meta analysis of mixed methods research

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• v.10(11); 2022 Nov

A mixed‐method systematic review and meta‐analysis of the influences of food environments and food insecurity on obesity in high‐income countries

Fatemeh eskandari.

1 Centre for Public Health Research, School of Health and Life Sciences, Teesside University, Middlesbrough UK

2 Fuse ‐ The Centre for Translational Research in Public Health, Newcastle upon Tyne UK

Amelia A. Lake

Mark butler, claire o'malley, associated data.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Obesity remains a serious public health concern in rich countries and the current obesogenic food environments and food insecurity are predictors of this disease. The impact of these variables on rising obesity trends is, however, mixed and inconsistent, due to measurement issues and cross‐sectional study designs. To further the work in this area, this review aimed to summarize quantitative and qualitative data on the relationship between these variables, among adults and children across high‐income countries. A mixed‐method systematic review was conducted using 13 electronic databases, up to August 2021. Two authors independently extracted data and evaluated quality of publications. Random‐effects meta‐analysis was used to estimate the odds ratio (OR) for the association between food insecurity and obesity. Where statistical pooling for extracted statistics related to food environments was not possible due to heterogeneity, a narrative synthesis was performed. Meta‐analysis of 36,113 adults and children showed statistically significant associations between food insecurity and obesity (OR: 1.503, 95% confidence interval: 1.432–1.577, p  < .05). Narrative synthesis showed association between different types of food environments and obesity. Findings from qualitative studies regarding a reliance on energy‐dense, nutrient‐poor foods owing to their affordability and accessibility aligned with findings from quantitative studies. Results from both qualitative and quantitative studies regarding the potential links between increased body weight and participation in food assistance programs such as food banks were supportive of weight gain. To address obesity among individuals experiencing food insecurity, wide‐reaching approaches are required, especially among those surrounded by unhealthy food environments which could potentially influence food choice.

This review aimed to summarize quantitative and qualitative data on the relationship betweenobesogenic food environments, food insecurity, and obesity among adults and children across high‐income countries. A mixed‐method systematic review was conducted using 13 electronic databases, up to August 2021.

1. INTRODUCTION

Obesity remains a serious public health concern in high‐income countries due to its alarming prevalence and costly long‐term health problems (Lee et al.,  2021 ). Overweight and obesity are defined by World Health Organization (WHO) as “abnormal or excessive fat accumulation that presents a risk to health” (Ryan et al.,  2020 ). The prevalence of overweight and obesity has dramatically increased since the 1980s (Swinburn et al.,  2019 ). While hunger is increasing in many countries, it is estimated that 2 billion people are affected by either overweight or obesity (Ryan et al.,  2020 ). Overweight or obesity is one of the major risk factors for noncommunicable diseases (NCDs) worldwide, including cardiovascular diseases, diabetes, and cancers (Kluge et al.,  2020 ). In the light of COVID‐19 (the coronavirus disease 2019) pandemic, increasing recent studies have also linked obesity with a higher risk of hospitalization, severe symptoms, and death from this disease (Department of Health and Social Care,  2020 ; Sattar et al.,  2020 ). The severity of these risks increase as body mass index (BMI) increases (Department of Health and Social Care,  2020 ). In more wealthy, developed countries, many efforts have been made thus far to tackle obesity, including the implementation of relevant national and international policies and programs. However, these measures have proved inadequate for controlling the obesity epidemic in these countries (Lee et al.,  2021 ). Because of this rapid increase in obesity prevalence, it has been argued that the cause is most likely to be related to environmental changes rather than biological changes (Jeffery et al.,  2006 ; Leal & Chaix,  2011 ). Modifying the food environments has been recognized to have a vital role in shaping individuals' eating behaviors and purchasing (Wang et al.,  2019 ). Food environments are defined as “the availability, affordability, convenience, and desirability of various foods surrounding individuals” (Wang et al.,  2019 ). A recent conceptual framework has been developed which defines personal and external food environment domains (Turner et al.,  2020 ). Dimensions related to individuals, such as food accessibility, affordability, convenience, and desirability form the personal domain, whereas exogenous dimensions such as food availability, prices, vendor, and product properties make the external domain (Turner et al.,  2020 ).

Food insecurity is also thought to play a role in the rising trend of obesity and related NCDs (Keenan et al.,  2020 ; Kirkman et al.,  2020 ). Food insecurity has been defined as “limited or uncertain availability of nutritionally adequate and safe foods or limited or uncertain ability to acquire acceptable foods in socially acceptable ways” (Taylor & Loopstra,  2016 ). The United Nations (UN) estimated that 135 million people around the world experienced severe food insecurity at the beginning of 2020 (before the coronavirus pandemic) (Covid‐19 and food security,  2020 ). This figure was estimated to be doubled to 265 million by the end of 2020 as a result of recent COVID‐19 crisis (Covid‐19 and food security,  2020 ) that has made clear disparities in the food supply and distribution chain, having a detrimental effect on availability and access (Power et al.,  2020 ). Food insecurity contributes to both malnutrition and the paradox of obesity in high‐income countries (Penne & Goedemé,  2020 ). Evidence indicates that those from lower socioeconomic status groups can only afford energy‐dense food that is low in nutrients, causing obesity, impaired liver function, hypertension, and iron deficiency (Thompson et al.,  2018 ). Significant disparities exist in access to healthier food as more socially deprived areas have more clusters of unhealthy food outlets (Kirkman et al.,  2020 ). In neighborhoods with high prevalence of food insecurity, it is thought that the higher availability of cheap, high‐energy‐dense foods plays an important role in the relationship between weight status and food insecurity (Keenan et al.,  2020 ). Due to high association between food insecurity and poverty, individuals with food insecurity are expected to belong to high‐poverty neighborhoods that have constrained access to healthy and nutritious foods (Ro & Osborn,  2018 ). There is a global “cost of living crisis” which is impacting households and their ability to purchase food (Hourston,  2022 ). Rising trends in food insecurity has led to the provision and inclusion of donated and surplus food by charity and third‐sector organizations, into the diets of people with low‐income conditions (Thompson et al.,  2019 ). As the provision of food aid is growing and diversifying in high‐income countries, these not‐for‐profit retail outlets have been proposed to be incorporated into concepts of the food environments (Thompson et al.,  2019 ). Participation in food assistance programs has been found to be associated with obesity (Ro & Osborn,  2018 ). A recent systematic review has indicated that the nutrition quality of food parcels is inconsistent, and is often poor compared with national nutritional recommendations (Oldroyd et al.,  2022 ).

Available literature demonstrates that the association between food insecurity, food environments, and risk for overweight and obesity is ambiguous and inconsistent (Biadgilign et al.,  2021 ; Chen et al.,  2016 ; Morales & Berkowitz,  2016 ; Nettle & Bateson,  2019 ). Therefore, a comprehensive mixed‐methods systematic review and meta‐analysis was conducted to assess these relationships among adults and children across high‐income countries. The specific objectives of this review were to (a) explore how the food environment and food insecurity are associated with obesity among adults and children, (b) examine the role of food and nutrition assistance programs (as an additional contemporary aspect of the food environments) on the relationship between food insecurity and weight status, and (c) understand the gaps and limitations in the literature. This review was limited to high‐income countries as most data collection assessments to evaluate food environments have been conducted and validated in high‐income countries context over the past two decades. Furthermore, food environments in high‐income countries dramatically differ from those found in low‐ and middle‐income countries (LMICs). For example, rural households in LMICs usually obtain foods from informal market food environments which have limited schedules and highly seasonal food offerings. In contrast, consumers in high‐income countries mainly access formal market food environments, such as supermarkets, restaurants, and fast‐food chains (Ahmed et al.,  2021 ).

To the best of authors' knowledge, this is the first mixed‐method systematic review that aimed to further the work in this area using both quantitative and qualitative studies. Mixed‐methods systematic reviews have become increasingly important as they provide a more complete basis for complex decision‐making than that currently offered by single‐method reviews to answer complex applied health and public health questions (Stern, Lizarondo, Carrier, Godfrey, et al.,  2020 ). Therefore, the current mixed‐method review substantially differs from two recent systematic reviews and meta‐analyses which only included quantitative study designs to investigate links between food insecurity and weight status regardless of considering the impact of food environments and qualitative study designs in their studies that were not limited to high‐income countries (Moradi et al.,  2019 ; Pourmotabbed et al.,  2020 ). Building on these, we reviewed literature for both quantitative and qualitative studies restricted to high‐income countries up to August 2021, with a strong focus on considering the impact of both different food environments (objective or perceived measures) and food insecurity status on obesity.

This mixed‐methods systematic review and meta‐analysis followed the Joanna Briggs Institute (JBI) Reviewers Manual 2020 (Aromataris & Munn,  2020 ) and the Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) statement (Moher et al.,  2009 )/Objectives, eligibility criteria, and methods of analysis were specified in advance and published in a priory protocol (PROSPERO [CRD42019124339]) (Eskandari et al.,  2019 ).

2.1. Search strategy and selection of studies

Thirteen electronic databases were searched including PubMed, CINAHL, EMBASE, MEDLINE, PsycINFO, ERIC, Scopus, Web of Science, EThoS, Cochrane Library, JBI Library, PROSPERO, and Google Scholar. Search strategy and full search example can be found in Tables  S1 and S2 . The reference lists of selected articles for critical appraisal were also checked for additional relevant studies. The searches were undertaken from January 2019 to 31 August 2021. The first author (FE) performed the searches and imported citations into Endnote version x9 for the screening process and for removing duplicates. The titles and abstracts were assessed separately by two investigators; all titles and abstracts were screened by the first author (FE) in the first screening stage. Twenty percent of titles and abstracts were then independently double screened by a second reviewer (KR). A third reviewer (CO) resolved any discrepancies by consensus or provided clarification. In the next stage, full‐text articles were evaluated against the eligibility and study quality criteria. The screening for full‐text papers was performed by (FE and KR). The PRISMA flow diagram shows the number of articles at each stage (Figure  1 ).

PRISMA flow diagram of the search and screening process for the current mixed‐method systematic review.

2.2. Eligibility criteria

Eligible study designs included observational studies such as cross‐sectional studies, cohort studies, and case–control studies, aimed at exploring the association between the food environment, food insecurity, and obesity in adults or children conducted in high‐income countries (as defined by the World Bank; The World Bank Group,  2021 ) published from 1992 onwards (as major studies related to the food environment and food insecurity began from this date). Furthermore, qualitative studies that investigated the perceptions and experiences of obesity arisen from food poverty/insecurity and unhealthy food environments were included. Systematic reviews were excluded, and studies published in English were included. Studies without scientific credibility or non‐peer‐reviewed were excluded. Animal studies and those investigating obesity grade 3 or more (BMI > 40 kg/m 2 ) were also excluded as the aim of this study was not to look at severe type of obesity.

2.3. Data extraction

Two reviewers (FE, KR) independently extracted data from quantitative studies using the data extraction tool from JBI‐MAStARI (Aromataris & Munn,  2020 ). Study characteristics including specific details of included studies, population demographics, methods and outcomes of interest to the review questions were extracted from each study (Aromataris & Munn,  2020 ). The qualitative data were extracted independently by both reviewers (FE, KR) using the standardized data extraction tool form JBI‐QARI (The Joanna Briggs Institute,  2020 ).

2.4. Assessment of methodological quality

Included studies were evaluated independently by two reviewers (FE, KR) for methodological validity and risk of bias using the standardized critical appraisal tools from JBI‐MAStARI (Aromataris & Munn,  2020 ), which were specific for each article's study design. The quality scores were based on the possibility of risk of bias in the methodology, conduct, and analysis in which “Yes” represented a quality score of 1 (Oldroyd et al.,  2022 ). The following maximum scores showed the highest quality: cross‐sectional, 8; cohort studies, 11; and qualitative, 10. For this review, methodological quality is reported; however, this did not influence inclusion or exclusion of studies.

2.5. Data synthesis

2.5.1. quantitative data synthesis.

To examine the heterogeneity and suitability of quantitative data for meta‐analysis, a statistician (AB) was consulted. The main statistics extracted from each study were the mean BMI for individuals experiencing food security and the mean BMI for individuals with food insecurity, together with odds ratio (OR) and 95% confidence intervals (CI). When an OR was not reported, it was estimated from other data based on the methods outlined in the Cochrane handbook (Higgins et al.,  2021 ). The Comprehensive Meta‐analysis software version 3 was used to pool effect sizes in a random‐effects meta‐analysis. A random‐effects model was applied to quantify pooled effect sizes and 95% CI. Using Tau statistics, heterogeneity was calculated. Where statistical pooling for extracted statistics (variables related to the food environments) was not possible, the results were presented in a narrative form according to the Synthesis Without Meta‐Analysis (SWiM) guidelines (Campbell et al.,  2020 ).

2.5.2. Qualitative data synthesis

An approach outlined by Thomas and Harden ( 2008 ) was used to develop thematic synthesis for qualitative data. To this end, data were firstly open‐coded using line‐by‐line coding technique (FE, CO). Then, based on similarities identified within the data, descriptive themes were developed. Finally, analytical themes were developed and were reviewed and agreed (FE, CO).

2.5.3. Data synthesis for mixed‐methods synthesis

The findings of quantitative and qualitative data were aggregated according to a convergent segregated approach outlined in the JBI Reviewers' Manual for JBI Mixed Methods Systematic Reviews (Stern, Lizarondo, Carrier, et al.,  2020 ). This included a configurative analysis approach to generate the links between the findings that represented aggregation. The finding themes from quantitative and qualitative synthesis are presented in narrative description (Campbell et al.,  2020 ; Stern, Lizarondo, Carrier, et al.,  2020 ).

3.1. Main characteristics of the studies

After removal of duplicates, a total of 6307 citations were found. After title and abstract screening, 149 full‐text papers were evaluated against the eligibility criteria. One more study was found from checking reference lists. One hundred and three publications were excluded at this stage. Reasons for exclusions are presented in Figure  1 . The study selection flowchart is presented according to PRISMA guidelines (Moher et al.,  2009 ; Figure  1 ). For the quantitative section of the review, 36 cross‐sectional studies (Bauer et al.,  2012 ; Bruening et al.,  2012 ; Dharod et al.,  2013 ; Domingo et al.,  2021 ; Gorski Findling et al.,  2018 ; Huelskamp et al.,  2021 ; Kaiser et al.,  2019 ; Keenan et al.,  2021 ; Kral et al.,  2017 ; Leung & Villamor,  2011 ; Matheson et al.,  2002 ; McCurdy et al.,  2015 ; Mercille et al.,  2012 ; Nettle & Bateson,  2019 ; Nguyen et al.,  2015 ; Niu et al.,  2021 ; Poulsen et al.,  2019 ; Ro & Osborn,  2018 ; Robaina & Martin,  2013 ; Rodriguez et al.,  2021 ; Sanjeevi et al.,  2018 ; Santarossa et al.,  2021 ; Shinwell et al.,  2021 ; Smith & Richards,  2008 ; Vadiveloo et al.,  2020 ; van der Velde et al.,  2020 ; Vedovato et al.,  2016 ; Walch & Holland,  2021 ; Watt et al.,  2013 ; Webb et al.,  2008 ; Widome et al.,  2009 ; Wilcox et al.,  2020 ; Wirth et al.,  2020 ; Yau et al.,  2020 ) and one cohort study (Benjamin‐Neelon et al.,  2020 ) were included. Eleven studies were included for the qualitative section of the review (Bhawra et al.,  2015 ; Byker Shanks et al.,  2020 ; Cooksey Stowers et al.,  2020 ; Franzen & Smith,  2009 ; Genuis et al.,  2015 ; Gosliner & Shah,  2020 ; Holston et al.,  2020 ; Jennings et al.,  2020 ; Kerpan et al.,  2015 ; Ong et al.,  2021 ; Thompson et al.,  2018 ).

Included studies were primarily conducted across the USA (36) (Bauer et al.,  2012 ; Benjamin‐Neelon et al.,  2020 ; Bruening et al.,  2012 ; Byker Shanks et al.,  2020 ; Cooksey Stowers et al.,  2020 ; Dharod et al.,  2013 ; Franzen & Smith,  2009 ; Gorski Findling et al.,  2018 ; Gosliner & Shah,  2020 ; Holston et al.,  2020 ; Hooper et al.,  2020 ; Huelskamp et al.,  2021 ; Jennings et al.,  2020 ; Kaiser et al.,  2019 ; Kral et al.,  2017 ; Leung & Villamor,  2011 ; Matheson et al.,  2002 ; McCurdy et al.,  2015 ; Nettle & Bateson,  2019 ; Nguyen et al.,  2015 ; Niu et al.,  2021 ; Poulsen et al.,  2019 ; Ro & Osborn,  2018 ; Robaina & Martin,  2013 ; Rodriguez et al.,  2021 ; Sanjeevi et al.,  2018 ; Santarossa et al.,  2021 ; Smith & Richards,  2008 ; Vadiveloo et al.,  2020 ; Vedovato et al.,  2016 ; Walch & Holland,  2021 ; Watt et al.,  2013 ; Webb et al.,  2008 ; Widome et al.,  2009 ; Wilcox et al.,  2020 ; Wirth et al.,  2020 ), with six in Canada (Bhawra et al.,  2015 ; Domingo et al.,  2021 ; Genuis et al.,  2015 ; Kerpan et al.,  2015 ; Mercille et al.,  2012 ; Ong et al.,  2021 ), four in the UK (Keenan et al.,  2021 ; Shinwell et al.,  2021 ; Thompson et al.,  2018 ; Yau et al.,  2020 ), and one in Netherlands (van der Velde et al.,  2020 ). Twenty‐two studies were published between 2020 and 2021 (Benjamin‐Neelon et al.,  2020 ; Byker Shanks et al.,  2020 ; Cooksey Stowers et al.,  2020 ; Domingo et al.,  2021 ; Gosliner & Shah,  2020 ; Holston et al.,  2020 ; Hooper et al.,  2020 ; Huelskamp et al.,  2021 ; Jennings et al.,  2020 ; Keenan et al.,  2021 ; Niu et al.,  2021 ; Ong et al.,  2021 ; Rodriguez et al.,  2021 ; Santarossa et al.,  2021 ; Shinwell et al.,  2021 ; Vadiveloo et al.,  2020 ; van der Velde et al.,  2020 ; Walch & Holland,  2021 ; Wilcox et al.,  2020 ; Wirth et al.,  2020 ; Yau et al.,  2020 ), 14 studies were published between 2015 and 2019 (Bhawra et al.,  2015 ; Genuis et al.,  2015 ; Gorski Findling et al.,  2018 ; Kaiser et al.,  2019 ; Kerpan et al.,  2015 ; Kral et al.,  2017 ; McCurdy et al.,  2015 ; Nettle & Bateson,  2019 ; Nguyen et al.,  2015 ; Poulsen et al.,  2019 ; Ro & Osborn,  2018 ; Sanjeevi et al.,  2018 ; Thompson et al.,  2018 ; Vedovato et al.,  2016 ) and the remaining were published between 2002 and 2014 (Bauer et al.,  2012 ; Bruening et al.,  2012 ; Dharod et al.,  2013 ; Franzen & Smith,  2009 ; Leung & Villamor,  2011 ; Matheson et al.,  2002 ; Mercille et al.,  2012 ; Robaina & Martin,  2013 ; Smith & Richards,  2008 ; Watt et al.,  2013 ; Webb et al.,  2008 ; Widome et al.,  2009 ). Total sample size was 63,152, ranging from 10 (Cooksey Stowers et al.,  2020 ) to 8333 (Nguyen et al.,  2015 ) in individual studies. Thirty‐two studies considered adult samples (>18 years) (Bauer et al.,  2012 ; Bhawra et al.,  2015 ; Bruening et al.,  2012 ; Byker Shanks et al.,  2020 ; Cooksey Stowers et al.,  2020 ; Dharod et al.,  2013 ; Domingo et al.,  2021 ; Franzen & Smith,  2009 ; Gosliner & Shah,  2020 ; Holston et al.,  2020 ; Huelskamp et al.,  2021 ; Kaiser et al.,  2019 ; Keenan et al.,  2021 ; Leung & Villamor,  2011 ; McCurdy et al.,  2015 ; Mercille et al.,  2012 ; Nettle & Bateson,  2019 ; Nguyen et al.,  2015 ; Ong et al.,  2021 ; Ro & Osborn,  2018 ; Robaina & Martin,  2013 ; Rodriguez et al.,  2021 ; Sanjeevi et al.,  2018 ; Santarossa et al.,  2021 ; Shinwell et al.,  2021 ; Vadiveloo et al.,  2020 ; van der Velde et al.,  2020 ; Vedovato et al.,  2016 ; Walch & Holland,  2021 ; Watt et al.,  2013 ; Webb et al.,  2008 ; Wilcox et al.,  2020 ; Yau et al.,  2020 ), 11 studies focused on children or adolescents (Benjamin‐Neelon et al.,  2020 ; Genuis et al.,  2015 ; Gorski Findling et al.,  2018 ; Jennings et al.,  2020 ; Kerpan et al.,  2015 ; Kral et al.,  2017 ; Matheson et al.,  2002 ; Poulsen et al.,  2019 ; Smith & Richards,  2008 ; Widome et al.,  2009 ; Wirth et al.,  2020 ) and three studies included both children/adolescents and adults (Hooper et al.,  2020 ; Niu et al.,  2021 ; Thompson et al.,  2018 ). Twenty‐three quantitative studies directly measured anthropometric indices (Bauer et al.,  2012 ; Benjamin‐Neelon et al.,  2020 ; Dharod et al.,  2013 ; Domingo et al.,  2021 ; Hooper et al.,  2020 ; Kral et al.,  2017 ; Matheson et al.,  2002 ; Mercille et al.,  2012 ; Nettle & Bateson,  2019 ; Nguyen et al.,  2015 ; Niu et al.,  2021 ; Poulsen et al.,  2019 ; Robaina & Martin,  2013 ; Rodriguez et al.,  2021 ; Sanjeevi et al.,  2018 ; Santarossa et al.,  2021 ; Smith & Richards,  2008 ; Vedovato et al.,  2016 ; Watt et al.,  2013 ; Widome et al.,  2009 ; Wilcox et al.,  2020 ; Wirth et al.,  2020 ) and 14 studies used self‐reported measures (Bruening et al.,  2012 ; Gorski Findling et al.,  2018 ; Huelskamp et al.,  2021 ; Kaiser et al.,  2019 ; Keenan et al.,  2021 ; Leung & Villamor,  2011 ; McCurdy et al.,  2015 ; Ro & Osborn,  2018 ; Shinwell et al.,  2021 ; Vadiveloo et al.,  2020 ; van der Velde et al.,  2020 ; Walch & Holland,  2021 ; Webb et al.,  2008 ; Yau et al.,  2020 ). A summary of all included quantitative and qualitative studies are presented in Tables  1 and ​ and2, 2 , respectively.

Characteristics of the included studies and a quality assessment, quantitative component

Note : JBI critical appraisal checklists, as appropriate to the study type, were used to assess the methodological quality of each included study. The scores indicate the reviewers (FE, KR) consideration of the possibility of bias in the design, conduct and analysis. Cross‐sectional studies, out of 8; Cohort studies, out of 11.

Reference: Joanna Briggs Institute ( 2017a ).

Abbreviations: CI, confidence interval; FSP, Food Stamp Program; SE , standard error; SNAP, the Supplemental Nutrition Assistance Program; SSB, Sugar‐sweetened beverages; SSI, Supplemental Security Income; WIC, the Special Supplemental Nutrition Program for Women, Infants, and Children.

Characteristics of the included studies and a quality assessment; qualitative component

Note : JBI critical appraisal checklists, as appropriate to the study type, were used to assess the methodological quality of each included record. The scores indicate the reviewers (FE, KR) consideration of the possibility of bias in the design, conduct and analysis. Qualitative studies, out of 10.

Reference: Joanna Briggs Institute ( 2017b ).

3.2. Methodological quality

Quantitative studies: Thirty‐five cross‐sectional studies were included in this review. Overall quality scores ranged from six to eight out of 8 (Table  1 and Table  S3 ). One longitudinal study was also included, having a maximum overall quality score of 11 (Table  1 and Table  S4 ). Thus, the studies were deemed to be of very good quality, with the risk of selection bias remaining low.

Qualitative studies: Eleven studies were critically appraised and considered of very good quality (Table  2 and Table  S5 ). Overall quality scores for these studies ranged from 7 to 10 out of 10, indicating a high level of data integrity and congruity between methodology and the research aims, data collecting methods, and analysis.

3.2.1. Characteristics of food insecurity exposures

Tools included in studies for assessment of food insecurity status were 6‐item US Household Food Security Survey Module (HHFSM) (Bauer et al.,  2012 ; Bruening et al.,  2012 ; Hooper et al.,  2020 ; Kaiser et al.,  2019 ; Kral et al.,  2017 ; Leung & Villamor,  2011 ; Poulsen et al.,  2019 ; Ro & Osborn,  2018 ; Shinwell et al.,  2021 ). 18‐item the Food Security Core Module (FSCM) or simply the US Household Food Security Survey Module (USDA HFSSM) (i.e., by ‘simply’ we mean that FSCM and USDA HFSSM are the same tool, but are called either interchangeably; Benjamin‐Neelon et al.,  2020 ; Domingo et al.,  2021 ; Matheson et al.,  2002 ; McCurdy et al.,  2015 ; Mercille et al.,  2012 ; Robaina & Martin,  2013 ; Rodriguez et al.,  2021 ; Santarossa et al.,  2021 ; van der Velde et al.,  2020 ; Vedovato et al.,  2016 ; Webb et al.,  2008 ), 10‐item US adult food security survey (Keenan et al.,  2021 ; Sanjeevi et al.,  2018 ; Walch & Holland,  2021 ; Webb et al.,  2008 ; Yau et al.,  2020 ), the National Health and Nutrition Examination Survey Food Security Survey Module (NHANES FSSM; Nguyen et al.,  2015 ; Watt et al.,  2013 ), 10‐item Radimer/Cornell Hunger Scale (Dharod et al.,  2013 ), 11‐item USDA Adult Food Security Survey Module (Huelskamp et al.,  2021 ), USDA's 30‐day Adult Food Security Scale (Gorski Findling et al.,  2018 ), 2‐item adapted from the 1999 USDA Food Security/Hunger Core Module (Widome et al.,  2009 ), the Hunger Vital Sign (HVS) screener tool (Wirth et al.,  2020 ), and a modified USDA's instrument on food security (Smith & Richards,  2008 ; Vadiveloo et al.,  2020 ).

3.2.2. Type of food environment characteristics

In this review, food environments were defined as objective (e.g., geographic information system) and/or perceived aspects of the physical and economic food environment inside and outside the home. Included studies were diverse in their measures and in their results for the food environment component.

3.2.2.1. Home food environment

Two studies used home food availability (HFA) scales; in a cross‐sectional study of 817 individuals, healthy and obesogenic HFA scales were used to assess how frequently particularly foods were available at the home (Poulsen et al.,  2019 ). In another study of 432 parents or caregivers of kindergarten‐age children, HFA was assessed using yes or no questions asking about availability of different types of fruits and vegetables (FV), energy‐dense foods, and beverages in their homes (Bauer et al.,  2012 ).

In a study of 4589 middle and high school students, household food availability was ascertained via two scales (Widome et al.,  2009 ), measuring both the availability of healthy and unhealthy foods in their homes. Fast food intake was also determined by asking how often they ate something from a fast‐food restaurant during past week.

In a study of 2095 parents, participants reported on home food environment using different items. Four items assessed perceptions of access to fruit and vegetables, addressing quality, variety, and cost of produce (Bruening et al.,  2012 ). The types of food consumed at family meals were measured using six items and one item reported on family meal frequency. Fast‐food consumption was also assessed.

A cross‐sectional study of 152 females participating in Supplemental Nutrition Assistance Program (SNAP) program (aged 18–50 years) used a multi‐dimensional home environmental scale (MHES). This scale was created to measure home environment from the perspective of adolescent children and their mothers (Sanjeevi et al.,  2018 ). The environmental influence was measured by questions related to availability of specific healthy and unhealthy food items at home (Sanjeevi et al.,  2018 ).

In a study of 124 largely Hispanic and fifth‐grade children (aged 9–13 years), their mothers provided reports of household food supplies (Matheson et al.,  2002 ). Mothers completed a 40‐item household inventory of food supplies.

In a cross‐sectional secondary study, 50 mothers of 8‐ to 10‐year‐old children completed different questionnaires (Kral et al.,  2017 ). Using these, meal and snack patterns of children, restriction of child's access to food by parents, extent to which children were pressured to eat more food, and children's susceptibility for eating more in the presence of palatable foods were assessed.

3.2.2.2. Neighborhood fresh produce environment

Two measures were included in one study (18–65 years old; Ro & Osborn,  2018 ) that measured perceptions of healthy foods in the neighborhood food environment, including availability and affordability of FV. For availability, participants answered how often they found fresh FV in their neighborhood. For affordability of FV, they answered if the fresh FV in their neighborhood were affordable. In a study of 301 individuals who resided within communities with food security and food insecurity, respondents answered about household's perception about different food accessibility within their neighborhood. They answered questions about easy access to fresh FV, and food support services such as food pantry (Kaiser et al.,  2019 ).

3.2.2.3. Food source destinations (neighborhood food access)

Twelve studies examined different types of neighborhood food access. In a large cross‐sectional study of 3748 children (2–18 years old), neighborhood retail food access was measured using numbers of food outlets such as stores, and restaurants located within 1 mile of youths' home (Gorski Findling et al.,  2018 ). Access was reported for specific food outlet types such as fast‐food and non‐fast‐food restaurants, convenience stores, supermarkets, grocery stores, and other stores. Alternative neighborhood food access measures included store type of closest the SNAP retailer (Gorski Findling et al.,  2018 ). Household food purchases and acquisitions were evaluated. To measure spending on unhealthy food items, the mean of sugary beverage spending by each child's household was also examined (Gorski Findling et al.,  2018 ). Another cross‐sectional survey of adults ( n  = 298) measured the sources of foods that were purchased from over the past month (Vedovato et al.,  2016 ). The food sources included fast‐food restaurants, convenience stores, bar or pub, food pantry, family and friends, church or community centre, street food vendor, etc. (Vedovato et al.,  2016 ). A study of parents or caregivers of kindergarten‐aged children asked parents to report the frequency of family fast‐food visits per week ( n  = 432; Bauer et al.,  2012 ).

In a study of adults aged 18 years and over who lived on low‐income neighborhoods ( n  = 435), participants were asked to answer questions regarding use of supermarkets or other store types for food purchases and use of free or low‐cost food from charitable sources (i.e., food banks, soup kitchens, church or community outreach programs, shelters, friends, and/or family; Webb et al.,  2008 ). A study of 212 food pantry users asked the participants about frequency of going to food pantries, and/or to soup kitchens (Robaina & Martin,  2013 ). Participants used food pantries on a long‐term basis, with 62.5% visiting at least once per week and 44% ate foods at a soup kitchen. A correlational study of 166 mothers of 2–5‐year‐old children on a low income used 8‐item Food Shopping Practices scale to ask about the use of shopping habits to stretch food dollars (i.e., to rate their use of food coupons, buying lower cost food to save money, shopping at specific stores due to a sale, buying food in bulk, and using a shopping list) in the previous 30 days (McCurdy et al.,  2015 ). Respondents also answered questions about using emergency food parcels from soup kitchens, food banks, community cupboards, or churches (McCurdy et al.,  2015 ).

In another study of 153 women on a low income, a self‐administrated survey asked women about their perceived food access (Watt et al.,  2013 ). Seventy‐five percent of women reported that they experienced limited food access. Finally, a cross‐sectional study of 107 women responsible for household food supplies measured grocery shopping practices and access to traditional foods (Mercille et al.,  2012 ). Most families made their grocery shopping from a supermarket within 145 km or more from their homes.

3.2.2.4. Food source destinations –nutrition assistance programs

Nine studies assessed participation in nutrition assistance programs; the interactions between SNAP participation, food insecurity, and BMI were examined in a study of 2003–2010 US NHANES ( n  = 8333; Nguyen et al.,  2015 ). SNAP is previously recognized as food stamps and is the largest federal program in the United States that offers support for the purchase of foods to low‐income US households to alleviate food insecurity (Sachdev et al.,  2019 ). In a cross‐sectional study of 7741 adult California Health Interview Survey, SNAP participation was assessed using questions related to receiving food stamp benefits. Supplemental Security Income (SSI) participation was also assessed (Leung & Villamor,  2011 ). In a cross‐sectional analysis of 3748 children, household participation in SNAP and WIC was examined (Gorski Findling et al.,  2018 ). In a study of 435 adult residents of low‐income neighborhoods, participation in 3 government nutrition assistance programs (i.e., the free or reduced‐price school meals program, the Food Stamp Program [FSP]), and the Special Supplemental Nutrition Program for Women, Infants, and Children [WIC]) was assessed (Webb et al.,  2008 ). In a study of 212 food pantry users, participants were asked whether they receive SNAP or WIC (Robaina & Martin,  2013 ). Over half (57%) of participants received SNAP. A cross‐sectional, correlational study of 166 mothers of young children on a low income assessed participation in government food assistance programs. Participants answered questions from a modified version of the Current Population Survey Food Security Supplement (FSS) to assess participation in SNAP and WIC (McCurdy et al.,  2015 ). Eighty percent of participants reported receipt of benefits from SNAP. In a study of 153 women on a low income, data on participation in WIC and/or SNAP were obtained from the self‐administrated survey (Watt et al.,  2013 ). Sixty‐four percent of participants used the benefits obtained from WIC and half of them benefited from food stamps (SNAP) over the previous year.

3.2.2.5. Healthy food beliefs and attitudes

Five studies measured this aspect of food environments; in a cross‐sectional survey of 298 adults, four subscales were developed to indicate different aspects of beliefs and opinions about healthy foods: affordability, convenience, importance, and taste (Vedovato et al.,  2016 ). In a study of 432 parents or caregivers of kindergarten‐aged children, barriers to healthy foods at the home were assessed (Bauer et al.,  2012 ). A cross‐sectional study of 107 females who were responsible for household food supplies measured self‐efficacy for food preparation using the calculation of two self‐efficacy scores. One scale measured food preparation in general and another one measured healthy food preparation (Mercille et al.,  2012 ). Self‐efficacy in food preparation was described as individuals' confidence in their ability to make dishes and balanced meals using store‐bought food. Women were fairly confident about their capability to prepare store‐bought food. However, the average score for self‐efficacy in healthy food was slightly lower, suggesting more difficulty in this regard (Mercille et al.,  2012 ). They also reported on their perceptions of FV supply in local stores. The local grocery store was perceived negatively, as it did not usually bring fresh FV and it was used mainly as a backup.

3.2.2.6. Dietary intake (diet quality)

This determinant was reported as intake of specific foods or food groups are associated with obesity and such diets may relate to different aspects of neighborhood food environments.

Thirteen studies measured dietary intakes of participants; in a cross‐sectional analysis of 7741 Adult California Health Interview Survey, dietary information was collected by asking about the frequency of eating of fruits (excluding fruit juice); vegetables (excluding fried potatoes); soda (excluding diet soda); French fries, and fast food (Leung & Villamor,  2011 ). A study of 432 parents or caregivers of kindergarten‐aged children, parents were asked about the frequency of fast‐food visits per week. They were also asked about the frequency of their child food consumption from hot or ready‐made food from a convenience store or gas station over the past 30 days (Bauer et al.,  2012 ). Another study of 212 food pantry users, diet quality of participants was measured using the Block Food Frequency Screener. These users reported about their usual consumption of fruit, vegetables, and fiber (Robaina & Martin,  2013 ). A cross‐sectional study of 202 young people (9–18 years) who were homeless and were living in two of the largest family shelters in the USA assessed dietary intake by completing a single 24‐h recall to provide information about the type and quantity of food consumed, preparation style, where food is eaten, and how it is spread over the day (Smith & Richards,  2008 ). Dairy, fruits, and vegetables were consumed less than recommended levels (below the estimated average requirements) by both males and females of all ages. All youths ate excessive servings of sweet groups, fats, and oil (18.6–22.7 servings). Another cross‐sectional study of 195 Somali refugee women in the United States estimated their regular dietary intakes by completing a short food frequency questionnaire. Questions were asked to estimate how often specific food items were consumed such as eggs, meats, beans/lentils, grains, dairy, fruits, and vegetables (Dharod et al.,  2013 ). In a study of 153 women on a low income, mother's diet was measured using an 8‐item index from Starting the Conversation (Watt et al.,  2013 ). It included questions about intakes of FV, sugar‐sweetened beverages (SSB), high‐fat foods, and desserts. Infant's diet was evaluated as breastfeeding initiation and consumption of particular foods such as fruits and French fries. The majority of women did not meet dietary guidelines and nearly 64% of them reported weekly intakes of fast‐food. Drinking SSB at a daily basis was reported by 44% of women. Most of the women breastfed after delivery. Usual feeding practices were that 39% of women reported they gave their infants high‐sugar fruit/vegetable juice daily and 24% of them reported feeding their infants sweets on a weekly basis (Watt et al.,  2013 ).

3.2.2.7. Coping strategies to alleviate hunger

One cross‐sectional study of 202 young people (9–18 years) evaluated coping strategies used by youths who were homeless and were living in two of the largest family shelters in the USA (Smith & Richards,  2008 ). Coping strategies to alleviate food insecurity included overeating, eating at the homes of family and friends, eating disliked foods, and eating anything.

3.3. Findings of the review

3.3.1. quantitative component, 3.3.1.1. associations between food insecurity and bmi.

To analyze the association between food insecurity and obesity, the OR of 36,113 cases in 24 studies (Bauer et al.,  2012 ; Benjamin‐Neelon et al.,  2020 ; Bruening et al.,  2012 ; Dharod et al.,  2013 ; Domingo et al.,  2021 ; Huelskamp et al.,  2021 ; Kaiser et al.,  2019 ; Keenan et al.,  2021 ; Kral et al.,  2017 ; Matheson et al.,  2002 ; Nettle & Bateson,  2019 ; Nguyen et al.,  2015 ; Poulsen et al.,  2019 ; Ro & Osborn,  2018 ; Robaina & Martin,  2013 ; Rodriguez et al.,  2021 ; Sanjeevi et al.,  2018 ; Santarossa et al.,  2021 ; Smith & Richards,  2008 ; van der Velde et al.,  2020 ; Vedovato et al.,  2016 ; Webb et al.,  2008 ; Widome et al.,  2009 ; Wirth et al.,  2020 ), involving both adults and children, was pooled together for the meta‐analysis. These studies used a cross‐sectional approach in addition to one cohort study. As shown in Figure  2 , meta‐analysis of these studies showed an overall small but statistically significant association between food insecurity and obesity (OR: 1.503, 95% CI: 1.432–1.577, p ‐value = .000) when all ORs were combined with the random‐effects model. This means food insecurity increased the risk of obesity among adults and children. Therefore, individuals experiencing food insecurity were more likely to be affected by obesity.

Forest plot showing OR with 95% CI of the association between food security status and obesity.

3.3.1.2. Associations between food environments and BMI

3.3.1.2.1. home food environment.

A cross‐sectional study of 10‐ to 15‐year‐old youths and their parents ( n  = 817) reported no associations between obesogenic HFA scores and body mass index z score (BMIz) (beta coefficient: −0.02 [95% CI: −0.04–0.01, p  = .143]; Poulsen et al.,  2019 ). In this study, there was no evidence that healthy or obesogenic HFA changed the food security or anthropometric associations in children. This study also found that households living with food insecurity had significantly lower mean healthy HFA scores compared to households living in food security (beta coefficient: −1.23 [95% CI: −2.29 to 0.18, p  = .022]). But there was no evidence of a difference in mean obesogenic HFA scores (beta coefficient: −1.02 [95% CI: −2.32 to 0.28, p  = .122]). In this study, young people from higher obesogenic HFA or lower healthy HFA households had fewer mean daily FV intakes (beta for healthy HFA: 0.08 (95% CI: 0.03–0.12, p  = .001); beta for obesogenic HFA: −0.06 (95% CI: −0.09 to −0.02, p  = .003).

Another study of parents or caregivers of kindergarten‐aged children ( n  = 432) found no differences in families' HFA by food security status (Bauer et al.,  2012 ).

A study of middle and high school students ( n  = 4589) demonstrated that youths experiencing food insecurity had several eating‐related risk factors for overweight/obesity (Widome et al.,  2009 ). Adolescents who “often” did not have enough to consume or that they suffered from hunger “some months” reported eating more fast food than did those who had food security (Widome et al.,  2009 ; mean score [95% CI]: 2.03 [1.75, 2.31] p  = .088). This group had the greater percentage of young people who were affected by obesity (≥95th percentile) ( p  = .01). These youths had less food available in the home (both healthy [ p  < .001] and unhealthy foods [ p  < .001]). This study found that youths with food insecurity were less likely to eat family meals than peers of a higher socioeconomic status ( p  < .001). The study suggested that this might be because of limited or irregular food availability that might be less likely to establish a regular family meal routine (Widome et al.,  2009 ).

The population‐based study of 2095 parents showed that the home food environment in households living with food insecurity was poorer than in households living in food security (Bruening et al.,  2012 ). Parents experiencing food insecurity reported having more fast food at family meals (95% CI = 0.1, 0.2, p  < .01) and more serving of SSB (95% CI = 2.5, 10.3, p  < .01) at family meals than parents living in food security. They also reported serving frequently less healthy food items such as green salad, vegetables, and fruits ( p  < .05). The study explained that the higher BMI and poorer eating patterns of parents with food insecurity may be due to the fact that more obstacles in accessing healthy foods such as FV were reported by this group than parents experiencing food security. Great differences in perceived access to FV were reported between parents living in food security and parents living with food insecurity. For example, near 40% of parents experiencing food insecurity compared to near 14% of parents experiencing food security perceived that fruits were too expensive to purchase (95% CI = 21.5, 30.6, p  < .01). Parents affected by food insecurity were 3–4 times more likely to find FV to be too expensive. These parents believed that the quality and variety of available FV were poor ( p  < .01).

Another study of 152 participants in SNAP program, availability of unhealthy food was significantly associated with BMI (beta coefficient = −0.227, p ‐value = .02; Sanjeevi et al.,  2018 ). Also, a significant difference between groups living in food security and groups living with food insecurity was reported for availability of unhealthy foods at home. Groups experiencing food security scored higher than groups with food insecurity for the availability of unhealthy foods at home by almost 14% ( p  < .01). This study indicated that the relationship between food insecurity and obesity was partially mediated by home food environment (beta coefficient: 0.19, 95% CI: 0.01–0.42, p  < .05). Thus, home food environment could play a vital role in mediating this relationship in this population.

The study of Hispanic and fifth‐grade children ( n  = 124) demonstrated a significant association between household food supplies and household food security ( p  < .01). Household food supplies were significantly correlated with youth's consumption of fruit, meat, sweets, and snacks at home ( p  < .05; Matheson et al.,  2002 ).

A cross‐sectional secondary study of 50 mothers of 8‐ to 10‐year‐old children ( n  = 100) found large percentage of youths from households living in food security reported eating 3–4 snacks per day (46% vs. 15.4%), while a greater percentage of youths from households living with food insecurity reported eating 5 or more snacks per day (15.4% vs. 0%) ( p  = .02; Kral et al.,  2017 ). Mothers from households experiencing food insecurity reported significantly more concern regarding their child's weight and subsequently limited access to food by their children at a greater extent than mothers from households living in food security ( p  < .03). Children from households with food insecurity had significantly more external eating, both past satiety and in the absence of hunger ( p  < .03).

3.3.1.2.2. Neighborhood fresh produce environment

In a cross‐sectional study of 5957 individuals, the OR between overweight/obesity and food insecurity became nonsignificant when the perceptions of neighborhood fresh produce environment were added (OR = 1.32, 95% CI: 0.89–1.98; Ro & Osborn,  2018 ). Only neighborhood affordability of fresh produce was statistically associated with overweight/obesity; women affected by severe food insecurity (in this study, there was no difference in the percent of overweight/obesity based on the food insecurity status for men) had lower odds of obesity when they usually or always afforded fresh FV in their neighborhood (OR = 0.70, 95% CI: 0.53–0.93; Ro & Osborn,  2018 ). This suggested that neighborhood affordability of fresh produce determined the statistical correlation between food insecurity and overweight/obesity.

A study of people who lived within food secure and food insecure communities ( n  = 301) demonstrated that those who lived in the food secure areas had the greatest satisfaction with easy access to FV. Food assistance users had higher incidences of obesity (OR: 1.46, 95% CI: 0.54–3.94) (the overall p ‐value, however, was not significant). Perceived farmers' market access was associated with a lower prevalence of overweight/obesity (OR: 0.46, 95% CI: 0.17–1.23) (the overall p ‐value, however, was not significant; Kaiser et al.,  2019 ).

3.3.1.2.3. Food source destinations (neighborhood food access)

A study of 2–18 years old children ( n  = 3748) demonstrated that more neighborhood access to combination groceries or other types of stores was related to greater prevalence of obesity among children overall and those participated in SNAP (Gorski Findling et al.,  2018 ). Odds of childhood overweight/obesity were higher with greater access to combination grocery/other stores overall (OR: 1.10, 95% CI: 1.03–1.17, p  < .05) and for children in SNAP (OR: 1.14, 95% CI: 1.05–1.24, p  < .05; Gorski Findling et al.,  2018 ). In this study, alternative access measures of food exposure were not associated with child overweight/obesity (Gorski Findling et al.,  2018 ). The average child lived in a household in which 6.3% of their total spending at food outlets was on sugary beverages (Gorski Findling et al.,  2018 ). Compared to non‐SNAP households, the average youths from households participated in SNAP also spent a higher percentage of their budget on sugary beverages ( p  < .05; Gorski Findling et al.,  2018 ).

A cross‐sectional survey of 298 households found no significant associations between food source use patterns (such as shopping from a convenience or grocery store) and excess body weight (Vedovato et al.,  2016 ).

A study of 432 parents and caregivers of kindergarten‐aged children found no differences in frequency of families' fast‐food visits by food security status (Bauer et al.,  2012 ).

In a study of 435 adults who were residents of low‐income neighborhoods, BMI was significantly higher for those cases who acquired their foods from charitable sources such as food banks or soup kitchens ( p  < .01). Also, those who reported shopping at convenience stores ( p  = .04) and those who consumed fast‐foods in the month prior to the survey had significantly higher BMI ( p  < .01; Webb et al.,  2008 ). In this study, no association was found between BMI and different use of supermarkets, ethnic grocery stores, or use of farmers' markets.

A study of mothers of young children on a low income ( n  = 166) found no significant association between maternal BMI and number of weekly shopping visits to supermarkets (McCurdy et al.,  2015 ). However, maternal BMI was significantly associated with variables related to food resources. Use of community food programs ( p  < .05) and more frequent use of food shopping practices to stretch food dollars ( p  = .04) were positively associated with maternal BMI.

In a study of women who were responsible for household food supplies ( n  = 107), no association was found between self‐efficacy scores, grocery shopping practices, and access to traditional food (Mercille et al.,  2012 ).

3.3.1.2.4. Food source destinations –nutrition assistance programs

Analysis of NHANES 2003–2010 ( n  = 8333) showed that, while those experiencing food insecurity or SNAP participants had a higher BMI and greater possibility of obesity ( p  < .05), the combined association of food insecurity and SNAP participation indicated a decrease in BMI across all three groups of food insecurity ( p  < .05) and reduced the chance of obesity among those who had marginal food security ( p  < .05; Nguyen et al.,  2015 ).

A cross‐sectional analysis of 7741 Adult California Health Interview Survey demonstrated that the incidence of obesity was 30% higher among those who participated in SNAP than among the nonparticipants ( p  = .01; Leung & Villamor,  2011 ). This association was more evident among males than females. Participation in SSI programs was positively associated to an adjusted 50% higher incidence of obesity compared to those who did not participate.

A large cross‐sectional study of 3748 children demonstrated that youths from SNAP families had higher odds of overweight/obesity with greater access to combination grocery/other stores (OR: 1.14, 95% CI: 1.05–1.24, p  < .05). Eligible non‐SNAP youths had higher odds of overweight/obesity with greater access to convenience stores (OR: 1.11, 95% CI: 1.04–1.18, p  < .05; Gorski Findling et al.,  2018 ).

In a study of 435 adult residents of low‐income neighborhoods, compared to those who reported no federal nutrition assistance, those who participated in the WIC, FSP, and free/reduced‐price school meals during the 12 months before to the survey had significantly higher BMI ( p  < .01; Webb et al.,  2008 ). However, participation in FSP on a chronic basis (≥6 months) was linked to lower BMI compared to those who participated for <6 months ( p  < .01; Webb et al.,  2008 ).

A cross‐sectional, correlational study of 166 mothers of young children on a low income reported that participation in WIC or SNAP did not appear to be as considerable determinants of maternal BMI (McCurdy et al.,  2015 ). In a study of 153 women on a low income, risk of overweight in infants was strongly associated with participation in the SNAP FSP (OR = 4.469, standard error: 0.0693, p  ≤ .05; Watt et al.,  2013 ). Participation in SNAP was significantly associated with greater intakes of SSB ( p  ≤ .005). Those women participated in SNAP were 4.5 times more likely to have a child in the 85th percentile or higher. SNAP participation contributed to child obesity through increased mothers' intakes of SSB.

3.3.1.2.5. Healthy food beliefs and attitudes

A cross‐sectional survey of 298 households demonstrated that those who greatly perceived healthy food as being convenient had a 57% decline in odds of BMI for caregivers and children (OR: 0.43, 95% CI: 0.21–2.4, p  < .05; Vedovato et al.,  2016 ). This study found that compared to those groups who had food insecurity, those participants who had food security more reported to agree that healthy foods are affordable and convenient. After adjusting for socioeconomic characteristics, odds of household food insecurity were 0.18 (95% CI: 0.09, 0.39) and 0.49 (95% CI: 0.24, 0.95) among those families who perceived healthy food to be affordable and convenient, respectively ( p  < .05; Vedovato et al.,  2016 ).

A study of 432 parents or caregivers of kindergarten‐aged children found that food security status changed parents' experience of barriers to having healthful food in their homes (Bauer et al.,  2012 ). Parents experiencing food insecurity were most likely to report that there was little variety of FV where they buy groceries ( p  = .003) and were more likely to agree that where they buy groceries the fruits and vegetables were in poor condition ( p  = .03). These parents were also more likely to report that their family does not like FV ( p  = .01).

A cross‐sectional study of 107 women responsible for household food supplies demonstrated among BMI categories, only women with severe obesity had less confidence in their healthy food preparation abilities (beta coefficient: −0.23, p  = .03; Mercille et al.,  2012 ). Both self‐efficacy scores were inversely associated with severe household food insecurity (beta coefficient: −0.25, p  = .01); however, the association became nonsignificant for the general food preparation score. Lack of availability as an excuse for not buying FV locally was positively correlated with self‐efficacy in healthy food preparation (beta coefficient: 0.29, p  < .01 (Mercille et al.,  2012 ).

3.3.1.2.6. Dietary intake (diet quality)

Thirteen studies measured dietary intakes of participants. A cross‐sectional analysis of 7741 adults in the California Health Interview Survey demonstrated that SNAP and SSI participants had higher consumption of soda than nonparticipants of any program (Leung & Villamor,  2011 ). The findings from another study of 432 parents or caregivers of kindergarten‐aged children demonstrated that children from families who experienced very low food security had higher intakes of hot or ready‐made foods bought from a convenience store or gas station than those youths from families living in food security ( p  = .002). Children living with food insecurity also ate pizza and fried chicken more often than children living in food security ( p  < .05; Bauer et al.,  2012 ). Based on the results from a study of 212 food pantry users, participants living in food security were twice as likely to eat fruit, vegetables, and fiber than those who had food insecurity (OR = 2.3, 95% CI: 1.1, 5.2, p  = .05; Robaina & Martin,  2013 ).

A cross‐sectional study of 202 young people who were homeless found significant associations with overeating (as a coping strategy) and higher food intakes of fat ( p  = .037), protein ( p  = .010), and the meat food group ( p  = .014) among females 9–13 years (Smith & Richards,  2008 ). Among youths' males 9–13 years, overeating was associated with increased intakes of calories ( p  = .012), carbohydrates ( p  = .030), fat ( p  = .011), protein ( p  = .015), bread ( p  = .028), and vegetables ( p  = .029). Eating at the homes of family and friends, as the coping strategies, was also associated with overeating ( p  = .017; Smith & Richards,  2008 ). These results suggested that these youths have used coping strategies for dealing with a food insecure environment by overeating and consuming high‐fat foods when tasty food was available. In this study, the major calorific snacks that were identified to be commonly consumed by youth were salty snacks, candy, soft drinks, fruit drinks, French fries, cheeseburgers, and pizza. These types of foods are largely offered by fast‐food restaurants and convenience stores, which are common stores in downtown urban neighborhoods (Smith & Richards,  2008 ). Another cross‐sectional study of 195 Somali refugee women in the United States demonstrated an association between BMI scores and daily intake from different food groups. A significant difference was noted in the fruits, vegetables, and beans groups (Dharod et al.,  2013 ). Intake from all of these food groups at least once a day was less common among participants who were affected by overweight/obesity than individuals with normal weight ( p  ≤ .05). Families with severe level of food insecurity or child hunger had higher intakes of eggs. Child hunger was 20 times greater among households who consumed eggs at least once a day (OR: 21.20; 95% CI: 7.83–57.34; p  < .001). Daily intake of FV also predicted food security. When participants reported eating leafy green vegetables at least once a day, the odds of food insecurity became 70%–80% lower (OR: 0.02; CI: 0.08–0.51; p  < .001; Dharod et al.,  2013 ). Finally, in a study of 153 women on a low income, while mothers had possible risk factors for childhood obesity such as fast‐food consumption, intakes of sweets and SSB by mothers were associated with infant overweight ( p  ≤ .05; Watt et al.,  2013 ). Mothers who drink SSB daily were 4.7 times more expected to have a child in the 85th percentile or higher on weight for length (standard error: 0.673, p  ≤ .05). Mothers who ate sweets twice a week or more were more than 11 times more expected to have a child in the 85th percentile or higher on weight for length (standard error: 0.892, p  ≤ .05).

3.3.1.2.7. Coping strategies to alleviate hunger

In a cross‐sectional study of 202 young people who were homeless, regression analyses of coping mechanisms found that the variable “when I am really hungry, I will eat anything” was predictive of males' BMI (beta coefficient: −1.046 [standard error: 0.426, p  = .000]). For females, the variable “if I am hungry, I will eat foods that I do not like” was predictive of BMI (beta coefficient: −0.804 [standard error: 0.379, p  = .036]).

3.3.2. Qualitative component

Two analytical themes were developed from 19 study findings extracted from included studies. The study findings and relations between descriptive themes are presented in Table  S6 and Figure  3 . The synthesized analytical findings are as below.

• A reliance on energy‐dense, nutrient‐poor foods due to their affordability, accessibility, and extended shelf life must be acknowledged. Policy efforts are needed to focus on affordability and availability of neighborhood fresh produce as well as to consider the importance of the food environment in mediating the relationship between food poverty/insecurity and BMI among low‐income individuals.
• Food banks and other food support networks, used as a coping strategy for food insecurity, have the potential to affect their users' health and body weight. Therefore, increasing the nutritional quality of food provided by them is essential.

Relations between descriptive themes

3.3.3. Mixed‐methods aggregation of qualitative and quantitative synthesized findings

Mixed‐methods syntheses were conducted to answer the following questions: “Are the results from each synthesis supportive or contradictory?”, “Do the qualitative data support to explain variations in the direction and size of correlations within the included quantitative studies?”. To facilitate the final aggregation of the individual syntheses, a convergent segregated approach was applied (Stern, Lizarondo, Carrier, et al.,  2020 ). The synthesized results from the qualitative components were combined with textual descriptions translated from quantitative findings (Table  S7 ). The mixed‐method synthesis: To reduce the prevalence of overweight/obesity, holistic interventional approaches are required to be implemented to remediate both food insecurity and unhealthy individuals' dietary behaviors that are influenced by different types of food environments. These efforts should emphasize affordability and availability of neighborhood fresh produce as well as to consider other components of food environments such as unhealthy obesogenic food environments in mediating the relationships between food insecurity and overweight/obesity, especially among low‐income families. It is essential that the nutritional quality of food provided by nutrition assistance programs is improved.

4. DISCUSSION

The results of our meta‐analysis ( n  = 36,113) showed an overall small, but statistically significant, association between food insecurity and obesity. These results demonstrated that food insecurity increased risk of obesity among adults and children. Therefore, individuals experiencing food insecurity were more likely to be affected by obesity. These findings are important given the context of the “cost of living crisis” and rising health inequalities (Limb,  2022 ).

There were also positive associations between different types of food environments and overweight and obesity. A study of female participants in SNAP program demonstrated that availability of unhealthy foods at home was significantly associated with BMI (Sanjeevi et al.,  2018 ). This study indicated that the relationship between food insecurity and obesity was partially mediated by home food environment. A large study revealed that women with severe food insecurity had lower odds of obesity if they were usually or always able to afford fresh fruits and vegetables in their neighborhood (Ro & Osborn,  2018 ). Therefore, neighborhood affordability of fresh produce was accounted as the driving factor that reduced the statistical association between food insecurity and overweight/obesity. Another large study of children aged 2–8 years demonstrated that the average child and adolescents in SNAP household spent a higher percentage of their budget on sugary beverages than on non‐SNAP households (Gorski Findling et al.,  2018 ). In another study of adult residents of low‐income neighborhoods, those who obtained food from charitable sources such as food bank or soup kitchens had significantly higher BMI than those who shopped at convenience stores and those who ate fast‐foods in the month before the survey (Webb et al.,  2008 ).

In the absence of data on the direct associations between food environment exposure and BMI outcome, the links between food insecurity (as outcome) and different types of food environments (as exposure) were also examined. This was performed to better understand the mechanisms behind the association between food environments and weight status as the links between food insecurity and overweight/obesity was established through our meta‐analysis. For example, a study of 4589 middle and high school students did not provide the association between BMI and home food environments directly (Widome et al.,  2009 ). However, it showed that youths with food insecurity had several eating‐related risk factors for overweight/obesity. It demonstrated that these youths, who had the greater percentage of obesity, reported eating more fast‐food than did those who experienced food security. This group also had less both healthy and unhealthy food available in their home. They were also less likely to eat family meals than their counterparts experiencing food security. The study suggested that this might be because of limited or irregular food availability, leading to less instilling a regular family meal routine (Widome et al.,  2009 ). In another population‐based study, the food environment in households living with food insecurity was poorer than in households living in food security (Bruening et al.,  2012 ). Large differences in perceived access to FV were available between parents experiencing food security and parents who experienced food insecurity. Parents who experienced food insecurity reported that the quality and variety of available FV were poor. More importantly, this group of parents perceived that fruits were too expensive to purchase compared to parents experiencing food security (Bruening et al.,  2012 ). Another study of parents and caregivers of kindergarten‐aged children found food security status influenced the parents' experience of obstacles to having healthful food in their home (Bauer et al.,  2012 ). Parents with food insecurity were most likely to report that there was little variety of fruit and vegetables in poor condition where they buy groceries. It also found youths from families who experienced very low food security reported eating more than twice ready‐made food or hot food from a convenience store or compared to children whose families experienced food security. In comparison to children living in food security, youths living with food insecurity also consumed fried chicken and pizza more often (Bauer et al.,  2012 ). The findings from qualitative studies ( n  = 409 participants) regarding a reliance on energy‐dense, nutrient‐poor foods due to their affordability and accessibility aligned with quantitative studies. A study of children's lived experience found that participants mentioned healthy food is more expensive and cost was a barrier to purchasing fresh fruit (Genuis et al.,  2015 ). They also raised the issue that accessibility and transportation play a key role as to reach the closest grocery store that sells a full range of healthy market choices, and that a vehicle is required. Also, findings from qualitative and quantitative studies regarding the potential links between increased body weight and participation in food assistance programs such as food banks, used as a coping strategy for food insecurity, were supportive. A qualitative study of food bank users revealed that although relying on food bank parcels meant that they could afford to pay bills, however, it also meant sacrificing fresh food that exacerbated their weight gain (Thompson et al.,  2018 ).

In the present study, our findings from the aggregation of qualitative and quantitative analyses recommend that holistic approaches (including policy) are required to remediate food insecurity and unhealthy individuals' dietary behaviors that are influenced by different types of food environments in order to reduce the prevalence of overweight/obesity.

4.1. Strengths and limitations

This review is particularly comprehensive by the inclusion of both quantitative and qualitative studies. However, the study is not without its limitations. The focus on the impact of both food insecurity status and the food environment on high BMI meant that articles that only measured one of these factors were excluded from this review. This could potentially limit the inclusion of relevant evidence. For example, those studies that considered the important role of smartphone technology but not the role of food insecurity on obesity risk were excluded, although food environments particularly expand into online settings that shape consumers' food choices (Vadiveloo et al.,  2021 ). Most included quantitative studies were cross‐sectional in their designs. Thus, it was not possible to identify causality or direction among key variables (Ro & Osborn,  2018 ). Moreover, it was not possible to assess whether food insecurity status and/or the food environment variables temporarily caused different behaviors and perceptions or if all these factors shared a common cause (Widome et al.,  2009 ). Therefore, further longitudinal studies are warranted to acknowledge the possible associations between these variables. For those studies that used self‐reported measures to evaluate anthropometric indices, they are subject to misclassification of subjects (Ro & Osborn,  2018 ). For example, if overweight/obesity were underestimated in those studies, these might make a more cautious interpretation of our results. Regarding food environment exposures, some studies used personal perceptions of neighborhoods. For instance, in a cross‐sectional study of 5957 individuals, neighborhood measures were personal perceptions of neighborhoods. As such, this may not represent objective neighborhood characteristics such as food (Ro & Osborn,  2018 ). Small sample size of qualitative studies ( n  = 11) could be also considered a limitation. These studies took place in specific context and communities which might limit transferability of findings from such small sample size. However, they provide some insights into the lived experience of individuals suffering from overweight or obesity and food insecurity. Finally, it is difficult to make comparisons between studies in relation to measurement tools used, as these differed from study to study. Measures of food insecurity used by included studies varied in their validity and focus on capturing different elements of food security status. For instance, although the USDA Adult Food Security Survey Module is a validated measure of food insecurity, it focuses on food adequacy, failing to capture other elements of food security status such as preferences, safety, and nutrition (Yau et al.,  2020 ).

4.2. Implications for policy and practice

This systematic review highlights that obesogenic food environments and food insecurity significantly contribute to obesity. This supports the evidence concerning reliance on cheap energy‐dense foods in favor of nutrient‐dense foods such as fruits and vegetables. For instance, this review indicates that those living with food insecurity have higher fruit and sugary beverage intakes compared to those living in food security. Since these beverages are cheaper than the equivalent whole fruits, this might be preferred by these individuals under economic constraint (Yau et al.,  2020 ). Since this review indicated that BMI is significantly higher for those who acquire their foods form charitable sources such as food banks, implementing policies and efforts to improve the nutritional quality of food parcels is essential to help food bank users to meet their individual dietary needs. A recent mixed‐method systematic review has explored the nutritional quality of food parcels provided by food banks and the effectiveness of food banks at reducing food insecurity in developed countries (Oldroyd et al.,  2022 ). The results of this study revealed that pre‐packaged food parcels provided by food banks were inconsistent at meeting nutritional requirements of their users and often failed to meet individual needs, including cultural and health preferences. Use of food banks improved food security and dietary quality of users, allowing otherwise unachievable access to food. Nevertheless, food insecurity remained, and was explained by limited food variety, quality, and choice (Oldroyd et al.,  2022 ).These mixed‐method findings encourage interventions to ensure consistent, adequate nutrition, and improved nutritional quality of food parcels at food banks to meet nutritional needs of those requiring food banks.

These findings emphasize the importance of structural and policy changes to the food and economic environment. There need to be societal changes to reduce inequalities to facilitate national and international goals of reducing overweight and/or obesity. This also provides scope for halting rising trends in food insecurity as well as eradicating food insecurity. A suggested approach to tackling such issues might be to address the high and rising cost of food, especially healthy foods (Yau et al.,  2020 ) particularly within the context of the global cost of living crisis. It is reported that in high‐income countries like the UK, even people who work full‐time on the National Living Wage cannot necessarily achieve the Minimum Income Standard (i.e., the income needed to reach a minimum socially acceptable standard of living; Yau et al.,  2020 ). Therefore, combined with the rising economic crises related to recent COVID‐19 pandemic and world events, addressing wage‐related policies to ensure sufficient income for adequate standards of living is critical to address health inequalities.

4.3. Implications for research

Further longitudinal studies investigating the impact of obesogenic food environments and food insecurity on obesity among general populations, rather than minority‐specific, and in countries beyond the USA, will strengthen the evidence base. Since this review indicated that BMI is significantly higher for those who acquire their foods from charitable sources such as food banks, further updated reviews for high‐income countries investigating the nutritional quality of food parcels and whether using foodbanks reduces the food insecurity and improves their users' diets will strengthen the evidence base.

5. CONCLUSIONS AND IMPLICATIONS OF THIS REVIEW

Drawing on evidence from research across high‐income countries, the present systematic review and meta‐analysis showed that food insecurity and some types of food environments are a risk factor for obesity. Wide‐reaching approaches (including policy changes) are required to address overweight/obesity among individuals experiencing food insecurity, especially among those whose food choices are influenced by unhealthy food environments. Our results suggest that these efforts should focus on affordability and availability of neighborhood fresh produce as well as to consider other components of food environments such as unhealthy obesogenic food environments in mediating the relationships between food insecurity and overweight/obesity, especially among low‐income families. It is also essential that the nutritional quality of food offered by nutrition assistance programs is improved.

CONFLICT OF INTEREST

No conflicts of interest.

ETHICAL STATEMENT

This review used only published sources of data. Ethical review by a Research Ethics Committee was not required.

Supporting information

Tables S1–S7

ACKNOWLEDGMENTS

This research was supported by Teesside University. Guidance with literature searches from Mrs Carol Dell Price, Teesside University, and Prof Alan Batterham, Teesside University for his advice with data analysis are gratefully acknowledged.

Eskandari, F. , Lake, A. A. , Rose, K. , Butler, M. , & O’Malley, C. (2022). A mixed‐method systematic review and meta‐analysis of the influences of food environments and food insecurity on obesity in high‐income countries . Food Science & Nutrition , 10 , 3689–3723. 10.1002/fsn3.2969 [ CrossRef ] [ Google Scholar ]

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Three techniques for integrating data in mixed methods studies

• Related content
• Peer review
• Alicia O’Cathain , professor 1 ,
• Elizabeth Murphy , professor 2 ,
• Jon Nicholl , professor 1
• 1 Medical Care Research Unit, School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
• 2 University of Leicester, Leicester, UK
• Correspondence to: A O’Cathain a.ocathain{at}sheffield.ac.uk
• Accepted 8 June 2010

Techniques designed to combine the results of qualitative and quantitative studies can provide researchers with more knowledge than separate analysis

Health researchers are increasingly using designs that combine qualitative and quantitative methods, and this is often called mixed methods research. 1 Integration—the interaction or conversation between the qualitative and quantitative components of a study—is an important aspect of mixed methods research, and, indeed, is essential to some definitions. 2 Recent empirical studies of mixed methods research in health show, however, a lack of integration between components, 3 4 which limits the amount of knowledge that these types of studies generate. Without integration, the knowledge yield is equivalent to that from a qualitative study and a quantitative study undertaken independently, rather than achieving a “whole greater than the sum of the parts.” 5

Barriers to integration have been identified in both health and social research. 6 7 One barrier is the absence of formal education in mixed methods research. Fortunately, literature is rapidly expanding to fill this educational gap, including descriptions of how to integrate data and findings from qualitative and quantitative methods. 8 9 In this article we outline three techniques that may help health researchers to integrate data or findings in their mixed methods studies and show how these might enhance knowledge generated from this approach.

Triangulation protocol

Researchers will often use qualitative and quantitative methods to examine different aspects of an overall research question. For example, they might use a randomised controlled trial to assess the effectiveness of a healthcare intervention and semistructured interviews with patients and health professionals to consider the way in which the intervention was used in the real world. Alternatively, they might use a survey of service users to measure satisfaction with a service and focus groups to explore views of care in more depth. Data are collected and analysed separately for each component to produce two sets of findings. Researchers will then attempt to combine these findings, sometimes calling this process triangulation. The term triangulation can be confusing because it has two meanings. 10 It can be used to describe corroboration between two sets of findings or to describe a process of studying a problem using different methods to gain a more complete picture. The latter meaning is commonly used in mixed methods research and is the meaning used here.

The process of triangulating findings from different methods takes place at the interpretation stage of a study when both data sets have been analysed separately (figure ⇓ ). Several techniques have been described for triangulating findings. They require researchers to list the findings from each component of a study on the same page and consider where findings from each method agree (convergence), offer complementary information on the same issue (complementarity), or appear to contradict each other (discrepancy or dissonance). 11 12 13 Explicitly looking for disagreements between findings from different methods is an important part of this process. Disagreement is not a sign that something is wrong with a study. Exploration of any apparent “inter-method discrepancy” may lead to a better understanding of the research question, 14 and a range of approaches have been used within health services research to explore inter-method discrepancy. 15

Point of application for three techniques for integrating data in mixed methods research

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The most detailed description of how to carry out triangulation is the triangulation protocol, 11 which although developed for multiple qualitative methods, is relevant to mixed methods studies. This technique involves producing a “convergence coding matrix” to display findings emerging from each component of a study on the same page. This is followed by consideration of where there is agreement, partial agreement, silence, or dissonance between findings from different components. This technique for triangulation is the only one to include silence—where a theme or finding arises from one data set and not another. Silence might be expected because of the strengths of different methods to examine different aspects of a phenomenon, but surprise silences might also arise that help to increase understanding or lead to further investigations.

The triangulation protocol moves researchers from thinking about the findings related to each method, to what Farmer and colleagues call meta-themes that cut across the findings from different methods. 11 They show a worked example of triangulation protocol, but we could find no other published example. However, similar principles were used in an iterative mixed methods study to understand patient and carer satisfaction with a new primary angioplasty service. 16 Researchers conducted semistructured interviews with 16 users and carers to explore their experiences and views of the new service. These were used to develop a questionnaire for a survey of 595 patients (and 418 of their carers) receiving either the new service or usual care. Finally, 17 of the patients who expressed dissatisfaction with aftercare and rehabilitation were followed up to explore this further in semistructured interviews. A shift of thinking to meta-themes led the researchers away from reporting the findings from the interviews, survey, and follow-up interviews sequentially to consider the meta-themes of speed and efficiency, convenience of care, and discharge and after care. The survey identified that a higher percentage of carers of patients using the new service rated the convenience of visiting the hospital as poor than those using usual care. The interviews supported this concern about the new service, but also identified that the weight carers gave to this concern was low in the context of their family member’s life being saved.

Morgan describes this move as the “third effort” because it occurs after analysis of the qualitative and the quantitative components. 17 It requires time and energy that must be planned into the study timetable. It is also useful to consider who will carry out the integration process. Farmer and colleagues require two researchers to work together during triangulation, which can be particularly important in mixed methods studies if different researchers take responsibility for the qualitative and quantitative components. 11

Following a thread

Moran-Ellis and colleagues describe a different technique for integrating the findings from the qualitative and quantitative components of a study, called following a thread. 18 They state that this takes place at the analysis stage of the research process (figure ⇑ ). It begins with an initial analysis of each component to identify key themes and questions requiring further exploration. Then the researchers select a question or theme from one component and follow it across the other components—they call this the thread. The authors do not specify steps in this technique but offer a visual model for working between datasets. An approach similar to this has been undertaken in health services research, although the researchers did not label it as such, probably because the technique has not been used frequently in the literature (box)

An example of following a thread 19

Adamson and colleagues explored the effect of patient views on the appropriate use of services and help seeking using a survey of people registered at a general practice and semistructured interviews. The qualitative (22 interviews) and quantitative components (survey with 911 respondents) took place concurrently.

The researchers describe what they call an iterative or cyclical approach to analysis. Firstly, the preliminary findings from the interviews generated a hypothesis for testing in the survey data. A key theme from the interviews concerned the self rationing of services as a responsible way of using scarce health care. This theme was then explored in the survey data by testing the hypothesis that people’s views of the appropriate use of services would explain their help seeking behaviour. However, there was no support for this hypothesis in the quantitative analysis because the half of survey respondents who felt that health services were used inappropriately were as likely to report help seeking for a series of symptoms presented in standardised vignettes as were respondents who thought that services were not used inappropriately. The researchers then followed the thread back to the interview data to help interpret this finding.

After further analysis of the interview data the researchers understood that people considered the help seeking of other people to be inappropriate, rather than their own. They also noted that feeling anxious about symptoms was considered to be a good justification for seeking care. The researchers followed this thread back into the survey data and tested whether anxiety levels about the symptoms in the standardised vignettes predicted help seeking behaviour. This second hypothesis was supported by the survey data. Following a thread led the researchers to conclude that patients who seek health care for seemingly minor problems have exceeded their thresholds for the trade-off between not using services inappropriately and any anxiety caused by their symptoms.

Mixed methods matrix

A unique aspect of some mixed methods studies is the availability of both qualitative and quantitative data on the same cases. Data from the qualitative and quantitative components can be integrated at the analysis stage of a mixed methods study (figure ⇑ ). For example, in-depth interviews might be carried out with a sample of survey respondents, creating a subset of cases for which there is both a completed questionnaire and a transcript. Cases may be individuals, groups, organisations, or geographical areas. 9 All the data collected on a single case can be studied together, focusing attention on cases, rather than variables or themes, within a study. The data can be examined in detail for each case—for example, comparing people’s responses to a questionnaire with their interview transcript. Alternatively, data on each case can be summarised and displayed in a matrix 8 9 20 along the lines of Miles and Huberman’s meta-matrix. 21 Within a mixed methods matrix, the rows represent the cases for which there is both qualitative and quantitative data, and the columns display different data collected on each case. This allows researchers to pay attention to surprises and paradoxes between types of data on a single case and then look for patterns across all cases 20 in a qualitative cross case analysis. 21

We used a mixed methods matrix to study the relation between types of team working and the extent of integration in mixed methods studies in health services research (table ⇓ ). 22 Quantitative data were extracted from the proposals, reports, and peer reviewed publications of 75 mixed methods studies, and these were analysed to describe the proportion of studies with integrated outputs such as mixed methods journal articles. Two key variables in the quantitative component were whether the study was assessed as attempting to integrate qualitative or quantitative data or findings and the type of publications produced. We conducted qualitative interviews with 20 researchers who had worked on some of these studies to explore how mixed methods research was practised, including how the team worked together.

Example of a mixed methods matrix for a study exploring the relationship between types of teams and integration between qualitative and quantitative components of studies* 22

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The shared cases between the qualitative and quantitative components were 21 mixed methods studies (because one interviewee had worked on two studies in the quantitative component). A matrix was formed with each of the 21 studies as a row. The first column of the matrix contained the study identification, the second column indicated whether integration had occurred in that project, and the third column the score for integration of publications emerging from the study. The rows were then ordered to show the most integrated cases first. This ordering of rows helped us to see patterns across rows.

The next columns were themes from the qualitative interview with a researcher from that project. For example, the first theme was about the expertise in qualitative research within the team and whether the interviewee reported this as adequate for the study. The matrix was then used in the context of the qualitative analysis to explore the issues that affected integration. In particular, it helped to identify negative cases (when someone in the analysis doesn’t fit with the conclusions the analysis is coming to) within the qualitative analysis to facilitate understanding. Interviewees reported the need for experienced qualitative researchers on mixed methods studies to ensure that the qualitative component was published, yet two cases showed that this was neither necessary nor sufficient. This pushed us to explore other factors in a research team that helped generate outputs, and integrated outputs, from a mixed methods study.

Themes from a qualitative study can be summarised to the point where they are coded into quantitative data. In the matrix (table ⇑ ), the interviewee’s perception of the adequacy of qualitative expertise on the team could have been coded as adequate=1 or not=2. This is called “quantitising” of qualitative data 23 ; coded data can then be analysed with data from the quantitative component. This technique has been used to great effect in healthcare research to identify the discrepancy between health improvement assessed using quantitative measures and with in-depth interviews in a randomised controlled trial. 24

We have presented three techniques for integration in mixed methods research in the hope that they will inspire researchers to explore what can be learnt from bringing together data from the qualitative and quantitative components of their studies. Using these techniques may give the process of integration credibility rather than leaving researchers feeling that they have “made things up.” It may also encourage researchers to describe their approaches to integration, allowing them to be transparent and helping them to develop, critique, and improve on these techniques. Most importantly, we believe it may help researchers to generate further understanding from their research.

We have presented integration as unproblematic, but it is not. It may be easier for single researchers to use these techniques than a large research team. Large teams will need to pay attention to team dynamics, considering who will take responsibility for integration and who will be taking part in the process. In addition, we have taken a technical stance here rather than paying attention to different philosophical beliefs that may shape approaches to integration. We consider that these techniques would work in the context of a pragmatic or subtle realist stance adopted by some mixed methods researchers. 25 Finally, it is important to remember that these techniques are aids to integration and are helpful only when applied with expertise.

Summary points

Health researchers are increasingly using designs which combine qualitative and quantitative methods

However, there is often lack of integration between methods

Three techniques are described that can help researchers to integrate data from different components of a study: triangulation protocol, following a thread, and the mixed methods matrix

Use of these methods will allow researchers to learn more from the information they have collected

Cite this as: BMJ 2010;341:c4587

Funding: Medical Research Council grant reference G106/1116

Competing interests: All authors have completed the unified competing interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare financial support for the submitted work from the Medical Research Council; no financial relationships with commercial entities that might have an interest in the submitted work; no spouses, partners, or children with relationships with commercial entities that might have an interest in the submitted work; and no non-financial interests that may be relevant to the submitted work.

Contributors: AOC wrote the paper. JN and EM contributed to drafts and all authors agreed the final version. AOC is guarantor.

Provenance and peer review: Not commissioned; externally peer reviewed.

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A mixed-methods systematic review and meta-analysis of barriers and facilitators to paediatric symptom management at end of life

Affiliations.

• 1 School of Psychology, University of Southampton, Southampton, UK.
• 2 Helen & Douglas House Hospices, Oxford, UK.
• 3 John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
• 4 Department of Anaesthesia and Pain Medicine, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
• 5 The Louis Dundas Centre, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
• 6 Alder Hey Children's Hospital, Liverpool, UK.
• 7 Rainbows Hospice, Loughborough, UK.
• 8 Patient & Public Representative, Cambridge, UK.
• 9 UCL School of Pharmacy, London, UK.
• 10 Psychological Medicine, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
• PMID: 32228216
• PMCID: PMC7521017
• DOI: 10.1177/0269216320907065

Background: Symptom management for infants, children and young people at end of life is complex and challenging due to the range of conditions and differing care needs of individuals of different ages. A greater understanding of these challenges could inform the development of effective interventions.

Aim: To investigate the barriers and facilitators experienced by patients, carers and healthcare professionals managing symptoms in infants, children and young people at end of life.

Design: A mixed-methods systematic review and meta-analysis was undertaken (PROSPERO ID: CRD42019124797).

Data sources: The Cochrane Library, PROSPERO, CINAHL, MEDLINE, PsycINFO, Web of Science Core Collection, ProQuest Dissertations & Theses Database, Evidence Search and OpenGrey were electronically searched from the inception of each database for qualitative, quantitative or mixed-methods studies that included data from patients, carers or healthcare professionals referring to barriers or facilitators to paediatric end-of-life symptom management. Studies underwent data extraction, quality appraisal, narrative thematic synthesis and meta-analysis.

Results: A total of 64 studies were included (32 quantitative, 18 qualitative and 14 mixed-methods) of medium-low quality. Themes were generated encompassing barriers/facilitators experienced by carers (treatment efficacy, treatment side effects, healthcare professionals' attitudes, hospice care, home care, families' symptom management strategies) and healthcare professionals (medicine access, treatment efficacy, healthcare professionals' demographics, treatment side effects, specialist support, healthcare professionals' training, health services delivery, home care). Only one study included patients' views.

Conclusion: There is a need for effective communication between healthcare professionals and families, more training for healthcare professionals, improved symptom management planning including anticipatory prescribing, and urgent attention paid to the patients' perspective.

Keywords: Child; caregivers; meta-analysis; paediatrics; pain management; palliative care; systematic review; terminal care.

Publication types

• Meta-Analysis
• Research Support, Non-U.S. Gov't
• Systematic Review
• Health Personnel / psychology
• Health Personnel / standards
• Health Personnel / statistics & numerical data
• Hospice Care* / standards
• Hospice Care* / statistics & numerical data
• Palliative Care* / standards
• Palliative Care* / statistics & numerical data
• Patient Care Management* / statistics & numerical data
• Qualitative Research

• Open access
• Published: 21 May 2024

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

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

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

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

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

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

Conclusions

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

Systematic review registration

PROSPERO CRD42021267355.

Peer Review reports

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

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

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

Objectives and research questions

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

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

Information sources, search strategy and eligibility criteria

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

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

Screening and selection process

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

Data collection process and data items

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

Assessment of risk of bias across reviews

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

Assessment of risk of bias within the reviews

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

Synthesis methods

Panoramic meta-analysis.

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

Narrative synthesis

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

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

Study selection results

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

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

Characteristics and diversity of included reviews

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

Results of the individual reviews

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

Results of quality assessment and confidence in the reviews

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

Risk of bias and evidence assessment within reviews

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

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

Overlap of reviews

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

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

Synthesis of results

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

Measures of adherence and related outcomes

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

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

Interventions used to promote adherence

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

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

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

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

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

Theoretical underpinning of interventions

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

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

Overall efficacy and heterogeneity according to the panoramic meta-analysis

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

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

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

Funnel plot of publication bias

Efficacy of informational and educational interventions

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

Efficacy of cognitive and behavioural motivational interventions

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

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

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

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

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

Efficacy of relational and psychosocial interventions

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

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

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

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

Efficacy according to the theoretical underpinning

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

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

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

In their Cochrane review, Jordan et al. [ 35 ] compared the evidence for the efficacy of different types of exercises and physiotherapy settings. Graded exercise is beneficial for adherence (moderate evidence). The exercise type does not appear to play an important role (moderate evidence). Whether water-based exercise favours adherence is unclear (low evidence and inconsistent results). Furthermore, the supervision of exercising (moderate evidence) is beneficial for adherence, but also self-management programmes improve exercise frequency compared to waiting list or no-intervention control groups (moderate evidence). Exercising individually seems to improve attendance at exercise classes more than exercising in a group (moderate evidence), as individual sessions could be scheduled at more convenient times and missed sessions could be rescheduled, whereas group sessions were scheduled at relatively inflexible times, and missed sessions could not be rescheduled [ 35 ]. However, adding group exercise to a home exercise programme can increase overall physical activity levels (moderate evidence) [ 35 ]. While the results of home- versus clinic-based interventions were conflicting and confounded by the intervention approaches, a combination of home- and clinic-based approaches may be promising [ 12 ] and aligns with the moderate-quality evidence that self-management programmes, refresher or booster sessions with a physiotherapist assist people to better adhere to therapeutic exercise [ 16 ].

No study was identified in the reviews that compared other settings, such as private- and public-funded physiotherapy or primary care and rehabilitation settings regarding adherence outcomes. No review and no study comparing the same educational, motivational, or BCT-based intervention across different conditions were identified.

This overview of systematic reviews addresses adherence in the physiotherapy and therapeutic exercise domain, aiming to summarise the evidence on the efficacy of interventions, to explore heterogeneity and to identify research gaps. The overview of reviews provided an adequate approach to generate answers to the research questions. Nineteen reviews, covering 205 unique trials, were included and narratively synthesised. In addition, four meta-analyses were pooled in a panoramic meta-analysis. The findings provide an overview of the diverse interventions and techniques aiming to enhance adherence, ranging from informational/educational to cognitive/behavioural/motivational and to relational/psychosocial intervention types. Furthermore, it synthesised their efficacy in physiotherapy for adults.

Confidence in the reviews was rated moderate or high in four reviews [ 7 , 16 , 27 , 35 ], but low or very low in the others (Table  3 ). The individual reviews considered the evidence levels as mostly low or very low (Table  4 ; see Risk of bias and evidence assessment ). Table 5 summarizes the evidence on the efficacy of each intervention and technique according to (a) whether the evidence supports efficacy, (b) the evidence level based on the report in the systematic reviews and (c) the confidence in the reviews as assessed with AMSTAR-2. It must be noted that the components of the intervention which caused the efficacy were not always clear. Some interventions lacked detailed definitions and descriptions of the specific BCTs included [ 33 ]. A single technique or mechanism of action was not always identifiable; moreover, various techniques seem to influence each other in such a way that they achieved efficacy only jointly [ 17 , 40 ].

No clear conclusion can be drawn on the efficacy of informational/educational interventions. Five reviews [ 12 , 30 , 31 , 34 , 36 ] showed low evidence for the efficacy of interventions on knowledge acquisition and low evidence for limited short-term efficacy on adherence. Providing knowledge alone seems not enough and should be complemented with supportive material (very low evidence) and combined with other interventions (low evidence). Patient education should also include social-cognitive or cognitive-behavioural approaches, psychoeducational interventions and collaborative processes as it is included in the therapeutic alliance approach [ 31 , 34 , 36 ]. Patient education with a more constructive educational approach builds upon the knowledge of the patient, supporting him/her in exploring and co-constructing knowledge which is very relevant in physiotherapy as research has shown [ 55 , 56 ].

The reviews on additional motivational, cognitive and behavioural interventions showed findings ranging from non-efficacy of behavioural counselling based on readiness to change (with low to moderate evidence) to moderate efficacy for booster sessions and behavioural graded physical activity (with moderate evidence) (see Table  5 ). Overall, a small overall effect size (SMD 0.24) for motivational interventions is indicative of the findings of the panoramic meta-analysis. The four pooled meta-analyses [ 7 , 16 , 27 , 28 ] included studies analysing interventions with a considerable amount of content overlap (e.g. goal-setting and booster sessions are BCTs and often part of motivational interventions), and no statistical heterogeneity of the true effect was found. Nevertheless, the diversity of interventions and techniques included constrain the explanatory power for potential components responsible for the efficacy of adherence. The sensitivity analyses in the meta-analysis of Eisele et al. [ 7 ] indicate that low-quality studies tend to overestimate the efficacy (cf. [ 31 ]). While some evidence exists on short- and medium-term effects of motivational programmes on adherence, no clear evidence for long-term effects can be concluded [ 7 , 30 ]. Furthermore, there is moderate and low evidence that additional motivational interventions and goal planning/setting improve adherence to self-efficacy beliefs [ 27 , 28 , 39 ]. Since self-efficacy beliefs play an important role in motivation and adherence [ 13 , 48 ], the results are relevant for physiotherapists to promote motivation and adherence. Experiencing that one can reach the set goals and manage daily challenges, complemented with feedback and reinforcement from the therapist (or important others), may increase self-efficacy beliefs and human agency [ 48 , 57 , 58 , 59 ].

A closer look at how and in which manner goals and actions are planned and reviewed seems crucial. The patient-led approach was only reported in 5 of the 26 interventions that incorporated the BCT goal setting (behaviour) , although it is associated with greater engagement and achievement than goals which are set by the therapist [ 17 ]. Goal setting and action planning should be informed by the patient’s motives, interests and values in order to promote intrinsic motivation, self-determination and subsequently better adherence ([ 17 ],cf. [ 27 , 28 , 60 , 61 ]). The reviews on the BCTs displayed various positive effects relating to the BCT cluster goals and planning ; however, they point out that the BCT goal setting is not used alone but in connection with several other BCTs. Feedback on outcomes of behaviour , behavioural contract and non-specific reward as well as patient led-goal setting , self-monitoring of behaviour and social support (unspecified) was included in efficacious interventions [ 17 ]. Social support seems to have an important influence on adherence [ 6 , 7 , 17 , 40 ], for example through regular phone-calls or home visits, encouraging messaging, supervision or community-based group programs (cf. [ 1 , 2 , 3 ],[ 37 , 62 ]). Social support also relates to the promotion of self-efficacy beliefs, if it endorses confidence in own abilities and competences [ 6 ].

Some BCTs seem inherent to standard practices of physiotherapy [ 6 ] even though physiotherapists seem to use rather a small number of BCTs [ 15 ]. Control groups also contained BCTs [ 6 , 7 ]; in particular instruction on how to perform a behaviour , generalisation of the target behaviour and social support (unspecified) were frequently coded [ 6 ]. Thus, it seems difficult to identify those BCTs that are (most) efficacious in promoting adherence ([ 7 ],cf. [ 50 ]). Unsurprisingly, the reviews revealed conflicting results and a high risk of bias in the individual studies. However, combining a greater number of BCTs (≥ 8) can be highly recommended, as this achieved a larger effect than interventions using fewer BCTs [ 7 ]. It is fairly unlikely that any single BCT changes adherence [ 6 , 7 , 17 , 40 ]. In that regard, Ariie et al. [ 63 ] argue that not only the amount of BCTs but also the quality, appropriateness and feasibility of the use of the BCTs is crucial.

Meaningful combinations of several BCTs are required. However, the combinations of BCTs may also differ among conditions, personal factors and therapeutic interventions ([ 7 ],cf. [ 63 , 64 ], [ 64 , 65 , 66 ]), and over the time. Two reviews consistently point to the same crucial time point (i.e. after 6 months) when BCT efficacy seems to drop, and more attention is required to maintain adherence [ 7 , 17 ]. Action planning , feedback on behaviour and behavioural practice/rehearsal seem efficacious particularly on short-term. Patient led-goal setting , self-monitoring of behaviour and social support (unspecified) are among those BCTs that seem more efficacious at long-term [ 17 ]. These findings are also in line with findings in non-clinical adults [ 54 ] and with motivational theories (e.g. the Health Action Process Approach [ 53 ]).

Limitations

Conducting an overview of reviews is per se associated with methodological limitations. A limitation is that reviews were analysed and not the original RCTs, which adds further risks of bias domains such as selection, analysis and reporting bias. A specific potential source of bias in overviews of reviews is the overlap of primary studies among the included reviews. The small overlap, caused by a few reviews with similar thematic scope, was controlled for in the data analysis. The substantial non-overlap of primary studies across the reviews reflects the clinical and methodological diversity of the included reviews and showcases the efforts to address (a) motivation and (non-)adherence as complex phenomena and from various perspectives.

Another methodological limitation originates from the search strategies. Considering different health-care systems and delimitations of the physiotherapy profession among countries, divergences among the definitions of terms and the use of diverse approaches to physical therapy, physiotherapy or the therapeutic use of exercise and physical activity, made a clear delimitation in the search strategy and inclusion/exclusion criteria difficult. Therefore, we may have missed out some relevant reviews by reducing our search to the two terms physiotherapy and physical therapy. Equally, we may also have included some aspects that were not primarily investigated for physiotherapists or physical therapists. Including only studies with adults, the findings may not be applicable to promote adherence among children.

While we did not exclude reviews from another language, the search was conducted only in English, which may omit important reviews in other languages. All included reviews (and as far as reported, also the original RCTs) were conducted in economically developed countries; however, social-cultural and context-specific factors influence participation and adherence [ 67 , 68 , 69 , 70 , 71 ]. Furthermore, we are aware that our own cultural background and experiences may have influenced the analysis and synthesis of the results and that conclusions drawn in this overview of reviews may not be suitable for every setting around the world. Therefore, we encourage the readers to critically assess the applicability of the findings to their specific context.

Another gap in coverage of this overview is that interventions that were analysed in RCTs but not included in any systematic review are not considered in this overview. Thus, there may be new or alternative intervention approaches that resulted efficacious but were not covered by this overview. Furthermore, reviews that focused only on the use of digital apps or tools, e.g. virtual reality, gamification, exergames or tele-rehabilitation, were excluded from this overview. Several reviews in this field include adherence-related outcomes, showing potential efficacy as well as limitations of the use of digital tools [ 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 ].

Research gaps, recommendations and measuring adherence

This overview of reviews highlighted some gaps in the existing knowledge. First, there is a lack of clear evidence on the efficacy of the interventions. The use of BCTs in the intervention as well as in the control groups may be a reason for inconsistent findings and conflicting evidence. Furthermore, the clinical and methodological heterogeneity constrains drawing clear conclusions on the efficacy. Second (and related to the previous), interventions are insufficiently described regarding their theoretical underpinning and active ingredients/techniques and thus limit the comparison of interventions. Theoretical underpinnings were used partly and translated into practise differently. Difficulties concerning the derivation or deduction of concrete, practical techniques or strategies from the theories were reported. A broader use of the BCT taxonomies would make interventions more comparable. Recently, the BCT Ontology was published, which claims to provide a standard terminology and a comprehensive classification system for the content of behaviour change interventions, suitable for describing interventions [ 84 ]. Third, there is a need for studies on holistic approaches, complex interventions based on integrative theories and the combination of multiple BCTs. While many theories are based on cognitive and behavioural approaches, affective and psychosocial factors are hardly investigated, overlooked and probably underestimated. Rhodes and Fiala [ 12 ] call for studying the influences of affective attitudes on adherence (e.g. enjoyment and pleasing behaviour) which may oppose the more cognitive, instrumental attitudes (e.g. the utility of behaviour). Jordan et al. [ 35 ] refer to a meta-analysis in another therapeutic regime [ 85 ] to explicit the potential efficacy of affective interventions (e.g. appealing to feelings, emotions or social relationships and social supports) in combination with educational and behavioural interventions on patient adherence [ 35 ]. Fourth, more research in patient-led approaches to goal setting and action planning and the relationship of patient-centeredness to adherence is promising [ 60 , 61 , 86 , 87 ].

Fifth, the reviews reported many studies that failed to provide evidence for intervention efficacy on adherence, particularly on long-term adherence. There is a need for prolonged observation to investigate long-term effects on adherence. Probably, intervention or follow-up interventions (e.g. booster sessions) must also be prolonged or repeated to avoid drop out to medium-term follow-ups (around 6 months) and to maintain participation. Sixth, studies should pay more attention to the actual efficacy of adherent behaviour on the desired therapeutic outcomes.

Seventh, another research gap lies in the analysis of the potential variation of the intervention efficacy across medical conditions, physiotherapeutic settings, personal characteristics (e.g. age, gender, sociocultural background) and dispositions (e.g. motives, affective attitudes, previous behaviour) and diverse context-related factors. Huynh et al. [ 79 ] showed for the case of multiple sclerosis that the efficacy of BCTs is not investigated in all disease stages or throughout the disease course; participants with mild-to-moderate level disability were more frequently included in the studies (cf. [ 18 ]). Ariie et al. [ 73 ] stated that the response to BCTs may be different according to the condition (cf. [ 76 ]). On the one hand, studies analysing the use of the same intervention or same combination of BCTs in different intervention groups (according to the categories mentioned above) could be beneficial for comparison purposes. On the other hand, studies should analyse how to find the ‘right’ (ideally, the ‘most efficacious’) adherence promotion intervention for the patient or target group. Qualitative studies may explore adequate combinations of BCTs and contribute to the understanding of complex intervention processes. The findings showcased that different interventions and BCTs may contribute to adherence and that the BCT Taxonomy defines a wide range of techniques, providing the physiotherapists with an overview of which techniques are useable and thus may inspire and support them to develop additional interventions and to enrich their current physiotherapeutic practise. The physiotherapist may use this knowledge to tailor interventions in a patient-centred manner to promote adherence, and to adapt to the condition, characteristics, dispositions and context-related factors of the patient. Hence, experimental studies could compare the efficacy of tailored to not-tailored interventions.

Finally, the outcome adherence should be better defined and holistically assessed. The definition of adherence (as the extent to which a person’s behaviour corresponds with treatment goals or plans) and calculation of adherence rates (by reported exercise or attended sessions divided by the recommended or prescribed exercise or sessions) are simplifying a complex phenomenon. The average or the percentages of attended or completed sessions do not picture interruptions, regularity or periods of more and less adherence. Attendance regularity can change over the time and different participation and fluctuation patterns can be identified [ 88 , 89 ]. For example, an adherence rate of 50% can imply (a) that a person attended regularly every second session throughout the period of observation or (b) that a person attended all sessions of the first half of the observation period and then stopped attending. The underlying reasons and motivational factors may be quite different in these two cases. Besides assessing participation and fluctuation patterns, the three dimensions of the SIRAS scale [ 42 ], i.e. frequency, intensity and reciprocity, could be considered for a holistic account of adherence. The findings of this overview emphasized the importance of a patient-led goal setting and planning, which includes a shared decision-making process and the mutual agreement to adhere to the jointly established plan (cf. WHO definition of adherence, [ 5 ]). The measurement of adherence should be able to distinguish a patient-led approach from a therapist-led approach (cf. [ 17 ]) and to appraise the extent of a shared decision-making process. In conclusion, a holistic approach to measure adherence in physiotherapy may include measures of the frequency of attendance/exercising (e.g. attended sessions out of the prescribed/recommended sessions), the regularity of participation and fluctuation (e.g. timeline with pauses and interruptions, visualizing more and less adherent periods), the intensity of attendance/exercising (e.g. the number or the increment of exercises and repetitions performed in comparison to the plan), reciprocity and fidelity to the agreed goals and plan (e.g. therapist’s and patient’s subjective appraisal of the degree of accomplishment of the agreed plan) and persistence/perseverance over the time (e.g. measuring volition via questionnaires or rating persistence in participation in spite of the experienced challenges and barriers).

We conclude that moderate certainty of evidence supports that (i) additional motivational interventions and behaviour change programmes can increase adherence and patients’ self-efficacy beliefs and (ii) interventions applying BCTs increase adherence, particularly when using a greater number of BCTs and combining various BCTs, and particularly on short to medium term. The BCTs’ patient-led goal setting , self-monitoring of behaviour and social support seem promising to promote maintenance; (iii) graded activities, booster sessions with a physiotherapist and supervision foster adherence.

There is low certainty of evidence that (i) goal setting and planning improves adherence to treatment regimens, particularly if a patient-centred approach is taken; (ii) motivational interventions including various techniques, such as positive reinforcement, social support, monitoring or feedback, can foster adherence; (iii) social support seems to play an important role in promoting adherence; however, evidence is low as this BCT is frequently found in the control group; and (iv) information provision and transfer of knowledge to the patient may improve adherence-related outcomes when combined with motivational techniques, as in psychoeducational programmes. Additional written information is superior to verbal instructions alone; (v) a combination of home-based exercise programmes with clinical supervision, refresher or booster sessions, or/and self-management programmes seems promising to increase adherence.

Regarding the implications for future research, a holistic approach to measure adherence in physiotherapy and the investigation of clearly defined interventions combining multiple BCTs is recommended.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Overview of reviews, umbrella review and reviews of reviews are considered as synonyms in this article (cf. [ 19 ]).

Abbreviations

Behaviour change technique

Cognitive behavioural/cognitive behavioural theory

Control/comparator group

Grades of Recommendation, Assessment, Development and Evaluation

Intervention/experimental group

Physical activity

Preferred Reporting Items for Overviews of Reviews

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

Physiotherapy

Randomised controlled trial

Standardised mean difference

Systematic review

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CL and PP conceived and designed the review. CL did the database search and data extraction. CL and PP did screening and quality assessment. CL did the narrative synthesis and drafted the manuscript. PP conducted the panoramic meta-analysis and critically revised and substantially contributed throughout the writing of the manuscript. The authors read and approved the final manuscript.

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Ley, C., Putz, P. Efficacy of interventions and techniques on adherence to physiotherapy in adults: an overview of systematic reviews and panoramic meta-analysis. Syst Rev 13 , 137 (2024). https://doi.org/10.1186/s13643-024-02538-9

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Systematic Reviews

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Healthcare-associated infections in long-term care facilities: a systematic review and meta-analysis of point prevalence studies

Stephanie K Tanamas ,

Rodney James ,

Courtney Ierano ,

Michael J Malloy ,

Eliza Watson ,

Janet K Sluggett ,

David Dunt ,

Karin Thursky ,

Leon J Worth .

https://doi.org/ 10.1136/bmjph-2023-000504

Objectives Residents of long-term care facilities (LTCFs) are especially vulnerable to acquiring healthcare-associated infections (HAIs). Our systematic review and meta-analysis estimated the burden of HAIs, identified the most frequent HAIs and explored the impact of facility-level and surveillance methodological differences on HAI burden in LTCFs, as determined by point prevalence studies (PPS).

Design Systematic review and meta-analysis.

Data sources Bibliographic databases MEDLINE (Ovid), Embase (Ovid) and CINAHL (EBSCOhost) were searched for potentially eligible English-language original research publications. References of short-listed full-text publications, the European Centre for Disease Control and Prevention website and an unpublished 2016–2022 Australian Aged Care PPS report were also checked.

Eligibility criteria PPS monitoring HAIs, published and undertaken between January 1991 and June 2023 in LTCFs.

Data extraction and synthesis Two reviewers independently screened for eligible PPS and if included, assessed risk of bias for each PPS using the Joanna Briggs Institute (JBI) critical appraisal tool for prevalence studies. Meta-analysis was performed using a generalised linear mixed model.

Results 31 publications (including 123 PPS from 33 countries encompassing 709 860 residents) were included. Nine PPS (7.3%) were allocated a JBI quality score greater than 80% while 30 PPS (24.4%) were allocated a score between 70% and 80%. The overall pooled HAI point prevalence was 3.5% (95% CI 3.1% to 4.0%); 3.9% (95% CI 3.2% to 4.7%) when higher bias-risk PPS (<70% quality score) were excluded. Of 120 PPS, the most frequently reported HAIs were urinary tract (UTIs; 38.9%), respiratory tract (RTIs; 33.6%) and skin or soft tissue (SSTIs; 23.7%) infections. HAI point prevalence varied by geographical region (p<0.001), study decade (p<0.001) and HAI surveillance definitions used (p<0.001). There was no difference across facility types (p=0.57) or season (p=0.46).

Conclusions HAIs remain a global public health problem and threat to the safety of LTCF residents; effective infection prevention and control strategies to reduce HAIs in LTCFs are still required. Guidance specifically about the prevention and management of UTIs, RTIs and SSTIs should be prioritised.

PROSPERO registration number CRD42023406844.

What is already known on this topic

Residents of long-term care facilities (LTCFs) are especially vulnerable to acquiring healthcare-associated infections (HAIs).

A systematic review of point prevalence studies (PPS) specifically undertaken in LTCFs to monitor HAIs has not been performed.

What this study adds

Our findings demonstrated HAIs remain a global public health problem and threat to the safety of LTCF residents.

How this study might affect research, practice or policy

Recommended future LTCF research projects include exploring why HAI PPS data from low-mid-income countries are lacking, standardising and validating HAI surveillance methodology and evaluating the impact on HAI point prevalence estimates of implementing specific infection prevention and control guidelines and/or repeated HAI PPS participation.

• Introduction

Healthcare-associated infections (HAIs), many of which are caused by multidrug-resistant organisms, are associated with significant morbidity, prolonged hospital stay, attributable mortality and excess financial costs. 1 Residents of long-term care facilities (LTCFs) are especially vulnerable to acquiring HAIs because of advanced age, underlying disease, impaired mental and functional status, administration of immunosuppressive medications and use of invasive devices such as indwelling urinary catheters. 2 3 These residents have close contact with staff and other residents, posing risks for HAI transmission and may require frequent and/or prolonged hospitalisation, posing risks for the incursion of HAIs from acute care settings. 4 5 Worldwide, LTCF residents have been disproportionately and devastatingly impacted by SARS-CoV-2 infection. 6

To protect residents and staff in LTCFs and other settings from acquiring HAIs, the World Health Organization (WHO) has identified eight evidence-based core components. These equally important components and their associated requirements together are considered the foundation for establishing or strengthening effective infection prevention and control (IPC) policies and practices. Aside from HAI surveillance, the components are titled IPC programme, IPC guidelines, IPC education and training, multimodal strategies, monitoring, auditing and feedback, workload, staffing and bed occupancy, and built environment, materials and equipment for IPC. 7 HAI surveillance, that is, the systematic collection, management, analysis, reporting and use of data, is necessary to identify IPC-related problems and priorities. 8

One option for an LTCF HAI surveillance system is undertaking point prevalence studies (PPS) to quantify at a particular point in time the number of residents with an HAI as a proportion of the total number of eligible residents. 9 Notably, the European Centre for Disease Control and Prevention (ECDC) in 2009 funded the HAIs in LTCFs (HALT) project; the aim of this major project is to oversee sustainable PPS that estimate the prevalence of HAIs and antimicrobial use in European LTCFs. 10 The pooled HAI prevalence for the HALT 1 (2010), 11 2 (2013) 12 and 3 (2016/17) 13 studies was 2.6%, 3.4% and 3.7%, respectively. The fourth HALT PPS protocol, 14 synchronous with the third HALT protocol, has been published with a recommendation for eligible European LTCFs to again perform the PPS during 2023.

Several systematic reviews mapping the global burden of HAIs, as reported mostly by hospitals, have been published 15–17 ; all have highlighted HAIs acquired by patients during hospital admissions remain a major worldwide safety problem. The 2011 WHO report found for hospitals in low-middle-income and high-income countries, an HAI pooled prevalence of 10.1% and 7.6%, respectively. 15 Ongoing support for the effective application of the WHO IPC core components in hospitals located in low-medium-income countries is now especially considered essential; this includes establishing reliable HAI surveillance systems to collect and analyse HAI burden data on a regular basis. 18

To the authors’ knowledge, a worldwide systematic review of HAI PPS data collected in LTCFs that exposes IPC-related problems and priorities specific to this unique setting has not been similarly performed. In view of this, our objectives were to review PPS undertaken in LTCFs to:

Estimate the global burden of HAIs.

Identify the most frequent HAI types.

Explore the impact of facility-level and surveillance methodological differences on the reported burden of HAIs.

Protocol and registration

The protocol for this systematic review and meta-analysis was registered with PROSPERO (No:CRD42023406844) and developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( online supplemental file S1 ). 19

Public involvement

LTCF residents and their representatives were not involved in the conduct of this systematic review and meta-analysis.

Information sources and search strategy

On 1 March 2023, an initial systematic search of bibliographic databases MEDLINE (Ovid), Embase (Ovid) and CINANL (EBSCOhost) was conducted to identify potentially eligible publications published between January 1991 and March 2023. This search strategy, repeated on 30 June 2023 with the eligibility criteria extended to include publications up to June 2023, used a combination of keywords and subject headings on ‘long-term care facilities’, ‘infections’ and ‘point prevalence’ ( online supplemental file S2 ). References of short-listed full-text publications, the ECDC HALT project website 10 and an unpublished 2016–2022 Australian Aged Care National Antimicrobial Prescribing Survey (NAPS) report (coauthored by NB, RJ, CI, MJM and others) were also reviewed. The annual Australian Aged Care NAPS PPS based on the ECDC HALT project officially commenced in 2016.

Eligibility criteria and study selection

For each publication that was identified as relevant to the area of interest, selection was based on the following inclusion and exclusion criteria ( box 1 ).

Inclusion and exclusion criteria

Area of interest: point prevalence studies (pps) of healthcare-associated infections (hais), inclusion criteria.

Publications focusing on long-term care facilities (LTCFs), including nursing homes, skilled nursing facilities, assisted living facilities and residential homes.

English language publications focusing on original PPS, published and undertaken between January 1991 and June 2023. 1991 was the first year HAI surveillance definitions specifically for use in LTCFs were published. 23

Note: While a PPS could be conducted over a span of several months, data collection at each participating LTCF within that PPS was to be completed at a particular point in time. The European Centre for Disease Control and Prevention (ECDC) Healthcare-Associated Infections in Long-Term Care Facility (HALT) project, for example, specifies data should be collected on one single day, noting in LTCFs with a large number of residents, data collection can be spread over two or more consecutive days. 70

Exclusion criteria

Publications classified as review articles (all types), case reports, qualitative studies and dissertations.

Publications focused on outbreak management.

Publications not focused on humans.

Publications focused on home-based long-term care, community centres and adult day care facilities.

Publications focused on LTCFs specialised in one specific care, for example, physical impairment, chronic diseases such as multiple sclerosis, dementia, psychiatric illnesses, palliative care and rehabilitation care.

Publications focused on those in (geriatric ward) hospitals.

Publications focused on the management of infectious diseases in general.

Publications focused on colonisation (and not infection).

Publications focused on subgroups defined with one specific infection (such as pneumonia or urinary tract infection) only.

Publications for which there is insufficient methodological information available to interpret the findings.

Publications (to avoid duplication) only focused on data already published in the ECDC HALT PPS reports.

Using Covidence, 20 the titles and abstracts of all publications resulting from the searches were independently screened for inclusion by two reviewers (NB and SKT). Full-text articles of the preselected publications were assessed before making a final decision on eligibility for inclusion. In case of discrepancy between the two reviewers, the publication was discussed until a consensus was reached.

Data extraction

Standardised data were collated from eligible full-text publications by reviewer NB and cross-checked by reviewer SKT. For each PPS, the following variables, if available, were recorded:

First author.

Year of publication.

Location (country and region).

Study year (and season).

Number and type of LTCFs (total no. or mean (range)).

Number of residents (total no. or mean (range)).

HAI surveillance definition used.

Prevalence (%).

Most frequent HAI (% of all HAIs or prevalence of residents with HAI).

Where prevalence information was available in figure format only, the numerical value was estimated using WebPlotDigitizer V.4.6 ( https://automeris.io/WebPlotDigitizer ) and clearly labelled in the footnote of table 1 .

Quality assessments

The quality of each PPS was independently assessed by two reviewers (NB and SKT) using a modified version of the recommended Joanna Briggs Institute (JBI) Critical Appraisal Tool for Prevalence Studies. 21 The JBI tool was developed following extensive peer review and has been approved by the JBI Scientific Board. Nine criteria are described to appraise the methodological quality of a study and the extent to which the possibility of bias in its design, conduct and analysis has been addressed. To enable a detailed assessment, in our modified version (11 criteria), some JBI criteria were subdivided or merged ( online supplemental file S3 ). For all included PPS, results were presented as yes (Y), no (N) or unknown (U) for each criterion and summarised as a percentage for yes (Y) scores.

Data synthesis

A meta-analysis was performed overall and by prespecified subgroups:

Geographical region (Oceania (Australia), Europe, North America (USA) and Asia (Hong Kong)). European countries were further grouped into Eastern, Western, Northern, and Southern Europe, and the British Isles. 22

Study decade (1991–2000, 2001–2010, 2011–2020, 2021–2023).

Season (winter vs other seasons).

Facility type (nursing homes (NHs), residential homes (RH) and mixed/unspecified LTCFs).

HAI surveillance definition used (McGeer et al 23 and Stone et al 24 with or without modification).

A PPS was classified as having been conducted in winter if any of their participating LTCFs collected data during any part of winter; in the northern hemisphere from December to February and in the southern hemisphere from June to August. NHs differ from RHs in that medical and skilled nursing care is provided in addition to assistance with daily living activities. HAI surveillance definitions specifically for use by LTCFs were first developed by McGeer et al 23 in 1991 and revised by Stone et al 24 in 2012.

Where provided by the original publication, the prevalence estimates by facility type were considered as separate prevalences such that a survey could contribute >1 estimate to the meta-analysis. A random intercept logistic regression 25 was used to model the average HAI prevalence across the various studies with a categorical indicator for study as the random effect. The R function metaprop() from R package meta 26 was used with CIs calculated using the Clopper-Pearson method. A random-effects meta-analysis was performed to allow for heterogeneity in true HAI prevalences, rather than a fixed-effects meta-analysis which assumes any interstudy differences in prevalence is due to sampling error. 27 The same method was used for all subgroup analyses, with data restricted to the subgroup of interest, and for the sensitivity analysis which included only studies assessed as high quality (quality score ≥70%). Heterogeneity in study estimates within and between subgroups was examined using Cochran’s Q test, the Higgins’ I 2 and τ. 2 , 28–30 Maximum-likelihood estimator was used to estimate τ 2 . Funnel plots and Egger’s regression test were used to check for publication bias. 31 Publication bias was accounted for using the Duval and Tweedie’s trim-and-fill method. 32

All analyses were performed by using RStudio V.2022.12.0. The R script used in our analysis is in online supplemental file S4 .

Search output

The initial search of three bibliographic databases identified 1354 publications. After duplicates were removed, the title and abstract of 979 publications were screened against the inclusion and exclusion criteria ( box 1 ). 47 of these publications and 13 additional publications identified by checking references of short-listed full-text publications, the ECDC website 10 and an unpublished 2016–2022 Australian Aged Care NAPS PPS report were considered eligible for full-text review ( figure 1 ). Of reviewed publications, 31 were eligible for inclusion, encompassing a total of 123 PPS (74 PPS were incorporated in one of three ECDC HALT reports 11–13 which are described separately in online supplemental file S5 5. Additional information for two PPS 33 34 was obtained by contacting the author (Dr A, Eikelenboom-Boskamp, 2023) ( online supplemental file S6 ). 17 publications (13 PPS) 35–54 were excluded because the reported findings had mostly been published in ECDC HALT reports.

Study identification; screening and inclusion flow chart. CINAHL, Cumulative Index to Nursing and Allied Health Literature; ECDC, European Centre for Disease Control and Prevention; Embase, Excerpta Medica Database; HALT, Healthcare-Associated Infections in Long-Term Care Facilities; NAPS, National Antimicrobial Prescribing Survey; NCAS, National Centre for Antimicrobial Stewardship; PPS, point prevalence studies; VICNISS, Victorian Healthcare-Associated Infection Surveillance System.

Study characteristics

The study characteristics of the 123 PPS (127 point prevalence estimates), including HAI prevalence and quality ratings, are presented in table 1 and online supplemental files S5 and S7 , disaggregated by study year where possible. Four PPS 55 56 contributed two point prevalence estimates each, separately for NHs and RHs.

The vast majority of PPS were undertaken in high-income European countries (87.8%, 108/123) and primarily between 2010 and 2019 (79.7%, 98/123). Over half (58.5%, 72/123) of PPS had less than 50 participating LTCFs, and in 41.5% (51/123), the cohort of participating LTCFs was NHs only. Norway (10 PPS between 1997 and 2016/17) and the Netherlands (14 PPS between 2007 and 2016/17) mostly participated with sample sizes ranging from 21 to 540 and 4 to 57 LTCFs, respectively.

Nine PPS (7.3%, 9/123) were allocated a modified JBI assessed quality score greater than 80% while 30 PPS (24.4%, 30/123) were allocated a score between 70% and 80%. The quality score range of PPS included in the three ECDC HALT reports improved over time: HALT 1 (2010) 36.4%–45.5%, HALT 2 (2013) 45.5%–54.6% and HALT 3 (2016/17) 63.6%–81.8%. Most PPS scored a ‘no’ or ‘unknown’ for having conducted data analysis with sufficient coverage of the identified sample (96.7%, 119/123) and reporting interobserver comparison where data collection was performed by more than one observer (87.8%, 108/123). Less than one-third (27.6%, 34/123) of PPS included at least the estimated minimum sample size for adequate precision of the prevalence estimate (6108 residents). Nearly all PPS scored a ‘yes’ for using valid methods (99.2%, 122/123) and reporting prevalences as % with N or n/N (100%, 123/123) where N=number of included eligible residents and n=number of eligible residents with one or more HAI ( online supplemental file S7 ).

Prevalence of HAIs

The HAI point prevalence ranged from 0% in Cyprus in the 2010 HALT study ( online supplemental file S5 ) to 18.7% in Poland ( table 1 ). The overall pooled point prevalence across 127 prevalence estimates encompassing 709 860 residents was 3.5% (95% CI 3.1% to 4.0%), with heterogeneity statistic I 2 =98.9% and τ 2 =0.553. A similar pooled point prevalence was found when the analysis was restricted to PPS with a quality score ≥70% (3.9% (95% CI 3.2 to 4.7%) across 39 prevalence estimates encompassing 221 942 residents.

Visual inspection of the funnel plot and Egger’s regression test (p=0.02) suggested possible publication bias. The pooled HAI point prevalence, corrected for publication bias using Duval and Tweedie’s trim-and-fill method, was 5.4% (95% CI 4.6% to 6.3%; I 2 =99.2%, τ 2 =1.045).

Type of HAIs

Of 120 PPS, the most frequently reported HAIs were urinary tract infections (UTIs; 38.9%, 51/131), respiratory tract infections (RTIs; 33.6%, 44/131), and skin and soft tissue infections (SSTIs; 23.7%, 31/131). For three PPS, the most frequently reported HAIs were not specified. For 10 PPS, 2 HAI types were most frequently and equally reported.

Variation in HAI point prevalence

Variation in HAI pooled point prevalence was observed by geographical region (p<0.001; figure 2 ), though with a notably uneven covariate distribution encompassing a range of overall sample sizes from 5460 residents among surveys conducted in Asia to 211 730 in Oceania. The prevalence by region ranged from 1.2% (95% CI 0.9% to 1.5%) in Oceania to 5.2% (95% CI 4.0% to 6.7%) in Southern Europe. HAI pooled point prevalence decreased over decades from 6.9% (95% CI 0.06% to 0.08%) between 1991 and 2000 to 3.0% (95% CI 0.03% to 0.04%) between 2011 and 2020 and 1.0% (95% CI 1.0% to 1.1%) between 2021 and 2023 (test for subgroup differences: p<0.001; figure 3 ), noting that the latter comprised two Australian Aged Care NAPS PPS only. The sample size ranged from 16 357 residents for surveys conducted between 1991 and 2000 to 430 756 for surveys conducted between 2011 and 2020. HAI pooled point prevalence was much higher in PPS that used the McGeer et al HAI surveillance definitions without modification (10.6% (95% CI 8.4% to 13.4%)) compared with PPS using McGeer et al definitions with modification (2.4% (95% CI 1.9% to 2.9%)) or the Stone et al definitions with (2.9% (95% CI 2.4% to 3.5%)) or without modification (3.4% (95% CI 1.9% to 6.0%)) (test for subgroup differences: p<0.001; figure 4 ). The sample size ranged from 6744 residents for studies that used McGeer et al definition without modification to 392 785 for studies that used the Stone et al definition with modification. HAI pooled point prevalence did not differ across facility types (p=0.57) nor by season (p=0.46).

Meta-analysis of healthcare-associated infection (HAI) point prevalence by geographical region. Markers are subgroup-specific pooled prevalence estimates from a random-effects meta-analysis and lines are 95% CI calculated using the Clopper-Pearson method. ‘n’ is the number of prevalence estimates contributing to each subgroup.

Meta-analysis of healthcare-associated infection (HAI) point prevalence by study decade. Markers are subgroup-specific pooled prevalence estimates from a random-effects meta-analysis and lines are 95% CI calculated using the Clopper-Pearson method. ‘n’ is the number of prevalence estimates contributing to each subgroup.

Meta-analysis of healthcare-associated infection (HAI) point prevalence by infection surveillance definition. Markers are subgroup-specific pooled prevalence estimates from a random-effects meta-analysis and lines are 95% CI calculated using the Clopper-Pearson method. ‘n’ is the number of prevalence estimates contributing to each subgroup.

Principal findings

Our comprehensive systematic review and meta-analysis provides valuable insight into HAIs as determined by PPS in LTCFs; literature detailing 123 PPS (127 point prevalence estimates) undertaken in 33 countries between 1991 and mid-2023 was compiled and analysed. The pooled HAI point prevalence (overall global burden) was 3.6%. The majority of PPS reported more than one infection type; most frequently UTIs, RTIs and SSTIs. There was some variation in HAI point prevalence by geographical region, study decade and HAI surveillance definition used; within these subgroups, the highest prevalence was reported for Southern Europe (5.2%), 1991–2000 (6.9%) and McGeer et al without modification (10.6%), respectively.

Our reported high level of LTCF participation in HAI PPS supports the general view that this surveillance method is easy to conduct, at least in high-income countries. 8 57 Low-income to mid-income countries may have been under-represented because comparatively their healthcare systems are inadequately funded and developed and HAI surveillance programmes need to be improved. 18 58 59 Three PPS (2020–2022 unpublished Australian Aged Care NAPS PPS) only reporting HAI prevalence since the onset of the COVID-19 pandemic may have been due to a reporting lag or it may have been that participation in a PPS was postponed or declined during this time as LTCFs were largely focused on responding to the COVID-19 pandemic. 60

We found the highest HAI point prevalence estimate was reported by one Polish NH; 18.7% in 2009. 56 Later, 3 and 24 Polish LTCFs (although still small sample sizes) reported their HAI prevalence as 1.9% (HALT 1; 2010) 11 and 3.9% (HALT 3; 2016/2017), 12 respectively. For both these studies, the Polish LTCFs point prevalence estimates were above the median estimates; 1.5% and 2.1%, respectively. Polish LTCFs did not participate in HALT 2 (2013).

For some LTCF cohorts in Italy and The Netherlands that similarly participated at least annually over a prolonged period, their HAI prevalence decreased from the first to last PPS. 34 61 62 A recently published study likewise found, over 10 years (2009–2019) the HAI risk for residents in Dutch LTCFs that participated ≥4 years in PPS was decreased (OR 0.72 (95% CI 0.57 to 0.92)) compared with the first year. 63 This large study was excluded from our review because our required variables were not reported for each biannual PPS.

Variation in HAI point prevalence estimates could have been attributable to other factors not specifically explored in our meta-analysis. HALT 3 reported, in addition to facility size, HAI prevalence within an LTCF was associated with the number of residents >85 years, wheelchair-bound, bedridden, disorientated, having a wound other than a pressure sore and/or with a urinary or vascular catheter. 13 Other reported factors include differences in the level of training and skills of surveyors, recommendations for and availability of diagnostic testing and surveillance reporting behaviour. 12 57 Comparing HAI point prevalence estimates in LTCFs within and between countries should at least take into account these factors to avoid misleading conclusions.

While we found that HAI prevalence differed by study decade and HAI definition used, we acknowledge that these factors are likely correlated because the McGeer et al definition 23 was primarily used prior to 2012 and thereafter superseded by the Stone et al definition. 24 We expected HAI pooled point prevalence estimates to vary too across seasons (winter vs other seasons); the seasonally high incidence of RTIs during winter in temperate regions and the rainy season in tropical regions is widely recognised. 64 Although this was not observed in our study, there was a particularly notable imbalance in sample size between the subgroups, with only 15 prevalence estimates contributing to the pooled HAI prevalence for winter vs 112 for other seasons. A recent study 65 suggests future LTCF HAI point prevalence estimates may reflect seasonal spikes of COVID-19 infection during winter despite continual transmission throughout the year. COVID-19 infection is to be reported for the first time by LTCFs participating in the fourth HALT PPS. 14

A notable strength of our systematic review and meta-analysis, aside from following PRISMA guidelines and using two independent reviewers for study selection, was undertaking a modified JBI assessment of the methodological quality of each PPS. This rigorous assessment assists in determining how the results should be interpreted.

Our modified JBI quality assessment found for the most PPS there was insufficient representative coverage of identified country samples. Many LTCFs within countries volunteered to participate, perhaps resulting in a selection of ‘PPS participants’ with higher awareness and implementation of optimal IPC policies and practices. It is unknown exactly how much non-representation impacted on the extent to which ‘national results’ could be extrapolated to entire countries.

We also found for most PPS interobserver comparisons were not reported. Exceptionally and as part of the HALT 2 and 3 studies, 12 13 17 and 20 LTCFs, respectively, from 10 countries or administrations participated in an optional validation study. External ‘gold standard’ teams re-examined resident charts independently from the primary PPS surveyors. The sensitivity of the HALT 2 and 3 HAI data was 79% and 76%, respectively. This may have led to a slight underestimation of HAI prevalence. For both studies, the specificity was nearly 100%.

Limitations

The foremost limitation was that some relevant PPS may have been missed. As is most common for scientific publications, 66 we because of resource constraints explicitly excluded non-English language publications. Ideally, systematic reviews and meta-analyses should include these publications if it is determined such publications are of comparable quality to those published in English. 67 While we included unpublished Australian Aged Care NAPS PPS reports (easily accessible to the authors), we did not actively seek to include other similar unpublished reports. Current empirical research has shown results may be impacted only for a minority of meta-analyses excluding unpublished publications. 68

Our subgroup analyses were limited by uneven covariate distribution and high heterogeneity within subgroups, and thus the results should be interpreted with caution. 69 When stratified by geographical region, the number of subgroup-specific prevalence estimates ranged from 2 in Asia to 24 in Southern Europe, and the number of residents ranged from 5460 in Asia to 198 605 in Oceania. An uneven distribution was also observed by study decade, season, facility type and HAI surveillance definition used. Heterogeneity was very high with I 2 ≥88% for all subgroups, indicating a large portion of the observed variation within the subgroup was due to variation in the real prevalences.

Future research

Some results of our systematic review and meta-analysis highlighted subsequent research projects are warranted, including:

Exploring why LTCF HAI PPS data from low-income to mid-income countries is lacking and determining the feasibility, resources needed and cost-effectiveness for LTCFs in these countries to participate in reliable HAI surveillance systems.

To support benchmarking (less variance) between and within countries, standardising and validating HAI surveillance methodology. In countries where microbiology laboratory access is limited or non-existent, HAI definitions based on clinical data only may be most appropriate.

Testing the (long-term) impact on HAI point prevalence estimates of implementing prioritised UTI, RTI and/or SSTI clinical guidelines and quality improvement activities.

Evaluating exactly why for some repeatedly participating LTCF cohorts, their HAI point prevalence estimates have decreased over time.

Our systematic review and meta-analysis highlighted that HAIs remain a global public health problem and threat to the safety of LTCF residents. Effective IPC policies and practices to reduce HAIs are required in LTCFs; guidance specifically about the prevention and management of UTIs, RTIs and SSTIs should be prioritised. Recommended future LTCF research projects include exploring why HAI PPS data from low-income to mid-income countries is lacking, standardising and validating HAI surveillance methodology and evaluating the impact on HAI point prevalence estimates of implementing specific IPC guidelines and/or repeated HAI PPS participation.

• Supplementary files
• Publication history

This paper is in the following e-collection/theme issue:

Published on 27.5.2024 in Vol 26 (2024)

Assessing the Content and Effect of Web-Based Decision Aids for Postmastectomy Breast Reconstruction: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Authors of this article:

• Lin Yu 1 , MD   ; 
• Jianmei Gong 1 , PhD   ; 
• Xiaoting Sun 1 , MD   ; 
• Min Zang 1 , MD   ; 
• Lei Liu 1 * , PhD   ; 
• Shengmiao Yu 2 * , BS  

1 School of Nursing, Liaoning University of Chinese Traditional Medicine, Shenyang, China

2 Outpatient Department, The Fourth Affiliated Hospital of China Medical University, Shenyang, China

*these authors contributed equally

Corresponding Author:

Lei Liu, PhD

School of Nursing, Liaoning University of Chinese Traditional Medicine

No.79 Chongshan Dong Road

Shenyang, 110000

Phone: 86 17824909908

Email: [email protected]

Background: Web-based decision aids have been shown to have a positive effect when used to improve the quality of decision-making for women facing postmastectomy breast reconstruction (PMBR). However, the existing findings regarding these interventions are still incongruent, and the overall effect is unclear.

Objective: We aimed to assess the content of web-based decision aids and its impact on decision-related outcomes (ie, decision conflict, decision regret, informed choice, and knowledge), psychological-related outcomes (ie, satisfaction and anxiety), and surgical decision-making in women facing PMBR.

Methods: This systematic review and meta-analysis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 6 databases, PubMed, Embase, Cochrane Library, CINAHL, PsycINFO, and Web of Science Core Collection, were searched starting at the time of establishment of the databases to May 2023, and an updated search was conducted on April 1, 2024. MeSH (Medical Subject Headings) terms and text words were used. The Cochrane Risk of Bias Tool for randomized controlled trials was used to assess the risk of bias. The certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation approach.

Results: In total, 7 studies included 579 women and were published between 2008 and 2023, and the sample size in each study ranged from 26 to 222. The results showed that web-based decision aids used audio and video to present the pros and cons of PMBR versus no PMBR, implants versus flaps, and immediate versus delayed PMBR and the appearance and feel of the PMBR results and the expected recovery time with photographs of actual patients. Web-based decision aids help improve PMBR knowledge, decisional conflict (mean difference [MD]=–5.43, 95% CI –8.87 to –1.99; P =.002), and satisfaction (standardized MD=0.48, 95% CI 0.00 to 0.95; P =.05) but have no effect on informed choice (MD=–2.80, 95% CI –8.54 to 2.94; P =.34), decision regret (MD=–1.55, 95% CI –6.00 to 2.90 P =.49), or anxiety (standardized MD=0.04, 95% CI –0.50 to 0.58; P =.88). The overall Grading of Recommendations, Assessment, Development, and Evaluation quality of the evidence was low.

Conclusions: The findings suggest that the web-based decision aids provide a modern, low-cost, and high dissemination rate effective method to promote the improved quality of decision-making in women undergoing PMBR.

Trial Registration: PROSPERO CRD42023450496; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=450496

Introduction

Breast cancer (BC) is a major global health problem. In 2020, more than 2.3 million newly diagnosed cases and 685,000 deaths were associated with BC [ 1 ]. There has been a gradual increase in the incidence of BC globally over the past few decades, which has been attributed to lifestyle changes (eg, increase in BMI and decrease in birth rate), as well as an increase in screening detection as BC becomes more recognized [ 2 - 4 ]. Although BC has the highest incidence rate among all types of cancer, its mortality rate declined by 43% between 1989 and 2020, and it is concentrated in larger areas [ 2 , 5 ]. Advances in the early detection and treatment of BC have improved patient survival rates, which, in turn, have led to an increased focus on improving the quality of life of the survivors of BC. The surgical approach to BC is complex and usually involves the decision to undergo breast-conserving surgery and mastectomy. For women undergoing mastectomy, the change in appearance due to the missing breast can lead to various types of psychological problems including physical imagery discomfort, psychological distress, anxiety, and depression [ 6 ]. Postmastectomy breast reconstruction (PMBR) is now an option for women to restore their appearance [ 7 ].

However, when women face a PMBR decision, they must decide whether to use PMBR, and if they choose to do so, they should further decide on the timing and type of PMBR (ie, implant, autologous tissue, or combination) [ 8 , 9 ]. Delayed autologous PMBR results in a localized or regional recurrence rate similar to immediate PMBR [ 10 ]. A BC diagnosis can leave patients feeling anxious and uncertain, which is often exacerbated by presenting multiple, complex treatment options for women to choose from in a short period [ 11 ]. Most patients with BC who are considering PMBR immediately have clinically substantially decision conflict [ 12 , 13 ]. Patients experience postoperative complications leading to decision regret [ 14 ]. These issues can lead to poorer health outcomes, negative perceptions of the health care system, and lower quality of life [ 14 , 15 ]. Therefore, more preoperative patient education about possible complications includes the patient’s anatomy, which PMBR to choose, the associated pros and cons, and previous surgical and medication history. Women should be fully informed of their options and given the tools to weigh the pros and cons of each option, which may reduce the incidence of these adverse effects [ 16 ]. At the same time, personalized medicine is increasingly becoming the standard of care for patients with BC [ 17 ], and based on the current evidence, patients should have equal access to all eligible PMBR options [ 10 ]. In a sample of 126 patients who underwent mastectomy, a minority of patients made high-quality decisions about PMBR. Specifically, 43.3% of patients were adequately informed and accepted treatment decisions that were consistent with their preferences [ 11 ]. Therefore, patients and providers must work together through dialogue to optimize treatment options and engage in shared decision-making. However, it is not easy for inexperienced physicians to perform shared decision-making in an orderly and correct manner in a limited amount of time [ 18 ]. Decision aids may be helpful before a patient decides to undergo PMBR. Some studies [ 19 ] also suggest that decision aids may be helpful for some women even after undergoing a PMBR, as some women exhibit decision conflicts after the consultation. Decision aids are powerful tools to support patients in making informed choices based on their own values and are available via the internet, DVDs, and printed materials [ 20 ]. With the increasing popularity of the internet worldwide, web-based dissemination of information has been recognized as one of the most promising of all available formats (eg, leaflets, brochures, audio, and video) for delivering decision aids to patients. Web-based decision aids are characterized as being interactive, dynamic, and customizable [ 21 ]. On the one hand, web-based decision aids have a greater advantage in facilitating patient access than face-to-face interaction with physicians. On the other hand, decision aids on the internet can store and disseminate information over a longer period than traditional, static decision aids and can personalize the visit according to the patient’s values and preferences [ 21 - 23 ].

Prior Research

Paraskeva et al [ 24 ] conducted a systematic review exploring the effectiveness of interventions to assist women in making decisions about PMBR, which consisted of 6 studies with mixed results in terms of knowledge, decision-making, overall satisfaction, and quality of life. Berlin et al [ 25 ] assessed PMBR decision aids in a systematic review and meta-analysis, concluding that PMBR reduces decision conflict, improves information satisfaction, promotes participation in the decision-making process, and enhances the awareness of participation in the decision-making process. However, the authors included all types of trials (ie, quantitative and qualitative) and only meta-analyzed decision conflict. This review also did not include the effects of decision aids on outcome indicators such as psychologically relevant outcomes. Yang et al [ 26 ] conducted a meta-analysis exploring the effects of decision aids on decision-making in PMBR; however, the authors did not compare whether different forms of decision aids would have different effects. Zhao et al [ 27 ] conducted a scoping review with the aim of reviewing, comparing, and discussing the current incorporation of the adverse effects of BC treatments into decision aids and examined how web-based decision aids personalized BC treatment decision-making tools in patient–health care provider communication, clinician decision-making processes, and shared decision-making, as yet unassessed patient outcomes (eg, knowledge and anxiety). In summary, there is a lack of descriptions of the impact of web-based decision aids on the decision-making of women facing PMBR. Overall, existing systematic evaluations on related topics have produced mixed results, and more importantly, many primary trials [ 28 - 31 ], following these reviews, have produced conflicting results, which may provide new evidence. Therefore, there is a need for a new systematic evaluation to provide a comprehensive overview of the effectiveness of web-based decision aids on the quality of decision-making for women faced with PMBR, drawn from all available evidence from randomized controlled trials (RCTs) that meet high standards for evidence-based research.

The aim of this systematic review and meta-analysis was to assess the content of the web-based decision aids and evaluate their effectiveness on decision-related outcomes (ie, decision conflict, decision regret, informed choice, and knowledge), psychological-related outcomes (ie, satisfaction and anxiety), and surgical decision-making in women facing PMBR.

This is a systematic review and meta-analysis reported in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; Multimedia Appendix 1 ) guidelines [ 32 ]. The protocol was registered in PROSPERO (CRD42023450496).

Eligibility and Exclusion Criteria

An overview of the inclusion and exclusion criteria can be found in Textbox 1 .

• The population included in the study was aged ≥18 years and women who had been diagnosed with breast cancer (BC) and were considering postmastectomy breast reconstruction (PMBR) but had not yet had the surgery and had internet access. If the patient, at the time of enrollment, had attempted PMBR; did not have BC (ie, were considering prophylactic mastectomy); and had an active psychiatric, cognitive, or visual impairment, they were not eligible.

Intervention

• Studies focusing on web-based decision aids (including websites and apps)
• Controls for usual care, counseling, health education pamphlets, and non–web-based decision aids
• The primary outcomes were decision-related outcomes (ie, informed choice, knowledge, decision conflict, and decision regret); psychological outcomes (ie, satisfaction and anxiety); and PMBR options and tool usability (ie, women’s feedback on use)
• Randomized controlled trials

Search Strategy

A systematic search of studies was carried out using English databases such as PubMed, Embase, Cochrane Library, CINAHL, PsycINFO, and Web of Science Core Collection from the date of inception of each database to May 2023, and an updated search was conducted on April 1, 2024, to cover new research. Medical Subject Headings terms and text words were used. The keywords used included “Mastectomy,” “mammaplasty,” “mastectomy,” “informed choice*,” “shared decision making,” “computer,” “web based,” and “Internet,” which are English search terms. These index terms and keywords were explored and modified according to the different grammatical rules of the database. Specific details of the search algorithm are available in Multimedia Appendix 2 . The reference lists of the included studies and relevant articles were hand-searched to identify other potentially eligible articles. The search was limited to articles in English and had no limitations with regard to publication year.

The results were input into EndNote X9, and duplicates were removed automatically. After removing duplicates, 2 reviewers independently screened the titles and abstracts of identified articles and removed irrelevant citations in accordance with the selection criteria. After the removal of irrelevant studies, the full texts of potentially relevant studies were retrieved. Next, both reviewers independently assessed the full texts. Any disagreements were settled by discussion with a third reviewer.

Data Extraction

Characteristics of the included RCTs (eg, author, year of publication, country, sample size, subject characteristics, form, content, development method and team, theoretical basis, duration of use, reading level, a brief description of the intervention in the control group, outcome measurements, follow-up, and results) were extracted into tables. We wrote to the authors to obtain more information about the results. Two reviewers compared the findings independently.

Risk of Bias Assessment

The quality of RCTs was evaluated using the Cochrane Handbook for RCTs [ 33 ]. The tool consists of 7 items: randomized sequence generation, allocation concealment, participant and personnel blinding, blinding for outcome assessment, incomplete outcome, data selective reporting, and other bias. The risk of bias for each domain was judged as low risk of bias, high risk of bias, or unclear risk of bias. The evaluation of study quality was performed independently by 2 reviewers, and a third reviewer was consulted if necessary.

Statistical Analysis

Statistical analysis was performed using Review Manager (version 5.3; Cochrane), illustrated using a forest plot when at least 2 studies were measured for the same outcomes for a PMBR decision at the longest follow-up time point [ 34 , 35 ]. We used mean differences (MDs) for continuous variables that were measured with the same instrument, standardized MDs (SMDs) when a similar outcome was assessed with different instruments, and relative risks for dichotomous variables. We calculated possible missing values such as SD and 95% CI [ 33 ]. In the study, heterogeneity was assessed via the Higgins I 2 statistic with I 2 values of ≤25%, 50%, and ≥75% deemed to represent low, medium, and high heterogeneity, respectively [ 33 ]. When there was no significant heterogeneity, the fixed effects model ( I 2 ≤50%) was used; otherwise, the random effects model was used, resulting in a more conservative summary effect estimate [ 33 ]. To identify potential sources of clinical heterogeneity, we also conducted a post hoc sensitivity analysis to determine the stability of the results by omitting each test [ 36 ].

Study Selection

Figure 1 shows the research selection process and results based on the PRISMA 2020 guidelines. A total of 844 studies were identified. A total of 129 of these studies were excluded because they were repetitive. After selecting titles and abstracts, 21 studies were included for the next stage. Consequently, 7 studies met the inclusion criteria.

Study Characteristics

The 7 studies included 579 women and were published between 2008 and 2023, and the sample size in each study ranged from 26 to 222. The average age of the women was approximately 50 years; they were in the early stages of BC and facing the PMBR decision. The studies were conducted in 3 countries; 6 studies were conducted in high-income countries—4 in the United States [ 30 , 31 , 37 , 38 ] and 2 in Australia [ 29 , 39 ]—and 1 in an upper–middle-income country, China [ 28 ]. Detailed characteristics of the included studies are shown in Table 1 .

a BC: breast cancer.

b DCS: Decision Conflict Scale.

c BR-DMPS: Breast Reconstruction-Decision-Making Process Scale.

d DRS: Decision Regret Scale.

e BIS: Body Image Scale.

f HADS: Hospital Anxiety and Depression Scale.

g DC: decisional conflict.

h DR: decisional regret.

i STAI: State-Trait Anxiety Inventory.

j BR: breast reconstruction.

k DQI: Decision Quality Index.

l DASS-21: Depression Anxiety Stress Scale.

m SSQ-6: Social Support Questionnaire.

Characteristics of the Interventions and Controls

The characteristics of the interventions and controls are shown in Multimedia Appendix 3 [ 28 - 31 , 37 - 39 ].

Characteristics of the Interventions

In total, 5 of the studies [ 28 - 30 , 38 , 39 ] explained that the web-based decision aids development team includes survivors of BC who have undergone mastectomy, plastic or reconstructive surgeons who perform PMBR, and software engineers. The methodology used to develop web-based decision aids includes qualitative research, evidence review and mentoring, and pilot study group meetings. The theoretical basis for the development of web-based decision aids is usually the International Patient Decision Aid Standards [ 29 , 30 , 39 ] or the Ottawa Decision Support Framework [ 28 ]. Except for 2 studies [ 28 , 37 ] that did not report the time of use, most web-based decision aids took between 20 and 74 minutes. Two web-based decision aids [ 29 , 30 , 39 ] were developed at a reading level written at a seventh- and eighth-grade reading level. The web-based decision aids content specifically includes the patient population and reconstruction options, including implant reconstruction (ie, tissue expanders and implant types), autologous flap reconstruction (ie, latissimus dorsi, rectus abdominis, and free flaps and deep epithelial perforator flaps in the lower abdomen), and skin-sparing and preserving mastectomies (ie, 1-phase and 2-phase procedures). There are also contraindications and general eligibility criteria. Timing of reconstruction includes immediate versus delayed reconstruction, as well as factors that influence the type and timing of reconstruction. It also includes information about the pros and cons of reconstruction versus no reconstruction, implants versus flaps, immediate versus delayed reconstruction, the look and feel of PMBR, and the expected recovery time. The probability of possible implant (eg, wrinkled breast appearance, periosteal contracture after radiation therapy, and possible need for implant replacement over time) and flap (eg, muscle weakness and flap failure) are clearly described in a balanced format with quotes of real patients’ opinions. The web-based decision aids show photographs, high-quality 3D animated images, pre- and postoperative photographs, audio, and video of actual patients of different skin colors and body types, A list of frequently asked questions from clinicians is also included. Elements in the tool include patient-tailored risk assessments, patient value clarification exercises, techniques for managing emotions, and strategies for communicating with family members about PMBR decisions. Women’s stories explaining their reasons for choosing particular methods and their impact on their lives are also included. Users enter their questions and the system prompts them to print a summary to use in a consultation with their physician. This customized printable page also helps patients discuss their concerns and options with their families.

Characteristics of the Controls

The control for the study by Politi et al [ 30 ] was the enhanced urgent care and American Society of Plastic Surgeons pamphlet on PMBR. Varelas et al [ 31 ] used traditional counseling. The control for the study by Fang et al [ 28 ] was the provider-provided urgent care+pamphlet, which describes the types of surgery, including mastectomy, implant-based PMBR, and autologous PMBR, as well as the advantages and disadvantages of the different types of surgeries. The control for the study by Manne et al [ 38 ] was the 56-page pamphlet available at no cost from the Cancer Support Community focusing on PMBR. For the study by Sherman et al [ 39 ], the control was the web-based access to excerpts of the public brochure, including basic information on breast surgery and reconstruction, but excluding content unique to the intervention group (ie, video interviews with patients or surgeons, and values clarification exercises). In the study by Mardinger et al [ 29 ], the control was the decision aids, which is unvalidated that contains 6 text-based pages that can be accessed in both interactive and noninteractive formats. The control for the study by Heller et al [ 37 ] was the group that received the standard patient education, including printed materials in books and pamphlets as well as personal instruction from the attending physician, physician-in-training, physician assistant, and nurse practitioner.

Outcome Measure

A total of 5 studies [ 28 , 29 , 31 , 38 , 39 ] measured decision conflict using the Decision Conflict Scale (DCS), and 1 study [ 30 ] measured decision conflict using the 4-item SURE scale. Three studies [ 28 , 29 , 39 ] measured decision regret using the Decision Regret Scale (DRS) and 2 studies [ 28 , 29 ] measured informed choice using the subdimension of the DCS—feeling informed. Knowledge was measured primarily by the percentage of correct answers to self-administered multiple-choice questions about specific plastic surgery procedures in 6 studies [ 28 - 31 , 37 , 38 ]. Satisfaction was measured using the Satisfaction with Decision Scale [ 29 ] and some scales adapted from those used in previous studies [ 28 , 37 - 39 ]. Anxiety was primarily measured using the Hospital Anxiety and Depression Scale [ 28 ] and the State-Trait Anxiety Inventory [ 31 , 38 ].

Decision-Related Outcomes

Decision conflict.

In total, 6 studies [ 28 - 31 , 38 , 39 ] investigated the impact of decision conflicts in PMBR. The 5 studies [ 28 , 29 , 31 , 38 , 39 ] that used DCS included in the meta-analysis showed a statistically significant positive impact of web-based decision aids interventions on decision conflict (MD=–5.43, 95% CI –8.87 to –1.99; P =.002). Heterogeneity experiments indicated that there was evidence of statistical heterogeneity in the expected summary results ( I 2 =63%; Figure 2 ). Politi et al [ 30 ] used the 4-item SURE DCS and reported that there was no difference between the 2 groups in terms of decisional conflict ( P >.05).

Decision Regret

In total, 3 studies [ 28 , 29 , 39 ] used DRS to investigate the impact of decision regret in PMBR. The meta-analysis showed that the difference in decision regret after the intervention was not statistically significant compared with the control group (MD=–1.55, 95% CI –6.00 to 2.90; P =.49). Heterogeneity experiments indicated that there was evidence of no statistical heterogeneity in the expected summary results ( I 2 =0%; Figure 2 ).

Informed Choice

In total, 2 studies [ 28 , 29 ] investigated the impact of informed choice by DCS in PMBR surgery. The meta-analysis showed that the difference in informed choice after the intervention was not statistically significant compared to the control group (MD=–2.80, 95% CI –8.54 to 2.94; P =.34). Heterogeneity experiments indicate that there was evidence of no statistical heterogeneity in the expected summary results ( I 2 =0%; Figure 2 ).

We did not conduct a meta-analysis of knowledge as an outcome because most of the instruments measuring knowledge were self-administered. The study by Heller et al [ 37 ] found significantly higher levels of knowledge in the web-based decision aids group, with a mean increase in correctly answered questions of 14% compared to 8% in the control group ( P =.02). Politi et al [ 30 ] found that participants using web-based decision aids had higher objective knowledge, answering an average of 85% (9.35/11) of the questions correctly compared to 58% (6.35/11) in the control group ( P <.001). Similarly, Varlas et al [ 31 ] showed improved knowledge assessment scores in both groups but significantly higher knowledge assessment scores in the intervention group (control=70.8%, SD 15.5%; intervention=83.1%, SD 13.8%; P =.02). However, Manne et al [ 38 ] reported similar effects of web-based decision aids on PMBR knowledge versus the booklet, and Fang et al [ 28 ] also reported no difference in the amount of PMBR-related medical information between web-based decision aids and the control group at 1 week after consultation ( P =.13), suggesting that women in both groups had a similar level of comprehension of medical information, whether using the booklet alone or in combination with the web-based decision aids. Mardinger et al [ 29 ] also reported that both groups had similar scores on the true or false PMBR knowledge questionnaire over time ( P >.05).

Psychological Outcomes

Satisfaction.

In total, 5 studies [ 28 , 29 , 31 , 38 , 39 ] used different scales to investigate the impact of satisfaction. The meta-analyses indicated that web-based decision aids may improve current form:satisfaction compared to controls, but the results were not statistically significant (SMD=0.48, 95% CI 0.00 to 0.95; P =.05). Heterogeneity experiments indicated that there was evidence of statistical heterogeneity in the expected summary results ( I 2 =79%; Figure 3 ). Similarly, Heller et al [ 37 ] reported a higher level of satisfaction with the way in which information about PMBR was obtained in the web-based decision aids group than in the control group ( P =.03).

A total of 3 studies used Hospital Anxiety and Depression Scale and State-Trait Anxiety Inventory [ 28 , 31 , 38 ] to investigate the impact of anxiety in PMBR. The meta-analysis showed that there was no statistically significant difference in the combination of SMD after intervention (SMD=0.04, 95% CI –0.50 to 0.58; P =.88). Heterogeneity experiments indicated that there was evidence of statistical heterogeneity in the expected summary results ( I 2 =61%; Figure 3 ). Heller et al [ 37 ] reported that in the web-based decision aids group, there was a trend toward lower levels of anxiety between the preoperative and postoperative visits, but the difference between the groups was not significant as determined by generalized estimating equation modeling.

Choice of Surgery

The surgical choices differed between the two groups in the study by Fang et al [ 28 ]: 56% (27/48) in the web-based decision aids group and 46% (22/48) in the control group opted for immediate PMBR ( P =.05). In addition, most patients chose implantable PMBR, with no difference between groups. Notably, the web-based decision aids group in the study by Mardinger et al [ 29 ] was unbalanced in terms of the choice of type of PMBR, with 10 (36%) women in the web-based decision aids group refusing PMBR compared with 6 (21%) women in the control group ( P =.20). The results of the study by Politi et al [ 30 ] showed that 95 (79.2%) women underwent reconstruction; among them, nearly all (92/95, 97%) underwent immediate PMBR, and there were no differences between groups in median preference scores for reconstruction, type, or time.

Evaluation of the Intervention

In total, 3 studies reported different benefits of web-based decision aids compared to controls. Heller et al [ 37 ] reported an upward trend in the number of patients in the web-based decision aids group who reported that they received all the necessary information and improved their ability to choose a PMBR plan, but the difference between the groups was not significant. Manne et al [ 38 ] reported that 81% of participants in the web-based decision aids found logging in and navigating easy and the length of time was rated as “just right,” and that the web-based decision aids were more helpful, interesting, and valuable than the brochures. Sherman et al [ 39 ] found that women in the intervention group found the web-based decision aids to be 2.94 (SD 0.76) informative, very useful, easy to use, contained enough information, and helped them to clarify their reconstruction ideas. However, Varelas et al [ 31 ] reported that surgeon satisfaction was also significantly higher in the intervention group than in the control group. Meanwhile, consultation time was shorter in the intervention group, but the difference was not statistically significant ( P =.46). Similarly, Politi et al [ 30 ] reported no difference between the web-based decision aids group and the control group in terms of mean counseling time after the intervention (29.7 vs 30.0 minutes; P >.05). Mardinger et al [ 29 ] showed that although women used both decision aids with comparable frequency, the total time spent counseling and the time spent per counseling session was significantly greater for women in the intervention than that for the control group ( P <.05). Women in the study by Fang et al [ 28 ] indicated no difference between the 2 groups in terms of perceived impact and utility of web-based decision aids on PMBR decisions.

Sensitivity Analysis

We conducted sensitivity analyses of decision conflict, satisfaction, and anxiety by removing each study. Sensitivity analysis showed that for decision conflict and satisfaction, after removing 1 study [ 31 ], contrary to the previous results, web-based decision aids did improve satisfaction (the I 2 range was 79%-12%) but did not improve decision conflict (the I 2 range was 63%-2%). We found that by removing the study by Manne et al [ 38 ], the stability of anxiety did not change but the heterogeneity was reduced from 62% to 0% ( Figure 4 ).

Risk of Bias

Figure 5 [ 28 - 31 , 37 - 39 ] presents the summary of the risk of deviation for the included studies. In 6 [ 28 - 31 , 37 , 39 ] of the 7 studies, the description of the method used in random assignment was clearly stated (ie, web-based automated randomization software and random number generator), and in the remaining study [ 38 ], the information obtained about random assignment was insufficient to make a definitive judgment. Of the 7 studies, 5 [ 30 , 31 , 37 - 39 ] were unable to make definitive judgments in this area because of underreporting, whereas in the remaining 2 trials [ 28 , 29 ] sufficient information was obtained about allocation concealment (individually sealed envelopes to conceal allocation). Furthermore, 6 studies [ 28 - 30 , 37 - 39 ] were judged to be at unclear risk of bias because the effect of unblinding was unknown, and 1 study [ 31 ] described the blinding of participants. Seven studies [ 28 - 31 , 37 - 39 ] achieved blinding of outcome evaluators (ie, clinic and surgical staff were blinded to condition assignment) or the blinding was unclear, but the outcome was objectively measured and not subjective to interpretation. Incomplete outcome data appeared to be adequately addressed in 7 studies [ 28 - 31 , 37 - 39 ] (ie, incomplete data were fairly evenly balanced across intervention groups or intention-to-treat analyses were reported). In addition, 3 studies [ 28 , 30 , 39 ] underwent clinical registration or reported relevant protocols, showing that outcomes were reported in full. The impact of selective reporting in the remaining 4 studies [ 29 , 31 , 37 , 38 ] was unclear, and this area was judged to be at unclear risk of bias. Information on other potential sources of bias was sufficient. Therefore, this area was judged to be at low risk of bias for all studies [ 28 - 31 , 37 - 39 ].

Certainty of Evidence

We assessed the certainty of evidence for the included RCTs using Grades of Recommendation Assessment, Development, and Evaluation (GRADE; Multimedia Appendix 4 ) except for decision regret, for which the certainty of evidence was low. The certainty of evidence was very low for the rest, that is, decision conflict, satisfaction, anxiety, and informed choice.

Principal Findings

Our systematic review and meta-analysis showed that the modules of web-based decision aids include basic information on PMBR, patient stories, risk assessment, value clarification, and emotion management and that patients can be directed to seek information and obtain personalized decision support based on their individual needs. Therefore, these web-based decision aids are helpful and recommended for women. Regarding the effectiveness of web-based decision aids, the results showed that they may improve PMBR knowledge, decision conflict, and satisfaction but have no effect on informed choice, decision regret, or anxiety. The overall GRADE quality of evidence for decision regret was low, and the overall GRADE quality of evidence for informed choice, decision conflict, and anxiety was very low.

The Content of Web-Based Decision Aids

First, regarding the content of web-based decision aids, few of the studies included in our systematic review and meta-analyses reported comprehensive development of their web-based decision aids. The types of decisions on which most web-based decision aids primarily focused were PMBR decision types and reconstruction times. In addition, some of the studies reported that the development of the tool was obtained through a decisional needs assessment. Research suggests that people tend to have decisional needs when confronted with known outcomes with multiple choices, uncertain outcomes, or valuing people differently and that unmet needs lead to poor quality decisions, which adversely affect health outcomes [ 40 ]. Research has shown that some patients have difficulty imagining plastic surgery without photos of women of different body types and skin colors when faced with a decision. Therefore, the use of 3D images during the counseling process is an acceptable web-based decision aid, and the results of our review suggest that web-based decision aids on PMBR decision-making show real photographs of patients by incorporating high-quality, 3D animated images and that viewing 3D images may increase presurgical preparation by giving patients a more realistic understanding of what is actually achievable after PMBR [ 41 ]. There are web-based decision aids that are designed with the goal of making patients more comfortable receiving information in a less-stressful environment outside of the hospital, and it also allows family members and friends who are members of the patient support group, but who may not necessarily be able to participate in the counseling, to receive specific information about the procedure and participate in the decision-making process. Women and their families are allowed to express their views about breast surgery because family members act as advocates and care coordinators in the decision-making process [ 42 ]. In this era of increasing emphasis on evidence-based medicine, the PMBR risk assessment calculator can help individualize and quantify risk to better inform surgical decisions and better manage patient expectations [ 43 ]. The purpose of the values clarification exercise is to help women assess, explore, and identify their personal values and to encourage them to think about how their values affect their decision-making. Using the values clarification exercise can help women increase their satisfaction with their appearance. Patient stories are also important to web-based decision aids, and research has shown that women express a need to learn about other women’s experiences to gain a deeper understanding of the impact of PMBR on their daily lives. Web-based decision aids have achieved this by telling the stories of patients who have had previous mastectomies, with or without PMBR. These stories illustrate the decision-making experiences of these patients and the impact of their decisions on their daily lives [ 44 ]. Another advantage of web-based decision aids is that they allow patients to absorb the information without being overwhelmed by other information or distracted by other issues. Research has shown that some people feel prepared and emotionally supported for PMBR decision-making, while others feel that the elements of supportive care are missing, making the inclusion of an emotion management module in web-based decision aids essential for women’s psychosocial support [ 45 ]. However, although the internet has become an easily accessible tool, there is still a persistent digital divide. Therefore, special attention should be given to the sociodemographic characteristics of the population, building more resources for health care infrastructure in underserved communities and providing free or discounted Wi-Fi connections and mobile devices in low-income areas [ 46 ]. These actions, combined with the popularity of smartphone users, are measures that may narrow the digital divide [ 21 ].

Effectiveness of Web-Based Decision Aids

In line with the results of a previous meta-analysis [ 26 ], web-based decision aids reduced decision conflict. Decision conflicts were as high as 45.68 (SD 23.40) among women who were newly diagnosed with early-stage BC in China [ 47 ]. Decision conflict was significantly higher among women who chose mastectomy with or without combined reconstruction compared to women who chose conservative breast surgery. Greater decision conflict is associated with less information, higher uncertainty in weighing choices based on personal values, and inadequate social support [ 40 ]. Women may second-guess their decisions after the fact, even if those decisions have already been made. Women who face PMBR decision-making need support in making this complex decision, especially those who do not have a strong preference for PMBR. Decision conflict can be reduced by addressing factors of uncertainty, such as providing information about the benefits and risks of each option and helping patients understand their own values [ 48 ]. Web-based decision aids can improve the quality of PMBR decision-making by enhancing patient knowledge and providing personalized risk assessments, reducing decision conflict [ 18 ].

Uncertainty about whether they are making the best decision can trigger emotional turmoil, and decision regret occurs when women compare the unfavorable outcome of a decision with alternative choices they may have [ 11 , 47 ]. The results of our meta-analysis showed that there was no effect of web-based decision aids on decision regret in the intervention group compared to the control. Women who choose decisions that result in unexpected clinical outcomes or lower-than-expected outcomes will inevitably experience decision regret, a very common but negative emotion, even though the patient’s preferences and needs are honored and considered in their treatment [ 49 ]. Decision regret can be used as an indicator of decision-making quality, which can contribute to performance improvement in the health care system. Other studies from a psychological perspective have shown that if a decision is regretted, the following “preference reversal” may cause patients to favor another unselected option, which may completely offset their health outcomes, with the degree of decision regret varying widely. However, Becerra Pérez et al [ 50 ] reported that most studies reported a low mean DRS, resulting in an overall mean score of 16.5 out of 100 across studies. It is important to note that there is no consensus on specific thresholds for clinically important decision regret based on DRS, and authors have rarely justified their choice of thresholds; therefore, minimum and maximum efficiency may limit our ability to perform statistical analyses [ 51 ].

Previous research has shown that women with BC who use decision aids receive more information that helps them make informed and values-based decisions [ 26 ]. Our results, in contrast, showed no effect of web-based decision aids on informed choice in the intervention group compared to the control group possibly because, compared to other forms, web-based decision aids require more effort. Therefore, some women in the web-based decision aids group may have been less inclined to seek more information and consider it carefully. This may explain why women in the web-based decision aids group did not feel less uninformed about their decisions [ 52 ]. The results of previous meta-analyses [ 25 , 26 ] suggest that web-based decision aids are promising interventions for improving knowledge related to PMBR decision-making and that web-based decision aids can help patients’ knowledge of PMBR and treatment options and can identify patients’ PMBR preferences and goals for quality decision-making with their health care providers; however, it is important to note that in this review, the impact of web-based decision aids on PMBR knowledge was mixed, which may be because most of the current instruments on PMBR decision-making knowledge measurement are self-administered scales. We found that web-based decision aids improved PMBR knowledge compared to a control group of some conventional education [ 37 ], traditional counseling [ 31 ], or conventional pamphlets [ 30 ]. When the control group was using pamphlets [ 19 , 28 ] or noninteractive decision aids [ 29 ] that contained similar information, web-based decision aids did not have a statistically significant effect on PMBR knowledge. Therefore, to elucidate the impact of web-based decision aids on knowledge, measurement studies using validated and sensitive instruments are needed.

Because the initial anxiety experienced by women may be related to the new diagnosis and anticipated surgery, this anxiety lessened once the surgery was over. There was no difference in the level of anxiety experienced after surgery between the 2 groups. Given the severity of a BC diagnosis, it is very reassuring that web-based decision aids did not exacerbate anxiety while providing benefits in terms of patient satisfaction and knowledge as well as surgeon satisfaction. Several studies have shown that patient satisfaction is higher when receiving PMBR information digitally [ 53 ]. Our study also suggests that web-based decision aids improve patient satisfaction with decision-making. Although most of the studies included in our systematic review reported that the use of web-based decision aids increased women’s satisfaction with PMBR, most of the measurement tools used to assess the outcomes used self-administered scales. Therefore, more high-quality evidence, including studies using validated and sensitive instruments, is needed to elucidate the impact of web-based decision aids on satisfaction [ 26 ].

Some of the outcome indicators in this review (ie, decision conflict, satisfaction, and anxiety) showed significant heterogeneity, which may be related to factors such as the fact that the measurement tools were different and the web-based decision aids were delivered in an inconsistent form and content. We conducted sensitivity analyses for decision conflict, satisfaction, and anxiety, and the adjusted total estimates of anxiety did not significantly change these results when studies were progressively omitted, excluding the study by Manne et al [ 38 ]. With respect to decision conflict and satisfaction, the adjusted total estimates changed significantly, a result that excludes the study by Varelas et al [ 31 ]. Contrary to the original results, the effect of web-based decision aids on improving satisfaction was statistically significant, and the effect of web-based decision aids on improving decision conflict was similar to the control group effect; therefore, the effect of web-based decision aids on decision conflict and satisfaction should be carefully interpreted. Regarding the heterogeneity of this meta-analysis, sensitivity analyses showed that the heterogeneity of all outcomes was also reduced by excluding 1 study.

Limitations

Some limitations of this review must be recognized. First, we did not perform an assessment of publication bias because only 7 studies were ultimately included in the analysis, which may cause publication bias. In addition, the included studies had no follow-up surveys and lacked evidence of the long-term impact of the interventions. Our findings serve as a reminder that even when statistical information is effectively communicated, participants may not make estimates of the same order of magnitude after a period. Finally, the number of included studies was small. Some studies had inconsistent outcome indicators and were therefore not included.

Conclusions

This review shows that web-based decision aids can increase knowledge and satisfaction, and reduce levels of decision conflict among women facing PMBR decision-making; however, there is no effect on informed choice, decision regret, or anxiety. Currently, web-based decision aids for women’s PMBR decision-making are relatively easy to implement in terms of content and form. Due to limitations in the number of included studies in our meta-analysis, well-designed studies, including multicenter RCTs using high-quality decision aids, are necessary in the future to further validate our conclusion that web-based decision aids play a role in the quality of decision-making for women facing PMBR.

Acknowledgments

The authors would like to thank the authors of studies in this review who took the time to become involved in guiding the development of this review.

Authors' Contributions

LY, LL, SY, JG, XS, and MZ made substantial contributions to the conception and design, acquisition of data, or analysis and interpretation of data and were involved in drafting the manuscript or revising it critically for important intellectual content. All authors provided final approval for the version to be published and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflicts of Interest

None declared.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement.

Search strategy.

Characteristics of the interventions and controls.

Certainty of evidence.

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Abbreviations

Edited by S Ma, S Gardezi; submitted 22.10.23; peer-reviewed by A Trojan, T Lund-Jacobsen; comments to author 13.03.24; revised version received 16.04.24; accepted 16.04.24; published 27.05.24.

©Lin Yu, Jianmei Gong, Xiaoting Sun, Min Zang, Lei Liu, Shengmiao Yu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Meta-Analysis and Meta-Synthesis Methodologies: Rigorously Piecing Together Research

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For a variety of reasons, education research can be difficult to summarize. Varying contexts, designs, levels of quality, measurement challenges, definition of underlying constructs, and treatments as well as the complexity of research subjects themselves can result in variability. Education research is voluminous and draws on multiple methods including quantitative, as well as, qualitative approaches to answer key research questions. With increased numbers of empirical research in Instructional Design and Technology (IDT), using various synthesis methods can provide a means to more deeply understand trends and patterns in research findings across multiple studies. The purpose of this article is to illustrate structured review or meta-synthesis procedures for qualitative research, as well as, novel meta-analysis procedures for the kinds of multiple treatment designs common to IDT settings. Sample analyses are used to discuss key methodological ideas as a way to introduce researchers to these techniques.

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Current status of electronic health literacy among pregnant women with gestational diabetes mellitus and their perceptions of online health information: a mixed-methods study

• Jingqi Xu 1   na1 ,
• Yujia Chen 1   na1 ,
• Jing Zhao 1   na1 ,
• Jiarun Wang 1 ,
• Jianfei Chen 1 ,
• Xinlong Pan 1 ,
• Wei Zhang 1 ,
• Jin Zheng 2 ,
• Zhijie Zou 1 ,
• Xiaoli Chen 1 &
• Yingzi Zhang 3  

BMC Pregnancy and Childbirth volume  24 , Article number:  392 ( 2024 ) Cite this article

Metrics details

Women diagnosed with gestational diabetes mellitus often rely on internet-based health information for managing their condition. This study aims to investigate the present state of electronic health literacy among women with gestational diabetes mellitus, analyze the influencing factors, and explore their experiences regarding accessing, comprehending, evaluating, and applying online health information pertinent to gestational diabetes mellitus.

A sequential explanatory mixed methods research design was adopted in this study. Initially, 235 women with gestational diabetes mellitus participated in a cross-sectional survey. The research tools included general information and the Chinese version of the electronic Health Literacy Scale (eHEALS). Descriptive analyses were conducted to describe the characteristics of the sample, and multiple linear regression analyses were used to explore the factors influencing electronic health literacy among women with gestational diabetes mellitus. Secondly, 11 women with gestational diabetes mellitus joined semi-structured in-depth interviews to obtain their perceptions about online health information. The data were analyzed using inductive content analysis to develop themes.

The median score of eHEALS in the Chinese version among 235 women diagnosed with gestational diabetes mellitus was 29 (interquartile range [IQR], 26 to 32). Factors influencing electronic health literacy among these women included accessing health information from medical professionals (β = 0.137, p  = 0.029) and utilizing health information from applications (β = 0.159, p  = 0.013). From the qualitative phase of the study, four thematic categories emerged: reasons and basis for accessing health information from the Internet; address barriers to accessing and applying online health information; desires for a higher level of online health information services; outcomes of accessing and applying online health information.

The electronic health literacy of women diagnosed with gestational diabetes mellitus remains suboptimal and warrants improvement. The sources of access to health information affect electronic health literacy in women with gestational diabetes mellitus. Moreover, women facing gestational diabetes encounter numerous impediments when attempting to access health-related information online, underscoring the necessity for enhanced online health information services to meet their needs.

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Gestational diabetes mellitus is a metabolic disorder occurring during pregnancy [ 1 ], primarily resulting from insulin resistance and the progressive dysfunction of pancreatic β-cell [ 2 ]. Symptoms of gestational diabetes mellitus often manifest insidiously, making detection challenging. Diagnosis typically occurs through the oral glucose tolerance test administered between the 24th and 28th weeks of gestation [ 3 ]. Although there have been some advancements in monitoring the fetal health of women with gestational diabetes [ 4 , 5 ], gestational diabetes mellitus remains one of the most important causes of adverse perinatal outcomes [ 6 , 7 ], which may also have a negative impact on maternal mental health [ 8 ]. To mitigate these adverse effects, a collaborative multidisciplinary approach is typically employed, with lifestyle and behavioral management serving as the preferred method of intervention [ 9 ]. Lifestyle and behavioral management strategies for gestational diabetes mellitus encompass a diverse array of medical knowledge, spanning medical nutrition therapy, physical activity recommendations, weight management strategies, and more [ 10 ]. Therefore, to effectively manage gestational diabetes mellitus, women typically require access to extensive health information regarding lifestyle and behavioral management strategies.

In recent years, with the development of information and communication technologies, electronic resources have been increasingly used in healthcare. The Internet, in particular, has emerged as a popular platform for accessing health information among women diagnosed with gestational diabetes mellitus [ 11 ]. However, despite the convenience afforded by the Internet for accessing health information, it is essential to acknowledge the challenges associated with online health information and services. These challenges include content duplication, the presence of unregulated information sources, inadequate quality control measures, and difficulty in verifying the credibility of information sources [ 12 ]. Therefore, for women managing gestational diabetes mellitus, discerning the most reliable and credible health information from the vast array of online resources is paramount.

According to Norman and Skinner, the ability of individuals to access reliable and credible health information from electronic resources hinges on their electronic health literacy, an extension of traditional health literacy within the digital realm [ 13 ]. Unlike traditional health literacy, which primarily emphasizes individual access to and understanding of health information [ 14 ], electronic health literacy focuses on the individual comprehensive ability to access, understand, and assess health information from electronic resources, and apply health information available online to address health issues or make health-related decisions [ 15 ]. Evidence suggests that individual electronic health literacy is positively associated with one’s health behaviors and health outcomes, including a higher level of medication adherence, psychosocial well-being, and quality of life, as well as adopting adaptive health behaviors [ 16 , 17 , 18 , 19 ]. Therefore, to enhance the health behaviors and outcomes of women diagnosed with gestational diabetes mellitus, a thorough understanding of their electronic health literacy is indispensable.

Most of the existing studies on electronic health literacy focus on adolescents, college students, and the elderly [ 20 , 21 , 22 ]. In recent years, a few researchers have explored electronic health literacy in people with chronic diseases and their caregivers, including cancer patients and their caregivers [ 23 , 24 ], individuals with systemic lupus erythematosus, and those diagnosed with diabetes [ 25 ]. To the best of our knowledge, there is relatively limited research on the electronic health literacy of pregnant women, and currently, no studies have investigated the electronic health literacy of women with gestational diabetes mellitus. Through a review of studies on electronic health literacy in other populations, it was found that demographic characteristics, pregnancy-related features, and sources of health information acquisition may influence the electronic health literacy of women with gestational diabetes mellitus, including factors such as age, education level, employment status, household income, residential location, gestational age, number of pregnancies, and online health information searching [ 26 , 27 , 28 , 29 , 30 , 31 ]. In addition, research on electronic health literacy is primarily quantitative, while comprehensive studies on the experience and needs related to electronic health information remain insufficient. Taking these factors into consideration, this study adopted a mixed-methods approach to investigate electronic health literacy among women with gestational diabetes mellitus. It thoroughly explored the factors that influence electronic health literacy in this population, while also delving into their experiences of accessing, comprehending, evaluating, and applying online health information. Based on the literature review above, before the study began, we hypothesized that demographic characteristics, pregnancy-related factors, and sources of health information acquisition are associated with the electronic health literacy of pregnant women with gestational diabetes.

A sequential explanatory mixed-methods research design was employed to investigate the current status of electronic health literacy and cognition of online health information among women diagnosed with gestational diabetes mellitus. This study is divided into two parts. The first part discusses the current status and influencing factors of electronic health literacy among women with gestational diabetes mellitus through quantitative analysis. In the second part, qualitative research was conducted to explore the perception and cognition of women with gestational diabetes mellitus on online health information.

Quantitative phase—questionnaire survey

Study design and setting.

The quantitative phase is a cross-sectional study conducted through questionnaire surveys. During this phase, we recruited pregnant women diagnosed with gestational diabetes mellitus from the obstetrics department of a tertiary maternity hospital in Wuhan City using a convenience sampling method. The inclusion criteria were as follows: (1) aged 18 years old and above; (2) diagnosed with gestational diabetes mellitus according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria; (3) native Chinese speakers or non-native Chinese speakers who could understand Chinese well; (4) no cognitive impairment and normal mental state; (5) signed informed consent. Exclusion criteria included the inability to complete the questionnaire due to poor physical condition.

The sample size for studies on variable influencing factors should be determined according to the requirements of statistical variable analysis, typically recommended to be at least 5 to 10 times the number of variables [ 32 ]. In this study, based on 19 variables (16 independent variables and the 3 dimensions of the electronic health literacy scale), the estimated sample size ranged from 95 to 190. Considering a 20% invalid questionnaire rate, this section ultimately included 235 participants.

Data collection

Data were obtained through a self-completed questionnaire between July 20, 2022 and September 10, 2022. The questionnaire included the collection of independent and dependent variable information. The collection of independent variable information was based on a review of previous studies, covering general data related to demographic characteristics, pregnancy features, and sources of obtaining healthcare information. The instrument for collecting dependent variable information is the Chinese version of the eHEALS.

The eHEALS is the original and most frequently used instrument for investigating electronic health literacy [ 33 ]. It was initially developed by Norman and Skinner in 2006 [ 34 ]. The Cronbach alpha coefficient of the original English version of eHEALS is 0.88. The Chinese version of eHEALS was translated by Guo in 2013 [ 35 ]. It consists of 3 dimensions with 8 items, scored on a 5-point Likert scale. The score of each item ranges from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater electronic health literacy. The Chinese version of eHEALS demonstrates good reliability and validity. Regarding reliability, the Cronbach’s α coefficient is 0.913 [ 35 ]. For validity, exploratory factor analysis reveals a KMO coefficient of 0.875 and a significant Bartlett’s test of sphericity with a χ2 value of 544.000 (df = 28); confirmatory factor analysis indicates factor loadings ranging from 0.692 to 0.869 [ 35 ]. In our study, the Cronbach’s alpha coefficient for eHEALS was 0.937.

Data analysis

IBM SPSS Statistics was employed for statistical analysis. Demographic and pregnancy characteristics of participants were presented using descriptive statistics. Continuous variables were described by means and standard deviations, or medians and interquartile, depending on the normality of the data. Categorical variables were described by frequencies and percentages. To investigate the correlation between general data and e-health literacy among pregnant women, univariate analysis was performed. Due to the non-normal distribution of the data, either the Mann-Whitney U test or Kruskal-Wallis H test was utilized. Subsequently, the general data of women with gestational diabetes mellitus ( p  < 0.05) from the univariate analysis were included as independent variables in a multiple linear regression model, with e-health literacy as the dependent variables, to explore the influencing factors of e-health literacy.

Qualitative phase—in-depth interviews

Study design and sample.

Qualitative data was collected through semi-structured in-depth interviews between September 1, 2022, and October 3, 2022. The sample size was determined based on the saturation principle, which means that sample recruitment continued until no new codes emerged [ 36 ]. Ultimately, a total of 11 participants were enrolled. Among these, four participants took part in both the qualitative and quantitative segments of the study, while the remaining seven exclusively contributed to the qualitative phase.

Before the interviews began, a survey was conducted on the personal basic information and electronic health literacy status of all 11 participants involved in the interviews.

The semi-structured interview instrument comprised 10 questions (Supplementary 1 ). The interview location was a quiet and clean reception room for pregnant women at the obstetrics clinic, which ensured the privacy of the interviews. Two researchers were involved: one recorded environmental information, interviewees’ non-verbal communication, and facial expressions, while the other conducted the interviews with pregnant women. Midway through the study, owing to the COVID-19 pandemic, researchers conducted interviews with pregnant women via online video calls. All interviews were audio-recorded and transcribed verbatim.

The qualitative data from 11 interview transcripts were coded using NVivo 11.0, and analyzed using the inductive content analysis method described by Elo and Kyngäs [ 37 ]. The process of inductive content analysis comprises three phases. Open coding (Phases 1): researchers immersed themselves in the text data, generating numerous notes and headings to capture the content comprehensively. Subsequently, the researchers organized the headings into coding sheets and freely generated categories. Creating categories (Phases 2): the researchers amalgamated akin or disparate categories into higher-order categories for reducing the number of categories. Abstraction (Phases 3): the researchers delineated research topics through the utilization of generalized descriptions, thereby shaping the themes.

Quantitative results

Description of the sample.

The eHEALS score in the Chinese version, obtained from 235 women diagnosed with gestational diabetes mellitus, spanned from 8 to 40, with a median score of 29 (IQR, 26 to 32). The median age of these participants was 31 (IQR, 29 to 34) years and their median gestational age was 34 (IQR, 32 to 36) weeks. All individuals involved in the study identified as Han Chinese. Further demographic and pregnancy characteristics of participants are shown in Table  1 .

Influencing factors of electronic health literacy in women with gestational diabetes mellitus

The results of single factor analysis indicated that educational status ( p  = 0.003), experience of accessing health information from clinicians or nurses ( p  = 0.022), experience of accessing health information from social forums or WeChat official accounts ( p  = 0.018), experience of accessing health information from applications ( p  = 0.016), experience of accessing health information from Internet pages ( p  = 0.046), and satisfaction with health information on the Internet ( p  = 0.002) had a statistically significant difference in electronic health literacy scores of women with gestational diabetes mellitus. The results are shown in Table  1 . Additionally, correlation analysis of gestational weeks and electronic health literacy scores showed that gestational weeks and electronic health literacy were not correlated in women with gestational diabetes mellitus ( p  = 0.346).

In the multiple linear regression analysis, the eHEALS score served as the dependent variable, while the statistically significant factors identified in the univariate analysis were considered independent variables. P  < 0.05 indicates statistical significance. Results showed that women with gestational diabetes mellitus who accessed health information from clinicians or nurses scored higher on the eHEALS than those who did not (β = 0.137, p  = 0.029). Similarly, women with gestational diabetes mellitus who accessed health information from applications demonstrated higher eHEALS scores than those who did not do (β = 0.159, p  = 0.013). These results are shown in Table  2 .

Qualitative findings

A total of 11 women with gestational diabetes mellitus participated in the interviews, designated with identifiers P1 to P11 based on the interview sequence. All interviewees were married and of Han nationality. Their age ranged from 27 to 36 years, with an average age of approximately 31 years. Three participants were in their second trimester, while the remaining were in their third trimester. Notably, only one interviewee, identified as P1, had prior pregnancy experience and already had one child. Furthermore, the ninth participant possessed a medical background and resided in a rural area. Among the participants, five individuals scored 32 points or more on the Chinese version of eHEALS (The score of eHEALS range from 26 to 40). The general information about the participants is presented in Supplementary 2 .

Based on the results of the interviews, a total of 4 themes and 12 sub-themes were identified. Supplementary 3 presents excerpts of selected quotes corresponding to each theme.

Reasons and basis for accessing health information from the internet

This theme revealed why and how women with gestational diabetes mellitus access health information from the Internet. They access information pertaining to maintaining a healthy pregnancy, managing their condition, monitoring fetal growth and development, and ensuring a successful delivery by utilizing Internet searches or subscribing to popular medical science articles disseminated via WeChat official accounts and pregnancy-related applications. The preference for electronic media among women with gestational diabetes mellitus is influenced by factors such as their previous information-seeking habits, recommendations from friends, and insights derived from data analysis. These information-seeking behaviors are motivated by concerns regarding health risks associated with disease exposure and perceived barriers to effective doctor-patient communication.

Reasons for accessing health information from the internet

The majority of interviewees reported actively seeking or passively receiving health information from the Internet. Their motivations included encountering abnormal prenatal examination results, experiencing personal or family physical discomfort, and lacking sufficient knowledge about various medical conditions.

Furthermore, some interviewees highlighted communication barriers between healthcare providers and patients, including distrust of doctors, dissatisfaction with their performance, and the impact of the COVID-19 pandemic, as factors prompting them to resort to the Internet for health information.

Basis for selecting electronic media providing health information

The interviewees utilize diverse electronic media platforms like Baidu, Little Red Book, and Baby Tree for accessing health information. Their choices are frequently influenced by previous preferences, recommendations from acquaintances, and the promotion of big data.

Address barriers to accessing and applying online health information

Many barriers impede women with gestational diabetes mellitus in accessing and applying health information available online, including advertising, inappropriate medical depth of health information, redundant and cluttered health information, conflicting opinions on the same health issue, wide period and content span for health information update, and difficulties in evaluating the quality, sources, and safety of online health information. In response, they adopted strategies to address these barriers, including asking for help, exploring and practicing independently, and assessing the credentials of health information providers.

Barriers abound

During the interviews, women with gestational diabetes mellitus indicated that they encountered many barriers in accessing information. Two interviewees noted excessive hidden advertisements in online health information. Additionally, two interviewees pointed out that the medical depth of the health information available online was inappropriate and they expressed that this health information was insufficient to address their health concerns. Furthermore, three interviewees expressed difficulty in making decisions due to the plethora of conflicting opinions encountered online regarding the same health issue. Two respondents highlighted that the frequency and scope of updates to online health information posed obstacles to their access. Three respondents expressed apprehensions regarding the quality, source, and safety of the information available online.

Respond to barriers

Whenever women with gestational diabetes mellitus encounter difficulties accessing valuable health information online or have doubts about the reliability of the information they find, they tend to seek guidance from individuals with more expertise or experience, such as hospital doctors, online healthcare professionals, and peers who have similar experiences. They said that if they did not know whether health information available online was credible, they would try to practice it personally and judge the truth of health information based on their health changes. In addition, they expressed that they would try to retrieve health information through multiple online sources, compare the information content, and finally trust the highly overlapping parts. Furthermore, they also evaluate the credibility of online health information by assessing the credentials of information providers.

Desires for a higher level of online health information services

Women diagnosed with gestational diabetes mellitus often turn to the Internet as a supplementary resource for obtaining health-related information, yet deficiencies persist within current online health information platforms. Their expressed aspirations for enhanced online health services manifest across four key dimensions, as outlined below.

Desires for online transmission media with simple design and easy-to-use search function

Women diagnosed with gestational diabetes mellitus express a preference for online health information platforms that prioritize user-friendly design and enhanced searchability. Such features streamline software navigation, thereby facilitating their information retrieval process.

Desires for diversified online transmission forms of health information

Women diagnosed with gestational diabetes mellitus expressed a clear preference for online health information dissemination to encompass not only simple textual descriptions but also incorporate videos and images, thereby enhancing the comprehensibility and appeal of the content.

Desires for online information platforms containing real cases and experience sharing

Women diagnosed with gestational diabetes mellitus articulated the wish for web-based platforms to feature shared experiences from pregnant women and real-life cases. This inclusion is seen as instrumental in fostering confidence in recovery, accessing credible health information, and gaining deeper insights into pregnancy-related matters.

Desires for online information platforms with strong interactivity and personalized health information push services

Women with gestational diabetes mellitus expressed their desire for the personalized push service of health information provided by the web-based platforms, preferably sending health information according to their pregnancy duration. They also seek increased interaction with medical professionals on web-based platforms to receive more personalized and relevant advice and guidance.

Outcomes of accessing and applying online health information

Women with gestational diabetes mellitus noted that applying and accessing online health information could not only enhance their health literacy but also foster greater awareness of adopting a healthy lifestyle and encourage increased involvement from their spouses. However, they also acknowledged potential adverse effects, such as heightened anxiety stemming from the treatment experiences shared by others.

Popularization of health knowledge

Women with gestational diabetes mellitus point out that accessing online health information has improved their health knowledge and helps them effectively control blood sugar levels.

Emotional feedback

Some women diagnosed with gestational diabetes mellitus remarked that the severity of the condition was often exaggerated on the Internet, leading to heightened anxiety. Furthermore, encountering accounts of successful disease management shared by others sometimes evoked feelings of self-doubt regarding their own ability to manage the condition, consequently causing stress and anxiety. Conversely, one woman with gestational diabetes mellitus expressed that upon encountering individuals facing similar health challenges online, she found solace in the shared experience of others facing similar struggles.

Increased awareness about adapting healthy lifestyles

Women diagnosed with gestational diabetes mellitus emphasized that their awareness of adopting healthy lifestyles had been heightened through their exploration of health information accessible on the Internet.

Increased husband’s sense of involvement and experience

Women diagnosed with gestational diabetes mellitus noted that their husbands also have the opportunity to access online health information, thereby enabling them to gain a deeper understanding of the pregnancy experience.

To the best of our knowledge, this is the first study to investigate electronic health literacy among women with gestational diabetes mellitus through a mixed-methods design. Our study indicates that the electronic health literacy of women with gestational diabetes warrants improvement. Additionally, we delved into reasons for seeking health information online, barriers encountered, aspirations for improved online health services, and the impacts of utilizing online health information.

In terms of the influencing factors on electronic health literacy, our results indicated that women with gestational diabetes mellitus who accessed health information from medical personnel scored higher on electronic health literacy compared to those who did not, which was inconsistent with Kim et al.‘s finding that there was no difference in electronic health literacy scores between those with type 2 diabetes who relied on health professionals for health information and those who did not [ 38 ]. One possible explanation for this discrepancy is the variation in disease self-management capabilities. The majority of people with type 2 diabetes surveyed had managed their diabetes for 1–10 years, while participants in our study were diagnosed with gestational diabetes for a maximum of three months. The duration of illness positively correlates with the level of self-management [ 39 ]. This suggests that gestational diabetes patients may have weaker disease self-management abilities compared to type 2 diabetes patients, leading to a greater need for healthcare professionals’ assistance in addressing more health issues and facilitating gestational diabetes women’s understanding and application of online health information [ 40 ]. Additionally, the reason for this outcome in our study may be attributed to inadequate communication between healthcare professionals and patients [ 41 ]. Evidence suggests that individuals turn to the internet for information when their health concerns are not addressed by healthcare providers during consultations [ 41 ]. In the qualitative portion of our study, some patients reported that their issues were not fully resolved after communication with healthcare providers or that new uncertainties arose from these interactions. Consequently, women diagnosed with gestational diabetes mellitus turn to the internet as an additional resource for health information, thereby augmenting their level of electronic health literacy [ 42 ].

The control of blood sugar levels is crucial for women with gestational diabetes mellitus, and continuous blood sugar monitoring, along with maintaining a healthy diet and lifestyle, is key to controlling blood sugar [ 43 , 44 , 45 , 46 ]. Our research findings indicate that by accessing online health information, women with gestational diabetes mellitus can gain a deeper understanding of information related to blood sugar control, thereby effectively managing their blood sugar levels. Amr Jamal et al. have also noted that patients who engage in online health information queries have a better understanding of diabetes-related knowledge and demonstrate stronger blood sugar management capabilities compared to those who do not [ 47 ]. Therefore, future research should continue to explore the impact of this online health information on blood sugar management among women with gestational diabetes mellitus, thus effectively improving the management and prognosis of the disease.

Studies have demonstrated that precise health guidance aids in both treating gestational diabetes and preventing its development in high-risk pregnant women [ 48 , 49 ]. Although the qualitative results of this study indicate that online health information searches play a role in health guidance, this depends on the quality of the information obtained. Accurate online medical information can assist patients in comprehending their condition and guide them toward suitable treatment options [ 50 ]. However, inaccurate or misleading information can result in confusion and treatment delays [ 51 ]. The results of our qualitative study showed that women with gestational diabetes mellitus were not competent in discerning the quality of health information available online. Therefore, it is necessary to evaluate the quality of online health information. Presently, several tools have been developed to assess the quality of websites providing health information, including DISCERN, HONcode, and CRAAP [ 52 ]. However, current investigations into the quality of online health information primarily focus on cancer patients [ 53 , 54 , 55 ], with relatively limited research on the quality of online health information for gestational diabetes. Future studies could address this gap to assist gestational diabetes women in better selecting online health information. Additionally, the authority of online health information publishers has a positive impact on the credibility of health information [ 56 ]. Medical professionals have traditionally been the primary source of health information for individuals, being widely regarded as the most authoritative [ 57 ]. In our study, participants expressed a greater willingness to trust online health information published by certified healthcare professionals. These indications suggest the necessity of encouraging healthcare professionals to take responsibility for providing online guidance and support to women with gestational diabetes, thereby facilitating their access to and utilization of high-quality online healthcare information.

In terms of the design of online health platforms, interviewees expressed desires for easy access to health information, receiving personalized push services of health information, and increased interaction with medical personnel through these platforms, aligning with findings by Nijland et al. [ 58 ]. These implied that at the outset of developing online health information platforms, platform designers need to consider how to deliver health information to users in an understandable and accessible manner, as well as how to tailor health information to users’ needs [ 59 ].

Due to the impact of the COVID-19 pandemic, we chose to conduct online video interviews with some participants. Compared to traditional offline interviews, online interviews offer more convenience in terms of time and space, but they also present some challenges [ 60 ]. Firstly, there are issues with internet connectivity, as online video interviews may be affected by network interruptions, thus disrupting the smooth progress of the interviews [ 61 ]. Secondly, online video interviews lack the emotional connection and interpersonal interaction of face-to-face communication, which may affect the richness of the information provided by the interviewees [ 62 ]. Lastly, due to issues with image quality and angles, online video interviews may not accurately capture the facial expressions and body language of the interviewees, thereby impacting the understanding and interpretation of the interview information [ 63 ]. The epidemic has sparked increased interest in video interviews, but video interviews should not be seen solely as expedient measures in response to the pandemic, but rather as an opportunity for long-term methodological advancement. Future research should further optimize the process of online video interviews to facilitate the development of virtual qualitative research methods.

Limitations

Some limitations needed to be reported. Firstly, the quantitative study utilized a self-assessment scale as the research instrument. Participants may have either exaggerated or minimized certain information to obtain more favorable results, potentially introducing reporting bias. Secondly, all participants were sourced from a single hospital, potentially impacting the generalizability of the findings. Lastly, participants who engaged in both quantitative and qualitative phases of the study appeared more prepared at qualitative interviews compared to those solely involved in the qualitative phase. This discrepancy may introduce bias into their responses.

Conclusions

Women with gestational diabetes mellitus have a low level of electronic health literacy and insufficient ability to assess online health information, and the source of health information could influence their electronic health literacy. They often accessed health information from the Internet due to perceived disease threats and blocked doctor-patient communication. Furthermore, they highlighted numerous barriers to accessing electronic health information and expressed a desire for enhanced quality in online information services. It is recommended to enhance doctor-patient communication and encourage medical staff to take on a guiding and supportive role to facilitate access to valuable information. Additionally, the development of assessment tools tailored to online health information suitable for women with gestational diabetes mellitus is proposed. Furthermore, improvements to online health information platforms are suggested to better align with user needs, thereby enhancing the electronic health literacy of women diagnosed with gestational diabetes mellitus.

Data availability

Owing to the confidentiality of the information, the datasets generated and analyzed in this study are not publicly available. Nevertheless, upon reasonable request, they can be made accessible through the corresponding author.

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Acknowledgements

We would like to thank all the pregnant women who participated in our study.

The Fundamental Research Funds for the Central Universities [grant number 2021PT073] supported this research.

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Jingqi Xu, Yujia Chen and Jing Zhao are considered as co-first authors.

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School of Nursing, Wuhan University, No. 115, Donghu Road, Wuhan, Hubei, 430071, China

Jingqi Xu, Yujia Chen, Jing Zhao, Jiarun Wang, Jianfei Chen, Xinlong Pan, Wei Zhang, Zhijie Zou & Xiaoli Chen

Hospital of Stomatology, Wuhan University, 237 Luoyu Road, Wuhan, Hubei, 430079, China

Magnet Program & Nursing Research Department, UT Southwestern Medical Center, 8200 Brookriver Dr, Dallas, TX, 75247, USA

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JX: Conceptualization, Methodology, Writing - Original Draft; YC: Methodology, Validation, Writing - Original Draft; JZ: Methodology, Investigation, Writing - Original Draft; JW: Methodology, Validation, Investigation; JC: Investigation, Data Curation; XP: Investigation, Data Curation; WZ: Validation, Data Curation; JZ: Conceptualization, Writing - Review & Editing, Supervision; ZZ: Conceptualization, Writing - Review & Editing, Supervision; XC: Conceptualization, Writing - Review & Editing, Supervision, Project administration; YZ: Validation, Data Curation.

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Correspondence to Jin Zheng , Zhijie Zou or Xiaoli Chen .

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Xu, J., Chen, Y., Zhao, J. et al. Current status of electronic health literacy among pregnant women with gestational diabetes mellitus and their perceptions of online health information: a mixed-methods study. BMC Pregnancy Childbirth 24 , 392 (2024). https://doi.org/10.1186/s12884-024-06594-w

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Systematic review and meta-analysis of hepatitis E seroprevalence in Southeast Asia: a comprehensive assessment of epidemiological patterns

• Ulugbek Khudayberdievich Mirzaev 1 , 2 ,
• Serge Ouoba 1 , 3 ,
• Zayar Phyo 1 ,
• Chanroth Chhoung 1 ,
• Akuffo Golda Ataa 1 ,
• Aya Sugiyama 1 ,
• Tomoyuki Akita 1 &
• Junko Tanaka 1  

BMC Infectious Diseases volume  24 , Article number:  525 ( 2024 ) Cite this article

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The burden of hepatitis E in Southeast Asia is substantial, influenced by its distinct socio-economic and environmental factors, as well as variations in healthcare systems. The aim of this study was to assess the pooled seroprevalence of hepatitis E across countries within the Southeast Asian region by the UN division.

The study analyzed 66 papers across PubMed, Web of Science, and Scopus databases, encompassing data from of 44,850 individuals focusing on anti-HEV seroprevalence. The investigation spanned nine countries, excluding Brunei and East Timor due to lack of data. The pooled prevalence of anti-HEV IgG was determined to be 21.03%, with the highest prevalence observed in Myanmar (33.46%) and the lowest in Malaysia (5.93%). IgM prevalence was highest in Indonesia (12.43%) and lowest in Malaysia (0.91%). The study stratified populations into high-risk (farm workers, chronic patients) and low-risk groups (general population, blood donors, pregnant women, hospital patients). It revealed a higher IgG—28.9%, IgM—4.42% prevalence in the former group, while the latter group exhibited figures of 17.86% and 3.15%, respectively, indicating occupational and health-related vulnerabilities to HEV.

A temporal analysis (1987–2023), indicated an upward trend in both IgG and IgM prevalence, suggesting an escalating HEV burden.

These findings contribute to a better understanding of HEV seroprevalence in Southeast Asia, shedding light on important public health implications and suggesting directions for further research and intervention strategies.

Research Question

Investigate the seroprevalence of hepatitis E virus (HEV) in Southeast Asian countries focusing on different patterns, timelines, and population cohorts.

Sporadic Transmission of IgG and IgM Prevalence:

• Pooled anti-HEV IgG prevalence: 21.03%

• Pooled anti-HEV IgM prevalence: 3.49%

Seroprevalence among specific groups:

High-risk group (farm workers and chronic patients):

• anti-HEV IgG: 28.9%

• anti-HEV IgM: 4.42%

Low-risk group (general population, blood donors, pregnant women, hospital patients):

• anti-HEV IgG: 17.86%

• anti-HEV IgM: 3.15%

Temporal Seroprevalence of HEV:

Anti-HEV IgG prevalence increased over decades (1987–1999; 2000–2010; 2011–2023): 12.47%, 18.43%, 29.17% as an anti-HEV IgM prevalence: 1.92%, 2.44%, 5.27%

Provides a comprehensive overview of HEV seroprevalence in Southeast Asia.

Highlights variation in seroprevalence among different population groups.

Reveals increasing trend in HEV seroprevalence over the years.

Distinguishes between sporadic and epidemic cases for a better understanding of transmission dynamics.

Peer Review reports

Introduction

Hepatitis E is a major global health concern caused by the hepatitis E virus (HEV), which is a small, nonenveloped, single-stranded, positive-sense RNA virus belonging to the Paslahepevirus genus in the Hepeviridae family. There are eight genotypes of HEV: HEV-1 and HEV-2 infect only humans, HEV-3, HEV-4, and HEV-7 infect both humans and animals, while HEV-5, HEV-6, and HEV-8 infect only animals [ 1 ].

HEV infections affect millions of people worldwide each year, resulting in a significant number of symptomatic cases and deaths. In 2015, the World Health Organization (WHO) reported approximately 44,000 deaths from hepatitis E, accounting for 3.3% of overall mortality attributed to viral hepatitis [ 2 ]. The primary mode of transmission for hepatitis E is through the fecal–oral route. Outbreaks of the disease are often associated with heavy rainfall and flooding [ 3 , 4 ]. Additionally, sporadic cases can occur due to poor sanitation, vertical transmission, blood transfusion or close contact with infected animals, which serve as hosts for the virus [ 5 ]. Southeast Asia carries a substantial burden of hepatitis E, influenced by its unique socio-economic and environmental factors as well as variations in healthcare systems. Understanding the seroprevalence of hepatitis E in this region is crucial for implementing targeted public health interventions and allocating resources. To achieve the effective control and prevention of HEV, it is required to address the waterborne transmission and considering the specific characteristics of each region. By taking these measures, healthcare authorities can work towards reducing the global impact of hepatitis E on public health. Systematic reviews and meta-analyses on hepatitis E play a crucial role in synthesizing and integrating existing research findings, providing comprehensive insights into the epidemiology, transmission, and burden of the disease, thereby aiding evidence-based decision-making and public health strategies [ 6 , 7 ].

Recent systematic reviews and meta-analysis conducted on hepatitis E have varied in their scope or were limited by a smaller number of source materials [ 8 , 9 ]. The objective of this study was to determine the pooled seroprevalence of hepatitis E in countries within Southeast Asia by aggregating findings from a multitude of primary studies conducted across the region.

To commence this systematic review and meta-analysis, we adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and used the PRISMA assessment checklist [Supplementary Table  1 ]. The study included pertinent research conducted within the population of Southeast Asian countries, as outlined by the United Nations [ 10 ], and perform a meta-analysis on the seroprevalence of hepatitis E in this specific region.

PICOT assessment

In this systematic review and meta-analysis, the eligible population comprised individuals from the Southeast Asia region, irrespective of age, gender, ethnic characteristics, or specific chronic diseases. However, studies involving populations outside the designated countries, travelers, migrants, animal species studies, and those lacking clear descriptions of the study population were excluded.

Intervention and comparison

Intervention and comparison are not applicable to the prevalence studies.

Anti-HEV antibodies positivity either total antibodies or IgG or IgM among the Southeast Asian countries' population was assessed.

All studies conducted between 1987 and 2023 were included in this meta-analysis.

Search strategy

To conduct the data search, we utilized three databases, namely “PubMed”, “Scopus”, and “Web of Science”. The search terms comprised keywords related to the Hepatitis E virus, such as “Hepatitis E virus” OR “Hepatitis E” OR “HEV” AND names of each country “Brunei”, “Cambodia”, “Timor-Leste” OR “East-Timor”, “Laos” OR “Lao PDR”, “Indonesia”, “Malaysia”, “Myanmar” OR “Burma”, “Philippines”, “Singapore”, “Thailand”, “Vietnam” and “Southeast Asia”.

The search process in the databases finished on May 29 th , 2023, with two members of the study team conducting independent searches. Subsequently, the search results were unified. A grey literature search was performed from June 25 th to 30 th , 2023, by examining the references of review manuscripts and conference materials, along with using specific keywords in the Google Scholar database. Notably, during the gray literature search, additional studies from the Philippines that were initially missing in the first search were identified and included. Moreover, due to the diverse language expertise of the team, studies in Russian and French related to Cambodia and Vietnam were also considered for inclusion.

After applying the inclusion and exclusion criteria, each article selected for this systematic review (SR) was considered relevant. The quality assessment of each article was conducted using specific JBI critical appraisal instruments [ 11 ] [Supplementary Table  2 ].

Sporadic transmission of HEV infection

For the systematic review and meta-analysis of sporadic infection of HEV, we divided the study population into cohorts by countries, by risk of acquiring HEV—low and high risk. The low risk cohort included the general population (apparently healthy individuals, students, some ethnic populations, or individuals included in original studies as “general population”), blood donors, pregnant women, and hospital patients, while pig farmers, those with chronic hepatitis, HIV positive patients, and solid organ transplant patients in the high-risk group.

Lastly, we analyzed data in three decades—1987–1999, 2000–2010, and 2011–2023—to reveal seroprevalence rates over time.

Epidemic outbreaks of HEV infection

We separated epidemic outbreaks from sporadic cases due to distinct patterns and scale of transmission in epidemy. Epidemics are characterized by rapid and widespread transmission, affecting a large population within a short period and often following a specific pattern or route of propagation.

Statistical analysis

A meta-analysis of proportions was conducted using the 'meta' and 'metafor' packages in the R statistical software. To account for small proportions, the Freeman-Tukey double arcsine method was applied to transform the data. The Dersimonian and Laird method, which employs a random-effects model, was utilized for the meta-analysis, and the results were presented in a forest plot. Confidence intervals (CIs) for the proportions of individual studies were computed using the Clopper-Pearson method.

Heterogeneity was evaluated using the Cochran Q test and quantified by the I 2 index. Heterogeneity was considered significant if the p -value of the Cochran Q test was below 0.05.

For the assessment of publication bias, a funnel plot displaying the transformed proportions against the sample size was created. The symmetry of the plot was examined using the Egger test ( p  < 0.1).

The initial search yielded 1641 articles, which covered 9 out of 11 Southeast Asia countries. We couldn't find any information on hepatitis E from Brunei. We excluded a study from East Timor because it focused on the wrong population (US Army troops). The final screening resulted in the selection of 57 relevant studies, and the grey literature search added 9 more papers that met our inclusion criteria (Fig.  1 ). Among 9 papers through a grey literature, two relevant studies from the Philippines [ 12 , 13 ], one each from Indonesia [ 14 ] and Lao PDR [ 15 ], one study covered both Vietnam and Cambodia [ 16 ], one study provided HEV seroepidemiology information for Myanmar, Thailand, and Vietnam [ 17 ], two studies reported in Russian [ 18 , 19 ] (from Vietnam) and one reported in French [ 16 ] (from Vietnam and Cambodia). In total, our analysis included 66 papers from which we extracted data. This involved a total of 44,850 individuals (Table  1 ).

Flowchart of the identification, inclusion, and exclusion of the study. Table under flowchart informing about the studies which were found by the initial search in databases

Sporadic transmission IgG and IgM prevalence in Southeast Asian countries (excluding outbreak settings)

The sporadic cases involving 42,248 participants out of 44,850 participants (the remaining 2,602 people are considered in the “ Epidemic outbreaks ” section) from Southeast Asian countries the pooled prevalence of IgG was found to be 21.03%, while for IgM, it was 3.49% among 34,480 individuals who were tested (Fig. 2 ). Among these countries, Myanmar registered the highest pooled prevalence of IgG at 33.46%, while Malaysia had the lowest at 5.93%. For IgM prevalence, Indonesia had the highest rate at 12.43%, and Malaysia again had the lowest at 0.91% (Table  2 ) [Supplementary Figures  1 and 6 ].

Forest plot of meta-analysis of the prevalence of anti-HEV IgG ( A ) and anti-HEV IgM ( B ) in Southeast Asian countries. The plot includes the number of study participants for each country

Seroprevalence among specific groups

High risk of acquiring hev.

The high-risk group, which included farm workers and chronic patients, demonstrated a pooled anti-HEV IgG prevalence of 28.9%, with IgM prevalence at 4.42% [Supplementary Figures  2 and 8 ].

Chronic patients

This group, comprising individuals with chronic liver disease, HIV infection, or solid organ transplantation, exhibited the highest prevalence of pooled IgG among all cohorts, standing at 29.2%. Additionally, IgM prevalence was 3.9% [Supplementary Figures  2 and 7 ].

Farm workers

Farm workers were divided into several subgroups based on exposure to animals (reservoirs of HEV), including pig or ruminant farmers, slaughterhouse workers, butchers, and meat retailers. Among this group, the highest IgG prevalence was observed at 28.4%, while the pooled IgM level was 6.21% [Supplementary Figures  2 and 7 ].

Low risk of acquiring HEV

The low-risk group, comprising the general population, blood donors, pregnant women, and hospital patients, exhibited anti-HEV IgG and IgM prevalence of 17.86% and 3.15%, respectively. [Supplementary Figures  2 and 9 ].

General population

The general population in Southeast Asian countries, represented by 22,571 individuals, showed a presence of IgG in 21.4% of them. IgM was tested in 10,304 participants, and 2.63% of acute infection cases were identified [Supplementary Figures  2 and 7 ].

Blood donors

Blood donors, as a selected subgroup of the general population, exhibit differences in health status, age, gender distribution, and representativeness, warranting separate assessment. Among blood donors in Southeast Asian countries, the pooled prevalence of IgG and IgM were found to be 11.77% and 0.83%, respectively [Supplementary Figures  2 and 7 ].

Pregnant women

Pregnant women considered a vulnerable group regarding disease consequences, demonstrated an anti-HEV IgG prevalence of 18.56% among 1,670 individuals included in the study. Furthermore, 1.54% of them tested positive for anti-HEV IgM [Supplementary Figures  2 and 7 ].

Hospital patients

A group of 18,792 patients who visited hospitals with clinical signs of acute infection, jaundice, high temperature, and elevated liver enzymes, showed anti-HEV IgG and IgM prevalence of 16.3% and 4.45%, respectively [Supplementary Figures  2 and 7 ].

Temporal seroprevalence of HEV

Given the studies' long duration, the data was presented by decades: 1987–1999, 2000–2010, and 2011–2023. The prevalence of IgG showed an upward trend over these decades, with rates of 12.47%, 18.43%, and 29.17%. Similarly, for IgM, the prevalence rates were 1.92%, 2.44%, and 5.27% for the first, second, and third decades, respectively (Fig. 3 ).

The prevalence of anti-HEV IgG and IgM in Southeast Asian countries throughout the decades

Evaluating the trend of seroprevalence over decades within the same population and country proved challenging due to the limited availability of research papers. Consequently, we assessed anti-HEV antibody prevalence over decades, considering population cohorts and individual countries.

In Fig.  4 , we can see that all population groups show a consistent increase in the prevalence of both IgG and IgM antibodies over the decades. Figure  5 , we analyze the prevalence of anti-HEV antibodies in different countries over time, except for Indonesia and Malaysia, where we observe an increase in prevalence.

The epidemiological data regarding the occurrence of anti-HEV IgG ( A ) and anti-HEV IgM ( B ) antibodies within population cohorts across Southeast Asian nations divided by decades. The population cohorts delineated by the disrupted lines in the figure lack comprehensive data representation, as they provide information for only two out of three decades. Blood donors group has the anti-HEV IgM only for the last decade

The epidemiological data regarding the occurrence of anti-HEV IgG ( A ) and anti-HEV IgM ( B ) antibodies within countries of Southeast Asia divided by decades. The countries delineated by the disrupted lines in the figure lack comprehensive data representation, as they provide information for only two out of three decades. Philippines has the anti-HEV IgG antibodies information only for the first decade. Philippines, Myanmar, Singapore have anti-HEV IgM information only for single decade

Some studies lacked information on the collection time of the samples [ 13 , 19 , 41 , 48 , 59 , 62 , 64 , 82 ]. In these studies, the pooled IgG and IgM prevalence was 26.5% and 4.75%, respectively [Supplementary Figures  3 , 4 , 5 , 10 , 11 , 12 ].

Epidemic outbreaks

We separated epidemic outbreaks from sporadic cases due to distinct patterns and scale of transmission in epidemy. Epidemics are characterized by rapid and widespread transmission, affecting a large population within a short period and often following a specific pattern or route of propagation. The outbreaks occurred between 1987 and 1998 in several Southeast Asian countries, namely Indonesia [ 31 , 33 , 34 ], Vietnam [ 77 ], and Myanmar [ 54 ] [Supplementary Figure  13 ]. These outbreak investigations involved a total of 2,602 individuals, with most participants from Indonesia (2,292 individuals). The studies were mainly conducted using a case–control design. Among the participants, 876 were considered controls, while 1,726 were classified as cases. The pooled prevalence of total anti-HEV immunoglobulins was estimated as 61.6% (95% CI 57.1–66) (Table  2 ).

Assessment of publication bias

We checked for publication bias using a funnel plot and Egger's test. Both the studies on anti-HEV IgG and IgM showed asymmetry with Egger's test indicating a p -value less than 0.001 for both cases (Fig. 6 ).

Funnel plot of anti-HEV IgG ( A ) and anti-HEV IgM prevalence. Double arcsine transformed proportion of individual studies is plotted against the sample size. The distribution of studies in the funnel plot revealed the presence of publication bias

A paper search yielded varying numbers of manuscripts from Southeast Asian countries. The Philippines had the fewest studies, while Thailand had the highest with 15 studies. No data was found for Brunei Darussalam and East Timor or Timor Leste on the human species.

The results of this study provide valuable insights into the seroprevalence of IgG and IgM antibodies against HEV in different populations across Southeast Asian countries. Understanding the prevalence of these antibodies is essential for assessing the burden of HEV infection and identifying high-risk groups.

The extensive analysis of anti-HEV IgG prevalence in this study covered a wide range of population groups in Southeast Asia, including the general population, blood donors, pregnant women, hospital patients, farm workers, and chronic patients. The results unveiled an overall pooled prevalence of 21.03%, indicating significant exposure to the Hepatitis E virus among individuals in the region at some point in their lives. Moreover, a consistent increase in IgG prevalence was observed over the years, with the highest prevalence occurring in the most recent decade (2011–2023). This suggests a progressive rise in HEV exposure within the region.

Upon examining the prevalence data across different decades and population cohorts, a uniform upward trend in HEV antibody prevalence became apparent across all groups. Several factors could be assessed as potential contributors to this trend:

Notably, the expanding population in Southeast Asian nations during this timeframe increased the number of individuals at risk of Hepatitis E infection.

The rapid urbanization, characterized by the migration from rural to urban areas, led to higher population density and conditions conducive to Hepatitis E virus transmission [ 84 ]. Access to clean drinking water and adequate sanitation facilities emerged as critical factors in preventing Hepatitis E. Regions with inadequate infrastructure, particularly in water and sanitation, faced an elevated risk due to contaminated water sources. Climate-related events, such as heavy rainfall and flooding, significantly impacted waterborne diseases like Hepatitis E. The increasing frequency and severity of such events emphasized the importance of considering climate-related factors in assessing prevalence trends [ 85 ]. Consumption of contaminated or undercooked meat, particularly pork, was identified as a source of Hepatitis E transmission. Changes in food consumption habits over time may have contributed to changes in seroprevalence [ 86 ]. Limited access to healthcare facilities in certain areas exacerbated the spread of Hepatitis E. Increased awareness together with advances in medical research and the establishment of robust surveillance systems likely improved the detection and reporting of Hepatitis E cases, contributing to the observed increase in seroprevalence [ 87 , 88 , 89 ]. These multifaceted factors have likely played a collective role in shaping the changing landscape of Hepatitis E seroprevalence in Southeast Asian nations over the past decades. The upward trend emphasizes the importance of continued monitoring, intervention, and public health measures to mitigate the spread of Hepatitis E in the region.

Among specific populations, pregnant women exhibited an IgG prevalence of 18.56%, indicating that a considerable number of pregnant individuals have been exposed to HEV. Pregnant women are particularly vulnerable to the consequences of HEV infection, as it can lead to severe outcomes for both the mother and the foetus.

Hospital patients with clinical signs of acute infection showed an IgG prevalence of 16.3%, suggesting that HEV is still a significant cause of acute hepatitis cases in the hospital setting. Similarly, farm workers, especially those exposed to animals (reservoirs of HEV), had a high prevalence of IgG (28.4%), highlighting the occupational risk associated with zoonotic transmission.

Chronic patients, including individuals with chronic liver disease, HIV infection, or solid organ transplantation, exhibited the highest pooled IgG prevalence among all cohorts at 29.2%. This finding underscores the importance of monitoring HEV infection in immunocompromised individuals, as they may develop chronic HEV infection, which can lead to severe liver complications.

The prevalence of IgM antibodies, which are indicative of recent or acute HEV infection, was lower overall compared to IgG. The general population showed an IgM prevalence of 2.63% among acute infection cases. Among hospital patients exhibiting clinical signs of acute infection, the prevalence of IgM antibodies indicative of recent or acute HEV infection was higher at 4.45%.

Farm workers, particularly those exposed to animals, demonstrated the highest IgM prevalence at 6.21%. This finding highlights the occupational risk of acquiring acute HEV infection in this population due to direct or indirect contact with infected animals.

The study also identified a high-risk group, consisting of farm workers and chronic patients, with a pooled IgG prevalence of 28.9% and an IgM prevalence of 4.42%. This group is particularly susceptible to HEV infection and requires targeted interventions to reduce transmission and prevent severe outcomes.

Overall, this study provides valuable data on the seroprevalence of HEV antibodies in different populations in Southeast Asian countries. It highlights the importance of continued surveillance and public health interventions to control HEV transmission, especially in vulnerable groups. Understanding the prevalence trends over time can aid in developing effective strategies for the prevention and management of HEV infections in the region. However, further research and studies are warranted to explore the underlying factors contributing to the observed seroprevalence trends and to design targeted interventions to reduce HEV transmission in specific populations. Among the countries of Southeast Asia Myanmar was the most for HEV infection, while Malaysia registered the lowest seroprevalence.

This study has some limitations that we should be aware of. We looked at studies in three languages (English, Russian, and French), but we couldn't find data from two out of the 11 countries. This means we might not have a complete picture of the disease's prevalence in the whole region.

The way we divided the groups based on occupation or status could be questioned. Different criteria might give us different results, so it's something we need to consider. Another challenge is that the study covers a long time from 1989 to 2023 by published research and involves many different countries. This makes it difficult to compare the results because the tests used, and the diagnostic abilities might have changed over time and vary across countries.

Despite these limitations, our study presents a detailed epidemiologic report of combined seroprevalence data for HEV in Southeast Asian countries following the UN division. It gives us a basic understanding of the disease's prevalence in the region and offers some insights into potential risk factors. However, to get a more accurate picture, future research should address these limitations and include data from all countries in the region. Furthermore, certain countries such as Myanmar and the Philippines have not reported HEV prevalence data since 2006 and 2015, respectively. The absence of recent HEV prevalence reports from certain countries raises concerns about the availability of up-to-date epidemiological data for assessing the current status of hepatitis E virus infections in these regions.

Our comprehensive analysis study involving Southeast Asian countries provides significant insights into the seroprevalence of hepatitis E virus (HEV) infection in this region and in various populations. The rates of anti-HEV antibodies observed among different groups, as well as the increasing trend in seroprevalence over decades, emphasize the dynamic nature of HEV transmission in the region. These findings contribute to a better understanding of HEV prevalence across countries, populations, and time periods in Southeast Asia, shedding light on important public health implications and suggesting directions for further research and intervention strategies.

Availability of data and materials

All data generated or analyzed during this study were included in this paper either in the results or supplementary information.

Abbreviations

Hepatitis E Virus

Preferred reporting items for systematic review and meta-analysis

Enzyme-Linked Immunosorbent Essay

Hepatitis E virus Immunoglobulin G

Hepatitis E Virus Immunoglobulin M

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Acknowledgements

The authors would like to thank all researchers of the primary research included in this study.

This work was supported by Project Research Center for Epidemiology and Prevention of Viral Hepatitis and Hepatocellular Carcinoma, Hiroshima University led by Prof. Junko Tanaka (PI).

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Ulugbek Khudayberdievich Mirzaev, Serge Ouoba, Ko Ko, Zayar Phyo, Chanroth Chhoung, Akuffo Golda Ataa, Aya Sugiyama, Tomoyuki Akita & Junko Tanaka

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Ulugbek Khudayberdievich Mirzaev

Unité de Recherche Clinique de Nanoro (URCN), Institut de Recherche en Sciences de La Santé (IRSS), Nanoro, Burkina Faso

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UM, TA, and JT conceptualized the study. UM and SO contributed to developing the study design and data acquisition. UM, CC, ZP, AG, SO, and JT analysed and interpreted the data. UM, KK, and AS drafted the manuscript. TA, AS, KK, SO, and JT contributed to the intellectual content of the manuscript. All authors read and approved the final manuscript. JT and TA shared the co-correspondence. 

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Mirzaev, U.K., Ouoba, S., Ko, K. et al. Systematic review and meta-analysis of hepatitis E seroprevalence in Southeast Asia: a comprehensive assessment of epidemiological patterns. BMC Infect Dis 24 , 525 (2024). https://doi.org/10.1186/s12879-024-09349-2

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Received : 30 October 2023

Accepted : 24 April 2024

Published : 24 May 2024

DOI : https://doi.org/10.1186/s12879-024-09349-2

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