example of systematic review in nursing research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Int J Prev Med

How to Write a Systematic Review: A Narrative Review

Ali hasanpour dehkordi.

Social Determinants of Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran

Elaheh Mazaheri

1 Health Information Technology Research Center, Student Research Committee, Department of Medical Library and Information Sciences, School of Management and Medical Information Sciences, Isfahan University of Medical Sciences, Isfahan, Iran

Hanan A. Ibrahim

2 Department of International Relations, College of Law, Bayan University, Erbil, Kurdistan, Iraq

Sahar Dalvand

3 MSc in Biostatistics, Health Promotion Research Center, Iran University of Medical Sciences, Tehran, Iran

Reza Ghanei Gheshlagh

4 Spiritual Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran

In recent years, published systematic reviews in the world and in Iran have been increasing. These studies are an important resource to answer evidence-based clinical questions and assist health policy-makers and students who want to identify evidence gaps in published research. Systematic review studies, with or without meta-analysis, synthesize all available evidence from studies focused on the same research question. In this study, the steps for a systematic review such as research question design and identification, the search for qualified published studies, the extraction and synthesis of information that pertain to the research question, and interpretation of the results are presented in details. This will be helpful to all interested researchers.

A systematic review, as its name suggests, is a systematic way of collecting, evaluating, integrating, and presenting findings from several studies on a specific question or topic.[ 1 ] A systematic review is a research that, by identifying and combining evidence, is tailored to and answers the research question, based on an assessment of all relevant studies.[ 2 , 3 ] To identify assess and interpret available research, identify effective and ineffective health-care interventions, provide integrated documentation to help decision-making, and identify the gap between studies is one of the most important reasons for conducting systematic review studies.[ 4 ]

In the review studies, the latest scientific information about a particular topic is criticized. In these studies, the terms of review, systematic review, and meta-analysis are used instead. A systematic review is done in one of two methods, quantitative (meta-analysis) and qualitative. In a meta-analysis, the results of two or more studies for the evaluation of say health interventions are combined to measure the effect of treatment, while in the qualitative method, the findings of other studies are combined without using statistical methods.[ 5 ]

Since 1999, various guidelines, including the QUORUM, the MOOSE, the STROBE, the CONSORT, and the QUADAS, have been introduced for reporting meta-analyses. But recently the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement has gained widespread popularity.[ 6 , 7 , 8 , 9 ] The systematic review process based on the PRISMA statement includes four steps of how to formulate research questions, define the eligibility criteria, identify all relevant studies, extract and synthesize data, and deduce and present results (answers to research questions).[ 2 ]

Systematic Review Protocol

Systematic reviews start with a protocol. The protocol is a researcher road map that outlines the goals, methodology, and outcomes of the research. Many journals advise writers to use the PRISMA statement to write the protocol.[ 10 ] The PRISMA checklist includes 27 items related to the content of a systematic review and meta-analysis and includes abstracts, methods, results, discussions, and financial resources.[ 11 ] PRISMA helps writers improve their systematic review and meta-analysis report. Reviewers and editors of medical journals acknowledge that while PRISMA may not be used as a tool to assess the methodological quality, it does help them to publish a better study article [ Figure 1 ].[ 12 ]

Screening process and articles selection according to the PRISMA guidelines

The main step in designing the protocol is to define the main objectives of the study and provide some background information. Before starting a systematic review, it is important to assess that your study is not a duplicate; therefore, in search of published research, it is necessary to review PREOSPERO and the Cochrane Database of Systematic. Sometimes it is better to search, in four databases, related systematic reviews that have already been published (PubMed, Web of Sciences, Scopus, Cochrane), published systematic review protocols (PubMed, Web of Sciences, Scopus, Cochrane), systematic review protocols that have already been registered but have not been published (PROSPERO, Cochrane), and finally related published articles (PubMed, Web of Sciences, Scopus, Cochrane). The goal is to reduce duplicate research and keep up-to-date systematic reviews.[ 13 ]

Research questions

Writing a research question is the first step in systematic review that summarizes the main goal of the study.[ 14 ] The research question determines which types of studies should be included in the analysis (quantitative, qualitative, methodic mix, review overviews, or other studies). Sometimes a research question may be broken down into several more detailed questions.[ 15 ] The vague questions (such as: is walking helpful?) makes the researcher fail to be well focused on the collected studies or analyze them appropriately.[ 16 ] On the other hand, if the research question is rigid and restrictive (e.g., walking for 43 min and 3 times a week is better than walking for 38 min and 4 times a week?), there may not be enough studies in this area to answer this question and hence the generalizability of the findings to other populations will be reduced.[ 16 , 17 ] A good question in systematic review should include components that are PICOS style which include population (P), intervention (I), comparison (C), outcome (O), and setting (S).[ 18 ] Regarding the purpose of the study, control in clinical trials or pre-poststudies can replace C.[ 19 ]

Search and identify eligible texts

After clarifying the research question and before searching the databases, it is necessary to specify searching methods, articles screening, studies eligibility check, check of the references in eligible studies, data extraction, and data analysis. This helps researchers ensure that potential biases in the selection of potential studies are minimized.[ 14 , 17 ] It should also look at details such as which published and unpublished literature have been searched, how they were searched, by which mechanism they were searched, and what are the inclusion and exclusion criteria.[ 4 ] First, all studies are searched and collected according to predefined keywords; then the title, abstract, and the entire text are screened for relevance by the authors.[ 13 ] By screening articles based on their titles, researchers can quickly decide on whether to retain or remove an article. If more information is needed, the abstracts of the articles will also be reviewed. In the next step, the full text of the articles will be reviewed to identify the relevant articles, and the reason for the removal of excluded articles is reported.[ 20 ] Finally, it is recommended that the process of searching, selecting, and screening articles be reported as a flowchart.[ 21 ] By increasing research, finding up-to-date and relevant information has become more difficult.[ 22 ]

Currently, there is no specific guideline as to which databases should be searched, which database is the best, and how many should be searched; but overall, it is advisable to search broadly. Because no database covers all health topics, it is recommended to use several databases to search.[ 23 ] According to the A MeaSurement Tool to Assess Systematic Reviews scale (AMSTAR) at least two databases should be searched in systematic and meta-analysis, although more comprehensive and accurate results can be obtained by increasing the number of searched databases.[ 24 ] The type of database to be searched depends on the systematic review question. For example, in a clinical trial study, it is recommended that Cochrane, multi-regional clinical trial (mRCTs), and International Clinical Trials Registry Platform be searched.[ 25 ]

For example, MEDLINE, a product of the National Library of Medicine in the United States of America, focuses on peer-reviewed articles in biomedical and health issues, while Embase covers the broad field of pharmacology and summaries of conferences. CINAHL is a great resource for nursing and health research and PsycINFO is a great database for psychology, psychiatry, counseling, addiction, and behavioral problems. Also, national and regional databases can be used to search related articles.[ 26 , 27 ] In addition, the search for conferences and gray literature helps to resolve the file-drawn problem (negative studies that may not be published yet).[ 26 ] If a systematic review is carried out on articles in a particular country or region, the databases in that region or country should also be investigated. For example, Iranian researchers can use national databases such as Scientific Information Database and MagIran. Comprehensive search to identify the maximum number of existing studies leads to a minimization of the selection bias. In the search process, the available databases should be used as much as possible, since many databases are overlapping.[ 17 ] Searching 12 databases (PubMed, Scopus, Web of Science, EMBASE, GHL, VHL, Cochrane, Google Scholar, Clinical trials.gov, mRCTs, POPLINE, and SIGLE) covers all articles published in the field of medicine and health.[ 25 ] Some have suggested that references management software be used to search for more easy identification and removal of duplicate articles from several different databases.[ 20 ] At least one search strategy is presented in the article.[ 21 ]

Quality assessment

The methodological quality assessment of articles is a key step in systematic review that helps identify systemic errors (bias) in results and interpretations. In systematic review studies, unlike other review studies, qualitative assessment or risk of bias is required. There are currently several tools available to review the quality of the articles. The overall score of these tools may not provide sufficient information on the strengths and weaknesses of the studies.[ 28 ] At least two reviewers should independently evaluate the quality of the articles, and if there is any objection, the third author should be asked to examine the article or the two researchers agree on the discussion. Some believe that the study of the quality of studies should be done by removing the name of the journal, title, authors, and institutions in a Blinded fashion.[ 29 ]

There are several ways for quality assessment, such as Sack's quality assessment (1988),[ 30 ] overview quality assessment questionnaire (1991),[ 31 ] CASP (Critical Appraisal Skills Program),[ 32 ] and AMSTAR (2007),[ 33 ] Besides, CASP,[ 34 ] the National Institute for Health and Care Excellence,[ 35 ] and the Joanna Briggs Institute System for the Unified Management, Assessment and Review of Information checklists.[ 30 , 36 ] However, it is worth mentioning that there is no single tool for assessing the quality of all types of reviews, but each is more applicable to some types of reviews. Often, the STROBE tool is used to check the quality of articles. It reviews the title and abstract (item 1), introduction (items 2 and 3), implementation method (items 4–12), findings (items 13–17), discussion (Items 18–21), and funding (item 22). Eighteen items are used to review all articles, but four items (6, 12, 14, and 15) apply in certain situations.[ 9 ] The quality of interventional articles is often evaluated by the JADAD tool, which consists of three sections of randomization (2 scores), blinding (2 scores), and patient count (1 scores).[ 29 ]

Data extraction

At this stage, the researchers extract the necessary information in the selected articles. Elamin believes that reviewing the titles and abstracts and data extraction is a key step in the review process, which is often carried out by two of the research team independently, and ultimately, the results are compared.[ 37 ] This step aimed to prevent selection bias and it is recommended that the chance of agreement between the two researchers (Kappa coefficient) be reported at the end.[ 26 ] Although data collection forms may differ in systematic reviews, they all have information such as first author, year of publication, sample size, target community, region, and outcome. The purpose of data synthesis is to collect the findings of eligible studies, evaluate the strengths of the findings of the studies, and summarize the results. In data synthesis, we can use different analysis frameworks such as meta-ethnography, meta-analysis, or thematic synthesis.[ 38 ] Finally, after quality assessment, data analysis is conducted. The first step in this section is to provide a descriptive evaluation of each study and present the findings in a tabular form. Reviewing this table can determine how to combine and analyze various studies.[ 28 ] The data synthesis approach depends on the nature of the research question and the nature of the initial research studies.[ 39 ] After reviewing the bias and the abstract of the data, it is decided that the synthesis is carried out quantitatively or qualitatively. In case of conceptual heterogeneity (systematic differences in the study design, population, and interventions), the generalizability of the findings will be reduced and the study will not be meta-analysis. The meta-analysis study allows the estimation of the effect size, which is reported as the odds ratio, relative risk, hazard ratio, prevalence, correlation, sensitivity, specificity, and incidence with a confidence interval.[ 26 ]

Estimation of the effect size in systematic review and meta-analysis studies varies according to the type of studies entered into the analysis. Unlike the mean, prevalence, or incidence index, in odds ratio, relative risk, and hazard ratio, it is necessary to combine logarithm and logarithmic standard error of these statistics [ Table 1 ].

Effect size in systematic review and meta-analysis

OR=Odds ratio; RR=Relative risk; RCT= Randomized controlled trial; PPV: positive predictive value; NPV: negative predictive value; PLR: positive likelihood ratio; NLR: negative likelihood ratio; DOR: diagnostic odds ratio

Interpreting and presenting results (answers to research questions)

A systematic review ends with the interpretation of results. At this stage, the results of the study are summarized and the conclusions are presented to improve clinical and therapeutic decision-making. A systematic review with or without meta-analysis provides the best evidence available in the hierarchy of evidence-based practice.[ 14 ] Using meta-analysis can provide explicit conclusions. Conceptually, meta-analysis is used to combine the results of two or more studies that are similar to the specific intervention and the similar outcomes. In meta-analysis, instead of the simple average of the results of various studies, the weighted average of studies is reported, meaning studies with larger sample sizes account for more weight. To combine the results of various studies, we can use two models of fixed and random effects. In the fixed-effect model, it is assumed that the parameters studied are constant in all studies, and in the random-effect model, the measured parameter is assumed to be distributed between the studies and each study has measured some of it. This model offers a more conservative estimate.[ 40 ]

Three types of homogeneity tests can be used: (1) forest plot, (2) Cochrane's Q test (Chi-squared), and (3) Higgins I 2 statistics. In the forest plot, more overlap between confidence intervals indicates more homogeneity. In the Q statistic, when the P value is less than 0.1, it indicates heterogeneity exists and a random-effect model should be used.[ 41 ] Various tests such as the I 2 index are used to determine heterogeneity, values between 0 and 100; the values below 25%, between 25% and 50%, and above 75% indicate low, moderate, and high levels of heterogeneity, respectively.[ 26 , 42 ] The results of the meta-analyzing study are presented graphically using the forest plot, which shows the statistical weight of each study with a 95% confidence interval and a standard error of the mean.[ 40 ]

The importance of meta-analyses and systematic reviews in providing evidence useful in making clinical and policy decisions is ever-increasing. Nevertheless, they are prone to publication bias that occurs when positive or significant results are preferred for publication.[ 43 ] Song maintains that studies reporting a certain direction of results or powerful correlations may be more likely to be published than the studies which do not.[ 44 ] In addition, when searching for meta-analyses, gray literature (e.g., dissertations, conference abstracts, or book chapters) and unpublished studies may be missed. Moreover, meta-analyses only based on published studies may exaggerate the estimates of effect sizes; as a result, patients may be exposed to harmful or ineffective treatment methods.[ 44 , 45 ] However, there are some tests that can help in detecting negative expected results that are not included in a review due to publication bias.[ 46 ] In addition, publication bias can be reduced through searching for data that are not published.

Systematic reviews and meta-analyses have certain advantages; some of the most important ones are as follows: examining differences in the findings of different studies, summarizing results from various studies, increased accuracy of estimating effects, increased statistical power, overcoming problems related to small sample sizes, resolving controversies from disagreeing studies, increased generalizability of results, determining the possible need for new studies, overcoming the limitations of narrative reviews, and making new hypotheses for further research.[ 47 , 48 ]

Despite the importance of systematic reviews, the author may face numerous problems in searching, screening, and synthesizing data during this process. A systematic review requires extensive access to databases and journals that can be costly for nonacademic researchers.[ 13 ] Also, in reviewing the inclusion and exclusion criteria, the inevitable mindsets of browsers may be involved and the criteria are interpreted differently from each other.[ 49 ] Lee refers to some disadvantages of these studies, the most significant ones are as follows: a research field cannot be summarized by one number, publication bias, heterogeneity, combining unrelated things, being vulnerable to subjectivity, failing to account for all confounders, comparing variables that are not comparable, just focusing on main effects, and possible inconsistency with results of randomized trials.[ 47 ] Different types of programs are available to perform meta-analysis. Some of the most commonly used statistical programs are general statistical packages, including SAS, SPSS, R, and Stata. Using flexible commands in these programs, meta-analyses can be easily run and the results can be readily plotted out. However, these statistical programs are often expensive. An alternative to using statistical packages is to use programs designed for meta-analysis, including Metawin, RevMan, and Comprehensive Meta-analysis. However, these programs may have limitations, including that they can accept few data formats and do not provide much opportunity to set the graphical display of findings. Another alternative is to use Microsoft Excel. Although it is not a free software, it is usually found in many computers.[ 20 , 50 ]

A systematic review study is a powerful and valuable tool for answering research questions, generating new hypotheses, and identifying areas where there is a lack of tangible knowledge. A systematic review study provides an excellent opportunity for researchers to improve critical assessment and evidence synthesis skills.

Authors' contributions

All authors contributed equally to this work.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

This website is intended for healthcare professionals

• { $refs.search.focus(); })" aria-controls="searchpanel" :aria-expanded="open" class="hidden lg:inline-flex justify-end text-gray-800 hover:text-primary py-2 px-4 lg:px-0 items-center text-base font-medium"> Search

Search menu

Bashir Y, Conlon KC. Step by step guide to do a systematic review and meta-analysis for medical professionals. Ir J Med Sci. 2018; 187:(2)447-452 https://doi.org/10.1007/s11845-017-1663-3

Bettany-Saltikov J. How to do a systematic literature review in nursing: a step-by-step guide.Maidenhead: Open University Press; 2012

Bowers D, House A, Owens D. Getting started in health research.Oxford: Wiley-Blackwell; 2011

Hierarchies of evidence. 2016. http://cjblunt.com/hierarchies-evidence (accessed 23 July 2019)

Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2008; 3:(2)37-41 https://doi.org/10.1191/1478088706qp063oa

Developing a framework for critiquing health research. 2005. https://tinyurl.com/y3nulqms (accessed 22 July 2019)

Cognetti G, Grossi L, Lucon A, Solimini R. Information retrieval for the Cochrane systematic reviews: the case of breast cancer surgery. Ann Ist Super Sanita. 2015; 51:(1)34-39 https://doi.org/10.4415/ANN_15_01_07

Dixon-Woods M, Cavers D, Agarwal S Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Med Res Methodol. 2006; 6:(1) https://doi.org/10.1186/1471-2288-6-35

Guyatt GH, Sackett DL, Sinclair JC Users' guides to the medical literature IX. A method for grading health care recommendations. JAMA. 1995; 274:(22)1800-1804 https://doi.org/10.1001/jama.1995.03530220066035

Hanley T, Cutts LA. What is a systematic review? Counselling Psychology Review. 2013; 28:(4)3-6

Cochrane handbook for systematic reviews of interventions. Version 5.1.0. 2011. https://handbook-5-1.cochrane.org (accessed 23 July 2019)

Jahan N, Naveed S, Zeshan M, Tahir MA. How to conduct a systematic review: a narrative literature review. Cureus. 2016; 8:(11) https://doi.org/10.7759/cureus.864

Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1997; 33:(1)159-174

Methley AM, Campbell S, Chew-Graham C, McNally R, Cheraghi-Sohi S. PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv Res. 2014; 14:(1) https://doi.org/10.1186/s12913-014-0579-0

Moher D, Liberati A, Tetzlaff J, Altman DG Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009; 6:(7) https://doi.org/10.1371/journal.pmed.1000097

Mueller J, Jay C, Harper S, Davies A, Vega J, Todd C. Web use for symptom appraisal of physical health conditions: a systematic review. J Med Internet Res. 2017; 19:(6) https://doi.org/10.2196/jmir.6755

Murad MH, Asi N, Alsawas M, Alahdab F. New evidence pyramid. Evid Based Med. 2016; 21:(4)125-127 https://doi.org/10.1136/ebmed-2016-110401

National Institute for Health and Care Excellence. Methods for the development of NICE public health guidance. 2012. http://nice.org.uk/process/pmg4 (accessed 22 July 2019)

Sambunjak D, Franic M. Steps in the undertaking of a systematic review in orthopaedic surgery. Int Orthop. 2012; 36:(3)477-484 https://doi.org/10.1007/s00264-011-1460-y

Siddaway AP, Wood AM, Hedges LV. How to do a systematic review: a best practice guide for conducting and reporting narrative reviews, meta-analyses, and meta-syntheses. Annu Rev Psychol. 2019; 70:747-770 https://doi.org/0.1146/annurev-psych-010418-102803

Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Med Res Methodol. 2008; 8:(1) https://doi.org/10.1186/1471-2288-8-45

Wallace J, Nwosu B, Clarke M. Barriers to the uptake of evidence from systematic reviews and meta-analyses: a systematic review of decision makers' perceptions. BMJ Open. 2012; 2:(5) https://doi.org/10.1136/bmjopen-2012-001220

Carrying out systematic literature reviews: an introduction

Alan Davies

Lecturer in Health Data Science, School of Health Sciences, University of Manchester, Manchester

View articles · Email Alan

Systematic reviews provide a synthesis of evidence for a specific topic of interest, summarising the results of multiple studies to aid in clinical decisions and resource allocation. They remain among the best forms of evidence, and reduce the bias inherent in other methods. A solid understanding of the systematic review process can be of benefit to nurses that carry out such reviews, and for those who make decisions based on them. An overview of the main steps involved in carrying out a systematic review is presented, including some of the common tools and frameworks utilised in this area. This should provide a good starting point for those that are considering embarking on such work, and to aid readers of such reviews in their understanding of the main review components, in order to appraise the quality of a review that may be used to inform subsequent clinical decision making.

Since their inception in the late 1970s, systematic reviews have gained influence in the health professions ( Hanley and Cutts, 2013 ). Systematic reviews and meta-analyses are considered to be the most credible and authoritative sources of evidence available ( Cognetti et al, 2015 ) and are regarded as the pinnacle of evidence in the various ‘hierarchies of evidence’. Reviews published in the Cochrane Library ( https://www.cochranelibrary.com) are widely considered to be the ‘gold’ standard. Since Guyatt et al (1995) presented a users' guide to medical literature for the Evidence-Based Medicine Working Group, various hierarchies of evidence have been proposed. Figure 1 illustrates an example.

Systematic reviews can be qualitative or quantitative. One of the criticisms levelled at hierarchies such as these is that qualitative research is often positioned towards or even is at the bottom of the pyramid, thus implying that it is of little evidential value. This may be because of traditional issues concerning the quality of some qualitative work, although it is now widely recognised that both quantitative and qualitative research methodologies have a valuable part to play in answering research questions, which is reflected by the National Institute for Health and Care Excellence (NICE) information concerning methods for developing public health guidance. The NICE (2012) guidance highlights how both qualitative and quantitative study designs can be used to answer different research questions. In a revised version of the hierarchy-of-evidence pyramid, the systematic review is considered as the lens through which the evidence is viewed, rather than being at the top of the pyramid ( Murad et al, 2016 ).

Both quantitative and qualitative research methodologies are sometimes combined in a single review. According to the Cochrane review handbook ( Higgins and Green, 2011 ), regardless of type, reviews should contain certain features, including:

• Clearly stated objectives
• Predefined eligibility criteria for inclusion or exclusion of studies in the review
• A reproducible and clearly stated methodology
• Validity assessment of included studies (eg quality, risk, bias etc).

The main stages of carrying out a systematic review are summarised in Box 1 .

Formulating the research question

Before undertaking a systemic review, a research question should first be formulated ( Bashir and Conlon, 2018 ). There are a number of tools/frameworks ( Table 1 ) to support this process, including the PICO/PICOS, PEO and SPIDER criteria ( Bowers et al, 2011 ). These frameworks are designed to help break down the question into relevant subcomponents and map them to concepts, in order to derive a formalised search criterion ( Methley et al, 2014 ). This stage is essential for finding literature relevant to the question ( Jahan et al, 2016 ).

It is advisable to first check that the review you plan to carry out has not already been undertaken. You can optionally register your review with an international register of prospective reviews called PROSPERO, although this is not essential for publication. This is done to help you and others to locate work and see what reviews have already been carried out in the same area. It also prevents needless duplication and instead encourages building on existing work ( Bashir and Conlon, 2018 ).

A study ( Methley et al, 2014 ) that compared PICO, PICOS and SPIDER in relation to sensitivity and specificity recommended that the PICO tool be used for a comprehensive search and the PICOS tool when time/resources are limited.

The use of the SPIDER tool was not recommended due to the risk of missing relevant papers. It was, however, found to increase specificity.

These tools/frameworks can help those carrying out reviews to structure research questions and define key concepts in order to efficiently identify relevant literature and summarise the main objective of the review ( Jahan et al, 2016 ). A possible research question could be: Is paracetamol of benefit to people who have just had an operation? The following examples highlight how using a framework may help to refine the question:

• What form of paracetamol? (eg, oral/intravenous/suppository)
• Is the dosage important?
• What is the patient population? (eg, children, adults, Europeans)
• What type of operation? (eg, tonsillectomy, appendectomy)
• What does benefit mean? (eg, reduce post-operative pyrexia, analgesia).

An example of a more refined research question could be: Is oral paracetamol effective in reducing pain following cardiac surgery for adult patients? A number of concepts for each element will need to be specified. There will also be a number of synonyms for these concepts ( Table 2 ).

Table 2 shows an example of concepts used to define a search strategy using the PICO statement. It is easy to see even with this dummy example that there are many concepts that require mapping and much thought required to capture ‘good’ search criteria. Consideration should be given to the various terms to describe the heart, such as cardiac, cardiothoracic, myocardial, myocardium, etc, and the different names used for drugs, such as the equivalent name used for paracetamol in other countries and regions, as well as the various brand names. Defining good search criteria is an important skill that requires a lot of practice. A high-quality review gives details of the search criteria that enables the reader to understand how the authors came up with the criteria. A specific, well-defined search criterion also aids in the reproducibility of a review.

Search criteria

Before the search for papers and other documents can begin it is important to explicitly define the eligibility criteria to determine whether a source is relevant to the review ( Hanley and Cutts, 2013 ). There are a number of database sources that are searched for medical/health literature including those shown in Table 3 .

The various databases can be searched using common Boolean operators to combine or exclude search terms (ie AND, OR, NOT) ( Figure 2 ).

Although most literature databases use similar operators, it is necessary to view the individual database guides, because there are key differences between some of them. Table 4 details some of the common operators and wildcards used in the databases for searching. When developing a search criteria, it is a good idea to check concepts against synonyms, as well as abbreviations, acronyms and plural and singular variations ( Cognetti et al, 2015 ). Reading some key papers in the area and paying attention to the key words they use and other terms used in the abstract, and looking through the reference lists/bibliographies of papers, can also help to ensure that you incorporate relevant terms. Medical Subject Headings (MeSH) that are used by the National Library of Medicine (NLM) ( https://www.nlm.nih.gov/mesh/meshhome.html) to provide hierarchical biomedical index terms for NLM databases (Medline and PubMed) should also be explored and included in relevant search strategies.

Searching the ‘grey literature’ is also an important factor in reducing publication bias. It is often the case that only studies with positive results and statistical significance are published. This creates a certain bias inherent in the published literature. This bias can, to some degree, be mitigated by the inclusion of results from the so-called grey literature, including unpublished work, abstracts, conference proceedings and PhD theses ( Higgins and Green, 2011 ; Bettany-Saltikov, 2012 ; Cognetti et al, 2015 ). Biases in a systematic review can lead to overestimating or underestimating the results ( Jahan et al, 2016 ).

An example search strategy from a published review looking at web use for the appraisal of physical health conditions can be seen in Box 2 . High-quality reviews usually detail which databases were searched and the number of items retrieved from each.

A balance between high recall and high precision is often required in order to produce the best results. An oversensitive search, or one prone to including too much noise, can mean missing important studies or producing too many search results ( Cognetti et al, 2015 ). Following a search, the exported citations can be added to citation management software (such as Mendeley or Endnote) and duplicates removed.

Title and abstract screening

Initial screening begins with the title and abstracts of articles being read and included or excluded from the review based on their relevance. This is usually carried out by at least two researchers to reduce bias ( Bashir and Conlon, 2018 ). After screening any discrepancies in agreement should be resolved by discussion, or by an additional researcher casting the deciding vote ( Bashir and Conlon, 2018 ). Statistics for inter-rater reliability exist and can be reported, such as percentage of agreement or Cohen's kappa ( Box 3 ) for two reviewers and Fleiss' kappa for more than two reviewers. Agreement can depend on the background and knowledge of the researchers and the clarity of the inclusion and exclusion criteria. This highlights the importance of providing clear, well-defined criteria for inclusion that are easy for other researchers to follow.

Full-text review

Following title and abstract screening, the remaining articles/sources are screened in the same way, but this time the full texts are read in their entirety and included or excluded based on their relevance. Reasons for exclusion are usually recorded and reported. Extraction of the specific details of the studies can begin once the final set of papers is determined.

Data extraction

At this stage, the full-text papers are read and compared against the inclusion criteria of the review. Data extraction sheets are forms that are created to extract specific data about a study (12 Jahan et al, 2016 ) and ensure that data are extracted in a uniform and structured manner. Extraction sheets can differ between quantitative and qualitative reviews. For quantitative reviews they normally include details of the study's population, design, sample size, intervention, comparisons and outcomes ( Bettany-Saltikov, 2012 ; Mueller et al, 2017 ).

Quality appraisal

The quality of the studies used in the review should also be appraised. Caldwell et al (2005) discussed the need for a health research evaluation framework that could be used to evaluate both qualitative and quantitative work. The framework produced uses features common to both research methodologies, as well as those that differ ( Caldwell et al, 2005 ; Dixon-Woods et al, 2006 ). Figure 3 details the research critique framework. Other quality appraisal methods do exist, such as those presented in Box 4 . Quality appraisal can also be used to weight the evidence from studies. For example, more emphasis can be placed on the results of large randomised controlled trials (RCT) than one with a small sample size. The quality of a review can also be used as a factor for exclusion and can be specified in inclusion/exclusion criteria. Quality appraisal is an important step that needs to be undertaken before conclusions about the body of evidence can be made ( Sambunjak and Franic, 2012 ). It is also important to note that there is a difference between the quality of the research carried out in the studies and the quality of how those studies were reported ( Sambunjak and Franic, 2012 ).

The quality appraisal is different for qualitative and quantitative studies. With quantitative studies this usually focuses on their internal and external validity, such as how well the study has been designed and analysed, and the generalisability of its findings. Qualitative work, on the other hand, is often evaluated in terms of trustworthiness and authenticity, as well as how transferable the findings may be ( Bettany-Saltikov, 2012 ; Bashir and Conlon, 2018 ; Siddaway et al, 2019 ).

Reporting a review (the PRISMA statement)

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) provides a reporting structure for systematic reviews/meta-analysis, and consists of a checklist and diagram ( Figure 4 ). The stages of identifying potential papers/sources, screening by title and abstract, determining eligibility and final inclusion are detailed with the number of articles included/excluded at each stage. PRISMA diagrams are often included in systematic reviews to detail the number of papers included at each of the four main stages (identification, screening, eligibility and inclusion) of the review.

Data synthesis

The combined results of the screened studies can be analysed qualitatively by grouping them together under themes and subthemes, often referred to as meta-synthesis or meta-ethnography ( Siddaway et al, 2019 ). Sometimes this is not done and a summary of the literature found is presented instead. When the findings are synthesised, they are usually grouped into themes that were derived by noting commonality among the studies included. Inductive (bottom-up) thematic analysis is frequently used for such purposes and works by identifying themes (essentially repeating patterns) in the data, and can include a set of higher-level and related subthemes (Braun and Clarke, 2012). Thomas and Harden (2008) provide examples of the use of thematic synthesis in systematic reviews, and there is an excellent introduction to thematic analysis by Braun and Clarke (2012).

The results of the review should contain details on the search strategy used (including search terms), the databases searched (and the number of items retrieved), summaries of the studies included and an overall synthesis of the results ( Bettany-Saltikov, 2012 ). Finally, conclusions should be made about the results and the limitations of the studies included ( Jahan et al, 2016 ). Another method for synthesising data in a systematic review is a meta-analysis.

Limitations of systematic reviews

Apart from the many advantages and benefits to carrying out systematic reviews highlighted throughout this article, there remain a number of disadvantages. These include the fact that not all stages of the review process are followed rigorously or even at all in some cases. This can lead to poor quality reviews that are difficult or impossible to replicate. There also exist some barriers to the use of evidence produced by reviews, including ( Wallace et al, 2012 ):

• Lack of awareness and familiarity with reviews
• Lack of access
• Lack of direct usefulness/applicability.

Meta-analysis

When the methods used and the analysis are similar or the same, such as in some RCTs, the results can be synthesised using a statistical approach called meta-analysis and presented using summary visualisations such as forest plots (or blobbograms) ( Figure 5 ). This can be done only if the results can be combined in a meaningful way.

Meta-analysis can be carried out using common statistical and data science software, such as the cross-platform ‘R’ ( https://www.r-project.org), or by using standalone software, such as Review Manager (RevMan) produced by the Cochrane community ( https://tinyurl.com/revman-5), which is currently developing a cross-platform version RevMan Web.

Carrying out a systematic review is a time-consuming process, that on average takes between 6 and 18 months and requires skill from those involved. Ideally, several reviewers will work on a review to reduce bias. Experts such as librarians should be consulted and included where possible in review teams to leverage their expertise.

Systematic reviews should present the state of the art (most recent/up-to-date developments) concerning a specific topic and aim to be systematic and reproducible. Reproducibility is aided by transparent reporting of the various stages of a review using reporting frameworks such as PRISMA for standardisation. A high-quality review should present a summary of a specific topic to a high standard upon which other professionals can base subsequent care decisions that increase the quality of evidence-based clinical practice.

• Systematic reviews remain one of the most trusted sources of high-quality information from which to make clinical decisions
• Understanding the components of a review will help practitioners to better assess their quality
• Many formal frameworks exist to help structure and report reviews, the use of which is recommended for reproducibility
• Experts such as librarians can be included in the review team to help with the review process and improve its quality

CPD reflective questions

• Where should high-quality qualitative research sit regarding the hierarchies of evidence?
• What background and expertise should those conducting a systematic review have, and who should ideally be included in the team?
• Consider to what extent inter-rater agreement is important in the screening process

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Systematic Review | Definition, Examples & Guide

Systematic Review | Definition, Examples & Guide

Published on 15 June 2022 by Shaun Turney . Revised on 17 October 2022.

A systematic review is a type of review that uses repeatable methods to find, select, and synthesise all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer.

They answered the question ‘What is the effectiveness of probiotics in reducing eczema symptoms and improving quality of life in patients with eczema?’

In this context, a probiotic is a health product that contains live microorganisms and is taken by mouth. Eczema is a common skin condition that causes red, itchy skin.

Table of contents

What is a systematic review, systematic review vs meta-analysis, systematic review vs literature review, systematic review vs scoping review, when to conduct a systematic review, pros and cons of systematic reviews, step-by-step example of a systematic review, frequently asked questions about systematic reviews.

A review is an overview of the research that’s already been completed on a topic.

What makes a systematic review different from other types of reviews is that the research methods are designed to reduce research bias . The methods are repeatable , and the approach is formal and systematic:

• Formulate a research question
• Develop a protocol
• Search for all relevant studies
• Apply the selection criteria
• Extract the data
• Synthesise the data
• Write and publish a report

Although multiple sets of guidelines exist, the Cochrane Handbook for Systematic Reviews is among the most widely used. It provides detailed guidelines on how to complete each step of the systematic review process.

Systematic reviews are most commonly used in medical and public health research, but they can also be found in other disciplines.

Systematic reviews typically answer their research question by synthesising all available evidence and evaluating the quality of the evidence. Synthesising means bringing together different information to tell a single, cohesive story. The synthesis can be narrative ( qualitative ), quantitative , or both.

Prevent plagiarism, run a free check.

Systematic reviews often quantitatively synthesise the evidence using a meta-analysis . A meta-analysis is a statistical analysis, not a type of review.

A meta-analysis is a technique to synthesise results from multiple studies. It’s a statistical analysis that combines the results of two or more studies, usually to estimate an effect size .

A literature review is a type of review that uses a less systematic and formal approach than a systematic review. Typically, an expert in a topic will qualitatively summarise and evaluate previous work, without using a formal, explicit method.

Although literature reviews are often less time-consuming and can be insightful or helpful, they have a higher risk of bias and are less transparent than systematic reviews.

Similar to a systematic review, a scoping review is a type of review that tries to minimise bias by using transparent and repeatable methods.

However, a scoping review isn’t a type of systematic review. The most important difference is the goal: rather than answering a specific question, a scoping review explores a topic. The researcher tries to identify the main concepts, theories, and evidence, as well as gaps in the current research.

Sometimes scoping reviews are an exploratory preparation step for a systematic review, and sometimes they are a standalone project.

A systematic review is a good choice of review if you want to answer a question about the effectiveness of an intervention , such as a medical treatment.

To conduct a systematic review, you’ll need the following:

• A precise question , usually about the effectiveness of an intervention. The question needs to be about a topic that’s previously been studied by multiple researchers. If there’s no previous research, there’s nothing to review.
• If you’re doing a systematic review on your own (e.g., for a research paper or thesis), you should take appropriate measures to ensure the validity and reliability of your research.
• Access to databases and journal archives. Often, your educational institution provides you with access.
• Time. A professional systematic review is a time-consuming process: it will take the lead author about six months of full-time work. If you’re a student, you should narrow the scope of your systematic review and stick to a tight schedule.
• Bibliographic, word-processing, spreadsheet, and statistical software . For example, you could use EndNote, Microsoft Word, Excel, and SPSS.

A systematic review has many pros .

• They minimise research b ias by considering all available evidence and evaluating each study for bias.
• Their methods are transparent , so they can be scrutinised by others.
• They’re thorough : they summarise all available evidence.
• They can be replicated and updated by others.

Systematic reviews also have a few cons .

• They’re time-consuming .
• They’re narrow in scope : they only answer the precise research question.

The 7 steps for conducting a systematic review are explained with an example.

Step 1: Formulate a research question

Formulating the research question is probably the most important step of a systematic review. A clear research question will:

• Allow you to more effectively communicate your research to other researchers and practitioners
• Guide your decisions as you plan and conduct your systematic review

A good research question for a systematic review has four components, which you can remember with the acronym PICO :

• Population(s) or problem(s)
• Intervention(s)
• Comparison(s)

You can rearrange these four components to write your research question:

• What is the effectiveness of I versus C for O in P ?

Sometimes, you may want to include a fourth component, the type of study design . In this case, the acronym is PICOT .

• Type of study design(s)
• The population of patients with eczema
• The intervention of probiotics
• In comparison to no treatment, placebo , or non-probiotic treatment
• The outcome of changes in participant-, parent-, and doctor-rated symptoms of eczema and quality of life
• Randomised control trials, a type of study design

Their research question was:

• What is the effectiveness of probiotics versus no treatment, a placebo, or a non-probiotic treatment for reducing eczema symptoms and improving quality of life in patients with eczema?

Step 2: Develop a protocol

A protocol is a document that contains your research plan for the systematic review. This is an important step because having a plan allows you to work more efficiently and reduces bias.

Your protocol should include the following components:

• Background information : Provide the context of the research question, including why it’s important.
• Research objective(s) : Rephrase your research question as an objective.
• Selection criteria: State how you’ll decide which studies to include or exclude from your review.
• Search strategy: Discuss your plan for finding studies.
• Analysis: Explain what information you’ll collect from the studies and how you’ll synthesise the data.

If you’re a professional seeking to publish your review, it’s a good idea to bring together an advisory committee . This is a group of about six people who have experience in the topic you’re researching. They can help you make decisions about your protocol.

It’s highly recommended to register your protocol. Registering your protocol means submitting it to a database such as PROSPERO or ClinicalTrials.gov .

Step 3: Search for all relevant studies

Searching for relevant studies is the most time-consuming step of a systematic review.

To reduce bias, it’s important to search for relevant studies very thoroughly. Your strategy will depend on your field and your research question, but sources generally fall into these four categories:

• Databases: Search multiple databases of peer-reviewed literature, such as PubMed or Scopus . Think carefully about how to phrase your search terms and include multiple synonyms of each word. Use Boolean operators if relevant.
• Handsearching: In addition to searching the primary sources using databases, you’ll also need to search manually. One strategy is to scan relevant journals or conference proceedings. Another strategy is to scan the reference lists of relevant studies.
• Grey literature: Grey literature includes documents produced by governments, universities, and other institutions that aren’t published by traditional publishers. Graduate student theses are an important type of grey literature, which you can search using the Networked Digital Library of Theses and Dissertations (NDLTD) . In medicine, clinical trial registries are another important type of grey literature.
• Experts: Contact experts in the field to ask if they have unpublished studies that should be included in your review.

At this stage of your review, you won’t read the articles yet. Simply save any potentially relevant citations using bibliographic software, such as Scribbr’s APA or MLA Generator .

• Databases: EMBASE, PsycINFO, AMED, LILACS, and ISI Web of Science
• Handsearch: Conference proceedings and reference lists of articles
• Grey literature: The Cochrane Library, the metaRegister of Controlled Trials, and the Ongoing Skin Trials Register
• Experts: Authors of unpublished registered trials, pharmaceutical companies, and manufacturers of probiotics

Step 4: Apply the selection criteria

Applying the selection criteria is a three-person job. Two of you will independently read the studies and decide which to include in your review based on the selection criteria you established in your protocol . The third person’s job is to break any ties.

To increase inter-rater reliability , ensure that everyone thoroughly understands the selection criteria before you begin.

If you’re writing a systematic review as a student for an assignment, you might not have a team. In this case, you’ll have to apply the selection criteria on your own; you can mention this as a limitation in your paper’s discussion.

You should apply the selection criteria in two phases:

• Based on the titles and abstracts : Decide whether each article potentially meets the selection criteria based on the information provided in the abstracts.
• Based on the full texts: Download the articles that weren’t excluded during the first phase. If an article isn’t available online or through your library, you may need to contact the authors to ask for a copy. Read the articles and decide which articles meet the selection criteria.

It’s very important to keep a meticulous record of why you included or excluded each article. When the selection process is complete, you can summarise what you did using a PRISMA flow diagram .

Next, Boyle and colleagues found the full texts for each of the remaining studies. Boyle and Tang read through the articles to decide if any more studies needed to be excluded based on the selection criteria.

When Boyle and Tang disagreed about whether a study should be excluded, they discussed it with Varigos until the three researchers came to an agreement.

Step 5: Extract the data

Extracting the data means collecting information from the selected studies in a systematic way. There are two types of information you need to collect from each study:

• Information about the study’s methods and results . The exact information will depend on your research question, but it might include the year, study design , sample size, context, research findings , and conclusions. If any data are missing, you’ll need to contact the study’s authors.
• Your judgement of the quality of the evidence, including risk of bias .

You should collect this information using forms. You can find sample forms in The Registry of Methods and Tools for Evidence-Informed Decision Making and the Grading of Recommendations, Assessment, Development and Evaluations Working Group .

Extracting the data is also a three-person job. Two people should do this step independently, and the third person will resolve any disagreements.

They also collected data about possible sources of bias, such as how the study participants were randomised into the control and treatment groups.

Step 6: Synthesise the data

Synthesising the data means bringing together the information you collected into a single, cohesive story. There are two main approaches to synthesising the data:

• Narrative ( qualitative ): Summarise the information in words. You’ll need to discuss the studies and assess their overall quality.
• Quantitative : Use statistical methods to summarise and compare data from different studies. The most common quantitative approach is a meta-analysis , which allows you to combine results from multiple studies into a summary result.

Generally, you should use both approaches together whenever possible. If you don’t have enough data, or the data from different studies aren’t comparable, then you can take just a narrative approach. However, you should justify why a quantitative approach wasn’t possible.

Boyle and colleagues also divided the studies into subgroups, such as studies about babies, children, and adults, and analysed the effect sizes within each group.

Step 7: Write and publish a report

The purpose of writing a systematic review article is to share the answer to your research question and explain how you arrived at this answer.

Your article should include the following sections:

• Abstract : A summary of the review
• Introduction : Including the rationale and objectives
• Methods : Including the selection criteria, search method, data extraction method, and synthesis method
• Results : Including results of the search and selection process, study characteristics, risk of bias in the studies, and synthesis results
• Discussion : Including interpretation of the results and limitations of the review
• Conclusion : The answer to your research question and implications for practice, policy, or research

To verify that your report includes everything it needs, you can use the PRISMA checklist .

Once your report is written, you can publish it in a systematic review database, such as the Cochrane Database of Systematic Reviews , and/or in a peer-reviewed journal.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

There are several reasons to conduct a literature review at the beginning of a research project:

• To familiarise yourself with the current state of knowledge on your topic
• To ensure that you’re not just repeating what others have already done
• To identify gaps in knowledge and unresolved problems that your research can address
• To develop your theoretical framework and methodology
• To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Turney, S. (2022, October 17). Systematic Review | Definition, Examples & Guide. Scribbr. Retrieved 14 May 2024, from https://www.scribbr.co.uk/research-methods/systematic-reviews/

Is this article helpful?

Shaun Turney

Other students also liked, what is a literature review | guide, template, & examples, exploratory research | definition, guide, & examples, what is peer review | types & examples.

• UWF Libraries

Evidence Based Nursing

• Types of Reviews
• What is Evidence -Based Practice & PICO
• PICO Widgets for Searching
• PubMed - Info & Tutorials
• Cochrane Library - Info & Tutorials
• Joanna Briggs Institute - Tutorial
• Finding the Evidence: Summaries & Clinical Guidlines
• Find Books & Background Resources
• Statistics and Data
• APA Formatting Guide
• Citation Managers
• Library Presentations

What are the types of reviews?

As you begin searching through the literature for evidence, you will come across different types of publications. Below are examples of the most common types and explanations of what they are. Although systematic reviews and meta-analysis are considered the highest quality of evidence, not every topic will have an Systematic Review or Metanalysis.

Use the PRISMA Checklist to assess systematic reviews

Literature Review Examples

Remember, a literature review provides an overview of a topic. There may or may not be a method for how studies are collected or interpreted. Lit reviews aren't always obviously labeled "literature review"; they may be embedded within sections such as the introduction or background. You can figure this out by reading the article . 

• Dance therapy for individuals with Parkinson's Disease Notice how the introduction and subheadings provide background on the topic and describe way it's important. Some studies are grouped together that convey a similar idea. Limitations of some studies are addressed as a way of showing the significance of the research topic.
• Ethical Issues Regarding Human Cloning: A Nursing Perspective Notice how this article is broken into several sections: background on human cloning, harms of cloning, and nursing issues in cloning. These are the themes of the different articles that were used in writing this literature review. Look at how the articles work together to form a cohesive piece of literature.

Systematic Review Examples

Systematic reviews address a clinical question.  Reviews are gathered using a specific, defined set of criteria.

• Selection criteria is defined
• The words "Systematic Review" may appear int he title or abstract
• BTW -> Cochrane Reviews aka Systematic Reviews
• Additional reviews can be found by using a systematic review limit 
• A Systematic Review of Animal-Assisted Therapy on Psychosocial Outcomes in People with Intellectual Disability
• The determinants and consequences of adult nursing staff turnover: a systematic review of systematic reviews
• Cochrane Library (Wiley) This link opens in a new window Over 5000 reviews of research on medical treatments, practices, and diagnostic tests are provided in this database. Cochrane Reviews is the premier resource for Evidence Based Practice.
• PubMed (NLM) This link opens in a new window PubMed comprises more than 22 million citations for biomedical literature from MEDLINE, life science journals, and online books.

Meta-Analysis Examples

Meta-analysis is a study that combines data from OTHER studies. All the studies are combined to argue whether a clinical intervention is statistically significant by combining the results from the other studies.  For example, you want to examine a specific headache intervention without running a clinical trial.  You can look at other articles that discuss your clinical intervention, combine all the participants from those articles, and run a statistical analysis to test if your results are significant. Guess what? There's a lot of math. 

• Include the words "meta-analysis" or "meta analysis" in your keywords
• Meta-analyses will always be accompanied by a systematic review, but a systematic review may not have a meta-analysis
• See if the abstract or results section mention a meta-analysis
• Use databases like Cochrane or PubMed
• Exercise Interventions for Preventing Falls Among Older People in Care Facilities: A Meta-Analysis
• Acupuncture for the prevention of tension-type headache This is a systematic review that includes a meta-analysis. Check out the Abstract and Results for an example of what a meta-analysis looks like!
• << Previous: What is Evidence -Based Practice & PICO
• Next: Finding the Evidence >>
• Last Updated: May 17, 2024 8:29 AM
• URL: https://libguides.uwf.edu/EBN

Systematic Reviews

• Introduction
• Guidelines and procedures
• Management tools
• Define the question
• Check the topic
• Determine inclusion/exclusion criteria
• Develop a protocol
• Identify keywords
• Databases and search strategies
• Grey literature
• Manage and organise
• Screen & Select
• Locate full text
• Extract data

Example reviews

• Examples of systematic reviews
• Accessing help This link opens in a new window
• Systematic Style Reviews Guide This link opens in a new window

Please choose the tab below for your discipline to see relevant examples.

For more information about how to conduct and write reviews, please see the Guidelines section of this guide.

• Health & Medicine
• Social sciences
• Vibration and bubbles: a systematic review of the effects of helicopter retrieval on injured divers. (2018).
• Nicotine effects on exercise performance and physiological responses in nicotine‐naïve individuals: a systematic review. (2018).
• Association of total white cell count with mortality and major adverse events in patients with peripheral arterial disease: A systematic review. (2014).
• Do MOOCs contribute to student equity and social inclusion? A systematic review 2014–18. (2020).
• Interventions in Foster Family Care: A Systematic Review. (2020).
• Determinants of happiness among healthcare professionals between 2009 and 2019: a systematic review. (2020).
• Systematic review of the outcomes and trade-offs of ten types of decarbonization policy instruments. (2021).
• A systematic review on Asian's farmers' adaptation practices towards climate change. (2018).
• Are concentrations of pollutants in sharks, rays and skates (Elasmobranchii) a cause for concern? A systematic review. (2020).
• << Previous: Write
• Next: Publish >>
• Last Updated: May 13, 2024 5:20 PM
• URL: https://libguides.jcu.edu.au/systematic-review

• Library Hours
• Strategic Plan
• Giving to the Libraries
• Jobs at the Libraries
• Find Your Librarian
• View All →
• Google Scholar
• Research Guides
• Textbook/Reserves
• Government Documents
• Get It For Me
• Print/Copy/Scan
• Renew Materials
• Study Rooms
• Use a Computer
• Borrow Tech Gear
• Student Services
• Faculty Services
• Users with Disabilities
• Visitors & Alumni
• Special Collections
• Find Information

Basics of Systematic Reviews

• About Systematic Review

Types of Reviews

Literature review.

Collects key sources on a topic and discusses those sources in conversation with each other

• Standard for research articles in most disciplines
• Tells the reader what is known, or not known, about a particular issue, topic, or subject
• Demonstrates knowledge and understanding of a topic
• Establishes context or background for a case or argument
• Helps develop the author’s ideas and perspective

Rapid Review

Thorough methodology but with process limitations in place to expeditethe completion of a review.

• For questions that require timely answers
• 3-4 months vs. 12-24 months
• Limitations - scope, comprehensiveness bias, and quality of appraisal
• Discusses potential effects that the limited methods may have had on results

Scoping Review

Determine the scope or coverage of a body of literature on a given topic and give clear indication of the volume of literature and studies available as well as an overview of its focus.

• Identify types of available evidence in a given field
• Clarify key concepts/definitions in the literature
• Examine how research is conducted on a certain topic or field
• Identify key factors related to a concept
• Key difference is focus
• Identify and analyze knowledge gaps

Systematic Review

Attempts to identify, appraise, and summarize all empirical evidence that fits pre-specified eligibility criteria to answer a specific research question.

• clearly defined question with inclusion/exclusion criteria
• rigorous and systematic search of the literature
• thorough screening of results
• data extraction and management
• analysis and interpretation of results
• risk of bias assessment of included studies

Meta-Analysis

Used to systematically synthesize or merge the findings of single, independent studies, using statistical methods to calculate an overall or ‘absolute’ effect.

• Combines results from multiple empirical studies
• Requires systematic review first
• Use well recognized, systematic methods to account for differences in sample size, variability (heterogeneity) in study approach and findings (treatment effects)
• Test how sensitive their results are to their own systematic review protocol

For additional types of reviews please see these articles:

• Sutton, A., Clowes, M., Preston, L. and Booth, A. (2019), Meeting the review family: exploring review types and associated information retrieval requirements. Health Info Libr J, 36: 202-222. https://doi.org/10.1111/hir.12276
• Grant, M.J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26: 91-108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
• << Previous: About Systematic Review
• Next: Sources >>
• Last Updated: May 17, 2024 10:04 AM
• URL: https://libguides.utsa.edu/systematicreview
• Library Locations
• Staff Directory
• 508 Compliance
• Site Search
• © The University of Texas at San Antonio
• Information: 210-458-4011
• Campus Alerts
• Required Links
• UTSA Policies
• Report Fraud

• Open access
• Published: 15 May 2024

The association between ultra-processed food and common pregnancy adverse outcomes: a dose-response systematic review and meta-analysis

• Sepide Talebi 1 , 2 ,
• Sanaz Mehrabani 3 ,
• Seyed Mojtaba Ghoreishy 4 , 5 ,
• Alexei Wong 6 ,
• Aliasghar Moghaddam 7 ,
• Peyman Rahimi Feyli 7 ,
• Parsa Amirian 8 ,
• Mahsa Zarpoosh 8 ,
• Mohammad Ali Hojjati Kermani 9 &
• Sajjad Moradi 10  

BMC Pregnancy and Childbirth volume  24 , Article number:  369 ( 2024 ) Cite this article

141 Accesses

Metrics details

Given the increasing incidence of negative outcomes during pregnancy, our research team conducted a dose-response systematic review and meta-analysis to investigate the relationship between ultra-processed foods (UPFs) consumption and common adverse pregnancy outcomes including gestational diabetes mellitus (GDM), preeclampsia (PE), preterm birth (PTB), low birth weight (LBW), and small for gestational age (SGA) infants. UPFs are described as formulations of food substances often modified by chemical processes and then assembled into ready-to-consume hyper-palatable food and drink products using flavors, colors, emulsifiers, and other cosmetic additives. Examples include savory snacks, reconstituted meat products, frozen meals that have already been made, and soft drinks.

A comprehensive search was performed using the Scopus, PubMed, and Web of Science databases up to December 2023. We pooled relative risk (RR) and 95% confidence intervals (CI) using a random-effects model.

Our analysis (encompassing 54 studies with 552,686 individuals) revealed a significant association between UPFs intake and increased risks of GDM (RR = 1.19; 95% CI: 1.10, 1.27; I 2  = 77.5%; p  < 0.001; studies = 44; number of participants = 180,824), PE (RR = 1.28; 95% CI: 1.03, 1.59; I 2  = 80.0%; p  = 0.025; studies = 12; number of participants = 54,955), while no significant relationships were found for PTB, LBW and SGA infants. Importantly, a 100 g increment in UPFs intake was related to a 27% increase in GDM risk (RR = 1.27; 95% CI: 1.07, 1.51; I 2  = 81.0%; p  = 0.007; studies = 9; number of participants = 39,812). The non-linear dose-response analysis further indicated a positive, non-linear relationship between UPFs intake and GDM risk P nonlinearity  = 0.034, P dose-response  = 0.034), although no such relationship was observed for PE (P nonlinearity  = 0.696, P dose-response  = 0.812).

In summary, both prior to and during pregnancy, chronic and excessive intake of UPFs is associated with an increased risk of GDM and PE. However, further observational studies, particularly among diverse ethnic groups with precise UPFs consumption measurement tools, are imperative for a more comprehensive understanding.

Peer Review reports

Introduction

The Centers for Disease Control and Prevention’s (CDC’s) 2022 National Center for Health Statistics report alarmingly indicates a persistent rise in pregnancy-related mortality in the US across three decades, highlighting significant disparities in “race” and maternal age [ 1 ]. This trend underscores the pivotal role of addressing common pregnancy adverse outcomes as a critical component of maternal morbidity and mortality prevention strategies [ 2 ].

Promoting healthy dietary habits during pregnancy is imperative to meet the increased physiological needs of expectant mothers. The phenomenon of “nutritional transition”, characterized by a shift towards high-calorie, low-micronutrient foods, culminates in malnutrition and obesity [ 3 ]. The significance of maternal nutrition in prenatal care is heavily emphasized by researchers as a preventive measure against adverse pregnancy outcomes [ 4 ]. The consumption of diets rich in refined carbohydrates, fats, and sweets is linked to an increased risk of gestational diabetes mellitus (GDM) and preterm birth (PTB) [ 5 ]. Moreover, such dietary patterns adversely affect women’s health by exacerbating hypertensive disorders and contributing to conditions like preeclampsia (PE), low birth weight (LBW), and small-for-gestational-age (SGA) infants [ 6 ]. Recognizing the detrimental impact of these unhealthy dietary patterns, it becomes crucial to consider the role of food processing in the maternal diet.

The NOVA classification, a framework for grouping edible substances, categorizes foods into four groups based on the extent and purpose of food processing applied, ranging from unprocessed or minimally processed foods to ultra-processed foods (UPFs) [ 7 , 8 ]. UPFs are characterized by their high content of additives such as preservatives, artificial flavors, colors, and sweeteners, and are typically devoid of whole or minimally processed ingredients [ 9 ]. The consumption of UPFs has been associated with higher risks of obesity, hypertension, cancer, and other chronic diseases [ 8 , 10 , 11 , 12 ]. These foods are implicated in disrupting insulin signaling, promoting excessive energy intake, weight gain, and increased urinary concentrations of phthalate metabolites, which act as endocrine disruptors [ 13 , 14 ]. In the context of adverse pregnancy outcomes, recent meta-analytic work highlighted a heightened risk of GDM (odds ratio (OR): 1.48; 95% confidence interval (CI): 1.17, 1.87) and PE (OR: 1.28; 95% CI: 1.15, 1.42) among high UPFs consumers, with no significant associations observed in LBW, PTB, and Large for Gestational Age (LGA) [ 15 ]. However, the previous meta-analysis did not encompass a comprehensive set of extant studies for each adverse outcome (as evidenced by the inclusion of only 10 studies for GDM in contrast to the 44 studies incorporated in our current investigation), thereby underscoring the challenge posed by the unutilized data in previous analyses. Additionally, recent studies of relevance have emerged [ 16 , 17 , 18 ] and the preceding meta-analytic work did not include a dose-response analysis [ 15 ]. The integration of dose-response analysis offers benefits such as facilitating the formulation of public health directives, augmenting precision, and quantifying the dose-response relationship. Consequently, we decided to conduct an updated dose-response systematic review and meta-analysis to rigorously evaluate the association between UPFs consumption and common adverse pregnancy outcomes, including GDM, SGA, LBW, PTB, and PE.

This systematic review and meta-analysis was conducted according to the guidelines specified in the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 19 ]. The study protocol was registered with the International Prospective Register of Systematic Reviews Database (PROSPERO) under the registration number CRD42023486135.

Literature search and selection

A systematic literature search was done employing PubMed/MEDLINE, ISI Web of Science and Scopus, with no date restrictions, up to December 6, 2023. The search keywords and strategy are reported in Supplementary Table 1 . Data from grey literature sources such as notes, conference abstracts, reviews, case reports, letters, short surveys, and reports were obtained from a manual search of references mentioned in original research articles published in one of these databases. To augment the breadth of research identified, references within reviews and pertinent studies that met eligibility criteria were further subjected to manual examination.

Inclusion and exclusion criteria

Inclusion criteria were defined as follows: a) observational studies (cohort, case-control, or cross-sectional,) in adult subjects (≥18 years) reporting data on the association between UPFs intake and the risk of adverse pregnancy outcomes (including GDM PE, PTB, LBW, and SGA infants), and reporting effect estimates in the form of hazard ratio (HR), relative risk (RR), or odds ratios (OR), each with at least 95% confidence interval (95% CI). Exclusion criteria included: a) studies done in children and adolescents (< 18 years), b) studies without sufficient data (for instance, those failing to report effect sizes or 95% CIs, instead reporting beta coefficients), and c) those with no relevant exposure. Study titles and abstracts, as well as full-text reviews from database searches meeting the inclusion criteria, were assessed by two reviewers (ST and SM). Any disagreements regarding study inclusion/exclusion criteria were resolved by consensus following discussion. The PICOS tool for each article was reported in Supplementary Table 2 .

Data extraction

Two investigators (FJ and SM) extracted the following data, based on a standardized extraction form, from the studies that met the inclusion criteria: a) first author’s name, year of publication, and country of origin; b) study characteristics (design, follow-up period, and source of data on health status); c) participant characteristics (number of participants/cases, age and sex); d) methods of evaluating UPFs intake; e) the risk of adverse pregnancy outcomes; f) main study results (outcomes), and g) covariates utilized for adjustments in multivariate analyses. Any disagreement regarding data extraction characteristics was decided by consensus following the discussion.

Quality assessment

Applying the Newcastle-Ottawa Scale (NOS) [ 20 ], two investigators assessed the quality of each shortlisted study. The NOS was specifically chosen due to its comprehensive framework designed to evaluate the quality of non-randomized studies. This scale excels in its design, content, and user-friendliness, making it particularly suitable for integrating quality assessments into the interpretation of meta-analytic results. The NOS scale for systematic reviews or meta-analyses, allocating up to 9 points across three domains: study group selection (four points), study group comparability (two points), and exposure and outcomes ascertainment for case-control or cohort studies (three points). Studies scoring 7–9 are deemed high quality/low risk of bias, whereas a score of 0–3 indicates a high risk of bias. Table 1 shows the consensus from this assessment.

Statistical analyses and data synthesis

Statistical analyses were performed applying STATA version 14.0 (StataCorp, College Station, TX, USA) and SPSS version 25.0 (IBM, Armonk, NY, USA). The RR and 95% CI were established as overall effect sizes in this work, similar to effect estimates reported in observational articles meeting the inclusion criteria for this meta-analysis [ 21 ]. The synthesized effect estimates were reported as pooled RR with 95% CI. Due to anticipated heterogeneity between studies, effect estimates were calculated using the DerSimonian-Laird weighted random-effects model [ 22 ]. A pairwise meta-analysis combined the effect size results for the highest and lowest categories of UPFs intake. Heterogeneity among the articles was examined by the Cochran Q and I-squared (I 2 ) statistics, with the I 2 value estimated from [(Q-df)/Q × 100%]; where Q is the χ 2 value and df the corresponding degrees of freedom. Between-study heterogeneity was considered significant when the Cochran Q statistic was significant ( p  < 0.05) or if I 2  > 50%; specifically, low, moderate, high, and extreme heterogeneity was defined based on the I 2 statistics cut-offs of < 25%, 25–50%, 50–75%, and >75%, respectively.

Furthermore, subgroup analyses were conducted to evaluate any potential effects due to the study design (cross-sectional, case-control, or cohort), UPFs classification method (NOVA food classification, Western-type diet pattern, fast-food, or sweets consumption), the study region of origin (North America, South America, Asia, Europe, and Australia), pre-pregnancy body mass index (< 25 kg/m 2 and ≥ 25 kg/m 2 ) [ 23 , 24 ], age (< 30 years and ≥ 30 years) [ 24 ], number of cases (< 100 or ≥ 100), number of participants (< 1000 or ≥ 1000), dietary assessment method (food frequency questionnaires [FFQ], 24 h recall, or food records), dietary assessment period (pre-pregnancy, early pregnancy, mid-pregnancy), and other covariate adjustments. Sensitivity analysis was conducted by omitting each study and evaluating the remaining pooled effect estimates. Publication bias was evaluated by visual inspection of funnel plots, and formal testing using Egger’s regression asymmetry and Begg’s rank correlation tests [ 25 , 26 ], with outcomes considered as significant at p  < 0.05.

A dose-response meta-analysis was completed to estimate the RRs per 100 g increment in UPFs intake, based on the method introduced by Greenland and colleagues [ 27 , 28 ]. For this process, studies needed to report the number of cases (i.e., participants with incidence) and non-cases (i.e., participants without incidence) or person-years (i.e., the number of people in the study and the duration of their participation) as well as the median point of UPFs intake across more than three categories of intake. Ultimately, a one-stage linear mixed-effects meta-analysis was undertaken to model the dose-response associations, estimating and combining study-specific slope lines to obtain an average slope in a single stage. This linear mixed-effects meta-analysis includes studies with two categories of exposures (at least two effect sizes) in the dose-response analysis.

Quality of evidence

The quality of evidence across articles was ranked employing the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group guidelines. The GRADE criteria categorize evidence quality into high, moderate, low, or very low levels [ 29 ].

Study characteristics

Our systematic search and examination of reference lists yielded a total of 3433 records. After omitting duplicates, 2787 articles remained for assessment (Fig. 1 ). A title and abstract review led to the removal of 2707 articles. Subsequent full-text assessment of the 80 remaining studies resulted in the exclusion of a further 26 articles for the following reasons: five articles reported outcomes not relevant to our research scope, six lacked sufficient data, and 15 did not focus on relevant exposure (Supplemental Table 3 ). Consequently, 54 studies met our inclusion criteria and were selected in the present meta-analysis [ 16 , 17 , 18 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ].

Flow chart of the process of the study selection

The selected studies (detailed in Supplemental Table 4 ) encompass 38 cohort studies [ 16 , 31 , 33 , 34 , 35 , 36 , 37 , 39 , 40 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 51 , 52 , 54 , 55 , 57 , 58 , 59 , 60 , 61 , 62 , 65 , 66 , 67 , 69 , 70 , 71 , 73 , 74 , 75 , 78 ], 11 case-control studies [ 17 , 18 , 30 , 32 , 34 , 50 , 63 , 66 , 68 , 72 , 77 ], and five cross-sectional studies [ 38 , 46 , 64 , 76 , 79 ]. These articles, conducted between 1988 and 2023, originated from different countries including the USA [ 33 , 36 , 53 , 58 , 60 , 62 , 69 , 74 , 78 ], the UK [ 16 ], China [ 43 , 49 , 51 , 71 , 73 ], Brazil [ 31 , 59 , 63 , 64 , 68 , 79 ], Spain [ 39 , 40 , 42 , 55 , 57 ], Iran [ 17 , 18 , 30 , 32 , 48 , 54 , 66 , 76 , 77 ], Malaysia [ 75 ], Palestine [ 72 ], Australia [ 45 , 46 , 65 ], Singapore [ 37 , 38 ], Norway [ 35 , 44 , 47 ], Japan [ 41 , 67 ], Czech Republic [ 34 ], Iceland [ 70 ] and Denmark [ 61 ]. The study-specific, maximally adjusted RR was reported for 552,686 individuals across the included articles and was pooled for meta-analysis to assess the association between UPFs and the risk GDM [ 16 , 32 , 33 , 34 , 36 , 38 , 39 , 40 , 41 , 43 , 48 , 49 , 50 , 51 , 53 , 54 , 55 , 56 , 59 , 60 , 64 , 65 , 66 , 70 , 71 , 72 , 73 , 74 , 75 , 77 , 78 , 79 ], PE [ 17 , 18 , 30 , 35 , 48 , 52 , 62 , 69 , 74 , 76 ], PTB [ 31 , 37 , 44 , 45 , 46 , 48 , 52 , 58 , 61 , 67 ], LBW [ 45 , 63 , 67 ] and SGA infants [ 46 , 67 , 68 ]. The Newcastle-Ottawa grade (used for quality assessment) categorized 27 studies as high quality [ 17 , 33 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 47 , 51 , 53 , 55 , 57 , 58 , 60 , 62 , 65 , 67 , 69 , 74 , 75 , 78 ] and 27 as medium quality [ 16 , 18 , 30 , 31 , 32 , 34 , 38 , 46 , 48 , 49 , 50 , 52 , 54 , 56 , 59 , 61 , 63 , 64 , 66 , 68 , 70 , 71 , 72 , 73 , 76 , 77 , 79 ]. Moreover, the outcomes revealed that the level of agreement between investigators for data collection as well as for quality assessment was appropriate (Kappa = 0.897).

Ultra-processed food and common adverse pregnancy outcomes

Our results suggested a significant relationship between higher UPF intake and an increased risk of GDM (RR = 1.19; 95% CI: 1.10, 1.27; I 2  = 77.5%; p  < 0.001; n  = 44), PE (RR = 1.28; 95% CI: 1.03, 1.59; I 2  = 80.0%; p  = 0.025; n  = 12), but not PTB (RR = 1.06; 95% CI: 0.97, 1.17; I 2  = 34.2%; p  = 0.231; n  = 8), LBW (RR = 1.01; 95% CI: 0.91, 1.12; I 2  = 52.2%; p  = 0.905; n = 4) and SGA infants (RR = 1.11; 95% CI: 0.81, 1.52; I 2  = 66.3%; p  = 0.532; n  = 3), (Refer to Table 1 , Supplementary Fig. 1 ).

In the context of GDM, subgroup analysis showed that a greater UPFs intake was significantly associated with an enhanced risk in cohort studies (vs. cross-sectional) (RR = 1.18; 95% CI: 1.09, 1.27; I 2  = 79.3%; p  < 0.001; n  = 31) and case-control studies (RR = 2.06; 95% CI: 1.31, 3.35; I 2  = 77.7%; p  = 0.002; n  = 10), particularly in studies assessed western dietary pattern (RR = 1.34; 95% CI: 1.01, 1.76; I 2  = 43.0%; p  = 0.040; n  = 7) or fast-foods (RR = 1.32; 95% CI: 1.15, 1.51; I 2  = 79.3%; p  < 0.001; n  = 22), (vs. NOVA classification or sweets consumption), in North America (vs. Europe, South America, Asia and Australia) (RR = 1.43; 95% CI: 1.27, 1.53; I 2  = 45.4%; p  < 0.001; n = 10), and across studies with > 100 number of case (RR = 1.38; 95% CI: 1.21, 1.58; I 2  = 74.8%; p < 0.001; n  = 12)(vs. < 100 number of case), in studies with > 1000 number of participants (RR = 1.33; 95% CI: 1.15, 1.54; I 2  = 76.9%; p < 0.001; n  = 21)(vs. < 1000 number of participants), in studies used FFQ for dietary assessment (RR = 1.27; 95% CI: 1.14, 1.43; I 2  = 78.6%; p < 0.001; n  = 34) (vs. 24 h recall or food record), particularly in studies where the period of dietary assessment was at early pregnancy (RR = 1.26; 95% CI: 1.09, 1.46; I 2  = 80.5%; p  = 0.002; n  = 19) (vs. pre-pregnancy or mid-pregnancy). Moreover, subgroup analysis for covariates adjustment showed that BMI and physical activity may influence the association between UPF intake and the risk of GDM (Table 2 ).

For PE, the subgroup analysis also highlighted that greater UPFs intake was significantly associated with an enhanced risk in studies assessed western dietary pattern (RR = 2.51; 95% CI: 1.13, 5.57; I 2  = 91.1%; p  = 0.023; n  = 3) or NOVA classification (RR = 1.22; 95% CI: 1.04, 1.42; I 2  = 0.0%; p  = 0.013; n = 3), (vs. sweets consumption), in Asia (vs. Europe or US areas) (RR = 1.65; 95% CI: 1.07, 2.55; I 2  = 86.1%; p  < 0.001; n  = 6), and across studies with > 100 number of case (RR = 1.57; 95% CI: 1.03, 2.40; I 2  = 93.2%; p < 0.001; n  = 4)(vs. < 100 number of case), in studies with number of < 1000 participants (RR = 1.65; 95% CI: 1.07, 2.55; I 2  = 86.1%; p = 0.023; n = 6)(vs. > 1000 number of participants), in participants aged ≥30 years (RR = 1.28; 95% CI: 1.07, 1.54; I 2  = 50.4%; p  = 0.089; n  = 5)(vs. participants aged < 30 years), in participants with pre-pregnancy-BMI > 25 (RR = 1.52; 95% CI: 1.07, 2.15; I 2  = 84.7%; p  = 0.021; n  = 1)(vs. participants with pre-pregnancy-BMI ≤ 25), in studies used FFQ for dietary assessment (RR = 1.38; 95% CI: 1.10, 1.72; I 2  = 82.6%; p  = 0.005; n  = 10) (vs. questions), and particularly in studies where the period of dietary assessment was at mid-pregnancy (RR = 1.23; 95% CI: 1.05, 1.43; I 2  = 38.8%; p  = 0.009; n  = 3) (vs. early pregnancy). Furthermore, subgroup analysis for covariates adjustment showed that BMI and physical activity may influence the association between UPF intake and the risk of PE (Table 3 ).

Linear and non-linear dose-response analysis

The linear dose-response analysis (refer to Table 1 and Fig. 2 ) indicates a 27% increase in GDM risk per 100 g increment in UPF intake RR = 1.27; 95% CI: 1.07, 1.51; I 2  = 81.0%; p  = 0.007; n  = 9). However, the linear dose-response analysis for other outcomes was not undertaken due to the limited number of studies available.

Forest plots showing the linear dose-response meta-analysis of mortality risk for 100 g change in ultra-processed food consumption in daily intake and risk of gestational diabetes mellitus

The non-linear dose-response analysis revealed a positive non-linear relationship between UPFs intake and GDM risk (P nonlinearity  = 0.034, P dose-response  = 0.034, Fig. 3 ), but not for PE (P nonlinearity  = 0.696, P dose-response  = 0.812, Fig. 4 ). The non-linear dose-response analysis was not conducted for other outcomes due to insufficient studies.

Non-linear dose-response indicated associations between UPF intake and the risk of gestational diabetes mellitus

Non-linear dose-response indicated associations between UPF intake and the risk of preeclampsia

Sensitivity analyses and publication bias

Sensitivity analysis across the highest to the lowest meta-analysis for GDM, PE, PTB, LBW and SGA infants showed no significant influence of any single study (Supplemental Fig. 2 ).

No evidence of publication bias was found in articles related to the association with an increased risk of PE ( p  = 0.529, Egger’s test; p  = 0.891, Begg’s), PTB ( p  = 0.458, Egger’s test; p  = 0.473, Begg’s), LBW ( p  = 0.905, Egger’s test; p  = 1.00, Begg’s test), and SGA infants ( p  = 0.348, Egger’s test; p = 1.00, Begg’s test). Although, for GDM, Egger’s test indicated potential publication bias ( p  < 0.001), not corroborated by Begg’s test ( p  = 0.241). As illustrated in Supplemental Fig. 3 , the funnel plot was symmetrical for the association between the UPFs intake and all outcomes, except for studies that reported the risk of GDM disease.

Utilizing the GRADE scale for quality evaluation, we detected the evidence for associations between UPFs intake and risk of GDM, PE, PTB, LBW and SGA infants was classified as moderate (Refer to Table 1 ).

In the realm of maternal and fetal health, the quality of dietary intake during pregnancy is of paramount significance. Accumulating evidence suggests a correlation between the consumption of UPFs and the deterioration of diet quality, potentially elevating the risk of various health complications [ 80 , 81 , 82 ]. This systematic review and meta-analysis aimed to elucidate the relationship between UPFs intake and adverse pregnancy outcomes including GDM, SGA, LBW, PTB, and PE, through an integrative analysis of existing studies. Our outcomes indicate a significant association between UPFs consumption and increased risks of PE, and GDM either prior to or during pregnancy. However, no significant association was found between UPFs intake and the risks of LBW, SGA, and PTB. Importantly, a 27% increment in the incidence of GDM was linked to a 100 g increase in UPF intake. Furthermore, a positive, non-linear relationship between UPF intake and GDM risk was identified through non-linear dose-response analysis, albeit no analogous association was found for PE.

The results of the current work showed a positive association between UPFs consumption and the risk of PE. In addition, subgroup analysis revealed this relationship to be more pronounced in studies using the NOVA-food classification and a Western dietary pattern for UPFs intake assessment, compared to those focusing on sweet intake. The NOVA classification categorizes foods based on the extent of processing, encompassing various UPFs. Moreover, the association between UPFs consumption and the risk of PE was significant in studies conducted in Asia (vs other regions). Prevalence of PE varies globally, ranging from 0.2–6.7% in Asia, 2.8–9.2% in Oceania, 2.8–5.2% in Europe, 2.6–4.0% in North America, and 1.8–7.7% in South America and the Caribbean [ 83 ]. However, the high heterogeneity in Asian studies should be noted when interpreting this result. Furthermore, a significant association was observed in studies involving women aged 30 years or older, aligning with the increased PE risk associated with advanced maternal age [ 84 ]. Additionally, a significant association was identified between PE risk and UPFs intake in women with pre-pregnancy BMI higher than 25 kg/m 2 (vs BMI ≤25). This aligns with previous findings linking excessive weight gain in expectant mothers to an elevated PE risk, with overweight and obese mothers facing substantially higher risks [ 85 ].

The association between UPFs intake and PE can be elucidated through several mechanisms. The risk factors for PE, including GDM, maternal obesity, and advanced maternal age, are extensively documented in the literature [ 84 , 85 , 86 ]. It has been established that adopting healthy lifestyle habits (including dietary patterns) can mitigate these risk factors [ 87 ]. A higher intake of UPFs is associated with a diminished dietary quality, marked by an increased consumption of sugars and fats, alongside a decrease in fiber, protein, vitamins, and minerals [ 88 , 89 ]. UPFs are known to contain elevated levels of pro-inflammatory agents such as refined sugars, salt, and trans fats. The ingestion of these inflammatory components can precipitate oxidative stress and systemic inflammation [ 90 , 91 , 92 ], which are implicated in the pathogenesis of preeclampsia [ 93 , 94 ]. Additionally, the presence of trans fatty acids, added phosphates, and a high salt content in UPFs may impair endothelial function [ 95 , 96 , 97 ], a critical factor in the pathophysiology of hypertension observed in preeclampsia [ 98 ]. Furthermore, the intake of UPFs can alter the composition and diversity of the gut microbiota [ 99 ]. Studies have shown that food additives commonly found in UPFs, such as emulsifiers, sweeteners, and colorants, adversely affect the gut flora [ 100 ]. The interplay between the gut microbiota and the placenta, referred to as the “gut–placenta” axis, is crucial for understanding the etiology of PE. Dysbiosis of the gut microbiota and bacterial products like lipopolysaccharide (LPS) have been identified as promotive of PE [ 101 , 102 ]. According to Kell et al., microbial infection, particularly through bacterial products such as LPS (also known as endotoxin), which is highly inflammatory, can initiate an innate immune response that exacerbates inflammation [ 103 ]. Hence, it is hypothesized that dysbiosis induced by UPFs consumption may play a significant role in the promotion of preeclampsia. Moreover, UPFs intake is positively associated with a risk of obesity [ 104 ], a condition marked by insulin resistance and hyperinsulinemia, crucial factors in PE development [ 105 ]. Pregnant women with obesity and PE exhibit higher leptin levels, correlating with increased Tumor Necrosis Factor-Alfa (TNF-α), Interleukin 6 (IL-6), and C-reactive protein concentrations [ 106 , 107 ]. Additionally, excessive adipose tissue near the reproductive tract is the source of increased complement components and fragments in preeclamptic pregnancies. These complement proteins may promote an imbalance in angiogenic factors (characterized by increased production of antiangiogenic factors and a decrease in proangiogenic factors). This imbalance leads to placental injury, resulting in decreased blood flow to the tissue, and is accompanied by changes in cytokines levels (decreased IL-10 and increased TNF-α) before the onset of PE [ 108 ].

Our pooled analysis also revealed that higher UPFs intake was related to an increased risk of GDM. This association was significant in studies employing cohort and case-control designs (as opposed to those with cross-sectional methodologies). The inherent recall bias in cross-sectional studies that rely on self-reporting, is a notable limitation affecting the reliability of outcomes [ 109 ]. Moreover, this association was more pronounced in studies that used Western dietary patterns and fast-food consumption for the assessment of UPFs intake (vs those employing NOVA food classification and sweet consumption metrics). The concept of a dietary pattern, which represents the aggregate of eating and drinking habits, is critical as it exerts a greater impact on health and chronic diseases than any individual food item [ 110 ]. Additionally, the application of the NOVA food classification in existing studies is less frequent, suggesting the need for further research utilizing this methodology to derive more meaningful results. Geographical variations were also evident, with significant associations observed in studies conducted in America, compared to those in Asia and Europe. This is in context with the differing regional prevalences of GDM: 7.1% in North America and the Caribbean, 7.8% in Europe and 20.8% in South-East Asia [ 111 ]. Despite the higher prevalence of GDM in Asian populations, the greater intake of UPFs in American and European cohorts may have influenced the study outcomes [ 112 , 113 , 114 , 115 ]. Additionally, a positive association between UPFs intake and GDM risk was observed in studies focusing on women with a pre-pregnancy BMI > 25. Previous research indicates that being overweight or obese before and during pregnancy is a significant risk factor for GDM [ 116 , 117 , 118 ]. However, the scarcity of studies in women with pre-pregnancy BMI > 25 kg/m 2 kg/m suggests the need for more research in this demographic for robust conclusions.

Our outcomes also indicated that a 100 g increase in UPF intake was associated with a 27% increase in the risk of GDM. Moreover, the non-linear dose-response analysis similarly showed a positive, non-linear association between the consumption of UPFs and the risk of GDM. These findings underscore the significant impact that UPF consumption can have on GDM risk. The evidence points towards a robust and worrying correlation where even moderate increases in UPF intake can precipitate a marked rise in GDM risk, highlighting the critical need for dietary awareness and intervention among pregnant women. This aligns with broader nutritional science, emphasizing the importance of minimizing UPF consumption to mitigate not only GDM risk but potentially other metabolic disorders as well, given the multitude of adverse mechanisms through which UPFs affect glucose metabolism and insulin sensitivity.

Pathophysiologically, UPFs intake may increase GDM risk through several mechanisms. In pregnant women with GDM, pre-pregnancy reduced insulin sensitivity and β-cell dysfunction lead to hyperglycemia [ 119 , 120 ]. The hypothesis that excessive sugar intake may augment body mass, thereby indirectly precipitating insulin resistance and subsequent diabetes, is widely recognized. Moreover, the liver’s capacity to assimilate and metabolize refined sugars prevalent in UPFs (such as fructose and sucrose) may be compromised, leading to augmented fat deposition and deteriorated insulin sensitivity [ 121 ]. Furthermore, insulin resistance may be induced by cosmetic ingredients present in UPFs. For example, dietary additives like carrageenan, employed as a thickening and stabilizing agent, may interfere with insulin signaling and thus foster insulin resistance [ 122 ]. Additionally, UPFs intake correlates with increased production of reactive oxygen species and inflammatory biomarkers [ 123 ], inducing insulin resistance through molecular pathways such as β-cell and mitochondrial dysfunction, decreased GLUT4 expression, impaired insulin signaling and heightened inflammatory responses [ 124 ]. Furthermore, UPFs often contain packaging materials like phthalates and bisphenol A, known to have endocrine disruption properties that may contribute to insulin resistance and diabetes development [ 125 , 126 ]. The ingestion of substantial quantities of UPFs also elevates inflammation, a pivotal factor in the genesis of insulin resistance, culminating in hyperglycemia and the development of GDM [ 127 ]. A diet replete with saturated fats, trans fats, sugars, and salt, characteristic of high UPFs consumption, may contribute to chronic inflammation [ 128 ]. Furthermore, excessive UPFs consumption may supplant essential components of a balanced and nutritious diet. For instance, fruits and vegetables are associated with an anti-inflammatory effect [ 129 ]. In addition, the leaching of chemicals from food packaging into UPFs could introduce non-nutritional elements such as phthalates or bisphenol A, potentially eliciting an inflammatory response [ 130 ].

The present study did not establish a significant association between UPFs consumption and the risk of LBW. This result may be attributable to several factors. Firstly, a limited number of studies have evaluated the association between UPFs intake and LBW risk. Additionally, the intake of high-sugar foods (such as sugar-sweetened beverages) has been correlated with an increased risk of LBW in non-GDM subjects [ 34 , 40 ]. This could be attributed to impaired fetal nutrition due to reduced vascular function, potentially induced by oxidative stress, inflammation, and endothelial dysfunction associated with high sugar consumption [ 131 ]. However, in GDM subjects this association may not be found due to the higher glucose loads in the fetus [ 47 ]. Therefore, additional research is warranted in both GDM and non-GDM populations to elucidate these relationships comprehensively.

Moreover, SGA was not associated with the intake of UPFs according to the pooled analysis of conducted studies. Although additional studies are necessary to explore this relationship further, existing evidence suggests that a fast-food dietary pattern may lead to increased fat intake and a reduced intake of essential micronutrients crucial for fetal development [ 132 ]. Maternal UPFs intake is linked to lower protein intake, reduced overall nutrition quality, and higher intake of trans fats, carbohydrates and saturated fats, which may increase the risk of neonatal adiposity [ 133 , 134 , 135 ]. Furthermore, higher fast-food intake during pregnancy has been linked with an elevated risk of maternal obesity, which in turn, may increase the likelihood of LGA babies [ 132 , 136 ].

Regarding PTB, the current study found no association with UPFs consumption. Previous research has indicated that dietary patterns rich in fruits and vegetables are associated with a lower risk of PTB [ 37 , 45 ]. Inadequate nutrition before and during pregnancy can lead to health issues for both the mother and fetus, increasing the risk of preterm delivery and intrauterine growth retardation [ 137 ]. The absence of an association between UPFs consumption and preterm delivery in this study could be due to various factors, including the need for a higher UPFs consumption threshold during pregnancy to manifest negative impacts on preterm birth. Additionally, the varied diet of pregnant women, typically including beneficial foods such as fruits, vegetables and nuts, may mitigate the adverse effects of UPFs.

The current investigation has several crucial strengths that make its findings highly significant. Firstly, by pooling all available observational data on the topic, the study provides a comprehensive and robust analysis of the relationship between UPFs intake and adverse pregnancy outcomes. Secondly, the study’s use of a dose-response analysis adds further weight to its conclusions and bolsters our understanding of the link between these two factors. However, there are limitations to consider. These include potential information and recall biases due to the self-reported nature of dietary intake assessments (such as the FFQ) and the absence of specific dietary tools for assessing UPFs consumption. Additionally, this meta-analysis included studies that did not use NOVA’s specialized dietary assessments. Moreover, dietary changes following pregnancy discovery could affect results, and the observational nature of the included studies precludes causal inference. Despite the inclusion of numerous confounding variables, several factors must be cautiously considered in the interpretation of the research findings. For instance, the socio-economic status of participants influences their dietary habits, while race and ethnicity may affect pregnancy outcomes. Furthermore, disparities in access to healthcare services can impact dietary choices and pregnancy outcomes. Other health statuses, such as mental health conditions among pregnant subjects, also influence dietary selections and pregnancy results [ 138 , 139 ]. Finally, the availability of data on broader categories such as diabetes in pregnancy and hypertensive disorders was limited, hindering our ability to conduct a comprehensive analysis on these broader categories.

Our outcomes indicate that prior to or during pregnancy, UPFs intake is associated with a higher risk of GDM and PE. However, no significant link tying UPFs intake to SGA, LWB and PTB was established. Importantly, a 100 g increment in UPFs intake was related to a 27% increase in GDM risk. This study aligns with global trends, where a rise in adverse pregnancy outcomes seems to align with the escalation of industrialization and the corresponding surge in UPFs production and consumption. Investigating the potential linkage between UPFs intake and the rise of adverse pregnancy outcomes may help in the development of nutrition-centric policies for expecting mothers and promote more health-conscious decision-making. To further substantiate these findings, extensive empirical research is required. Future studies should encompass observational research across diverse ethnic groups. Moreover, the adoption of more precise tools for measuring UPFs consumption is imperative. In observational research, it may be challenging to ascertain whether the consumption of UPFs directly contributes to adverse pregnancy outcomes or if it serves as an indicator of other underlying factors. Components of UPFs may escalate the risk of negative pregnancy outcomes. Moreover, UPF consumption could be part of a complex interplay affecting other variables that directly result in adverse outcomes. For instance, UPF intake could influence gestational weight gain, potentially leading to insulin resistance, which is known to correlate with unfavorable pregnancy outcomes, including GDM. Additionally, the consumption of UPFs may not only diminish dietary quality but also be linked with various lifestyle and dietary factors, such as poor diet quality, thereby increasing the risk of adverse pregnancy outcomes [ 140 ]. The bidirectional correlation between UPF consumption and unfavorable pregnancy outcomes also merits consideration. For example, individuals experiencing depression or other health conditions might alter their dietary patterns to include a higher intake of UPFs [ 138 ]. Evaluating changes in UPF consumption over time, utilizing precise questionnaires that assess food items classified as UPFs according to the NOVA food classification system, and their association with other health outcomes affecting pregnancy, such as obesity, could offer insights into this relationship. Considering these approaches is essential to enhance the depth and accuracy of investigations into the potential association between UPFs intake and the incidence of adverse pregnancy outcomes.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due but are available from the corresponding author on reasonable request.

Hoyert DL, Miniño AM. Maternal Mortality in the United States: Changes in Coding, Publication, and Data Release, 2018. Natl Vital Stat Rep. 2020;69(2):1–18.

PubMed   Google Scholar  

Parikh NI, Gonzalez JM, Anderson CA, Judd SE, Rexrode KM, Hlatky MA, et al. Adverse pregnancy outcomes and cardiovascular disease risk: unique opportunities for cardiovascular disease prevention in women: a scientific statement from the American Heart Association. Circulation. 2021;143(18):e902–e16.

Article   CAS   PubMed   Google Scholar  

Hashmi AH, Solomon N, Lee SJ, Min AM, Gilder ME, Wiladphaingern J, et al. Nutrition in transition: historical cohort analysis summarising trends in under-and over-nutrition among pregnant women in a marginalised population along the Thailand–Myanmar border from 1986 to 2016. Br J Nutr. 2019;121(12):1413–23.

Lind JM, Hennessy A, McLean M. Cardiovascular disease in women: the significance of hypertension and gestational diabetes during pregnancy. Curr Opin Cardiol. 2014;29(5):447–53.

Article   PubMed   Google Scholar  

Englund-Ögge L, Birgisdottir BE, Sengpiel V, Brantsæter AL, Haugen M, Myhre R, et al. Meal frequency patterns and glycemic properties of maternal diet in relation to preterm delivery: results from a large prospective cohort study. PLoS One. 2017;12(3):e0172896.

Article   PubMed   PubMed Central   Google Scholar  

Mekie M, Mekonnen W, Assegid M. Cohabitation duration, obstetric, behavioral and nutritional factors predict preeclampsia among nulliparous women in West Amhara zones of Ethiopia: age matched case control study. PLoS One. 2020;15(1):e0228127.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Monteiro CA, Cannon G, Levy R, Moubarac J-C, Jaime P, Martins AP, et al. NOVA The star shines bright. World Nutrition. 2016;7(1–3):28–38.

Google Scholar  

da Costa Louzada ML, Baraldi LG, Steele EM, Martins APB, Canella DS, Moubarac J-C, et al. Consumption of ultra-processed foods and obesity in Brazilian adolescents and adults. Prev Med. 2015;81:9–15.

Article   Google Scholar  

Monteiro CA, Levy RB, Claro RM, Castro IRR, Cannon G. A new classification of foods based on the extent and purpose of their processing. Cadernos de saude publica. 2010;26:2039–49.

Mendonça RD, Pimenta AM, Gea A, de la Fuente-Arrillaga C, Martinez-Gonzalez MA, Lopes ACS, et al. Ultraprocessed food consumption and risk of overweight and obesity: the University of Navarra Follow-up (SUN) cohort study. Am J Clin Nutr. 2016;104(5):1433–40.

Mendonça RD, Lopes ACS, Pimenta AM, Gea A, Martinez-Gonzalez MA, Bes-Rastrollo M. Ultra-processed food consumption and the incidence of hypertension in a Mediterranean cohort: the Seguimiento Universidad de Navarra project. Am J Hypertens. 2017;30(4):358–66.

Fiolet T, Srour B, Sellem L, Kesse-Guyot E, Allès B, Méjean C, Deschasaux M, Fassier P, Latino-Martel P, Beslay M, Hercberg S, Lavalette C, Monteiro CA, Julia C, Touvier M. Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ. 2018;360:k322. https://doi.org/10.1136/bmj.k322 .

Bhattacharyya S, Feferman L, Tobacman JK. Carrageenan inhibits insulin signaling through GRB10-mediated decrease in Tyr (P)-IRS1 and through inflammation-induced increase in Ser (P) 307-IRS1. J Biol Chem. 2015;290(17):10764–74.

Buckley JP, Kim H, Wong E, Rebholz CM. Ultra-processed food consumption and exposure to phthalates and bisphenols in the US National Health and nutrition examination survey, 2013–2014. Environ Int. 2019;131:105057.

Paula WO, Patriota ES, Gonçalves VS, Pizato N. Maternal consumption of ultra-processed foods-rich diet and perinatal outcomes: a systematic review and meta-analysis. Nutrients. 2022;14(15):3242.

Mahendra A, Kehoe SH, Kumaran K, Krishnaveni GV, Arun N, Pidaparthy P, et al. Periconceptional diet and the risk of gestational diabetes in south Indian women: findings from the BAngalore nutrition gestational diabetes LiFEstyle study (BANGLES). Lancet Glob Health. 2023;11:S4.

Kooshki A, Sovizi B, Mahmoudi R, Ghezi S, Foroumandi E. The association between food groups and preeclampsia: a case-control study. Hyperten Pregnancy. 2022;41(1):64–9.

Article   CAS   Google Scholar  

Moradi M, Niazi A, Selajgeh F, Mazloumi E. Comparing dietary patterns during pregnancy in women with preeclampsia and healthy women: a case-control study. J Midwife Reproduct Health. 2022;10(1)

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.

Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Oxford: Ottawa Hospital Research Institute; 2000.

Symons M, Moore D. Hazard rate ratio and prospective epidemiological studies. J Clin Epidemiol. 2002;55(9):893–9.

DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.

Jayedi A, Soltani S, Zargar MS, Khan TA, Shab-Bidar SJB. Central fatness and risk of all cause mortality: systematic review and dose-response meta-analysis of 72 prospective cohort studies. 2020;370.

Song C, Lyu Y, Li C, Liu P, Li J, Ma R, et al. Long-term risk of diabetes in women at varying durations after gestational diabetes: a systematic review and meta-analysis with more than 2 million women 2018;19(3):421–9.

Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088–101.

Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ (Clin res ed). 1997;315(7109):629–34.

Berlin JA, Longnecker MP, Greenland S. Meta-analysis of epidemiologic dose-response data. Epidemiology. 1993;4(3):218–28. https://doi.org/10.1097/00001648-199305000-00005 .

Orsini N, Bellocco R, Greenland SJTsj. Generalized least squares for trend estimation of summarized dose–response data 2006;6(1):40–57.

Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. Bmj. 2008;336(7650):924–6.

Abbasi R, Bakhshimoghaddam F, Alizadeh M. Major dietary patterns in relation to preeclampsia among Iranian pregnant women: a case–control study. J Matern Fetal Neonatal Med. 2021;34(21):3529–36.

Alves-Santos NH, Cocate PG, Benaim C, Farias DR, Emmett PM, Kac G. Prepregnancy dietary patterns and their association with perinatal outcomes: a prospective cohort study. J Acad Nutr Diet. 2019;119(9):1439–51.

Asadi M, Shahzeidi M, Nadjarzadeh A, Hashemi Yusefabad H, Mansoori A. The relationship between pre-pregnancy dietary patterns adherence and risk of gestational diabetes mellitus in Iran: a case–control study. Nutr Diet. 2019;76(5):597–603.

Bao W, Tobias DK, Olsen SF, Zhang C. Pre-pregnancy fried food consumption and the risk of gestational diabetes mellitus: a prospective cohort study. Diabetol. 2014;57(12):2485–91.

Bartáková V, Kuricová K, Zlámal F, Bělobrádková J, Kaňková K. Differences in food intake and genetic variability in taste receptors between Czech pregnant women with and without gestational diabetes mellitus. Eur J Nutr. 2018;57(2):513–21.

Brantsæter AL, Haugen M, Samuelsen SO, Torjusen H, Trogstad L, Alexander J, et al. A dietary pattern characterized by high intake of vegetables, fruits, and vegetable oils is associated with reduced risk of preeclampsia in nulliparous pregnant Norwegian women. J Nutr. 2009;139(6):1162–8.

Chen LW, Hu FB, Yeung E, Willett W, Zhang CL. Prospective study of pre-gravid sugar-sweetened beverage consumption and the risk of gestational diabetes mellitus. Diabetes Care. 2009;32(12):2236–41.

Chia AR, de Seymour JV, Colega M, Chen LW, Chan YH, Aris IM, et al. A vegetable, fruit, and white rice dietary pattern during pregnancy is associated with a lower risk of preterm birth and larger birth size in a multiethnic Asian cohort: the growing up in Singapore towards healthy outcomes (GUSTO) cohort study. Am J Clin Nutr. 2016;104(5):1416–23.

de Seymour J, Chia AR, Colega M, Jones B, McKenzie E, Cai SR, et al. Maternal dietary patterns and gestational diabetes mellitus in a multi-ethnic Asian cohort: the GUSTO study. Nutrients. 2016;8(9)

Dominguez LJ, Martinez-Gonzalez MA, Basterra-Gortari FJ, Gea A, Barbagallo M, Bes-Rastrollo M. Fast food consumption and gestational diabetes incidence in the SUN project. PLoS One. 2014;9(9):e106627.

Donazar-Ezcurra M, Lopez-del Burgo C, Martinez-Gonzalez MA, Basterra-Gortari FJ, de Irala J, Bes-Rastrollo M. Soft drink consumption and gestational diabetes risk in the SUN project. Clin Nutr. 2018;37(2):638–45.

Dong JY, Kimura T, Ikehara S, Cui M, Kawanishi Y, Yamagishi K, et al. Chocolate consumption and risk of gestational diabetes mellitus: the Japan environment and Children's study. Br J Nutr. 2019;122(8):936–41.

Drozd-Dąbrowska M, Trusewicz R, Ganczak M. Selected risk factors of developmental delay in polish infants: a case-control study. Int J Environ Res Public Health. 2018;15(12)

Du HY, Jiang H, Karmin O, Bo C, Xu LJ, Liu SP, et al. Association of dietary pattern during pregnancy and gestational diabetes mellitus: a prospective cohort study in northern China. Biomed Environ Sci. 2017;30(12):887–97.

CAS   PubMed   Google Scholar  

Englund-Ögge L, Brantsæter AL, Sengpiel V, Haugen M, Birgisdottir BE, Myhre R, et al. Maternal dietary patterns and preterm delivery: Results from large prospective cohort study. BMJ (Online). 2014;348

Gete DG, Waller M, Mishra GD. Prepregnancy dietary patterns and risk of preterm birth and low birth weight: findings from the Australian longitudinal study on Women's health. Am J Clin Nutr. 2020;111(5):1048–58.

Grieger JA, Grzeskowiak LE, Clifton VL. Preconception dietary patterns in human pregnancies are associated with preterm delivery. J Nutr. 2014;144(7):1075–80.

Grundt JH, Eide GE, Brantsæter AL, Haugen M, Markestad T. Is consumption of sugar-sweetened soft drinks during pregnancy associated with birth weight? Mater Child Nutrit. 2017;13(4):e12405.

Hajianfar H, Esmaillzadeh A, Feizi A, Shahshahan Z, Azadbakht L. The association between major dietary patterns and pregnancy-related complications. Archiv Iran Med. 2018;21(10):443–51.

Hehua Z, Yang X, Qing C, Shanyan G, Yuhong Z. Dietary patterns and associations between air pollution and gestational diabetes mellitus. Environ Int. 2021;147

Hezaveh ZS, Feizy Z, Dehghani F, Sarbakhsh P, Moini A, Vafa M. The association between maternal dietary protein intake and risk of gestational diabetes mellitus. International journal of. Prev Med. 2019;10(1)

Hu JJ, Oken E, Aris IM, Lin PID, Ma YA, Ding N, et al. Dietary patterns during pregnancy are associated with the risk of gestational diabetes mellitus: evidence from a Chinese prospective birth cohort study. Nutrients. 2019;11(2)

Ikem E, Halldorsson TI, Birgisdóttir BE, Rasmussen MA, Olsen SF, Maslova E. Dietary patterns and the risk of pregnancy-associated hypertension in the Danish National Birth Cohort: a prospective longitudinal study. BJOG: An Int J Obstetr Gynaecol. 2019;126(5):663–73.

Kahr MK, Suter MA, Ballas J, Ramin SM, Monga M, Lee W, et al. Geospatial analysis of food environment demonstrates associations with gestational diabetes. Am J Obstet Gynecol. 2016;214(1):110.e1-.e9.

Lamyian M, Hosseinpour-Niazi S, Mirmiran P, Banaem LM, Goshtasebi A, Azizi F. Pre-pregnancy fast food consumption is associated with gestational diabetes mellitus among tehranian women. Nutrients. 2017;9(3)

Leone A, Martínez-González M, Craig W, Fresán U, Gómez-Donoso C, Bes-Rastrollo M. Pre-gestational consumption of ultra-processed foods and risk of gestational diabetes in a Mediterranean cohort. The SUN project. Nutrients. 2021;13(7)

Lotfi MH, Fallahzadeh H, Rahmanian M, Hosseinzadeh M, Lashkardoost H, Doaei S, et al. Association of food groups intake and physical activity with gestational diabetes mellitus in Iranian women. J Mater -Fetal Neonatal Med. 2020;33(21):3559–64.

Marí-Sanchis A, Díaz-Jurado G, Basterra-Gortari FJ, de la Fuente-Arrillaga C, Martínez-González MA, Bes-Rastrollo M. Association between pre-pregnancy consumption of meat, iron intake, and the risk of gestational diabetes: the SUN project. Eur J Nutr. 2018;57(3):939–49.

Martin CL, Sotres-Alvarez D, Siega-Riz AM. Maternal dietary patterns during the second trimester are associated with preterm birth. J Nutr. 2015;145(8):1857–64.

Nascimento GR, Alves LV, Fonseca CL, Figueiroa JN, Alves JG. Dietary patterns and gestational diabetes mellitus in a low income pregnant women population in Brazil - a cohort study. Archivos Latinoamericanos de Nutricion. 2016;66(4):301–8.

Osorio-Yáñez C, Gelaye B, Qiu C, Bao W, Cardenas A, Enquobahrie DA, et al. Maternal intake of fried foods and risk of gestational diabetes mellitus. Ann Epidemiol. 2017;27(6):384–90.e1.

Rasmussen MA, Maslova E, Halldorsson TI, Olsen SF. Characterization of dietary patterns in the Danish National Birth Cohort in relation to preterm birth. PLoS One. 2014;9(4)

Saftlas AF, Triche EW, Beydoun H, Bracken MB. Does chocolate intake during pregnancy reduce the risks of preeclampsia and gestational hypertension? Ann Epidemiol. 2010;20(8):584–91.

Santos IS, Victora CG, Huttly S, Carvalhal JB. Caffeine intake and low birth weight: a population-based case-control study. Am J Epidemiol. 1998;147(7):620–7.

Sartorelli DS, Crivellenti LC, Zuccolotto DCC, Franco LJ. Relationship between minimally and ultra-processed food intake during pregnancy with obesity and gestational diabetes mellitus. Cadernos de Saude Publica. 2019;35(4)

Schoenaker DA, Soedamah-Muthu SS, Callaway LK, Mishra GD. Pre-pregnancy dietary patterns and risk of gestational diabetes mellitus: results from an Australian population-based prospective cohort study. Diabetol. 2015;58(12):2726–35. https://doi.org/10.1007/s00125-015-3742-1 . Epub 2015 Sep 10.

Sedaghat F, Akhoondan M, Ehteshami M, Aghamohammadi V, Ghanei N, Mirmiran P, Rashidkhani B. Maternal Dietary Patterns and Gestational Diabetes Risk: A Case-Control Study. J Diabetes Res. 2017;2017:5173926. https://doi.org/10.1155/2017/5173926 . Epub 2017 Dec 6.

Tamada H, Ebara T, Matsuki T, Kato S, Sato H, Ito Y, et al. Ready-meal consumption during pregnancy is a risk factor for stillbirth: the Japan environment and Children’s study (JECS). Japan environment and Children’s study, Ready-Meal Consumption During Pregnancy is a Risk Factor for Stillbirth: The Japan Environment and Children’s Study (JECS).

Teixeira JA, Hoffman DJ, Castro TG, Saldiva S, Francisco RPV, Vieira SE, et al. Pre-pregnancy dietary pattern is associated with newborn size: results from ProcriAr study. Br J Nutr. 2021;126(6):903–12.

Triche EW, Grosso LM, Belanger K, Darefsky AS, Benowitz NL, Bracken MB. Chocolate consumption in pregnancy and reduced likelihood of preeclampsia. Epidemiol. 2008;19(3):459–64.

Tryggvadottir EA, Medek H, Birgisdottir BE, Geirsson RT, Gunnarsdottir I. Association between healthy maternal dietary pattern and risk for gestational diabetes mellitus. Eur J Clin Nutr. 2016;70(2):237–42.

Tsoi KY, Chan RSM, Tam CH, Li LS, Tam WH, Ma RCW. Dietary patterns of Chinese pregnant women in Hong Kong. Asia Pac J Clin Nutr. 2022;31(3):378–93.

Wahedy K, El Bilbeisi AHH, Bakry MJ. DIETARY PATTERNS and THEIR ASSOCIATION with GLYCEMIC CONTROL and RISK of GESTATIONAL DIABETES MELLITUS in GAZA STRIP, PALESTINE: a CASE-CONTROL STUDY. Bullet Pharmaceut Sci Assiut. 2021;44(2):537–49.

Wen L, Ge H, Qiao J, Zhang L, Chen X, Kilby MD, et al. Maternal dietary patterns and risk of gestational diabetes mellitus in twin pregnancies: a longitudinal twin pregnancies birth cohort study. Nutr J. 2020;19(1)

Yisahak SF, Hinkle SN, Mumford SL, Gleason JL, Grantz KL, Zhang C, et al. Periconceptional and first trimester Ultraprocessed food intake and maternal Cardiometabolic outcomes. Diabetes Care. 2022;45(9):2028–36.

Yong HY, Mohd Shariff Z, Mohd Yusof BN, Rejali Z, Tee YYS, Bindels J, van der Beek EM. Beverage Intake and the Risk of Gestational Diabetes Mellitus: The SECOST. Nutrients. 2021;13(7):2208. https://doi.org/10.3390/nu13072208 .

Zareei S, Homayounfar R, Naghizadeh MM, Ehrampoush E, Amiri Z, Rahimi M, et al. Dietary pattern in patients with preeclampsia in Fasa. Iran Shiraz E Med J. 2019;20(11)

Zareei S, Homayounfar R, Naghizadeh MM, Ehrampoush E, Rahimi M. Dietary pattern in pregnancy and risk of gestational diabetes mellitus (GDM). Diabet Metabol Syndrome: Clin Res Rev. 2018;12(3):399–404.

Zhang C, Schulze MB, Solomon CG, Hu FB. A prospective study of dietary patterns, meat intake and the risk of gestational diabetes mellitus. Diabetologia. 2006;49(11):2604-13. https://doi.org/10.1007/s00125-006-0422-1 . Epub 2006 Sep 7.

Zuccolotto DCC, Crivellenti LC, Franco LJ, Sarotelli DS. Dietary patterns of pregnant women, maternal excessive body weight and gestational diabetes. Revista de Saude Publica. 2019;53.

Moradi S, Hojjati Kermani MA, Bagheri R, Mohammadi H, Jayedi A, Lane MM, et al. Ultra-processed food consumption and adult diabetes risk: a systematic review and dose-response Meta-analysis. Nutrients. 2021;13(12)

Suksatan W, Moradi S, Naeini F, Bagheri R, Mohammadi H, Talebi S, Mehrabani S, Hojjati Kermani MA, Suzuki K. Ultra-Processed Food Consumption and Adult Mortality Risk: A Systematic Review and Dose-Response Meta-Analysis of 207,291 Participants. Nutrients. 2021;14(1):174. https://doi.org/10.3390/nu14010174 .

Babaei A, Pourmotabbed A, Talebi S, Mehrabani S, Bagheri R, Ghoreishy SM, et al. The association of ultra-processed food consumption with adult inflammatory bowel disease risk: a systematic review and dose-response meta-analysis of 4 035 694 participants. Nutr Rev. 2023;

Umesawa M, Kobashi G. Epidemiology of hypertensive disorders in pregnancy: prevalence, risk factors, predictors and prognosis. Hyperten res: official j Japan Soc Hypert. 2017;40(3):213–20.

Sutan R, Aminuddin NA, Mahdy ZA. Prevalence, maternal characteristics, and birth outcomes of preeclampsia: a cross-sectional study in a single tertiary healthcare center in greater Kuala Lumpur Malaysia. Front Public Health. 2022;10:973271.

Bodnar LM, Ness RB, Markovic N, Roberts JM. The risk of preeclampsia rises with increasing prepregnancy body mass index. Ann Epidemiol. 2005;15(7):475–82.

Catalano PM, McIntyre HD, Cruickshank JK, McCance DR, Dyer AR, Metzger BE, et al. The hyperglycemia and adverse pregnancy outcome study: associations of GDM and obesity with pregnancy outcomes. Diabetes Care. 2012;35(4):780–6.

Perry A, Stephanou A, Rayman MP. Dietary factors that affect the risk of pre-eclampsia. BMJ nutrit prevent health. 2022;5(1):118–33.

Martini D, Godos J, Bonaccio M, Vitaglione P, Grosso G. Ultra-processed foods and nutritional dietary profile: a Meta-analysis of nationally representative samples. Nutrients. 2021;13(10)

Maldonado-Pereira L, Barnaba C, de Los CG, Medina-Meza IG. Evaluation of the nutritional quality of ultra-processed foods (ready to eat + fast food): fatty acids, sugar, and sodium. J Food Sci. 2022;87(8):3659–76.

Monguchi T, Hara T, Hasokawa M, Nakajima H, Mori K, Toh R, et al. Excessive intake of trans fatty acid accelerates atherosclerosis through promoting inflammation and oxidative stress in a mouse model of hyperlipidemia. J Cardiol. 2017;70(2):121–7.

Ma X, Nan F, Liang H, Shu P, Fan X, Song X, et al. Excessive intake of sugar: an accomplice of inflammation. Front Immunol. 2022;13:988481.

Wojcik M, Krawczyk M, Zieleniak A, Mac Marcjanek K, Wozniak LA. Chapter 14 - associations of high blood sugar with oxidative stress and inflammation in patients with type 2 diabetes. In: Preuss HG, Bagchi D, editors. Dietary Sugar Salt and Fat in Human Health. Academic Press; 2020. p. 305–23.

Chapter   Google Scholar  

Chiarello DI, Abad C, Rojas D, Toledo F, Vázquez CM, Mate A, et al. Oxidative stress: Normal pregnancy versus preeclampsia. Biochim Biophys Acta (BBA)-Mol Basis Dis. 2020;1866(2):165354.

Harmon AC, Cornelius DC, Amaral LM, Faulkner JL, Cunningham MW, Jr., Wallace K, et al. The role of inflammation in the pathology of preeclampsia. Clinical science (London, England : 1979). 2016;130(6):409–19.

Patik JC, Lennon SL, Farquhar WB, Edwards DG. Mechanisms of dietary sodium-induced impairments in Endothelial function and potential countermeasures. Nutrients. 2021;13(1)

Shuto E, Taketani Y, Tanaka R, Harada N, Isshiki M, Sato M, et al. Dietary phosphorus acutely impairs endothelial function. J Am Soc Nephrol: JASN. 2009;20(7):1504–12.

Lopez-Garcia E, Schulze MB, Meigs JB, Manson JE, Rifai N, Stampfer MJ, et al. Consumption of trans fatty acids is related to plasma biomarkers of inflammation and Endothelial Dysfunction1. J Nutr. 2005;135(3):562–6.

Lamarca B. Endothelial dysfunction. An important mediator in the pathophysiology of hypertension during pre-eclampsia. Minerva Ginecol 2012;64(4):309–20.

Fernandes AE, Rosa PWL, Melo ME, Martins RCR, Santin FGO, Moura AMSH, et al. Differences in the gut microbiota of women according to ultra-processed food consumption. Nutr Metab Cardiovasc Dis. 2023;33(1):84–9. https://doi.org/10.1016/j.numecd.2022.09.025 . Epub 2022 Oct 11.

Whelan K, Bancil AS, Lindsay JO, Chassaing B. Ultra-processed foods and food additives in gut health and disease. Nat Rev Gastroenterol Hepatol. 2024;22:1–22.

Fan M, Li X, Gao X, Dong L, Xin G, Chen L, et al. LPS induces preeclampsia-like phenotype in rats and HTR8/SVneo cells dysfunction through TLR4/p38 MAPK pathway. Front Physiol. 2019;10:1030.

Jin J, Gao L, Zou X, Zhang Y, Zheng Z, Zhang X, et al. Gut Dysbiosis promotes preeclampsia by regulating macrophages and trophoblasts. Circ Res. 2022;131(6):492–506.

Kell DB, Kenny LC. A dormant microbial component in the development of preeclampsia. Front Med. 2016;3:60.

Rauber F, Chang K, Vamos EP, da Costa Louzada ML, Monteiro CA, Millett C, et al. Ultra-processed food consumption and risk of obesity: a prospective cohort study of UK biobank. Eur J Nutr. 2021;60(4):2169–80.

Wondmkun YT. Obesity, insulin resistance, and type 2 diabetes: associations and therapeutic implications. Diabet metab syndrome obes: targets therapy. 2020;13:3611–6.

Teran E, Escudero C, Moya W, Flores M, Vallance P, Lopez-Jaramillo P. Elevated C-reactive protein and pro-inflammatory cytokines in Andean women with pre-eclampsia. Int J Gynecol Obstet. 2001;75(3):243–9.

Palei AC, Spradley FT, Granger JP. Chronic hyperleptinemia results in the development of hypertension in pregnant rats. Am J Phys Regul Integr Comp Phys. 2015;308(10):R855–R61.

CAS   Google Scholar  

Olson KN, Redman LM, Sones JL. Obesity "complements" preeclampsia. Physiol Genom. 2019;51(3):73–6.

Wang X, Cheng Z. Cross-sectional studies: strengths, weaknesses, and recommendations. Chest. 2020;158(1 Supplement):S65–71.

Mozaffarian D, Ludwig DS. Dietary guidelines in the 21st century—a time for food. Jama. 2010;304(6):681–2.

Wang H, Li N, Chivese T, Werfalli M, Sun H, Yuen L, et al. IDF diabetes atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by International Association of Diabetes in pregnancy study Group's criteria. Diabetes Res Clin Pract. 2022;183:109050.

Kityo A, Lee SA. The intake of ultra-processed foods and prevalence of chronic kidney disease: the health examinees study. Nutrients. 2022;14(17)

Nardocci M, Leclerc B-S, Louzada M-L, Monteiro CA, Batal M, Moubarac J-C. Consumption of ultra-processed foods and obesity in Canada. Can J Public Health. 2019;110:4–14.

Rauber F, Louzada MLC, Steele EM, Millett C, Monteiro CA, Levy RB. Ultra-processed food consumption and chronic non-communicable diseases-related dietary nutrient profile in the UK (2008–2014). Nutrients. 2018;10(5):587.

Lavigne-Robichaud M, Moubarac J-C, Lantagne-Lopez S, Johnson-Down L, Batal M, Sidi EAL, et al. Diet quality indices in relation to metabolic syndrome in an indigenous Cree (Eeyouch) population in northern Québec. Canada Pub health nutrit. 2018;21(1):172–80.

Sun Y, Shen Z, Zhan Y, Wang Y, Ma S, Zhang S, et al. Effects of pre-pregnancy body mass index and gestational weight gain on maternal and infant complications. BMC pregnan childbirth. 2020;20(1):1–13.

Linder T, Eder A, Monod C, Rosicky I, Eppel D, Redling K, et al. Impact of prepregnancy overweight and obesity on treatment modality and pregnancy outcome in women with gestational diabetes mellitus. Front Endocrinol. 2022;13:799625.

Najafi F, Hasani J, Izadi N, Hashemi-Nazari S-S, Namvar Z, Shamsi H, et al. Risk of gestational diabetes mellitus by pre-pregnancy body mass index: a systematic review and meta-analysis. Diabet Metab Syndrome: Clin Res Rev. 2021;15(4):102181.

McIntyre HD, Catalano P, Zhang C, Desoye G, Mathiesen ER, Damm P. Gestational diabetes mellitus. Nat Rev Dis Primers. 2019;5(1):47.

Plows JF, Stanley JL, Baker PN, Reynolds CM, Vickers MH. The pathophysiology of gestational diabetes mellitus. Int J Mol Sci. 2018;19(11):3342.

Stanhope KL. Sugar consumption, metabolic disease and obesity: the state of the controversy. Crit Rev Clin Lab Sci. 2016;53(1):52–67.

Bhattacharyya S, I OS, Katyal S, Unterman T, Tobacman JK. Exposure to the common food additive carrageenan leads to glucose intolerance, insulin resistance and inhibition of insulin signalling in HepG2 cells and C57BL/6J mice. Diabetologia. 2012;55(1):194–203.

Quetglas-Llabrés MM, Monserrat-Mesquida M, Bouzas C, Mateos D, Ugarriza L, Gómez C, et al. Oxidative stress and inflammatory biomarkers are related to high intake of ultra-processed food in old adults with metabolic syndrome. Antioxidants (Basel, Switzerland). 2023;12(8).

Yaribeygi H, Sathyapalan T, Atkin SL, Sahebkar A. Molecular mechanisms linking oxidative stress and diabetes mellitus. Oxidative Med Cell Longev. 2020;2020:8609213.

Bertoli S, Leone A, Battezzati A. Human bisphenol a exposure and the “diabesity phenotype”. Dose-Response. 2015;13(3):1559325815599173.

Rolfo A, Nuzzo AM, De Amicis R, Moretti L, Bertoli S, Leone A. Fetal–maternal exposure to endocrine disruptors: correlation with diet intake and pregnancy outcomes. Nutrients. 2020;12(6):1744.

Lorenzo PI, Martín-Montalvo A, Cobo Vuilleumier N, Gauthier BR. Molecular modelling of islet β-cell adaptation to inflammation in pregnancy and gestational diabetes mellitus. Int J Mol Sci. 2019;20(24)

Christ A, Lauterbach M, Latz E. Western diet and the immune system: an inflammatory connection. Immunity. 2019;51(5):794–811.

Neale E, Batterham M, Tapsell LC. Consumption of a healthy dietary pattern results in significant reductions in C-reactive protein levels in adults: a meta-analysis. Nutr Res. 2016;36(5):391–401.

Liu Z, Lu Y, Zhong K, Wang C, Xu X. The associations between endocrine disrupting chemicals and markers of inflammation and immune responses: a systematic review and meta-analysis. Ecotoxicol Environ Saf. 2022;234:113382.

Node K, Inoue T. Postprandial hyperglycemia as an etiological factor in vascular failure. Cardiovasc Diabetol. 2009;8:23.

Hu Z, Tylavsky FA, Kocak M, Fowke JH, Han JC, Davis RL, et al. Effects of maternal dietary patterns during pregnancy on early childhood growth trajectories and obesity risk: the CANDLE study. Nutrients. 2020;12(2)

Wang Y, Wang K, Du M, Khandpur N, Rossato SL, Lo C-H, et al. Maternal consumption of ultra-processed foods and subsequent risk of offspring overweight or obesity: results from three prospective cohort studies. Bmj. 2022:379.

Paula WO, Gonçalves VSS, Patriota ESO, Franceschini SCC, Pizato N. Impact of ultra-processed food consumption on quality of diet among Brazilian pregnant women assisted in primary health care. Int J Environ Res Public Health. 2023;20(2)

Crume TL, Brinton JT, Shapiro A, Kaar J, Glueck DH, Siega-Riz AM, et al. Maternal dietary intake during pregnancy and offspring body composition: the healthy start study. Am J Obstet Gynecol. 2016;215(5):609–e1-e8.

Article   PubMed Central   Google Scholar  

Wan Y, Chen Y, Wu X, Yin A, Tian F, Zhang H, et al. Mediation effect of maternal triglyceride and fasting glucose level on the relationship between maternal overweight/ obesity and fetal growth: a prospective cohort study. BMC Pregnancy and Childbirth. 2023;23(1):449.

Akbari Z, Mansourian M, Kelishadi R. Relationship of the intake of different food groups by pregnant mothers with the birth weight and gestational age: need for public and individual educational programs. J educ health promot. 2015;4:23.

Fischer S, Morales-Suárez-Varela M. The bidirectional relationship between gestational diabetes and depression in pregnant women: a systematic search and review. Healthcare (Basel, Switzerland). 2023;11(3).

Khan R, Waqas A, Bilal A, Mustehsan ZH, Omar J, Rahman A. Association of Maternal depression with diet: a systematic review. Asian J Psychiatr. 2020;52:102098.

Ancira-Moreno M, O'Neill MS, Rivera-Dommarco J, Batis C, Rodríguez Ramírez S, Sánchez BN, et al. Dietary patterns and diet quality during pregnancy and low birthweight: the PRINCESA cohort. Matern Child Nutr. 2020;16(3):e12972.

Download references

Acknowledgments

Author information, authors and affiliations.

Students’ Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran

Sepide Talebi

Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran

Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Sanaz Mehrabani

Department of Nutrition, School of Public Health, Iran University of Medical Sciences, Tehran, Iran

Seyed Mojtaba Ghoreishy

Student research committee, School of Public Health, Iran University of Medical Sciences, Tehran, Iran

Department of Health and Human Performance, Marymount University, Arlington, VA, USA

Alexei Wong

Department of Clinical Sciences, Faculty of Veterinary Medicine, Razi University, Kermanshah, Iran

Aliasghar Moghaddam & Peyman Rahimi Feyli

General Practitioner, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran

Parsa Amirian & Mahsa Zarpoosh

Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Mohammad Ali Hojjati Kermani

Department of Nutrition and Food Sciences, Research Center for Evidence-Based Health Management, Maragheh University of Medical Sciences, Maragheh, Iran

Sajjad Moradi

You can also search for this author in PubMed   Google Scholar

Contributions

SM and ST designed this study. SM, MZ and PA contributed to the conduct of the search. SM and ST performed the statistical analysis and interpreted the results. SM, SM-GH, Sanaz Merabani, and MA-HK wrote the initial manuscript. AW, AM and PR_F critically revised the manuscript and contributed to the subsequent drafts of the manuscript. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Sajjad Moradi .

Ethics declarations

Ethics approval and consent to participate.

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., supplementary material 2., supplementary material 3., supplementary material 4., supplementary material 5., supplementary material 6., supplementary material 7., supplementary material 8., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Talebi, S., Mehrabani, S., Ghoreishy, S.M. et al. The association between ultra-processed food and common pregnancy adverse outcomes: a dose-response systematic review and meta-analysis. BMC Pregnancy Childbirth 24 , 369 (2024). https://doi.org/10.1186/s12884-024-06489-w

Download citation

Received : 18 January 2024

Accepted : 07 April 2024

Published : 15 May 2024

DOI : https://doi.org/10.1186/s12884-024-06489-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Ultra-processed foods
• Gestational diabetes mellitus
• Preeclampsia
• Preterm birth

BMC Pregnancy and Childbirth

ISSN: 1471-2393

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Open access
• Published: 14 May 2024

Developing a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in Medical Assistance in Dying (MAiD): a mixed method modified e-Delphi study

• Jocelyn Schroeder 1 ,
• Barbara Pesut 1 , 2 ,
• Lise Olsen 2 ,
• Nelly D. Oelke 2 &
• Helen Sharp 2  

BMC Nursing volume  23 , Article number:  326 ( 2024 ) Cite this article

150 Accesses

Metrics details

Medical Assistance in Dying (MAiD) was legalized in Canada in 2016. Canada’s legislation is the first to permit Nurse Practitioners (NP) to serve as independent MAiD assessors and providers. Registered Nurses’ (RN) also have important roles in MAiD that include MAiD care coordination; client and family teaching and support, MAiD procedural quality; healthcare provider and public education; and bereavement care for family. Nurses have a right under the law to conscientious objection to participating in MAiD. Therefore, it is essential to prepare nurses in their entry-level education for the practice implications and moral complexities inherent in this practice. Knowing what nursing students think about MAiD is a critical first step. Therefore, the purpose of this study was to develop a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in MAiD in the Canadian context.

The design was a mixed-method, modified e-Delphi method that entailed item generation from the literature, item refinement through a 2 round survey of an expert faculty panel, and item validation through a cognitive focus group interview with nursing students. The settings were a University located in an urban area and a College located in a rural area in Western Canada.

During phase 1, a 56-item survey was developed from existing literature that included demographic items and items designed to measure experience with death and dying (including MAiD), education and preparation, attitudes and beliefs, influences on those beliefs, and anticipated future involvement. During phase 2, an expert faculty panel reviewed, modified, and prioritized the items yielding 51 items. During phase 3, a sample of nursing students further evaluated and modified the language in the survey to aid readability and comprehension. The final survey consists of 45 items including 4 case studies.

Systematic evaluation of knowledge-to-date coupled with stakeholder perspectives supports robust survey design. This study yielded a survey to assess nursing students’ attitudes toward MAiD in a Canadian context.

The survey is appropriate for use in education and research to measure knowledge and attitudes about MAiD among nurse trainees and can be a helpful step in preparing nursing students for entry-level practice.

Peer Review reports

Medical Assistance in Dying (MAiD) is permitted under an amendment to Canada’s Criminal Code which was passed in 2016 [ 1 ]. MAiD is defined in the legislation as both self-administered and clinician-administered medication for the purpose of causing death. In the 2016 Bill C-14 legislation one of the eligibility criteria was that an applicant for MAiD must have a reasonably foreseeable natural death although this term was not defined. It was left to the clinical judgement of MAiD assessors and providers to determine the time frame that constitutes reasonably foreseeable [ 2 ]. However, in 2021 under Bill C-7, the eligibility criteria for MAiD were changed to allow individuals with irreversible medical conditions, declining health, and suffering, but whose natural death was not reasonably foreseeable, to receive MAiD [ 3 ]. This population of MAiD applicants are referred to as Track 2 MAiD (those whose natural death is foreseeable are referred to as Track 1). Track 2 applicants are subject to additional safeguards under the 2021 C-7 legislation.

Three additional proposed changes to the legislation have been extensively studied by Canadian Expert Panels (Council of Canadian Academics [CCA]) [ 4 , 5 , 6 ] First, under the legislation that defines Track 2, individuals with mental disease as their sole underlying medical condition may apply for MAiD, but implementation of this practice is embargoed until March 2027 [ 4 ]. Second, there is consideration of allowing MAiD to be implemented through advanced consent. This would make it possible for persons living with dementia to receive MAID after they have lost the capacity to consent to the procedure [ 5 ]. Third, there is consideration of extending MAiD to mature minors. A mature minor is defined as “a person under the age of majority…and who has the capacity to understand and appreciate the nature and consequences of a decision” ([ 6 ] p. 5). In summary, since the legalization of MAiD in 2016 the eligibility criteria and safeguards have evolved significantly with consequent implications for nurses and nursing care. Further, the number of Canadians who access MAiD shows steady increases since 2016 [ 7 ] and it is expected that these increases will continue in the foreseeable future.

Nurses have been integral to MAiD care in the Canadian context. While other countries such as Belgium and the Netherlands also permit euthanasia, Canada is the first country to allow Nurse Practitioners (Registered Nurses with additional preparation typically achieved at the graduate level) to act independently as assessors and providers of MAiD [ 1 ]. Although the role of Registered Nurses (RNs) in MAiD is not defined in federal legislation, it has been addressed at the provincial/territorial-level with variability in scope of practice by region [ 8 , 9 ]. For example, there are differences with respect to the obligation of the nurse to provide information to patients about MAiD, and to the degree that nurses are expected to ensure that patient eligibility criteria and safeguards are met prior to their participation [ 10 ]. Studies conducted in the Canadian context indicate that RNs perform essential roles in MAiD care coordination; client and family teaching and support; MAiD procedural quality; healthcare provider and public education; and bereavement care for family [ 9 , 11 ]. Nurse practitioners and RNs are integral to a robust MAiD care system in Canada and hence need to be well-prepared for their role [ 12 ].

Previous studies have found that end of life care, and MAiD specifically, raise complex moral and ethical issues for nurses [ 13 , 14 , 15 , 16 ]. The knowledge, attitudes, and beliefs of nurses are important across practice settings because nurses have consistent, ongoing, and direct contact with patients who experience chronic or life-limiting health conditions. Canadian studies exploring nurses’ moral and ethical decision-making in relation to MAiD reveal that although some nurses are clear in their support for, or opposition to, MAiD, others are unclear on what they believe to be good and right [ 14 ]. Empirical findings suggest that nurses go through a period of moral sense-making that is often informed by their family, peers, and initial experiences with MAID [ 17 , 18 ]. Canadian legislation and policy specifies that nurses are not required to participate in MAiD and may recuse themselves as conscientious objectors with appropriate steps to ensure ongoing and safe care of patients [ 1 , 19 ]. However, with so many nurses having to reflect on and make sense of their moral position, it is essential that they are given adequate time and preparation to make an informed and thoughtful decision before they participate in a MAID death [ 20 , 21 ].

It is well established that nursing students receive inconsistent exposure to end of life care issues [ 22 ] and little or no training related to MAiD [ 23 ]. Without such education and reflection time in pre-entry nursing preparation, nurses are at significant risk for moral harm. An important first step in providing this preparation is to be able to assess the knowledge, values, and beliefs of nursing students regarding MAID and end of life care. As demand for MAiD increases along with the complexities of MAiD, it is critical to understand the knowledge, attitudes, and likelihood of engagement with MAiD among nursing students as a baseline upon which to build curriculum and as a means to track these variables over time.

Aim, design, and setting

The aim of this study was to develop a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in MAiD in the Canadian context. We sought to explore both their willingness to be involved in the registered nursing role and in the nurse practitioner role should they chose to prepare themselves to that level of education. The design was a mixed-method, modified e-Delphi method that entailed item generation, item refinement through an expert faculty panel [ 24 , 25 , 26 ], and initial item validation through a cognitive focus group interview with nursing students [ 27 ]. The settings were a University located in an urban area and a College located in a rural area in Western Canada.

Participants

A panel of 10 faculty from the two nursing education programs were recruited for Phase 2 of the e-Delphi. To be included, faculty were required to have a minimum of three years of experience in nurse education, be employed as nursing faculty, and self-identify as having experience with MAiD. A convenience sample of 5 fourth-year nursing students were recruited to participate in Phase 3. Students had to be in good standing in the nursing program and be willing to share their experiences of the survey in an online group interview format.

The modified e-Delphi was conducted in 3 phases: Phase 1 entailed item generation through literature and existing survey review. Phase 2 entailed item refinement through a faculty expert panel review with focus on content validity, prioritization, and revision of item wording [ 25 ]. Phase 3 entailed an assessment of face validity through focus group-based cognitive interview with nursing students.

Phase I. Item generation through literature review

The goal of phase 1 was to develop a bank of survey items that would represent the variables of interest and which could be provided to expert faculty in Phase 2. Initial survey items were generated through a literature review of similar surveys designed to assess knowledge and attitudes toward MAiD/euthanasia in healthcare providers; Canadian empirical studies on nurses’ roles and/or experiences with MAiD; and legislative and expert panel documents that outlined proposed changes to the legislative eligibility criteria and safeguards. The literature review was conducted in three online databases: CINAHL, PsycINFO, and Medline. Key words for the search included nurses , nursing students , medical students , NPs, MAiD , euthanasia , assisted death , and end-of-life care . Only articles written in English were reviewed. The legalization and legislation of MAiD is new in many countries; therefore, studies that were greater than twenty years old were excluded, no further exclusion criteria set for country.

Items from surveys designed to measure similar variables in other health care providers and geographic contexts were placed in a table and similar items were collated and revised into a single item. Then key variables were identified from the empirical literature on nurses and MAiD in Canada and checked against the items derived from the surveys to ensure that each of the key variables were represented. For example, conscientious objection has figured prominently in the Canadian literature, but there were few items that assessed knowledge of conscientious objection in other surveys and so items were added [ 15 , 21 , 28 , 29 ]. Finally, four case studies were added to the survey to address the anticipated changes to the Canadian legislation. The case studies were based upon the inclusion of mature minors, advanced consent, and mental disorder as the sole underlying medical condition. The intention was to assess nurses’ beliefs and comfort with these potential legislative changes.

Phase 2. Item refinement through expert panel review

The goal of phase 2 was to refine and prioritize the proposed survey items identified in phase 1 using a modified e-Delphi approach to achieve consensus among an expert panel [ 26 ]. Items from phase 1 were presented to an expert faculty panel using a Qualtrics (Provo, UT) online survey. Panel members were asked to review each item to determine if it should be: included, excluded or adapted for the survey. When adapted was selected faculty experts were asked to provide rationale and suggestions for adaptation through the use of an open text box. Items that reached a level of 75% consensus for either inclusion or adaptation were retained [ 25 , 26 ]. New items were categorized and added, and a revised survey was presented to the panel of experts in round 2. Panel members were again asked to review items, including new items, to determine if it should be: included, excluded, or adapted for the survey. Round 2 of the modified e-Delphi approach also included an item prioritization activity, where participants were then asked to rate the importance of each item, based on a 5-point Likert scale (low to high importance), which De Vaus [ 30 ] states is helpful for increasing the reliability of responses. Items that reached a 75% consensus on inclusion were then considered in relation to the importance it was given by the expert panel. Quantitative data were managed using SPSS (IBM Corp).

Phase 3. Face validity through cognitive interviews with nursing students

The goal of phase 3 was to obtain initial face validity of the proposed survey using a sample of nursing student informants. More specifically, student participants were asked to discuss how items were interpreted, to identify confusing wording or other problematic construction of items, and to provide feedback about the survey as a whole including readability and organization [ 31 , 32 , 33 ]. The focus group was held online and audio recorded. A semi-structured interview guide was developed for this study that focused on clarity, meaning, order and wording of questions; emotions evoked by the questions; and overall survey cohesion and length was used to obtain data (see Supplementary Material 2  for the interview guide). A prompt to “think aloud” was used to limit interviewer-imposed bias and encourage participants to describe their thoughts and response to a given item as they reviewed survey items [ 27 ]. Where needed, verbal probes such as “could you expand on that” were used to encourage participants to expand on their responses [ 27 ]. Student participants’ feedback was collated verbatim and presented to the research team where potential survey modifications were negotiated and finalized among team members. Conventional content analysis [ 34 ] of focus group data was conducted to identify key themes that emerged through discussion with students. Themes were derived from the data by grouping common responses and then using those common responses to modify survey items.

Ten nursing faculty participated in the expert panel. Eight of the 10 faculty self-identified as female. No faculty panel members reported conscientious objector status and ninety percent reported general agreement with MAiD with one respondent who indicated their view as “unsure.” Six of the 10 faculty experts had 16 years of experience or more working as a nurse educator.

Five nursing students participated in the cognitive interview focus group. The duration of the focus group was 2.5 h. All participants identified that they were born in Canada, self-identified as female (one preferred not to say) and reported having received some instruction about MAiD as part of their nursing curriculum. See Tables  1 and 2 for the demographic descriptors of the study sample. Study results will be reported in accordance with the study phases. See Fig.  1 for an overview of the results from each phase.

Fig. 1  Overview of survey development findings

Phase 1: survey item generation

Review of the literature identified that no existing survey was available for use with nursing students in the Canadian context. However, an analysis of themes across qualitative and quantitative studies of physicians, medical students, nurses, and nursing students provided sufficient data to develop a preliminary set of items suitable for adaptation to a population of nursing students.

Four major themes and factors that influence knowledge, attitudes, and beliefs about MAiD were evident from the literature: (i) endogenous or individual factors such as age, gender, personally held values, religion, religiosity, and/or spirituality [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ], (ii) experience with death and dying in personal and/or professional life [ 35 , 40 , 41 , 43 , 44 , 45 ], (iii) training including curricular instruction about clinical role, scope of practice, or the law [ 23 , 36 , 39 ], and (iv) exogenous or social factors such as the influence of key leaders, colleagues, friends and/or family, professional and licensure organizations, support within professional settings, and/or engagement in MAiD in an interdisciplinary team context [ 9 , 35 , 46 ].

Studies of nursing students also suggest overlap across these categories. For example, value for patient autonomy [ 23 ] and the moral complexity of decision-making [ 37 ] are important factors that contribute to attitudes about MAiD and may stem from a blend of personally held values coupled with curricular content, professional training and norms, and clinical exposure. For example, students report that participation in end of life care allows for personal growth, shifts in perception, and opportunities to build therapeutic relationships with their clients [ 44 , 47 , 48 ].

Preliminary items generated from the literature resulted in 56 questions from 11 published sources (See Table  3 ). These items were constructed across four main categories: (i) socio-demographic questions; (ii) end of life care questions; (iii) knowledge about MAiD; or (iv) comfort and willingness to participate in MAiD. Knowledge questions were refined to reflect current MAiD legislation, policies, and regulatory frameworks. Falconer [ 39 ] and Freeman [ 45 ] studies were foundational sources for item selection. Additionally, four case studies were written to reflect the most recent anticipated changes to MAiD legislation and all used the same open-ended core questions to address respondents’ perspectives about the patient’s right to make the decision, comfort in assisting a physician or NP to administer MAiD in that scenario, and hypothesized comfort about serving as a primary provider if qualified as an NP in future. Response options for the survey were also constructed during this stage and included: open text, categorical, yes/no , and Likert scales.

Phase 2: faculty expert panel review

Of the 56 items presented to the faculty panel, 54 questions reached 75% consensus. However, based upon the qualitative responses 9 items were removed largely because they were felt to be repetitive. Items that generated the most controversy were related to measuring religion and spirituality in the Canadian context, defining end of life care when there is no agreed upon time frames (e.g., last days, months, or years), and predicting willingness to be involved in a future events – thus predicting their future selves. Phase 2, round 1 resulted in an initial set of 47 items which were then presented back to the faculty panel in round 2.

Of the 47 initial questions presented to the panel in round 2, 45 reached a level of consensus of 75% or greater, and 34 of these questions reached a level of 100% consensus [ 27 ] of which all participants chose to include without any adaptations) For each question, level of importance was determined based on a 5-point Likert scale (1 = very unimportant, 2 = somewhat unimportant, 3 = neutral, 4 = somewhat important, and 5 = very important). Figure  2 provides an overview of the level of importance assigned to each item.

Ranking level of importance for survey items

After round 2, a careful analysis of participant comments and level of importance was completed by the research team. While the main method of survey item development came from participants’ response to the first round of Delphi consensus ratings, level of importance was used to assist in the decision of whether to keep or modify questions that created controversy, or that rated lower in the include/exclude/adapt portion of the Delphi. Survey items that rated low in level of importance included questions about future roles, sex and gender, and religion/spirituality. After deliberation by the research committee, these questions were retained in the survey based upon the importance of these variables in the scientific literature.

Of the 47 questions remaining from Phase 2, round 2, four were revised. In addition, the two questions that did not meet the 75% cut off level for consensus were reviewed by the research team. The first question reviewed was What is your comfort level with providing a MAiD death in the future if you were a qualified NP ? Based on a review of participant comments, it was decided to retain this question for the cognitive interviews with students in the final phase of testing. The second question asked about impacts on respondents’ views of MAiD and was changed from one item with 4 subcategories into 4 separate items, resulting in a final total of 51 items for phase 3. The revised survey was then brought forward to the cognitive interviews with student participants in Phase 3. (see Supplementary Material 1 for a complete description of item modification during round 2).

Phase 3. Outcomes of cognitive interview focus group

Of the 51 items reviewed by student participants, 29 were identified as clear with little or no discussion. Participant comments for the remaining 22 questions were noted and verified against the audio recording. Following content analysis of the comments, four key themes emerged through the student discussion: unclear or ambiguous wording; difficult to answer questions; need for additional response options; and emotional response evoked by questions. An example of unclear or ambiguous wording was a request for clarity in the use of the word “sufficient” in the context of assessing an item that read “My nursing education has provided sufficient content about the nursing role in MAiD.” “Sufficient” was viewed as subjective and “laden with…complexity that distracted me from the question.” The group recommended rewording the item to read “My nursing education has provided enough content for me to care for a patient considering or requesting MAiD.”

An example of having difficulty answering questions related to limited knowledge related to terms used in the legislation such as such as safeguards , mature minor , eligibility criteria , and conscientious objection. Students were unclear about what these words meant relative to the legislation and indicated that this lack of clarity would hamper appropriate responses to the survey. To ensure that respondents are able to answer relevant questions, student participants recommended that the final survey include explanation of key terms such as mature minor and conscientious objection and an overview of current legislation.

Response options were also a point of discussion. Participants noted a lack of distinction between response options of unsure and unable to say . Additionally, scaling of attitudes was noted as important since perspectives about MAiD are dynamic and not dichotomous “agree or disagree” responses. Although the faculty expert panel recommended the integration of the demographic variables of religious and/or spiritual remain as a single item, the student group stated a preference to have religion and spirituality appear as separate items. The student focus group also took issue with separate items for the variables of sex and gender, specifically that non-binary respondents might feel othered or “outed” particularly when asked to identify their sex. These variables had been created based upon best practices in health research but students did not feel they were appropriate in this context [ 49 ]. Finally, students agreed with the faculty expert panel in terms of the complexity of projecting their future involvement as a Nurse Practitioner. One participant stated: “I certainly had to like, whoa, whoa, whoa. Now let me finish this degree first, please.” Another stated, “I'm still imagining myself, my future career as an RN.”

Finally, student participants acknowledged the array of emotions that some of the items produced for them. For example, one student described positive feelings when interacting with the survey. “Brought me a little bit of feeling of joy. Like it reminded me that this is the last piece of independence that people grab on to.” Another participant, described the freedom that the idea of an advance request gave her. “The advance request gives the most comfort for me, just with early onset Alzheimer’s and knowing what it can do.” But other participants described less positive feelings. For example, the mature minor case study yielded a comment: “This whole scenario just made my heart hurt with the idea of a child requesting that.”

Based on the data gathered from the cognitive interview focus group of nursing students, revisions were made to 11 closed-ended questions (see Table  4 ) and 3 items were excluded. In the four case studies, the open-ended question related to a respondents’ hypothesized actions in a future role as NP were removed. The final survey consists of 45 items including 4 case studies (see Supplementary Material 3 ).

The aim of this study was to develop and validate a survey that can be used to track the growth of knowledge about MAiD among nursing students over time, inform training programs about curricular needs, and evaluate attitudes and willingness to participate in MAiD at time-points during training or across nursing programs over time.

The faculty expert panel and student participants in the cognitive interview focus group identified a need to establish core knowledge of the terminology and legislative rules related to MAiD. For example, within the cognitive interview group of student participants, several acknowledged lack of clear understanding of specific terms such as “conscientious objector” and “safeguards.” Participants acknowledged discomfort with the uncertainty of not knowing and their inclination to look up these terms to assist with answering the questions. This survey can be administered to nursing or pre-nursing students at any phase of their training within a program or across training programs. However, in doing so it is important to acknowledge that their baseline knowledge of MAiD will vary. A response option of “not sure” is important and provides a means for respondents to convey uncertainty. If this survey is used to inform curricular needs, respondents should be given explicit instructions not to conduct online searches to inform their responses, but rather to provide an honest appraisal of their current knowledge and these instructions are included in the survey (see Supplementary Material 3 ).

Some provincial regulatory bodies have established core competencies for entry-level nurses that include MAiD. For example, the BC College of Nurses and Midwives (BCCNM) requires “knowledge about ethical, legal, and regulatory implications of medical assistance in dying (MAiD) when providing nursing care.” (10 p. 6) However, across Canada curricular content and coverage related to end of life care and MAiD is variable [ 23 ]. Given the dynamic nature of the legislation that includes portions of the law that are embargoed until 2024, it is important to ensure that respondents are guided by current and accurate information. As the law changes, nursing curricula, and public attitudes continue to evolve, inclusion of core knowledge and content is essential and relevant for investigators to be able to interpret the portions of the survey focused on attitudes and beliefs about MAiD. Content knowledge portions of the survey may need to be modified over time as legislation and training change and to meet the specific purposes of the investigator.

Given the sensitive nature of the topic, it is strongly recommended that surveys be conducted anonymously and that students be provided with an opportunity to discuss their responses to the survey. A majority of feedback from both the expert panel of faculty and from student participants related to the wording and inclusion of demographic variables, in particular religion, religiosity, gender identity, and sex assigned at birth. These and other demographic variables have the potential to be highly identifying in small samples. In any instance in which the survey could be expected to yield demographic group sizes less than 5, users should eliminate the demographic variables from the survey. For example, the profession of nursing is highly dominated by females with over 90% of nurses who identify as female [ 50 ]. Thus, a survey within a single class of students or even across classes in a single institution is likely to yield a small number of male respondents and/or respondents who report a difference between sex assigned at birth and gender identity. When variables that serve to identify respondents are included, respondents are less likely to complete or submit the survey, to obscure their responses so as not to be identifiable, or to be influenced by social desirability bias in their responses rather than to convey their attitudes accurately [ 51 ]. Further, small samples do not allow for conclusive analyses or interpretation of apparent group differences. Although these variables are often included in surveys, such demographics should be included only when anonymity can be sustained. In small and/or known samples, highly identifying variables should be omitted.

There are several limitations associated with the development of this survey. The expert panel was comprised of faculty who teach nursing students and are knowledgeable about MAiD and curricular content, however none identified as a conscientious objector to MAiD. Ideally, our expert panel would have included one or more conscientious objectors to MAiD to provide a broader perspective. Review by practitioners who participate in MAiD, those who are neutral or undecided, and practitioners who are conscientious objectors would ensure broad applicability of the survey. This study included one student cognitive interview focus group with 5 self-selected participants. All student participants had held discussions about end of life care with at least one patient, 4 of 5 participants had worked with a patient who requested MAiD, and one had been present for a MAiD death. It is not clear that these participants are representative of nursing students demographically or by experience with end of life care. It is possible that the students who elected to participate hold perspectives and reflections on patient care and MAiD that differ from students with little or no exposure to end of life care and/or MAiD. However, previous studies find that most nursing students have been involved with end of life care including meaningful discussions about patients’ preferences and care needs during their education [ 40 , 44 , 47 , 48 , 52 ]. Data collection with additional student focus groups with students early in their training and drawn from other training contexts would contribute to further validation of survey items.

Future studies should incorporate pilot testing with small sample of nursing students followed by a larger cross-program sample to allow evaluation of the psychometric properties of specific items and further refinement of the survey tool. Consistent with literature about the importance of leadership in the context of MAiD [ 12 , 53 , 54 ], a study of faculty knowledge, beliefs, and attitudes toward MAiD would provide context for understanding student perspectives within and across programs. Additional research is also needed to understand the timing and content coverage of MAiD across Canadian nurse training programs’ curricula.

The implementation of MAiD is complex and requires understanding of the perspectives of multiple stakeholders. Within the field of nursing this includes clinical providers, educators, and students who will deliver clinical care. A survey to assess nursing students’ attitudes toward and willingness to participate in MAiD in the Canadian context is timely, due to the legislation enacted in 2016 and subsequent modifications to the law in 2021 with portions of the law to be enacted in 2027. Further development of this survey could be undertaken to allow for use in settings with practicing nurses or to allow longitudinal follow up with students as they enter practice. As the Canadian landscape changes, ongoing assessment of the perspectives and needs of health professionals and students in the health professions is needed to inform policy makers, leaders in practice, curricular needs, and to monitor changes in attitudes and practice patterns over time.

Availability of data and materials

The datasets used and/or analysed during the current study are not publicly available due to small sample sizes, but are available from the corresponding author on reasonable request.

Abbreviations

British Columbia College of Nurses and Midwives

Medical assistance in dying

Nurse practitioner

Registered nurse

University of British Columbia Okanagan

Nicol J, Tiedemann M. Legislative Summary: Bill C-14: An Act to amend the Criminal Code and to make related amendments to other Acts (medical assistance in dying). Available from: https://lop.parl.ca/staticfiles/PublicWebsite/Home/ResearchPublications/LegislativeSummaries/PDF/42-1/c14-e.pdf .

Downie J, Scallion K. Foreseeably unclear. The meaning of the “reasonably foreseeable” criterion for access to medical assistance in dying in Canada. Dalhousie Law J. 2018;41(1):23–57.

Nicol J, Tiedeman M. Legislative summary of Bill C-7: an act to amend the criminal code (medical assistance in dying). Ottawa: Government of Canada; 2021.

Google Scholar  

Council of Canadian Academies. The state of knowledge on medical assistance in dying where a mental disorder is the sole underlying medical condition. Ottawa; 2018. Available from: https://cca-reports.ca/wp-content/uploads/2018/12/The-State-of-Knowledge-on-Medical-Assistance-in-Dying-Where-a-Mental-Disorder-is-the-Sole-Underlying-Medical-Condition.pdf .

Council of Canadian Academies. The state of knowledge on advance requests for medical assistance in dying. Ottawa; 2018. Available from: https://cca-reports.ca/wp-content/uploads/2019/02/The-State-of-Knowledge-on-Advance-Requests-for-Medical-Assistance-in-Dying.pdf .

Council of Canadian Academies. The state of knowledge on medical assistance in dying for mature minors. Ottawa; 2018. Available from: https://cca-reports.ca/wp-content/uploads/2018/12/The-State-of-Knowledge-on-Medical-Assistance-in-Dying-for-Mature-Minors.pdf .

Health Canada. Third annual report on medical assistance in dying in Canada 2021. Ottawa; 2022. [cited 2023 Oct 23]. Available from: https://www.canada.ca/en/health-canada/services/medical-assistance-dying/annual-report-2021.html .

Banner D, Schiller CJ, Freeman S. Medical assistance in dying: a political issue for nurses and nursing in Canada. Nurs Philos. 2019;20(4): e12281.

Article   PubMed   Google Scholar  

Pesut B, Thorne S, Stager ML, Schiller CJ, Penney C, Hoffman C, et al. Medical assistance in dying: a review of Canadian nursing regulatory documents. Policy Polit Nurs Pract. 2019;20(3):113–30.

Article   PubMed   PubMed Central   Google Scholar  

College of Registered Nurses of British Columbia. Scope of practice for registered nurses [Internet]. Vancouver; 2018. Available from: https://www.bccnm.ca/Documents/standards_practice/rn/RN_ScopeofPractice.pdf .

Pesut B, Thorne S, Schiller C, Greig M, Roussel J, Tishelman C. Constructing good nursing practice for medical assistance in dying in Canada: an interpretive descriptive study. Global Qual Nurs Res. 2020;7:2333393620938686. https://doi.org/10.1177/2333393620938686 .

Article   Google Scholar  

Pesut B, Thorne S, Schiller CJ, Greig M, Roussel J. The rocks and hard places of MAiD: a qualitative study of nursing practice in the context of legislated assisted death. BMC Nurs. 2020;19:12. https://doi.org/10.1186/s12912-020-0404-5 .

Pesut B, Greig M, Thorne S, Burgess M, Storch JL, Tishelman C, et al. Nursing and euthanasia: a narrative review of the nursing ethics literature. Nurs Ethics. 2020;27(1):152–67.

Pesut B, Thorne S, Storch J, Chambaere K, Greig M, Burgess M. Riding an elephant: a qualitative study of nurses’ moral journeys in the context of Medical Assistance in Dying (MAiD). Journal Clin Nurs. 2020;29(19–20):3870–81.

Lamb C, Babenko-Mould Y, Evans M, Wong CA, Kirkwood KW. Conscientious objection and nurses: results of an interpretive phenomenological study. Nurs Ethics. 2018;26(5):1337–49.

Wright DK, Chan LS, Fishman JR, Macdonald ME. “Reflection and soul searching:” Negotiating nursing identity at the fault lines of palliative care and medical assistance in dying. Social Sci & Med. 2021;289: 114366.

Beuthin R, Bruce A, Scaia M. Medical assistance in dying (MAiD): Canadian nurses’ experiences. Nurs Forum. 2018;54(4):511–20.

Bruce A, Beuthin R. Medically assisted dying in Canada: "Beautiful Death" is transforming nurses' experiences of suffering. The Canadian J Nurs Res | Revue Canadienne de Recherche en Sci Infirmieres. 2020;52(4):268–77. https://doi.org/10.1177/0844562119856234 .

Canadian Nurses Association. Code of ethics for registered nurses. Ottawa; 2017. Available from: https://www.cna-aiic.ca/en/nursing/regulated-nursing-in-canada/nursing-ethics .

Canadian Nurses Association. National nursing framework on Medical Assistance in Dying in Canada. Ottawa: 2017. Available from: https://www.virtualhospice.ca/Assets/cna-national-nursing-framework-on-maidEng_20170216155827.pdf .

Pesut B, Thorne S, Greig M. Shades of gray: conscientious objection in medical assistance in dying. Nursing Inq. 2020;27(1): e12308.

Durojaiye A, Ryan R, Doody O. Student nurse education and preparation for palliative care: a scoping review. PLoS ONE. 2023. https://doi.org/10.1371/journal.pone.0286678 .

McMechan C, Bruce A, Beuthin R. Canadian nursing students’ experiences with medical assistance in dying | Les expériences d’étudiantes en sciences infirmières au regard de l’aide médicale à mourir. Qual Adv Nurs Educ - Avancées en Formation Infirmière. 2019;5(1). https://doi.org/10.17483/2368-6669.1179 .

Adler M, Ziglio E. Gazing into the oracle. The Delphi method and its application to social policy and public health. London: Jessica Kingsley Publishers; 1996

Keeney S, Hasson F, McKenna H. Consulting the oracle: ten lessons from using the Delphi technique in nursing research. J Adv Nurs. 2006;53(2):205–12.

Keeney S, Hasson F, McKenna H. The Delphi technique in nursing and health research. 1st ed. City: Wiley; 2011.

Willis GB. Cognitive interviewing: a tool for improving questionnaire design. 1st ed. Thousand Oaks, Calif: Sage; 2005. ISBN: 9780761928041

Lamb C, Evans M, Babenko-Mould Y, Wong CA, Kirkwood EW. Conscience, conscientious objection, and nursing: a concept analysis. Nurs Ethics. 2017;26(1):37–49.

Lamb C, Evans M, Babenko-Mould Y, Wong CA, Kirkwood K. Nurses’ use of conscientious objection and the implications of conscience. J Adv Nurs. 2018;75(3):594–602.

de Vaus D. Surveys in social research. 6th ed. Abingdon, Oxon: Routledge; 2014.

Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL. Best practices for developing and validating scales for health, social, and behavioral research: A primer. Front Public Health. 2018;6:149. https://doi.org/10.3389/fpubh.2018.00149 .

Puchta C, Potter J. Focus group practice. 1st ed. London: Sage; 2004.

Book   Google Scholar  

Streiner DL, Norman GR, Cairney J. Health measurement scales: a practical guide to their development and use. 5th ed. Oxford: Oxford University Press; 2015.

Hsieh H-F, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277–88.

Adesina O, DeBellis A, Zannettino L. Third-year Australian nursing students’ attitudes, experiences, knowledge, and education concerning end-of-life care. Int J of Palliative Nurs. 2014;20(8):395–401.

Bator EX, Philpott B, Costa AP. This moral coil: a cross-sectional survey of Canadian medical student attitudes toward medical assistance in dying. BMC Med Ethics. 2017;18(1):58.

Beuthin R, Bruce A, Scaia M. Medical assistance in dying (MAiD): Canadian nurses’ experiences. Nurs Forum. 2018;53(4):511–20.

Brown J, Goodridge D, Thorpe L, Crizzle A. What is right for me, is not necessarily right for you: the endogenous factors influencing nonparticipation in medical assistance in dying. Qual Health Res. 2021;31(10):1786–1800.

Falconer J, Couture F, Demir KK, Lang M, Shefman Z, Woo M. Perceptions and intentions toward medical assistance in dying among Canadian medical students. BMC Med Ethics. 2019;20(1):22.

Green G, Reicher S, Herman M, Raspaolo A, Spero T, Blau A. Attitudes toward euthanasia—dual view: Nursing students and nurses. Death Stud. 2022;46(1):124–31.

Hosseinzadeh K, Rafiei H. Nursing student attitudes toward euthanasia: a cross-sectional study. Nurs Ethics. 2019;26(2):496–503.

Ozcelik H, Tekir O, Samancioglu S, Fadiloglu C, Ozkara E. Nursing students’ approaches toward euthanasia. Omega (Westport). 2014;69(1):93–103.

Canning SE, Drew C. Canadian nursing students’ understanding, and comfort levels related to medical assistance in dying. Qual Adv Nurs Educ - Avancées en Formation Infirmière. 2022;8(2). https://doi.org/10.17483/2368-6669.1326 .

Edo-Gual M, Tomás-Sábado J, Bardallo-Porras D, Monforte-Royo C. The impact of death and dying on nursing students: an explanatory model. J Clin Nurs. 2014;23(23–24):3501–12.

Freeman LA, Pfaff KA, Kopchek L, Liebman J. Investigating palliative care nurse attitudes towards medical assistance in dying: an exploratory cross-sectional study. J Adv Nurs. 2020;76(2):535–45.

Brown J, Goodridge D, Thorpe L, Crizzle A. “I am okay with it, but I am not going to do it:” the exogenous factors influencing non-participation in medical assistance in dying. Qual Health Res. 2021;31(12):2274–89.

Dimoula M, Kotronoulas G, Katsaragakis S, Christou M, Sgourou S, Patiraki E. Undergraduate nursing students’ knowledge about palliative care and attitudes towards end-of-life care: A three-cohort, cross-sectional survey. Nurs Educ Today. 2019;74:7–14.

Matchim Y, Raetong P. Thai nursing students’ experiences of caring for patients at the end of life: a phenomenological study. Int J Palliative Nurs. 2018;24(5):220–9.

Canadian Institute for Health Research. Sex and gender in health research [Internet]. Ottawa: CIHR; 2021 [cited 2023 Oct 23]. Available from: https://cihr-irsc.gc.ca/e/50833.html .

Canadian Nurses’ Association. Nursing statistics. Ottawa: CNA; 2023 [cited 2023 Oct 23]. Available from: https://www.cna-aiic.ca/en/nursing/regulated-nursing-in-canada/nursing-statistics .

Krumpal I. Determinants of social desirability bias in sensitive surveys: a literature review. Qual Quant. 2013;47(4):2025–47. https://doi.org/10.1007/s11135-011-9640-9 .

Ferri P, Di Lorenzo R, Stifani S, Morotti E, Vagnini M, Jiménez Herrera MF, et al. Nursing student attitudes toward dying patient care: a European multicenter cross-sectional study. Acta Bio Medica Atenei Parmensis. 2021;92(S2): e2021018.

PubMed   PubMed Central   Google Scholar  

Beuthin R, Bruce A. Medical assistance in dying (MAiD): Ten things leaders need to know. Nurs Leadership. 2018;31(4):74–81.

Thiele T, Dunsford J. Nurse leaders’ role in medical assistance in dying: a relational ethics approach. Nurs Ethics. 2019;26(4):993–9.

Download references

Acknowledgements

We would like to acknowledge the faculty and students who generously contributed their time to this work.

JS received a student traineeship through the Principal Research Chairs program at the University of British Columbia Okanagan.

Author information

Authors and affiliations.

School of Health and Human Services, Selkirk College, Castlegar, BC, Canada

Jocelyn Schroeder & Barbara Pesut

School of Nursing, University of British Columbia Okanagan, Kelowna, BC, Canada

Barbara Pesut, Lise Olsen, Nelly D. Oelke & Helen Sharp

You can also search for this author in PubMed   Google Scholar

Contributions

JS made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and drafting and substantively revising the work. JS has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. BP made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and drafting and substantively revising the work. BP has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. LO made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and substantively revising the work. LO has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. NDO made substantial contributions to the conception of the work; data acquisition, analysis, and interpretation; and substantively revising the work. NDO has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. HS made substantial contributions to drafting and substantively revising the work. HS has approved the submitted version and agreed to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

Authors’ information

JS conducted this study as part of their graduate requirements in the School of Nursing, University of British Columbia Okanagan.

Corresponding author

Correspondence to Barbara Pesut .

Ethics declarations

Ethics approval and consent to participate.

The research was approved by the Selkirk College Research Ethics Board (REB) ID # 2021–011 and the University of British Columbia Behavioral Research Ethics Board ID # H21-01181.

All participants provided written and informed consent through approved consent processes. Research was conducted in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1., supplementary material 2., supplementary material 3., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Schroeder, J., Pesut, B., Olsen, L. et al. Developing a survey to measure nursing students’ knowledge, attitudes and beliefs, influences, and willingness to be involved in Medical Assistance in Dying (MAiD): a mixed method modified e-Delphi study. BMC Nurs 23 , 326 (2024). https://doi.org/10.1186/s12912-024-01984-z

Download citation

Received : 24 October 2023

Accepted : 28 April 2024

Published : 14 May 2024

DOI : https://doi.org/10.1186/s12912-024-01984-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Medical assistance in dying (MAiD)
• End of life care
• Student nurses
• Nursing education

BMC Nursing

ISSN: 1472-6955

• General enquiries: [email protected]

• Systematic Review
• Open access
• Published: 12 May 2024

Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis

• Mingxuan Du 1 ,
• Chengjia Zhao 2 ,
• Haiyan Hu 1 ,
• Ningning Ding 1 ,
• Jiankang He 1 ,
• Wenwen Tian 1 ,
• Wenqian Zhao 1 ,
• Xiujian Lin 1 ,
• Gaoyang Liu 1 ,
• Wendan Chen 1 ,
• ShuangLiu Wang 1 ,
• Pengcheng Wang 3 ,
• Dongwu Xu 1 ,
• Xinhua Shen 4 &
• Guohua Zhang 1  

BMC Psychology volume  12 , Article number:  263 ( 2024 ) Cite this article

309 Accesses

Metrics details

A growing number of studies have reported that problematic social networking use (PSNU) is strongly associated with anxiety symptoms. However, due to the presence of multiple anxiety subtypes, existing research findings on the extent of this association vary widely, leading to a lack of consensus. The current meta-analysis aimed to summarize studies exploring the relationship between PSNU levels and anxiety symptoms, including generalized anxiety, social anxiety, attachment anxiety, and fear of missing out. 209 studies with a total of 172 articles were included in the meta-analysis, involving 252,337 participants from 28 countries. The results showed a moderately positive association between PSNU and generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO) respectively (GA: r  = 0.388, 95% CI [0.362, 0.413]; SA: r  = 0.437, 95% CI [0.395, 0.478]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]), and there were different regulatory factors between PSNU and different anxiety subtypes. This study provides the first comprehensive estimate of the association of PSNU with multiple anxiety subtypes, which vary by time of measurement, region, gender, and measurement tool.

Peer Review reports

Introduction

Social network refers to online platforms that allow users to create, share, and exchange information, encompassing text, images, audio, and video [ 1 ]. The use of social network, a term encompassing various activities on these platforms, has been measured from angles such as frequency, duration, intensity, and addictive behavior, all indicative of the extent of social networking usage [ 2 ]. As of April 2023, there are 4.8 billion social network users globally, representing 59.9% of the world’s population [ 3 ]. The usage of social network is considered a normal behavior and a part of everyday life [ 4 , 5 ]. Although social network offers convenience in daily life, excessive use can lead to PSNU [ 6 , 7 ], posing potential threats to mental health, particularly anxiety symptoms (Rasmussen et al., 2020). Empirical research has shown that anxiety symptoms, including generalized anxiety (GA), social anxiety (SA), attachment anxiety (AA), and fear of missing out (FoMO), are closely related to PSNU [ 8 , 9 , 10 , 11 , 12 ]. While some empirical studies have explored the relationship between PSNU and anxiety symptoms, their conclusions are not consistent. Some studies have found a significant positive correlation [ 13 , 14 , 15 ], while others have found no significant correlation [ 16 , 17 , 18 , 19 ]. Furthermore, the degree of correlation varies widely in existing research, with reported r-values ranging from 0.12 to 0.80 [ 20 , 21 ]. Therefore, a systematic meta-analysis is necessary to clarify the impact of PSNU on individual anxiety symptoms.

Previous research lacks a unified concept of PSNU, primarily due to differing theoretical interpretations by various authors, and the use of varied standards and diagnostic tools. Currently, this phenomenon is referred to by several terms, including compulsive social networking use, problematic social networking use, excessive social networking use, social networking dependency, and social networking addiction [ 22 , 23 , 24 , 25 , 26 ]. These conceptual differences hinder the development of a cohesive and systematic research framework, as it remains unclear whether these definitions and tools capture the same underlying construct [ 27 ]. To address this lack of uniformity, this paper will use the term “problematic use” to encompass all the aforementioned nomenclatures (i.e., compulsive, excessive, dependent, and addictive use).

Regarding the relationship between PSNU and anxiety symptoms, two main perspectives exist: the first suggests a positive correlation, while the second proposes a U-shaped relationship. The former perspective, advocating a positive correlation, aligns with the social cognitive theory of mass communication. It posits that PSNU can reinforce certain cognitions, emotions, attitudes, and behaviors [ 28 , 29 ], potentially elevating individuals’ anxiety levels [ 30 ]. Additionally, the cognitive-behavioral model of pathological use, a primary framework for explaining factors related to internet-based addictions, indicates that psychiatric symptoms like depression or anxiety may precede internet addiction, implying that individuals experiencing anxiety may turn to social networking platforms as a coping mechanism [ 31 ]. Empirical research also suggests that highly anxious individuals prefer computer-mediated communication due to the control and social liberation it offers and are more likely to have maladaptive emotional regulation, potentially leading to problematic social network service use [ 32 ]. Turning to the alternate perspective, it proposes a U-shaped relationship as per the digital Goldilocks hypothesis. In this view, moderate social networking usage is considered beneficial for psychosocial adaptation, providing individuals with opportunities for social connection and support. Conversely, both excessive use and abstinence can negatively impact psychosocial adaptation [ 33 ]. In summary, both perspectives offer plausible explanations.

Incorporating findings from previous meta-analyses, we identified seven systematic reviews and two meta-analyses that investigated the association between PSNU and anxiety. The results of these meta-analyses indicated a significant positive correlation between PSNU and anxiety (ranging from 0.33 to 0.38). However, it is evident that these previous meta-analyses had certain limitations. Firstly, they focused only on specific subtypes of anxiety; secondly, they were limited to adolescents and emerging adults in terms of age. In summary, this systematic review aims to ascertain which theoretical perspective more effectively explains the relationship between PSNU and anxiety, addressing the gaps in previous meta-analyses. Additionally, the association between PSNU and anxiety could be moderated by various factors. Drawing from a broad research perspective, any individual study is influenced by researcher-specific designs and associated sample estimates. These may lead to bias compared to the broader population. Considering the selection criteria for moderating variables in empirical studies and meta-analyses [ 34 , 35 ], the heterogeneity of findings on problematic social network usage and anxiety symptoms could be driven by divergence in sample characteristics (e.g., gender, age, region) and research characteristics (measurement instrument of study variables). Since the 2019 coronavirus pandemic, heightened public anxiety may be attributed to the fear of the virus or heightened real life stress. The increased use of electronic devices, particularly smartphones during the pandemic, also instigates the prevalence of problematic social networking. Thus, our analysis focuses on three moderators: sample characteristics (participants’ gender, age, region), measurement tools (for PSNU and anxiety symptoms) and the time of measurement (before COVID-19 vs. during COVID-19).

The present study was conducted in accordance with the 2020 statement on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 36 ]. To facilitate transparency and to avoid unnecessary duplication of research, this study was registered on PROSPERO, and the number is CRD42022350902.

Literature search

Studies on the relationship between the PSNU and anxiety symptoms from 2000 to 2023 were retrieved from seven databases. These databases included China National Knowledge Infrastructure (CNKI), Wanfang Data, Chongqing VIP Information Co. Ltd. (VIP), Web of Science, ScienceDirect, PubMed, and PsycARTICLES. The search strings consisted of (a) anxiety symptoms, (b) social network, and (c) Problematic use. As shown in Table  1 , the keywords for anxiety are as follows: anxiety, generalized anxiety, social anxiety, attachment anxiety, fear of missing out, and FoMO. The keywords for social network are as follows: social network, social media, social networking site, Instagram, and Facebook. The keywords for addiction are as follows: addiction, dependence, problem/problematic use, excessive use. The search deadline was March 19, 2023. A total of 2078 studies were initially retrieved and all were identified ultimately.

Inclusion and exclusion criteria

Retrieved studies were eligible for the present meta-analysis if they met the following inclusion criteria: (a) the study provided Pearson correlation coefficients used to measure the relationship between PSNU and anxiety symptoms; (b) the study reported the sample size and the measurement instruments for the variables; (c) the study was written in English and Chinese; (d) the study provided sufficient statistics to calculate the effect sizes; (e) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, they were coded by the first measurement. In addition, studies were excluded if they: (a) examined non-problematic social network use; (b) had an abnormal sample population; (c) the results of the same sample were included in another study and (d) were case reports or review articles. Two evaluators with master’s degrees independently assessed the eligibility of the articles. A third evaluator with a PhD examined the results and resolved dissenting views.

Data extraction and quality assessment

Two evaluators independently coded the selected articles according to the following characteristics: literature information, time of measurement (before the COVID-19 vs. during the COVID-19), sample source (developed country vs. developing country), sample size, proportion of males, mean age, type of anxiety, and measurement instruments for PSNU and anxiety symptoms. The following principles needed to be adhered to in the coding process: (a) effect sizes were extracted from independent samples. If multiple independent samples were investigated in the same study, they were coded separately; if the study was a longitudinal study, it was coded by the first measurement; (b) if multiple studies used the same data, the one with the most complete information was selected; (c) If studies reported t or F values rather than r , the following formula \( r=\sqrt{\frac{{t}^{2}}{{t}^{2}+df}}\) ; \( r=\sqrt{\frac{F}{F+d{f}_{e}}}\) was used to convert them into r values [ 37 , 38 ]. Additionally, if some studies only reported the correlation matrix between each dimension of PSNU and anxiety symptoms, the following formula \( {r}_{xy}=\frac{\sum {r}_{xi}{r}_{yj}}{\sqrt{n+n(n-1){r}_{xixj}}\sqrt{m+m(m-1){r}_{yiyj}}}\) was used to synthesize the r values [ 39 ], where n or m is the number of dimensions of variable x or variable y, respectively, and \( {r}_{xixj} \) or \( {r}_{yiyj}\) represents the mean of the correlation coefficients between the dimensions of variable x or variable y, respectively.

Literature quality was determined according to the meta-analysis quality evaluation scale developed [ 40 ]. The quality of the post-screening studies was assessed by five dimensions: sampling method, efficiency of sample collection, level of publication, and reliability of PSNU and anxiety symptom measurement instruments. The total score of the scale ranged from 0 to 10; higher scores indicated better quality of the literature.

Data analysis

All data were performed using Comprehensive Meta Analysis 3.3 (CMA 3.3). Pearson’s product-moment coefficient r was selected as the effect size index in this meta-analysis. Firstly, \( {\text{F}\text{i}\text{s}\text{h}\text{e}\text{r}}^{{\prime }}\text{s} Z=\frac{1}{2}\times \text{ln}\left(\frac{1+r}{1-r}\right)\) was used to convert the correlation coefficient to Fisher Z . Then the formula \( SE=\sqrt{\frac{1}{n-3}}\) was used to calculate the standard error ( SE ). Finally, the summary of r was obtained from the formula \( r=\frac{{e}^{2z}-1}{{e}^{2z}+1}\) for a comprehensive measure of the relationship between PSNU and anxiety symptoms [ 37 , 41 ].

Although the effect sizes estimated by the included studies may be similar, considering the actual differences between studies (e.g., region and gender), the random effects model was a better choice for data analysis for the current meta-analysis. The heterogeneity of the included study effect sizes was measured for significance by Cochran’s Q test and estimated quantitatively by the I 2 statistic [ 42 ]. If the results indicate there is a significant heterogeneity (the Q test: p -value < 0.05, I 2  > 75) and the results of different studies are significantly different from the overall effect size. Conversely, it indicates there are no differences between the studies and the overall effect size. And significant heterogeneity tends to indicate the possible presence of potential moderating variables. Subgroup analysis and meta-regression analysis were used to examine the moderating effect of categorical and continuous variables, respectively.

Funnel plots, fail-safe number (Nfs) and Egger linear regression were utilized to evaluate the publication bias [ 43 , 44 , 45 ]. The likelihood of publication bias was considered low if the intercept obtained from Egger linear regression was not significant. A larger Nfs indicated a lower risk of publication bias, and if Nfs < 5k + 10 (k representing the original number of studies), publication bias should be a concern [ 46 ]. When Egger’s linear regression was significant, the Duval and Tweedie’s trim-and-fill was performed to correct the effect size. If there was no significant change in the effect size, it was assumed that there was no serious publication bias [ 47 ].

A significance level of P  < 0.05 was deemed applicable in this study.

Sample characteristics

The PRISMA search process is depicted in Fig.  1 . The database search yielded 2078 records. After removing duplicate records and screening the title and abstract, the full text was subject to further evaluation. Ultimately, 172 records fit the inclusion criteria, including 209 independent effect sizes. The present meta-analysis included 68 studies on generalized anxiety, 44 on social anxiety, 22 on attachment anxiety, and 75 on fear of missing out. The characteristics of the selected studies are summarized in Table  2 . The majority of the sample group were adults. Quality scores for selected studies ranged from 0 to 10, with only 34 effect sizes below the theoretical mean, indicating high quality for the included studies. The literature included utilized BSMAS as the primary tool to measure PSNU, DASS-21-A to measure GA, IAS to measure SA, ECR to measure AA, and FoMOS to measure FoMO.

Flow chart of the search and selection strategy

Overall analysis, homogeneity tests and publication bias

As shown in Table  3 , there was significant heterogeneity between PSNU and all four anxiety symptoms (GA: Q  = 1623.090, I 2  = 95.872%; SA: Q  = 1396.828, I 2  = 96.922%; AA: Q  = 264.899, I 2  = 92.072%; FoMO: Q  = 1847.110, I 2  = 95.994%), so a random effects model was chosen. The results of the random effects model indicate a moderate positive correlation between PSNU and anxiety symptoms (GA: r  = 0.350, 95% CI [0.323, 0.378]; SA: r  = 0.390, 95% CI [0.347, 0.431]; AA: r  = 0.345, 95% CI [0.286, 0.402]; FoMO: r  = 0.496, 95% CI [0.461, 0.529]).

Figure  2 shows the funnel plot of the relationship between PSNU and anxiety symptoms. No significant symmetry was seen in the funnel plot of the relationship between PSNU and GA and between PSNU and SA. And the Egger’s regression results also indicated that there might be publication bias ( t  = 3.775, p  < 0.001; t  = 2.309, p  < 0.05). Therefore, it was necessary to use fail-safe number (Nfs) and the trim and fill method for further examination and correction. The Nfs for PSNU and GA as well as PSNU and SA are 4591 and 7568, respectively. Both Nfs were much larger than the standard 5 k  + 10. After performing the trim and fill method, 14 effect sizes were added to the right side of the funnel plat (Fig.  2 .a), the correlation coefficient between PSNU and GA changed to ( r  = 0.388, 95% CI [0.362, 0.413]); 10 effect sizes were added to the right side of the funnel plat (Fig.  2 .b), the correlation coefficient between PSNU and SA changed to ( r  = 0.437, 95% CI [0.395, 0.478]). The correlation coefficients did not change significantly, indicating that there was no significant publication bias associated with the relationship between PSNU and these two anxiety symptoms (GA and SA).

Funnel plot of the relationship between PSNU and anxiety symptoms. Note: Black dots indicated additional studies after using trim and fill method; ( a ) = Funnel plot of the PSNU and GA; ( b ) = Funnel plot of the PSNU and SA; ( c ) = Funnel plot of the PSNU and AA; ( d ) = Funnel plot of the PSNU and FoMO

Sensitivity analyses

Initially, the findings obtained through the one-study-removed approach indicated that the heterogeneities in the relationship between PSNU and anxiety symptoms were not attributed to any individual study. Nevertheless, it is important to note that sensitivity analysis should be performed based on literature quality [ 223 ] since low-quality literature could potentially impact result stability. In the relationship between PSNU and GA, the 10 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.402, 95% CI [0.375, 0.428]); In the relationship between PSNU and SA, the 8 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.431, 95% CI [0.387, 0.472]); In the relationship between PSNU and AA, the 5 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.367, 95% CI [0.298, 0.433]); In the relationship between PSNU and FoMO, the 11 effect sizes below the theoretical mean scores were excluded from analysis, and the sensitivity analysis results were recalculated ( r  = 0.508, 95% CI [0.470, 0.544]). The revised estimates indicate that meta-analysis results were stable.

Moderator analysis

The impact of moderator variables on the relation between psnu and ga.

The results of subgroup analysis and meta-regression are shown in Table  4 , the time of measurement significantly moderated the correlation between PSNU and GA ( Q between = 19.268, df  = 2, p  < 0.001). The relation between the two variables was significantly higher during the COVID-19 ( r  = 0.392, 95% CI [0.357, 0.425]) than before the COVID-19 ( r  = 0.270, 95% CI [0.227, 0.313]) or measurement time uncertain ( r  = 0.352, 95% CI [0.285, 0.415]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). The relation was significantly higher when PSNU was measured with the BSMAS ( r  = 0.373, 95% CI [0.341, 0.404]) compared to others ( r  = 0.301, 95% CI [0.256, 0.344]).

The moderating effect of the GA measurement was significant ( Q between = 60.061, df  = 5, p  < 0.001). Specifically, when GA measured by the GAD ( r  = 0.398, 95% CI [0.356, 0.438]) and the DASS-21-A ( r  = 0.433, 95% CI [0.389, 0.475]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the STAI ( r  = 0.232, 95% CI [0.187, 0.276]).

For the relation between PSNU and GA, the moderating effect of region, gender and age were not significant.

The impact of moderator variables on the relation between PSNU and SA

The effects of the moderating variables in the relation between PSNU and SA were shown in Table  5 . The results revealed a gender-moderated variances between the two variables (b = 0.601, 95% CI [ 0.041, 1.161], Q model (1, k = 41) = 4.705, p  = 0.036).

For the relation between PSNU and SA, the moderating effects of time of measurement, region, measurement of PSNU and SA, and age were not significant.

The impact of moderator variables on the relation between PSNU and AA

The effects of the moderating variables in the relation between PSNU and AA were shown in Table  6 , region significantly moderated the correlation between PSNU and AA ( Q between = 6.410, df  = 2, p  = 0.041). The correlation between the two variables was significantly higher in developing country ( r  = 0.378, 95% CI [0.304, 0.448]) than in developed country ( r  = 0.242, 95% CI [0.162, 0.319]).

The moderating effect of the PSNU measurement was significant ( Q between = 6.852, df  = 1, p  = 0.009). Specifically, when AA was measured by the GPIUS-2 ( r  = 0.484, 95% CI [0.200, 0.692]) and the PMSMUAQ ( r  = 0.443, 95% CI [0.381, 0.501]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the BSMAS ( r  = 0.248, 95% CI [0.161, 0.331]) and others ( r  = 0.313, 95% CI [0.250, 0.372]).

The moderating effect of the AA measurement was significant ( Q between = 17.283, df  = 2, p  < 0.001). The correlation was significantly higher when measured using the ECR ( r  = 0.386, 95% CI [0.338, 0.432]) compared to the RQ ( r  = 0.200, 95% CI [0.123, 0.275]).

For the relation between PSNU and AA, the moderating effects of time of measurement, region, gender, and age were not significant.

The impact of moderator variables on the relation between PSNU and FoMO

The effects of the moderating variables in the relation between PSNU and FoMO were shown in Table  7 , the moderating effect of the PSNU measurement was significant ( Q between = 8.170, df  = 2, p  = 0.017). Among the sub-dimensions, the others was excluded because there was only one sample. Specifically, when measured using the FoMOS-MSME ( r  = 0.630, 95% CI [0.513, 0.725]), a moderate positive correlation was observed. However, the correlation was less significant when measured using the FoMOS ( r  = 0.472, 95% CI [0.432, 0.509]) and the T-S FoMOS ( r  = 0.557, 95% CI [0.463, 0.639]).

For the relationship between PSNU and FoMO, the moderating effects of time of measurement, region, measurement of PSNU, gender and age were not significant.

Through systematic review and meta-analysis, this study established a positive correlation between PSNU and anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out), confirming a linear relationship and partially supporting the Social Cognitive Theory of Mass Communication [ 28 ] and the Cognitive Behavioral Model of Pathological Use [ 31 ]. Specifically, a significant positive correlation between PSNU and GA was observed, implying that GA sufferers might resort to social network for validation or as an escape from reality, potentially alleviating their anxiety. Similarly, the meta-analysis demonstrated a strong positive correlation between PSNU and SA, suggesting a preference for computer-mediated communication among those with high social anxiety due to perceived control and liberation offered by social network. This preference is often accompanied by maladaptive emotional regulation, predisposing them to problematic use. In AA, a robust positive correlation was found with PSNU, indicating a higher propensity for such use among individuals with attachment anxiety. Notably, the study identified the strongest correlation in the context of FoMO. FoMO’s significant association with PSNU is multifaceted, stemming from the real-time nature of social networks that engenders a continuous concern about missing crucial updates or events. This drives frequent engagement with social network, thereby establishing a direct link to problematic usage patterns. Additionally, social network’s feedback loops amplify this effect, intensifying FoMO. The culture of social comparison on these platforms further exacerbates FoMO, as users frequently compare their lives with others’ selectively curated portrayals, enhancing both their social networking usage frequency and the pursuit for social validation. Furthermore, the integral role of social network in modern life broadens FoMO’s scope, encompassing anxieties about staying informed and connected.

The notable correlation between FoMO and PSNU can be comprehensively understood through various perspectives. FoMO is inherently linked to the real-time nature of social networks, which cultivates an ongoing concern about missing significant updates or events in one’s social circle [ 221 ]. This anxiety prompts frequent engagement with social network, leading to patterns of problematic use. Moreover, the feedback loops in social network algorithms, designed to enhance user engagement, further intensify this fear [ 224 ]. Additionally, social comparison, a common phenomenon on these platforms, exacerbates FoMO as users continuously compare their lives with the idealized representations of others, amplifying feelings of missing out on key social experiences [ 225 ]. This behavior not only increases social networking usage but also is closely linked to the quest for social validation and identity construction on these platforms. The extensive role of social network in modern life further amplifies FoMO, as these platforms are crucial for information exchange and maintaining social ties. FoMO thus encompasses more than social concerns, extending to anxieties about staying informed with trends and dynamics within social networks [ 226 ]. The multifaceted nature of FoMO in relation to social network underscores its pronounced correlation with problematic social networking usage. In essence, the combination of social network’s intrinsic characteristics, psychological drivers of user behavior, the culture of social comparison, and the pervasiveness of social network in everyday life collectively make FoMO the most pronouncedly correlated anxiety type with PSNU.

Additionally, we conducted subgroup analyses on the timing of measurement (before COVID-19 vs. during COVID-19), measurement tools (for PSNU and anxiety symptoms), sample characteristics (participants’ region), and performed a meta-regression analysis on gender and age in the context of PSNU and anxiety symptoms. It was found that the timing of measurement, tools used for assessing PSNU and anxiety, region, and gender had a moderating effect, whereas age did not show a significant moderating impact.

Firstly, the relationship between PSNU and anxiety symptoms was significantly higher during the COVID-19 period than before, especially between PSNU and GA. However, the moderating effect of measurement timing was not significant in the relationship between PSNU and other types of anxiety. This could be attributed to the increased uncertainty and stress during the pandemic, leading to heightened levels of general anxiety [ 227 ]. The overuse of social network for information seeking and anxiety alleviation might have paradoxically exacerbated anxiety symptoms, particularly among individuals with broad future-related worries [ 228 ]. While the COVID-19 pandemic altered the relationship between PSNU and GA, its impact on other types of anxiety (such as SA and AA) may not have been significant, likely due to these anxiety types being more influenced by other factors like social skills and attachment styles, which were minimally impacted by the epidemic.

Secondly, the observed variance in the relationship between PSNU and AA across different economic contexts, notably between developing and developed countries, underscores the multifaceted influence of socio-economic, cultural, and technological factors on this dynamic. The amplified connection in developing countries may be attributed to greater socio-economic challenges, distinct cultural norms regarding social support and interaction, rising social network penetration, especially among younger demographics, and technological disparities influencing accessibility and user experience [ 229 , 230 ]. Moreover, the role of social network as a coping mechanism for emotional distress, potentially fostering insecure attachment patterns, is more pronounced in these settings [ 231 ]. These findings highlight the necessity of considering contextual variations in assessing the psychological impacts of social network, advocating for a nuanced understanding of how socio-economic and cultural backgrounds mediate the relationship between PSNU and mental health outcomes [ 232 ]. Additionally, the relationship between PSNU and other types of anxiety (such as GA and SA) presents uniform characteristics across different economic contexts.

Thirdly, the significant moderating effects of measurement tools in the context of PSNU and its correlation with various forms of anxiety, including GA, and AA, are crucial in interpreting the research findings. Specifically, the study reveals that the Bergen Social Media Addiction Scale (BSMAS) demonstrates a stronger correlation between PSNU and GA, compared to other tools. Similarly, for AA, the Griffiths’ Problematic Internet Use Scale 2 (GPIUS2) and the Problematic Media Social Media Use Assessment Questionnaire (PMSMUAQ) show a more pronounced correlation with AA than the BSMAS or other instruments, but for SA and FoMO, the PSNU instrument doesn’t significantly moderate the correlation. The PSNU measurement tool typically contains an emotional change dimension. SA and FoMO, due to their specific conditional stimuli triggers and correlation with social networks [ 233 , 234 ], are likely to yield more consistent scores in this dimension, while GA and AA may be less reliable due to their lesser sensitivity to specific conditional stimuli. Consequently, the adjustment effects of PSNU measurements vary across anxiety symptoms. Regarding the measurement tools for anxiety, different scales exhibit varying degrees of sensitivity in detecting the relationship with PSNU. The Generalized Anxiety Disorder Scale (GAD) and the Depression Anxiety Stress Scales 21 (DASS-21) are more effective in illustrating a strong relationship between GA and PSNU than the State-Trait Anxiety Inventory (STAI). In the case of AA, the Experiences in Close Relationships-21 (ECR-21) provides a more substantial correlation than the Relationship Questionnaire (RQ). Furthermore, for FoMO, the Fear of Missing Out Scale - Multi-Social Media Environment (FoMOS-MSME) is more indicative of a strong relationship with PSNU compared to the standard FoMOS or the T-S FoMOS. These findings underscore the importance of the selection of appropriate measurement tools in research. Different tools, due to their unique design, focus, and sensitivity, can reveal varying degrees of correlation between PSNU and anxiety disorders. This highlights the need for careful consideration of tool characteristics and their potential impact on research outcomes. It also cautions against drawing direct comparisons between studies without acknowledging the possible variances introduced by the use of different measurement instruments.

Fourthly, the significant moderating role of gender in the relationship between PSNU and SA, particularly pronounced in samples with a higher proportion of females. Women tend to engage more actively and emotionally with social network, potentially leading to an increased dependency on these platforms when confronting social anxiety [ 235 ]. This intensified use might amplify the association between PSNU and SA. Societal and cultural pressures, especially those related to appearance and social status, are known to disproportionately affect women, possibly exacerbating their experience of social anxiety and prompting a greater reliance on social network for validation and support [ 236 ]. Furthermore, women’s propensity to seek emotional support and express themselves on social network platforms [ 237 ] could strengthen this link, particularly in the context of managing social anxiety. Consequently, the observed gender differences in the relationship between PSNU and SA underscore the importance of considering gender-specific dynamics and cultural influences in psychological research related to social network use. In addition, gender consistency was observed in the association between PSNU and other types of anxiety, indicating no significant gender disparities.

Fifthly, the absence of a significant moderating effect of age on the relationship between PSNU and various forms of anxiety suggests a pervasive influence of social network across different age groups. This finding indicates that the impact of PSNU on anxiety is relatively consistent, irrespective of age, highlighting the universal nature of social network’s psychological implications [ 238 ]. Furthermore, this uniformity suggests that other factors, such as individual psychological traits or socio-cultural influences, might play a more crucial role in the development of anxiety related to social networking usage than age [ 239 ]. The non-significant role of age also points towards a potential generational overlap in social networking usage patterns and their psychological effects, challenging the notion that younger individuals are uniquely susceptible to the adverse effects of social network on mental health [ 240 ]. Therefore, this insight necessitates a broader perspective in understanding the dynamics of social network and mental health, one that transcends age-based assumptions.

Limitations

There are some limitations in this research. First, most of the studies were cross-sectional surveys, resulting in difficulties in inferring causality of variables, longitudinal study data will be needed to evaluate causal interactions in the future. Second, considerable heterogeneity was found in the estimated results, although heterogeneity can be partially explained by differences in study design (e.g., Time of measurement, region, gender, and measurement tools), but this can introduce some uncertainty in the aggregation and generalization of the estimated results. Third, most studies were based on Asian samples, which limits the generality of the results. Fourth, to minimize potential sources of heterogeneity, some less frequently used measurement tools were not included in the classification of measurement tools, which may have some impact on the results of heterogeneity interpretation. Finally, since most of the included studies used self-reported scales, it is possible to get results that deviate from the actual situation to some extent.

This meta-analysis aims to quantifies the correlations between PSNU and four specific types of anxiety symptoms (i.e., generalized anxiety, social anxiety, attachment anxiety, and fear of missing out). The results revealed a significant moderate positive association between PSNU and each of these anxiety symptoms. Furthermore, Subgroup analysis and meta-regression analysis indicated that gender, region, time of measurement, and instrument of measurement significantly influenced the relationship between PSNU and specific anxiety symptoms. Specifically, the measurement time and GA measurement tools significantly influenced the relationship between PSNU and GA. Gender significantly influenced the relationship between PSNU and SA. Region, PSNU measurement tools, and AA measurement tools all significantly influenced the relationship between PSNU and AA. The FoMO measurement tool significantly influenced the relationship between PSNU and FoMO. Regarding these findings, prevention interventions for PSNU and anxiety symptoms are important.

Data availability

The datasets are available from the corresponding author on reasonable request.

Abbreviations

• Problematic social networking use
• Generalized anxiety
• Social anxiety
• Attachment anxiety

Fear of miss out

Bergen Social Media Addiction Scale

Facebook Addiction Scale

Facebook Intrusion Questionnaire

Generalized Problematic Internet Use Scale 2

Problematic Mobile Social Media Usage Assessment Questionnaire

Social Network Addiction Tendency Scale

Brief Symptom Inventory

The anxiety subscale of the Depression Anxiety Stress Scales

Generalized Anxiety Disorder

The anxiety subscale of the Hospital Anxiety and Depression Scale

State-Trait Anxiety Inventory

Interaction Anxiousness Scale

Liebowitz Social Anxiety Scale

Social Anxiety Scale for Social Media Users

Social Anxiety for Adolescents

Social Anxiety Subscale of the Self-Consciousness Scale

Social Interaction Anxiety Scale

Experiences in Close Relationship Scale

Relationship questionnaire

Fear of Missing Out Scale

FoMO Measurement Scale in the Mobile Social Media Environment

Trait-State Fear of missing Out Scale

Rozgonjuk D, Sindermann C, Elhai JD, Montag C. Fear of missing out (FoMO) and social media’s impact on daily-life and productivity at work: do WhatsApp, Facebook, Instagram, and Snapchat Use disorders mediate that association? Addict Behav. 2020;110:106487.

Article   PubMed   Google Scholar  

Mieczkowski H, Lee AY, Hancock JT. Priming effects of social media use scales on well-being outcomes: the influence of intensity and addiction scales on self-reported depression. Social Media + Soc. 2020;6(4):2056305120961784.

Article   Google Scholar  

Global digital population as of April. 2023 [ https://www.statista.com/statistics/617136/digital-population-worldwide/ ].

Marengo D, Settanni M, Fabris MA, Longobardi C. Alone, together: fear of missing out mediates the link between peer exclusion in WhatsApp classmate groups and psychological adjustment in early-adolescent teens. J Social Personal Relationships. 2021;38(4):1371–9.

Marengo D, Fabris MA, Longobardi C, Settanni M. Smartphone and social media use contributed to individual tendencies towards social media addiction in Italian adolescents during the COVID-19 pandemic. Addict Behav. 2022;126:107204.

Müller SM, Wegmann E, Stolze D, Brand M. Maximizing social outcomes? Social zapping and fear of missing out mediate the effects of maximization and procrastination on problematic social networks use. Comput Hum Behav. 2020;107:106296.

Sun Y, Zhang Y. A review of theories and models applied in studies of social media addiction and implications for future research. Addict Behav. 2021;114:106699.

Boustead R, Flack M. Moderated-mediation analysis of problematic social networking use: the role of anxious attachment orientation, fear of missing out and satisfaction with life. Addict Behav 2021, 119.

Hussain Z, Griffiths MD. The associations between problematic social networking Site Use and Sleep Quality, attention-deficit hyperactivity disorder, Depression, anxiety and stress. Int J Mental Health Addict. 2021;19(3):686–700.

Gori A, Topino E, Griffiths MD. The associations between attachment, self-esteem, fear of missing out, daily time expenditure, and problematic social media use: a path analysis model. Addict Behav. 2023;141:107633.

Marino C, Manari T, Vieno A, Imperato C, Spada MM, Franceschini C, Musetti A. Problematic social networking sites use and online social anxiety: the role of attachment, emotion dysregulation and motives. Addict Behav. 2023;138:107572.

Tobin SJ, Graham S. Feedback sensitivity as a mediator of the relationship between attachment anxiety and problematic Facebook Use. Cyberpsychology Behav Social Netw. 2020;23(8):562–6.

Brailovskaia J, Rohmann E, Bierhoff H-W, Margraf J. The anxious addictive narcissist: the relationship between grandiose and vulnerable narcissism, anxiety symptoms and Facebook Addiction. PLoS ONE 2020, 15(11).

Kim S-S, Bae S-M. Social Anxiety and Social Networking Service Addiction Proneness in University students: the Mediating effects of Experiential Avoidance and interpersonal problems. Psychiatry Invest. 2022;19(8):702–702.

Zhao J, Ye B, Yu L, Xia F. Effects of Stressors of COVID-19 on Chinese College Students’ Problematic Social Media Use: A Mediated Moderation Model. Front Psychiatry 2022, 13.

Astolfi Cury GS, Takannune DM, Prates Herrerias GS, Rivera-Sequeiros A, de Barros JR, Baima JP, Saad-Hossne R, Sassaki LY. Clinical and Psychological Factors Associated with Addiction and Compensatory Use of Facebook among patients with inflammatory bowel disease: a cross-sectional study. Int J Gen Med. 2022;15:1447–57.

Balta S, Emirtekin E, Kircaburun K, Griffiths MD. Neuroticism, trait fear of missing out, and Phubbing: the mediating role of state fear of missing out and problematic Instagram Use. Int J Mental Health Addict. 2020;18(3):628–39.

Boursier V, Gioia F, Griffiths MD. Do selfie-expectancies and social appearance anxiety predict adolescents’ problematic social media use? Comput Hum Behav. 2020;110:106395.

Worsley JD, McIntyre JC, Bentall RP, Corcoran R. Childhood maltreatment and problematic social media use: the role of attachment and depression. Psychiatry Res. 2018;267:88–93.

de Bérail P, Guillon M, Bungener C. The relations between YouTube addiction, social anxiety and parasocial relationships with YouTubers: a moderated-mediation model based on a cognitive-behavioral framework. Comput Hum Behav. 2019;99:190–204.

Naidu S, Chand A, Pandaram A, Patel A. Problematic internet and social network site use in young adults: the role of emotional intelligence and fear of negative evaluation. Pers Indiv Differ. 2023;200:111915.

Apaolaza V, Hartmann P, D’Souza C, Gilsanz A. Mindfulness, compulsive Mobile Social Media Use, and derived stress: the mediating roles of self-esteem and social anxiety. Cyberpsychology Behav Social Netw. 2019;22(6):388–96.

Demircioglu ZI, Goncu-Kose A. Antecedents of problematic social media use and cyberbullying among adolescents: attachment, the dark triad and rejection sensitivity. Curr Psychol (New Brunsw NJ) 2022:1–19.

Gao Q, Li Y, Zhu Z, Fu E, Bu X, Peng S, Xiang Y. What links to psychological needs satisfaction and excessive WeChat use? The mediating role of anxiety, depression and WeChat use intensity. BMC Psychol. 2021;9(1):105–105.

Article   PubMed   PubMed Central   Google Scholar  

Malak MZ, Shuhaiber AH, Al-amer RM, Abuadas MH, Aburoomi RJ. Correlation between psychological factors, academic performance and social media addiction: model-based testing. Behav Inform Technol. 2022;41(8):1583–95.

Song C. The effect of the need to belong on mobile phone social media dependence of middle school students: Chain mediating roles of fear of missing out and maladaptive cognition. Sichuan Normal University; 2022.

Tokunaga RS, Rains SA. A review and meta-analysis examining conceptual and operational definitions of problematic internet use. Hum Commun Res. 2016;42(2):165–99.

Bandura A. Social cognitive theory of mass communication. Media effects. edn.: Routledge; 2009. pp. 110–40.

Valkenburg PM, Peter J, Walther JB. Media effects: theory and research. Ann Rev Psychol. 2016;67:315–38.

Slater MD. Reinforcing spirals: the mutual influence of media selectivity and media effects and their impact on individual behavior and social identity. Communication Theory. 2007;17(3):281–303.

Ahmed E, Vaghefi I. Social media addiction: A systematic review through cognitive-behavior model of pathological use. 2021.

She R, han Mo PK, Li J, Liu X, Jiang H, Chen Y, Ma L, fai Lau JT. The double-edged sword effect of social networking use intensity on problematic social networking use among college students: the role of social skills and social anxiety. Comput Hum Behav. 2023;140:107555.

Przybylski AK, Weinstein N. A large-scale test of the goldilocks hypothesis: quantifying the relations between digital-screen use and the mental well-being of adolescents. Psychol Sci. 2017;28(2):204–15.

Ran G, Li J, Zhang Q, Niu X. The association between social anxiety and mobile phone addiction: a three-level meta-analysis. Comput Hum Behav. 2022;130:107198.

Fioravanti G, Casale S, Benucci SB, Prostamo A, Falone A, Ricca V, Rotella F. Fear of missing out and social networking sites use and abuse: a meta-analysis. Comput Hum Behav. 2021;122:106839.

Moher D, Liberati A, Tetzlaff J, Altman DG, Group* P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264–9.

Card NA. Applied meta-analysis for social science research. Guilford; 2015.

Peterson RA, Brown SP. On the use of beta coefficients in meta-analysis. J Appl Psychol. 2005;90(1):175.

Hunter JE, Schmidt FL. Methods of meta-analysis: correcting error and bias in research findings. Sage; 2004.

Zhang Y, Li S, Yu G. The relationship between self-esteem and social anxiety: a meta-analysis with Chinese students. Adv Psychol Sci. 2019;27(6):1005–18.

Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to meta-analysis. Wiley; 2021.

Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.

Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34.

Light RJ, Pillemer DB. Summing up: the science of reviewing research. Harvard University Press; 1984.

Rosenthal R. Meta-Analytic Procedures for Social Science Research Sage Publications: Beverly Hills, 1984, 148 pp. Educational Researcher 1986;15(8):18–20.

Rothstein HR, Sutton AJ, Borenstein M. Publication bias in meta-analysis. Publication bias meta‐analysis: Prev Assess Adjustments 2005:1–7.

Duval S, Tweedie R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta‐analysis. Biometrics. 2000;56(2):455–63.

Al-Mamun F, Hosen I, Griffiths MD, Mamun MA. Facebook use and its predictive factors among students: evidence from a lower- and middle-income country, Bangladesh. Front Psychiatry 2022, 13.

Schou Andreassen C, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, Pallesen S. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behaviors: J Soc Psychologists Addict Behav. 2016;30(2):252–62.

Arikan G, Acar IH, Ustundag-Budak AM. A two-generation study: The transmission of attachment and young adults’ depression, anxiety, and social media addiction. Addict Behav 2022, 124.

Arpaci I, Karatas K, Kiran F, Kusci I, Topcu A. Mediating role of positivity in the relationship between state anxiety and problematic social media use during the COVID-19 pandemic. Death Stud. 2022;46(10):2287–97.

Brailovskaia J, Margraf J. Facebook Addiction Disorder (FAD) among German students-A longitudinal approach. PLoS ONE 2017, 12(12).

Brailovskaia J, Margraf J. The relationship between burden caused by coronavirus (Covid-19), addictive social media use, sense of control and anxiety. Comput Hum Behav. 2021;119:106720–106720.

Brailovskaia J, Margraf J. Addictive social media use during Covid-19 outbreak: validation of the Bergen Social Media Addiction Scale (BSMAS) and investigation of protective factors in nine countries. Curr Psychol (New Brunsw NJ) 2022:1–19.

Brailovskaia J, Krasavtseva Y, Kochetkov Y, Tour P, Margraf J. Social media use, mental health, and suicide-related outcomes in Russian women: a cross-sectional comparison between two age groups. Women’s Health (London England). 2022;18:17455057221141292–17455057221141292.

PubMed   Google Scholar  

Chang C-W, Huang R-Y, Strong C, Lin Y-C, Tsai M-C, Chen IH, Lin C-Y, Pakpour AHH, Griffiths MDD. Reciprocal relationships between Problematic Social Media Use, problematic gaming, and psychological distress among University students: a 9-Month Longitudinal Study. Front Public Health 2022, 10.

Charzynska E, Sussman S, Atroszko PA. Profiles of potential behavioral addictions’ severity and their associations with gender, personality, and well-being: A person-centered approach. Addict Behav 2021, 119.

Chen C-Y, Chen IH, Pakpour AH, Lin C-Y, Griffiths MD. Internet-related behaviors and psychological distress among Schoolchildren during the COVID-19 School Hiatus. Cyberpsychology Behav Social Netw. 2021;24(10):654–63.

Da Veiga GF, Sotero L, Pontes HM, Cunha D, Portugal A, Relvas AP. Emerging adults and Facebook Use: the validation of the Bergen Facebook Addiction Scale (BFAS). Int J Mental Health Addict. 2019;17(2):279–94.

Dadiotis A, Bacopoulou F, Kokka I, Vlachakis D, Chrousos GP, Darviri C, Roussos P. Validation of the Greek version of the Bergen Social Media Addiction Scale in Undergraduate Students. EMBnetjournal 2021, 26.

Fekih-Romdhane F, Jahrami H, Away R, Trabelsi K, Pandi-Perumal SR, Seeman MV, Hallit S, Cheour M. The relationship between technology addictions and schizotypal traits: mediating roles of depression, anxiety, and stress. BMC Psychiatry 2023, 23(1).

Flynn S, Noone C, Sarma KM. An exploration of the link between adult attachment and problematic Facebook use. BMC Psychol. 2018;6(1):34–34.

Fung XCC, Siu AMH, Potenza MN, O’Brien KS, Latner JD, Chen C-Y, Chen IH, Lin C-Y. Problematic use of internet-related activities and Perceived Weight Stigma in Schoolchildren: a longitudinal study across different epidemic periods of COVID-19 in China. Front Psychiatry 2021, 12.

Gonzalez-Nuevo C, Cuesta M, Muniz J, Postigo A, Menendez-Aller A, Kuss DJ. Problematic Use of Social Networks during the First Lockdown: User Profiles and the Protective Effect of Resilience and Optimism. Journal of Clinical Medicine 2022, 11(24).

Hou X-L, Wang H-Z, Hu T-Q, Gentile DA, Gaskin J, Wang J-L. The relationship between perceived stress and problematic social networking site use among Chinese college students. J Behav Addictions. 2019;8(2):306–17.

Hussain Z, Wegmann E. Problematic social networking site use and associations with anxiety, attention deficit hyperactivity disorder, and resilience. Computers Hum Behav Rep. 2021;4:100125.

Imani V, Ahorsu DK, Taghizadeh N, Parsapour Z, Nejati B, Chen H-P, Pakpour AH. The mediating roles of anxiety, Depression, Sleepiness, Insomnia, and Sleep Quality in the Association between Problematic Social Media Use and Quality of Life among patients with Cancer. Healthcare 2022, 10(9).

Islam MS, Sujan MSH, Tasnim R, Mohona RA, Ferdous MZ, Kamruzzaman S, Toma TY, Sakib MN, Pinky KN, Islam MR et al. Problematic smartphone and Social Media Use among Bangladeshi College and University students amid COVID-19: the role of Psychological Well-Being and Pandemic related factors. Front Psychiatry 2021, 12.

Islam MS, Jahan I, Dewan MAA, Pontes HM, Koly KN, Sikder MT, Rahman M. Psychometric properties of three online-related addictive behavior instruments among Bangladeshi school-going adolescents. PLoS ONE 2022, 17(12).

Jahan I, Hosen I, Al Mamun F, Kaggwa MM, Griffiths MD, Mamun MA. How has the COVID-19 pandemic impacted Internet Use behaviors and facilitated problematic internet use? A Bangladeshi study. Psychol Res Behav Manage. 2021;14:1127–38.

Jiang Y. Problematic social media usage and anxiety among University Students during the COVID-19 pandemic: the mediating role of Psychological Capital and the moderating role of academic burnout. Front Psychol. 2021;12:612007–612007.

Kim M-R, Oh J-W, Huh B-Y. Analysis of factors related to Social Network Service Addiction among Korean High School Students. J Addictions Nurs. 2020;31(3):203–12.

Koc M, Gulyagci S. Facebook addiction among Turkish college students: the role of psychological health, demographic, and usage characteristics. Cyberpsychology Behav Social Netw. 2013;16(4):279–84.

Lin C-Y, Namdar P, Griffiths MD, Pakpour AH. Mediated roles of generalized trust and perceived social support in the effects of problematic social media use on mental health: a cross-sectional study. Health Expect. 2021;24(1):165–73.

Lin C-Y, Imani V, Griffiths MD, Brostrom A, Nygardh A, Demetrovics Z, Pakpour AH. Temporal associations between morningness/eveningness, problematic social media use, psychological distress and daytime sleepiness: mediated roles of sleep quality and insomnia among young adults. J Sleep Res 2021, 30(1).

Lozano Blasco R, Latorre Cosculluela C, Quilez Robres A. Social Network Addiction and its impact on anxiety level among University students. Sustainability 2020, 12(13).

Marino C, Musetti A, Vieno A, Manari T, Franceschini C. Is psychological distress the key factor in the association between problematic social networking sites and poor sleep quality? Addict Behav 2022, 133.

Meshi D, Ellithorpe ME. Problematic social media use and social support received in real-life versus on social media: associations with depression, anxiety and social isolation. Addict Behav 2021, 119.

Mitropoulou EM, Karagianni M, Thomadakis C. Social Media Addiction, Self-Compassion, and Psychological Well-Being: a structural equation Model. Alpha Psychiatry. 2022;23(6):298–304.

Ozimek P, Brailovskaia J, Bierhoff H-W. Active and passive behavior in social media: validating the Social Media Activity Questionnaire (SMAQ). Telematics Inf Rep. 2023;10:100048.

Phillips WJ, Wisniewski AT. Self-compassion moderates the predictive effects of social media use profiles on depression and anxiety. Computers Hum Behav Rep. 2021;4:100128.

Reer F, Festl R, Quandt T. Investigating problematic social media and game use in a nationally representative sample of adolescents and younger adults. Behav Inform Technol. 2021;40(8):776–89.

Satici B, Kayis AR, Griffiths MD. Exploring the Association between Social Media Addiction and relationship satisfaction: psychological distress as a Mediator. Int J Mental Health Addict 2021.

Sediri S, Zgueb Y, Ouanes S, Ouali U, Bourgou S, Jomli R, Nacef F. Women’s mental health: acute impact of COVID-19 pandemic on domestic violence. Archives Womens Mental Health. 2020;23(6):749–56.

Shabahang R, Shim H, Aruguete MS, Zsila A. Oversharing on Social Media: anxiety, Attention-Seeking, and Social Media Addiction Predict the breadth and depth of sharing. Psychol Rep 2022:332941221122861–332941221122861.

Sotero L, Ferreira Da Veiga G, Carreira D, Portugal A, Relvas AP. Facebook Addiction and emerging adults: the influence of sociodemographic variables, family communication, and differentiation of self. Escritos De Psicología - Psychol Writings. 2019;12(2):81–92.

Stockdale LA, Coyne SM. Bored and online: reasons for using social media, problematic social networking site use, and behavioral outcomes across the transition from adolescence to emerging adulthood. J Adolesc. 2020;79:173–83.

Wang Z, Yang H, Elhai JD. Are there gender differences in comorbidity symptoms networks of problematic social media use, anxiety and depression symptoms? Evidence from network analysis. Pers Indiv Differ. 2022;195:111705.

White-Gosselin C-E, Poulin F. Associations Between Young Adults’ Social Media Addiction, Relationship Quality With Parents, and Internalizing Problems: A Path Analysis Model. 2022.

Wong HY, Mo HY, Potenza MN, Chan MNM, Lau WM, Chui TK, Pakpour AH, Lin C-Y. Relationships between Severity of Internet Gaming Disorder, Severity of Problematic Social Media Use, Sleep Quality and Psychological Distress. Int J Environ Res Public Health 2020, 17(6).

Yam C-W, Pakpour AH, Griffiths MD, Yau W-Y, Lo C-LM, Ng JMT, Lin C-Y, Leung H. Psychometric testing of three Chinese online-related addictive Behavior instruments among Hong Kong University students. Psychiatr Q. 2019;90(1):117–28.

Yuan Y, Zhong Y. A survey on the use of social networks and mental health of college students during the COVID-19 pandemic. J Campus Life Mental Health\. 2021;19(3):209–12.

Google Scholar  

Yurdagul C, Kircaburun K, Emirtekin E, Wang P, Griffiths MD. Psychopathological consequences related to problematic Instagram Use among adolescents: the mediating role of body image dissatisfaction and moderating role of gender. Int J Mental Health Addict. 2021;19(5):1385–97.

Zhang W, Pu J, He R, Yu M, Xu L, He X, Chen Z, Gan Z, Liu K, Tan Y, et al. Demographic characteristics, family environment and psychosocial factors affecting internet addiction in Chinese adolescents. J Affect Disord. 2022;315:130–8.

Zhang L, Wu Y, Jin T, Jia Y. Revision and validation of the Chinese short version of social media disorder. Mod Prev Med. 2021;48(8):1350–3.

Zhang X, Fan L. The influence of anxiety on colleges’ life satisfaction. Chin J Health Educ. 2021;37(5):469–72.

Zhao M, Wang H, Dong Y, Niu Y, Fang Y. The relationship between self-esteem and wechat addiction among undergraduate students: the multiple mediating roles of state anxiety and online interpersonal trust. J Psychol Sci. 2021;44(1):104–10.

Zhao J, Zhou Z, Sun B, Zhang X, Zhang L, Fu S. Attentional Bias is Associated with negative emotions in problematic users of Social Media as measured by a dot-probe Task. Int J Environ Res Public Health 2022, 19(24).

Atroszko PA, Balcerowska JM, Bereznowski P, Biernatowska A, Pallesen S, Schou Andreassen C. Facebook addiction among Polish undergraduate students: validity of measurement and relationship with personality and well-being. Comput Hum Behav. 2018;85:329–38.

Chen Y, Li R, Zhang P, Liu X. The moderating role of state attachment anxiety and avoidance between social anxiety and social networking sites Addiction. Psychol Rep. 2020;123(3):633–47.

Chen B, Zheng X, Sun X. The relationship between problematic social media use and online social anxiety: the roles of social media cognitive overload and dispositional mindfulness. Psychol Dev Educ. 2023;39(5):743–51.

Chentsova VO, Bravo AJ, Mezquita L, Pilatti A, Hogarth L, Cross-Cultural AS. Internalizing symptoms, rumination, and problematic social networking site use: a cross national examination among young adults in seven countries. Addict Behav 2023, 136.

Chu X, Ji S, Wang X, Yu J, Chen Y, Lei L. Peer phubbing and social networking site addiction: the mediating role of social anxiety and the moderating role of Family Financial Difficulty. Front Psychol. 2021;12:670065–670065.

Dempsey AE, O’Brien KD, Tiamiyu MF, Elhai JD. Fear of missing out (FoMO) and rumination mediate relations between social anxiety and problematic Facebook use. Addict Behav Rep. 2019;9:100150–100150.

PubMed   PubMed Central   Google Scholar  

Yildiz Durak H, Seferoglu SS. Modeling of variables related to problematic social media usage: Social desirability tendency example. Scand J Psychol. 2019;60(3):277–88.

Ekinci N, Akat M. The relationship between anxious-ambivalent attachment and social appearance anxiety in adolescents: the serial mediation of positive Youth Development and Instagram Addiction. Psychol Rep 2023:332941231159600–332941231159600.

Foroughi B, Griffiths MD, Iranmanesh M, Salamzadeh Y. Associations between Instagram Addiction, academic performance, social anxiety, Depression, and life satisfaction among University students. Int J Mental Health Addict. 2022;20(4):2221–42.

He L. Influence mechanism and intervention suggestions on addiction of social network addiction. Gannan Normal University; 2021.

Hu Y. The influencing mechanism of type D personality on problematic social networking sites use among adolescents and intervention research. Central China Normal University; 2020.

Jia L. A study of the relationship between neuroticism, perceived social support, social anxiety and problematic social network use in high school students. Harbin Normal University; 2022.

Lee-Won RJ, Herzog L, Park SG. Hooked on Facebook: the role of social anxiety and need for Social Assurance in Problematic Use of Facebook. Cyberpsychology Behav Social Netw. 2015;18(10):567–74.

Li H. Social anxiety and internet interpersonal addiction in adolescents and countermeasures. Central China Normal University; 2022.

Lin W-S, Chen H-R, Lee TS-H, Feng JY. Role of social anxiety on high engagement and addictive behavior in the context of social networking sites. Data Technol Appl. 2019;53(2):156–70.

Liu Y. The influence of family function on social media addiction in adolescents: the chain mediation effect of social anxiety and resilience. Hunan Normal University; 2021.

Lyvers M, Salviani A, Costan S, Thorberg FA. Alexithymia, narcissism and social anxiety in relation to social media and internet addiction symptoms. Int J Psychology: J Int De Psychologie. 2022;57(5):606–12.

Majid A, Yasir M, Javed A, Ali P. From envy to social anxiety and rumination: how social media site addiction triggers task distraction amongst nurses. J Nurs Adm Manag. 2020;28(3):504–13.

Mou Q, Zhuang J, Gao Y, Zhong Y, Lu Q, Gao F, Zhao M. The relationship between social anxiety and academic engagement among Chinese college students: a serial mediation model. J Affect Disord. 2022;311:247–53.

Ruggieri S, Santoro G, Pace U, Passanisi A, Schimmenti A. Problematic Facebook use and anxiety concerning use of social media in mothers and their offspring: an actor-partner interdependence model. Addict Behav Rep. 2020;11:100256–100256.

Ruiz MJ, Saez G, Villanueva-Moya L, Exposito F. Adolescent sexting: the role of body shame, Social Physique anxiety, and social networking site addiction. Cyberpsychology Behav Social Netw. 2021;24(12):799–805.

She R, Kit Han Mo P, Li J, Liu X, Jiang H, Chen Y, Ma L, Tak Fai Lau J. The double-edged sword effect of social networking use intensity on problematic social networking use among college students: the role of social skills and social anxiety. Comput Hum Behav. 2023;140:107555.

Stănculescu E. The Bergen Social Media Addiction Scale Validity in a Romanian sample using item response theory and network analysis. Int J Mental Health Addict 2022.

Teng X, Lei H, Li J, Wen Z. The influence of social anxiety on social network site addiction of college students: the moderator of intentional self-regulation. Chin J Clin Psychol. 2021;29(3):514–7.

Tong W. Influence of boredom on the problematic mobile social networks usage in adolescents: multiple chain mediator. Chin J Clin Psychol. 2019;27(5):932–6.

Tu W, Jiang H, Liu Q. Peer victimization and adolescent Mobile Social Addiction: mediation of social anxiety and gender differences. Int J Environ Res Public Health 2022, 19(17).

Wang S. The influence of college students self-esteem, social anxiety and fear of missing out on the problematic mobile social networks usage. Huaibei Normal University; 2021.

Wang X. The impact of peer relationship and social anxiety on secondary vocational school students’ problematic social network use and intervention study. Huaibei Normal University; 2022.

Wegmann E, Stodt B, Brand M. Addictive use of social networking sites can be explained by the interaction of internet use expectancies, internet literacy, and psychopathological symptoms. J Behav Addictions. 2015;4(3):155–62.

Yang W. The relationship between the type of internet addiction and the personality traits in college students. Huazhong University of Science and Technology; 2004.

Yang Z. The relationship between social variables and social networking usage among shanghai working population. East China Normal University; 2013.

Zhang C. The relationship between perceived social support and problematic social network use among junior high school students: a chain mediation model and an intervention study. Hebei University; 2022.

Zhang J, Chang F, Huang D, Wen X. The relationship between neuroticism and the problematic mobile social networks use in adolescents: the mediating role of anxiety and positive self-presentation. Chin J Clin Psychol. 2021;29(3):598–602.

Zhang Z. College students’ loneliness and problematic social networking use: Chain mediation of social self-efficacy and social anxiety. Shanghai Normal University; 2019.

Zhu B. Discussion on mechanism of social networking addiction——Social anxiety, craving and excitability. Liaoning Normal University; 2017.

Blackwell D, Leaman C, Tramposch R, Osborne C, Liss M. Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Pers Indiv Differ. 2017;116:69–72.

Chen A. From attachment to addiction: the mediating role of need satisfaction on social networking sites. Comput Hum Behav. 2019;98:80–92.

Chen Y, Zhong S, Dai L, Deng Y, Liu X. Attachment anxiety and social networking sites addiction in college students: a moderated mediating model. Chin J Clin Psychol. 2019;27(3):497–500.

Li J. The relations among problematic social networks usage behavior, Childhood Trauma and adult attachment in University students. Hunan Agricultural University; 2020.

Liu C, Ma J-L. Adult attachment orientations and social networking site addiction: the Mediating effects of Online Social Support and the fear of missing out. Front Psychol. 2019;10:2629–2629.

Mo S, Huang W, Xu Y, Tang Z, Nie G. The impact of medical students’ attachment anxiety on the use of problematic social networking sites during the epidemic. Psychol Monthly. 2022;17(9):1–4.

Teng X. The effect of attachment anxiety on problematic mobile social network use: the role of loneliness and self-control. Harbin Normal University; 2021.

Worsley JD, Mansfield R, Corcoran R. Attachment anxiety and problematic social media use: the Mediating Role of Well-Being. Cyberpsychology Behav Social Netw. 2018;21(9):563–8.

Wu Z. The effect of adult attachment on problematic social network use: the chain mediating effect of loneliness and fear of missing out. Jilin University; 2022.

Xia N. The impact of attachment anxiety on adolescent problem social networking site use: a moderated mediation model. Shihezi University; 2022.

Young L, Kolubinski DC, Frings D. Attachment style moderates the relationship between social media use and user mental health and wellbeing. Heliyon 2020, 6(6).

Bakioglu F, Deniz M, Griffiths MD, Pakpour AH. Adaptation and validation of the online-fear of missing out inventory into Turkish and the association with social media addiction, smartphone addiction, and life satisfaction. BMC Psychol. 2022;10(1):154–154.

Bendayan R, Blanca MJ. Spanish version of the Facebook Intrusion Questionnaire (FIQ-S). Psicothema. 2019;31(2):204–9.

Blachnio A, Przepiorka A. Facebook intrusion, fear of missing out, narcissism, and life satisfaction: a cross-sectional study. Psychiatry Res. 2018;259:514–9.

Casale S, Rugai L, Fioravanti G. Exploring the role of positive metacognitions in explaining the association between the fear of missing out and social media addiction. Addict Behav. 2018;85:83–7.

Chen Y, Zhang Y, Zhang S, Wang K. Effect of fear of’ missing out on college students negative social adaptation: Chain¬ - mediating effect of rumination and problematic social media use. China J Health Psychol. 2022;30(4):581–6.

Cheng S, Zhang X, Han Y. Relationship between fear of missing out and phubbing on college students: the chain intermediary effect of intolerance of uncertainty and problematic social media use. China J Health Psychol. 2022;30(9):1296–300.

Cui Q, Wang J, Zhang J, Li W, Li Q. The relationship between loneliness and negative emotion in college students: the chain-mediating role of fear of missing out and social network sites addiction. J Jining Med Univ. 2022;45(4):248–51.

Ding Q, Wang Z, Zhang Y, Zhou Z. The more gossip, the more addicted: the relationship between interpersonal curiosity and social networking sites addiction tendencies in college students. Psychol Dev Educ. 2022;38(1):118–25.

Fabris MA, Marengo D, Longobardi C, Settanni M. Investigating the links between fear of missing out, social media addiction, and emotional symptoms in adolescence: the role of stress associated with neglect and negative reactions on social media. Addict Behav. 2020;106:106364.

Fang J, Wang X, Wen Z, Zhou J. Fear of missing out and problematic social media use as mediators between emotional support from social media and phubbing behavior. Addict Behav. 2020;107:106430.

Gao Z. The study on the relationship and intervention among fear of missing out self-differentiation and problematic social media use of college students. Yunnan Normal University; 2021.

Gioia F, Fioravanti G, Casale S, Boursier V. The Effects of the Fear of Missing Out on People’s Social Networking Sites Use During the COVID-19 Pandemic: The Mediating Role of Online Relational Closeness and Individuals’ Online Communication Attitude. Front Psychiatry 2021, 12.

Gu X. Study on the Inhibitory Effect of Mindfulness Training on Social Media Addiction of College Students. Wuhan University; 2020.

Gugushvili N, Taht K, Schruff-Lim EM, Ruiter RA, Verduyn P. The Association between Neuroticism and problematic social networking sites Use: the role of fear of missing out and Self-Control. Psychol Rep 2022:332941221142003–332941221142003.

Hou J. The study on FoMO and content social media addiction among young people. Huazhong University of Science and Technology; 2021.

Hu R, Zhang B, Yang Y, Mao H, Peng Y, Xiong S. Relationship between college students’ fear of missing and wechat addiction: a cross-lagged analysis. J Bio-education. 2022;10(5):369–73.

Hu G. The relationship between basic psychological needs satisfaction and the use of problematic social networks by college students: a moderated mediation model and online intervention studies. Jiangxi Normal University; 2020.

Jiang Y, Jin T. The relationship between adolescents’ narcissistic personality and their use of problematic mobile social networks: the effects of fear of missing out and positive self-presentation. Chin J Special Educ 2018(11):64–70.

Li J. The effect of positive self-presentation on social networking sites on problematic use of social networking sites: a moderated mediation model. Henan University; 2020.

Li J, Zhang Y, Zhang X. The impact of Freshmen Social Exclusion on problematic Social Network Use: a Moderated Mediation Model. J Heilongjiang Vocat Inst Ecol Eng. 2023;36(1):118–22.

Li M. The relationship between fear of missing out and social media addiction among middle school students——The moderating role of self-control. Kashi University; 2022.

Li R, Dong X, Wang M, Wang R. A study on the relationship between fear of missing out and social network addiction. New Educ Era 2021(52):122–3.

Li Y. Fear of missing out or social avoidance? The influence of peer exclusion on problematic social media use among adolescents in Guangdong Province and Macao. Guangzhou University; 2020.

Ma J, Liu C. The effect of fear of missing out on social networking sites addiction among college students: the mediating roles of social networking site integration use and social support. Psychol Dev Educ. 2019;35(5):605–14.

Mao H. A follow-up study on the mechanism of the influence of university students’ Qi deficiency quality on WeChat addiction. Hunan University of Chinese Medicine; 2021.

Mao Y. The effect of dual filial piety to the college students ’internet social dependence: the mediation of maladaptive cognition and fear of missing out. Huazhong University of Science and Technology; 2021.

Moore K, Craciun G. Fear of missing out and personality as predictors of Social networking sites usage: the Instagram Case. Psychol Rep. 2021;124(4):1761–87.

Niu J. The relationship of college students’ basic psychological needs and social media dependence: the mediating role of fear of missing out. Huazhong University of Science and Technology; 2021.

Pi L, Li X. Research on the relationship between loneliness and problematic mobile social media usage: evidence from variable-oriented and person-oriented analyses. China J Health Psychol. 2023;31(6):936–42.

Pontes HM, Taylor M, Stavropoulos V. Beyond Facebook Addiction: the role of cognitive-related factors and Psychiatric Distress in Social networking site addiction. Cyberpsychol Behav Soc Netw. 2018;21(4):240–7.

Quaglieri A, Biondi S, Roma P, Varchetta M, Fraschetti A, Burrai J, Lausi G, Marti-Vilar M, Gonzalez-Sala F, Di Domenico A et al. From Emotional (Dys) Regulation to Internet Addiction: A Mediation Model of Problematic Social Media Use among Italian Young Adults. Journal of Clinical Medicine 2022, 11(1).

Servidio R, Koronczai B, Griffiths MD, Demetrovics Z. Problematic smartphone Use and Problematic Social Media Use: the predictive role of Self-Construal and the Mediating Effect of Fear Missing Out. Front Public Health 2022, 10.

Sheldon P, Antony MG, Sykes B. Predictors of problematic social media use: personality and life-position indicators. Psychol Rep. 2021;124(3):1110–33.

Sun C, Li Y, Kwok SYCL, Mu W. The relationship between intolerance of uncertainty and problematic Social Media Use during the COVID-19 pandemic: a serial mediation model. Int J Environ Res Public Health 2022, 19(22).

Tang Z. The relationship between loneliness and problematic social networks use among college students: the mediation of fear of missing out and the moderation of social support. Jilin University; 2022.

Tomczyk Ł, Selmanagic-Lizde E. Fear of missing out (FOMO) among youth in Bosnia and Herzegovina — Scale and selected mechanisms. Child Youth Serv Rev. 2018;88:541–9.

Unal-Aydin P, Ozkan Y, Ozturk M, Aydin O, Spada MM. The role of metacognitions in cyberbullying and cybervictimization among adolescents diagnosed with major depressive disorder and anxiety disorders: a case-control study. Clinical Psychology & Psychotherapy; 2023.

Uram P, Skalski S. Still logged in? The Link between Facebook Addiction, FoMO, Self-Esteem, Life satisfaction and loneliness in social media users. Psychol Rep. 2022;125(1):218–31.

Varchetta M, Fraschetti A, Mari E, Giannini AM. Social Media Addiction, fear of missing out (FoMO) and online vulnerability in university students. Revista Digit De Investigación en Docencia Universitaria. 2020;14(1):e1187.

Wang H. Study on the relationship and intervention between fear of missing and social network addiction in college students. Yunnan Normal University; 2021.

Wang M, Yin Z, Xu Q, Niu G. The relationship between shyness and adolescents’ social network sites addiction: Moderated mediation model. Chin J Clin Psychol. 2020;28(5):906–9.

Wegmann E, Oberst U, Stodt B, Brand M. Online-specific fear of missing out and internet-use expectancies contribute to symptoms of internet-communication disorder. Addict Behav Rep. 2017;5:33–42.

Wegmann E, Brandtner A, Brand M. Perceived strain due to COVID-19-Related restrictions mediates the Effect of Social needs and fear of missing out on the risk of a problematic use of Social Networks. Front Psychiatry 2021, 12.

Wei Q. Negative emotions and problematic social network sites usage: the mediating role of fear of missing out and the moderating role of gender. Central China Normal University; 2018.

Xiong L. Effect of social network site use on college students’ social network site addiction: A moderated mediation model and attention bias training intervention study. Jiangxi Normal University; 2022.

Yan H. The influence of college students’ basic psychological needs on social network addiction: The intermediary role of fear of missing out. Wuhan University; 2020.

Yan H. The status and factors associated with social media addiction among young people——Evidence from WeChat. Chongqing University; 2021.

Yang L. Research on the relationship of fear of missing out, excessive use of Wechat and life satisfaction. Beijing Forestry University; 2020.

Yin Y, Cai X, Ouyang M, Li S, Li X, Wang P. FoMO and the brain: loneliness and problematic social networking site use mediate the association between the topology of the resting-state EEG brain network and fear of missing out. Comput Hum Behav. 2023;141:107624.

Zhang C. The parental rejection and problematic social network sites with adolescents: the chain mediating effect of basic psychological needs and fear of missing out. Central China Normal University; 2022.

Zhang J. The influence of basic psychological needs on problematic mobile social networks usage of adolescent: a moderated mediation model. Liaocheng University; 2020.

Zhang Y, Chen Y, Jin J, Yu G. The relationship between fear of missing out and social media addiction: a cross-lagged analysis. Chin J Clin Psychol. 2021;29(5):1082–5.

Zhang Y, Jiang W, Ding Q, Hong M. Social comparison orientation and social network sites addiction in college students: the mediating role of fear of missing out. Chin J Clin Psychol. 2019;27(5):928–31.

Zhou J, Fang J. Social network sites support and addiction among college students: a moderated mediation model. Psychology: Techniques Appl. 2021;9(5):293–9.

Andreassen CS, Torsheim T, Brunborg GS, Pallesen S. Development of a Facebook addiction scale. Psychol Rep. 2012;110(2):501–17.

Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, Pallesen S. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav. 2016;30(2):252.

Elphinston RA, Noller P. Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychology Behav Social Netw. 2011;14(11):631–5.

Caplan SE. Theory and measurement of generalized problematic internet use: a two-step approach. Comput Hum Behav. 2010;26(5):1089–97.

Jiang Y. Development of problematic mobile social media usage assessment questionnaire for adolescents. Psychology: Techniques Appl. 2018;6(10):613–21.

Wang X. College students’ social network addiction tendency: Questionnaire construction and correlation research. Master’s thesis Southwest University; 2016.

Derogatis LR. Brief symptom inventory 18. Johns Hopkins University Baltimore; 2001.

Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression anxiety stress scales (DASS) with the Beck Depression and anxiety inventories. Behav Res Ther. 1995;33(3):335–43.

Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7.

Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica. 1983;67(6):361–70.

Spielberger CD, Gonzalez-Reigosa F, Martinez-Urrutia A, Natalicio LF, Natalicio DS. The state-trait anxiety inventory. Revista Interamericana de Psicologia/Interamerican Journal of Psychology 1971, 5(3&4).

Marteau TM, Bekker H. The development of a six-item short‐form of the state scale of the Spielberger State—trait anxiety inventory (STAI). Br J Clin Psychol. 1992;31(3):301–6.

Leary MR. Social anxiousness: the construct and its measurement. J Pers Assess. 1983;47(1):66–75.

Liebowitz MR. Social phobia. Modern problems of pharmacopsychiatry 1987.

Alkis Y, Kadirhan Z, Sat M. Development and validation of social anxiety scale for social media users. Comput Hum Behav. 2017;72:296–303.

La Greca AM, Stone WL. Social anxiety scale for children-revised: factor structure and concurrent validity. J Clin Child Psychol. 1993;22(1):17–27.

Fenigstein A, Scheier MF, Buss AH. Public and private self-consciousness: Assessment and theory. J Consult Clin Psychol. 1975;43(4):522.

Mattick RP, Clarke JC. Development and validation of measures of social phobia scrutiny fear and social interaction anxiety. Behav Res Ther. 1998;36(4):455–70.

Peters L, Sunderland M, Andrews G, Rapee RM, Mattick RP. Development of a short form Social Interaction anxiety (SIAS) and Social Phobia Scale (SPS) using nonparametric item response theory: the SIAS-6 and the SPS-6. Psychol Assess. 2012;24(1):66.

Brennan KA, Clark CL, Shaver PR. Self-report measurement of adult attachment: an integrative overview. Attachment Theory Close Relationships. 1998;46:76.

Wei M, Russell DW, Mallinckrodt B, Vogel DL. The experiences in Close Relationship Scale (ECR)-short form: reliability, validity, and factor structure. J Pers Assess. 2007;88(2):187–204.

Bartholomew K, Horowitz LM. Attachment styles among young adults: a test of a four-category model. J Personal Soc Psychol. 1991;61(2):226.

Przybylski AK, Murayama K, DeHaan CR, Gladwell V. Motivational, emotional, and behavioral correlates of fear of missing out. Comput Hum Behav. 2013;29(4):1841–8.

Xiaokang S, Yuxiang Z, Xuanhui Z. Developing a fear of missing out (FoMO) measurement scale in the mobile social media environment. Libr Inform Service. 2017;61(11):96.

Bown M, Sutton A. Quality control in systematic reviews and meta-analyses. Eur J Vasc Endovasc Surg. 2010;40(5):669–77.

Turel O, Qahri-Saremi H. Problematic use of social networking sites: antecedents and consequence from a dual-system theory perspective. J Manage Inform Syst. 2016;33(4):1087–116.

Chou H-TG, Edge N. They are happier and having better lives than I am: the impact of using Facebook on perceptions of others’ lives. Cyberpsychology Behav Social Netw. 2012;15(2):117–21.

Beyens I, Frison E, Eggermont S. I don’t want to miss a thing: adolescents’ fear of missing out and its relationship to adolescents’ social needs, Facebook use, and Facebook related stress. Comput Hum Behav. 2016;64:1–8.

Di Blasi M, Gullo S, Mancinelli E, Freda MF, Esposito G, Gelo OCG, Lagetto G, Giordano C, Mazzeschi C, Pazzagli C. Psychological distress associated with the COVID-19 lockdown: a two-wave network analysis. J Affect Disord. 2021;284:18–26.

Yang X, Hu H, Zhao C, Xu H, Tu X, Zhang G. A longitudinal study of changes in smart phone addiction and depressive symptoms and potential risk factors among Chinese college students. BMC Psychiatry. 2021;21(1):252.

Kuss DJ, Griffiths MD. Social networking sites and addiction: ten lessons learned. Int J Environ Res Public Health. 2017;14(3):311.

Ryan T, Chester A, Reece J, Xenos S. The uses and abuses of Facebook: a review of Facebook addiction. J Behav Addictions. 2014;3(3):133–48.

Elhai JD, Levine JC, Dvorak RD, Hall BJ. Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use. Comput Hum Behav. 2017;69:75–82.

Jackson LA, Wang J-L. Cultural differences in social networking site use: a comparative study of China and the United States. Comput Hum Behav. 2013;29(3):910–21.

Ahrens LM, Mühlberger A, Pauli P, Wieser MJ. Impaired visuocortical discrimination learning of socially conditioned stimuli in social anxiety. Soc Cognit Affect Neurosci. 2014;10(7):929–37.

Elhai JD, Yang H, Montag C. Fear of missing out (FOMO): overview, theoretical underpinnings, and literature review on relations with severity of negative affectivity and problematic technology use. Brazilian J Psychiatry. 2020;43:203–9.

Barker V. Older adolescents’ motivations for social network site use: the influence of gender, group identity, and collective self-esteem. Cyberpsychology Behav. 2009;12(2):209–13.

Krasnova H, Veltri NF, Eling N, Buxmann P. Why men and women continue to use social networking sites: the role of gender differences. J Strateg Inf Syst. 2017;26(4):261–84.

Palmer J. The role of gender on social network websites. Stylus Knights Write Showc 2012:35–46.

Vannucci A, Flannery KM, Ohannessian CM. Social media use and anxiety in emerging adults. J Affect Disord. 2017;207:163–6.

Primack BA, Shensa A, Sidani JE, Whaite EO, yi Lin L, Rosen D, Colditz JB, Radovic A, Miller E. Social media use and perceived social isolation among young adults in the US. Am J Prev Med. 2017;53(1):1–8.

Twenge JM, Campbell WK. Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Prev Med Rep. 2018;12:271–83.

Download references

This research was supported by the Social Science Foundation of China (Grant Number: 23BSH135).

Author information

Authors and affiliations.

School of Mental Health, Wenzhou Medical University, 325035, Wenzhou, China

Mingxuan Du, Haiyan Hu, Ningning Ding, Jiankang He, Wenwen Tian, Wenqian Zhao, Xiujian Lin, Gaoyang Liu, Wendan Chen, ShuangLiu Wang, Dongwu Xu & Guohua Zhang

School of Education, Renmin University of China, 100872, Beijing, China

Chengjia Zhao

School of Media and Communication, Shanghai Jiao Tong University, Dongchuan Road 800, 200240, Shanghai, China

Pengcheng Wang

Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, 313002, Huzhou, China

Xinhua Shen

You can also search for this author in PubMed   Google Scholar

Contributions

GZ, XS, XL and MD prepared the study design, writing - review and editing. MD and CZ wrote the main manuscript text. MD and HH analyzed data and edited the draft. ND, JH, WT, WZ, GL, WC, SW, PW and DX conducted resources and data curation. All authors have approved the final version of the manuscript.

Corresponding authors

Correspondence to Xinhua Shen or Guohua Zhang .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Du, M., Zhao, C., Hu, H. et al. Association between problematic social networking use and anxiety symptoms: a systematic review and meta-analysis. BMC Psychol 12 , 263 (2024). https://doi.org/10.1186/s40359-024-01705-w

Download citation

Received : 25 January 2024

Accepted : 03 April 2024

Published : 12 May 2024

DOI : https://doi.org/10.1186/s40359-024-01705-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Fear of missing out
• Meta-analysis

BMC Psychology

ISSN: 2050-7283

• General enquiries: [email protected]

Staff Members

222 results returned.

Samantha Agbeblewu

Lending Assistant

Jack (John) Ahern

Director of Annual Giving

Kimberly Allen

Projects and Gov Docs Metadata Assistant

Chris (Christopher) Allen

Library Facilities Manager

Nate (Nathaniel) Allen

Carolina Blu Delivery & Weekend Supervisor

Meaghan Alston

Assistant Curator for African American Collections

Katelyn Ander

Media Center Manager

Juan Arango

Records Management & Searching Coordinator

Baz Armstrong

Circulation Supervisor/Technology Lending Coordinator

Jess (Jessica) Aylor

Executive Director of Library Development