problem solving techniques #3 cause and effect diagrams

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• Cause and Effect Diagrams: A Problem-Solving Technique

A comprehensive guide to understanding and utilizing cause and effect diagrams for problem solving and root cause analysis.

Cause and effect diagrams are a powerful problem-solving technique that can help you identify the root cause of any issue, from small everyday annoyances to complex organizational problems. By following a systematic process of gathering data, analyzing it and then developing solutions, cause and effect diagrams can be used to effectively tackle any problem that you may face. This article will provide an overview of the cause and effect diagram technique, outlining how it works and the steps involved in using it. We will also look at some examples of how it can be used to solve a variety of problems. When trying to solve a problem, it is important to break down all the possible causes.

A cause and effect diagram is a visual tool used to represent all the possible causes of a problem or event. It can be used to brainstorm different causes in order to identify the root cause of a problem. The process of creating a cause and effect diagram begins by identifying the problem or event, then listing all the possible causes that could be contributing to it. The causes can then be grouped together into categories.

For example, if the problem is a defect in a product, the categories might include “materials”, “suppliers”, “manufacturing process”, “transport”, etc. After the categories have been identified, arrows can be added to show the relationship between each cause and the effect. Once the cause and effect diagram is complete, it can be used to identify potential causes and solutions. By looking at the diagram as a whole, it is possible to identify which causes may have the most impact on the problem.

This can help narrow down potential solutions and make it easier to identify the root cause of a problem. There are several benefits of using cause and effect diagrams in problem-solving. First, they provide an organized way of looking at problems. By breaking down each component of the problem, it is easier to identify potential causes and solutions. Second, cause and effect diagrams can help individuals think more creatively about problems.

Benefits of Cause and Effect Diagrams

• they are useful for documenting problems and their solutions, which can be helpful when troubleshooting future issues., creating a cause and effect diagram.

Next, list all possible causes that could contribute to the problem or event. This includes both direct and indirect causes. Then, similar causes should be grouped into categories . This helps to narrow down the potential causes and makes the diagram easier to read.

After this, arrows should be added to show the relationship between each cause and the effect. Finally, the diagram should be reviewed as a whole to identify potential causes and solutions . Cause and effect diagrams can be an invaluable tool for problem solving and root cause analysis. By taking the time to create a diagram, individuals can better understand the various factors that may be contributing to a problem or event and identify potential solutions.

Cause and effect diagrams are an invaluable tool for problem solving and root cause analysis. By organizing problems into component parts, they allow individuals to think more creatively and identify potential causes and solutions more effectively. Furthermore, they provide a documented record of the problem and its solution for future reference. By following these steps, individuals can use cause and effect diagrams to effectively solve problems.

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Cause and Effect Diagram (Fishbone, Ishikawa): Ultimate Guide with Examples

Last Updated on January 8, 2024 by andrewshih

The Cause and Effect Diagram, also known as the Fishbone or Ishikawa Diagram, is a useful visual tool that aids in identifying and understanding the root causes of a problem. 

In this comprehensive guide, we will explore the intricacies of the Cause and Effect Diagram, providing a step-by-step procedure, a practical example, and resources for implementation.

What Is Cause And Effect Diagram

Cause and Effect Diagram is a graphical representation that illustrates the cause-and-effect relationships within a system or a specific problem. 

Developed by Kaoru Ishikawa, this diagram is widely used in various industries to analyze complex issues and facilitate effective problem-solving. 

The diagram gets its name from its fishbone-like structure, where the problem statement is placed at the “head,” and various potential causes branch out like the “bones” of the fish.

The primary purpose of an Ishikawa Diagram is to provide a comprehensive and structured method for identifying, categorizing, and analyzing the factors that contribute to a specific problem. 

By visually organizing potential causes into distinct categories, the diagram helps teams and individuals gain a clearer understanding of the relationships between different elements of a system, process, or situation.

When To Use A Fishbone Diagram

Here are some instances when using a Fishbone Diagram is particularly beneficial:

Complex Problem Solving

When faced with a complex problem that involves multiple factors and potential causes, a Fishbone Diagram can help break down the complexity into manageable categories. It provides a structured approach to understanding the interrelationships among different elements contributing to the issue.

Quality Improvement

In industries that prioritize quality control, such as manufacturing or healthcare, a Fishbone Diagram is often used to investigate defects, errors, or deviations from standards. It helps teams explore and address the underlying causes affecting product or service quality.

Process Improvement

When analyzing and improving business processes, a Fishbone Diagram can be instrumental in identifying bottlenecks, inefficiencies, or breakdowns in the workflow. By categorizing potential causes, teams can streamline processes for increased efficiency.

Project Planning

During the planning phase of a project, a Fishbone Diagram can be used to anticipate potential challenges and risks. It aids in proactive problem identification and mitigation strategies, contributing to more successful project outcomes.

Team Collaboration

When teams are working on a shared goal or project, a Fishbone Diagram encourages collaborative brainstorming sessions. It provides a visual platform for team members to contribute their insights and perspectives, fostering a collective understanding of the problem.

Customer Complaints

In customer-centric industries, a Fishbone Diagram can be deployed to investigate and address customer complaints. It helps identify the underlying causes of dissatisfaction and guides the development of strategies to enhance customer satisfaction.

Training and Development

In educational settings or corporate training programs, a Fishbone Diagram can be utilized to analyze challenges or obstacles hindering effective learning and development. It assists in identifying areas for improvement in training processes.

Strengths of Cause and Effect Diagrams

Visual representation.

One of the main strengths of cause and effect diagrams is their ability to provide a visual representation of complex issues. The diagram’s fishbone structure allows for a clear and organized display of potential causes and their relationships, making it easier for teams to understand and communicate.

Systematic Analysis

Cause and effect diagrams promote a systematic approach to problem-solving. By categorizing potential causes into major branches, teams can methodically explore and analyze each factor, leading to a comprehensive understanding of the root causes of a problem.

Collaborative Problem Solving

The creation of a cause and effect diagram often involves collaborative brainstorming sessions. This encourages team members to share their insights and perspectives, fostering a collaborative problem-solving environment that capitalizes on collective knowledge and expertise.

Identification of Multiple Causes

The diagram facilitates the identification of multiple causes contributing to a single problem. This multiperspective analysis helps ensure that the root causes are thoroughly explored, reducing the likelihood of overlooking critical factors.

Weaknesses of Cause and Effect Diagrams

Oversimplification.

One weakness of cause and effect diagrams is the potential for oversimplification. The structured nature of the diagram may not capture the complexity or interdependencies of certain issues, leading to a superficial understanding of the problem.

Subjectivity in Prioritization

The prioritization of causes within the diagram can be subjective. Teams may struggle to objectively rank the importance of various factors, and the process may be influenced by individual perspectives, biases, or incomplete information.

Lack of Quantitative Data

Cause and effect diagrams are primarily qualitative tools and do not inherently incorporate quantitative data. While they are effective in identifying potential causes, they may not provide a precise measurement of the impact or significance of each cause, requiring supplementary quantitative analysis.

How to Create Fishbone Diagram Step-by-Step

Creating a Cause and Effect Diagram involves systemically identifying and categorizing potential causes of a specific problem. Here are the steps to create a cause-and-effect diagram:

Step 1: Define the Problem Statement

Clearly articulate the problem or issue that you want to address. Write it as a concise statement at the “head” of the Fishbone Diagram.

Step 2: Identify Major Categories (Fishbone Bones)

The next step is to create the main branches, or “bones,” of the fishbone diagram.

Teams using cause-effect diagrams often use additional mnemonic lists to ensure a thorough consideration of potential causes. For example, the manufacturing industry frequently utilizes 5Ms, and the service industry often uses 5Ps.

While these lists serve as helpful aids, they are not universally applicable, and individuals may create their own or modify existing ones. The key is to consider all potential sources of causation by asking targeted questions like, “What procedures might be causing this problem?”

Step 3: Brainstorm Causes Within Each Category

With your team, brainstorm potential causes within each category. 

Encourage open discussion and gather as many ideas as possible. There are several techniques that may be helpful during the storming process.

• Use Affinity Diagrams to organize the information and group related ideas.
• Utilize 5 Whys Analysis to repeatedly ask why for a thorough cause and effect chain analysis.
• Ask the 4 Ws – What, Why, When, and Where to help explore the cause and effect.

Step 4: Add Subcauses and Details

If necessary, the team can further expand on each identified cause by adding subcauses or specific details. This adds granularity to the diagram, making it more comprehensive.

Using the image above, the team has identified two sub-causes for the main cause #1.

Continue this process until the team feels that the causes and sub-causes are sufficiently detailed. The team can continue to add additional sub-levels to sub-causes if necessary, although the diagram can become more difficult to visualize with multiple levels.

Step 5: Prioritize Causes

If applicable, prioritize the causes based on their significance. This can be done through team discussion, voting, or using data such as the Pareto Principle to identify the most critical factors.

Step 6:  Review, Validate, and implement

Review the completed Fishbone Diagram with the team. Ensure that all relevant causes have been included and that the relationships between causes and effects are accurate. Finally, share the completed diagram with relevant stakeholders and use it as a visual aid to identify and solve the problem.

By following these steps, the team can utilize the Fishbone Diagram to systematically analyze complex problems, identify root causes, and drive effective solutions.

Tips for Creating a Cause and Effect Diagram

Here are some tips to enhance the process and maximize the value of the fishbone diagram:

Tips for Creating the Diagram

• Clearly Define the Problem : A well-defined problem statement ensures that the cause-and-effect analysis remains focused and relevant.
• Select Appropriate Categories : Tailor these categories to fit the specific context of your analysis.
• Use Facilitation Techniques : Employ facilitation techniques such as affinity diagrams to organize and group related causes. Or use the 5 Whys technique to dig deeper into the root causes of each identified issue.
• Keep it Visual and Simple : Maintain clarity and simplicity in the Fishbone Diagram. Use clear and concise language, and avoid overcrowding the diagram with excessive detail. Make the diagram easy to understand and communicate.

Tips for Collaboration

• Encourage Open Brainstorming : Encourage team members to share their insights and ideas freely. Avoid judgment during the initial brainstorming phase to stimulate creativity.
• Include Relevant Stakeholders : Involve stakeholders with diverse perspectives in the cause-and-effect analysis to capture a comprehensive view of the problem and its contributing factors.
• Review and Iterate : After creating the initial diagram, review it with the team to confirm its accuracy and completeness. Be open to feedback and make necessary adjustments and several iterations.
• Facilitate Follow-Up Action : Use the insights gained from the Fishbone Diagram to develop actionable plans and solutions. Assign responsibilities for implementing changes and track progress over time to ensure continuous improvement.

By following these tips, you can facilitate a more effective cause-and-effect analysis, leading to a Fishbone Diagram that serves as a valuable tool for problem-solving and decision-making.

Cause and Effect Diagram Examples

Here are three examples of Ishikawa Diagrams representing different scenarios:

Example 1: Production Delays in a Manufacturing Plant

In this scenario, you are tasked with identifying the various factors contributing to production delays, allowing the team to identify root causes and implement targeted solutions.

Problem Statement : Unexpected delays in the production process lead to reduced output.

From the brainstorming exercise, the team identified the following causes within each category:

• Manpower : Insufficient Training, Staff Shortage, Lack of Motivation
• Machinery : Equipment Malfunctions, Inadequate Maintenance, Outdated Technology
• Material : Defective Raw Materials, Supply Chain Issues, Inventory Management
• Methods : Inefficient Workflows, Lack of Standard Operating Procedures, Poor Planning
• Measurement : Inaccurate Performance Metrics, Lack of Key Performance Indicators (KPIs), Monitoring and Reporting Issues

Example 2: High Employee Turnover in a Company

In this scenario, you are tasked with identifying the root causes of high employee turnover and guiding the development of strategies to improve retention.

Problem Statement : High turnover rates lead to instability and decreased morale among employees.

• People : Poor Leadership, Lack of Recognition, Limited Growth Opportunities
• Processes : Inefficient Recruitment, Inadequate Onboarding, Poor Performance Management
• Policies : Lack of Work-Life Balance, Inflexible Policies, Unclear Communication
• Work Environment : Toxic Culture, Inadequate Facilities, Lack of Team Collaboration
• Compensation & Benefits : Below-market Salaries, Inadequate Benefits, Limited Incentives

While taking a deep dive and examining the potential causes of insufficient recruitment, the team identified poor job descriptions, limited recruitment channels, and a competitive job market.

Example 3: Customer Complaints in a Service Industry

In this scenario, you are tasked with understanding the causes of multifaceted customer complaints and helping the organization to enhance its service quality and address customer concerns effectively.

Problem Statement : Increasing customer complaints leads to a decline in customer satisfaction.

• People : Inadequate Training, Lack of Empathy, Communication Issues
• Process : Inefficient Service Delivery, Long Waiting Times, Lack of Quality Control
• Product : Defective Products, Inadequate Product Information, Poor Product Design
• Policies : Inconsistent Policies, Lack of Customer-Focused Policies, Misleading Information
• Environment : Unpleasant Atmosphere, Poor Customer Service Culture, Lack of Feedback Mechanism

While taking a deeper dive into the long waiting time, the team identified insufficient staffing, and technical malfunctions as potential causes.

As the team continued to look into the cause of insufficient staffing, the team identified budget constraints, poor scheduling, and unexpected sickness as the causes.

Cause And Effect Diagram Complimentary Techniques

While the Fishbone Diagram (Ishikawa Diagram) is the most well-known type of Cause and Effect Diagram, there are other variations and complementary techniques used for cause and effect analysis. Here are some notable ones:

Affinity Diagram

An affinity diagram serves as the starting point by organizing diverse ideas and grouping them into clusters. It acts as a foundation for the fishbone diagram, which then delves into a more detailed analysis of root causes within specific categories. 

Together, these tools provide a comprehensive approach to problem-solving, transitioning from idea generation and organization to in-depth cause-and-effect analysis.

5 Whys Analysis

Although not a diagram per se, the 5 Whys is a questioning technique that helps delve deeper into the root cause of a problem. It involves asking “Why?” repeatedly (typically five times) to trace the cause-and-effect chain until the fundamental issue is uncovered. The 5 Whys Analysis is often used in conjunction with Fishbone Diagrams for a more thorough investigation.

Pareto Chart

A Pareto Chart is often used in conjunction with Fishbone Diagrams. It helps prioritize issues by illustrating the relative importance of different causes. The Pareto Principle suggests that 80% of problems may be attributed to 20% of the causes, guiding teams to focus on the most significant contributors.

How do I prioritize causes in a Fishbone Diagram?

Causes can be prioritized based on their significance or impact. This can be done through team discussion, voting, or using data such as the Pareto Principle to identify the most critical factors that require immediate attention.

Can I use a Fishbone Diagram for project planning?

Yes, a Fishbone Diagram can be a useful tool in the planning phase of a project. It helps anticipate potential challenges, risks, and issues, allowing teams to develop proactive strategies for successful project outcomes.

How often should I revisit a Fishbone Diagram?

Revisit a Fishbone Diagram as needed, especially when there are changes in the problem, new information becomes available, or when monitoring progress on implemented solutions. It’s a dynamic tool that can be iteratively improved over time.

What is the difference between an affinity diagram and a fishbone diagram?

An affinity diagram is used for brainstorming and organizing ideas, providing a foundation for a Fishbone Diagram. The Fishbone Diagram then conducts a more detailed analysis of root causes within specific categories identified by the affinity diagram. They work together in a collaborative problem-solving process.

What are the 6 elements of a Fishbone Diagram?

The Fishbone Diagram typically consists of six major elements or “bones.” These elements represent different categories under which potential causes of a problem are grouped. The six categories are People, Processes, Equipment, Materials, Environment, and Management . Each category helps organize and analyze potential causes systematically, providing a comprehensive view of the factors contributing to a specific issue.

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Cause and Effect Analysis

Identifying the likely causes of problems.

By the Mind Tools Content Team

(Also known as Cause and Effect Diagrams, Fishbone Diagrams, Ishikawa Diagrams, Herringbone Diagrams, and Fishikawa Diagrams.)

When you have a serious problem, it's important to explore all of the things that could cause it, before you start to think about a solution.

That way you can solve the problem completely, first time round, rather than just addressing part of it and having the problem run on and on.

Cause and Effect Analysis gives you a useful way of doing this. This diagram-based technique, which combines Brainstorming with a type of Mind Map , pushes you to consider all possible causes of a problem, rather than just the ones that are most obvious.

Click here to view a transcript of this video.

About the Tool

Cause and Effect Analysis was devised by professor Kaoru Ishikawa, a pioneer of quality management, in the 1960s. The technique was then published in his 1990 book, " Introduction to Quality Control ." [1]

The diagrams that you create with are known as Ishikawa Diagrams or Fishbone Diagrams (because a completed diagram can look like the skeleton of a fish).

Although it was originally developed as a quality control tool, you can use the technique just as well in other ways. For instance, you can use it to:

• Discover the root cause of a problem.
• Uncover bottlenecks in your processes.
• Identify where and why a process isn't working.

How to Use the Tool

Follow these steps to solve a problem with Cause and Effect Analysis:

Step 1: Identify the Problem

First, write down the exact problem you face. Where appropriate, identify who is involved, what the problem is, and when and where it occurs.

Then, write the problem in a box on the left-hand side of a large sheet of paper, and draw a line across the paper horizontally from the box. This arrangement, looking like the head and spine of a fish, gives you space to develop ideas.

In this simple example, a manager is having problems with an uncooperative branch office.

Figure 1 – Cause and Effect Analysis Example Step 1

(Click image to view full size.)

Some people prefer to write the problem on the right-hand side of the piece of paper, and develop ideas in the space to the left. Use whichever approach you feel most comfortable with.

It's important to define your problem correctly. CATWOE can help you do this – this asks you to look at the problem from the perspective of Customers, Actors in the process, the Transformation process, the overall World view, the process Owner, and Environmental constraints.

By considering all of these, you can develop a comprehensive understanding of the problem.

Step 2: Work Out the Major Factors Involved

Next, identify the factors that may be part of the problem. These may be systems, equipment, materials, external forces, people involved with the problem, and so on.

Try to draw out as many of these as possible. As a starting point, you can use models such as the McKinsey 7S Framework (which offers you Strategy, Structure, Systems, Shared values, Skills, Style and Staff as factors that you can consider) or the 4Ps of Marketing (which offers Product, Place, Price, and Promotion as possible factors).

Brainstorm any other factors that may affect the situation.

Then draw a line off the "spine" of the diagram for each factor, and label each line.

The manager identifies the following factors, and adds these to his diagram:

Figure 2 – Cause and Effect Analysis Example Step 2

Step 3: Identify Possible Causes

Now, for each of the factors you considered in step 2, brainstorm possible causes of the problem that may be related to the factor.

Show these possible causes as shorter lines coming off the "bones" of the diagram. Where a cause is large or complex, then it may be best to break it down into sub-causes. Show these as lines coming off each cause line.

For each of the factors he identified in step 2, the manager brainstorms possible causes of the problem, and adds these to his diagram, as shown in figure 3.

Figure 3 – Cause and Effect Analysis Example Step 3

Step 4: Analyze Your Diagram

By this stage you should have a diagram showing all of the possible causes of the problem that you can think of.

Depending on the complexity and importance of the problem, you can now investigate the most likely causes further. This may involve setting up investigations, carrying out surveys, and so on. These will be designed to test which of these possible causes is actually contributing to the problem.

The manager has now finished his analysis. If he hadn't looked at the problem this way, he might have dealt with it by assuming that people in the branch office were "being difficult."

Instead he thinks that the best approach is to arrange a meeting with the Branch Manager. This would allow him to brief the manager fully on the new strategy, and talk through any problems that she may be experiencing.

A useful way to use this technique with a team is to write all of the possible causes of the problem down on sticky notes. You can then group similar ones together on the diagram.

This approach is sometimes called CEDAC (Cause and Effect Diagram with Additional Cards) and was developed by Dr. Ryuji Fukuda, a Japanese expert on continuous improvement.

Professor Kaoru Ishikawa created Cause and Effect Analysis in the 1960s. The technique uses a diagram-based approach for thinking through all of the possible causes of a problem. This helps you to carry out a thorough analysis of the situation.

There are four steps to using the tool.

• Identify the problem.
• Work out the major factors involved.
• Identify possible causes.
• Analyze your diagram.

You'll find this method is particularly useful when you're trying to solve complicated problems.

[1] Ishikawa, K (1990).  'Introduction to Quality Control ,' Tokyo: 3A Corporation.

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Cause and effect diagrams: what they are and how to use them

April 27, 2021 by MindManager Blog

By: Leanna Armstrong

Figuring out what lies at the root of a complicated production or work process challenge is sometimes easier said than done. Fortunately, a cause and effect diagram makes for a handy tool any time you’re called on to solve a problem or prevent one from happening.

What is a cause and effect diagram?

A cause and effect diagram is a visual map that functions as a problem-solving tool . As the name suggests, its main purpose is to help you identify the cause of a troubling situation in relation to the effect (or results) you’ve observed.

To clarify the cause and effect diagram definition further, it may help to understand that this tool is also known as a fishbone diagram . By setting your problem on one side – and radiating out to the various elements that may be causing it on the other – you end up with a diagram loosely resembling the skeleton of a fish.

How to use a cause and effect diagram

If you’re in any way involved in manufacturing or selling a product or service, chances are you can benefit from using a cause and effect diagram tool.

Fishbone diagrams are designed to help you:

• Get clear on the nature of a problem and why it’s occurring
• Identify contributing factors and how they’re connected
• Determine the best course of action for correcting a setback

You’ll get more from your cause and effect diagram if you and your team use it while brainstorming which areas of a particular process are most likely involved.

By visually breaking those areas down into their various components – and noting the impact of specific activities – you can increase your understanding of where things have gone wrong, and find a solution much faster.

You may discover, for example, that you need to:

• Change out materials or suppliers
• Revise the steps in a new or established workflow
• Tweak the tasks carried out by certain departments or individuals

With that in mind, here are 3 key areas where fishbone diagrams are commonly used.

1. Manufacturing a product.

This usually includes analyzing the 6 (or 8) M-factors involved in most production processes (Machinery, Method, Materials, Measurement, Mother Nature, Manpower, Management, Maintenance).

2. Providing a service.

This may include examining the 4 S-factors involved in many service delivery procedures (Surroundings, Suppliers, Skills, Systems).

3. Marketing a product or service.

This may include exploring the 8 P-factors involved in many product or service marketing systems (Price, People, Place, Product, Packaging, Procedures/Processes, Policies, Promotion).

No matter the source of your predicament, creating a cause and effect diagram template will help you and your team drill down and sort out the changes required to put it right.

How to create a cause and effect diagram

With a versatile program like MindManager , you can use a fishbone diagram template to capture, view, and organize all the potential causes of a problem – then use (and link to) an idea map for brainstorming ways to resolve it.

Here’s how to create a cause and effect diagram in 4 easy steps.

Label a box or node at one end of your diagram with the name of your issue (this represents the “head” of your fish).

Extend a main “spine” line from this node out to one side.

Add “rib” lines that branch off from either side of your main line, each leading to its own node. Label these nodes with the names of areas or factors that may be contributing to your problem.

Break down and explore each of these potential causes further by branching off to the specific processes, tasks, or resources involved (these are your sub-causes).

And that’s it!

Now, you and your team can spot suspect activity and identify the areas that are most likely causing your issue more easily. To better focus on problem-solving , you might even prioritize these factors by ranking them in the order in which they should be addressed.

Example of a cause and effect diagram

Creating a fishbone diagram is a great way to see what’s behind a particular problem so you can both resolve it and prevent it from occurring again. Here’s a simple cause and effect diagram example to help demonstrate this.

Let’s say you want to build out your line of travel backpacks and have recently launched a second product. Despite the success of your original backpack however, your new, sturdier pack is falling short of sales expectations .

You decide to investigate the problem from a marketing perspective with the help of a cause and effect diagram.

First, you identify the problem at hand by labeling a box on the middle left side of your diagram:

Next, at a loss for where to actually start, you construct the skeleton of your diagram by adding a spine line and branching out to all 8 areas of your marketing process:

At this point, you realize you should probably sit down with your promotional team and examine the key factors affecting these categories.

After some inspired brainstorming, the ribs of your diagram look like this:

As your team sits back and analyzes their finished diagram, 2 key findings emerge:

• Instead of travelers, your existing customers are predominantly students who love your product but can’t afford – and don’t want or need – a larger, heavier, more expensive version
• The travelers your new product was geared toward can buy a similar quality pack for significantly less from your local competitor

Now that you have a better understanding of the reasons behind your new pack’s lack of success, you can create a plan to resolve the problem (possibly by sourcing less costly materials) and ensure you always perform sufficient marketing research in future.

Cause and effect diagram use cases

Crafting a simple cause and effect diagram template is the best way to shift your team’s focus from the symptoms associated with a problem, to the construction of a practical plan for meeting it head-on.

Here are some simple use cases that illustrate a few more common cause and effect diagram examples.

Company A wants to fix a product quality problem

Production quality at the company’s newest widget manufacturing facility is below that of other locations. After ruling out factors in common, the QC team meets to create a cause and effect diagram around two key areas – Mother Nature (environment) and Manpower (plant workers) – and makes some impactful discoveries: the new plant is located in a very humid location where skilled labor is limited.

Making process adjustments to correct for the humidity and ramping up training allows the company to normalize quality.

Company B wants to resolve an image setback

As a newer massage and body treatment spa, image is especially important to the company. After a promising start, however, customer reviews show their reputation for calm and relaxation has taken a hit. Management sits down to brainstorm the problem and, with the help of a fishbone diagram, resolves that recent renovations and staff turnover have combined to disrupt the spa’s serene surroundings.

The company makes a plan to both confine renos to off-days and improve employee engagement.

Company C wants to overcome a marketing challenge

With a full line of successful breakfast cereals under their belt, the company is at a loss as to why their latest cereal isn’t selling. Working with their marketing department, they use a cause and effect diagram tool to pinpoint the problem and conclude that the only real difference lies in the packaging.

The company creates several new packaging designs and polls their target audience to determine which holds the greatest appeal.

Remember, mapping your way out of a problem is the easiest way to see where you are, how you got there, and what the most promising route is to get back on track.

Downloadable cause and effect diagram templates from MindManager

Click the images below to access the cause and effect diagram example shared above, and a blank template created using MindManager. Click “Menu” in the bottom left corner of your browser window, and then click “Download” to get a copy of the template. Open the template in MindManager to start working.

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Cause and Effect Analysis

Identifying the likely causes of problems, (also known as cause and effect diagrams, fishbone diagrams, ishikawa diagrams, herringbone diagrams, and fishikawa diagrams.).

When you have a serious problem, it's important to explore all of the things that could cause it, before you start to think about a solution.

That way you can solve the problem completely, first time round, rather than just addressing part of it and having the problem run on and on.

Cause and Effect Analysis gives you a useful way of doing this. This diagram-based technique, which combines Brainstorming   with a type of Mind Map   , pushes you to consider all possible causes of a problem, rather than just the ones that are most obvious.

Click here   to view a transcript of this video.

About the Tool

Cause and Effect Analysis was devised by professor Kaoru Ishikawa, a pioneer of quality management, in the 1960s. The technique was then published in his 1990 book, " Introduction to Quality Control ."

The diagrams that you create with are known as Ishikawa Diagrams or Fishbone Diagrams (because a completed diagram can look like the skeleton of a fish).

Although it was originally developed as a quality control tool, you can use the technique just as well in other ways. For instance, you can use it to:

• Discover the root cause of a problem.
• Uncover bottlenecks   in your processes.
• Identify where and why a process isn't working.

How to Use the Tool

Follow these steps to solve a problem with Cause and Effect Analysis:

Step 1: Identify the Problem

First, write down the exact problem you face. Where appropriate , identify who is involved, what the problem is, and when and where it occurs.

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Then, write the problem in a box on the left-hand side of a large sheet of paper, and draw a line across the paper horizontally from the box. This arrangement, looking like the head and spine of a fish, gives you space to develop ideas.

In this simple example, a manager is having problems with an uncooperative branch office.

Figure 1 – Cause and Effect Analysis Example Step 1

(Click image to view full size.)

Some people prefer to write the problem on the right-hand side of the piece of paper, and develop ideas in the space to the left. Use whichever approach you feel most comfortable with.

It's important to define your problem correctly. CATWOE   can help you do this – this asks you to look at the problem from the perspective of Customers, Actors in the process, the Transformation process, the overall World view, the process Owner, and Environmental constraints.

By considering all of these, you can develop a comprehensive understanding of the problem.

Step 2: Work Out the Major Factors Involved

Next, identify the factors that may be part of the problem. These may be systems, equipment, materials, external forces, people involved with the problem, and so on.

Try to draw out as many of these as possible. As a starting point, you can use models such as the McKinsey 7S Framework   (which offers you Strategy, Structure, Systems, Shared values, Skills, Style and Staff as factors that you can consider) or the 4Ps of Marketing   (which offers Product, Place, Price, and Promotion as possible factors).

Brainstorm   any other factors that may affect the situation.

Then draw a line off the "spine" of the diagram for each factor, and label each line.

The manager identifies the following factors, and adds these to his diagram:

Figure 2 – Cause and Effect Analysis Example Step 2

Step 3: Identify Possible Causes

Now, for each of the factors you considered in step 2, brainstorm possible causes of the problem that may be related to the factor.

Show these possible causes as shorter lines coming off the "bones" of the diagram. Where a cause is large or complex, then it may be best to break it down into sub-causes. Show these as lines coming off each cause line.

For each of the factors he identified in step 2, the manager brainstorms possible causes of the problem, and adds these to his diagram, as shown in figure 3.

Figure 3 – Cause and Effect Analysis Example Step 3

Step 4: Analyze Your Diagram

By this stage you should have a diagram showing all of the possible causes of the problem that you can think of.

Depending on the complexity and importance of the problem, you can now investigate the most likely causes further. This may involve setting up investigations, carrying out surveys, and so on. These will be designed to test which of these possible causes is actually contributing to the problem.

The manager has now finished his analysis. If he hadn't looked at the problem this way, he might have dealt with it by assuming that people in the branch office were "being difficult."

Instead he thinks that the best approach is to arrange a meeting with the Branch Manager. This would allow him to brief the manager fully on the new strategy, and talk through any problems that she may be experiencing.

A useful way to use this technique with a team is to write all of the possible causes of the problem down on sticky notes. You can then group similar ones together on the diagram.

This approach is sometimes called CEDAC (Cause and Effect Diagram with Additional Cards) and was developed by Dr. Ryuji Fukuda, a Japanese expert on continuous improvement.

Professor Kaoru Ishikawa created Cause and Effect Analysis in the 1960s. The technique uses a diagram-based approach for thinking through all of the possible causes of a problem. This helps you to carry out a thorough analysis of the situation.

There are four steps to using the tool.

• Identify the problem.
• Work out the major factors involved.
• Identify possible causes.
• Analyze your diagram.

You'll find this method is particularly useful when you're trying to solve complicated problems.

This site teaches you the skills you need for a happy and successful career; and this is just one of many tools and resources that you'll find here at Mind Tools. Subscribe to our free newsletter , or join the Mind Tools Club and really supercharge your career!

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Comments (36)

• Over a month ago BillT wrote Hi AnotherFrancis, and Welcome to the Club! As with my response to Alex81, not everyone will find the example the most useful, as is the case with you. We appreciate your feedback. BillT Mind Tools Team
• Over a month ago AnotherFrancis wrote Nice article but, like Alex81, I didn't find the example to be very helpful.
• Over a month ago BillT wrote Hi Alex81, and Welcome to the Club! Thank you for your feedback on the Cause and Effect Analysis. I agree that not everyone may find the example the best way to demonstrate the resource. This diagram has also been known as the Ishikawa Diagram or Fishbone Diagram. You may find additional resources with better examples for you. Also, you may wish to post this in the Forums so that our members can provide their input into the topic. BillT Mind Tools Team

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Cause and Effect

Understand relationships quickly and easily with smartdraw's cause and effect or fishbone diagrams, what is a cause and effect diagram, cause and effect analysis, how to make a cause and effect diagram, cause and effect diagram tutorial, cause and effect chart examples, with smartdraw, you can create many different types of diagrams, charts, and visuals.

A cause and effect diagram examines why something happened or might happen by organizing potential causes into smaller categories. It can also be useful for showing relationships between contributing factors. One of the Seven Basic Tools of Quality, it is often referred to as a fishbone diagram or Ishikawa diagram.

One of the reasons cause & effect diagrams are also called fishbone diagrams is because the completed diagram ends up looking like a fish's skeleton with the fish head to the right of the diagram and the bones branching off behind it to the left.

To begin making a cause and effect diagram, write the main issue or problem to be analyzed in a box that is typically on the right edge of the page, halfway down the drawing area or page. A line called the "spine" or "backbone" should extend to the left starting from the edge of the main box (if you're using a SmartDraw template, this will already be there for you). Next, angle branches off of the spine, each representing a cause or effect of the main issue. Each of these branches can contain additional branches.

Most cause and effect diagrams examine a similar set of possible causes for any issue analyzed.

In the manufacturing industry, these are referred to as the 6Ms:

• Methods. Are there well-written and appropriate training guidelines in place? Are certain policies or regulations causing slow-downs or creating unnecessary steps?
• Machines. Are there any maintenance issues with the tools used or the number of tools available?
• Materials. Are there any issues getting raw materials from suppliers? Any problems with transportation (timing) or with the quality of the supplies?
• Measurements. Could there be errors in calculation or contamination that caused false readings? Could the way you measure be inconsistent in some way? Is your equipment regularly calibrated and maintained?
• Mother Nature/Environment. Is there too much moisture in the environment? Are temperatures too hot or too cold? Is there excessive dust or other contamination?
• Manpower/People. Do you have too little of your workforce devoted to a process? Are new people adequately trained? Is the training consistent? Are the right people with the right experience being hired or promoted? Is there a specific position creating a bottleneck or making frequent mistakes?

Occasionally, a manufacturing analysis will also include two other categories: Management and Maintenance.

In the service industry, these are described as the 4S:

• Surroundings. Does your establishment project the right image? Is it run-down? Is it impersonal? Is it comfortable?
• Suppliers. Are there any issues delivering your service? Do you have problems with low quality food deliveries? Are there too many dropped phone calls? Can your server handle traffic spikes? Are you getting enough traffic through advertising channels you're paying for?
• Systems. Do you have policies and procedures in place for all scenarios? Do you have modern cash registers that help your servers place orders and deliver checks efficiently?
• Skill. Are your employees properly trained? Do they have the right experience?

Occasionally, a fifth category will be included called "Safety".

In the marketing industry, cause and effect diagrams will often consist of 7Ps:

• Product. Consider all aspects of what you're selling including its quality, its perceived image, availability, warranties, support and customer service.
• People. When people buy your product or service they may interact with many people: sales people, customer service people, delivery people, and so on. Are there any potential problems with your company culture?
• Process/Procedure. How do you handle problems when they arise? Are they escalated properly? Is your staff trained appropriately and do they follow their training?
• Promotion. Consider advertising, sales, PR, branding, direct marketing, partnerships, and social media.
• Price. How does the price of your product or service compare to competitors? What discounts and payment methods are available?
• Physical evidence/Packaging. How is your product or service consumed? Is how or where you present your product hurting your ability to convert? Are your facilities clean and tidy? Is the packaging cheap or expensive?
• Place/Plant. Is your distribution efficient and cost-effective? Is your product sold in the right stores or neighborhoods? Are your stores convenient for your target customers?

How to Make Cause and Effect Diagrams with SmartDraw

SmartDraw makes creating cause and effect diagrams easy with built-in smart templates that let you add new causes in a single click and format your diagram automatically. Learn how by reading this cause and effect chart tutorial.

Best Practices in Cause and Effect Analysis

These are the best and most common practices when creating cause and effect diagrams.

• Identify the problem. Define the process or issue to be examined.
• Brainstorm. Discuss all possible causes and group them into categories.
• Draw the backbone. Once the topic is identified, draw a straight, horizontal line (this is called the spine or backbone) on the page, and on the right side, draw a rectangle at the end. Write a brief description of the problem in the rectangle.
• Add causes and effects. Causes are added with lines branching off from the main backbone at an angle. Write the description of the cause at the end of the branch. These are usually one of the main categories discussed above. Details related to the cause or effect may be added as sub-categories branching off further from the main branch. Continue to add branches and a cause or effect until all factors have been documented. The end result should resemble a fish skeleton.
• Analyze. Once the diagram has been completed, analyze the information as it has been organized in order to come to a solution and create action items.

Cause and Effect Examples

The best way to understand cause and effect diagrams is to look at some examples of cause and effect diagrams.

Click on any of these cause and effect diagrams included in SmartDraw and edit them:

Browse SmartDraw's entire collection of cause and effect examples and templates

More Cause and Effect Diagram Information

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Cause and effect diagrams: A Guide

Table of Contents

What is a cause and effect diagram.

A cause and effect diagram is a powerful visual tool that helps identify and organize potential causes of an issue or effect. Also known as a fishbone diagram or Ishikawa diagram, it's widely used in many industries to identify the root causes of issues.

The diagram consists of a central spine, resembling a fishbone, with branches that represent different categories of potential causes. The categories can vary based on the context and nature of the problem.

For example:

In manufacturing, the categories may include people, machines, materials, methods, and the environment.

In healthcare, the categories may include patients, processes, equipment, environment, and staff.

Why use the cause and effect diagram?

The cause and effect diagram is an effective diagramming tool that can help teams tackle complex problems and improve their problem-solving capabilities.

Here are some benefits of using a cause and effect diagram:

It helps teams identify the root cause(s) of a problem, which is crucial for developing effective solutions to address the issue.

The diagram enables teams to leverage diverse perspectives and insights by encouraging collaboration and participation from team members. This helps them to arrive at a more comprehensive understanding of the problem.

The diagram can be used across various industries and sectors, making it a versatile tool for problem-solving and decision-making.

A fishbone diagram can help streamline a free workflow process by identifying bottlenecks or inefficiencies in a process.

Key concepts of cause and effect diagrams

Cause and effect diagrams are a useful tool for problem-solving. However, it's essential to understand their limitations and underlying concepts.

Visual representation of factors contributing to an observed effect

Cause and effect diagrams start with the effect at the center of the diagram, represented by a box or a circle. Then, branches get drawn out from the center, representing possible causes of the effect. This helps teams to identify multiple factors that contribute to an observed effect.

Each branch is a category of causes, such as “manpower,” “machine,” “method,” “measurement,” or “material.”

The branches are further divided into smaller sub-branches that represent specific causes. The diagram can be as simple or as complex as needed, depending on the problem at hand.

Interrelationships among causal factors are clearly shown

This type of diagram can help teams identify the relationships between different factors contributing to the observed effect.

By organizing the possible causes into categories and subcategories, the diagram can reveal the interdependencies among them. This can be particularly helpful when analyzing complex problems.

For example, a healthcare team uses a cause and effect diagram to identify the potential causes of patient falls in a hospital.

The diagram could show multiple branches with subcategories, such as:

Environmental factors

Medication-related factors

Patient-related factors.

The team identifies that patient confusion, environmental factors, and medication side effects can all contribute to falls, and they're interrelated.

One causal factor may appear in several places

A single factor may contribute to the observed effect in multiple ways.

The cause and effect diagram can help identify multiple pathways. It does this by showing where a single factor appears in different categories or subcategories.

A manufacturing team uses a cause and effect diagram to identify the causes of product defects. The diagram shows that a faulty machine part may contribute to defects in the product's size, shape, and color, as well as affect the machine's overall performance. The team realizes that addressing the faulty machine parts could significantly impact the final product's quality.

It’s qualitative and hypothetical

While cause and effect diagrams can provide a valuable visual representation, their qualitative and hypothetical nature limit them.

The diagram doesn't provide any quantitative data, making it difficult to prioritize and compare different possible causes.

However, even with these limitations, the diagram can still be useful as a brainstorming tool for identifying possible causes and interrelationships.

How to create a cause and effect diagram

Creating a cause and effect diagram is a straightforward process that can help teams identify and address problems.

Here are the steps for creating a cause and effect diagram:

1. Identify the problem that needs to be addressed

The first step in creating a cause and effect diagram is to clearly identify the problem that needs to be addressed.

This problem should be clearly defined and specific so that the resulting diagram can accurately reflect the potential causes.

2. Determine the effect that is being observed

After identifying the problem, the next step is to determine the observed effect. This effect is typically a symptom of the underlying problem, and understanding it's crucial to identify the potential causes.

3. Brainstorm potential causes and categorize them into different groups

Once you have identified the problem and effect, the team can begin to brainstorm potential causes. These causes can then be categorized into different groups, such as people, processes, equipment, or materials.

4. Draw the cause and effect diagram using a visual tool like Miro

With potential causes identified and categorized, the team can then draw the cause and effect diagram using a visual tool like Miro.

This diagram should have the effect at the center and the different categories of potential causes branching out from it.

Miro has both a fishbone diagram template that is are easy to customize and will help you get started in no time.

5. Review and refine the diagram with input from team members

Finally, the team should review and refine the cause and effect diagram with input from team members.

This feedback can help to identify any missing causes, refine the categorization of causes, and ensure accuracy.

Importance of involving all team members in the process

Involving all team members in the creation of a cause and effect diagram is crucial for identifying and addressing the root causes of a problem.

Each team member brings a unique perspective and expertise to the process, which can result in identifying more potential causes and the evolution of better solutions.

Additionally, involving all team members fosters a sense of ownership and credibility. It can also improve communication and collaboration, leading to more effective problem-solving.

Next, let’s look at how you would make one.

Example of a simple cause and effect diagram and how it was created

To illustrate the process of creating a simple cause and effect diagram, let's consider a scenario.

In this example, we'll create a cause and effect diagram to understand why 40% of users cancel their subscriptions after the first month.

First, we identified the problem: a high cancellation rate after the first month of a subscription.

Next, we determined the effect that was being observed: 40% of users were canceling their subscription after the first month.

We then brainstormed potential causes and categorized them into different groups, including:

Customer Service

Product Quality

We identified the Customer Service category subcategories, such as‌ 'Difficult to Contact,' 'Unhelpful Support Staff,' and 'Long Wait Times.'

We identified subcategories for the Product Quality category, such as 'Technical Issues' and 'Poor User Experience.' For the Price category, we identified subcategories such as 'High Cost' and 'Hidden Fees.' And for the Marketing categories like 'Misleading Information' and 'Inaccurate Targeting.'

After categorizing the potential causes, we drew the cause and effect diagram using a visual tool like Miro. We then reviewed and refined the diagram with input from team members.

The resulting cause and effect diagram showed that the primary causes of the high cancellation rate were 'Difficult to Contact' and 'Poor User Experience.' These issues were further refined by analyzing the subcategories and identifying specific actions that could be taken to address them.

In the next section, we'll discuss how to analyze and interpret the diagram to identify the most likely causes of the problem.

How to analyze and interpret a cause and effect diagram

After creating a cause and effect diagram, the team needs to review it and identify potential causes that most likely contribute to the problem.

Several techniques can be used to analyze the diagram.

Techniques for analyzing and interpreting the diagram

One technique is to look for patterns or clusters of related causes. For example, if multiple causes in different categories all relate to a particular aspect of the problem, that may suggest that this is a particularly important area to investigate further.

Another technique is identifying potential causes that appear multiple times or in different categories. This can highlight the most important factors contributing to the problem and suggest areas where solutions could be targeted.

Teams can also use hypothesis testing, looking for evidence to support or disprove each potential cause. By testing hypotheses, the team can identify which potential causes are most likely to be the root cause(s) of the problem.

The goal of analyzing the diagram is to identify the most likely root cause(s) of the problem, which can then be addressed with targeted solutions.

Importance of considering both direct and indirect causes

It's essential to consider both direct and indirect causes when analyzing a cause and effect diagram. Direct causes are factors that directly contribute to the observed effect, such as malfunctioning machines. Indirect causes refer to underlying factors, such as insufficient training, that may not be immediately obvious.

An example could be a production problem caused by a broken machine and an untrained operator. While fixing the machine is a direct solution, providing the operator with proper training is indirect.

Addressing indirect causes can be more effective in the long term since they can have a wider impact and can contribute to solving future problems. So, considering both direct and indirect causes can help the team develop more effective solutions to the problem.

Example of a complex cause and effect diagram

A complex cause and effect diagram may include multiple categories with many potential causes or represent a system with many interrelated parts. For instance, a diagram related to supply chain management could include categories such as logistics, inventory, and procurement, with dozens of potential causes under each category.

To analyze such a diagram, the team would need to use techniques like:

Looking for patterns or clusters of related causes

Identifying potential causes that appear multiple times or in different categories

Testing hypotheses by looking for evidence to support or disprove each potential cause

Since complex diagrams can be overwhelming, involving all team members in the analysis is important. This ensures that different perspectives and knowledge can contribute to identifying the root cause(s).

After analyzing the diagram, the team should have a better understanding of the most likely root cause(s) of the problem. This information can then be used to develop targeted solutions to address the problem and prevent it from occurring again.

Use Miro to build an effective cause and effect diagram

Whether you're mapping out the root causes of a problem or analyzing the consequences of an action, Miro makes it easy so visually and collaboratively create a cause and effect diagram.

Using intuitive drag-and-drop functionality you can easily add and connect elements, label branches, and customize your diagram to suit your specific needs. Collaborate in real-time with your team, regardless of their location, and benefit from their insights as you build a comprehensive diagram. Sign up for free to get started!

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Ishikawa Diagram: Examples and Applications

By Status.net Editorial Team on May 18, 2023 — 9 minutes to read

Ishikawa diagrams, also known as Fishbone diagrams or Cause-and-effect diagrams, are essential tools in the world of quality management and problem-solving. Developed by Dr. Kaoru Ishikawa in the 1960s, these diagrams provide a visual representation of factors contributing to a specific outcome. By analyzing the various causes and effects, teams can identify root problems and implement effective solutions.

Organizations across diverse industries use Ishikawa diagrams to identify potential causes of issues and simplify complex problems. By categorizing causes into groups, such as equipment, processes, people, or materials, teams can better pinpoint the root causes of an issue. This structured approach enables organizations to develop targeted solutions, improving efficiency and productivity.

For example, a manufacturing company might use an Ishikawa diagram to determine the factors causing delays in its production line. By breaking down the possible causes into categories like equipment, workforce, and materials, management can address specific concerns and optimize the overall production process. Similarly, a software development team could use this method to identify and address the root causes of recurring bugs, ultimately enhancing the quality of the final product.

See also: Fishbone Diagram (Components, Factors, Examples)

• 5 Whys: How to Uncover Root Causes [Examples]

History and Background

Kaoru Ishikawa, a Japanese quality control expert, introduced the Ishikawa Diagram in the 1960s. His aim was to provide a visual representation of the potential causes of a specific problem or event, making it easier for teams to identify and address issues. This method has since become popular in various industries to analyze and solve complex problems.

The Ishikawa Diagram, also known as the fishbone diagram or cause-and-effect diagram, is a graphical representation of the relationship between a problem’s root causes and its effects. It is a simple and effective tool for process improvement and quality management. The diagram is designed to encourage systematic and comprehensive problem-solving by exploring all possible factors contributing to the issue, ensuring that no element is overlooked.

With this tool, teams can quickly identify the root causes of a problem or inefficiency in a process. By addressing these causes, organizations can make improvements that lead to better performance and higher quality outcomes. The Ishikawa Diagram is an effective method for identifying root causes and driving continuous improvement in various industries, such as manufacturing, healthcare, and service sectors.

• One example of the Ishikawa Diagram in action is in a manufacturing plant experiencing a decline in product quality. By utilizing the diagram, the team can identify factors such as ineffective training, outdated equipment, and poor communication as potential root causes. By addressing these issues, the plant can work to improve the overall quality of its products.
• Another example is in the healthcare industry, where a hospital may use the Ishikawa Diagram to analyze patient wait times. By exploring contributing factors such as staff shortages, patient volume, or outdated scheduling software, the hospital can pinpoint the areas that require improvement and work on solutions to reduce wait times for patients.
• Another example: a service-oriented business, such as a restaurant, can use the Ishikawa Diagram to investigate customer dissatisfaction. Factors such as slow service, unappetizing food, or an unclean environment can be identified and addressed, resulting in an improved dining experience and increased customer satisfaction.

Fundamentals

Fishbone diagram.

Another name for the Ishikawa Diagram is the Fishbone Diagram. Its name stems from its resemblance to a fish skeleton, with the main “spine” representing the problem and the branches illustrating the causes. The diagram’s primary purpose is to find the root causes and determine how to resolve them to improve a process or situation.

When trying to improve customer satisfaction, a company might create a Fishbone Diagram to identify potential causes of dissatisfaction and develop strategies to enhance customer experience.

Learn more: Fishbone Diagram (Components, Factors, Examples)

Seven Basic Quality Tools

The Ishikawa Diagram is among the Seven Basic Quality Tools, a set of simple and effective methods used to identify, analyze, and resolve quality-related problems. These tools are essential for process improvement and are widely used in manufacturing, engineering, and other industries.

Some examples of the Seven Basic Quality Tools include flowcharts, check sheets, and Pareto charts, which are all applied to streamline processes and improve efficiency.

Categories of Causes

The Ishikawa Diagram organizes causes by categories to simplify analysis and discussion. These categories vary depending on the industry or problem under consideration, such as materials, methods, machines, and people in the manufacturing sector. By breaking down the causes into categories, the diagram helps teams trace the root of a problem and develop targeted solutions.

For example, a software development team might categorize causes as people, process, and technology when examining the reason behind project delays. This categorization allows the team to pinpoint specific areas for improvement and develop appropriate action plans.

Applications and Benefits

Problem solving and root cause analysis.

Ishikawa diagrams allow teams to visualize the relationship between a problem statement and its potential causes. By identifying root causes through cause and effect analysis, organizations can focus on addressing these fundamental issues, leading to more effective problem solving. Quality circles often use Ishikawa diagrams to collaborate on problem identification and the development of solutions.

• Troubleshooting a drop in manufacturing productivity.
• Identifying factors causing a high employee turnover rate.
• Uncovering reasons for customer dissatisfaction with a service.

Quality Control and Management

Ishikawa diagrams are often used in quality control and management processes. By mapping out potential causes of defects or errors, teams can target specific areas for improvement, reducing the likelihood of future issues. This approach enables a proactive stance on quality management, minimizing costs and optimizing production processes.

• Identifying sources of variation in product dimensions.
• Pinpointing reasons for inconsistent product quality.
• Addressing potential causes of delayed shipments.

Product Development

In product development, Ishikawa diagrams can be used to ensure a thorough understanding of potential problems or risks associated with new products. By anticipating these issues, product teams can implement mitigation strategies or design improvements early in the development process, saving time and resources.

• Identifying potential safety hazards in a new product design.
• Brainstorming the possible causes of component failure in a prototype.
• Optimizing the user experience by systematically addressing potential user-perceived issues.

Service Improvement

In service industries, Ishikawa diagrams help uncover areas for improvement in customer relations, service delivery, and other processes. By analyzing the causes and effects of subpar service, businesses can prioritize and implement changes that enhance customer satisfaction and loyalty.

• Discovering factors contributing to long wait times at a restaurant.
• Identifying issues causing miscommunication between customer service agents and clients.
• Analyzing root causes of frequent late deliveries by a courier company.

Ishikawa diagrams have a wide range of applications in healthcare. Medical professionals use them to analyze factors affecting patient outcomes, streamline workflows, and identify areas for process improvement. This approach helps maintain a high standard of care and supports continuous improvement in the healthcare setting.

• Investigating the root causes of a high patient readmission rate.
• Identifying factors contributing to medication errors in a hospital.
• Analyzing the root causes of patient dissatisfaction with various healthcare services.

Creating an Ishikawa Diagram

Materials and equipment.

To create an Ishikawa (fishbone) diagram, you will need:

• A large whiteboard, flip chart, or paper
• Markers or pens
• Sticky notes

An Ishikawa diagram template can be helpful to visualize the process. The template comprises a head, skeleton, and bones structure.

Here are some Ishikawa (fishbone) diagram templates:

https://www.edrawmind.com/templates/fishbone-diagram-template.html

https://www.vertex42.com/ExcelTemplates/fishbone-diagram.html

https://creately.com/diagram-community/popular/t/fishbone-diagram

https://www.presentationgo.com/presentation/fishbone-diagram-powerpoint-google-slides/

Brainstorming Session

Organize a brainstorming session with the team or stakeholders involved in the project. The aim is to identify possible causes of a problem or effect. Encourage open communication and active participation during the session.

When identifying potential causes, consider the six Ms of management: Material, Method, Measurement, Manpower, Machine, and Mother Nature (Environment). These categories can act as a guide to ensure comprehensive analysis.

Categorizing Cause and Effect Relationships

After the brainstorming session, categorize and document the identified causes by arranging them along the Fishbone diagram’s “bones.” Categorization helps in understanding cause and effect relationships and further analysis.

As causes are categorized, aim to find any relationships between them, and group them accordingly.

Example 1: In a production line, if the material quality and machine calibration are both identified as possible causes, they may be grouped under the broader category of “Manufacturing Process.”

Example 2: In customer service, poor communication and lack of training may be related issues, and they can be grouped under the “Manpower” category.

Challenges and Limitations

Ishikawa diagrams provide a convenient way to represent potential causes of a problem or issue. However, they also come with their own set of challenges and limitations.

One disadvantage of using an Ishikawa diagram is that it may not accurately represent complex situations. In cases where multiple factors interact with each other, the diagram may oversimplify the relationships between causes and effects. As a result, users may not fully grasp the nuances of the situation, making it challenging to address the root cause.

Another challenge is the need for proper training to create and interpret Ishikawa diagrams effectively. If the team members working on the diagram lack adequate knowledge and expertise, they may struggle to identify relevant possible causes and organize them logically. This could lead to biased or incomplete diagrams, which subsequently hinder problem-solving efforts.

Variations in the use of Ishikawa diagrams can also present challenges. Different organizations or industries might employ their own conventions and terminologies when creating these diagrams, making it difficult for practitioners from diverse backgrounds to collaborate.

Ishikawa diagrams may not be equally advantageous for all types of problems. Certain issues may have a single root cause or multiple discrete causes that are better represented using alternative tools or methodologies.

Despite these limitations, Ishikawa diagrams can still be valuable in numerous situations listed in the “Application and benefits” section above.

Tools and Techniques

Mind mapping and software applications.

Mind mapping is a valuable method for visualizing and organizing information, which can be particularly helpful when using Ishikawa diagrams. Many software applications are available today to create mind maps, which can enhance performance and streamline the creation of cause-and-effect diagrams. These apps provide a user-friendly interface and allow for easy customization, enabling users to tailor their Ishikawa diagrams to their specific needs.

Examples of popular mind mapping software include:

• MindMeister

Quality Toolbox

The Quality Toolbox is a resource for understanding and implementing Ishikawa diagrams and other root cause analysis tools. It contains a variety of methods and systems to help improve the identification and management of potential problem sources. Utilizing the Quality Toolbox can boost effectiveness and efficiency in creating and analyzing cause-and-effect diagrams.

Six Sigma is a performance improvement methodology that promotes efficient and effective problem-solving. It complements Ishikawa diagrams by providing a structured approach to identify, analyze, and resolve issues. Implementing Six Sigma alongside cause-and-effect diagrams can significantly improve systems and processes, resulting in a higher level of quality.

Examples of Six Sigma concepts related to Ishikawa diagrams include:

• DMAIC (Define, Measure, Analyze, Improve, Control)
• Root Cause Hypothesis
• Process Mapping

The 5 Whys technique is an essential component of root cause analysis, often used in conjunction with Ishikawa diagrams. This tool helps users dig deeper into the root of a problem by repeatedly asking “Why?” until the underlying cause is uncovered. By applying the 5 Whys method, one can gain a better understanding of the issue at hand and subsequently develop more successful solutions.

Learn more: 5 Whys: How to Uncover Root Causes [Examples]

• 3 Root Cause Analysis Templates (and Examples)
• Fishbone Diagram (Components, Factors, Examples)
• Root Cause Analysis (RCA) Methods for Effective Problem Solving

How to use cause and effect analysis

Reading time: about 4 min

Regardless of the industry you work in, problem-solving will be a part of your job. And when a problem does arise, you can either ignore it and hope it goes away (not recommended) or determine the cause of the problem and find ways to fix it.

It’s more common to ignore issues than you may think. But to maximize your team’s efficiency, you’ll need to establish a method of problem-solving that works for you. A cause and effect diagram is a perfect way to get started.

What is a cause and effect diagram?

A cause and effect diagram (also called a fishbone diagram) is a visual tool that helps you examine the possible causes of a problem. Once you’ve determined the root issue, you can quickly solve it.

Fishbone diagram

The fishbone diagram was developed by Kaoru Ishikawa, a Japanese professor and expert in quality management. It got its name because it looks like a fish skeleton when completed. The “head” is the problem, and the “bones” represent the possible causes branching out from the head. 

Fishbone diagrams are often called Ishikawa diagrams, herringbone diagrams, or cause and effect diagrams.

Fishbone diagram template

If you would rather create a fishbone diagram from scratch, follow these basic steps:

• Identify your main issue or problem and write it in a box.
• Draw a long, horizontal line from the left edge of the box. This line is known as the spine.
• From the spine, draw short, angled branches. Each branch represents a potential cause of the main problem. Each branch can also have sub-branches.

How to perform a cause and effect analysis

A cause and effect analysis should be done in a team setting. Working with a team encourages creative thinking and allows you to examine the problem from different angles and perspectives.

If you perform an analysis independently, you risk introducing cognitive biases to the analysis. This can lead to identifying the wrong cause and trying to solve the wrong problem.

When performing a cause and effect analysis, consider:

• The present : What problem do you want to analyze, and what are the significant factors involved?
• The past : What might have caused the problem? 
• The future : What are some solutions you can implement to keep the problem from happening again?

Step 1: Clarify the problem

To clarify the problem, you should include information like where and how often the problem occurs and who is involved. The more specific you can be, the narrower your focus is as you problem-solve.

Once you know the real issue, write it in the “problem” box in the diagram.

Step 2: Identify the major factors

What else is contributing to the problem? Factors might include systems or equipment, materials, environment, or people.

On the fishbone diagram, these factors are placed in boxes at the end of each angled branch (or bone) extending up or down the spine. Leave enough room to add possible causes under each.

Step 3: Identify possible causes

For each factor you’ve identified, perform a brainstorming session with your team to list possible causes.

For example, if you’re experiencing a bottleneck, you may have identified team members as one of the possible factors. In this step, you’d take it a step further and determine how team members could be affecting the issue. Maybe their training was inadequate, or their skills were insufficient.

On the fishbone diagram, draw horizontal lines branching from each angled factor line. Add possible causes to these horizontal lines.

Step 4: Analyze your diagram

Your fishbone diagram should now include all the possible causes of the problem. Next, narrow down your list by eliminating the least likely causes. This might require a lot of investigative work, interviews with employees, sending out surveys, and more. 

For example, if there seems to be a consistent bottleneck at a specific workstation, your analysis and investigation can help you determine if the root cause is the equipment, the environment, or the employee working at that station.

Once you have identified the most likely root cause, you can create a plan to implement a solution that will fix the problem and prevent it from reoccurring. If you discovered that the cause of the issue was inadequate training, you could implement a standardized training process. That way, all employees—existing or new—would have the same understanding of the work processes.

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Cause and Effect Analysis: Using Fishbone Diagram and 5 Whys

Cause and effect is a relationship between events or things, where one is the result of the other or others. This is a combination of action and reaction. A cause-and-effect analysis generates and sorts hypotheses about possible causes of problems within a process by asking participants to list all of the possible causes and effects for the identified problem.

What is a Fishbone Diagram?

Cause and effect analysis is often conducted by drawing cause-and-effect diagrams (also known as Fishbone Diagram), which organize a large amount of information by showing links between events and their potential or actual causes and provide a means of generating ideas about why the problem is occurring and possible effects of that cause.

The cause-and-effect analysis allows problem solvers to broaden their thinking and look at the overall picture of a problem. Cause-and-effect diagrams can reflect either cause that blocks the way to the desired state or helpful factors needed to reach the desired state.

Fishbone Diagram (also known as Cause and Effect Diagram or Ishikawa Diagram) It visually displays multiple causes for a problem and helps us to identify stakeholder ideas about the causes of problems which Allows us to immediately categorize ideas into themes for analysis or further data gathering. People often use the 5-whys technique in conjunction with the fishbone diagram.

When to Use a Fishbone Diagram?

A cause-and-effect analysis stimulates and broadens thinking about potential or real causes and facilitates further examination of individual causes. Because everyone’s ideas can find a place on the diagram, a cause-and-effect analysis helps to generate consensus about causes. It can help to focus attention on the process where a problem is occurring and to allow for constructive use of facts revealed by reported events.

• When identifying possible causes for a problem
• When having difficulty understanding contributing factors or causes of a system failure
• Most helpful as a team process for brainstorming collaboratively

Steps for Creating a Fishbone Diagram

• Identify the problem statement (also referred to as the effect).
• Written at the mouth of the fish
• Identify the major categories of causes of the problem
• Written as branches from the main arrow for each of the major categories include: equipment or supply factors, environmental factors, rules/policy/procedure factors, and people/staff factors
• Brainstorm all the possible causes of the problem
• Ask “why does this happen?”
• Write it as a branch from the appropriate category
• Repeat the other categories by asking “Why does this happen?” about each cause
• Write sub-causes branching off the cause branches
• Ask “Why?” and generate deeper levels of causes and continue organizing them under related causes or categories

Use 5 Whys in Combination with Fishbone Diagram

The 5 Whys can be used individually or as a part of the fishbone (also known as the cause and effect or Ishikawa) diagram. The fishbone diagram helps you explore all potential or real causes that result in a single defect or failure. Once all inputs are established on the fishbone, you can use the 5 Whys technique to drill down to the root causes.

For example:

Statement – Your car stops in the middle of the road.  

1. Why did your car stop?

• Because it ran out of gas.

2. Why did it run out of gas?

• Because I didn’t buy any gas on my way to work.

3. Why didn’t you buy any gas this morning?

• Because I didn’t have any money.

4. Why didn’t you have any money?

• Because I lost it all last night in a poker game.

5. Why did you lose your money in last night’s poker game?

• Because I’m not very good at “bluffing” when I don’t have a good hand.

As you can see, in both examples the final Why leads the team to a statement (root cause) that the team can take action upon. It is much quicker to come up with a system that keeps the sales director updated on recent sales or teach a person to “bluff” a hand than it is to try to directly solve the stated problems above without further investigation.

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Cause and effect analysis with a fishbone (Ishikawa) diagram

Imagine you have been tasked with solving a problem: customers are experiencing lag while attempting to check out from their carts. Specifically, the app takes two minutes to transition from the cart page to the payment page once a customer clicks Continue .

What would you do? The first question that comes to mind is “why,” right? To answer this question, you’ll need to identify all the steps that customers take when checking out, and then assess how each step is performing. Together, these answers will enable you to determine the exact point in the checkout process where the problem is occurring.

This is called “root cause analysis” — you tried to list all the possibilities that could have created this problem. And out of all possibilities, you found which likely created the problem.

Let’s refer to the problem as the “effect,” and all the potential causes you identified during your analysis as the “causes.” Now, imagine trying to represent this cause-and-effect analysis visually. What types of diagrams could you create?

Maybe a tree whose roots are the effect and all the branches as the cause(s). Or maybe an Excel table listing all the causes and sub-causes in columns. Those are all good options, but in this article, we’ll discuss a cause-and-effect diagram (also called a fishbone diagram or Ishikawa diagram).

What is a fishbone diagram?

To start, a fishbone diagram (or Ishikawa diagram) is a tool to visually explore and represent the possible causes of an effect. The tool helps in identifying the potential causes that could have caused the problem.

A sample fishbone diagram looks like this:

It is called a fishbone as it looks like the skeleton of a fish. The head of the fish explains the problem statement (or the effect) and the bones attached explain the possible cause and sub-causes.

The history behind fishbone diagrams

Although the early history of fishbone analysis is slightly unclear, fishbone diagrams are believed to have been in use since the 1920s. However, it was during the 1960s that the diagram gained widespread popularity, thanks to the work of Dr. Kaoru Ishikawa on quality management processes for Kawasaki Shipyards.

It’s believed that Ishikawa first presented the fishbone analysis method in 1945, as part of the development of a company-wide quality control process. The creation of quality improvement processes and tools, along with the introduction of quality circles, played a significant role in the evolution of the concept of total quality management. The Ishikawa diagram became recognized as one of the seven basic quality tools.

When to use a fishbone diagram

The purpose of the fishbone diagram is to identify all the root causes of a problem. You can use fishbone analysis in product development — let’s list a few cases where you should use fishbone analysis:

• When you have to identify the possible causes of a problem
• When you have to develop a feature or product to fix the cause of a problem. This is a reverse analysis where business analysts find the gap in market need and fill that gap with a product or feature
• When evaluating a business process to find loopholes or gaps that create problems
• When you’re defining a process for quality control. Evaluate and find the current gap in quality and build processes to fill it

You can apply the fishbone analysis tool to most applications that need an establishment of quality control and management.

Now, we’ll go over the fishbone diagram and how you can apply it to multiple industries. Most businesses fall into one of these operation areas: services, manufacturing, and marketing, each of which can use a different fishbone diagram. While manufacturing may not be directly relevant to digital products, the fishbone diagram can still be a useful tool for uncovering problems and identifying potential causes. You can modify them accordingly to fit the context of your product.

Let’s start with the 4 Ws: what, why, when, and where. Applying these will help you find the possible potential causes for any effect. This is a good way to brainstorm and all fishbone diagrams must start with these, regardless of the industry you’re in. The 4 Ws are:

• What? There will be materials, products, lines of code, and other resources involved in the problem creation. Ask “what?” to help to pinpoint the specific components or resources that may be contributing to the issue
• Why? There will be situations or conditions that lead to the problem. Asking “why?” may help to uncover factors such as network failure, temperature, weather conditions, or other external considerations that may be impacting the system
• When? There will be a moment when a particular problem occurs. Ask “when?” to find out the time the problem occurs and figure out if it’s a recurring or isolated incident
• Where? Asking this question can help to identify specific areas, such as during the checkout process, a different part of the application, or within a specific context where the issue is most prominent.

Many businesses in the service industry indeed share similarities when it comes to cause analysis in their operational areas. Specifically, the 5 S’s (systems, suppliers, surroundings, safety, and skills) can be applied to cause and effect analysis within the service business.

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While all service businesses don’t need to conduct a complete cause analysis using the 5 S’s, many find that these principles can be applied at the initial levels and then gradually approached more thoroughly as they identify potential causes within their unique environment:

Systems are the methods, policies, processes, products, and tools used to build operational excellence and provide service without any failure.

Suppliers are any issues in delivering the service itself, like lower quality of service, failure to support customers, delay in refunding payments, agents or vendors delay, and more.

Surroundings are any external factors such as market, competition, public relations, brand value/image, etc. that may contribute to the issue.

Skills focus on finding issues in training, qualifications, skill set, and the experience of employees providing the service.

Safety focuses on finding issues in the system’s overall safety, products, operational procedures, and work environment.

Though the 6 Ms apply mainly to the manufacturing industry, you can transfer this framework into the software industry as well. Since digital products don’t have physical parts, not all of them will be relevant, but we’ll highlight them anyway:

Material focuses on finding issues in any raw materials used for manufacturing. This includes issues with the quantity or quality of supplies, any issues with the timeline of procurement and supply, and more.

Method focuses on finding issues in processes, policies, regulations, training, and guidelines used by companies. Though this is relevant to manufacturing physical goods, these apply to digital products as well (in context).

Machine focuses on finding issues in machines that are used in manufacturing (production). This will help uncover any issues with the maintenance of machines, any failures in the machine or assembly line, etc.

Mother nature , aka the environment, focuses on finding issues in environmental conditions. For physical goods, this could mean issues with temperature, light conditions, etc. For digital products, this could mean issues with servers going down, weather causing latency problems, and more.

Manpower (aka people) focuses on finding issues in the workforce. This means any issues with the work itself, employee burnout, training and skill sets, and more.

Measurement focuses on finding issues in measuring the process and results. This is huge in any business, as metrics and measuring success are vital to the health of a company. Use this to find any issues in quality readings, calculations, and more.

There are 7 Ps that can be commonly applied in cause analysis. These are all related to marketing businesses, but nearly all physical and digital products have marketing functions that affect their product and that this can apply to:

People focuses on finding issues in people involved in marketing a product or service to customers. This implies issues with not targeting the right audience or marketers not having a good understanding of the product.

Product is focused on finding issues in the product or service of an organization. This could be several things, including the perceived image of the product , issues with availability to reach customers, or failure to meet customer needs .

Process works on finding issues in procedures for promoting and marketing the product or service. Are there any gaps in cross-functional team collaboration ? Are they any issues with the escalation matrix?

Price is a big one. This is where to uncover issues in the pricing of the product or service. Is the price too low? Maybe it’s too high? Are there any issues with the price range not matching the competition or with accepting certain payment methods?

Promotion focuses on finding issues in promotion methods, mediums, and strategies. Are social media advertisements reaching the right audience? Is it generating enough clicks?

Place identifies problems with the location of your product. Are there any issues with the availability of your product on particular devices?

Physical evidence is the last of the Ps. It focuses on finding issues in the direct visibility of your product or service. If it has a physical component, is there a problem with the packaging? Physical evidence literally implies any physical issues with the product getting into the hands of customers.

Advantages and disadvantages of fishbone diagrams

There are many advantages you carry when you use a fishbone diagram as a tool for your cause analysis, including:

• Simplicity : the visual representation is simple and easy to understand. Anyone who understands the problem can easily go through all the possible causes of that problem
• Flexibility : you can dissect the cause into sub-causes and go deeper to find the cause
• Associativity : any cause becomes a potential candidate to generate the effect. The relationship with the problem is tightly defined, helping you conclude your analysis
• Ease of use : it’s easy to brainstorm with a team and explain how to use the diagram. Everyone can instantly adapt and be involved in a brainstorm
• Fast : it helps you analyze the root causes quickly by applying the 5 Why’s and drill down potential causes
• Prioritization : visual representation helps you to prioritize your causes and narrow down the most important ones

There are also a few limitations that can make fishbone diagrams difficult to use, including:

• Causes may not be as relevant as you once thought. This could lead to confusion on the path forward or create another problem by accident
• If oversimplified, the fishbone analysis may not uncover what you’re looking for
• There’s potential to prioritize smaller causes over critical ones. Since prioritization is so easy, the team may focus on small causes and waste time in fixing them
• The more dissection into sub-clauses you do leads to more complex diagrams. These may eventually become messy and difficult to understand
• The analysis is mostly based on understanding and brainstorming. It needs additional evaluation of the cause, which in case all possibilities have to be evaluated, can become inefficient and time-consuming

Fishbone diagram template

If you’d like a fishbone diagram template to work with, you can download this one I made on Google Sheets . Feel free to make a copy of it and customize it for your own use.

If you’d like to try to create a fishbone diagram yourself for your own team and organization, here are some common steps to make and analyze it:

• Identify and write the effect (problem statement) as the fish head
• Brainstorm and identify major causes. Write a major cause on each side bone. These major causes can be the Ms, Ps, or S’s with regard to the industry you’re in, or a combination of multiple
• Brainstorm and identify sub-causes. Write the sub-cause on each smaller bone
• Check for completeness. Evaluate or review for too few causes or narrow it down if you have too many
• Analyze each potential cause to narrow it down further. The goal is to end up with one or more that need to be fixed
• Prioritize the potential cause based on its severity on the effect

Let’s draw the fishbone diagram for a digital product. We’ll use the problem we listed at the beginning where customers are facing problems during the checkout process:

While this list may not be comprehensive, it does cover many potential causes that could lead to delays in opening the payments page. It’s important to note that during a brainstorming session, many potential causes can arise, but it’s important to focus on the most likely causes to address first. In this case, we’ve highlighted a sub-cause in red that represents the result of the analysis and the area that should be addressed.

How product managers can use fishbone diagrams

There are many cases where product managers have to analyze the cause of a problem, and a fishbone diagram is a powerful tool for product managers to benefit from. Its easy and quick creation helps quickly narrow down potential causes and act upon them.

A few examples where product managers can make use of fishbone diagram are:

• Finding the root cause for a reduction in feature usage
• Finding the root cause of churn
• Learn the effects of new features on the application upfront
• Analyze what the team should focus on while prioritizing enhancements for a feature
• Presenting the quality measures taken on certain features to senior management

The process type fishbone diagram

A not-so-popular and not-so-in-use flavor for creating fishbone diagrams is the process-type fishbone diagram. It’s very similar but has a small change in the drawing:

The main bone of the fish is divided into multiple cause areas. Team brainstorms potential causes that could have created the problem in each of these areas. It is called process type because each area in the main bone (mostly) represents a process, but each area in the main bone also represents a department. Causes are explored in the process of each department as a result.

These diagrams are not that popular but are an efficient way to involve multiple cross-functional teams from various departments.

Kaoru Ishikawa popularized the fishbone diagram to analyze the root cause of any problem. It is also called the Ishikawa diagram or cause and effect diagram.

One can use the 4 Ws — what, why, when, and where — to begin cause analysis. Based on industry type, there are common areas of cause analysis: the 5 S’ for services, 6 Ms for manufacturing, and 7 Ps for marketing. If your company or product has a combination of these, you can create multiple fishbone diagrams to get to the root cause of your issue.

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• Guide: Fishbone Diagram

Daniel Croft

Daniel Croft is an experienced continuous improvement manager with a Lean Six Sigma Black Belt and a Bachelor's degree in Business Management. With more than ten years of experience applying his skills across various industries, Daniel specializes in optimizing processes and improving efficiency. His approach combines practical experience with a deep understanding of business fundamentals to drive meaningful change.

• Last Updated: October 19, 2023
• Learn Lean Sigma

The Fishbone Diagram, also known by various other names such as Ishikawa Diagram, Cause and Effect Diagram or 6Ms, is a visual tool used in problem-solving and root cause analysis . Originating from the quality management sector, it is used as a systematic approach to identify, explore, and display possible causes of a specific problem.

Table of Contents

What is a fishbone diagram.

The Fishbone Diagram is a graphical tool used for identifying and organizing possible causes of a specific problem or outcome. The diagram resembles the skeletal structure of a fish, hence its name. At the “head” of the fish is the problem you’re trying to solve, and the “bones” branching off the spine are the multiple potential causes contributing to that problem.

The benefit of a Fishbone Diagram lies in its simplicity. It gives you a structured way to brainstorm and categorize the various factors affecting a specific issue. The diagram can get as detailed as you need it to be, allowing for sub-causes to branch off the main causes, offering even deeper insights.

The Components of a Fishbone Diagram

Head : The fish’s head represents the problem or effect you’re analyzing.

Spine : The long, horizontal line connecting to the head serves as the timeline or sequence of the problem.

Bones : These are the categories of potential causes. They branch off the spine, leading towards the head.

Sub-Bones : These are the more specific factors or sub-causes that stem from the main categories.

Why Use a Fishbone Diagram?

The Fishbone Diagram is a flexible tool that can be used for a range of applications. Here’s why it’s so useful:

1. Easy Visualization:

One of the primary advantages of using a Fishbone Diagram is its ability to simplify complex problems. With all potential causes visually represented in one place, it becomes easier to analyze and discuss the issues.

2. Team Collaboration:

The Fishbone Diagram is excellent for brainstorming sessions. It encourages team members to think critically and contributes to a shared understanding of the problem. It’s collaborative by design, allowing for the collective intelligence of the group to shine.

3. Root Cause Analysis:

Identifying symptoms of a problem is one thing; uncovering the root cause is another. The Fishbone Diagram excels at this by forcing you to dig deep into various contributing factors. By isolating these causes, you’re better positioned to find a lasting solution.

By using a Fishbone Diagram, you’re not just addressing a problem with a short-term fix; you’re conducting a thorough investigation to eliminate issues from the root up.

How to Create a Fishbone Diagram: A Detailed Guide

Creating a Fishbone Diagram might seem like a daunting task, but it’s actually a straightforward process. Here, we’ll break down each step in detail to ensure you can construct a Fishbone Diagram that serves its purpose effectively.

Materials You’ll Need:

• A whiteboard or large sheet of paper
• Markers or pens
• Sticky notes (optional)
• A team of people for brainstorming

Step 1: Identify the Problem

The first and most crucial step is to clearly identify the problem you’re trying to solve. This statement should be specific and concise. Write this problem statement at the far right side of your whiteboard or paper, as it will serve as the “head” of your fishbone diagram.

• Use data to define the problem whenever possible.
• Make sure the problem is mutually understood and agreed upon by all team members.

Step 2: Determine the Main Categories

Now, draw a horizontal line from the problem statement towards the left side of the board. This is the “spine” of your fish. Next, you’ll identify the major categories of causes that could be contributing to the problem. These categories will serve as the main “bones” branching off from the spine.

Common categories often include:

• Measurement
• Environment
• The categories can be industry-specific or general, depending on the problem you’re analyzing.
• Aim for 4-6 main categories for better manageability.

Step 3: Brainstorm Causes

Once you’ve determined the main categories, it’s time to brainstorm potential causes for each category. These causes will be the smaller “bones” that branch off from the main bones. If you’re working with a team, this is an excellent time for a brainstorming session.

• Use sticky notes to write down ideas so you can easily move them around.
• Encourage every team member to contribute; sometimes the most unexpected insights come from diverse perspectives.

The output of the root cause analysis at this point may look something like the below example. With the lack of preventative maintenance being explored with the 5Whys analysis.

Step 4: Dig Deeper with Sub-Causes

Sometimes, the primary causes can be broken down into smaller, more specific sub-causes. This is where the “ 5 Whys ” technique can be beneficial. For each primary cause, ask “Why?” up to five times to dig deeper into the root of the issue.

• Not all primary causes will need sub-causes; use your discretion.
• Keep the diagram organized to ensure readability.

Step 5: Analyze and Take Action

After all the causes and sub-causes have been identified, it’s time to analyze the Fishbone Diagram as a team. Highlight or circle the causes that seem most likely to be contributing to the problem. These are the areas that will need immediate attention and action.

• Use data or evidence to support your conclusions.
• Create an action plan assigning responsibility for each highlighted cause.

And there you have it! You’ve successfully created a Fishbone Diagram that will help you get to the root of your problem.

Examples and Case Studies: Understanding Fishbone Diagrams in Action

Case study: manufacturing unit with quality issues.

Imagine you run a manufacturing unit, and you’ve been receiving complaints about the quality of your products. You decide to use a Fishbone Diagram to get to the root of the issue.

Step 1: Identify the Problem Problem Statement: “High number of defective products in the last quarter.”

Step 2: main categories.

• People : Untrained staff, high employee turnover
• Process : Inconsistent quality checks, outdated SOPs (Standard Operating Procedures)
• Equipment : Aged machinery, lack of maintenance
• Environment : Poor lighting, extreme temperature fluctuations

Step 4: Dig Deeper (5 Whys)

• Untrained Staff : Why? No formal training program.
• Aged Machinery : Why? No budget allocated for equipment upgrades.

After analyzing the Fishbone Diagram, you realize that untrained staff and aged machinery are your primary culprits. Your next steps could involve investing in staff training programs and allocating budget for machinery upgrades.

By following these steps, you can develop targeted strategies to improve product quality significantly.

Common Mistakes and Tips: How to Make the Most of Your Fishbone Diagram

1. overlooking minor causes.

While it’s easy to focus on the most glaring issues, minor causes can accumulate and have a significant impact.

Tip : Don’t disregard a cause just because it seems minor. Sometimes, fixing smaller issues can lead to big improvements.

2. Ignoring Data

It’s tempting to rely solely on brainstorming and intuition, but data should be your guiding star.

Tip : Use metrics and KPIs to support or refute the causes you’ve identified. This adds credibility to your findings and helps you prioritize effectively.

3. Stopping at Symptoms

Identifying symptoms is just the first step; your ultimate goal should be to uncover the root causes.

Tip : Use techniques like the “5 Whys” to dig deeper into each cause and ensure you’re addressing the root of the issue, not just its manifestations.

Fishbone Diagrams are a fantastic asset in the toolbox of anyone interested in continuous improvement. Whether you’re a seasoned professional or just getting started, this simple yet powerful tool can help you dissect complex problems and develop targeted solutions.

They encourage you to look beyond the obvious and delve into the intricacies that contribute to each problem. So go ahead, grab that pen and paper (or a whiteboard, if you’re feeling fancy), and start your journey towards effective problem-solving.

• Ilie, G. and Ciocoiu, C.N., 2010. Application of fishbone diagram to determine the risk of an event with multiple causes.   Management research and practice ,  2 (1), pp.1-20.
• Coccia, M., 2018. The Fishbone diagram to identify, systematize and analyze the sources of general purpose Technologies.   Journal of Social and Administrative Sciences ,  4 (4), pp.291-303.

Q: What is the origin of the Fishbone Diagram?

A: The Fishbone Diagram was originally developed by Dr. Kaoru Ishikawa, a Japanese quality control statistician, in the 1960s. It’s also sometimes referred to as the Ishikawa Diagram in his honor.

Q: Can Fishbone Diagrams be used for non-manufacturing problems?

A: Absolutely! While Fishbone Diagrams are popular in manufacturing and quality management, their application is not limited to these areas. They can be used in healthcare, software development, event planning, and even for personal problem-solving.

Q: How many main categories should my Fishbone Diagram have?

A: The number of main categories can vary depending on the complexity of the problem. However, it’s generally advisable to have between 4-6 main categories for easier analysis and readability.

Q: Can I use software to create a Fishbone Diagram?

A: Yes, there are several software tools available for creating Fishbone Diagrams, such as Microsoft Visio, Lucidchart, and various other project management software options. However, a simple pen and paper or a whiteboard can be just as effective for smaller teams or simpler problems.

Q: How do I know which causes to prioritize after creating a Fishbone Diagram?

A: After creating your Fishbone Diagram, you should analyze it with your team to identify the most likely root causes of the problem. Using data to support your conclusions can be very helpful. You may also employ techniques like the Pareto Analysis to prioritize causes based on their impact.

Daniel Croft is a seasoned continuous improvement manager with a Black Belt in Lean Six Sigma. With over 10 years of real-world application experience across diverse sectors, Daniel has a passion for optimizing processes and fostering a culture of efficiency. He's not just a practitioner but also an avid learner, constantly seeking to expand his knowledge. Outside of his professional life, Daniel has a keen Investing, statistics and knowledge-sharing, which led him to create the website learnleansigma.com, a platform dedicated to Lean Six Sigma and process improvement insights.

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Cause and Effect Analysis Diagram

Cause and Effect Analysis Diagram: this article provides you with a practical explanation of the Cause and Effect Analysis Diagram . Next to what it is, this article also highlights two differnt tools to perform this type of analysis, the steps and an example. After reading, you will understand the basics of this problem analysis tool. Enjoy reading!

What is a Cause and Effect Analysis?

Each organisation has its own problems, big and small. A problem within an organisation can be either a chance to grow, or a setback that leads to failure. Which of the two it ends up being depends on how the problem is solved.

An effective method to prevent undesirable outcomes is to conduct a proper analysis of the situation, establish the causes of the problem, and subsequently developing a fitting solution for the situation.

For a Cause and Effect Analysis, the analyst sums up and analyses all the potential causes and effects of the identified problem, after which the analyst generates and sorts through various hypotheses about the problem’s potential causes.

The most frequently used methods to process and draw connections between large amounts of information involve the use of cause-and-effect diagrams.

Cause and Effect Analyses enable problem solvers to broaden their minds, and to look at the bigger picture with regards to the problem. Aside from reflecting the causes standing in the way of the desired outcome, cause-and-effect diagrams can also be used to map out the necessary factors for achieving this desired outcome.

Two tools for setting up a Cause and Effect Analysis

Fishbone diagram as a cause and effect analysis.

In the ’60s of the last century, professor Kaoru Ishikawa , a pioneer in the field of quality management, developed his Cause and Effect Analysis diagram. This analysis is also known as the Ishikawa diagram, Ishikawa, or Fishbone diagram .

It is called that because of the resemblance of the finished diagram to a fish bone. The diagram was initially used for quality improvement, but soon proved a highly effective problem analysis tool as well, used to analyse the causes of impediments within corporate processes, as well as potential way to improve these processes.

The Cause and Effect Analysis entails two important steps which enable the problem solver to both look back and ahead in time. In looking back, the analysis is geared towards identifying the areas where mistakes were made, or money was lost. This is how the diagram helps understand what happened before.

By establishing the causes of the problem, the problem solver is able to work towards swiftly solving, or entirely avoiding, future instances of it.

When looking ahead, the analysis attempts to find workable solutions that can be easily implemented in the future as a means to build upon the success of the organisation.

Fishbone Diagram Steps

The fishbone diagram can be drawn up using the steps below. Or you can download a free fishbone diagram template here and fill it out by following the steps below:

Step 1: Identify the problem

Write down the exact problem situation: Identify who is – or was – involved, what the problem is, where, and when it occurs. Next, briefly summarise the problem on the left side of a large sheet of paper and draw a long horizontal line across the length of the sheet.

This setup resembles the head and spine of a fish and ensures that there will be space both on top and on the bottom to develop ideas.

Step 2: Elaborate on the key factors of the problem

Establish which factors likely make up a part of the problem. These might be systems, but also equipment, materials, external parties, or people involved in the problem. A helpful tool to do this, is the McKinsey 7S framework . Brainstorm about all the potential factors that might influence the situation, drawing a line across the spine of the Fishbone diagram for each one. Label every subsequent line.

Step 3: Identify potential causes

Evaluate each factor from step two for potential causes of the problem that might correlate with these aforementioned factors. Note down these potential causes as shorter lines coming off of the ‘vertebrae’ of the fish bone. For far-reaching or complex causes, it is possible to subdivide into sub-causes that each have their own causal line.

Step 4: Analyse the Cause and Effect Analysis diagram

The final step entails an analysis of the entire diagram, containing all the potential causes of the problem. Dependent upon the complexity and the importance or impact of the problem, the most likely causes may warrant closer inspection. This can be done via further research or surveys. Which potential causes actually contribute to the problem can be tested like this.

Five Whys method

The Five Whys method or 5 Whys Analysis also aids in establishing the cause and effect of a problem situation. Whereas the Fishbone diagram can offer solutions to a problem with known causes, the Five Whys method shines in situations where the true cause of a problem is still unclear.

The use of the Five Why’s method is a simple way to solve an established problem without need for a large-scale, highly detailed investigation. The method is also referred to as the Why Tree, and it enables the problem solver to peel back multiple layers of a problem via the repeated asking of the question ‘why?’

This technique was originally developed by Sakichi Toyoda and utilised by the Toyota Motor Corporation in the testing of manufacturing methods.

Five Whys Steps with example

Carry out a Five Whys analysis via these five steps:

Step 1: Note down the specific problem

By describing the problem, it can be precisely and comprehensively formulated. In case a specific team is designated to solve the problem, the description will ensure that they are all on the same page, concentrating on the same problem.

Step 2: Ask why

Note down why the problem is occurring right below the description of the issue. Should the answer to this question fail to give a clear enough cause, move on to step 3.

Step 3: Continue asking why

Ask why with regards to the answer of step 2 and write the new answer below it.

Step 4: Repeat step 3 until the cause has been identified

Should there still be a lack of clarity as to the problem’s cause, the question of why needs to continue being asked. The cause might be identified after 2 times, or it might take 8 times.

Cause and Effect Analysis tips and Focus areas

• Utilise the Fishbone diagram to keep the team’s focus on causes instead of symptoms.
• Utilise the Five Whys method if the true cause isn’t known yet.
• Keep enough room open between the different categories of the Fishbone diagram for potential adjustments.
• Consider a brainstorming session to think about the problem and its causes, making the participants put down their thoughts on post-its.
• Combine the Five Why’s method with the Fishbone diagram to still be able to discern the cause in spite of disappointing initial results.
• Be careful about premature assumptions with regards to causality. Question whether or not connections can be proven or not.

It’s Your Turn

What do you think? Do you recognise the explanation about the different kinds of cause-and-effect analysis? Do you use the Five Whys method or the Ishikawa Diagram? What do you feel are the factors that contribute to an effective resolution of problem situations? Do you have any tips or additions?

Share your experience and knowledge in the comments box below.

More information

• Feld, W. M. (2002). Lean manufacturing: tools, techniques, and how to use them . CRC Press.
• Ishikawa K. & Loftus, J.H. (1990). Introduction to quality control . Tokyo, Japan: 3A Corporation; 1990.
• Martin, J. (2006). Lean six sigma for supply chain management . McGraw Hill Professional.

How to cite this article: Janse, B. (2018). Cause and Effect Analysis Diagram . Retrieved [insert date] from ToolsHero: https://www.toolshero.com/problem-solving/cause-and-effect-analysis/

Original publication date: 04/09/2018 | Last update: 10/30/2023

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Ben Janse is a young professional working at ToolsHero as Content Manager. He is also an International Business student at Rotterdam Business School where he focusses on analyzing and developing management models. Thanks to his theoretical and practical knowledge, he knows how to distinguish main- and side issues and to make the essence of each article clearly visible.

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Cause & Effect

What is a cause & effect diagram.

Also known as fishbone, C&E, Ishikawa diagram

Fishbone shaped diagram with Effect (Y, output) being the head and Bones as the major causes (Xs). This is about cause and effect and not symptoms

It can have a fair amount of detail in a visual format, which is easy to read and relate to. 

Removes personal biases - a team tool

Keeps focus on the problem

When to use Cause & Effect Diagram

To arrive at a root cause

Focus on causes and not symptoms

To brainstorm & visually present, the output of a brainstorming session

How to make a Cause & Effect Diagram

The tool involves brainstorming - so the rules apply

Place the problem statement on the right side of the board, vertically in the middle

Now, either ask the team for major cause areas or use 

6 Ms: Man, Machine, Method, Mother Nature, Material & Measurement - remove any that don't apply

Major process steps 

Use regions (increase sales problem)

previously collected data to write bones and get consensus

So now you have the head and major bones  

Let's brainstorm on causes related to each bone

Drill down further into each sub cause - ask "why", ask "what causes it" "why does this happen" - this may then be placed a sub bone

This goes on, until either you start going out of your control area  or team can't answer any more

Once completed

look for causes that appear repeatedly

you can highlight the prominent ones and get them voted upon to prioritize

do confirm the frequency of causes, if low, their impact on the problem may also be low 

Variations of Cause & Effect Diagram

CEDAC - Cause and Effect with Addition of Cards

Example of Cause & Effect Diagram

The head is why low core production hours 

Major bones are based on 6 Ms

This example only shows till level 2 causes , you can create further bones by adding  a vertical / slanting arrow to the sub bone

How to Use Cause and Effect Analysis to Solve Business Problems

Updated on: 24 July 2023

Every business has its own problems, big and small.

A problem in your business can be an opportunity for the business to grow or can be the setback that leads to failure. It all depends on how you embark on problem-solving.

A good way of preventing the occurrence of undesirable things is by proper analysis of the business situation, identification of the root causes and solving the situation. Hence the need to have an efficient and effective cause and effect analysis . Here we offer you a clear guide on how to go about it.

How to Do a Cause and Effect Analysis

Cause and effect analysis is also known as a fishbone diagram . It was developed by Professor Kaoru Ishikawa, a pioneer professional in the field of quality management in the 1960s.

The diagram is called fishbone as its final look is in the form of a fishbone. It was initially used for quality improvement but it can also be helpful in analyzing business processes through the identification of its causes and hindrances, as well as on ways to improve it.

The cause and effect analysis basically involves two key steps which are looking backward and looking forward.

• In looking backward, the analysis focuses on identifying areas in which mistakes were made or money was lost. It looks into what had happened before. In identifying where the issues are, you are able to work towards avoiding the problem in the future. Another advantage of this is that, you’ll be able to replicate a positive outcome.
• Looking into the future and planning for it, the analysis seeks to find workable solutions that are to be implemented in the future and build on the future success of the business. This analysis enables you to determine your future outcome by planning what to do now to achieve it.

This method is unique in that it encourages creative thinking as well as brainstorming in the process of breaking down issues and aligning them in the process of finding its solutions.

Among the key advantages of this method is that it ensures that you don’t seek out to solve the wrong problems, it also ensures that you solve the problem well enough for it to go away.

Step 1: Problem Identification

The way to use this tool is very simple. You first need to identify the problem area that needs analysis. A good way to do this is by giving a brief description of the current business situation and the consequences as well as the reasons for why they occurred.

Here you can replace the problem with a goal that you want to achieve. For instance, high profitability. You need to be very specific about the problem that you’re facing as this forms the basis of your analysis. If the identification is wrong, then the analysis also tends to be wrong.

You should also identify the people involved as well as what the exact problem is and how it occurs. For example, low productivity due to absenteeism especially in the production department, which includes production personnel and the line manager, for the period from January to November 2017.

Step 2: Main Problem Causes

The next step is identifying the main causes of the problem, for example, the people, the procedures your business utilized as well as the materials or equipment. Here, you will need to do a lot of brainstorming to come up as many reasons as you possibly can.

You can use various models to help you identify as many problems as you can in a clear manner. For example, the McKinsey 7S Framework which includes, Strategy, Systems, the business Structures, Shared values of the business, Staff, Skills, and Style.

Step 3: Identify Plausible Sub-causes of the Main Causes

Thereafter, you can narrow down to further plausible causes of the main problems that you’ve listed. This is important for the problem-solving stage of analyzing the diagram, as you will know what to do exactly to rectify the situation. This can include issues like wrong norms and values that could be as a result of poor training of employees.

Step 4: Analyze the Diagram

By now, your diagram should be showing all the main and minor causes that you’ve thought of.

Depending on how complex the information is, you can do a much deeper investigation on the causes. This can be by conducting a survey as well as drawing up questionnaires.

For example, you can ask your employees to fill in a survey on how much they understand the products you sell in your business. The information you get from this will help you build upon areas of training in order to address the problem of lack or insufficient knowledge.

From here, a corresponding step is developing an action plan with clear priorities that can be implemented immediately. This is made simple as the plans can comprise of actions to solve the subproblem causes.

For the action plan to be effective, it needs to be clear and to be executed aggressively. For example, a sub cause like lack of staff morale can be turned into an action plan, which will be giving incentives for staff motivation, hence, improved productivity and in turn higher profitability which was the main goal.

Ready to Use Cause and Effect Analysis in Business Problem-Solving? 

The cause and effect analysis uses brainstorming and critical analysis by way of visual representation to enable problem-solving .

The main problem is drawn out on the left while the main causes are drawn out an extension of the problem. This then calls for a look at the sub causes which are written out as branches of the main problems.

Various models are also used as a guide to identifying the problem-causes. The models look into various business elements including, the people relation the processes, the systems and equipment use, the management as well as the general organizational structure.

Among the advantages of this analysis method include correct identification of problem-causers hence proper problem-solving, better visualization and hence the effectiveness of the problem-solving process.

Would you like to try drawing up your own Fishbone Diagram ?

BIO : Lori Wade is the content writer and a career specialist for college students. She is a content marketer, sharing experience in writing, education, and self-development in her publications, for example, https://eliteessaywriters.com/blog/toefl-essay-topics/ . Connect with her on Facebook and follow her on Twitter .

Join over thousands of organizations that use Creately to brainstorm, plan, analyze, and execute their projects successfully.

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Thanks for sharing your thought! Helps me a lot to do the same thing in my company. I agree that before you decide what o plan you need to have to weigh its cause and effect.

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How to Use the Fishbone Diagram in Root Cause Analysis?

Solving problems is considered an initial part of designers’ practice as they aim for user-centred design. The target that requires root-cause analysis of problems using tools such as the Fishbone diagram. A clear definition for the problem should be highlighted in order to target the proper solution, including analysing the causes behind this problem and its impact on the business. Tools such as TRIZ , SCAMPER Technique and Fishbone Diagram are commonly used to allow exploring a specific problem before providing the proper solution.

Related Articles:

What is the 8D Problem Solving? And How to use the 8D Report?

Using the TRIZ Method for Creative Problem Solving

Problem Solving Using Hurson’s Production Thinking Model

What is the Fishbone Diagram?

The Fishbone diagram (also known as the root-cause analysis and Ishikawa diagram) is used to identify the root causes of problems by identifying the different factors that may cause the problem. Based on the results, the team can build an effective solution to eliminating the problem.

The term, Fishbone Diagram, was first coined by professor Kaoru Ishikawa in his book “Introduction to Quality Control,” published in 1990. The diagram is also known as the Ishikawa Diagram, Cause and Effect Diagram, and Herringbone Diagram.

The Cause and Effect Diagram can be implemented during the  development brainstorming session  to discover the roots of a specific problem or identify the bottleneck in a particular process by categorising the causes of problems into six main types: people, methods, machines, materials, measurements, and environment. The categorisation of the cause of a problem varies based on the industry, such as the following:

– Manufacturing industry (5 Ms) : Machine, Method, Material, Man Power, and Measurement

– Marketing industry (7 Ps) : Product, Price, Place, Promotion, People, Process, and Physical Evidence

– Service industry (5 Ss) : Surrounding, Suppliers, Systems, Skills, and Safety

The name Fishbone Diagram comes from the final shape of the diagram after analysing the problem because the structure looks like a fishbone, which is built gradually from right to left during the problem-solving session. The diagram starts with two main sections; Cause on the left and Effect on the right (video 1). 

Video 1. Definition of the Fishbone Diagram.

Then, the possible causes of the problem are explored by creating branches from the line that link between cause and effect sections, as we will explore in the following example. While this diagram looks similar to other creative thinking methods, such as  mind maps  and  starbursting , it focuses on solving problems rather than exploring ideas.

Using the Fishbone Diagram Example

To understand how the Cause-Effect Diagram works, we will explore an example of a problem and see how to use the Fishbone Diagram to investigate a problem, which is why a design studio company always fails to reach an innovative product or service.

Step 1: Identify the problem (Effect)

The effect, or problem, results from one or multiple causes. Therefore, the problem should be clearly addressed to investigate the related causes. In the Design Studio example, we start by writing the Effect (Lack of Creativity) or the problem on the right side, as shown in the figure below.

Step 2: Define the main causes model

Then, define the main categories of factors that can lead to the root causes of the problem. You can use any of the above highlighted three cause models. In this example, we’ll use the manufacturing industry model (5 Ms): Machine, Method, Material, Man Power, and Measurement.

Step 3: Investigate possible causes of the problem

After drawing the main general categories, where we can add causes, the discussion starts to investigate all the possible causes for the problem and organize them under the five categories. For example, under Man Power, causes can be a lack of motivation, lack of skills, under-evaluated skills, hiring inappropriate skills, or fixed working hours.

Step 4: Analyse the findings

Once the diagram is complete, and causes are organized in the model, the next step is to investigate and discuss each possible cause and rank them based on their priority and influence on the problem. Understanding the different causes may include using research methods such as interviews, focus groups, and surveys.

The below video provides another example of how to use the Fishbone Diagram in identifying the root causes of problems.

Video 2. Example of how to apply the Ishikawa Diagram in identifying the root-causes of problems.

Further resources:

Solve It!: The Mindset and Tools of Smart Problem Solvers

Root Cause Analysis Handbook: A Guide to Efficient and Effective Incident Investigation

When it comes to problems, both designers and managers can fail to find the solution, and part of this failur

e is their inability to find the correct causes of problems. Many methods to achieve this goal, such as the Fishbone Diagram, investigate the relationship between the effect of the problem and its possible causes. In this tool, the causes are categorised and organised in the diagram to build a better understanding of how these causes contribute to the final problem and to order causes based on their priority as a means of addressing the problem in the final solution plan.

Article cover copyrights: Image by storyset on Freepik

The steps to apply the Fishbone Diagram

– Manufacturing industry (5 Ms) : Machine, Method, Material, Man Power, and Measurement – Marketing industry (7 Ps) : Product, Price, Place, Promotion, People, Process, and Physical Evidence – Service industry (5 Ss) : Surrounding, Suppliers, Systems, Skills, and Safety

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Dr Rafiq Elmansy

As an academic and author, I've had the privilege of shaping the design landscape. I teach design at the University of Leeds and am the Programme Leader for the MA Design, focusing on design thinking, design for health, and behavioural design. I've developed and taught several innovative programmes at Wrexham Glyndwr University, Northumbria University, and The American University in Cairo. I'm also a published book author and the proud founder of Designorate.com, a platform that has been instrumental in fostering design innovation. My expertise in design has been recognised by prestigious organizations. I'm a fellow of the Higher Education Academy (HEA), the Design Research Society (FDRS), and an Adobe Education Leader. Over the course of 20 years, I've had the privilege of working with esteemed clients such as the UN, World Bank, Adobe, and Schneider, contributing to their design strategies. For more than 12 years, I collaborated closely with the Adobe team, playing a key role in the development of many Adobe applications.

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2 thoughts on “ how to use the fishbone diagram in root cause analysis ”.

Is there a tool with which I can create such a cause-effect diagram easily?

I’m sorry for the late reply as I was busy with my research. You can use online brainstorming tools such as Mural or Miro. It allows you to create the fish bone shape there.

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7 Powerful Root Cause Analysis Tools and Techniques

By Sebastian Traeger

Updated: April 21, 2024

Reading Time: 5 minutes

1. The Ishikawa Fishbone Diagram (IFD)

2. pareto chart, 4. failure mode and effects analysis (fmea), 5. proact® rca method, 6. affinity diagram, 7. fault tree analysis (fta).

With over two decades in business – spanning strategy consulting, tech startups and executive leadership – I am committed to helping your organization thrive. At Reliability, we’re on a mission to help enhance strategic decision-making and operational excellence through the power of Root Cause Analysis, and I hope this article will be helpful!  Our goal is to help you better understand these root cause analysis techniques by offering insights and practical tips based on years of experience. Whether you’re new to doing RCAs or a seasoned pro, we trust this will be useful in your journey towards working hard and working smart.

Root Cause Analysis (RCA) shines as a pivotal process that helps organizations identify the underlying reasons for problems, failures, and inefficiencies. The goal is simple: find the cause, fix it, and prevent it from happening again. But the process can be complex, and that’s where various RCA techniques come into play. 

Let’s dive into seven widely utilized RCA techniques and explore how they can empower your team’s problem-solving efforts.

Named after Japanese quality control statistician Kaoru Ishikawa, the Fishbone Diagram is a visual tool designed for group discussions. It helps teams track back to the potential root causes of a problem by sorting and relating them in a structured way. The diagram resembles a fishbone, with the problem at the head and the causes branching off the spine like bones. This visualization aids in categorizing potential causes and studying their complex interrelationships.

The Pareto Chart, rooted in the Pareto Principle, is a visual tool that helps teams identify the most significant factors in a set of data. In most situations, 80% of problems can be traced back to about 20% of causes. By arranging bar heights from tallest to shortest, teams can prioritize the most significant factors and focus their improvement efforts where they can have the most impact.

The 5 Whys method is the epitome of simplicity in getting to the bottom of a problem. By repeatedly asking ‘why’ (typically five times), you can delve beneath the surface-level symptoms of a problem to unearth the root cause. This iterative interrogation is most effective when answers are grounded in factual evidence.

When prevention is better than cure, Failure Mode and Effects Analysis (FMEA) steps in. This systematic, proactive method helps teams identify where and how a process might fail. By predicting and examining potential process breakdowns and their impacts, teams can rectify issues before they turn into failures. FMEA is a three-step process that involves identifying potential failures, analyzing their effects, and prioritizing them based on severity, occurrence, and detection ratings.

The PROACT ® RCA technique is a robust process designed to drive significant business results. Notably used to identify and analyze ‘chronic failures,’ which can otherwise be overlooked, this method is defined by its name:

PReserving Evidence and Acquiring Data: Initial evidence collection step based on the 5-P’s – Parts, Position, People, Paper, and Paradigms.

Order Your Analysis Team and Assign Resources: Assembling an unbiased team to analyze a specific failure.

Analyze the Event: Reconstructing the event using a logic tree to identify Physical, Human, and Latent Root Causes.

Communicate Findings and Recommendations: Developing and implementing solutions to prevent root cause recurrence.

Track and Measure Impact for Bottom Line Results: Tracking the success of implemented recommendations and correlating the RCA’s effectiveness with ROI.

PROACT® RCA excels in mitigating risk, optimizing cost, and boosting performance, making it a valuable addition to any RCA toolkit.

The Affinity Diagram is a powerful tool for dealing with large amounts of data. It organizes a broad range of information into groups based on their natural relationships, creating a clear, visual representation of complex situations. It’s particularly beneficial for condensing feedback from brainstorming sessions into manageable categories, fostering a better understanding of the broader picture.

Fault Tree Analysis (FTA) is a top-down, deductive failure analysis that explores the causes of faults or problems. It involves graphically mapping multiple causal chains to track back to possible root causes, using a tree-like diagram. FTA is particularly useful in high-risk industries, such as aerospace and nuclear power, where preventing failure is crucial.

Each RCA technique provides a unique approach for viewing and understanding problems, helping you pinpoint the root cause more effectively. The key is to understand when and how to use each tool, which can significantly enhance your team’s problem-solving capabilities.

Power up your RCA analysis with our EasyRCA and revolutionize your problem-solving process. Start Your Free Trial.

In conclusion, the techniques presented offer a diverse set of tools to help organizations address problems and inefficiencies effectively. From visual representations like the Ishikawa Fishbone Diagram and Pareto Chart to more proactive approaches such as the 5 Whys and Failure Mode and Effects Analysis (FMEA), each technique provides a unique perspective on identifying and mitigating root causes.

The PROACT® RCA Method stands out for its comprehensive process, particularly suited for chronic failures. Additionally, the Affinity Diagram and Fault Tree Analysis (FTA) contribute valuable insights by organizing data and exploring causal chains, respectively. Leveraging these techniques strategically enhances a team’s problem-solving capabilities, enabling them to make informed decisions and drive continuous improvement.

I hope you found these 7 techniques insightful and actionable! Stay tuned for more thought-provoking articles as we continue to share our knowledge. Success is rooted in a thorough understanding and consistent application, and we hope this article was a step in unlocking the full potential of Root Cause Analysis for your organization. Reliability runs initiatives such as an online learning center focused on the proprietary PROACT® RCA methodology and EasyRCA.com software. For additional resources, visit our Reliability Resources .

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Cause and Effect

Cause and effect generator.

In every aspect of life, actions lead to outcomes. Understanding cause and effect is crucial for making informed decisions, predicting consequences, and solving problems effectively. This principle underpins scientific discoveries, historical events, and daily interactions. By recognizing the connections between actions and their impacts, we gain insights into the world around us and improve our ability to navigate complex situations. In this article, we will explore the fundamental nature of cause and effect, illustrating its importance through various examples and highlighting its role in critical thinking and decision-making processes.

What is Cause and Effect?

Cause and effect is a concept used to describe the relationship between events or things, where one is the result of the other or others. This concept is fundamental in various fields, including science, philosophy, and everyday reasoning. Here’s a detailed definition and meaning:

• Cause: The reason something happens; an event or action that leads to a certain outcome.
• Effect: The result or outcome that occurs due to a specific cause.

The relationship between cause and effect implies that certain events (causes) directly bring about other events (effects). Understanding this relationship helps in explaining why things happen and predicting what might happen next based on current actions or events.

Historical Context of Cause and Effect

The exploration of cause and effect can be traced back to ancient philosophies. Aristotle, for instance, discussed the principles of causality in his works, emphasizing the importance of understanding causes to comprehend the world around us.

Cause and Effect Examples

In Sentence

• Heavy rain causes flooding in the streets .
• Studying hard for a test leads to getting a high score .
• Eating too much junk food results in gaining weight .
• Exercising regularly improves physical fitness .
• Forgetting to water the plants causes plants to wilt .
• Leaving a candle unattended can start a fire .
• Not wearing sunscreen on a sunny day results in getting sunburned .
• Saving money every month leads to building a substantial savings account .
• Breaking a bone requires needing a cast to heal .
• Global warming leads to melting polar ice caps .
• Not getting enough sleep results in feeling tired and irritable .
• Overfishing in the oceans causes a decrease in fish populations .
• Practicing a musical instrument daily leads to becoming proficient at playing it .
• Not paying attention while driving can result in a car accident .
• Smoking cigarettes leads to developing lung disease .
• Spending too much time on social media causes decreased productivity .
• Implementing green energy solutions results in a reduction in carbon footprint .
• Conflict between countries can lead to war or heightened political tensions .
• Cutting down forests results in loss of wildlife habitat .
• High levels of stress cause health problems such as high blood pressure and anxiety .

For Students

• Not completing homework can lead to lower grades .
• Attending all classes results in better understanding of the material .
• Studying in groups can help with improving problem-solving skills .
• Using a planner helps in managing time effectively .
• Participating in class discussions can boost confidence .
• Sleeping well the night before ensures alertness during the exam .
• Reading additional resources can enhance knowledge .
• Turning in assignments on time leads to better grades .
• Taking notes during lectures aids in better retention of information .
• Seeking help when needed prevents falling behind in studies .

In Research

• Increasing sample size often results in more accurate data .
• Applying a new technique can yield different results compared to traditional methods.
• Proper citation of sources prevents plagiarism .
• Using control groups ensures validity of the experiment .
• Consistent methodology leads to reproducible results .
• Analyzing data carefully avoids misinterpretation .
• Formulating a clear hypothesis guides the research direction .
• Reviewing literature extensively provides context for the study .
• Collaborating with peers can enhance research quality .
• Adhering to ethical guidelines maintains research integrity .
• If you touch a hot stove , you will burn your hand .
• Eating too much candy can cause a stomach ache .
• Sharing your toys will make your friends happy .
• Brushing your teeth prevents cavities .
• Doing your chores can earn you an allowance .
• If you run in the house , you might break something .
• Being kind to others makes you a good friend .
• Going to bed late means feeling sleepy the next day .
• Playing outside helps you stay healthy .
• Listening to your parents keeps you safe .

For Grade 3

• Reading every day helps you learn new words .
• If you water the plants , they will grow .
• Eating healthy food gives you more energy .
• Paying attention in class helps you understand the lesson .
• Playing outside makes you feel happy .
• Helping your classmates makes you a good friend .
• Finishing your homework on time makes your teacher proud .
• Wearing a coat in winter keeps you warm .
• If you clean your room , it will look nice .
• If you follow the rules , you will stay out of trouble .

In Real Life

• Driving too fast can cause car accidents .
• Not saving money may result in financial problems .
• Getting regular exercise improves overall health .
• Not getting enough sleep leads to feeling tired .
• Using public transportation reduces traffic congestion .
• Eating a balanced diet contributes to better health .
• Not paying bills on time results in late fees .
• Being on time for work shows professionalism .
• Using a budget helps in managing finances .
• Recycling helps protect the environment .

Types of Causes

• Immediate Causes : Directly responsible for an effect.
• Contributory Causes : Factors that play a role in creating an effect but are not solely responsible.
• Necessary and Sufficient Causes : Necessary causes must be present for an effect to occur, while sufficient causes alone can produce the effect.

Importance of Cause and Effect in Decision Making

Understanding cause and effect is crucial for making informed decisions. By anticipating the outcomes of actions, individuals and organizations can plan more effectively and avoid unintended consequences.

Signal Words in Cause and Effect Sentence

Signal words in cause and effect sentences help indicate the relationship between actions or events. They clearly show that one event is the result of another. Here are some common signal words and phrases used in cause and effect sentences:

Cause Signal Words

These words indicate the cause or reason something happens.

• As a result of
• On account of
• Because it rained, the event was canceled.
• Due to heavy traffic, she was late to the meeting.

Effect Signal Words

These words indicate the effect or outcome of a cause.

• Consequently
• As a result
• For this reason
• She studied hard; therefore, she passed the exam.
• The power went out; as a result, we had to use candles.

Combined Signal Words

These words or phrases can indicate both cause and effect in a sentence.

• If it rains, then the game will be postponed.
• She was so tired that she fell asleep immediately.

Sample Sentences

• Because it was raining, we stayed inside.
• The roads were icy; consequently , there were many accidents.
• Due to the high demand, prices increased.
• He didn’t study; therefore , he didn’t pass the exam.
• Since the store was closed, we couldn’t buy groceries.

Activities to Learn About Cause and Effect

For young children.

• Simple Experiments : Use water, food coloring, and paper towels to show how colors mix. Explain how one action (adding color) causes a change (new color formation).
• Storytime Discussions : Read stories and discuss the events that lead to outcomes. Ask questions like, “What happened when the character did this?”
• Domino Effect : Set up a line of dominos and show how knocking one down causes a chain reaction. Let them experiment with different setups.
• Cooking Activities : Baking cookies can illustrate how mixing ingredients and applying heat (the cause) results in baked cookies (the effect).

For Older Children

• Science Experiments : Conduct experiments where they can change one variable at a time and observe the outcomes, like growing plants under different conditions.
• Interactive Games : Use games that require problem-solving and critical thinking, such as “The Incredible Machine” or “Minecraft”.
• Chain Reactions : Create Rube Goldberg machines. These are complex devices that perform simple tasks through a series of cause-and-effect steps.
• Historical Events : Study historical events and discuss how one event led to another. This helps in understanding broader cause-and-effect relationships.
• Physics Projects : Building simple machines or engaging in robotics projects can illustrate cause and effect in a hands-on manner.
• Programming : Learning to code can show direct cause and effect as students write commands (causes) and see the results (effects) immediately.
• Debates and Discussions : Analyze current events or historical scenarios, debating how certain actions led to specific outcomes.
• Environmental Studies : Investigate how human actions impact the environment, such as pollution leading to climate change.
• Case Studies : Examine case studies in various fields (business, medicine, engineering) to understand cause-and-effect relationships.
• Simulations and Models : Use computer simulations to see how changing variables affects outcomes in systems like economics or weather patterns.
• Critical Analysis : Write essays or reports analyzing cause-and-effect relationships in literature, film, or real-world events.
• Project Management : Implement project management techniques to see how planning and execution impact project outcomes.

General Activities

• Mind Mapping : Create mind maps that outline causes and their potential effects.
• If-Then Scenarios : Play “if-then” games, predicting what will happen if certain actions are taken.
• Reflective Journals : Keep journals where you reflect on daily actions and their outcomes to see patterns over time.
• Experiential Learning : Engage in hands-on learning activities, where the process and results are directly linked, such as gardening, carpentry, or art projects.

Cause and Effect Questions and Answers

What is the definition of cause and effect.

Cause and effect describe the relationship where one event (cause) makes another event happen (effect).

Why is understanding cause and effect important?

Understanding cause and effect helps identify reasons behind occurrences, allowing for better decision-making and problem-solving.

How can cause and effect be identified?

Identify cause and effect by looking for events that trigger other events, using words like “because,” “therefore,” and “as a result.”

What are examples of cause and effect in everyday life?

Examples include eating healthy (cause) leading to better health (effect) or studying (cause) resulting in good grades (effect).

How do cause and effect impact science?

In science, cause and effect help explain natural phenomena and develop theories by identifying the reasons behind events.

Can cause and effect be used in writing?

Yes, writers use cause and effect to structure arguments, explain reasons behind actions, and enhance narratives.

What is a cause and effect diagram?

A cause and effect diagram, also known as a fishbone diagram, visually maps out the causes of a specific event.

How do you teach cause and effect to children?

Teach cause and effect to children through simple examples, storytelling, and hands-on activities that show direct relationships.

What is the difference between cause and correlation?

Cause directly leads to an effect, while correlation indicates a relationship between two variables without proving one causes the other.

How does cause and effect relate to critical thinking?

Cause and effect analysis enhances critical thinking by encouraging examination of reasons behind events and their outcomes.

Text prompt

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Write about the Cause and Effect of global warming on polar ice caps.

Create an essay discussing the Cause and Effect of daily exercise on mental health.

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A Better Framework for Solving Tough Problems

Start with trust and end with speed.

• Apple Podcasts

When it comes to solving complicated problems, the default for many organizational leaders is to take their time to work through the issues at hand. Unfortunately, that often leads to patchwork solutions or problems not truly getting resolved.

But Anne Morriss offers a different framework. In this episode, she outlines a five-step process for solving any problem and explains why starting with trust and ending with speed is so important for effective change leadership. As she says, “Let’s get into dialogue with the people who are also impacted by the problem before we start running down the path of solving it.”

Morriss is an entrepreneur and leadership coach. She’s also the coauthor of the book, Move Fast and Fix Things: The Trusted Leader’s Guide to Solving Hard Problems .

Key episode topics include: strategy, decision making and problem solving, strategy execution, managing people, collaboration and teams, trustworthiness, organizational culture, change leadership, problem solving, leadership.

HBR On Strategy curates the best case studies and conversations with the world’s top business and management experts, to help you unlock new ways of doing business. New episodes every week.

• Listen to the full HBR IdeaCast episode: How to Solve Tough Problems Better and Faster (2023)
• Find more episodes of HBR IdeaCast
• Discover 100 years of Harvard Business Review articles, case studies, podcasts, and more at HBR.org .

HANNAH BATES: Welcome to HBR On Strategy , case studies and conversations with the world’s top business and management experts, hand-selected to help you unlock new ways of doing business.

When it comes to solving complicated problems, many leaders only focus on the most apparent issues. Unfortunately that often leads to patchwork or partial solutions. But Anne Morriss offers a different framework that aims to truly tackle big problems by first leaning into trust and then focusing on speed.

Morriss is an entrepreneur and leadership coach. She’s also the co-author of the book, Move Fast and Fix Things: The Trusted Leader’s Guide to Solving Hard Problems . In this episode, she outlines a five-step process for solving any problem. Some, she says, can be solved in a week, while others take much longer. She also explains why starting with trust and ending with speed is so important for effective change leadership.

This episode originally aired on HBR IdeaCast in October 2023. Here it is.

CURT NICKISCH: Welcome to the HBR IdeaCast from Harvard Business Review. I’m Curt Nickisch.

Problems can be intimidating. Sure, some problems are fun to dig into. You roll up your sleeves, you just take care of them; but others, well, they’re complicated. Sometimes it’s hard to wrap your brain around a problem, much less fix it.

And that’s especially true for leaders in organizations where problems are often layered and complex. They sometimes demand technical, financial, or interpersonal knowledge to fix. And whether it’s avoidance on the leaders’ part or just the perception that a problem is systemic or even intractable, problems find a way to endure, to keep going, to keep being a problem that everyone tries to work around or just puts up with.

But today’s guest says that just compounds it and makes the problem harder to fix. Instead, she says, speed and momentum are key to overcoming a problem.

Anne Morriss is an entrepreneur, leadership coach and founder of the Leadership Consortium and with Harvard Business School Professor Francis Frei, she wrote the new book, Move Fast and Fix Things: The Trusted Leaders Guide to Solving Hard Problems . Anne, welcome back to the show.

ANNE MORRISS: Curt, thank you so much for having me.

CURT NICKISCH: So, to generate momentum at an organization, you say that you really need speed and trust. We’ll get into those essential ingredients some more, but why are those two essential?

ANNE MORRISS: Yeah. Well, the essential pattern that we observed was that the most effective change leaders out there were building trust and speed, and it didn’t seem to be a well-known observation. We all know the phrase, “Move fast and break things,” but the people who were really getting it right were moving fast and fixing things, and that was really our jumping off point. So when we dug into the pattern, what we observed was they were building trust first and then speed. This foundation of trust was what allowed them to fix more things and break fewer.

CURT NICKISCH: Trust sounds like a slow thing, right? If you talk about building trust, that is something that takes interactions, it takes communication, it takes experiences. Does that run counter to the speed idea?

ANNE MORRISS: Yeah. Well, this issue of trust is something we’ve been looking at for over a decade. One of the headlines in our research is it’s actually something we’re building and rebuilding and breaking all the time. And so instead of being this precious, almost farbege egg, it’s this thing that is constantly in motion and this thing that we can really impact when we’re deliberate about our choices and have some self-awareness around where it’s breaking down and how it’s breaking down.

CURT NICKISCH: You said break trust in there, which is intriguing, right? That you may have to break trust to build trust. Can you explain that a little?

ANNE MORRISS:  Yeah, well, I’ll clarify. It’s not that you have to break it in order to build it. It’s just that we all do it some of the time. Most of us are trusted most of the time. Most of your listeners I imagine are trusted most of the time, but all of us have a pattern where we break trust or where we don’t build as much as could be possible.

CURT NICKISCH: I want to talk about speed, this other essential ingredient that’s so intriguing, right? Because you think about solving hard problems as something that just takes a lot of time and thinking and coordination and planning and designing. Explain what you mean by it? And also, just  how we maybe approach problems wrong by taking them on too slowly?

ANNE MORRISS: Well, Curt, no one has ever said to us, “I wish I had taken longer and done less.” We hear the opposite all the time, by the way. So what we really set out to do was to create a playbook that anyone can use to take less time to do more of the things that are going to make your teams and organizations stronger.

And the way we set up the book is okay, it’s really a five step process. Speed is the last step. It’s the payoff for the hard work you’re going to do to figure out your problem, build or rebuild trust, expand the team in thoughtful and strategic ways, and then tell a real and compelling story about the change you’re leading.

Only then do you get to go fast, but that’s an essential part of the process, and we find that either people under emphasize it or speed has gotten a bad name in this world of moving fast and breaking things. And part of our mission for sure was to rehabilitate speed’s reputation because it is an essential part of the change leader’s equation. It can be the difference between good intentions and getting anything done at all.

CURT NICKISCH: You know, the fact that nobody ever tells you, “I wish we had done less and taken more time.” I think we all feel that, right? Sometimes we do something and then realize, “Oh, that wasn’t that hard and why did it take me so long to do it? And I wish I’d done this a long time ago.” Is it ever possible to solve a problem too quickly?

ANNE MORRISS: Absolutely. And we see that all the time too. What we push people to do in those scenarios is really take a look at the underlying issue because in most cases, the solution is not to take your foot off the accelerator per se and slow down. The solution is to get into the underlying problem. So if it’s burnout or a strategic disconnect between what you’re building and the marketplace you’re serving, what we find is the anxiety that people attach to speed or the frustration people attach to speed is often misplaced.

CURT NICKISCH: What is a good timeline to think about solving a problem then? Because if we by default take too long or else jump ahead and we don’t fix it right, what’s a good target time to have in your mind for how long solving a problem should take?

ANNE MORRISS: Yeah. Well, we’re playful in the book and talking about the idea that many problems can be solved in a week. We set the book up five chapters. They’re titled Monday, Tuesday, Wednesday, Thursday, Friday, and we’re definitely having fun with that. And yet, if you count the hours in a week, there are a lot of them. Many of our problems, if you were to spend a focused 40 hours of effort on a problem, you’re going to get pretty far.

But our main message is, listen, of course it’s going to depend on the nature of the problem, and you’re going to take weeks and maybe even some cases months to get to the other side. What we don’t want you to do is take years, which tends to be our default timeline for solving hard problems.

CURT NICKISCH: So you say to start with identifying the problem that’s holding you back, seems kind of obvious. But where do companies go right and wrong with this first step of just identifying the problem that’s holding you back?

ANNE MORRISS: And our goal is that all of these are going to feel obvious in retrospect. The problem is we skip over a lot of these steps and this is why we wanted to underline them. So this one is really rooted in our observation and I think the pattern of our species that we tend to be overconfident in the quality of our thoughts, particularly when it comes to diagnosing problems.

And so we want to invite you to start in a very humble and curious place, which tends not to be our default mode when we’re showing up for work. We convince ourselves that we’re being paid for our judgment. That’s exactly what gets reinforced everywhere. And so we tend to counterintuitively, given what we just talked about, we tend to move too quickly through the diagnostic phase.

CURT NICKISCH: “I know what to do, that’s why you hired me.”

ANNE MORRISS: Exactly. “I know what to do. That’s why you hired me. I’ve seen this before. I have a plan. Follow me.” We get rewarded for the expression of confidence and clarity. And so what we’re inviting people to do here is actually pause and really lean into what are the root causes of the problem you’re seeing? What are some alternative explanations? Let’s get into dialogue with the people who are also impacted by the problem before we start running down the path of solving it.

CURT NICKISCH: So what do you recommend for this step, for getting to the root of the problem? What are questions you should ask? What’s the right thought process? What do you do on Monday of the week?

ANNE MORRISS: In our experience of doing this work, people tend to undervalue the power of conversation, particularly with other people in the organization. So we will often advocate putting together a team of problem solvers, make it a temporary team, really pull in people who have a particular perspective on the problem and create the space, make it as psychologically safe as you can for people to really, as Chris Argyris so beautifully articulated, discuss the undiscussable.

And so the conditions for that are going to look different in every organization depending on the problem, but if you can get a space where smart people who have direct experience of a problem are in a room and talking honestly with each other, you can make an extraordinary amount of progress, certainly in a day.

CURT NICKISCH: Yeah, that gets back to the trust piece.

ANNE MORRISS: Definitely.

CURT NICKISCH: How do you like to start that meeting, or how do you like to talk about it? I’m just curious what somebody on that team might hear in that meeting, just to get the sense that it’s psychologically safe, you can discuss the undiscussable and you’re also focusing on the identification part. What’s key to communicate there?

ANNE MORRISS: Yeah. Well, we sometimes encourage people to do a little bit of data gathering before those conversations. So the power of a quick anonymous survey around whatever problem you’re solving, but also be really thoughtful about the questions you’re going to ask in the moment. So a little bit of preparation can go a long way and a little bit of thoughtfulness about the power dynamic. So who’s going to walk in there with license to speak and who’s going to hold back? So being thoughtful about the agenda, about the questions you’re asking about the room, about the facilitation, and then courage is a very infectious emotion.

So if you can early on create the conditions for people to show up bravely in that conversation, then the chance that you’re going to get good information and that you’re going to walk out of that room with new insight in the problem that you didn’t have when you walked in is extraordinarily high.

CURT NICKISCH: Now, in those discussions, you may have people who have different perspectives on what the problem really is. They also bear different costs of addressing the problem or solving it. You talked about the power dynamic, but there’s also an unfairness dynamic of who’s going to actually have to do the work to take care of it, and I wonder how you create a culture in that meeting where it’s the most productive?

ANNE MORRISS: For sure, the burden of work is not going to be equitably distributed around the room. But I would say, Curt, the dynamic that we see most often is that people are deeply relieved that hard problems are being addressed. So it really can create, and more often than not in our experience, it does create this beautiful flywheel of action, creativity, optimism. Often when problems haven’t been addressed, there is a fair amount of anxiety in the organization, frustration, stagnation. And so credible movement towards action and progress is often the best antidote. So even if the plan isn’t super clear yet, if it’s credible, given who’s in the room and their decision rights and mandate, if there’s real momentum coming out of that to make progress, then that tends to be deeply energizing to people.

CURT NICKISCH: I wonder if there’s an organization that you’ve worked with that you could talk about how this rolled out and how this took shape?

ANNE MORRISS: When we started working with Uber, that was wrestling with some very public issues of culture and trust with a range of stakeholders internally, the organization, also external, that work really started with a campaign of listening and really trying to understand where trust was breaking down from the perspective of these stakeholders?

So whether it was female employees or regulators or riders who had safety concerns getting into the car with a stranger. This work, it starts with an honest internal dialogue, but often the problem has threads that go external. And so bringing that same commitment to curiosity and humility and dialogue to anyone who’s impacted by the problem is the fastest way to surface what’s really going on.

CURT NICKISCH: There’s a step in this process that you lay out and that’s communicating powerfully as a leader. So we’ve heard about listening and trust building, but now you’re talking about powerful communication. How do you do this and why is it maybe this step in the process rather than the first thing you do or the last thing you do?

ANNE MORRISS: So in our process, again, it’s the days of the week. On Monday you figured out the problem. Tuesday you really got into the sandbox in figuring out what a good enough plan is for building trust. Wednesday, step three, you made it better. You created an even better plan, bringing in new perspectives. Thursday, this fourth step is the day we’re saying you got to go get buy-in. You got to bring other people along. And again, this is a step where we see people often underinvest in the power and payoff of really executing it well.

CURT NICKISCH: How does that go wrong?

ANNE MORRISS: Yeah, people don’t know the why. Human behavior and the change in human behavior really depends on a strong why. It’s not just a selfish, “What’s in it for me?” Although that’s helpful, but where are we going? I may be invested in a status quo and I need to understand, okay, if you’re going to ask me to change, if you’re going to invite me into this uncomfortable place of doing things differently, why am I here? Help me understand it and articulate the way forward and language that not only I can understand, but also that’s going to be motivating to me.

CURT NICKISCH: And who on my team was part of this process and all that kind of stuff?

ANNE MORRISS: Oh, yeah. I may have some really important questions that may be in the way of my buy-in and commitment to this plan. So certainly creating a space where those questions can be addressed is essential. But what we found is that there is an architecture of a great change story, and it starts with honoring the past, honoring the starting place. Sometimes we’re so excited about the change and animated about the change that what has happened before or what is even happening in the present tense is low on our list of priorities.

Or we want to label it bad, because that’s the way we’ve thought about the change, but really pausing and honoring what came before you and all the reasonable decisions that led up to it, I think can be really helpful to getting people emotionally where you want them to be willing to be guided by you. Going back to Uber, when Dara Khosrowshahi came in.

CURT NICKISCH: This is the new CEO.

ANNE MORRISS: The new CEO.

CURT NICKISCH: Replaced Travis Kalanick, the founder and first CEO, yeah.

ANNE MORRISS: Yeah, and had his first all-hands meeting. One of his key messages, and this is a quote, was that he was going to retain the edge that had made Uber, “A force of nature.” And in that meeting, the crowd went wild because this is also a company that had been beaten up publicly for months and months and months, and it was a really powerful choice. And his predecessor, Travis was in the room, and he also honored Travis’ incredible work and investment in bringing the company to the place where it was.

And I would use words like grace to also describe those choices, but there’s also an incredible strategic value to naming the starting place for everybody in the room because in most cases, most people in that room played a role in getting to that starting place, and you’re acknowledging that.

CURT NICKISCH: You can call it grace. Somebody else might call it diplomatic or strategic. But yeah, I guess like it or not, it’s helpful to call out and honor the complexity of the way things have been done and also the change that’s happening.

ANNE MORRISS: Yeah, and the value. Sometimes honoring the past is also owning what didn’t work or what wasn’t working for stakeholders or segments of the employee team, and we see that around culture change. Sometimes you’ve got to acknowledge that it was not an equitable environment, but whatever the worker, everyone in that room is bringing that pass with them. So again, making it discussable and using it as the jumping off place is where we advise people to start.

Then you’ve earned the right to talk about the change mandate, which we suggest using clear and compelling language about the why. “This is what happened, this is where we are, this is the good and the bad of it, and here’s the case for change.”

And then the last part, which is to describe a rigorous and optimistic way forward. It’s a simple past, present, future arc, which will be familiar to human beings. We love stories as human beings. It’s among the most powerful currency we have to make sense of the world.

CURT NICKISCH: Yeah. Chronological is a pretty powerful order.

ANNE MORRISS: Right. But again, the change leaders we see really get it right, are investing an incredible amount of time into the storytelling part of their job. Ursula Burns, the Head of Xerox is famous for the months and years she spent on the road just telling the story of Xerox’s change, its pivot into services to everyone who would listen, and that was a huge part of her success.

CURT NICKISCH: So Friday or your fifth step, you end with empowering teams and removing roadblocks. That seems obvious, but it’s critical. Can you dig into that a little bit?

ANNE MORRISS: Yeah. Friday is the fun day. Friday’s the release of energy into the system. Again, you’ve now earned the right to go fast. You have a plan, you’re pretty confident it’s going to work. You’ve told the story of change the organization, and now you get to sprint. So this is about really executing with urgency, and it’s about a lot of the tactics of speed is where we focus in the book. So the tactics of empowerment, making tough strategic trade-offs so that your priorities are clear and clearly communicated, creating mechanisms to fast-track progress. At Etsy, CEO Josh Silverman, he labeled these projects ambulances. It’s an unfortunate metaphor, but it’s super memorable. These are the products that get to speed out in front of the other ones because the stakes are high and the clock is sticking.

CURT NICKISCH: You pull over and let it go by.

ANNE MORRISS: Yeah, exactly. And so we have to agree as an organization on how to do something like that. And so we see lots of great examples both in young organizations and big complex biotech companies with lots of regulatory guardrails have still found ways to do this gracefully.

And I think we end with this idea of conflict debt, which is a term we really love. Leanne Davey, who’s a team scholar and researcher, and anyone in a tech company will recognize the idea of tech debt, which is this weight the organization drags around until they resolve it. Conflict debt is a beautiful metaphor because it is this weight that we drag around and slows us down until we decide to clean it up and fix it. The organizations that are really getting speed right have figured out either formally or informally, how to create an environment where conflict and disagreements can be gracefully resolved.

CURT NICKISCH: Well, let’s talk about this speed more, right? Because I think this is one of those places that maybe people go wrong or take too long, and then you lose the awareness of the problem, you lose that urgency. And then that also just makes it less effective, right? It’s not just about getting the problem solved as quickly as possible. It’s also just speed in some ways helps solve the problem.

ANNE MORRISS: Oh, yeah. It really is the difference between imagining the change you want to lead and really being able to bring it to life. Speed is the thing that unlocks your ability to lead change. It needs a foundation, and that’s what Monday through Thursday is all about, steps one through four, but the finish line is executing with urgency, and it’s that urgency that releases the system’s energy, that communicates your priorities, that creates the conditions for your team to make progress.

CURT NICKISCH: Moving fast is something that entrepreneurs and tech companies certainly understand, but there’s also this awareness that with big companies, the bigger the organization, the harder it is to turn the aircraft carrier around, right? Is speed relative when you get at those levels, or do you think this is something that any company should be able to apply equally?

ANNE MORRISS: We think this applies to any company. The culture really lives at the level of team. So we believe you can make a tremendous amount of progress even within your circle of control as a team leader. I want to bring some humility to this and careful of words like universal, but we do think there’s some universal truths here around the value of speed, and then some of the byproducts like keeping fantastic people. Your best people want to solve problems, they want to execute, they want to make progress and speed, and the ability to do that is going to be a variable in their own equation of whether they stay or they go somewhere else where they can have an impact.

CURT NICKISCH: Right. They want to accomplish something before they go or before they retire or finish something out. And if you’re able to just bring more things on the horizon and have it not feel like it’s going to be another two years to do something meaningful.

ANNE MORRISS: People – I mean, they want to make stuff happen and they want to be around the energy and the vitality of making things happen, which again, is also a super infectious phenomenon. One of the most important jobs of a leader, we believe, is to set the metabolic pace of their teams and organizations. And so what we really dig into on Friday is, well, what does that look like to speed something up? What are the tactics of that?

CURT NICKISCH: I wonder if that universal truth, that a body in motion stays in motion applies to organizations, right? If an organization in motion stays in motion, there is something to that.

ANNE MORRISS: Absolutely.

CURT NICKISCH: Do you have a favorite client story to share, just where you saw speed just become a bit of a flywheel or just a positive reinforcement loop for more positive change at the organization?

ANNE MORRISS: Yeah. We work with a fair number of organizations that are on fire. We do a fair amount of firefighting, but we also less dramatically do a lot of fire prevention. So we’re brought into organizations that are working well and want to get better, looking out on the horizon. That work is super gratifying, and there is always a component of, well, how do we speed this up?

What I love about that work is there’s often already a high foundation of trust, and so it’s, well, how do we maintain that foundation but move this flywheel, as you said, even faster? And it’s really energizing because often there’s a lot of pent-up energy that… There’s a lot of loyalty to the organization, but often it’s also frustration and pent-up energy. And so when that gets released, when good people get the opportunity to sprint for the first time in a little while, it’s incredibly energizing, not just for us, but for the whole organization.

CURT NICKISCH: Anne, this is great. I think finding a way to solve problems better but also faster is going to be really helpful. So thanks for coming on the show to talk about it.

ANNE MORRISS:  Oh, Curt, it was such a pleasure. This is my favorite conversation. I’m delighted to have it anytime.

HANNAH BATES: That was entrepreneur, leadership coach, and author Anne Morriss – in conversation with Curt Nickisch on HBR IdeaCast.

We’ll be back next Wednesday with another hand-picked conversation about business strategy from Harvard Business Review. If you found this episode helpful, share it with your friends and colleagues, and follow our show on Apple Podcasts, Spotify, or wherever you get your podcasts. While you’re there, be sure to leave us a review.

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MindMap Gallery Section 3 Basic Tools for Quality Data Analysis (Two Figures and One Table Pareto Chart, Cause and Effect Diagram, Countermeasure Table)

Section 3 Basic Tools for Quality Data Analysis (Two Figures and One Table Pareto Chart, Cause and Effect Diagram, Countermeasure Table)

Section 3: Mind map, the basic tool for quality data analysis, including arrangement charts, cause and effect diagrams, countermeasure tables, histograms, Scatter plots, matrix data analysis methods, etc.

• Recommended to you

Section 3 Basic Tools for Quality Data Analysis (Two Figures and One Table: Pareto Chart, Cause and Effect Diagram, Countermeasure Table)

Pareto chart (Pareto chart)

Pareto charts are based on the Pareto principle, which means that 80% of results come from 20% of the causes.

The purpose of the Pareto Chart is to compare the results or impact on customers of different defect types in order to identify the most important issues that need to be resolved first.

Pareto is a diagram that arranges quality improvement projects from the most important to the least important. It is a diagram used to find major problems or major factors affecting quality.

Application steps

Clarify the analysis object, collect data within a certain period of time, and identify defective items

Arrange the frequency of each defect from large to small, and calculate the proportion and cumulative proportion of each defect to the total number of defects.

List the various defect items on the abscissa in order from large to small. Group the item or items with the smallest value into "other" items and place them at the far right. The number can exceed the second item from the bottom.

The ordinate on the left is the frequency of defect occurrence, and the ordinate on the right is the ratio. The total frequency scale on the left is the same height as the total frequency scale (100%) on the right

At each defect on the abscissa, draw a bar chart corresponding to its frequency of occurrence.

Accumulate the proportion of each defect item from left to right and draw a cumulative frequency curve.

Cause and effect diagram (Ishikawa Kaoru diagram, fishbone diagram)

A cause-and-effect diagram is a tool used to analyze quality characteristics (results) and factors that may affect the quality characteristics (causes)

A cause-and-effect diagram is a diagram that reveals the relationship between a process output defect or problem and its potential causes. It is also an important tool and document for expressing and analyzing its cause-and-effect relationship.

Identify the quality characteristics and fill in the boxes to the right of the cause and effect diagram

Determine the main categories of possible causes and draw each "main branch" of the cause-and-effect diagram. A typical classification method (5M1E classification method) can be used: personnel, machinery and equipment, materials, methods, measurement, environment

Use brainstorming to fill in all possible causes of the problem into each main branch according to their different classifications

Identify the key factors (3-5) that affect quality problems, enclose them in circles or boxes, and use them as key considerations for implementing quality improvement measures.

Countermeasure table (measure plan table)

The countermeasure list generally includes the current status of important reasons, countermeasures, goals, measures, completion location, completion time and person in charge, etc.

The countermeasure table is not only a table for the implementation of the improvement measure plan, but also a basis for checking whether the improvement measure plan has been completed.

Identify important causes to correct

Clarify the current status of important causes, countermeasures and measures to be taken, personnel to complete the work and completion time, etc.

Tabulate the above

Histogram (frequency histogram)

A histogram is a graph that divides the data into a number of equally spaced groups in order. On the abscissa, the distance between the groups is the base and the frequency of falling into each group is the highest.

A histogram is often used to understand the distribution of data. It is a graphical representation of a set of data. This method of displaying data can clearly reflect the dispersion and central trend of the data and compare it with the required distribution.

Standard type (symmetric type): The average value of the data is the same as or close to the middle value of the maximum value and the minimum value. The frequency of data near the average value is the highest. The frequency starts from the middle value and slowly decreases to both sides. The average value is symmetrical, which is the most common shape

Zigzag shape: This shape will appear if there are too many groups when making a frequency distribution table; this shape will also appear when there is a problem with the measurement method or the measurement data is misread.

Skewed peak type: the average value of the data is located to the left (right side) of the middle value, from left to right (right to left), the frequency of the data distribution increases and then suddenly decreases, and the shape is asymmetrical; the lower limit (upper line) is subject to tolerance When other factors are limited, this shape often appears due to psychological factors

Steep wall type: the average value is far away from the middle value of the histogram, the frequency decreases (increases) from left to right, and the histogram is asymmetrical; when the process capacity is insufficient, use qualified product data or processes after full inspection This shape often appears when there is automatic feedback adjustment in

Flat-top type: This shape often occurs when several distributions with different average values ​​are mixed together, or when a process deteriorates slowly.

Bimodal: There are fewer frequencies near the middle value of the histogram, and there is a "peak" on each side; this shape often occurs when two different distributions with widely different average values ​​are mixed together.

Island type: There is an "island" on one side of the standard histogram.

Steps to create a histogram

Collect and record data required for analysis

Determine the maximum and minimum values ​​of data and calculate the range

Determine the number of groups and group spacing

Determine group boundaries

Make a frequency distribution table

Draw a histogram

Scatter plot (scatter plot)

Scatter plot is a graphing method used to analyze and study whether there is a correlation between two corresponding variables.

Scatter plots draw the data points of two corresponding variables on plane coordinates to determine whether there is a correlation between the two sets of variables and the degree of correlation.

Scatter plots can only qualitatively and approximately determine whether there is a linear correlation between two variables. In order to quantitatively and accurately measure the degree of linear correlation between two variables, their correlation coefficients need to be calculated.

Clarify the research object, collect paired data and organize it into a data table. The data should generally be more than 30 groups.

Establish a plane rectangular coordinate system on the graph paper. In order to facilitate the analysis of the correlation, the range between the maximum and minimum values ​​of the two coordinate values ​​should be basically equal.

Mark the data groups at the corresponding positions of the rectangular coordinates of the plane.

When there are outliers on the scatter plot that clearly deviate from other data points, the reasons should be identified so that you can decide whether to delete or correct them.

If necessary, relevant information can be noted on the scatter chart

Strong positive correlation: y increases as x increases, and the point dispersion is small

Weak positive correlation: y increases as x increases, and the point dispersion is large

Strong negative correlation: y decreases as x increases, and the point dispersion is small

Weak negative correlation: y decreases as x increases, and the point dispersion is large

Irrelevant: There is no obvious pattern between x and y

Nonlinear correlation: x and y have a curvilinear relationship

Matrix data analysis method (principal component analysis method)

Concept: Express the relationship between various factors with a certain amount through a matrix diagram, that is, numerical data can be marked on their intersection points, and the corresponding relationships between multiple quality factors or multiple variables can be expressed quantitatively, thereby A method of predicting, calculating, organizing and analyzing large amounts of data is a method of multi-parameter analysis.

Identify the various aspects that require analysis

form data matrix

Determine the comparison score

Main purpose: can be applied to all aspects of product realization, including the entire process

for prediction

For process quality analysis

Used to understand customer satisfaction during the product design process

• Open access
• Published: 21 May 2024

Efficacy of interventions and techniques on adherence to physiotherapy in adults: an overview of systematic reviews and panoramic meta-analysis

• Clemens Ley   ORCID: orcid.org/0000-0003-1700-3905 1 &
• Peter Putz   ORCID: orcid.org/0000-0003-2314-3293 2  

Systematic Reviews volume  13 , Article number:  137 ( 2024 ) Cite this article

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Adherence to physiotherapeutic treatment and recommendations is crucial to achieving planned goals and desired health outcomes. This overview of systematic reviews synthesises the wide range of additional interventions and behaviour change techniques used in physiotherapy, exercise therapy and physical therapy to promote adherence and summarises the evidence of their efficacy.

Seven databases (PEDro, PubMed, Cochrane Library, Web of Science, Scopus, PsycINFO and CINAHL) were systematically searched with terms related to physiotherapy, motivation, behaviour change, adherence and efficacy (last searched on January 31, 2023). Only systematic reviews of randomised control trials with adults were included. The screening process and quality assessment with AMSTAR-2 were conducted independently by the two authors. The extracted data was synthesised narratively. In addition, four meta-analyses were pooled in a panoramic meta-analysis.

Of 187 reviews identified in the search, 19 were included, comprising 205 unique trials. Four meta-analyses on the effects of booster sessions, behaviour change techniques, goal setting and motivational interventions showed a significantly small overall effect (SMD 0.24, 95% CI 0.13, 0.34) and no statistical heterogeneity ( I 2  = 0%) in the panoramic meta-analysis. Narrative synthesis revealed substantial clinical and methodological diversity. In total, the certainty of evidence is low regarding the efficacy of the investigated interventions and techniques on adherence, due to various methodological flaws. Most of the RCTs that were included in the reviews analysed cognitive and behavioural interventions in patients with musculoskeletal diseases, indicating moderate evidence for the efficacy of some techniques, particularly, booster sessions, supervision and graded exercise. The reviews provided less evidence for the efficacy of educational and psychosocial interventions and partly inconsistent findings. Most of the available evidence refers to short to medium-term efficacy. The combination of a higher number of behaviour change techniques was more efficacious.

Conclusions

The overview of reviews synthesised various potentially efficacious techniques that may be combined for a holistic and patient-centred approach and may support tailoring complex interventions to the patient’s needs and dispositions. It also identifies various research gaps and calls for a more holistic approach to define and measure adherence in physiotherapy.

Systematic review registration

PROSPERO CRD42021267355.

Peer Review reports

Adherence to physiotherapeutic1 treatment and recommendations is crucial to achieving the planned goals and desired effects [ 1 , 2 ]. This is because the desired effects are usually only achieved in the long term if the recommended treatment and home-based exercises are carried out regularly. However, non-adherence in physiotherapy can be as high as 70%, particularly in unsupervised home exercise programmes [ 1 , 3 ] and may differ among medical conditions [ 4 ]. The World Health Organization defines adherence to therapy as ‘the extent to which a person’s behaviour—taking medication, following a diet and/or executing lifestyle changes, corresponds with agreed recommendations from a health care provider’ [ 5 ]. Long-term adherence often requires lifestyle changes, which can be supported by behaviour change techniques (BCTs). BCTs are considered the ‘active, replicable and measurable component of any intervention designed to modify behaviour’ ([ 6 ],cf. [ 7 ]). BCTs are defined and operationalised in the behaviour change taxonomy [ 8 ], based on theoretical underpinnings and a Delphi study. Theoretical models to explain (non-)adherence and (a) motivation as well as techniques to promote behaviour change have been extensively studied in health and exercise psychology [ 9 , 10 , 11 ]. Rhodes and Fiala [ 12 ] argue that despite several strong psychological theories that have been developed to explain behaviour, few provide guidance for the design and development of interventions. Furthermore, theories may not be equally applicable to all behavioural domains, therapeutic regimes and settings. For example, the factors determining adherence to (passive) medication use differ from those influencing adherence to (active) physical therapies and exercise behaviour (cf. [ 5 ]). This review specifically addresses the domain of physiotherapy and therapeutic exercise.

Existing reviews of predictive studies identified factors influencing adherence positively or negatively, showing the predominately conflicting and low evidence of a wide range of predictive factors for adherence [ 1 , 2 , 13 ]. Moderate to strong evidence was shown for some factors, referring to previous (adherence) behaviour and treatment experiences, physical activity level, social support and psychosocial conditions, number of exercises and motivational dispositions. Such predictive studies have identified the possible targets for intervention but do not provide evidence on the efficacy of interventions. In contrast, randomised control trials (RCTs) are recognized as the preferred study design for investigating the efficacy of interventions. Thus, this overview of reviews Footnote 1 aimed at providing a synthesis of reviews that examined RCTs, allowing for the discussion of the efficacy of different interventions and BCTs on adherence-related outcomes.

There are numerous reviews on adherence to physiotherapy and (home-based) exercise, and on BCTs to increase physical activity levels, therapeutic exercise or self-organised exercise [ 1 , 2 , 3 , 14 , 15 , 16 , 17 , 18 ]. Yet, no systematic overview of reviews has been identified that specifically synthesised the efficacy of interventions and techniques to enhance adherence to physiotherapy.

Objectives and research questions

Therefore, the aim of this overview of reviews was to synthesise the evidence on the efficacy of interventions and techniques on adherence in physiotherapy, to explore heterogeneity regarding the theoretical underpinnings, types of interventions used, and the adherence-related measures and outcomes reported, and finally to identify research gaps. Thus, the primary research question is the following: How efficacious are interventions and techniques in increasing adherence to physiotherapy? Secondary research questions are as follows: What types of intervention and behaviour change techniques were investigated? Which theoretical underpinning was reported? How was adherence defined and related outcomes measured?

This overview of reviews is guided by the research questions and aligns with the common purposes of overviews [ 19 , 20 ] and the three functions for overviews proposed by Ballard and Montgomery [ 21 ], i.e. to explore heterogeneity, to summarize the evidence and to identify gaps. This overview approach is appropriate for addressing the research questions specified above by exploring different types of interventions and behaviour change techniques and by synthesising the evidence from systematic reviews of RCTs on their efficacy. The review protocol was registered ahead of the screening process in PROSPERO (reg.nr. CRD42021267355). The only deviations from the registration were that we excluded reviews of only cohort studies, due to the already broad heterogeneity of intervention and outcome measures, and that we additionally performed a panoramic meta-analysis.

Information sources, search strategy and eligibility criteria

The search in seven databases, PEDro, PubMed, Cochrane Library, Web of Science, Scopus, PsycInfo and CINAHL (Cumulative Index to Nursing and Allied Health Literature), was last updated on January 31, 2023. The search strategy was structured according to the PICOS (Population, Intervention, Comparison, Outcome and Study Type) scheme. The search terms related to physiotherapy and motivation or behaviour change and adherence and effectiveness/efficacy (details on the searches are listed in Additional file 1 ). A filter was applied limiting the search to (systematic) reviews. No publication date restrictions were applied.

Table 1 outlines the study inclusion and exclusion criteria. Only studies published in peer-reviewed journals were included. The review addressed adult patients, with any illness, disease or injury, and thus excluded studies on healthy populations. Reviews in the field of physiotherapy, physical therapy or the therapeutic use of exercise or physical activity were included if they investigated adherence as a primary outcome. Studies measuring adherence as a secondary outcome were excluded as they do analyse interventions that were not primarily designed to promote adherence and thus are outside the scope of this overview. Reviews that analysed only studies on digital apps or tools (e.g. virtual reality, gamification, exergames or tele-rehabilitation) were excluded from this overview, as they were outside of the scope of this overview. Only systematic reviews that appraised RCTs were included. Reviews appraising RCTs and other study designs were included if RCT results could be extracted separately. Systematic reviews are in our understanding literature reviews of primary studies with a comprehensive description of objectives, materials and methods; considering the risk of bias and confidence in the findings; and reporting according to the PRISMA statement [ 22 , 23 , 24 ]. Adherence is defined as the extent to which a person’s behaviour corresponds with treatment goals, plans or recommendations [ 5 ]. Related terms used in the literature are compliance, maintenance, attendance, participation and behaviour change or lifestyle modification and were thus included in the search strategy.

Screening and selection process

Author CL conducted the search in the seven different databases and removed duplicates, using the Zotero bibliography management tool. Following this, authors CL and PP both independently screened the titles and abstracts of the resulting sources (see Fig.  1 Flow diagram). After removing the excluded studies, PP and CL independently screened the remaining full texts in an unblinded standardised manner. Reasons for exclusion were noted in a screening spreadsheet. Any discrepancy was discussed, verified and resolved by consensus.

Data collection process and data items

Data extraction was done by CL after agreeing with PP on the criteria. A spreadsheet was created with the following data extraction components: (i) objectives and main topic of the review; (ii) study design(s) and number of studies included and excluded; (iii) search strategies (incl. PICO); (iv) population including diagnosis, sample sizes and age; (v) intervention and comparison, theoretical foundations and models used for designing the intervention; (vi) time frames, including follow-up; (vii) adherence-related outcome and outcome measures; (viii) key findings; (ix) analysis of primary studies (meta-analytical, other statistical or narrative analysis); and (x) tools used for the quality assessment, risk of bias and evidence grading. Primary outcomes on adherence included, adherence rates or categories, engagement, attendance and participation, and accomplished physical activity levels. PP verified the data extraction results. The data was extracted as reported in the systematic reviews, then reformatted and displayed in the tables and used for the narrative synthesis.

Assessment of risk of bias across reviews

Systematic reviews of RCTs are ranked highest in the evidence level [ 25 ], but are subjected to risk of bias (RoB). In an overview of reviews of systematic reviews, there are further risks of bias, in addition to those deriving from the primary studies and those deriving from the review of those studies. Particularly, the overlap of reviews regarding the included individual studies may bias the findings. According to the purpose of this overview, i.e. to synthesise the wide range of interventions and behaviour change techniques used to promote adherence and to summarise the evidence of their efficacy, the overlap of reviews regarding intervention or population was not an exclusion criterion. For considering the overlap of primary studies among the reviews, CL extracted the primary RCTs from the included reviews, identified the unique trials and compared the frequency of their use across the reviews (see results overlap of review and Additional file 2 ). Furthermore, where two or more reviews provided findings on the same technique (e.g. on the efficacy of behavioural graded activities), the overlap of primary studies was assessed specifically for that finding. If the evidence came from the same study, this was taken into account and marked accordingly in Table  5 to avoid double counting and overestimation of evidence.

Assessment of risk of bias within the reviews

CL and PP independently assessed the quality and risk of bias of the systematic reviews included, using the AMSTAR-2 tool [ 26 ]. Any discrepancy was discussed and resolved by consensus. AMSTAR (A MeaSurement Tool to Assess systematic Reviews) was developed to evaluate systematic reviews of randomised trials. The AMSTAR-2 revision enables a more detailed assessment of systematic reviews which may also include non-randomised studies of healthcare interventions. The applied AMSTAR-2 checklist consists of 16 items, whereof seven are classified as critical, and the appraisal results in an overall confidence rating distinguishing between critically low, low, moderate or high [ 26 ]. In addition, the overall confidence in the review was stipulated by the number of positive assessments in relation to the applicable domains (depending if meta-analysis was performed or not) and considering whether an item represents a critical domain or not [ 26 ].

Synthesis methods

Panoramic meta-analysis.

Among the included reviews, there were four meta-analyses [ 7 , 16 , 27 , 28 ], which were pooled as a panoramic meta-analysis based on the reported effect sizes and standard errors using IBM SPSS Version 29 (IBM Corp., Armonk, NY, USA). All four meta-analyses used the standardized mean difference as effect size. Standard errors were calculated from the reported 95% CI as \(\frac{\mathrm{upper bound }-\mathrm{ lower bound}}{3.92}\) . Inverse variance was used to weight the meta-analyses, statistical heterogeneity was assessed by I -squared and a fixed-effects model was selected based on the absence of statistical heterogeneity of true effects. Eisele et al. [ 7 ] included 15 primary trials that examined the effect of BCTs on physical activity adherence. They pooled results for medium-term (3–6 months) and long-term (7–12 months) interventions, from which we selected the medium-term model that best matched the eligibility criteria of the other included meta-analyses. Levack et al. [ 27 ] included nine primary trials that examined the effect of goal-setting strategies on engagement in rehabilitation. Among models with other outcomes, we selected this model because it best matched the aim of this overview, and it was most consistent with the outcomes of the other included meta-analyses. McGrane et al. [ 28 ] included six primary trials, representing 378 subjects that examined the effects of motivational interventions on physiotherapy session attendance. They reported another model with perceived self-efficacy as an outcome, but we selected the attendance model because it best matched the aim of this overview, and it was most consistent with the outcomes of the other included meta-analyses. Nicolson et al. [ 16 ] included two primary trials that examined the effect of booster sessions on self-rated adherence. Results were summarized by a forest plot and publication bias was assessed graphically by a funnel plot, although the small number of individual meta-analyses included limits its interpretability. Alpha was set at 0.05.

Narrative synthesis

The narrative synthesis was performed by CL in constant dialogue with and verification of PP. Guided by the research questions, the narrative synthesis of the extracted data was manifold. First, we explored the heterogeneity of interventions, measures and adherence-related outcomes across and within the reviews using the data extraction table. Definitions and measures of adherence were compared among the reviews and discussed. Second, analysis of the descriptions of the interventions and their respective components/techniques, their theoretical underpinning and their objectives was used to classify the interventions according to different types of intervention, namely the informational/educational, the cognitive/behavioural/motivational and the relational/psychosocial intervention. Consequently, for each type of intervention, the results on the efficacy were narratively synthesised. In addition, reported differences in efficacy among medical conditions, theoretical underpinnings and physiotherapeutic settings were summarised based on the data extraction table. Third, the results on the efficacy of the interventions and BCTs were further summarised in a table and then restructured according to the evidence level as reported in the systematic reviews and the confidence in the reviews as analysed by the AMSTAR-2. Therefore, the levels of evidence were extracted as reported in the reviews, which are based on different evidence appraisal schemes: GRADE (high, moderate, low, very low certainty of evidence), Cochrane Collaboration Back Review Group Evidence Levels (strong, moderate, conflicting, limited, no evidence) and self-developed tools. Afterwards, they were compared for the respective intervention/technique across the relevant reviews, considering the confidence in the review and the comprehensiveness of the review as well. The levels of evidence are presented in the table with the categories high, moderate, low and very low. The efficacy supported by the evidence is also based on the results reported in the reviews. In case of overlapping reviews or discrepancies between the reviews, the primary studies were consulted. The category yes refers to results of merely positive effects, and inconsistent refers to findings of positive and no effects of the intervention (techniques) analysed. The category no indicates that the intervention was not efficacious. No negative effects (i.e. favouring the control condition) were reported for the intervention (techniques) shown.

The reporting of findings followed the PRIOR reporting guideline for overviews of reviews of healthcare interventions [ 29 ].

Study selection results

Of the 187 records screened, 19 were included (see Fig.  1 ). Main reasons for exclusion were not a systematic review of RCTs ( n  = 79), adherence not the primary outcome ( n  = 60), and lack of physiotherapy relevance ( n  = 39) (see Fig.  1 ).

Flow diagram, based on PRISMA [ 24 ] and PRIOR [ 29 ] guidelines. Legend: *Multiple reasons for exclusion were possible

Characteristics and diversity of included reviews

The selection strategy resulted in a broad heterogeneity of included reviews. The 19 included reviews differed in their eligibility criteria of the primary studies as well, resulting in substantial clinical diversity, i.e. the inclusion of heterogenous conditions, intervention types and settings (see Table  2 ) and methodological diversity, i.e. the variability in study design, outcome measurements and risk of bias (see Tables 3 , 4 and 5 ). Musculoskeletal diseases [ 6 , 7 , 17 , 30 , 31 , 32 ] and pain [ 13 , 16 , 33 , 34 , 35 ] were the most investigated medical conditions. Those reviews that did not limit their search to a specific disease [ 12 , 27 , 28 , 36 , 37 , 38 , 39 , 40 ] yielded predominantly studies on musculoskeletal diseases. All reviews included adults only (18 and older). One focused on elderly (65 and older) people [ 40 ] and one on older (45 and older) adults [ 16 ]. Fourteen of the 19 reviews analysed RCTs only [ 6 , 7 , 16 , 17 , 27 , 28 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 39 , 40 ]; one also included besides RCT cohort studies [ 13 ] and three [ 12 , 37 , 38 ] also included any other quantitative study design (see Table  3 ). Four reviews performed a meta-analysis [ 7 , 16 , 27 , 28 ], and two studies were Cochrane Reviews [ 27 , 35 ]. Four reviews [ 6 , 7 , 17 , 40 ] analysed the use of BCTs and rated the interventions according to a BCT taxonomy [ 8 ].

Results of the individual reviews

The 19 reviews contained a total of 205 unique RCTs. Table 3 shows the main results of each review.

Results of quality assessment and confidence in the reviews

The critical appraisal with the AMSTAR-2 tool (see Table  4 ) showed that four reviews were rated with moderate to high quality [ 7 , 16 , 27 , 35 ], whereas all others resulted in a critically low to low overall confidence in the review. Frequent shortcomings were not explaining the reasons for the inclusion of primary study designs, and an insufficient discussion of the heterogeneity observed. Furthermore, as many reviews did not explicitly mention a pre-established, published or registered protocol or study plan, it is uncertain whether the research followed a pre-specified protocol and whether there were changes and/or deviations from it, and, if so, whether decisions during the review process may have biased the results [ 26 ].

Risk of bias and evidence assessment within reviews

The reviews used various approaches to appraise the evidence, particularly the GRADE (Grades of Recommendation, Assessment, Development and Evaluation) system [ 13 , 16 , 26 , 27 ], the evidence levels by the Oxford Centre for Evidence-Based Medicine [ 28 ] or the system by Cochrane Collaboration Back Review Group [published by 25,30] [ 31 , 32 , 33 , 34 ]. Three reviews modified existing or developed their own tool or checklist [ 12 , 35 , 36 ]. For the assessment of the risk of bias and/or quality of the individual studies, the reviews used the following tools: PEDro Scale [ 7 , 13 , 26 , 32 , 37 ], Cochrane Collaboration Back Review Group Quality Assessment Tool [ 31 , 34 ], Cochrane Risk of Bias criteria [ 6 , 16 , 17 , 27 , 33 , 37 , 38 , 39 ], the Delphi List [ 40 ] or modified or developed own tools [ 12 , 35 , 36 ].

A recurring concern regarding potential performance bias was the lack of therapist blinding, which is almost impossible to implement in this research field [ 7 ]. Attrition bias, due to low sample size or drop-outs, and measurement bias, due to the mere use of subjective measures, were also highlighted in the reviews. Another concern was the availability and selection of adequate control groups. Control groups, such as usual practice, unspecific exercise group or alternative intervention commonly include varying numbers of BCTs which must be considered when assessing and comparing contents of interventions [ 7 ]. The comparability of the intervention and control group regarding adherence-related outcomes is further hindered by poor descriptions of the intervention, uncertainty about treatment fidelity and implementation processes, varying competences and proficiency of the therapist, and the diverse translation of theoretical models and use of intervention techniques [ 7 , 34 , 39 ]. Rhodes and Fiala [ 12 ] pointed out that procedures of RCTs, such as several pre-screenings and measurement batteries, may lead to a potential self-selecting of only the most motivated individuals. This may limit the ability to compare intervention to the control group, as both groups are (already) highly motivated, and to detect changes, due to the already high motivation and disposition to adhere. This may explain in part, that the reviews reported many studies that failed to provide evidence for intervention efficacy on adherence. In addition, the restricted timeline (limited duration for observation and follow-up) of the studies may confound/skew the results, as drop-out may occur shortly after the end of the study and long-term adherence is not measured [ 12 ].

Overlap of reviews

The 19 reviews included from 3 to 42 individual RCTs. In sum, the reviews included 261 RCTs (multiple publications on the same trial were counted as one; thus, the number of trials was counted), whereby 34 trials were included in various reviews (see Additional file 2 , Overlap of reviews), resulting in 205 unique RCTs. Of these 34 trials included in multiple reviews, 25 were included in two different reviews. The following trials were included more than twice: Basler et al. 2007 (8x), Friedrich et al. 1998 (7x), Schoo et al. 2005 (4x), Vong et al. 2011 (4x), Asenlof et al. 2005 (3x), Bassett and Petrie 1999 (3x), Brosseau et al. 2012 (3x), Bennell et al. 2017 (3x), Gohner and Schlicht 2006 (3x) and Duncan and Pozehl 2002, 2003 (3x).

In total, the overlap of primary trials in the reviews is considered low; except among reviews [ 27 , 39 ] and among reviews [ 12 , 16 , 28 , 30 ]. Two reviews [ 27 ] and [ 39 ] were conducted by the same authors, within the same field, i.e. goal planning and setting, however with a different approach and research question. Reviews [ 12 , 16 , 28 , 30 ] have a considerable amount of overlap. Still, each of these reviews included unique RCTs, not analysed in any of the other reviews, and they do focus on different research questions, foci and analyses. Therefore, we did not exclude an entire review due to an overlap of studies.

Synthesis of results

The synthesis focused on answering the research questions. We began by presenting the narrative synthesis findings on how adherence was measured, what types of intervention and BCTs were investigated, and which theoretical underpinnings were reported. Afterwards, we synthesised the evidence on the efficacy of the interventions and BCTs, both meta-analytically and narratively.

Measures of adherence and related outcomes

The reviews included studies with a heterogeneous use, breadth and measures of adherence. Mostly, they refer to adherence as the extent to which a person’s behaviour corresponds with treatment goals, plans or recommendations ([ 30 ],cf. [ 5 ]). McLean and colleagues [ 30 ] expressed that within physiotherapy, the concept of adherence is multi-dimensional and could refer to attending appointments, following advice or undertaking prescribed exercises. The terms adherence and compliance were sometimes used interchangeably, referring to the degree of treatment attendance or accomplishment of physical activity levels, participation and recommendations, irrespective of how the treatment goals and plans were established. Yet, for definition purposes, the distinction between agreed and prescribed goals and plans was occasionally used in the reviews to distinguish adherence from compliance .

For analytical purposes, adherence was frequently dichotomised, establishing a cutoff point or percentage used to distinguish adherence from non-adherence. One was considered adherent, for example, if he/she achieved more than 70% or 80% of the targeted, recommended or prescribed sessions. Few studies graded the degree of adherence according to multi-categorical cut-off points (e.g. very low, low, moderate and high adherence). Only in one review [ 13 ], one study was named that distinguished a certain fluctuation in the adherence pattern, i.e. Dalager et al. [ 41 ] included besides the minutes exercised in a week the regularity of participation, distinguishing regular from irregular participation. Self-reported diaries, exercise logs and attendance lists were the most commonly used data recording instruments [ 33 , 35 , 37 ]. Adherence to home-based programmes was mainly measured with self-reported diaries, which are problematic as the only source, due to poor completion rates, and the possibility of inaccurate recall and self-presentation bias [ 18 , 33 ]. Digital devices (e.g. accelerometers or pedometers) may be used additionally to measure adherence; however, their use may also be problematic, as they require certain adherence to a systematic use of the device and the mere use of the device also may increase adherence [ 18 , 33 ]. One study reported the use of the Sport Injury Rehabilitation Adherence Scale (SIRAS) [ 42 ], which measures the patients’ degree and manner of participation in a session and compliance with the therapist’s instructions and plan. Thus, it does not measure adherence over a certain period of time nor adherence to recommendations or home-based exercise, but it can be used to assess the intensity of rehabilitation exercises, the frequency with which they follow the practitioner’s instructions and advice, and their receptivity to changes in the rehabilitation programme during that day’s appointment [ 42 ].

Interventions used to promote adherence

The reviews included a wide range of different interventions, which we grouped into three different intervention types:

Information provision and patient education were investigated in seven reviews [ 12 , 13 , 30 , 31 , 33 , 34 , 36 ], including (i) video- and audio-assisted patient education, (ii) phone calls, (iii) use of supporting materials and spoken or graphically presented information or (iv) other didactical interventions. Patient education has been defined as ‘any combination of learning experiences designed to facilitate voluntary adoption of behaviour conducive to health’ [ 43 ]. Niedermann et al. [ 31 ] distinguished between ‘purely’ educational programs based on knowledge transfer and psychoeducational programs. In the latter, motivational techniques and shared knowledge-building processes are added to the educational programme, which is done similarly in health coaching [ 34 ], and thus also relate to the cognitive, behavioural and relational/psychosocial interventions.

Cognitive and behavioural motivational interventions were relating frequently to cognitive-behavioural and social-cognitive theories, and applied (i) behavioural graded exercise; (ii) booster sessions, refresher or follow-up in situ by the therapist or via phone call; (iii) behavioural counselling (focusing on readiness to change); (iv) psychoeducational counselling; (v) supervision; (vi) (unspecified) motivational intervention; (vii) positive reinforcement; (viii) action and coping planning; and (ix) goal setting [ 7 , 12 , 13 , 16 , 27 , 28 , 30 , 32 , 33 , 34 , 39 ].

Relational and psychosocial interventions were less investigated overall. Related aspects included (i) social support; (ii) patient-centeredness, in particular patient-led goal setting, motivational interviewing and the therapeutic or working alliance; and (iii) emotional components [ 6 , 13 , 17 , 33 ].

The included reviews focused either on one particular or several types of intervention. Particularly, four reviews [ 6 , 7 , 17 , 40 ], which used a BCT taxonomy to analyse the interventions of the primary studies, described BCTs relating to all three intervention types. While this distinction of different types of interventions is useful to showcase the range of diverse interventions and techniques, they do have a great overlap and include a mix of different BCTs. For example, the way of facilitation of information, supervision or goal setting was approached differently according to the relational approach, i.e. being more instructive, directive or more collaborative, participatory, patient-led ([ 31 ],cf. [ 34 ]).

Theoretical underpinning of interventions

No review focused on only one theoretical foundation or excluded studies based on any theoretical model or not underpinning the intervention. In total, the reviews included studies with diverse theoretical models and varying degrees of theoretical underpinning. References to the cognitive behavioural theory (CBT) and to the social-cognitive theory were frequent in the individual studies. Furthermore, the self-determination theory, the transtheoretical model, the health belief model, the social learning theory and the socioemotional selectivity theory were used in some individual studies (cf. [ 11 ]). The heterogeneity in the theoretical underpinning of the interventions is reinforced by the given overlap of the theories and models (cf. [ 11 ],[ 28 ]) and various BCTs are key components of several theories [ 17 ]. Furthermore, theories were not used enough to explicitly inform and underpin interventions and they were translated into practise in different ways; thus, interventions based on the same theory may differ substantially [ 17 ].

The BCT Taxonomy v1 [ 8 ], which relates to various theoretical models, was used in four reviews [ 6 , 7 , 17 , 40 ] to identify BCTs in interventions in a standardized manner. The Behaviour Change Wheel [ 44 ], which is linked to the BCT Taxonomy v1, was referred to in one review [ 40 ] pointing to its usefulness for designing a behaviour change intervention. The number of BCTs used appears to be relevant, as interventions using a higher number (≥ 8) of BCTs achieved a significant effect (pooled SMD = 0.29, 95% CI 0.19–0.40, p  < 0.001), whereas interventions using a lower number (< 8) of BCTs did not (pooled SMD = 0.08, 95% CI -0.11 to 0.27, p  = 0.41).

Overall efficacy and heterogeneity according to the panoramic meta-analysis

Although there was statistical heterogeneity ( I 2 from 41 to 63%) between the primary studies included in each meta-analysis [ 7 , 16 , 27 , 28 ], there was no heterogeneity between the pooled effects of these four meta-analyses ( I 2 0%). This means that all variability in the effect size estimates (SMD from 0.20 to 0.39) was attributable to sampling error, but there was no variability in the true effects. Although the interventions were selected based on different eligibility criteria (BCTs, goal-setting strategies, motivational interventions and booster sessions), they appear to be very similar in terms of the effects they trigger. There was no overlap between the primary trials included in the meta-analyses. The pooled SMD was 0.24 (95% CI 0.13, 0.34) (Fig.  2 ). Effect size estimates were somewhat larger in those meta-analyses with less weight in the model (i.e. due to a larger standard error). However, no obvious publication bias could be detected in the funnel plot (Fig.  3 ). Sensitivity analyses in the meta-analysis in Eisele et al. [ 7 ], considering only studies with PEDro scores of 6 or more, revealed slightly lower effect sizes but still statistically significant effect sizes regarding medium-term effects (SMD PEDro>=6 0.16, 95% CI 0.04–0.28, p  < 0.01 versus SMD all 0.20, 95% CI 0.08–0.33, p  < 0.01) and higher numbers of BCTs (SMD PEDro>=6  = 0.26, 95% CI 0.16–0.37, p  < 0.001 versus SMD all  = 0.29, 95% CI 0.19–0.40, p  < 0.001), indicating that low-quality studies may tend to overestimate the efficacy ([ 7 ],cf. [ 31 ]).

Forest plot of panoramic meta-analysis: interventions aiming at improving adherence, adherence-related outcomes

Legend: Eisele 2019. Intervention: Interventions aiming at improving physical activity levels or adherence, containing at least one BCT. Comparison: Usual care, minimal intervention, placebo or no intervention. Outcome: Any measure of physical activity level or adherence to any kind of physical activity. Levack 2015. Intervention: Goal setting (with or without strategies to enhance goal pursuit). Comparison: No goal setting. Outcome: Engagement in rehabilitation. McGrane 2015. Intervention: Motivational interventions as part of a package, psychological strategies, theory-based instructional manuals, Internet-based behavioural programmes and relapse prevention, and re-inforcement strategies. Comparison: Any comparison (not specified). Outcome: Attendance at physiotherapy sessions/exercise classes. Nicolson 2017. Intervention: Booster sessions to increase adherence to therapeutic exercise. Comparison: Contextually equivalent control treatments. Outcome: Self-rated adherence

Funnel plot of publication bias

Efficacy of informational and educational interventions

The results of five—partly overlapping—reviews [ 12 , 30 , 31 , 34 , 36 ] showed, with a very low evidence base, that interventions that primarily aimed at information provision and knowledge transfer to the patient had limited efficacy on adherence-related outcomes. There was conflicting evidence and inconsistent efficacy of video-assisted patient education [ 36 ] and individualised exercise videos [ 12 , 30 ] in modifying behaviour or adherence. However, the authors identified the format in which the educational information is presented and the complexity of the addressed behaviour as crucial factors [ 36 ]. Videos that provide only spoken or graphically presented health information are inappropriate tools for changing patient behaviour. However, videos with a narrative format appear to be a powerful education tool [ 36 ]. Low evidence based on one study [ 12 , 30 ] indicates that additional written information seems superior to verbal instructions alone (mean difference between groups 39.3%, p  < 0.001). With a high overlap of studies, two reviews [ 30 , 31 ] showed that there is limited evidence for long-term effects of patient education targeting knowledge acquisition. While the informative and instructive educational approach is an essential part of patient education, patient education often involves more than the transfer of knowledge [ 30 , 31 , 34 ]. Niedermann et al. [ 31 ] compared educational and psychoeducational interventions and provided arguments in favour of psychoeducational approaches that enrich patient education with motivational strategies and techniques (cf. [ 34 ]).

Efficacy of cognitive and behavioural motivational interventions

Several (though partly overlapping) reviews [ 12 , 16 , 28 , 30 , 33 , 37 ] examined studies on additional motivational interventions that were based on social-cognitive or cognitive-behavioural theories. McGrane et al. [ 28 ] concluded heterogeneity of motivational interventions, outcomes and measurements as potential causes for conflicting evidence regarding effects on exercise attendance and PT adherence, as they found no significant difference ( p  = 0.07) in exercise attendance between additional motivational intervention groups and their controls (pooled SMD 0.33, 95% CI -0.03 to 0.68, I 2 62%), but a significant ( p  < 0.01) medium-sized effect of additional motivational interventions on self-efficacy beliefs (pooled SMD 0.71, 95% CI 0.55 to 0.87, I 2 41%). The heterogeneity hindered in this meta-analysis the statistical analysis of subgroups to determine and compare the efficacy of different components and approaches to motivational interventions [ 28 ]. Another meta-analysis [ 16 ] found moderate-quality evidence that booster sessions with a physiotherapist helped people with hip/knee osteoarthritis to better adhere to therapeutic exercise (pooled SMD 0.39, 95% CI 0.05 to 0.72, p  = 0.02, I 2 35%). Moderate evidence for the efficacy of supervision (2 studies, n  = 193) favouring adherence was shown [ 13 , 33 , 35 ].

In four reviews [ 16 , 32 , 33 , 35 ], four unique high-quality trials supported the use of motivational strategies and behavioural graded exercise to improve adherence to exercise (effect sizes 0.26–1.23)[ 16 ]. Behavioural graded exercise includes a preset gradual increase of the physical activity through facility-based interventions followed by booster sessions [ 45 ] and uses principles of operant conditioning and self-regulation [ 16 ].

While cognitive behavioural programmes seem superior to exercise alone for short-term adherence and clinical attendance [ 30 ], behavioural counselling focusing on readiness to change, action and coping plans and/or audio/video exercise cues seem not to improve adherence significantly [ 16 ]. Holden [ 34 ] concludes inconsistent evidence for health coaching based on the transtheoretical model of change, with one RCT showing some efficacy on exercise compliance (SMD = 1.3). However, the frequently referred to study of Göhner and Schlicht [ 46 ], who analysed a cognitive-behavioural intervention with a strong emphasis on action and coping planning [ 12 ], showed no difference between experimental and control groups in the first 11 weeks, but a significant difference 5 months later on behaviour (SMD = 0.83) as well as differences over all time-points on self-efficacy (interaction effect of time by group, F (3, 43) 10.36, p  < 0.001, n  = 47) favouring the intervention [ 46 ]. Motivational interventions, including positive reinforcement, increased (i) adherence to home exercise in one RCT [ 33 ], (ii) reported frequency of exercise in two RCTs [ 35 ] and (iii) self-efficacy beliefs in two RCTs, in the short-term (SMD = 1.23) and in the long-term (SMD = 0.44) ([ 16 ],cf. [ 30 ]). Self-efficacy beliefs relate to the trust in one’s capacities/competencies to cope with daily demands [ 47 ] and are associated (moderate evidence) with adherence [ 13 , 48 ].

Levack et al. [ 27 ] conclude some evidence that goal planning/setting improves engagement in rehabilitation (motivation, involvement and adherence) over the duration of the programme (9 studies, 369 participants, SMD 0.30, 95% CI -0.07 to 0.66). Furthermore, they show a low-quality evidence for effects on patient self-efficacy from more structured goal setting compared to usual care with or without goal setting (2 studies, 134 participants; SMD 0.37, 95% CI 0.02 to 0.71) and from goal setting compared to no goal setting (3 studies; 108 participants; SMD 1.07, 95% CI 0.64 to 1.49). The review did not detect differences in efficacy between the approach taken to goal planning. However and similar to patient education [ 34 ], the review authors argue that the lack of clarity about the effects and the low evidence is due to the heterogeneity of the implementation of goal planning, lack of detailed descriptions of the goal-setting process in the intervention groups but also in the control groups, and methodological flaws ([ 27 , 39 ],cf. [ 13 ]).

The BCTs from the cluster goals and planning showed various positive effects, although not fully consistently [ 6 , 7 , 40 ]. Eisele et al. [ 7 ] identified goal setting (behaviour) , problem-solving , goal setting (outcome) , action planning and reviewing behaviour goal(s) as often used in non-effective interventions but also in effective ones. A trial that showed negative effects included problem-solving and goal setting (outcome) as well. Room et al. [ 40 ] found one study on older people and Thacker et al. [ 6 ] two home-exercise-related studies that used BCTs from the goals and planning cluster (i.e. problem-solving and action planning), but none of the studies found differences in favour of the intervention. Willett et al. [ 17 ] adjusted the BCTv1 taxonomy to differentiate patient-led and therapist-led goal setting and showed that patient-led goal setting (behaviour) achieved among the highest efficacy ratios across time points.

Efficacy of relational and psychosocial interventions

The BCT Social Support (unspecified) refers to ‘advise on, arrange or provide social support (e.g. from friends, relatives, colleagues, ’buddies’ or staff) or non-contingent praise or reward for the performance of the behaviour . It includes encouragement and counselling, but only when it is directed at the behaviour’ [8, Supplementary Material]. Eisele et al. [ 7 ] identified this BCT in 19 interventions and 10 control conditions. They found this BCT in three trials supporting efficacy and in seven trials supporting inefficacy. In contrast, Thacker et al. [ 6 ] found this BCT in all effective interventions but not in the non-effective ones. Willet et al. [ 17 ] concluded from their review that this BCT has among the highest efficacy ratios across time points to promote adherence to physical activity.

Social support may come along with monitoring and feedback, which can be graphically or narratively presented by the therapist. Willett et al. [ 17 ] recommend that self-monitoring (e.g. activity diaries), feedback on behaviour as well as social support should be used—beyond monitoring purposes—for explicit intervention purposes (e.g. to foster self-efficacy beliefs). Feedback on behaviour alone does not seem to be efficacious [ 6 ], but feedback can be efficacious for instance in combination with social support or goal setting and planning [ 17 , 40 ].

Patient-centred approaches were also included in the relational/psychosocial intervention type. Motivational interviewing, which is a collaborative, patient-centred communication style to promote behaviour change [ 49 ], was used in three studies, indicating positive effects on exercise compliance, physical activity and exercise at home in two trials, whereas no effect in a pilot study [ 28 ]. There is low evidence from three RCTs for positive effects of the therapist-patient alliance on global assessments; however, the efficacy on adherence-related outcomes is unclear [ 36 ]. The terms working or therapeutic alliance refer to the social connection or bond between therapist and patient/client, including reciprocal positive feelings, (assertive) communication, empathy, and mutual respect as well as collaboration, shared decision-making, agreement on the treatment goals and tasks [ 36 , 50 ]. The therapeutic alliance is a patient-centred approach as well. Patient-led goal setting was more often a component within efficacious interventions than therapist-led goal setting [ 17 ].

None of the included reviews focused specifically on affective interventions. However, some interventions relate to affective components, for example patient-led goal setting or motivational interviewing may cover emotional needs [ 27 ]; health coaching, therapeutic alliance or social support may include emotional support [ 13 , 34 , 35 , 38 ]; monitoring may consider emotional consequences [ 6 ]; or messaging and information provision may include emotional components [ 36 ]. Room et al. [ 40 ] included one RCT [ 51 ], comparing emotionally meaningful messages against factual informational messages, but with no significant differences between the groups.

Efficacy according to the theoretical underpinning

McGrane et al. [ 28 ] provide a narrative analysis of the efficacy of interventions according to the different theoretical underpinnings. In their review, the cognitive-behavioural theory (CBT) was the most popular theory (4 primary studies) and showed to be efficacious in improving self-efficacy and activity limitations, but not consistently regarding attendance and attrition [ 28 ]. The social-cognitive theory was used in three studies, showing improvements in self-efficacy, action and coping planning, and attendance, but conflicting results for exercising in the short and long term. One intervention [ 52 ] based on self-determination theory showed to be efficacious to improve adherence to physical activity. In contrast to McGrane et al. [ 28 ], the reviews [ 12 , 30 , 35 ] point to moderate to conflicting evidence for no or inconsistent efficacy of CBT-based approaches to physiotherapy programmes (see Efficacy of cognitive and behavioural motivational interventions ). Jordan [ 35 ] concluded that the addition of transtheoretical model-based counselling to physiotherapy is no more effective than physiotherapy and a sham intervention (GRADE: High (high quality); Silver). Notably, the interventions may not be representative of the theory described due to diverse translations of the theory into practice and the overlap of the same BCTs among the theories.

Various theories (e.g. the transtheoretical model or the Health Action Process Approach [ 53 ]) and studies [ 54 ] distinguish the action or adoption phase from the maintenance phase at 6 months. Interestingly, Willet et al. [ 17 ] found in total higher short (< 3 months) and long-term (12 months and more) than medium-term (around 6 months) efficacy ratios, pointing to the risk of drop-out when changing from the (short-term) adoption phase to the (long-term) maintenance phase [ 17 ]. Eisele et al. [ 7 ] divided in their meta-analysis the short-term (< 3 months), medium-term (3–6 months) and long-term (7–12 months post-intervention) differently, showing a small medium-term overall effect (pooled SMD 0.20, 95% CI 0.08–0.33, p  < 0.01), but no significant long-term effect of interventions comprising BCTs in enhancing physical activity adherence (pooled SMD 0.13, 95% CI 0.02–0.28, p  = 0.09).

Efficacy according to the different types of exercise, physiotherapeutic settings and medical condition

In their Cochrane review, Jordan et al. [ 35 ] compared the evidence for the efficacy of different types of exercises and physiotherapy settings. Graded exercise is beneficial for adherence (moderate evidence). The exercise type does not appear to play an important role (moderate evidence). Whether water-based exercise favours adherence is unclear (low evidence and inconsistent results). Furthermore, the supervision of exercising (moderate evidence) is beneficial for adherence, but also self-management programmes improve exercise frequency compared to waiting list or no-intervention control groups (moderate evidence). Exercising individually seems to improve attendance at exercise classes more than exercising in a group (moderate evidence), as individual sessions could be scheduled at more convenient times and missed sessions could be rescheduled, whereas group sessions were scheduled at relatively inflexible times, and missed sessions could not be rescheduled [ 35 ]. However, adding group exercise to a home exercise programme can increase overall physical activity levels (moderate evidence) [ 35 ]. While the results of home- versus clinic-based interventions were conflicting and confounded by the intervention approaches, a combination of home- and clinic-based approaches may be promising [ 12 ] and aligns with the moderate-quality evidence that self-management programmes, refresher or booster sessions with a physiotherapist assist people to better adhere to therapeutic exercise [ 16 ].

No study was identified in the reviews that compared other settings, such as private- and public-funded physiotherapy or primary care and rehabilitation settings regarding adherence outcomes. No review and no study comparing the same educational, motivational, or BCT-based intervention across different conditions were identified.

This overview of systematic reviews addresses adherence in the physiotherapy and therapeutic exercise domain, aiming to summarise the evidence on the efficacy of interventions, to explore heterogeneity and to identify research gaps. The overview of reviews provided an adequate approach to generate answers to the research questions. Nineteen reviews, covering 205 unique trials, were included and narratively synthesised. In addition, four meta-analyses were pooled in a panoramic meta-analysis. The findings provide an overview of the diverse interventions and techniques aiming to enhance adherence, ranging from informational/educational to cognitive/behavioural/motivational and to relational/psychosocial intervention types. Furthermore, it synthesised their efficacy in physiotherapy for adults.

Confidence in the reviews was rated moderate or high in four reviews [ 7 , 16 , 27 , 35 ], but low or very low in the others (Table  3 ). The individual reviews considered the evidence levels as mostly low or very low (Table  4 ; see Risk of bias and evidence assessment ). Table 5 summarizes the evidence on the efficacy of each intervention and technique according to (a) whether the evidence supports efficacy, (b) the evidence level based on the report in the systematic reviews and (c) the confidence in the reviews as assessed with AMSTAR-2. It must be noted that the components of the intervention which caused the efficacy were not always clear. Some interventions lacked detailed definitions and descriptions of the specific BCTs included [ 33 ]. A single technique or mechanism of action was not always identifiable; moreover, various techniques seem to influence each other in such a way that they achieved efficacy only jointly [ 17 , 40 ].

No clear conclusion can be drawn on the efficacy of informational/educational interventions. Five reviews [ 12 , 30 , 31 , 34 , 36 ] showed low evidence for the efficacy of interventions on knowledge acquisition and low evidence for limited short-term efficacy on adherence. Providing knowledge alone seems not enough and should be complemented with supportive material (very low evidence) and combined with other interventions (low evidence). Patient education should also include social-cognitive or cognitive-behavioural approaches, psychoeducational interventions and collaborative processes as it is included in the therapeutic alliance approach [ 31 , 34 , 36 ]. Patient education with a more constructive educational approach builds upon the knowledge of the patient, supporting him/her in exploring and co-constructing knowledge which is very relevant in physiotherapy as research has shown [ 55 , 56 ].

The reviews on additional motivational, cognitive and behavioural interventions showed findings ranging from non-efficacy of behavioural counselling based on readiness to change (with low to moderate evidence) to moderate efficacy for booster sessions and behavioural graded physical activity (with moderate evidence) (see Table  5 ). Overall, a small overall effect size (SMD 0.24) for motivational interventions is indicative of the findings of the panoramic meta-analysis. The four pooled meta-analyses [ 7 , 16 , 27 , 28 ] included studies analysing interventions with a considerable amount of content overlap (e.g. goal-setting and booster sessions are BCTs and often part of motivational interventions), and no statistical heterogeneity of the true effect was found. Nevertheless, the diversity of interventions and techniques included constrain the explanatory power for potential components responsible for the efficacy of adherence. The sensitivity analyses in the meta-analysis of Eisele et al. [ 7 ] indicate that low-quality studies tend to overestimate the efficacy (cf. [ 31 ]). While some evidence exists on short- and medium-term effects of motivational programmes on adherence, no clear evidence for long-term effects can be concluded [ 7 , 30 ]. Furthermore, there is moderate and low evidence that additional motivational interventions and goal planning/setting improve adherence to self-efficacy beliefs [ 27 , 28 , 39 ]. Since self-efficacy beliefs play an important role in motivation and adherence [ 13 , 48 ], the results are relevant for physiotherapists to promote motivation and adherence. Experiencing that one can reach the set goals and manage daily challenges, complemented with feedback and reinforcement from the therapist (or important others), may increase self-efficacy beliefs and human agency [ 48 , 57 , 58 , 59 ].

A closer look at how and in which manner goals and actions are planned and reviewed seems crucial. The patient-led approach was only reported in 5 of the 26 interventions that incorporated the BCT goal setting (behaviour) , although it is associated with greater engagement and achievement than goals which are set by the therapist [ 17 ]. Goal setting and action planning should be informed by the patient’s motives, interests and values in order to promote intrinsic motivation, self-determination and subsequently better adherence ([ 17 ],cf. [ 27 , 28 , 60 , 61 ]). The reviews on the BCTs displayed various positive effects relating to the BCT cluster goals and planning ; however, they point out that the BCT goal setting is not used alone but in connection with several other BCTs. Feedback on outcomes of behaviour , behavioural contract and non-specific reward as well as patient led-goal setting , self-monitoring of behaviour and social support (unspecified) was included in efficacious interventions [ 17 ]. Social support seems to have an important influence on adherence [ 6 , 7 , 17 , 40 ], for example through regular phone-calls or home visits, encouraging messaging, supervision or community-based group programs (cf. [ 1 , 2 , 3 ],[ 37 , 62 ]). Social support also relates to the promotion of self-efficacy beliefs, if it endorses confidence in own abilities and competences [ 6 ].

Some BCTs seem inherent to standard practices of physiotherapy [ 6 ] even though physiotherapists seem to use rather a small number of BCTs [ 15 ]. Control groups also contained BCTs [ 6 , 7 ]; in particular instruction on how to perform a behaviour , generalisation of the target behaviour and social support (unspecified) were frequently coded [ 6 ]. Thus, it seems difficult to identify those BCTs that are (most) efficacious in promoting adherence ([ 7 ],cf. [ 50 ]). Unsurprisingly, the reviews revealed conflicting results and a high risk of bias in the individual studies. However, combining a greater number of BCTs (≥ 8) can be highly recommended, as this achieved a larger effect than interventions using fewer BCTs [ 7 ]. It is fairly unlikely that any single BCT changes adherence [ 6 , 7 , 17 , 40 ]. In that regard, Ariie et al. [ 63 ] argue that not only the amount of BCTs but also the quality, appropriateness and feasibility of the use of the BCTs is crucial.

Meaningful combinations of several BCTs are required. However, the combinations of BCTs may also differ among conditions, personal factors and therapeutic interventions ([ 7 ],cf. [ 63 , 64 ], [ 64 , 65 , 66 ]), and over the time. Two reviews consistently point to the same crucial time point (i.e. after 6 months) when BCT efficacy seems to drop, and more attention is required to maintain adherence [ 7 , 17 ]. Action planning , feedback on behaviour and behavioural practice/rehearsal seem efficacious particularly on short-term. Patient led-goal setting , self-monitoring of behaviour and social support (unspecified) are among those BCTs that seem more efficacious at long-term [ 17 ]. These findings are also in line with findings in non-clinical adults [ 54 ] and with motivational theories (e.g. the Health Action Process Approach [ 53 ]).

Limitations

Conducting an overview of reviews is per se associated with methodological limitations. A limitation is that reviews were analysed and not the original RCTs, which adds further risks of bias domains such as selection, analysis and reporting bias. A specific potential source of bias in overviews of reviews is the overlap of primary studies among the included reviews. The small overlap, caused by a few reviews with similar thematic scope, was controlled for in the data analysis. The substantial non-overlap of primary studies across the reviews reflects the clinical and methodological diversity of the included reviews and showcases the efforts to address (a) motivation and (non-)adherence as complex phenomena and from various perspectives.

Another methodological limitation originates from the search strategies. Considering different health-care systems and delimitations of the physiotherapy profession among countries, divergences among the definitions of terms and the use of diverse approaches to physical therapy, physiotherapy or the therapeutic use of exercise and physical activity, made a clear delimitation in the search strategy and inclusion/exclusion criteria difficult. Therefore, we may have missed out some relevant reviews by reducing our search to the two terms physiotherapy and physical therapy. Equally, we may also have included some aspects that were not primarily investigated for physiotherapists or physical therapists. Including only studies with adults, the findings may not be applicable to promote adherence among children.

While we did not exclude reviews from another language, the search was conducted only in English, which may omit important reviews in other languages. All included reviews (and as far as reported, also the original RCTs) were conducted in economically developed countries; however, social-cultural and context-specific factors influence participation and adherence [ 67 , 68 , 69 , 70 , 71 ]. Furthermore, we are aware that our own cultural background and experiences may have influenced the analysis and synthesis of the results and that conclusions drawn in this overview of reviews may not be suitable for every setting around the world. Therefore, we encourage the readers to critically assess the applicability of the findings to their specific context.

Another gap in coverage of this overview is that interventions that were analysed in RCTs but not included in any systematic review are not considered in this overview. Thus, there may be new or alternative intervention approaches that resulted efficacious but were not covered by this overview. Furthermore, reviews that focused only on the use of digital apps or tools, e.g. virtual reality, gamification, exergames or tele-rehabilitation, were excluded from this overview. Several reviews in this field include adherence-related outcomes, showing potential efficacy as well as limitations of the use of digital tools [ 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 ].

Research gaps, recommendations and measuring adherence

This overview of reviews highlighted some gaps in the existing knowledge. First, there is a lack of clear evidence on the efficacy of the interventions. The use of BCTs in the intervention as well as in the control groups may be a reason for inconsistent findings and conflicting evidence. Furthermore, the clinical and methodological heterogeneity constrains drawing clear conclusions on the efficacy. Second (and related to the previous), interventions are insufficiently described regarding their theoretical underpinning and active ingredients/techniques and thus limit the comparison of interventions. Theoretical underpinnings were used partly and translated into practise differently. Difficulties concerning the derivation or deduction of concrete, practical techniques or strategies from the theories were reported. A broader use of the BCT taxonomies would make interventions more comparable. Recently, the BCT Ontology was published, which claims to provide a standard terminology and a comprehensive classification system for the content of behaviour change interventions, suitable for describing interventions [ 84 ]. Third, there is a need for studies on holistic approaches, complex interventions based on integrative theories and the combination of multiple BCTs. While many theories are based on cognitive and behavioural approaches, affective and psychosocial factors are hardly investigated, overlooked and probably underestimated. Rhodes and Fiala [ 12 ] call for studying the influences of affective attitudes on adherence (e.g. enjoyment and pleasing behaviour) which may oppose the more cognitive, instrumental attitudes (e.g. the utility of behaviour). Jordan et al. [ 35 ] refer to a meta-analysis in another therapeutic regime [ 85 ] to explicit the potential efficacy of affective interventions (e.g. appealing to feelings, emotions or social relationships and social supports) in combination with educational and behavioural interventions on patient adherence [ 35 ]. Fourth, more research in patient-led approaches to goal setting and action planning and the relationship of patient-centeredness to adherence is promising [ 60 , 61 , 86 , 87 ].

Fifth, the reviews reported many studies that failed to provide evidence for intervention efficacy on adherence, particularly on long-term adherence. There is a need for prolonged observation to investigate long-term effects on adherence. Probably, intervention or follow-up interventions (e.g. booster sessions) must also be prolonged or repeated to avoid drop out to medium-term follow-ups (around 6 months) and to maintain participation. Sixth, studies should pay more attention to the actual efficacy of adherent behaviour on the desired therapeutic outcomes.

Seventh, another research gap lies in the analysis of the potential variation of the intervention efficacy across medical conditions, physiotherapeutic settings, personal characteristics (e.g. age, gender, sociocultural background) and dispositions (e.g. motives, affective attitudes, previous behaviour) and diverse context-related factors. Huynh et al. [ 79 ] showed for the case of multiple sclerosis that the efficacy of BCTs is not investigated in all disease stages or throughout the disease course; participants with mild-to-moderate level disability were more frequently included in the studies (cf. [ 18 ]). Ariie et al. [ 73 ] stated that the response to BCTs may be different according to the condition (cf. [ 76 ]). On the one hand, studies analysing the use of the same intervention or same combination of BCTs in different intervention groups (according to the categories mentioned above) could be beneficial for comparison purposes. On the other hand, studies should analyse how to find the ‘right’ (ideally, the ‘most efficacious’) adherence promotion intervention for the patient or target group. Qualitative studies may explore adequate combinations of BCTs and contribute to the understanding of complex intervention processes. The findings showcased that different interventions and BCTs may contribute to adherence and that the BCT Taxonomy defines a wide range of techniques, providing the physiotherapists with an overview of which techniques are useable and thus may inspire and support them to develop additional interventions and to enrich their current physiotherapeutic practise. The physiotherapist may use this knowledge to tailor interventions in a patient-centred manner to promote adherence, and to adapt to the condition, characteristics, dispositions and context-related factors of the patient. Hence, experimental studies could compare the efficacy of tailored to not-tailored interventions.

Finally, the outcome adherence should be better defined and holistically assessed. The definition of adherence (as the extent to which a person’s behaviour corresponds with treatment goals or plans) and calculation of adherence rates (by reported exercise or attended sessions divided by the recommended or prescribed exercise or sessions) are simplifying a complex phenomenon. The average or the percentages of attended or completed sessions do not picture interruptions, regularity or periods of more and less adherence. Attendance regularity can change over the time and different participation and fluctuation patterns can be identified [ 88 , 89 ]. For example, an adherence rate of 50% can imply (a) that a person attended regularly every second session throughout the period of observation or (b) that a person attended all sessions of the first half of the observation period and then stopped attending. The underlying reasons and motivational factors may be quite different in these two cases. Besides assessing participation and fluctuation patterns, the three dimensions of the SIRAS scale [ 42 ], i.e. frequency, intensity and reciprocity, could be considered for a holistic account of adherence. The findings of this overview emphasized the importance of a patient-led goal setting and planning, which includes a shared decision-making process and the mutual agreement to adhere to the jointly established plan (cf. WHO definition of adherence, [ 5 ]). The measurement of adherence should be able to distinguish a patient-led approach from a therapist-led approach (cf. [ 17 ]) and to appraise the extent of a shared decision-making process. In conclusion, a holistic approach to measure adherence in physiotherapy may include measures of the frequency of attendance/exercising (e.g. attended sessions out of the prescribed/recommended sessions), the regularity of participation and fluctuation (e.g. timeline with pauses and interruptions, visualizing more and less adherent periods), the intensity of attendance/exercising (e.g. the number or the increment of exercises and repetitions performed in comparison to the plan), reciprocity and fidelity to the agreed goals and plan (e.g. therapist’s and patient’s subjective appraisal of the degree of accomplishment of the agreed plan) and persistence/perseverance over the time (e.g. measuring volition via questionnaires or rating persistence in participation in spite of the experienced challenges and barriers).

We conclude that moderate certainty of evidence supports that (i) additional motivational interventions and behaviour change programmes can increase adherence and patients’ self-efficacy beliefs and (ii) interventions applying BCTs increase adherence, particularly when using a greater number of BCTs and combining various BCTs, and particularly on short to medium term. The BCTs’ patient-led goal setting , self-monitoring of behaviour and social support seem promising to promote maintenance; (iii) graded activities, booster sessions with a physiotherapist and supervision foster adherence.

There is low certainty of evidence that (i) goal setting and planning improves adherence to treatment regimens, particularly if a patient-centred approach is taken; (ii) motivational interventions including various techniques, such as positive reinforcement, social support, monitoring or feedback, can foster adherence; (iii) social support seems to play an important role in promoting adherence; however, evidence is low as this BCT is frequently found in the control group; and (iv) information provision and transfer of knowledge to the patient may improve adherence-related outcomes when combined with motivational techniques, as in psychoeducational programmes. Additional written information is superior to verbal instructions alone; (v) a combination of home-based exercise programmes with clinical supervision, refresher or booster sessions, or/and self-management programmes seems promising to increase adherence.

Regarding the implications for future research, a holistic approach to measure adherence in physiotherapy and the investigation of clearly defined interventions combining multiple BCTs is recommended.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Overview of reviews, umbrella review and reviews of reviews are considered as synonyms in this article (cf. [ 19 ]).

Abbreviations

Behaviour change technique

Cognitive behavioural/cognitive behavioural theory

Control/comparator group

Grades of Recommendation, Assessment, Development and Evaluation

Intervention/experimental group

Physical activity

Preferred Reporting Items for Overviews of Reviews

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

Physiotherapy

Randomised controlled trial

Standardised mean difference

Systematic review

Essery R, Geraghty AW, Kirby S, Yardley L. Predictors of adherence to home-based physical therapies: a systematic review. Disabil Rehabil. 2017;39:519–34.

Article   PubMed   Google Scholar  

Jack K, McLean SM, Moffett JK, Gardiner E. Barriers to treatment adherence in physiotherapy outpatient clinics: a systematic review. Man Ther. 2010;15:220–8.

Article   PubMed   PubMed Central   Google Scholar  

Peek K, Sanson-Fisher R, Mackenzie L, Carey M. Interventions to aid patient adherence to physiotherapist prescribed self-management strategies: a systematic review. Physiotherapy. 2016;102:127–35.

Bullard T, Ji M, An R, Trinh L, Mackenzie M, Mullen SP. A systematic review and meta-analysis of adherence to physical activity interventions among three chronic conditions: cancer, cardiovascular disease, and diabetes. BMC Public Health. 2019;19:636.

World Health Organization. Adherence to long-term therapies: evidence for action. World Health Organization; 2003. Available from: https://apps.who.int/iris/handle/10665/42682

Thacker J, Bosello F, Ridehalgh C. Do behaviour change techniques increase adherence to home exercises in those with upper extremity musculoskeletal disorders? A systematic review. Musculoskeletal care. 2020;19(3):340-62.

Eisele A, Schagg D, Kramer L, Bengel J, Gohner W. Behaviour change techniques applied in interventions to enhance physical activity adherence in patients with chronic musculoskeletal conditions: a systematic review and meta-analysis. Patient Educ Couns. 2019;102:25–36.

Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013;46:81–95.

Davis R, Campbell R, Hildon Z, Hobbs L, Michie S. Theories of behaviour and behaviour change across the social and behavioural sciences: a scoping review. Health Psychol Rev. 2015;9:323–44.

Michie S, Johnston M. Theories and techniques of behaviour change: developing a cumulative science of behaviour change. Health Psychol Rev. 2012;6:1–6.

Article   Google Scholar  

Rhodes RE, McEwan D, Rebar AL. Theories of physical activity behaviour change: a history and synthesis of approaches. Psychol Sport Exerc. 2019;42:100–9.

Rhodes RE, Fiala B. Building motivation and sustainability into the prescription and recommendations for physical activity and exercise therapy: the evidence. Physiother Theory Pract. 2009;25:424–41.

Areerak K, Waongenngarm P, Janwantanakul P. Factors associated with exercise adherence to prevent or treat neck and low back pain: a systematic review. Musculoskeletal Science and Practice. 2021;52.

Husebø AML, Dyrstad SM, Søreide JA, Bru E. Predicting exercise adherence in cancer patients and survivors: a systematic review and meta-analysis of motivational and behavioural factors. J Clin Nurs. 2013;22:4–21.

Kunstler BE, Cook JL, Freene N, Finch CF, Kemp JL, O’Halloran PD, et al. Physiotherapists use a small number of behaviour change techniques when promoting physical activity: a systematic review comparing experimental and observational studies. J Sci Med Sport. 2018;21:609–15.

Nicolson PJA, Bennell KL, Dobson FL, Van Ginckel A, Holden MA, Hinman RS. Interventions to increase adherence to therapeutic exercise in older adults with low back pain and/or hip/knee osteoarthritis: a systematic review and meta-analysis. Br J Sports Med. 2017;51:791–9.

Willett M, Duda J, Fenton S, Gautrey C, Greig C, Rushton A. Effectiveness of behaviour change techniques in physiotherapy interventions to promote physical activity adherence in lower limb osteoarthritis patients: a systematic review. Regnaux J-P, editor. PLoS ONE. 2019;14:e0219482.

Kim Y, Mehta T, Lai B, Motl RW. Immediate and sustained effects of interventions for changing physical activity in people with multiple sclerosis: meta-analysis of randomized controlled trials. Arch Phys Med Rehabil. 2020;101:1414–36.

Pollock M, Fernandes R, Becker L, Pieper D, Hartling L. Chapter V: overviews of reviews. In: Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions version 63 (updated February 2022). Cochrane; 2022 [cited 2022 May 19]. Available from: https://training.cochrane.org/handbook/current/chapter-v

Aromataris E, Fernandez R, Godfrey C, Holly C, Khalil H, Tungpunkom P. Chapter 10: umbrella reviews. In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis. JBI; 2020 [cited 2021 Apr 19]. Available from: https://jbi-global-wiki.refined.site/space/MANUAL/4687363/Chapter+10%3A+Umbrella+reviews

Ballard M, Montgomery P. Risk of bias in overviews of reviews: a scoping review of methodological guidance and four-item checklist. Res Synth Methods. 2017;8:92–108.

Centre for Reviews and Dissemination. Undertaking systematic reviews of research on effectiveness: CRD’s guidance for carrying out or commissioning reviews. York, UK: NHSCentre for Reviews and Dissemination, University of York; 2001 [cited 2023 Feb 20]. Available from: http://www.york.ac.uk/inst/crd/crdreports.htm

Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.3 (updated February 2022). Cochrane; 2022 [cited 2022 May 19]. Available from: www.training.cochrane.org/handbook

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372: n71.

Furlan AD, Malmivaara A, Chou R, Maher CG, Deyo RA, Schoene M, et al. 2015 Updated Method Guideline for Systematic Reviews in the Cochrane Back and Neck Group. Spine (Phila Pa 1976). 2015;40:1660–73.

Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008.

Levack WMM, Weatherall M, Hay-Smith EJC, Dean SG, Mcpherson K, Siegert RJ. Goal setting and strategies to enhance goal pursuit for adults with acquired disability participating in rehabilitation. Cochrane Database of Systematic Reviews. 2015;2015.

McGrane N, Galvin R, Cusack T, Stokes E. Addition of motivational interventions to exercise and traditional Physiotherapy: a review and meta-analysis. Physiotherapy. 2015;101:1–12.

Article   CAS   PubMed   Google Scholar  

Gates M, Gates A, Pieper D, Fernandes RM, Tricco AC, Moher D, et al. Reporting guideline for overviews of reviews of healthcare interventions: development of the PRIOR statement. BMJ. 2022;378: e070849.

McLean SM, Burton M, Bradley L, Littlewood C. Interventions for enhancing adherence with physiotherapy: a systematic review. Man Ther. 2010;15:514–21.

Niedermann K, Fransen J, Knols R, Uebelhart D. Gap between short- and long-term effects of patient education in rheumatoid arthritis patients: a systematic review. Arthritis Care Res. 2004;51:388–98.

Cinthuja P, Krishnamoorthy N, Shivapatham G. Effective interventions to improve long-term physiotherapy exercise adherence among patients with lower limb osteoarthritis. A systematic review BMC Musculoskelet Disord. 2022;23:147.

Beinart NA, Goodchild CE, Weinman JA, Ayis S, Godfrey EL. Individual and intervention-related factors associated with adherence to home exercise in chronic low back pain: a systematic review. The Spine Journal. 2013;13:1940–50.

Holden J, Davidson M, O’Halloran PD. Health coaching for low back pain: a systematic review of the literature. Int J Clin Pract. 2014;68:950–62.

Jordan JL, Holden MA, Mason EE, Foster NE. Interventions to improve adherence to exercise for chronic musculoskeletal pain in adults. Cochrane Database Syst Rev. 2010;CD005956.

Abu Abed M, Himmel W, Vormfelde S, Koschack J. Video-assisted patient education to modify behavior: a systematic review. Patient Educ Couns. 2014;97:16–22.

Bachmann C, Oesch P, Bachmann S. Recommendations for improving adherence to home-based exercise: a systematic review. Phys Med Rehab Kuror. 2018;28:20–31.

Hall AM, Ferreira PH, Maher CG, Latimer J, Ferreira ML. The influence of the therapist-patient relationship on treatment outcome in physical rehabilitation: a systematic review. Phys Ther. 2010;90:1099–110.

Levack WMM, Taylor K, Siegert RJ, Dean SG, McPherson KM, Weatherall M. Is goal planning in rehabilitation effective? A systematic review Clin Rehabil. 2006;20:739–55.

Room J, Hannink E, Dawes H, Barker K. What interventions are used to improve exercise adherence in older people and what behavioural techniques are they based on? A systematic review BMJ Open. 2017;7: e019221.

Dalager T, Bredahl TGV, Pedersen MT, Boyle E, Andersen LL, Sjøgaard G. Does training frequency and supervision affect compliance, performance and muscular health? A cluster randomized controlled trial. Man Ther. 2015;20:657–65.

Kolt GS, Brewer BW, Pizzari T, Schoo AMM, Garrett N. The Sport Injury Rehabilitation Adherence Scale: a reliable scale for use in clinical physiotherapy. Physiotherapy. 2007;93:17–22.

Green LW. Determining the impact and effectiveness of health education as it relates to federal policy. Health Educ Monogr. 1978;6:28–66.

Google Scholar  

Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42.

Pisters MF, Veenhof C, de Bakker DH, Schellevis FG, Dekker J. Behavioural graded activity results in better exercise adherence and more physical activity than usual care in people with osteoarthritis: a cluster-randomised trial. J Physiother. 2010;56:41–7.

Göhner W, Schlicht W. Preventing chronic back pain: evaluation of a theory-based cognitive-behavioural training programme for patients with subacute back pain. Patient Educ Couns. 2006;64:87–95.

Bandura A. Toward a psychology of human agency: pathways and reflections. Perspect Psychol Sci. 2018;13:130–6.

Ashford S, Edmunds J, French DP. What is the best way to change self-efficacy to promote lifestyle and recreational physical activity? A systematic review with meta-analysis. Br J Health Psychol. 2010;15:265–88.

Frost H, Campbell P, Maxwell M, O’Carroll RE, Dombrowski SU, Williams B, et al. Effectiveness of motivational interviewing on adult behaviour change in health and social care settings: a systematic review of reviews. PLoS ONE. 2018;13: e0204890.

Michie S, West R, Sheals K, Godinho CA. Evaluating the effectiveness of behavior change techniques in health-related behavior: a scoping review of methods used. Translational Behavioral Medicine. 2018;8:212–24.

Gallagher KM. Helping older adults sustain their physical therapy gains: a theory-based intervention to promote adherence to home exercise following rehabilitation. Journal of Geriatric Physical Therapy. 2016;39:20–9.

Silva MN, Vieira PN, Coutinho SR, Minderico CS, Matos MG, Sardinha LB, et al. Using self-determination theory to promote physical activity and weight control: a randomized controlled trial in women. J Behav Med. 2010;33:110–22.

Schwarzer R, Lippke S, Luszczynska A. Mechanisms of health behavior change in persons with chronic illness or disability: the Health Action Process Approach (HAPA). Rehabil Psychol. 2011;56:161–70.

Murray JM, Brennan SF, French DP, Patterson CC, Kee F, Hunter RF. Effectiveness of physical activity interventions in achieving behaviour change maintenance in young and middle aged adults: a systematic review and meta-analysis. Soc Sci Med. 2017;192:125–33.

Areskoug Josefsson K, Andersson A-C. The co-constructive processes in physiotherapy. Lee A, editor. Cogent Medicine. 2017;4:1290308.

Qasem M. Constructivist learning theory in physiotherapy education: a critical evaluation of research. Journal of Novel Physiotherapies. 2015;5.

Brinkman C, Baez SE, Genoese F, Hoch JM. Use of goal setting to enhance self-efficacy after sports-related injury: a critically appraised topic. J Sport Rehabil. 2019;29:498–502.

Fillipas S, Oldmeadow LB, Bailey MJ, Cherry CL. A six-month, supervised, aerobic and resistance exercise program improves self-efficacy in people with human immunodeficiency virus: a randomised controlled trial. Australian Journal of Physiotherapy. 2006;52:185–90.

Ley C, Karus F, Wiesbauer L, Rato Barrio M, Spaaij R. Health, integration and agency: sport participation experiences of asylum seekers. J Refug Stud. 2021;34:4140–60.

Melin J, Nordin Å, Feldthusen C, Danielsson L. Goal-setting in physiotherapy: exploring a person-centered perspective. Physiother Theory Pract. 2021;37:863–80.

Wijma AJ, Bletterman AN, Clark JR, Vervoort SC, Beetsma A, Keizer D, et al. Patient-centeredness in physiotherapy: what does it entail? A systematic review of qualitative studies. Physiother Theory Pract. 2017;33:825–40.

Meade LB, Bearne LM, Sweeney LH, Alageel SH, Godfrey EL. Behaviour change techniques associated with adherence to prescribed exercise in patients with persistent musculoskeletal pain: systematic review. Br J Health Psychol. 2019;24:10–30.

Ariie T, Takasaki H, Okoba R, Chiba H, Handa Y, Miki T, et al. The effectiveness of exercise with behavior change techniques in people with knee osteoarthritis: a systematic review with meta-analysis. PM R. 2022;

Demmelmaier I, Iversen MD. How are behavioral theories used in interventions to promote physical activity in rheumatoid arthritis? A systematic review. Arthritis Care Res. 2018;70:185–96.

Larkin L, Gallagher S, Cramp F, Brand C, Fraser A, Kennedy N. Behaviour change interventions to promote physical activity in rheumatoid arthritis: a systematic review. Rheumatol Int. 2015;35:1631–40.

Rausch Osthoff A-K, Juhl CB, Knittle K, Dagfinrud H, Hurkmans E, Braun J, et al. Effects of exercise and physical activity promotion: meta-analysis informing the 2018 EULAR recommendations for physical activity in people with rheumatoid arthritis, spondyloarthritis and hip/knee osteoarthritis. RMD Open. 2018;4: e000713.

Armstrong TL, Swartzman LC. 3 - cross-cultural differences in illness models and expectations for the health care provider-client/patient interaction. In: Shané S. Kazarian, David R. Evans, editors. Handbook of Cultural Health Psychology. San Diego: Academic Press; 2001 [cited 2013 Aug 20]. p. 63–84. Available from: http://www.sciencedirect.com/science/article/pii/B9780124027718500052

Brady B, Veljanova I, Chipchase L. Culturally informed practice and physiotherapy. J Physiother. 2016;62:121–3.

Jimenez DE, Burrows K, Aschbrenner K, Barre LK, Pratt SI, Alegria M, et al. Health behavior change benefits: perspectives of Latinos with serious mental illness. Transcult Psychiatry. 2016;53:313–29.

Jorgensen P. Concepts of body and health in physiotherapy: the meaning of the social/cultural aspects of life. Physiother Theory Pract. 2000;16:105–15.

Teng B, Rosbergen ICM, Gomersall S, Hatton A, Brauer SG. Physiotherapists’ experiences and views of older peoples’ exercise adherence with respect to falls prevention in Singapore: a qualitative study. Disabil Rehabil. 2022;44:5530–8.

Alfieri FM, da Silva DC, de Oliveira NC, Battistella LR. Gamification in musculoskeletal rehabilitation. Curr Rev Musculoskelet Med. 2022;15:629–36.

Cox NS, Dal Corso S, Hansen H, McDonald CF, Hill CJ, Zanaboni P, et al. Telerehabilitation for chronic respiratory disease. Cochrane Database Syst Rev. 2021;1:CD013040.

Cruz-Cobo C, Bernal-Jiménez MÁ, Vázquez-García R, Santi-Cano MJ. Effectiveness of mHealth interventions in the control of lifestyle and cardiovascular risk factors in patients after a coronary event: systematic review and meta-analysis. JMIR Mhealth Uhealth. 2022;10: e39593.

Darabseh MZ, Aburub A, Davies S. The effects of virtual reality physiotherapy interventions on cardiopulmonary function and breathing control in cystic fibrosis: a systematic review. Games Health J. 2023;12:13–24.

Fernandes CS, Magalhães B, Gomes JA, Santos C. Exergames to improve rehabilitation for shoulder injury: Systematic Review and GRADE Evidence Synthesis. REHABIL NURS. 2022;47:147–59.

García-Bravo S, Cuesta-Gómez A, Campuzano-Ruiz R, López-Navas MJ, Domínguez-Paniagua J, Araújo-Narváez A, et al. Virtual reality and video games in cardiac rehabilitation programs. A systematic review Disabil Rehabil. 2021;43:448–57.

Hawley-Hague H, Lasrado R, Martinez E, Stanmore E, Tyson S. A scoping review of the feasibility, acceptability, and effects of physiotherapy delivered remotely. Disability and Rehabilitation. 2022;

Melillo A, Chirico A, De Pietro G, Gallo L, Caggianese G, Barone D, et al. Virtual reality rehabilitation systems for cancer survivors: a narrative review of the literature. Cancers. 2022;14.

Moulaei K, Sheikhtaheri A, Nezhad MS, Haghdoost A, Gheysari M, Bahaadinbeigy K. Telerehabilitation for upper limb disabilities: a scoping review on functions, outcomes, and evaluation methods. Arch Public Health. 2022;80:196.

Patsaki I, Dimitriadi N, Despoti A, Tzoumi D, Leventakis N, Roussou G, et al. The effectiveness of immersive virtual reality in physical recovery of stroke patients: a systematic review. Frontiers in Systems Neuroscience. 2022;16.

Skov Schacksen C, Henneberg NC, Muthulingam JA, Morimoto Y, Sawa R, Saitoh M, et al. Effects of telerehabilitation interventions on heart failure management (2015–2020): scoping review. JMIR Rehabil Assist Technol. 2021;8: e29714.

Thompson D, Rattu S, Tower J, Egerton T, Francis J, Merolli M. Mobile app use to support therapeutic exercise for musculoskeletal pain conditions may help improve pain intensity and self-reported physical function: a systematic review. J Physiother. 2023;69:23–34.

Marques MM, Wright AJ, Corker E, Johnston M, West R, Hastings J, et al. The behaviour change technique ontology: transforming the behaviour change technique taxonomy v1. Wellcome Open Res. 2023;8:308.

Roter DL, Hall JA, Merisca R, Nordstrom B, Cretin D, Svarstad B. Effectiveness of interventions to improve patient compliance: a meta-analysis. Med Care. 1998;36:1138–61.

Hansen LS, Præstegaard J, Lehn-Christiansen S. Patient-centeredness in physiotherapy–a literature mapping review. Physiotherapy theory and practice. 2022;38(12):1843-56.

Robinson JH, Callister LC, Berry JA, Dearing KA. Patient-centered care and adherence: definitions and applications to improve outcomes. J Am Acad Nurse Pract. 2008;20:600–7.

Seelig H, Fuchs R. Physical exercise participation: a continuous or categorical phenomenon? Psychol Sport Exerc. 2011;12:115–23.

Shang B, Duan Y, Huang WY, Brehm W. Fluctuation – a common but neglected pattern of physical activity behaviour: an exploratory review of studies in recent 20 years. European Journal of Sport Science. 2018;18(2):266-78.

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Ley, C., Putz, P. Efficacy of interventions and techniques on adherence to physiotherapy in adults: an overview of systematic reviews and panoramic meta-analysis. Syst Rev 13 , 137 (2024). https://doi.org/10.1186/s13643-024-02538-9

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Hierarchical reinforcement learning for handling sparse rewards in multi-goal navigation

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• Volume 57 , article number  156 , ( 2024 )

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• Jiangyue Yan 1 ,
• Biao Luo 1 &
• Xiaodong Xu 1  

Reinforcement learning (RL) has achieved remarkable advancements in navigation tasks in recent years. However, tackling multi-goal navigation tasks with sparse rewards remains a complex and challenging problem due to the long-sequence decision-making involved. Such multi-goal navigation tasks inherently incorporate a hybrid action space, where the robot needs to select a navigation endpoint first before executing primitive actions. To address the problem of multi-goal navigation with sparse rewards, we introduce a novel hierarchical RL framework named Hierarchical RL with Multi-Goal (HRL-MG). The main idea of HRL-MG is to divide and conquer the hybrid action space, splitting long-sequence decisions into short-sequence decisions. The HRL-MG framework is composed of two main modules: a selector and an actuator. The selector employs a temporal abstraction hierarchical architecture designed to specify a desired end goal based on the discrete action space. Conversely, the actuator utilizes a continuous goal-oriented hierarchical architecture developed to enact continuous action sequences to reach the desired end goal specified by the selector. In addition, we incorporate a dynamic goal detection mechanism, grounded in hindsight experience replay, to mitigate the challenges posed by sparse reward landscapes. We validated the algorithm’s efficacy on both the discrete environment Maze_2D and the continuous robotic environment MuJoCo ‘Ant’. The results indicate that HRL-MG significantly outperforms other methods in multi-goal navigation tasks with sparse rewards.

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1 Introduction

Autonomous navigation is a crucial challenge that mobile robots must overcome to successfully execute complex tasks. Traditional path-planning algorithms often depend on pre-existing global maps for navigation (Hart et al. 1968 ; Dijkstra 2022 ; LaValle 1998 ). This dependency implies that the robot requires complete knowledge of its environment beforehand. These approaches result in a clear division between perception and decision-making, which can be problematic when dealing with environments where the map is unknown or subject to change. Furthermore, real-world applications frequently demand that mobile robots navigate towards multiple goals, such as automated robotic sorting (Shah and Pandey 2018 ), delivery systems (Hernandez et al. 2017 ) and museum tour guide robots (Burgard et al. 1999 ). To address the above problems together, we formulate the task as a multi-goal navigation problem in an unknown environment with sparse rewards.

It is reasonable to consider the navigation task as a sequential decision problem, and thus RL-based approaches played an important role in solving robot navigation problems (Faust et al. 2017 ; Garaffa et al. 2021 ; Wang et al. 2020 ; Zhu et al. 2017 ; Li et al. 2020 ). However, it is worth noting that the majority of current navigation tasks are designed for single-goal and the multi-goal navigation task still remains an open problem.

Typically, in a single-goal navigation task, the reward function is set up using artificial reward shaping, e.g. \(R= - \Vert (x_t,y_t)-(x_d,y_d)\Vert _{2}\) , where \((x_t,y_t)\) and \((x_d,y_d)\) represent the coordinates of the agent and goal at step t , respectively. However, the extension of such reward function to a multi-goal navigation task has significant limitations: (i) In practical applications of multi-goal navigation, environments are often highly complex, making it challenging to precisely define reward functions that accurately reflect the real-world environment. (ii) Reward shaping for multi-goal tasks will lead to sub-optimal outcomes. Under typical distance-based reward settings, multi-goal navigation tasks may encounter difficulties in completion due to the reliance on distance rewards, often leading to suboptimal solutions. For example, in a multi-goal navigation task with n goals, \(G\{ g_1,g_2,\ldots,g_n \}\) . If reward shaping is done using method such as \(L_2\) -norm, the agent will tend to hover around an already-reached goal \(g_i\) , \(\left( g_i\in G, i\in \left[ 1,n\right] \right)\) . Exploration is premised on the fact that the agent needs to move away from the current location first (i.e. increase the Euclidean distance from current goal \(d(s_t,g_i)\) ), Footnote 1 at which point reward shaping \(R=-d(s_t,g_i)\) (Trott et al. 2019 ) would prevent the agent from exploring outwards. Thus, introducing the agent to sub-optimal and preventing it from completing the multi-goal navigation task.

In this paper, the multi-goal navigation problem is formulated in an unknown environment with sparse rewards. It implies that positive rewards can only be earned after all desired goals have been completed:

where n is the number of end goals and ‘achieved_goal_num’ is the number of goals that have been reached so far.

While the definition of sparse rewards may be straightforward and basic, the challenges of dealing with reward scarcity and training difficulties in sparse reward scenarios are intractable. Presently, a series of methods are used to solve sparse reward problems. Inverse RL (IRL) learns a reward function in reverse by expert demonstration and then uses the learned reward function for forward training of the exploration policy (Abbeel and Ng 2004 ; Ho and Ermon 2016 ; Ng et al. 2000 ; Ziebart et al. 2008 ; Fu et al. 2017 ; Kretzschmar et al. 2016 ). In other words, instead of learning a policy or value function directly from the environment’s rewards, IRL aims to understand what rewards would best explain the observed behaviour of an agent. However, in this case, the learned reward function usually has a random bias. There may be a mismatch between the assumed reward function model and the true underlying reward structure of the environment. Counting-based (Bellemare et al. 2016 ; Tang et al. 2017 ) or curiosity-based approaches (Pathak et al. 2017 ; Burda et al. 2018 ; Reizinger and Szemenyei 2020 ; Zhelo et al. 2018 ) provide additional intrinsic rewards by increasing the agent’s exploration of unseen states. Hindsight experience replay (HER) (Andrychowicz et al. 2017 ; Fang et al. 2019 ) increases the intermediate rewards of the agent by hindsight relabeling the goal \(g_t\) and reward \(r_t\) in the experience replay transitions \(\tau = \big \{(s_{t}, g_{t}, a_{t},\,s_{t}^{\prime },\, r_{t})\big \}^{T-1}_{t=0}\) . Hierarchical RL (HRL) makes it easier for agent to reach shorter goals by splitting long-sequence decision tasks into shorter decision tasks, thus reducing the difficulty of sparse rewards in long-sequence decisions (Barto and Mahadevan 2003 ; Kulkarni et al. 2016 ; Levy et al. 2017 ; Nachum et al. 2018 ). In HRL, the agent learns a hierarchy of policies, each responsible for accomplishing a specific sub-task. This approach reduces the search space and allows for more efficient learning, as the agent can focus on mastering smaller components of the task before integrating them into larger policies.

The above approaches are effective for most single-goal tasks with sparse reward, but there is a lack of research for multi-goal navigation tasks under similar sparse reward conditions. Furthermore, these methods are complicated to directly extend to navigation task scenarios where the robot must solve multiple desired goals.

In this paper, we proposed a novel composite HRL framework called Hierarchical Reinforcement Learning with Multi-Goal (HRL-MG) . Our approach consists of the following main contributions:

We proposed a novel HRL framework that integrates both a temporal abstraction architecture and a continuous goal-orientation architecture to address the challenges posed by hybrid action spaces in multi-goal tasks. HRL-MG framework is designed to decompose long-sequence decisions in the context of multi-goal scenarios.

A dynamic goal-testing approach in a hierarchical architecture is proposed to further overcome the complex sparse reward problem encountered by both selector and actuator in multi-goal navigation tasks.

2 Related works

2.1 hierarchical architecture in hrl techniques.

A range of approaches have been developed in the field of HRL to address the problem of sparse rewards and intractable training in RL (Sutton et al. 1999 ; Parr and Russell 1997 ; Dietterich 2000 ; Bacon et al. 2017 ; Vezhnevets et al. 2017 ; Kulkarni et al. 2016 ; Nachum et al. 2018 ; Levy et al. 2017 ; Botvinick 2012 ). Current HRL architectures fall into two main categories: (i) discrete temporal abstraction architecture, (ii) continuous goal-oriented architecture. Discrete temporal abstraction hierarchy usually contains a high-level meta-controller and a low-level controller (Kulkarni et al. 2016 ). This framework involves organizing tasks into hierarchical structures, allowing for the abstraction of actions over different time scales. In continuous goal-oriented hierarchy framework, goals are continuously updated based on the interaction between the agent and the environment, and actions are selected to achieve these dynamic goals (Levy et al. 2017 ; Nachum et al. 2018 ). This framework emphasizes real-time adaptation and goal pursuit, where the agent continuously adjusts its actions to achieve evolving goals. However, both types of hierarchical approaches are aimed at tasks that involve a single desired goal, such as reaching a single destination in a navigation task or grasping a single object with a robotic arm. Our work bridges the multi-goal navigation task, a widespread application potential in practice scenarios.

2.2 Tasks with hybrid action space

In practical tasks of robot navigation, the action space is not only just a single discrete or continuous action space but usually a hybrid action space where both exist. For RL methods based on environment-provided rewards, mastering a complex task requires precise shaping of the reward function. The reward function must accurately formalize the task description into a mathematical representation. Due to challenges in reward shaping within complex scenarios, a simpler approach is to split the composite task. For example, in a multi-goal navigation scenario, a destination to be reached is selected first involving a discrete action space. Then the primitive actions such as the robot’s velocity and position are chosen involving a continuous action space. For hybrid action spaces, the most naive approach is to convert the hybrid action space into a unified action space through discretization or continuity, however, it changes the structure of the original action space. A parametric action space is proposed where each discrete action corresponds to a set of continuous parameters (Hausknecht and Stone 2015 ; Fan et al. 2019 ; Fu et al. 2019 ; Xiong et al. 2018 ). Authors in Li et al. ( 2021 ) construct an implicit action space containing both discrete and continuous actions via conditional variational autoencoder (CVAE). As can easily be seen, the main ideas for hybrid action space at present either aim at the representation of action space or adopt the hierarchy of action space. Below is the analysis of the advantages of hierarchical decision-making, explaining why we choose to adopt a hierarchical framework in this work to address implicit hybrid action space tasks.

Complexity Management: Hybrid tasks involve multiple decision factors, such as local actions and global strategies. Hierarchical decision-making breaks tasks into simpler sub-tasks or sub-goals, effectively managing complexity and reducing search space.

Iterative Optimization: Hierarchical decision-making allows systems to independently optimize and learn at each level, and then integrate the results into higher-level decisions. This iterative optimization approach is often more effective than directly parameterized searches across the entire action space, especially in tasks with high dimensionality and large-scale action spaces.

Adaptability and Intuition: Hierarchical decision-making aligns with human intuition and decision-making sequences, making it more intuitive and easier to understand. Additionally, it fosters adaptability, allowing flexible adjustment of strategies at different levels, making systems better suited for complex and changing environments.

In summary, hierarchical decision-making excels in managing complexity, achieving iterative optimization, and aligning with human intuition, thereby providing greater adaptability and flexibility in facing hybrid action space tasks.

3 Preliminaries

3.1 semi-markov decision processes.

HRL builds upon the theoretical underpinning of semi-markov decision processes (SMDPs) (Feinberg 1994 ; Pateria et al. 2021 ; Sutton et al. 1999 ), a framework that extends the fundamental concept of time present in MDP \(<\mathcal {S},\mathcal {A},P,r,\gamma>\) (Sutton and Barto 2018 ). A MDP is defined by the state space \(\mathcal {S}\) , action space A , reward function \(R:{\mathcal {S}}\times {\mathcal {A}}\rightarrow \mathbb {R}\) , and transition function \(P:{\mathcal {S}}\times {\mathcal {A}}\rightarrow \mathcal{S}^{\prime}\) . At each discrete time step t , the agent and the environment interact, the agent observes the state \(s_t\) from the environment, \(s_t\in \mathcal {S}\) , and on this basis chooses an action \(a_t\in \mathcal {A}\) . At the next time step \(t+1\) , as a result of its action, the agent receives a numerical reward \(r_{t+1}\in \mathcal {R}\in \mathbb {R}\) and turns to the next state \(s_{t+1}\) . The objective is to maximize the expected discounted cumulative rewards \(G(\pi _\theta )=\mathbb {E}\left[ \sum _{t=0}^{\infty }\gamma ^{t}r_{t}\mid a_{t}\sim \pi _\theta ,s_{t}\sim P\right]\) , where \(\gamma\) is a discount factor. In option-to-option SMDPs framework, an option is defined as \(\mathcal {O} <\chi ,\pi ,\beta>\) , consisting of an initiation set \(\chi \in \mathcal {S}\) , a policy \(\pi : \mathcal {S}\times \mathcal {A} \rightarrow [0,1]\) and a termination condition \(\beta: {\mathcal{S}}^{+} \rightarrow [0,1]\) . For any option \(o\in \mathcal {O}\) , let \({\mathcal {E}}(o,s,t)\) denote the event of option o being initiated in state \(s\in \chi\) at time t . Then the reward of the option o for any state \(s\in S\) is \(r_{s}^{o}=E\big \{r_{t+1}+\gamma r_{t+2}+\cdot \cdot \cdot +\gamma ^{k-1}r_{t+k}\,\big |\mathcal {E}(o,s,t)\big \}\) .

3.2 Goal-oriented reinforcement learning (GoRL)

GoRL is an RL paradigm that aims to train a policy that empowers the agent to be able to achieve various goals, which is also referred to as goal-conditional RL or multi-goal RL. In this paper, we refer to it simply as GoRL. GoRL tells the agent what to do by giving it an additional goal input. The value function approximator we normally use is \(V(s;\theta )\;\approx \;V^{\ast}(s)\) . To allow the value function to be extended to both state and goal inputs, we follow the universal value function approximator (UVFA) (Schaul et al. 2015 ), which the value function \(V(s,g;\theta )\;\approx \;V_{g}^{\ast}(s)\) or \(Q(s,a,g;\theta )\,\approx \,Q_{g}^{\ast}(s,a)\) . In this paper, we use the most direct approach, \(F:{\mathcal {S}}\times {\mathcal {G}}\rightarrow \mathbb {R}\) , simply concatenate state and goal together as a joint input, F denotes the connection function. GoRL naturally has the property of binary sparse rewards, i.e., the agent is given a positive reward when the input goal is reached, otherwise 0. Thus, GoRL is by default accompanied by the problem of reward sparsity (Liu et al. 2022 ; Plappert et al. 2018 ).

We proposed an innovative hierarchical framework called HRL-MG, specifically designed to tackle the complex problem of multi-goal navigation for robots operating in sparse reward scenarios. Our hierarchical framework applies to goal-oriented tasks, i.e., tasks where both state and goal are vectorial input. HRL-MG consists of two components: (i) a specific hierarchical framework compounded by temporal abstraction and continuous goal orientation and (ii) an HER-based dynamic goal testing method. Later in this section, the two components will be described in turn.

4.1 HRL-MG hierarchical framework

The HRL-MG framework is divided into two sections. The outer loop section is a discrete action-space decision mechanism referred to as the selector, which is responsible for choosing a desired goal among the multi-goal set. The inner loop section is a continuous action-space decision based on the primitive action, referred to as the actuator, as shown in Fig.  1 . In this case, the interaction between the selector and actuator is a hierarchical structure of temporal abstraction for splitting multi-goal navigation tasks into individual desired goal navigation tasks. At the same time, the actuator itself is a continuous goal-oriented hierarchy for the inner loop of one desired goal navigation task. The inner loop continuously selects sub-goals, using them as the expected goals at the primitive action layer, to guide the agent towards achieving the desired targets provided by the selector. Once the actuator reaches the desired goal provided by the selector, it returns to the outer loop. The selector selects the next desired goal and repeats this process until all desired goals are reached by the agent.

An overview of the HRL-MG hierarchical framework: a hierarchical architecture combining temporal abstraction and continuous goal orientation, consisting of two parts: a selector and an actuator. The outer loop selector is responsible for selecting the desired goal g (current end goal) among the multi-goal set G and the inner loop actuator is responsible for reaching the desired goal g given by the selector

4.1.1 Selector

In this phase, our focus is on developing a policy for the outer loop of the HRL-MG framework, called selector. The selector enables choosing a current navigation goal among multiple desired goals. The detailed network structure of the selector can be seen in the left part of Fig.  2 . The details of the Markov model setup for the selector are as follows:

Goal : The goal of the selector is to reach all desired goals \(\mathcal {G}_{final}\{g_0,g_1,\ldots,g_n\}\) under the condition of sparse reward as quickly as possible in a multi-goal navigation task.

State : According to the GoRL paradigm, the input of the selector is the concatenation of the state \(s_t\) and the multi-goal set \(\mathcal {G}_{final}\) , thus the joint input is \((s_t,\mathcal {G}_{final})\) . As shown in Fig.  2 , the state of the continuous robot environment ‘Ant’ is represented as \(\mathcal {S}[\theta _p,\theta _v,\theta _g]\) , where the \(\theta _p\) , \(\theta _v\) , \(\theta _g\) represent the parameters of the agent’s position vector, velocity vector and goal vector respectively.

Action : The selector’s action space is a discrete action space consisting of \(\mathcal {G}_{final}\{g_0,g_1,\ldots,g_n\}\) and the dimension of the action space depends on the number of multi-goal n . The selector gives a definite goal \(g_n \in \mathcal {G}_{final}\) as the action \(a_t\) .

Reward : The selector’s reward design follows a sparse reward mechanism, where the selector is positively rewarded if and only if the agent successfully reaches up to a desired goal and 0 otherwise. If all the desired goals are met, then a larger positive reward is returned.

where \(\phi\) is a mapping function that retrieves the position information in the current state, and \(\epsilon\) is a very small positive constant threshold.

Diagram of the HRL-MG network architecture: the \(Q\_ net\) on the left represents the process by which the selector gives a desired goal \(g_n\) to be reached by the actuator, and the two actor-critic structures on the right represent the division of the actuator into two layers, with the \(Actor\_ 1\) outputting a sub-goal \(g^{\prime }\) based on \(g_n\) given by the selector and the \(Actor\_ 0\) outputting the primitive action space to reach the sub-goal \(g^{\prime }\)

The selector aims to learn a policy \(\pi _s=(a_t|s_t, \mathcal {G}_{final};\theta _2)\) for guiding the agent to accomplish all desired goals under the sparse reward condition. Note that in an episode, at time step t , when the selector has given the desired goal \(g_n\) , handing over to the actuator for execution. And then after N time steps returning to the next state of the selector \(s_{t+N}\) . We store the transition \((s_t,\mathcal {G}_{final},g_n,R_{t+N},s_{t+N})\) in this episode into experience memory pool \(D_{selector}\) .

For a goal-oriented task with multiple desired goals, in each episode, the set \(\mathcal {G}_{final}\{g_0,g_1,\ldots,g_n\}\) are randomly initialized by the environment which satisfies a minimum threshold between the initial desired goals and the initial position of the agent, where \(g_n\) satisfies the following equation:

\(s_0\) is the random initial position of the agent and \(D_{threshold}\) is the minimum distance between the agent and each desired goal.

The selector will choose a deterministic desired goal \(g_n\) from \(\mathcal {G}_{final}\) using the Deep Q-Network (DQN) algorithm with hyperparameter \(\theta _2\) . The objective function of the selector can be stated as:

The selector’s desired goal selection acts as the outer loop of the HRL-MG hierarchical framework, decomposing a navigation task with multi-goal in a sparse scenario into each individual desired goal. The sparsity of multiple desired goal navigation rewards can be effectively mitigated by hierarchical splitting.

4.1.2 Actuator

In this phase, our focus is on developing a policy for the inner loop named actuator that enables reaching the goal \(g_n\) given by the selector. The actuator consists of a continuous subgoal-directed hierarchical structure. The upper layer in the actuator gives a more attainable subgoal \(g^{\prime }\) based on the current desired goal \(g_n\) generated by the selector, guiding the agent to explore the environment as much as possible. The bottom layer in the actuator reaches the subgoal \(g^{\prime }\) by performing the primitive actions based on the simulation environment, as shown in the right part of Fig.  2 . The details of the Markov model setup for the actuator are as follows:

Goal : The goal for the upper level of the actuator is to reach the desired goal \(g_n\) given by the selector. The goal for the bottom level of the actuator is to reach the subgoal \(g^{\prime }\) given by the upper level of the actuator.

State : The observation of the environment \(\mathcal {S}_a\) for the actuator is identical to the observation \(\mathcal {S}_e\) for the selector. Actually, in the HRL-MG hierarchical framework, each layer has the same state set or state description. Consequently, whether the agent acting as the selector or the actuator, can inhabit identical states or state configurations. In other words, in the HRL-MG hierarchical framework, the same state space at each layer can be interpreted as the state of the system being consistent or shared between different layers. This consistency simplifies the difficulty of analyzing and managing the hierarchical framework. Similar to the selector’s input, the input for the upper level of the actuator is the concatenation of the current state \(s_t\) and the desired goal \(g_n\) given by the selector. And the input for the bottom level of the actuator is the concatenation of the current state \(s_t\) and the subgoal \(g'\) given by the upper level of the actuator. The joint input is \((s_t,g_n)\) for the upper layer and \((s_t,g')\) for the bottom layer. Footnote 2

Action : Let \(A_u\) and \(A_b\) denote the action space for the upper and bottom layers of the actuator, respectively. Both \(A_u\) and \(A_b\) are continuous action spaces. \(A_u\) is a subset of the state space \(\mathcal {S}_a\) ( \(A_u\subseteq \mathcal {S}_a\) ). \(A_b\) is the primitive action space for navigation tasks.

Reward : The actuator’s reward settings still follow the sparse reward mechanism, and the reward functions for the upper and bottom layers are represented as follows:

The actuator aims to learn two policies \(\pi _u=(a_t|s_t, g_n;\theta _1)\) and \(\pi _b=(a_b|s_t, g^{\prime };\theta _0)\) which correspond to the upper and bottom layers respectively. The actuator splits again based on a single desired goal navigation, decomposing the long sequence of decisions into shorter subgoal navigation decisions. Utilizing short-term subgoals can be very effective in guiding the agent’s exploration of the environment, thus further overcoming the sparse reward dilemma.

The actuator uses the deep deterministic policy gradient (DDPG) algorithm with hyperparameter \(\theta _1\) and \(\theta _0\) for the upper level and the bottom level respectively. The objective function for the upper level of the actuator can be stated as:

The objective function for the bottom level of the actuator can be stated as:

where Q represents the critic network and \(Q^{\prime }\) represents the target-critic network. \(\mu\) represents the actor network and \(\mu ^{\prime }\) represents the target-actor network.

4.2 Dynamic goal testing

Once we have disentangled the task within the HRL-MG hierarchical framework, to further address the challenging convergence issues during training in a sparse reward environment, we implemented a strategy involving dynamic goal testing for both the selector and actuator, leveraging the principles of HER.

For the selector, at time step t , the selector begins with the state \(s_t\) and desired goals \(\mathcal {G}_{final}\) . A desired goal \(g_n\) is chosen as the current endpoint for the actuator based on the \(\epsilon\) -greedy algorithm. Normally, at each time step t , we would store the transition \((s_t,g_n,a_t,r_t,s_{t+1})\) into experience memory \(D_1\) , where \(r_t=1\) if the actuator reaches \(g_n\) and 0 otherwise. After N time steps, we would store the transition \((s_t,\mathcal {G}_{final},g_n,R_{t+N},s_{t+N})\) into experience memory \(D_2\) , where \(R_{t+N}=1\) if the selector reaches all desired goals \(\mathcal {G}_{final}\) , 0 if one desired goal is achieved and − 1 otherwise. In this case, if the actuator reaches a non- \(g_n\) goal \(g_n^{\prime }\subseteq \mathcal {G}_{final}\) during execution, it also does not receive a positive reward and will be much less efficient for the selector to complete the overall task. Therefore, after the actuator performs each primitive action, the current state \(s^{\prime }\) is checked. If \(g_n\) has been reached, \(s^{\prime }\) is returned to the selector for another desired goal \(g_n^{\prime} \subseteq {\mathcal{G}}_{final}\) . If not \(g_n\) but \(g_n^{\prime} \subseteq {\mathcal{G}}_{final}\)  has been reached again  s ′ is returned and the transition of the selector is re-labelled from \((s_t,\mathcal {G}_{final},g_n,R,s^{\prime })\) into \((s_t,\mathcal {G}_{final},g_n^{\prime },R,s^{\prime })\) , the transition of the actuator is re-labelled from \((s_t,g_n^{\prime },a_t,r_t,s^{\prime })\) into \((s_t,g_n^{\prime },a_t,r_t^{\prime },s^{\prime }).\)  Due to the re-labelling of the actuator’s goal, the reward recalculation has \(r^{\prime }=1\) .

4.3 Algorithm

Algorithm  1 shows the overall procedure of the Hierarchical RL with Multi-Goal algorithm (HRL-MG). Algorithm  2 shows the pseudocode of the dynamic goal testing procedure.

Dynamic goal test

5 Experiments

We perform experiments in both discrete and continuous multi-goal navigation environments. The two-dimensional maze game with a discrete state and action space serves as a motivating example to test the feasibility of our idea. Thereafter, we experiment in a continuous robot environment based on MuJoCo, called ‘Ant’ (Schulman et al. 2015 ). Next, we will show the results of our experiments in discrete and continuous environments respectively.

Maze_2D: Discrete 2D maze tasks, where the green squares represent the desired goals and the red squares represent the agent’s position. The left side of figures ( a – d ) shows the agent’s local observation and the right side shows “God’s view”. m represents the difficulty of the map, and n is the corresponding state space dimension. The agent’s task is to navigate through the maze to all the green squares to be successful. (Color figure online)

5.1 A discrete motivating example

Consider a discrete two-dimensional maze environment with the state space \(\mathcal {S}=n \times n\) ( n specified by user) and the observation space \(\mathcal {O}\) : its surrounding \(\left( 2k+1\right) \times \left( 2k+1\right)\) ( k specified by view_grid parameter) grids and the action space \(\mathcal {A}=\left\{ 0,1,2,3\right\}\) which correspond to the four directions of movement. For every episode, we randomly sample an initial state as well as two desired goals and the policy gets a reward of − 1 as long as the agent has not completed the exploration of two desired goals.

In the practical experimental setup for the maze environment, the default is to take  k  = 1, and the observation range of the agent is a 3 × 3 size local observation that can be used to determine the action that can avoid obstacles in the next step. In this work, the parameter n is set to \(n=11+2 \times m\) to realize a navigation task with increasing difficulty of the environment, and the parameter m denotes the number of state space expansions. With the increasing number of m , the state space dimension n will start from the initial value of 11 and increase the difficulty of the task by 2 dimensions at a time. Regarding the value range of parameter m , we set a set of integers [0, 19]. As m increases, the dimension of the environment’s state space n will gradually increase from 11 to 49. This implies that the difficulty of the environment will continually increase, as the state space will grow exponentially. At this time, the difficulty of the task has far exceeded the difficulty of exploring the general discrete task, which is enough to verify the effectiveness of our algorithm. Excluding the space occupied by the walls around the maze environment map, thus the overall state space size is \((n-1) \times (n-1)\) and the observation space size of the agent is \(3 \times 3\) , as shown in Fig.  3 .

We perform the two-goal tasks in the maze environment using our hierarchical framework with the subgoal guide method and Q-Learning (Watkins and Dayan 1992 ) algorithm respectively. In this setup, for each difficulty level m , we conduct 20 episodes of experiments. We keep track of the number of successful experiments out of the 20 episodes and calculate the success rate for that difficulty level m . Subsequently, we increase the difficulty level m and cyclically repeat the process. The number of random seeds used for the experiment setup is 4. The smoothing parameter during image processing is 0.6. The results of the experiment are presented in Fig.  4 .

From the experimental results, it is evident that around \(m=5\) , both Q-Learning and our method achieve a 100% success rate in completing tasks with two goals. As m increases, around \(m=10\) , the success rate of the Q-Learning algorithm gradually decreases to approximately 80%, while our method experiences a slight decline to around 90%. When m reaches 15, the success rate of the Q-Learning algorithm significantly drops, even reaching 0, while our method maintains a success rate of above 80%. Q-Learning can only solve for \(m<10\) while our method can easily keep the success rate around 80% for m up to 19. The primary challenge leading to the declining efficacy of Q-Learning algorithms is the exploration dilemma in large state spaces. As the state space continues to grow, the difficulty of Q-Learning’s exploration of the environment continues to rise. Due to the large state space, random exploration becomes very inefficient or even impractical. Consequently, Q-Learning may converge to local optima and fail to reach the global optimal solution. By structuring the learning process into hierarchical levels and setting intermediate subgoals, our method effectively decomposes the task into more manageable sub-tasks. This hierarchical organization facilitates more targeted exploration, allowing the agent to focus on relevant regions of the state space and thereby mitigating the adverse effects of its expansion.

Next, we extend our experiments toward continuous robotics tasks.

Mission success rates for maps with different levels of difficulty m . Graph a shows the exponential growth of the state-space dimension of the map corresponding to the increase of the difficulty m , reaching a maximum of 2209, making navigation over such a large map area extremely hard. Graph b shows the success rates of our method and Q-Learning as m increases. The number of randomized seeds for the experiment was 4

5.2 Continuous robot environment

The continuous robot simulation environment is based on the MuJoCo (Todorov et al. 2012 ) physics engine. The ‘Ant’ is a 3D robot consisting of a freely rotatable torso and four legs, which are connected by two hinges between the legs and the torso.

In this paper, We considered two different ‘Ant’ environments for the navigation task:

Ant_Reacher : In the Ant_Reacher task, the agent’s mission is to perform a multi-goal navigation task in a \(24 \times 24\) barrier-free map under the sparse reward condition. At the beginning of each episode, the distance from each initialized goal to the agent’s initial position in the environment is at least 8 units.

Ant_Four_Rooms : In the Ant_Four_Rooms task, the agent is required to perform multi-goal navigation on a \(16.5 \times 16.5\) map that is divided into four rooms by walls. Increased the difficulty of wall obstacles based on the Ant_Reacher environment. At the same time, the need for sparse reward settings is further emphasized in the context of this obstacle environment. At the beginning of each episode, the environment is initialization will ensure that each goal and agent are at least not in the same room.

The details of the Markov model setup for the continuous robot environment ‘Ant’ are as follows. In order to make the HRL-MG hierarchical framework easier to read and understand, all the details have been organized in Table  1 .

State : The ‘Ant’ robot’s state contains a 15-dimensional position vector and a 14-dimensional velocity vector. At each time step, the agent can obtain the 29-dimensional state value of the current time step from the observation of the environment.

Goal : We have designed three levels of difficulty for both the Ant_Reacher and the Ant_Four_Rooms environment, incorporating multi-goal tasks ranging from 2 goals, increasing to 5 goals, and eventually up to 8 or 10 goals respectively, to validate our method.

Action : In the selector network, the action space is an n-dimensional discrete action space, which depends on the number of final goals, n . In the actuator network, the action space is continuous. In the upper level networks, the agent’s action space overlaps with the state space and is a 5-dimensional vector, including the 3D coordinates and the two-dimensional velocity values of the sub-goals. In the bottom level network, the agent’s action space corresponds to the primitive action space of the ‘Ant’ environment based on the MuJoCo physics engine, an 8-dimensional action vector used to control the hip and ankle joints of the Ant robot’s four legs.

Reward : In all experimental setups, we used a reward function designed with the sparse reward mechanism. The reward function of the selector is shown in Eq.  2 . The reward function for the upper level of the actuator is shown in Eq.  6 and the reward function for the bottom level of the actuator is shown in Eq.  7 .

5.3 Results analysis

To verify whether HRL-MG improves the performance of multi-goal navigation tasks, we compared its performance with the HAC algorithm, which is a superior hierarchical algorithm for solving sparse rewards (Levy et al. 2017 ) and the algorithm with a non-hierarchical structure DDPG+HER (Andrychowicz et al. 2017 ), both under equivalent experimental rewards and environmental settings (Table 2 ).

5.3.1 Baselines

In this subsection, we present two baseline algorithms used for comparison experiments.

Hierarchial Actor-Critic (HAC) (Levy et al. 2017 ): The HAC hierarchical framework employs a continuous goal-oriented hierarchical architecture that accelerates learning and sampling efficiency by simultaneously learning multiple levels of policies.

DDPG+HER (Andrychowicz et al. 2017 ): The DDPG+HER method combines hindsight experience replay with off-policy RL algorithms to cope with the sparse binary reward problem in RL algorithms by sampling and relabeling experience.

5.3.2 Results

Figure  5 shows the success rate of the navigation task given the desired goal number of two. Figure  6 and Fig.  7 demonstrate the navigation paths in the simulation environment for Ant_Reacher and Ant_Four_Rooms tasks under different numbers of desired goals, respectively.

DDPG+HER: The DDPG+HER approach is completely incapable of achieving success in the task of multi-goal navigation in both Ant_Reacher and Ant_Four_Rooms environment. We tried to change the setting of the DDPG+HER method that a 10 reward would be returned after one desired is achieved. In this setting, we trained 5k episodes, but the agent still unable to complete the multi-goal task. We analyze that learning a good policy for walking and not tipping over in a robotic environment is already difficult, while at the same time having a multi-goal navigation task makes the whole process a very long-term decision-making process, leading to the failure of the approach.

HAC: The HAC algorithm is able to perform multi-objective navigation tasks, but the success rate of the tasks does not increase after stabilizing at about 60–70%. The algorithm started to have success examples around in the fourth batch of training, a batch containing 100 episodes of training and updating, so it can be seen that the algorithm’s overall convergence rate was slow. Meanwhile, when training HAC, we found that the multi-level synchronous learning of HAC leads to a particularly large occupation of GPU storage space, which can easily lead to the out-of-memory (OOM) problem and thus interrupt the program.

HRL-MG: Our method effectively overcomes the challenges of multi-goal navigation tasks with sparse reward in both Ant_Reacher and Ant_Four_Rooms environment, achieving a success rate of over 80%. By the tenth batch of training, the success rate of our method was already over 80%, and as we get closer to the 20th batch, the success rate of our method is even close to 95%. Our method demonstrates a near-perfect task completion capability on successive robotics tasks with two desired goals. Therefore, we gradually increase the number of desired goals in the next in order to verify the processing capability of HRL-MG.

The success rate of the two desired goals in the Ant_Reacher environment. The blue line represents the DDPG+HER algorithm, which has a task success rate of 0 because it is unable to complete the multi-goal navigation task, as can be seen in the bottom right corner of the figure. All results were smoothed with a parameter of 0.3. (Color figure online)

Ant_Reacher: Demonstration of the navigation task in the Ant_Reacher environment with the number of the desired goals of 2, 5, and 10 respectively. The yellow spheres represent the desired goals, the two green spheres represent the subgoals and the small red spheres represent the path walked by the agent. The left column, labelled “Pathway_Begin”, represents the initial state of the task. The middle column, labelled “Pathway_Mid”, represents the intermediate state during the task. The right column, labelled “Pathway_End”, represents the final state upon task completion. (Color figure online)

Ant_Four_Rooms: Demonstration of the navigation task in the Ant_Four_Rooms environment with the number of the desired goals of 2, 5, and 8 respectively. The yellow spheres represent the desired goals, the two green spheres represent the subgoals and the small red spheres represent the path walked by the agent. The left column, labelled “Pathway_Begin”, represents the initial state of the task. The middle column, labelled “Pathway_Mid”, represents the intermediate state during the task. The right column, labelled “Pathway_End”, represents the final state upon task completion. (Color figure online)

5.4 Increase the number of desired goals

Based on the validation of the effectiveness of the HRL-MG algorithm, we further explored whether the number of goals has an impact on the performance of the algorithm. Table  3 shows the average success rates for different numbers of desired goals in both the Ant_Reacher and Ant_Four_Rooms environments.

For the Ant_Reacher environment, we respectively set 2, 5, and 10 different desired goals. For the Ant_Four_Rooms, since its map size is slightly smaller than the Ant_Reacher environment, we respectively set 2, 5, and 8 different desired goals. In the Ant_Reacher environment with no obstacles in the way, the average success rates for tasks with 2, 5, and 10 desired goals all exceed 80%. In the Ant_Four_Rooms environment, due to the presence of wall obstacles, the success rates of multi-goal navigation tasks are slightly lower compared to the Ant_Reacher environment. The average success rate of tasks is around 60%. When the number of desired goals increased to 10, the success rate of the algorithm plummeted to 37%, and we speculate that it was the obstruction of the wall and the 10 goals that caused the number of steps to complete the task to grow exponentially. While keeping the step limit constant, causes the task success rate to plummet. Another possible reason is the selection of sub-goals. Monitoring of the training process revealed that the selection of sub-goals could not ignore the location of the wall presence and the robot trying to reach a subgoal will keep moving closer to the wall. Therefore tended to cause the ‘Ant’ robot to roll over, and once the robot had rolled over it was difficult to return to a normal stance in a short time. Meanwhile such behavior is also contrary to the penalty for collision with obstacles.

5.5 Dynamic goal testing

In the training phase, we adopt the dynamic goal testing mechanism mentioned earlier, where each step in an episode is tested based on the current state and all the desired goals, to avoid the problem of an agent passing by another goal in the middle of navigation without realizing it.

Therefore, during the testing phase, when the agent detects that it has passed by another goal on the way of navigation, it will directly end the execution of the current inner loop of the actuator and return to the outer loop of the selector to perform the desired goal selection again. Meanwhile, the current experience is relabeled and stored in the corresponding experience replay buffer. Dramatically improves the speed of completing multi-goal navigation tasks. For specific experimental demonstrations, please refer to our experimental video at https://youtu.be/28HB0CxnZPE .

6 Discussion

6.1 comparison to the tsp.

The multi-goal navigation task proposed in this paper is similar to the traveller’s problem (TSP) regarding top-level control. However we accomplished the multi-goal navigation problem in a situation where the map environment is unknown, the goal location is unknown, and the robot’s behavioural learning needs to be taken into account. A hierarchical reinforcement learning architecture and rewarded feedback allows the robot to learn how to perform the multi-goal navigation task in such a situation, and to adapt the top-level path to the current state. This is fundamentally different from the approach of route planning followed by traversal in navigation with maps. Our hierarchical framework has the potential to be extended to real task scenarios with multi-goal navigation problems in more complex situations.

6.2 Task scenario generality analysis

The HRL-MG hierarchical framework is not only applicable to the ‘Ant_Reacher’ and ‘Ant_Four_Room’ environments used in simulation experiments but also possesses the ability to generalize to other goal-oriented RL tasks. Particularly, it exhibits strong performance in tasks involving vector-represented objectives, such as goal-oriented manipulation and navigation tasks in simulated environments, including mechanical arm manipulation, swinging, and various navigation tasks.

Additionally, the versatility of the HRL-MG framework extends to scenarios where tasks require complex decision-making processes. Its ability to handle diverse environments and tasks underscores its robustness and adaptability. Furthermore, the framework’s hierarchical structure facilitates efficient learning and decision-making by decomposing complex tasks into manageable sub-tasks, enabling more effective exploration and exploitation strategies. These characteristics make the HRL-MG framework a promising approach for addressing a wide range of real-world problems in robotics, autonomous systems, and beyond.

7 Conclusion

In this work, we have proposed a novel and composite hierarchical reinforcement learning framework, called HRL-MG. The main idea of HRL-MG is to divide and conquer the hybrid action space, splitting long-sequence decisions into short-sequence decision tasks one by one. The HRL-MG framework consists of two main loops: the outer loop called selector and the inner loop called actuator, each playing a crucial role in the multi-goal navigation decision-making process. First, the outer loop selector will select based on multiple desired goals, giving a current desired goal to the inner loop to execute. After the inner loop executor receives the desired goal, it will again perform subgoal disassembly of the desired goal to further decompose the task into a short-term decision process. Finally, the bottom layer of the actuator will perform the output of the primitive action. At the same time, we propose a dynamic goal testing method to relabel the experience of the selector and the actuator in case there is a situation where some other desired goal is reached in the middle of the process but the agent is not aware of it. Through a series of experiments conducted in goal-oriented tasks within a robotic environment utilizing the MuJoCo physics engine, we have successfully demonstrated the efficacy of the HRL-MG framework. Our results have showcased its robust performance in various scenarios, including tasks that encompassed different numbers of the desired goal.

Data availability

No datasets were generated or analysed during the current study.

Where d is a distance metric, such as \(L_1\) , \(L_2\) -norm, usually works between agent and target.

Let \(\mathcal {S}\) denote the observable state space of agent, \(\mathcal {S}_e\) denote the state space of selector, \(\mathcal {S}_a\) denote the state space of actuator. Generally, \(\mathcal {S}_a=\mathcal {S}_e\subseteq \mathcal {S}\) .

Abbeel P, Ng AY (2004) Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the 21st international conference on machine learning, p1

Andrychowicz M, Wolski F, Ray A etal (2017) Hindsight experience replay. In: Proceedings of the 31st international conference on neural information processing systems, pp 5055–5065

Bacon PL, Harb J, Precup D (2017) The option-critic architecture. In: Proceedings of the association advancement artificial intelligence, pp 1726–1734

Barto AG, Mahadevan S (2003) Recent advances in hierarchical reinforcement learning. Discret Event Dyn Syst 13(1–2):41–77

Article   MathSciNet   Google Scholar  

Bellemare M, Srinivasan S, Ostrovski G et al (2016) Unifying count-based exploration and intrinsic motivation. In: Advances in neural information processing systems 29 (NIPS 2016), pp. 1471–1479

Botvinick MM (2012) Hierarchical reinforcement learning and decision making. Curr Opin Neurobiol 22(6):956–962

Article   Google Scholar  

Burda Y, Edwards H, Pathak D etal (2018) Large-scale study of curiosity-driven learning. arXiv preprint. arXiv:1808.04355

Burgard W, Cremers AB, Fox D et al (1999) Experiences with an interactive museum tour-guide robot. Artif Intell 114(1–2):3–55

Dietterich TG (2000) Hierarchical reinforcement learning with the maxq value function decomposition. J Artif Intell Res 13:227–303

Dijkstra EW (2022) A note on two problems in connexion with graphs. In: Edsger Wybe Dijkstra: His Life, Work, and Legacy, pp 287-290

Fan Z, Su R, Zhang W etal (2019) Hybrid actor-critic reinforcement learning in parameterized action space. arXiv preprint. arXiv:1903.01344

Fang M, Zhou T, Du Y etal (2019) Curriculum-guided hindsight experience replay. In: Advances in neural information processing systems, vol 32

Faust A, Palunko I, Cruz P et al (2017) Automated aerial suspended cargo delivery through reinforcement learning. Artif Intell 247:381–398

Feinberg EA (1994) Constrained semi-markov decision processes with average rewards. Z Oper Res 39:257–288

MathSciNet   Google Scholar  

Fu J, Luo K, Levine S (2017) Learning robust rewards with adversarial inverse reinforcement learning. arXiv preprint. arXiv:1710.11248

Fu H, Tang H, Hao J etal (2019) Deep multi-agent reinforcement learning with discrete-continuous hybrid action spaces. arXiv preprint. arXiv:1903.04959

Garaffa LC, Basso M, Konzen AA et al (2021) Reinforcement learning for mobile robotics exploration: a survey. IEEE Trans Neural Netw Learn Syst 34(8):3796–3810

Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107

Hausknecht M, Stone P (2015) Deep reinforcement learning in parameterized action space. arXiv preprint. arXiv:1511.04143

Hernandez K, Bacca B, Posso B (2017) Multi-goal path planning autonomous system for picking up and delivery tasks in mobile robotics. IEEE Lat Am Trans 15(2):232–238

Ho J, Ermon S (2016) Generative adversarial imitation learning. In: Advances in Neural Information Processing Systems (NeurIPS), vol 29, pp 4565–4573

Kretzschmar H, Spies M, Sprunk C et al (2016) Socially compliant mobile robot navigation via inverse reinforcement learning. Int J Robot Res 35(11):1289–1307

Kulkarni TD, Narasimhan K, Saeedi A etal (2016) Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation. In: Advances in neural information processing systems, vol 29

LaValle S (1998) Rapidly-exploring random trees: a new tool for path planning. Research Report 9811

Levy A, Konidaris G, Platt R etal (2017) Learning multi-level hierarchies with hindsight. arXiv preprint. arXiv:1712.00948

Li B, Tang H, Zheng Y etal (2021) Hyar: Addressing discrete-continuous action reinforcement learning via hybrid action representation. arXiv preprint. arXiv:2109.05490

Li C, Xia F, Martin-Martin R etal (2020) Hrl4in: Hierarchical reinforcement learning for interactive navigation with mobile manipulators. In: Conference on robot learning, PMLR, pp 603–616

Liu M, Zhu M, Zhang W (2022) Goal-conditioned reinforcement learning: problems and solutions. arXiv preprint. arXiv:2201.08299

Nachum O, Gu SS, Lee H etal (2018) Data-efficient hierarchical reinforcement learning. In: Advances in neural information processing systems, vol 31

Ng AY, Russell S etal (2000) Algorithms for inverse reinforcement learning. In: Proceedings of 17th international conference on machine learning, pp 663–670

Parr R, Russell S (1997) Reinforcement learning with hierarchies of machines. In: Advances in neural information processing systems, vol 10

Pateria S, Subagdja B, Tan Ah et al (2021) Hierarchical reinforcement learning: a comprehensive survey. ACM Comput Surv (CSUR) 54(5):1–35

Pathak D, Agrawal P, Efros AA etal (2017) Curiosity-driven exploration by self-supervised prediction. In: International conference on machine learning, PMLR, pp 2778–2787

Plappert M, Andrychowicz M, Ray A etal (2018) Multi-goal reinforcement learning: challenging robotics environments and request for research. arXiv preprint. arXiv:1802.09464

Reizinger P, Szemenyei M (2020) Attention-based curiosity-driven exploration in deep reinforcement learning. In: International conference on acoustics, speech and signal processing. IEEE, pp 3542–3546

Schaul T, Horgan D, Gregor K etal (2015) Universal value function approximators. In: International conference on machine learning, PMLR, pp 1312–1320

Schulman J, Moritz P, Levine S etal (2015) High-dimensional continuous control using generalized advantage estimation. arXiv preprint. arXiv:1506.02438

Shah R, Pandey A (2018) Concept for automated sorting robotic arm. Procedia Manuf 20:400–405

Sutton RS, Barto AG (2018) Reinforcement learning: an introduction. MIT, Cambridge

Google Scholar  

Sutton RS, Precup D, Singh S (1999) Between MDPS and semi-MDPS: a framework for temporal abstraction in reinforcement learning. Artif Intell 112(1–2):181–211

Tang H, Houthooft R, Foote D etal (2017) # exploration: a study of count-based exploration for deep reinforcement learning. In: Proceedings of the 31st international conference on neural information processing systems, pp 2750–2759

Todorov E, Erez T, Tassa Y (2012) Mujoco: a physics engine for model-based control. In: International conference on intelligent robots and systems. IEEE, pp 5026–5033

Trott A, Zheng S, Xiong C etal (2019) Keeping your distance: solving sparse reward tasks using self-balancing shaped rewards. In: Advances in neural information processing systems, vol 32

Vezhnevets AS, Osindero S, Schaul T etal (2017) Feudal networks for hierarchical reinforcement learning. In: International conference on machine learning, PMLR, pp 3540–3549

Wang C, Wang J, Wang J et al (2020) Deep-reinforcement-learning-based autonomous UAV navigation with sparse rewards. IEEE Internet Things J 7(7):6180–6190

Watkins CJ, Dayan P (1992) Q-learning. Mach Learn 8:279–292

Xiong J, Wang Q, Yang Z etal (2018) Parametrized deep q-networks learning: Reinforcement learning with discrete-continuous hybrid action space. arXiv preprint. arXiv:1810.06394

Zhelo O, Zhang J, Tai L etal (2018) Curiosity-driven exploration for mapless navigation with deep reinforcement learning. arXiv preprint. arXiv:1804.00456

Zhu Y, Mottaghi R, Kolve E etal (2017) Target-driven visual navigation in indoor scenes using deep reinforcement learning. In: International conference on robotics and automation. IEEE, pp 3357–3364

Ziebart BD, Maas AL, Bagnell JA et al (2008) Maximum entropy inverse reinforcement learning. Aaai, Chicago, pp 1433–1438

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Acknowledgements

This study was supported in part by the National Natural Science Foundation of China under Grants 62373375, U2341216 and 62022094.

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Yan, J., Luo, B. & Xu, X. Hierarchical reinforcement learning for handling sparse rewards in multi-goal navigation. Artif Intell Rev 57 , 156 (2024). https://doi.org/10.1007/s10462-024-10794-3

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DOI : https://doi.org/10.1007/s10462-024-10794-3

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