Who Gets Included? Equity in Digital and Decentralised Mental Health and Neurodevelopmental Trials: A Systematic Review

Who Gets Included? Equity in Digital and Decentralised Mental Health and Neurodevelopmental Trials: A Systematic Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Who Gets Included? Equity in Digital and Decentralised Mental Health and Neurodevelopmental Trials: A Systematic Review Sophie S Hall, Charlotte L Hall, Christopher Partlett, Alexia Jeayes, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7565290/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Decentralised clinical trials (DCTs) may improve access to digital mental health interventions, but equity remains limited. This review of 60 papers (58 DCTs) found key PROGRESS-Plus variables—race/ethnicity, socioeconomic status, occupation, and residence—were underreported. Underserved groups were consistently underrepresented. Barriers included digital exclusion and cognitive or sensory challenges, while therapist support and simplified onboarding facilitated participation. Standardised reporting and inclusive DCT design are needed. Health sciences/Health care Health sciences/Medical research Biological sciences/Psychology Social science/Psychology Mental health digital health randomised controlled trials minority groups Figures Figure 1 Figure 2 BACKGROUND Remote and digitised delivery of mental health care is increasingly recognised as essential to meet rising demand and improve accessibility nationally and globally ( 1 – 4 ). Digital tools, such as apps, teletherapy, and AI-driven diagnostics, offer scalable, cost-effective interventions that can reduce waiting times, ease pressure on services and support personalised care ( 5 , 6 ). However, for the benefits of mental health digitisation to be realised by those most at need they need be evaluated in underserved populations. Randomised Controlled Trials (RCTs) are regarded as the ‘gold standard’ for evaluating healthcare interventions ( 7 ), but their reliance on selective samples and inadequate demographic reporting ( 8 – 10 ), limits real-world applicability and produces a skewed data base on which decisions are made ( 11 – 14 ). Decentralised clinical trials (DCTs), also called ‘remote’, ‘online’, ‘site-less’, or ‘hybrid’ trial, conduct some or all activities outside traditional trial sites, and grew in popularity during COVID-19 ( 15 – 17 ). DCTs rely on digital technologies, offering a flexible, scalable model for evaluating digital mental health interventions, with potential to improve access, reduce costs, and support sustainability ( 18 , 19 ). Whilst DCTs are increasingly positioned to promote inclusive research, evidence of their impact on equity remains uncertain, particularly in mental health, where challenges such as stigma, digital exclusion, and lack of face-to-face interaction/support can limit participation ( 20 , 21 ). Neurodevelopmental conditions, such as autism and attention-deficit hyperactivity disorder (ADHD), frequently co-occur with mental health conditions ( 22 , 23 ) and present similar challenges ( 24 ). In addition, requirements for digital literacy and access may further exclude disadvantaged groups disproportionately affected by mental health disorders ( 25 – 29 ). Frameworks like NIHR INCLUDE and recommendations by Aiyegbusi et al. ( 30 , 31 ) provide guidance on engaging underserved groups, but their application to mental health DCTs is limited. Realising the inclusive potential of DCTs requires evaluating their effectiveness in reaching diverse populations and adapting designs to overcome intersecting barriers. The aims of this review were to: Assess the extent to which equity-relevant data are reported in DCTs, using the Place, Race, Occupation, Gender/sex, Religion, Education, Socioeconomic status, Social capital + personas characteristics associated with discrimination (PROGRESS-plus) framework. Explore whether specific population groups (using the PROGRESS framework) are under-represented in mental health/neuro-developmental conditions DCTs compared to general population statistics. Identify barriers and enablers to inclusive participation in DCTs in a mental health/neuro-developmental context. METHODS We report methodology in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines using the Equity extension ( 32 ) (see Supplementary Material 1), following a preregistered International Prospective Register of Systematic Reviews protocol (CRD420251080321). Patient and Public Involvement (PPI) Four PPI members with mental health and neurodevelopmental conditions (including one Black female and one Asian female) were involved in the conception of the study. Their concerns regarding inclusivity in DCT informed the development of the data extraction tool. A PPI member also reviewed the data codes derived from the thematic analysis. Inclusion criteria Studies were assessed for inclusion based on the population, intervention, comparator, outcome and study design ( 33 ) (Table 1 ). Table 1 Inclusion criteria based on population, intervention, comparator, outcome and study design Population Children (aged up to 17 years) and/or adults (aged 18 years and above) with a diagnosis of a mental health or neurodevelopmental condition (as defined by the DSM-5 ( 38 )), in any setting. Intervention Studies that described or evaluated decentralised, virtual, or remote interventions (fully or partially remote), e.g., teletherapy, app-based interventions, remote assessments. Interventions must involve assessment, treatment, prevention, or management of mental health or neurodevelopmental conditions. Comparator Studies with the following controls were considered: normal practice (‘usual care’), waiting-list control, or any other intervention described by the authors as a comparator. Outcomes Studies that reported psychological, emotional and/or behavioural outcomes (e.g., agitation, anxiety, social behaviour). Study designs Randomised controlled trials (including randomised feasibility and pilot trials). Exclusion criteria Studies were excluded if they: (a) lacked demographic data or reflections on representation; (b) involved interventions unrelated to mental health or neurodevelopmental conditions; (c) delivered non-digital or non-remote interventions; (d) had no other online trial components beyond the intervention; or (e) were published before 2020, as the COVID-19 pandemic marked a shift in DCT adoption ( 34 )ensuring the review reflects contemporary practices and technologies. Search strategy MEDLINE, PsycINFO, Embase, CINAHL, Cochrane Central, and Web of Science were searched up to 1 July 2025 using terms related to DCTs (e.g., decentralised, remote, digital), demographics (e.g., ethnicity, education), inclusivity, and mental health or neurodevelopmental conditions in children and adults. Searches were limited to English-language studies. The MEDLINE strategy is provided in Supplementary Material 2 and adapted for other databases. Key journals, reference lists of included studies, and relevant systematic reviews were hand-searched. Publications were managed in Covidence (Fig. 1). Titles and abstracts were independently screened by three authors (S.S.H., C.L.H., E.S.); disagreements led to inclusion in full-text review. Full texts were independently reviewed by the same authors, with discrepancies resolved through discussion. Data extraction Using a pre-defined data extraction spreadsheet in Microsoft Excel, relevant data were extracted by four authors (S.S.H., E.S., N.R., A.J.). Information included research methodology, sample size, type of intervention, remote trial design elements (e.g., recruitment strategies, screening, assessments), intervention setting, digital literacy and/or access, and demographic information (e.g., race/ethnicity, gender, age, religion, occupational level, educational level) as well as any identified barriers/enablers to engagement in the trial. A complete list of data extracted is provided in Supplementary Material 3. Data extraction commenced on 14th July 2025. Risk of bias assessment Given the objective of this review was to evaluate representation and the quality of demographic reporting, rather than the effectiveness or validity of study findings, a formal risk of bias assessment was not undertaken. The focus was placed on the completeness and transparency of reporting, as measured against established reporting guidelines (PROGRESS-Plus) ( 35 ) rather than on the internal validity of the studies or the potential for systematic bias in their results, consistent with the approach taken in previous reviews ( 10 , 36 ). Data synthesis We assessed reporting quality using PROGRESS-Plus guidelines ( 35 ). Four authors (S.S.H., E.S., N.R., A.J.) independently reviewed each paper, categorising each item as reported, not reported, or unclear. Supplementary materials were accepted if explicitly referenced. PROGRESS includes place of residence, race/ethnicity, occupation, gender/sex, religion, education, socioeconomic status (SES), and social capital. For synthesis, place of residence was coded as rural/urban; occupation as employed/unemployed; education as above/below compulsory level; social capital by relationship status; and SES as low-, middle-, or high-income using country-specific definitions. SES reporting varied (income frequency, household vs. individual, currencies), and no standard cut-offs were used; where available, numbers below the national poverty line were extracted separately. The “Plus” category captured additional characteristics, including non-mental health conditions and age. Descriptive statistics summarised demographics. Country-level synthesis included only countries represented in ≥ 2 studies; multi-country studies without disaggregated data were excluded. Where possible, chi-squared tests compared sample sociodemographics with national statistics. Perceived or measured barriers/enablers to participation were thematically analysed ( 37 ). One author (S.S.H.) generated initial codes, which were grouped into themes. A second author (E.S.) and a PPI member independently reviewed and validated theme construction. RESULTS Description of studies Database and manual searching yielded a total of 6,760 records. After removal of duplicates and screening of titles, abstracts and full-text papers, 60 papers were included in the review (Fig. 1), reporting a total of 59 studies. Two papers refer to the same RCT ( 38 , 39 ), but assessed different outcomes. Both RCTs met the inclusion criteria but as the data was derived from the same sample, demographics are only included once. A list of all included papers is provided in Supplementary Material 4. Across the 58 included RCTs, sample sizes ranged from 8 to 1,225 participants (as reported at baseline). Most studies were conducted in the USA (n = 15; 25.4%), followed by the UK (n = 8; 13.6%), Australia (n = 7; 11.9%), Sweden (n = 4; 6.8%), and China (n = 4; 6.8%). Two studies (3.4%) were conducted in multiple geographic regions (e.g., USA and UK ( 40 ) and Netherlands and UK ( 41 )). Interventions were delivered to a variety of study populations, primarily anxiety (n = 10; 16.9%), depression (n = 8; 13.6%), attention-deficit hyperactivity disorder (ADHD; n = 8, 13.6%), and autism (n = 6; 10.2%). Fourteen studies (23.7%) delivered interventions to participants with mixed diagnoses (e.g., anxiety and depression, post-traumatic stress disorder and depression, all mental health conditions). The studies were split in terms of models of delivery, with 28 (47.5%) being fully remote, and 30 (50.8%) adopting a hybrid model that combined remote and in-person trial elements. Remote recruitment (e.g., via social media, online platforms, electronic health records) was reported in 40 studies (67.8%). Remote screening or eligibility assessments (e.g., via telephone, online self-reporting) were used in 41 studies (69.5%). Remote outcome assessments were performed in most studies (n = 53, 89.8%). Table 2 provides a full overview of study characteristics. Table 2 Study characteristics Author/Year Country Participants (n) Diagnosis Full or hybrid remote Remote recruitment strategies (Y/N) Remote screening and/or eligibility (Y/N) Remote assessments/ follow-up (Y/N) Andrews et al. (2023) Australia 103 Anxiety or depression Full Yes Yes Yes Backman et al. (2024) Sweden 141 Autism Full Yes Yes Yes Bennett et al. (2021) UK 34 Any mental health condition Hybrid No No Yes Bikic et al. (2018) Denmark 70 ADHD Hybrid No Yes Yes Bilan et al. (2025) Spain 49 ADHD Hybrid No Yes No Carl et al. (2020) USA and UK 256 Generalised anxiety disorder Full Yes Yes Yes Chan et al. (2023) Hong Kong 320 Comorbid depressive disorder and insomnia Full Yes Yes Yes Chien et al. (2024) Hong Kong 50 Any neurodevelopmental conditions Hybrid Not clear Not clear Not clear Clark et al. (2023) UK 102 Social anxiety Hybrid No No Yes Creswell et al. (2024) UK 443 Anxiety Hybrid No Yes Yes Dopfner et al. (2025) Germany 431 ADHD Hybrid No Yes Yes Eto et al. (2025) Japan 30 ADHD Full Yes Yes Yes Eylem et al. (2021) Netherlands and UK 18 Suicidal ideation Hybrid Yes Not clear Yes Fatouros et al. (2025) Greece 200 Depression and generalised anxiety disorder Full Yes Yes Yes Felder et al. (2020, 2022) USA 208 Insomnia Full Yes Yes Yes Grenier-Martin et al. (2022) Canada 29 Intellectual and developmental disability Hybrid Yes Not clear Yes Guzick et al. (2023) USA 8 ADHD Hybrid Not clear Yes Yes Guzick et al. (2024) USA 57 ADHD Full Yes Yes Yes Hall et al. (2024) N/A N/A Across all conditions N/A N/A N/A N/A Hartley et al. (2022) Australia 16 Autism Hybrid Yes Not clear Yes Haun et al. (2023) USA 265 dyads PTSD Full Yes Yes Yes He et al. (2022) China 148 Depression Hybrid Yes Not clear Yes Heller et al. (2020) Netherlands 159 Depression and anxiety Hybrid Yes Not clear Yes Hoffmann et al. (2021) Denmark 101 Anxiety Full Yes Yes Yes Hollis et al. (2021) UK 224 Tourette’s syndrome Hybrid Yes No Yes Huberty et al. (2021) USA 239 Insomnia Full Yes Yes Yes Jamali et al. (2022) Iran 43 Autism Full Yes Yes Yes Jent et al. (2021) Spain 130 Disruptive behaviour Hybrid Not clear Yes Yes Kalmbach et al. (2020) USA 91 Insomnia Full Yes Yes Yes Kandola et al. (2024) UK 908 Depression Full Yes Yes Yes Kenworthy et al. (2023) USA 97 Autism Hybrid Not clear No Yes Kwon et al. (2024) Korea 74 ADHD Hybrid No No Yes Lewis et al. (2024) Australia 61 Autism Full Yes Yes Yes Lindgren et al. (2020) USA 38 Autism Hybrid Not clear Not clear Yes Lippke et al. (2021) Germany 300 Psychosomatic diagnosis Hybrid No No Yes Malarkey et al. (2024) USA 125 Insomnia Hybrid Yes Not clear Yes March et al. (2023) Australia 137 Anxiety Hybrid Not clear Yes Yes March et al. (2025) Australia 137 Anxiety Full Yes Yes Yes McCloud et al. (2020) UK 168 Anxiety and depression Full Yes Yes Yes McLellan et al. (2024) Australia 95 Anxiety Hybrid No Yes Yes Mechler et al. (2022) Sweden 272 Depression Full Yes Yes Yes Moshe et al. (2022) Germany 253 Depression Hybrid Yes Not clear Yes Murray et al. (2021) Australia 302 Bipolar disorder Full Yes Yes Yes Nardi et al. (2022) USA 27 Generalised anxiety disorder Hybrid Yes No Yes Nissling et al. (2023) Sweden 52 Anxiety Full Yes Yes Yes Nordh et al. (2021) Sweden 103 Social anxiety disorder Hybrid Yes Yes Not clear Ong et al. (2024) USA 178 PTSD and/or depression Full Yes Yes Yes Piscitello et al. (2024) USA 43 ADHD Hybrid Yes Yes Not clear Possemato et al. (2022) USA 81 Depression, anxiety and PTSD Full Yes Yes Yes Richards et al. (2020) UK 361 Depression and anxiety Full Yes Yes Yes Sabri et al. (2025) USA 144 PTSD and/or depression Full Yes Yes Yes Sayal et al. (2025) UK 1225 Emotional difficulties Hybrid No Yes Yes Segal et al. (2020) USA 460 Depression Hybrid Yes Yes Not clear Seo et al. (2022) South Korea 73 Postpartum depression Full Yes Yes Yes Sun et al. (2021) China 168 Perinatal depression Hybrid Not clear Yes Yes Tan et al. (2023) Malaysia 48 Depression and anxiety Hybrid No No Yes Tan et al. (2024) China 69 Learning disabilities Full Yes Yes Yes Wong et al. (2021) Hong Kong 79 Depression Full Yes Yes Yes Wu et al. (2023) China 93 OCD Full Yes Yes Yes To what extent are equity-relevant data reported in DCTs in mental health, as defined by the PROGRESS-Plus guidelines? In line with the PROGRESS-Plus guidelines, 59 of the included 60 papers (98.3%) reported participant demographics. One study did not report demographic characteristics, as this was a perspective piece, for which reporting was not applicable ( 42 ). Two papers reported on the same RCT, therefore demographics summarised below are taken from 58 studies ( 38 , 39 ). The most frequently reported PROGRESS-Plus items related to gender (58/50: 100%) and age (58/50: 100%). The least frequently reported PROGRESS-Plus items related to non-mental health disabilities (1/58: 1.7%), religion (3/58: 5.2%) and place of residence or setting (rural vs. urban areas) (7/58: 12.1%) (Table 3 ). Table 3 Number of studies (total n = 58) reporting PROGRESS-Plus items PROGRESS-Plus item Number of studies reporting demographic information n (%) Place (rural vs urban) 7 (12.1%) Race/ethnicity 23 (39.7%) Occupation 21 (36.2%) Gender 58 (100%) Religion 3 (5.2%) Education 41 (70.7%) Socioeconomic status 16 (27.6%) Social capital 25 (43.1%) Age 58 (100%) Non mental health disability 1 (1.7%) Demographic information from all included studies, disaggregated by country, is presented below. Descriptive statistics are limited to countries represented in at least two studies. Studies conducted across two or more countries in which data were not reported separately by country are excluded. A comprehensive overview of demographic information reported in each included study is available in Supplementary Material 5 (Supplementary Table 5a), alongside documentation of the source data used to acquire the representation of each sociodemographic variable by country (Supplementary Table 5b). Place : Seven studies (12.1%) reported participants’ place of residence categorised as rural or urban. Among the 1,211 participants reported in these seven studies, 626 (51.7%) resided in urban areas, 306 (25.3%) in rural areas, and data for 279 participants (23.0%) were either missing or not reported. One of the seven studies was in the USA ( 43 ) and one in China ( 44 ) demographics for these counties are reported in Supplementary Material 5 but were insufficient for further comparison. The remaining five studies were conducted in Australia ( 45 – 49 ), encompassing 533 participants: 260 (48.8%) lived in urban areas (population statistics: 86.5%), 160 (30.0%) in rural areas (population statistics: 13.5%), and residence data were unavailable for 113 participants (21.2%). Chi-squared tests identified there were significantly greater representation of rural based participants in these Australian studies compared to general population statistics ( p < .001). Race/ethnicity Ethnicity data were reported in 23 studies (39.7%), encompassing a total of 5,381 participants. Among these, 4,054 individuals (75.3%) were identified as White, 493 (9.2%) as Asian, 268 (4.9%) as Black, 208 (3.9%) as ‘Other’, and 152 (2.8%) as of ‘Multiple’ ethnicities. From the 23 studies, ethnicity information was either missing or not reported for 261 participants (4.9%). Three studies were the only representation from that country ( 50 – 52 ), and one study reported demographics aggregated across multiple countries without separating data by country ( 53 ). Therefore, the tabulated papers include studies conducted in the USA ( 38 , 43 , 54 – 63 ), the UK ( 64 – 68 ), and China ( 44 , 69 ) (Table 4 ). It was not possible to extract ethnicity of the population in China as China only officially report Han or non-Han statistics. Chi-squared test revealed a statistically significant difference between the racial representation reported in the included studies and national population statistics in the UK and USA, indicating over representation of white participants relative to their population proportions ( p’s < .0001). Table 4 Ethnicity reported across the included studies and separated by countries (represented in ≥ 2 studies) Total participants (n) White n (%) Asian n (%) Black n (%) Multiple n (%) Other n (%) Missing/not reported n (%) Across all geographic regions (n = 23*) 5,381* 4,054 (75.3%) 493 (9.2%) 268 (4.9%) 152 (2.8%) 208 (3.9%) 261 (4.9%) USA (n = 12) 2,117 1,564 (73.9%) 72 (3.4%) 224 (10.6%) 55 (2.6%) 129 (6.1%) 128 (6.0%) USA Population Statistics 61.6% 4.8% 12.4% 10.2% 0.1% UK (n = 5) 2,355 2,011 (85.4%) 60 (2.5%) 18 (0.8%) 64 (2.7%) 72 (3.1%) 130 (5.5%) UK Population Statistics 81.7% 9.3% 4% 2.9% 2.1% China (n = 2) 316 0 (0.0%) 305 (96.5%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 11 (3.5%) *Including four studies that are only represented by one country or combined countries Occupation Of the 21 studies (36.2%) that reported participants’ occupational status, a total of 3,059 individuals were classified as either 'employed' (n = 2023, 66.1%) or 'not employed' (n = 649, 21.2%). From the 21 studies, occupational status data were missing or unreported for 387 participants (12.7%). Only one study, conducted in Hong Kong, provided detailed information on occupational type ( 70 ). Nine studies were the only representation from that country ( 45 , 50 , 52 , 70 – 75 ), and one study reported demographics aggregated across multiple countries without separating data by country ( 41 ). Therefore, the tabulated papers include studies conducted in the USA ( 59 , 62 , 63 , 76 ), the UK ( 64 – 67 ), and Sweden ( 77 – 79 ) (Table 5 ). Chi-squared test revealed a statistically significant difference between the representation of employed and unemployed participants reported in the included studies and national population statistics for all three countries ( p’s < .001), with the data indicating underrepresentation of unemployed individuals relative to their population proportions ( p’s < .0001). Table 5 Occupational status reported across the included studies and separated by countries (represented in ≥ 2 studies) Total participants (n) Employed n (%) Not employed n (%) Missing/not reported n (%) Across all geographic regions (n = 21*) 3,059* 2,023 (66.1%) 649 (21.2%) 387 (12.7%) USA (n = 4) 693 438 (63.2%) 234 (33.8%) 21 (3.0%) USA Population Statistics 52.7% 42.8% UK (n = 4) 1,130 857 (75.8%) 174 (15.4%) 99 (8.8%) UK Population Statistics 75.1% 24.9% Sweden (n = 3) 296 129 (43.6%) 64 (21.6%) 103 (34.8%) Sweden Population Statistics 69% 31% *Including 10 studies that are only represented by one country or combined countries Gender Among studies that reported gender (n = 58; 100%), a total of 10,401 participants were included. Of these, 6,685 (64.3%) identified as female, 2,691 (25.9%) as male, 30 (0.3%) as non-binary, and 15 (0.1%) as ‘other’. Gender data were missing or not reported for 980 participants (9.4%) across the included studies reporting Gender. Eight studies were the only representation from that country ( 50 , 52 , 71 , 72 , 75 , 80 – 82 ), and two studies reported demographics aggregated across multiple countries without separating data by country ( 41 , 53 ). Therefore, the tabulated papers include studies conducted in the USA ( 38 , 43 , 54 – 63 , 76 , 83 , 84 ), the UK ( 64 – 68 , 85 – 87 ), Australia ( 45 – 49 , 88 , 89 ), Sweden ( 77 – 79 , 90 ), China ( 44 , 69 , 91 , 92 ), Germany ( 74 , 93 , 94 ), Hong Kong ( 70 , 95 , 96 ), Denmark ( 73 , 97 ), and Spain ( 51 , 98 ) (Table 6 ). Chi-squared test revealed a statistically significant difference between the representation of male and female participants reported in the included studies and national population statistics for all countries ( p’s < .001) except Hong Kong ( p = 0.5) and Denmark ( p = 0.2) (both have comparatively small sample sizes) with the data indicating underrepresentation of males relative to their population proportions. It should be noted that despite the small sample size, Spain was significantly different to expected population statistics in the opposite manner to the other studies, with females being underrepresented. However, this is likely due to a reflection on poor reporting as one study ( 51 ) only reported the number of males participating, contributing to a comparably large amount of ‘missing/not reported’ data, as we cannot assume the remaining sample are female. Table 6 Gender and sex reported across the included studies and separated by countries (represented in ≥ 2 studies) Total participants (n) Females n (%) Males n (%) Non-binary n (%) Other n (%) Missing/not reported n (%) Across all geographic regions (n = 58*) 10,401* 6,685 (64.3%) 2,691 (25.9%) 30 (0.3%) 15 (0.1%) 980 (9.4%) USA (n = 15) 2,326 1,542 (66.3%) 765 (32.9%) 0 (0.0%) 3 (0.1%) 16 (0.7%) USA Population Statistics 50.5% 49.5% UK (n = 8) 3,465 2,173 (62.7%) 700 (20.2%) 30 (0.9%) 3 (0.1%) 559 (16.1%) UK Population Statistics 49.6% 51.4% Australia (n = 7) 851 568 (66.7%) 271 (31.8%) 0 (0.0%) 3 (0.4%) 9 (1.1%) Australia Population Statistics 50.7% 49.3% Sweden (n = 4) 568 441 (77.6%) 118 (20.8%) 0 (0.0%) 0 (0.0%) 9 (1.6%) Sweden Population Statistics 49.7% 50.3% China (n = 4) 478 327 (68.4%) 151 (31.6%) 0 (0.0%) 0 (0.0%) 0 (0.0%) China Population Statistics 48.8% 51.2% Germany (n = 3) 984 740 (75.2%) 143 (14.5%) 0 (0.0%) 0 (0.0%) 101 (10.3%) Germany Population Statistics 50.6% 49.4% Hong Kong (n = 3) 449 136 (30.3%) 102 (22.7%) 0 (0.0%) 0 (0.0%) 211 (46.9%) Hong Kong Population Statistics 55% 45% Denmark (n = 2) 171 77 (45.0%) 94 (55.0% 0 (0.0%) 0 (0.0%) 0 (0.0%) Denmark Population Statistics 50.3% 49.8% Spain (n = 2) 179 6 (3.4%) 92 (51.3%) 0 (0.0%) 0 (0.0%) 81 (45.3%) Spain Population Statistics 50.9% 49.1% *Including 10 studies that are only represented by one country or combined countries Religion Only three studies (5.2%), across different countries, reported data on participants’ religious affiliation, comprising a total of 482 participants. One study conducted in the UK ( 67 ) included 361 participants, of whom 221 (61.2%) reported no religious affiliation, 88 (24.4%) identified as Christian, and 52 (14.4%) reported their religion as ‘other’. A study in South Korea ( 75 ) included 73 participants, with 38 (52.1%) reporting no religion, 18 (24.7%) identifying as Buddhist, 13 (17.8%) as Christian, and 4 (5.5%) as Catholic. Lastly, in a study conducted in Malaysia ( 52 ), 38 of the 48 participants (79.2%) identified as Muslim, while 10 (20.8%) reported their religion as 'other'. There were no missing data on religious affiliation in any of the three studies that reported this information. Education Forty-one studies (70.7%) reported educational attainment. However, three of these studies presented only the mean number of years of education, which could not be categorised into educational levels ( 58 , 71 , 92 ). Therefore, 38 studies were classified based on whether participants had completed only compulsory education or education beyond the compulsory level (reporting on 5,698 participants). Of these, 4,310 (75.6%) participants had completed education beyond the compulsory level, 1,185 (20.8%) participants had completed compulsory education (e.g., primary and/or secondary school). Educational data were missing or not reported for 203 (3.6%) participants across the 41 studies. Seven studies were the only representation from that country ( 50 – 52 , 72 , 73 , 75 , 81 ), and two studies reported demographics aggregated across multiple countries without separating data by country ( 41 , 53 ). Therefore, the tabulated papers include studies conducted in the USA ( 38 , 43 , 54 , 55 , 59 , 61 – 63 , 76 , 84 ), the UK ( 64 – 66 , 87 ), Australia ( 45 , 46 , 49 , 88 ), Germany ( 74 , 93 , 94 ), Hong Kong ( 70 , 95 , 96 ), Sweden ( 77 – 79 ), and China ( 44 , 91 ). On average, across these studies, more than half of the participants had pursued education beyond the compulsory level. Only studies conducted in Germany and Sweden had samples in which the majority of participants had not continued their education beyond the compulsory level (Table 7 ). The chi-squared test indicated that participants who pursued education beyond the compulsory level were overrepresented compared to national population statistics in all countries ( p’s < .001), except for Germany ( p = 0.3) and Sweden ( p = 0.9), where the difference not statistically significant. Table 7 Educational data reported across the included studies and separated by countries (represented in ≥ 2 studies) Total participants (n) Compulsory n (%) Higher n (%) Missing/not reported n (%) Across all geographic regions (n = 38*) 5,698 1,185 (20.8%) 4,310 (75.6%) 203 (3.6%) USA (n = 10) 1,683 152 (9.0%) 1,461 (86.8%) 70 (4.2%) USA Populations Statistics 39% 61% UK (n = 4) 937 141 (15.0%) 796 (85.0%) 0 (0.0%) UK Population Statistics 66% 33.8% Australia (n = 4) 275 26 (9.5%) 249 (90.5%) 0 (0.0%) Australia Population Statistics 16% 84% Germany (n = 3) 984 602 (61.2%) 377 (38.3%) 5 (0.5%) Germany Population Statistics 63% 37% Hong Kong (n = 3) 449 33 (7.3%) 389 (86.6%) 27 (6.1%) Hong Kong Population Statistics 59% 41% Sweden (n = 3) 296 138 (46.6%) 94 (31.8%) 64 (21.6%) Sweden Population Statistics 60% 40% China (n = 2) 217 2 (0.9%) 215 (99.1%) 0 (0.0%) China Population Statistics 39% 61% *Including 9 studies that are only represented by one country or combined countries Socioeconomic status : Twenty studies reported participant income, however, one study reported frequency of financial stress and therefore was not included in this analysis ( 84 ). Of the remaining 19 studies, there was only one study conducted in each the following countries: Canada, Germany, South Korea ( 72 , 74 , 75 ). Five studies conducted in Australia ( 45 – 49 ) are included in the table, of which two reported Socio-economic Index for Areas percentiles ( 47 , 48 ) making classification more reliable. One study in China reported income in USD ( $ ) ( 69 ) and one in RMB (Chinese Yuan) ( 52 ). One study in Hong Kong reported the number of participants with a monthly family income equal to or greater than the median (classified as middle) ( 96 ) and the other reporting monthly family income in HK $ ( 70 ). One UK study reported Index of Multiple Deprivation quintiles ( 68 ) and one reported annual total household income ( 65 ). Of the five USA based studies, one reported the number of participants who had an income ≥ $ 100,000 ( 38 ), these were classified as middle SES for the purpose of the table. One study only reported participants who were in poverty ( 57 ), and these were classified as low SES in the table. The remaining three papers reported annual income ( 61 , 63 , 76 ). The data sources used for classification of income for each country is presented in Supplementary Material 6. Table 8 Socioeconomic status as calculated based on area of deprivation status or income reported across the included studies and separated by countries (represented in ≥ 2 studies) Total Participants (n) Low n (%) Middle n (%) High n (%) Missing/not reported n (%) Across all geographic regions (n = 19*) 3798 1087 (28.6%) 1203 (31.7%) 999 (26.3%) 509 (13.4%) Australia (n = 5) 533 121 (22.7%) 201 (37.7%) 178 (33.4%) 33 (6.2%) China (n = 2) 237 84 (35.4%) 107 (45.1%) 30 (12.7%) 16 (6.7%) Hong Kong (n = 2) 129 65 (50.4%) 35 (27.1%) 36 (12.7%) 23 (6.8%) UK (n = 2) 1668 539 (32.3%) 330 (19.8%) 629 (37.7%) 170 (10.2%) USA (n = 5) 829 88 (10.6%) 468 (56.4%) 124 (15%) 149 (17.8%) *Including 3 studies that are only represented by one country It was possible to extract a proxy for the number of participants potentially living in poverty as per their country’s definition for seven studies, including three in Australia ( 45 , 47 , 48 ), one in Hong Kong ( 70 ), two in the UK ( 65 , 68 ) and one in the USA ( 57 ). This data is presented in Table 9 . See Supplementary Material 6 (Table 6 b) for information on the classifications. Chi-squared tests were not computed on this sub-selection of papers. The data indicates considerable variation amongst papers in the degree to which the sample is representative of national poverty statistics. Table 9 Number and % living in poverty represented in the sample Author Country n (%) in poverty March 2025 Australia 15 (10.9%) March 2023 Australia 12 (8.7%) Andrews 2023 Australia 15 (14.5%) Total Australia 42 (11.1%) Australia Population Statistics 13.4% Wong 2021 Hong Kong 39 (49.3%) Hong Kong Population Statistics 20.2% Creswell 2024 UK 35 (7.9%) Sayal 2025 UK 214 (17.4%) Total UK 249 (14.9%) UK Population Statistics 15% Kalmbach USA 16 (17.5%) USA Population Statistics 11.4% Social capital Of the 25 studies (43.1%) that reported on social capital, 21 categorised participants by relationship status (e.g., married or in a relationship, single, divorced, separated, or widowed), while four studies used living arrangements as the categorisation (e.g., living alone, with family, or with other adults). Among the 21 studies that reported relationship status, data were reported for 3,531 participants. Of these, 2,106 (59.6%) were married, in a civil partnership, or in a long-term relationship; 470 (13.3%) were single and/or never married; 300 (8.5%) were divorced, separated, or widowed; and 2 participants (0.1%) chose not to disclose this information. Relationship status data were missing or not reported for 653 participants (18.5%). Three studies were the only representation from that country ( 52 , 71 , 73 ), and two studies reported demographics aggregated across multiple countries without separating data by country ( 41 , 53 ). Therefore, the tabulated papers include studies conducted in the USA ( 38 , 43 , 61 – 63 , 84 ), China ( 69 , 91 , 92 ), Hong Kong ( 70 , 95 , 96 ), the UK ( 64 , 65 ), and Germany ( 74 , 94 ) (Table 9 ). National population statistics were not possible to obtain for China and Hong Kong. For the studies in UK, USA and Germany there was a greater representation of individuals who were married than would be expected based on their countries population statistics ( p < .0001). See Table 10 . Table 10 Social capital reported across the included studies and separated by countries (represented in ≥ 2 studies) Total participants (n) Married, civil partnership or relationship (n, %) Never married or single (n, %) Divorced, separated or widowed (n, %) Prefer not to say (n, %) Missing/not reported (n, %) Across all geographic regions (n = 21*) 3,531 2,106 (59.6%) 470 (13.3%) 300 (8.5%) 2 (0.1%) 663 (18.5%) USA (n = 6) 1,201 765 (63.7%) 175 (14.6%) 233 (19.4%) 0 (0.0%) 28 (2.3%) USA Marriage Population Statistics 49.4% China (n = 3) 330 285 (86.4%) 37 (11.2%) 4 (1.2%) 0 (0.0%) 4 (1.2%) Hong Kong (n = 3) 449 89 (19.8%) 59 (13.1%) 2 (0.4%) 0 (0.0%) 299 (66.6%) UK (n = 2) 545 408 (74.9%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 137 (25.1%) UK Marriage Population Statistics 46.9% Germany (n = 2) 553 337 (60.9%) 34 (6.1%) 39 (7.1%) 0 (0.0%) 143 (25.9%) Germany Marriage Population Statistics 51% *Including 5 studies that are only represented by one country or combined countries Four studies reported social capital in terms of living arrangements. Three of these were conducted in Australia. One study ( 45 ) reported that participants lived with a mean of 1.67 other residents. Another found that 9 of 61 participants (14.8%) were from single-parent households ( 46 ), while a third reported that 86 of 95 participants (90.5%) were from two-parent households ( 49 ). The fourth study, conducted in Sweden ( 78 ), reported that among 52 participants, 47 (90.4%) lived with a family member, 1 (1.9%) with another adult, 2 (3.8%) lived alone, and data were missing for 2 participants (3.8%). Non-mental health conditions : Only one study, which was conducted in South Korea ( 75 ), reported on 'diagnosis of physical illness (Yes: n = 9, 12.3%; No: n = 64, 87.7%). Age : Age was predominantly reported in terms of mean values therefore it was not possible to categorise the sample accurately into age-brackets. From the 60 papers included in the review, age was presented in 59 papers (one being a perspective piece), but one included the duplication of the same sample. The majority of the studies delivered interventions to adults (34/58: 58.6%), followed by children under 15 years of age (19/58: 32.5%), two studies conducted in Sweden tested the intervention in adolescence (15–19 years) (2/58: 3.4%) ( 78 , 90 ) and a third study in Sweden involved both younger adults and adolescence (16–25 years) (1/58: 1.7%) ( 77 ). Two other studies included a mixed age population of adults and children (1/58: 1.7%) ( 88 ) and children and adolescence (1/58: 1.7%) ( 68 ). Digital literacy/access Digital access was a common inclusion criterion for the trials (29/58 trials), as well as being mentioned in the perspective piece ( 42 ), typically participants were required to have a computer, smartphone, or tablet with stable internet ( 41 , 49 , 64 , 68 , 73 , 79 ). Some studies specified device types (e.g., Apple/Android smartphones), specific internet speeds, or a regularly used email ( 59 , 83 , 87 , 95 ) and a county specific (Australian) IP address ( 47 ). Reliable access to both internet and phone was also required in some cases ( 59 ). Technical issues were frequently reported, including connectivity problems and device limitations, which disrupted interventions and may have contributed to dropout ( 71 , 88 ). The role of digital skills was only considered by the perspective piece ( 42 ) and a 7/58 trials, typically in the form of assessing digital literacy/competence through self-report or purpose-built questionnaires ( 43 , 77 , 87 , 89 , 94 ). Some studies reported challenges when participants lacked digital familiarity, such as difficulties using computers ( 74 , 92 ), but only one study ( 74 ) incorporated support mechanisms, such as how to check internet connectivity. The recent perspective piece ( 42 ) highlighted the importance of structured support programs offering digital skills training both online and in person. It also identified digital navigators—clinic staff trained to provide technical assistance—as a promising way to support users without overburdening clinical teams. These studies highlight that digital access and literacy are critical factors for equitable access and engagement in digital mental health research and interventions. What are the identified barriers and enablers to participation in DCTs in a mental health context? High dropout and recruitment challenges Many studies reported challenges in keeping participants engaged and enrolled. High dropout rates were common ( 43 , 44 , 48 , 72 , 85 – 87 , 92 ), and some trials struggled with recruitment, especially where stigma or low mental health literacy were factors ( 41 , 50 , 88 ). Participants drop out was associated with feeling confused or unsupported ( 48 , 85 ). Some mistook surveys for the intervention or wanted more personal contact during onboarding ( 43 ). Dropout was also linked to factors like age, education, and social support ( 94 ), and some participants simply weren’t comfortable with digital tools ( 92 ). However, measuring engagement itself is challenging. There is no standard way to define or track it—some use clicks, others look at time spent, or pages visited—making it hard to compare across studies and complicating efforts to identify best practices ( 42 ). Support/navigators Support from therapists or trained staff consistently emerged as a key facilitator of engagement in digital mental health trials. Participants often preferred therapist-assisted formats ( 45 ), and regular check-ins, whether via email or phone, helped sustain involvement ( 54 , 80 ). Eto et al demonstrated that adapting face-to-face therapy techniques for digital delivery, such as co-creating worksheets and focusing on patient interests, could enhance engagement ( 71 ). In culturally specific contexts, like Muslim communities, directive guidance was especially valued, with unclear instructions perceived as incompetence ( 41 ). Beyond therapeutic input, practical support and project navigators played a crucial role from on-boarding, technical demonstrations, through to reminders to prevent disengagement ( 43 , 47 , 80 , 85 , 87 ). However, in resource-limited settings or among people with severe anxiety, therapist-led models may not be feasible ( 42 ). Additional strategies to boost engagement were suggested included motivational interviewing, short instructional videos, and text reminders ( 51 ). However, not all digital support features, such as social networking tools, will be effective or accepted by all ( 88 ). Participant burden The burden of participation, whether cognitive, emotional, sensory, or logistical, was another major barrier. Complex onboarding processes, long sessions, and excessive paperwork discouraged engagement ( 43 ) and participants dropped out due to tedious content or lack of engagement ( 44 , 81 ). In particular, children may struggle with focus and motivation ( 88 ) and some participants found digital formats (e.g., reading on screens) uncomfortable or inaccessible ( 92 ). To mitigate these issues, studies recommended simplifying onboarding, reducing the use of passwords, digitising forms ( 42 , 43 ) and simplifying data collection methods including the numbers of measures required ( 85 ). More specific recommendations were identified in one paper, which reported that expert as opposed to caregiver/lay explanations and demonstrations supported participant engagement with the intervention and short videos (~ 2 minutes) were more engaging than longer content ( 51 ). A further paper highlighted the potential of gamification and repeatable content may sustain interest, but this was not assessed directly ( 88 ). Furthermore, online formats were beneficial to aspects of participant burden, including that associated with commuting time, reducing stigma, and supporting a more comfortable therapeutic relationship—possibly due to the “online disinhibition effect ( 74 ). DISCUSSION This review of 60 papers (58 RCTs) found that, although DCTs are increasingly used in mental health research, reporting of equity-related demographics is inconsistent. Gender and age were universally reported, but items such as race/ethnicity, occupation, SES, place of residence, and religion were often missing. Evidence that DCTs enhance inclusion of underserved groups was limited. Barriers to equitable participation included digital access, literacy, and participant burden, while enablers included therapist/navigator support and simplified onboarding This review has several strengths. Unlike many others, it incorporated PPI from the outset, shaping the research focus and data extraction (99). To our knowledge, it is the first systematic review to apply PROGRESS-Plus to DCTs in mental health and to compare sociodemographic representation against national statistics. Previous reviews have focused on reporting quality (10, 100), whereas ours addressed representativeness. It is also uniquely specific to digital/remotely delivered mental healthcare, a global priority, and included international studies, strengthening generalisability. Our structured approach identified gaps in reporting of disadvantage-related demographics and highlighted at-risk groups for exclusion. Consistent with earlier findings, we show that despite claims that DCTs support inclusive participation, marginalised groups, particularly by ethnicity and SES, remain underreported, with religion, disability, and residence, are often overlooked (101–104). While digital literacy and comfort with telemedicine are improving (105, 106), remote/digital methods may not be sufficient to achieve inclusion in mental health research without consideration to specific methodological design. Nonetheless, the conclusions of this review should be interpreted alongside its limitations, such as that the exclusion of non-English studies and pre-2020 trials. Furthermore, inconsistent reporting of variables like socioeconomic status and social capital hindered synthesis, and dynamic demographics such as employment and education limit direct comparison with national statistics. Therefore, comparisons with national statistics should be interpreted as proxies rather than exact benchmarks. These findings have important implications for the design and implementation of digital mental health trials (Fig. 2). For clinicians and service providers, the lack of consistent equity-relevant data reporting limits the ability to assess whether digital interventions are reaching and benefiting those most in need. Without robust demographic data, it is difficult to tailor interventions or address disparities in access and outcomes—particularly for groups historically underserved by mental health services (107, 108). For policymakers, the findings underscore the need for clearer guidance and accountability mechanisms to ensure equity is embedded in digital trial design and reporting. This includes mandating the collection and transparent reporting of PROGRESS-Plus variables, as recommended by the PRISMA-Equity extension and other equity-focused frameworks (32, 35), to monitor progress towards inclusive research and to inform equitable policy decisions. Methodologically, equity must be embedded from the outset. Researchers need support to design trials that promote inclusion across the entire lifecycle, not just at recruitment. While our review focused on baseline representation, we found limited reporting on attrition by sociodemographic group, highlighting the need to prioritise equitable participation as well as access. DCTs can expand access to innovations in mental healthcare—but only if equity is embedded in their design and infrastructure. Realising this potential demands collaboration across disciplines, including methodologists, clinicians, industry, and patient representatives. It also important to explore how different models of decentralisation (e.g., remote recruitment, virtual intervention delivery, and digital follow-up) affect participation, engagement, and outcomes across diverse groups. Inconsistent practices and limited reporting continue to obscure representativeness and limit generalisability. Conclusion. Although DCTs are promoted as a way to broaden participation in mental health research, equity in access remains unachieved. Socially disadvantaged groups are inconsistently reported and often underrepresented. Without standardised, equity-focused data collection, it is difficult to identify who is included or excluded. As digital mental health expands, the field must address structural and methodological barriers alongside technological innovation. Contributors SSH, ES, HH, CP, and JM, were responsible for the concept. SSH, ES, CLH, NR, AJ were responsible for carrying out the searches and synthesis. SSH and ES drafted the manuscript with CP conducting statical evaluations. All authors critically revised the manuscript for important intellectual content and approved the final version. SSH is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Declarations Competing interests: The authors have no competing interests to declare. Human Ethics and Consent to Participate declarations Not applicable Funding: SSH time was supported by an NIHR Development and Skills Enhancement (DSE) Fellowship (NIHR306083). The time provided by CLH was supported by an NIHR Development and Skills Enhancement Award (NIHR304774). The NIHR had no role in design of the study, data collection, analysis, or interpretation; in the writing of the report; or in the decision to submit the article for publication. The views expressed are those of the authors and not necessarily those of the NIHR or Department of Health and Social Care. Author Contribution SSH, ES, HH, CP, and JM, were responsible for the concept. SSH, ES, CLH, NR, AJ were responsible for carrying out the searches and synthesis. SSH and ES drafted the manuscript with CP conducting statical evaluations. 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Lindgren S, Wacker D, Schieltz K, Suess A, Pelzel K, Kopelman T, et al. A Randomized Controlled Trial of Functional Communication Training via Telehealth for Young Children with Autism Spectrum Disorder. J Autism Dev Disord. 2020;50(12):4449–62. Sabri B, Perrin N, Hagos M. The Being Safe, Health and Positively Empowered Pilot Randomized Controlled Trial: A Digital Multicomponent Intervention for Immigrant Women With Cumulative Exposures to Violence. Cultural Diversity and Ethnic Minority Psychology. 2025;31(2):271EP-84. Bennett SD, Heyman I, Coughtrey AE, Varadkar S, Stephenson T, Shafran R. Telephone-guided self-help for mental health difficulties in neurological conditions: a randomised pilot trial. Archives of disease in childhood. 2021;106(9):862–7. Kandola A, Edwards K, Muller MA, Dührkoop B, Hein B, Straatman J, et al. Digitally managing depression: A fully remote randomised attention-placebo controlled trial. Digit Health. 2024;10:20552076241260409. McCloud T, Jones R, Lewis G, Bell V, Tsakanikos E. Effectiveness of a Mobile App Intervention for Anxiety and Depression Symptoms in University Students: Randomized Controlled Trial. JMIR Mhealth Uhealth. 2020;8(7):e15418. Hartley M, Dorstyn D, Due C. Challenges encountered with a mindfulness app: Lessons learnt from a pilot randomized trial involving caregivers and individuals with autism. Research in Autism Spectrum Disorders. 2022;96(Ahmed, K.L., Simon, A.R., Dempsey, J.R., Samaco, R.C., & Goin-Kochel, R.P. (2020). Evaluating two common strategies for research participant recruitment into autism studies: Observational study. Journal of Medical Internet Research, 22(9) https://dx.doi.o):1-9. Murray G, Thomas N, Michalak EE, Jones SH, Lapsley S, Bowe SJ, et al. Mindfulness-Based Online Intervention to Improve Quality of Life in Late-Stage Bipolar Disorder: A Randomized Clinical Trial. Journal of Consulting and Clinical Psychology. 2021;89(10):830EP-44. Mechler J, Lindqvist K, Carlbring P, Topooco N, Falkenström F, Lilliengren P, et al. Therapist-guided internet-based psychodynamic therapy versus cognitive behavioural therapy for adolescent depression in Sweden: a randomised, clinical, non-inferiority trial. Lancet Digit Health. 2022;4(8):e594-e603. Tan Y, Lyu R, Lu S. Reducing parenting stress in Chinese parents of children with learning disabilities with a mindful parenting program: A randomized controlled trial. Research in developmental disabilities. 2024;151(8709782, rid):104794. Wu Y, Li X, Zhou Y, Gao R, Wang K, Ye H, et al. Efficacy and Cost-Effectiveness Analysis of Internet-Based Cognitive Behavioral Therapy for Obsessive-Compulsive Disorder: Randomized Controlled Trial. Journal of Medical Internet Research. 2023;25e41283. Döpfner M, Plück J, Rosenberger KD, Klemp MT, Mühlenmeister J, Wähnke L, et al. Efficacy of guided and unguided web-assisted self-help for parents of children with attention-deficit/hyperactivity disorder and oppositional defiant disorder: A three-arm randomized controlled trial. J Child Psychol Psychiatry. 2025. https://doi.org/10.1111/jcpp.14153 Moshe I, Terhorst Y, Paganini S, Schlicker S, Pulkki-Raback L, Baumeister H, et al. Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients with Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials. Journal of Medical Internet Research. 2022;24(8):e38261. Chan CS, Wong CYF, Yu BYM, Hui VKY, Ho FYY, Cuijpers P. Treating depression with a smartphone-delivered self-help cognitive behavioral therapy for insomnia: a parallel-group randomized controlled trial. Psychol Med. 2023;53(5):1799–813. Chien C-W, Lin C-Y, Lai CYY, Graham F. Parent coaching to enhance community participation in young children with developmental disabilities: A pilot randomized controlled trial. Research in developmental disabilities. 2024;147(8709782, rid):104696. Bikic A, Leckman JF, Christensen T, Bilenberg N, Dalsgaard S. Attention and executive functions computer training for attention-deficit/hyperactivity disorder (ADHD): results from a randomized, controlled trial. Eur Child Adolesc Psychiatry. 2018;27(12):1563–74. Bilan DS, Chicchi Giglioli IA, Cuesta P, Cañadas E, de Ramón I, Maestú F, et al. Decreased impulsiveness and MEG normalization after AI-digital therapy in ADHD children: a RCT. Npj Ment Health Res. 2025;4(1):1. Zhou Q, He H, Li Q, Zhao J, Wang L, Luo Z, et al. Patient and public involvement in systematic reviews: frequency, determinants, stages, barriers, and dissemination. Journal of Clinical Epidemiology. 2024;170:111356. Ong SW, Blagojevic C, Bryce A, Ovadia A, Slater M, Pryal D, et al. Reporting of sociodemographic characteristics of trial participants in infectious diseases clinical trials–a systematic review. Clinical Microbiology and Infection. 2025. https://doi.org/10.1016/j.cmi.2025.04.030 Vayena E, Blasimme A, Sugarman J. Decentralised clinical trials: ethical opportunities and challenges. The Lancet Digital Health. 2023;5(6):e390-e4. Al M, Levison S, Berdel WE, Andersen DZ. Decentralised elements in clinical trials: recommendations from the European Medicines Regulatory Network. Lancet (London, England). 2023;401(10385):1339. Selvakumaran K, Sleeman KE, Davies JM. How good are we at reporting the socioeconomic position, ethnicity, race, religion and main language of research participants? A review of the quality of reporting in palliative care intervention studies. Palliative Medicine. 2024;38(3):396–9. Cunningham S, Russell AM, Lidington E, Shiely F. Lack of data collection in clinical trials prevents us from evaluating inclusion of people with disabilities. Journal of Clinical Epidemiology. 2025;181:111715. Rosa C, Marsch LA, Winstanley EL, Brunner M, Campbell AN. Using digital technologies in clinical trials: current and future applications. Contemporary clinical trials. 2021;100:106219. Aiyegbusi OL, Davies EH, Myles P, Williams T, Frost C, Haroon S, et al. Digitally enabled decentralised research: opportunities to improve the efficiency of clinical trials and observational studies. BMJ evidence-based medicine. 2023;28(5):328–31. Kammer-Kerwick M, Cox K, Purohit I, Watkins SC. The role of social determinants of health in mental health: an examination of the moderating effects of race, ethnicity, and gender on depression through the all of us research program dataset. PLOS Mental Health. 2024;1(3):e0000015. Pierce M, McManus S, Jessop C, John A, Hotopf M, Ford T, et al. Says who? The significance of sampling in mental health surveys during COVID-19. The Lancet Psychiatry. 2020;7(7):567–8. 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11:41:39","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":22702,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.PRISMAEquityChecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-7565290/v1/f0cb7e3d3008e9a8b34ff184.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Who Gets Included? Equity in Digital and Decentralised Mental Health and Neurodevelopmental Trials: A Systematic Review","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eRemote and digitised delivery of mental health care is increasingly recognised as essential to meet rising demand and improve accessibility nationally and globally (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Digital tools, such as apps, teletherapy, and AI-driven diagnostics, offer scalable, cost-effective interventions that can reduce waiting times, ease pressure on services and support personalised care (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, for the benefits of mental health digitisation to be realised by those most at need they need be evaluated in underserved populations.\u003c/p\u003e \u003cp\u003eRandomised Controlled Trials (RCTs) are regarded as the \u0026lsquo;gold standard\u0026rsquo; for evaluating healthcare interventions (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), but their reliance on selective samples and inadequate demographic reporting (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), limits real-world applicability and produces a skewed data base on which decisions are made (\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Decentralised clinical trials (DCTs), also called \u0026lsquo;remote\u0026rsquo;, \u0026lsquo;online\u0026rsquo;, \u0026lsquo;site-less\u0026rsquo;, or \u0026lsquo;hybrid\u0026rsquo; trial, conduct some or all activities outside traditional trial sites, and grew in popularity during COVID-19 (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). DCTs rely on digital technologies, offering a flexible, scalable model for evaluating digital mental health interventions, with potential to improve access, reduce costs, and support sustainability (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Whilst DCTs are increasingly positioned to promote inclusive research, evidence of their impact on equity remains uncertain, particularly in mental health, where challenges such as stigma, digital exclusion, and lack of face-to-face interaction/support can limit participation (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Neurodevelopmental conditions, such as autism and attention-deficit hyperactivity disorder (ADHD), frequently co-occur with mental health conditions (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and present similar challenges (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In addition, requirements for digital literacy and access may further exclude disadvantaged groups disproportionately affected by mental health disorders (\u003cspan additionalcitationids=\"CR26 CR27 CR28\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Frameworks like NIHR INCLUDE and recommendations by Aiyegbusi et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) provide guidance on engaging underserved groups, but their application to mental health DCTs is limited. Realising the inclusive potential of DCTs requires evaluating their effectiveness in reaching diverse populations and adapting designs to overcome intersecting barriers. The aims of this review were to:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAssess the extent to which equity-relevant data are reported in DCTs, using the Place, Race, Occupation, Gender/sex, Religion, Education, Socioeconomic status, Social capital\u0026thinsp;+\u0026thinsp;personas characteristics associated with discrimination (PROGRESS-plus) framework.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExplore whether specific population groups (using the PROGRESS framework) are under-represented in mental health/neuro-developmental conditions DCTs compared to general population statistics.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIdentify barriers and enablers to inclusive participation in DCTs in a mental health/neuro-developmental context.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eWe report methodology in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines using the Equity extension (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) (see Supplementary Material 1), following a preregistered International Prospective Register of Systematic Reviews protocol (CRD420251080321).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient and Public Involvement (PPI)\u003c/h2\u003e \u003cp\u003eFour PPI members with mental health and neurodevelopmental conditions (including one Black female and one Asian female) were involved in the conception of the study. Their concerns regarding inclusivity in DCT informed the development of the data extraction tool. A PPI member also reviewed the data codes derived from the thematic analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion criteria\u003c/h3\u003e\n\u003cp\u003eStudies were assessed for inclusion based on the population, intervention, comparator, outcome and study design (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInclusion criteria based on population, intervention, comparator, outcome and study design\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChildren (aged up to 17 years) and/or adults (aged 18 years and above) with a diagnosis of a mental health or neurodevelopmental condition (as defined by the DSM-5 (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)), in any setting.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies that described or evaluated decentralised, virtual, or remote interventions (fully or partially remote), e.g., teletherapy, app-based interventions, remote assessments. Interventions must involve assessment, treatment, prevention, or management of mental health or neurodevelopmental conditions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies with the following controls were considered: normal practice (\u0026lsquo;usual care\u0026rsquo;), waiting-list control, or any other intervention described by the authors as a comparator.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies that reported psychological, emotional and/or behavioural outcomes (e.g., agitation, anxiety, social behaviour).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy designs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandomised controlled trials (including randomised feasibility and pilot trials).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eExclusion criteria\u003c/h3\u003e\n\u003cp\u003eStudies were excluded if they: (a) lacked demographic data or reflections on representation; (b) involved interventions unrelated to mental health or neurodevelopmental conditions; (c) delivered non-digital or non-remote interventions; (d) had no other online trial components beyond the intervention; or (e) were published before 2020, as the COVID-19 pandemic marked a shift in DCT adoption (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)ensuring the review reflects contemporary practices and technologies.\u003c/p\u003e\n\u003ch3\u003eSearch strategy\u003c/h3\u003e\n\u003cp\u003eMEDLINE, PsycINFO, Embase, CINAHL, Cochrane Central, and Web of Science were searched up to 1 July 2025 using terms related to DCTs (e.g., decentralised, remote, digital), demographics (e.g., ethnicity, education), inclusivity, and mental health or neurodevelopmental conditions in children and adults. Searches were limited to English-language studies. The MEDLINE strategy is provided in Supplementary Material 2 and adapted for other databases. Key journals, reference lists of included studies, and relevant systematic reviews were hand-searched. Publications were managed in Covidence (Fig.\u0026nbsp;1). Titles and abstracts were independently screened by three authors (S.S.H., C.L.H., E.S.); disagreements led to inclusion in full-text review. Full texts were independently reviewed by the same authors, with discrepancies resolved through discussion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eUsing a pre-defined data extraction spreadsheet in Microsoft Excel, relevant data were extracted by four authors (S.S.H., E.S., N.R., A.J.). Information included research methodology, sample size, type of intervention, remote trial design elements (e.g., recruitment strategies, screening, assessments), intervention setting, digital literacy and/or access, and demographic information (e.g., race/ethnicity, gender, age, religion, occupational level, educational level) as well as any identified barriers/enablers to engagement in the trial. A complete list of data extracted is provided in Supplementary Material 3. Data extraction commenced on 14th July 2025.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRisk of bias assessment\u003c/h2\u003e \u003cp\u003eGiven the objective of this review was to evaluate representation and the quality of demographic reporting, rather than the effectiveness or validity of study findings, a formal risk of bias assessment was not undertaken. The focus was placed on the completeness and transparency of reporting, as measured against established reporting guidelines (PROGRESS-Plus) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) rather than on the internal validity of the studies or the potential for systematic bias in their results, consistent with the approach taken in previous reviews (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData synthesis\u003c/h3\u003e\n\u003cp\u003eWe assessed reporting quality using PROGRESS-Plus guidelines (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Four authors (S.S.H., E.S., N.R., A.J.) independently reviewed each paper, categorising each item as reported, not reported, or unclear. Supplementary materials were accepted if explicitly referenced. PROGRESS includes place of residence, race/ethnicity, occupation, gender/sex, religion, education, socioeconomic status (SES), and social capital. For synthesis, place of residence was coded as rural/urban; occupation as employed/unemployed; education as above/below compulsory level; social capital by relationship status; and SES as low-, middle-, or high-income using country-specific definitions. SES reporting varied (income frequency, household vs. individual, currencies), and no standard cut-offs were used; where available, numbers below the national poverty line were extracted separately. The \u0026ldquo;Plus\u0026rdquo; category captured additional characteristics, including non-mental health conditions and age. Descriptive statistics summarised demographics. Country-level synthesis included only countries represented in \u0026ge;\u0026thinsp;2 studies; multi-country studies without disaggregated data were excluded. Where possible, chi-squared tests compared sample sociodemographics with national statistics. Perceived or measured barriers/enablers to participation were thematically analysed (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). One author (S.S.H.) generated initial codes, which were grouped into themes. A second author (E.S.) and a PPI member independently reviewed and validated theme construction.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDescription of studies\u003c/h2\u003e \u003cp\u003eDatabase and manual searching yielded a total of 6,760 records. After removal of duplicates and screening of titles, abstracts and full-text papers, 60 papers were included in the review (Fig.\u0026nbsp;1), reporting a total of 59 studies. Two papers refer to the same RCT (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), but assessed different outcomes. Both RCTs met the inclusion criteria but as the data was derived from the same sample, demographics are only included once. A list of all included papers is provided in Supplementary Material 4.\u003c/p\u003e \u003cp\u003e Across the 58 included RCTs, sample sizes ranged from 8 to 1,225 participants (as reported at baseline). Most studies were conducted in the USA (n\u0026thinsp;=\u0026thinsp;15; 25.4%), followed by the UK (n\u0026thinsp;=\u0026thinsp;8; 13.6%), Australia (n\u0026thinsp;=\u0026thinsp;7; 11.9%), Sweden (n\u0026thinsp;=\u0026thinsp;4; 6.8%), and China (n\u0026thinsp;=\u0026thinsp;4; 6.8%). Two studies (3.4%) were conducted in multiple geographic regions (e.g., USA and UK (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and Netherlands and UK (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)). Interventions were delivered to a variety of study populations, primarily anxiety (n\u0026thinsp;=\u0026thinsp;10; 16.9%), depression (n\u0026thinsp;=\u0026thinsp;8; 13.6%), attention-deficit hyperactivity disorder (ADHD; n\u0026thinsp;=\u0026thinsp;8, 13.6%), and autism (n\u0026thinsp;=\u0026thinsp;6; 10.2%). Fourteen studies (23.7%) delivered interventions to participants with mixed diagnoses (e.g., anxiety and depression, post-traumatic stress disorder and depression, all mental health conditions).\u003c/p\u003e \u003cp\u003eThe studies were split in terms of models of delivery, with 28 (47.5%) being fully remote, and 30 (50.8%) adopting a hybrid model that combined remote and in-person trial elements. Remote recruitment (e.g., via social media, online platforms, electronic health records) was reported in 40 studies (67.8%). Remote screening or eligibility assessments (e.g., via telephone, online self-reporting) were used in 41 studies (69.5%). Remote outcome assessments were performed in most studies (n\u0026thinsp;=\u0026thinsp;53, 89.8%). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a full overview of study characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor/Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipants (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull or hybrid remote\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRemote recruitment strategies (Y/N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRemote screening and/or eligibility (Y/N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRemote assessments/\u003c/p\u003e \u003cp\u003efollow-up (Y/N)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndrews et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety or depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBackman et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBennett et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAny mental health condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBikic et al. (2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilan et al. (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarl et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA and UK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneralised anxiety disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChan et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHong Kong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComorbid depressive disorder and insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChien et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHong Kong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAny neurodevelopmental conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClark et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSocial anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreswell et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDopfner et al. (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEto et al. (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEylem et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetherlands and UK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSuicidal ideation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatouros et al. (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression and generalised anxiety disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFelder et al. (2020, 2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrenier-Martin et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntellectual and developmental disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuzick et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuzick et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHall et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcross all conditions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHartley et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaun et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265 dyads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHe et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeller et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression and anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoffmann et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHollis et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTourette\u0026rsquo;s syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuberty et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJamali et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJent et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisruptive behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKalmbach et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKandola et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKenworthy et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKwon et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLewis et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLindgren et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLippke et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePsychosomatic diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalarkey et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch et al. (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMcCloud et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety and depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMcLellan et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechler et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoshe et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMurray et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBipolar disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNardi et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeneralised anxiety disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNissling et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNordh et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSocial anxiety disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOng et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTSD and/or depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiscitello et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePossemato et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression, anxiety and PTSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichards et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression and anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSabri et al. (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTSD and/or depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSayal et al. (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmotional difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegal et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeo et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePostpartum depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSun et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerinatal depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTan et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression and anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTan et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLearning disabilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWong et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHong Kong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWu et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003e To what extent are equity-relevant data reported in DCTs in mental health, as defined by the PROGRESS-Plus guidelines?\u003c/b\u003e\u003c/p\u003e \u003cp\u003e In line with the PROGRESS-Plus guidelines, 59 of the included 60 papers (98.3%) reported participant demographics. One study did not report demographic characteristics, as this was a perspective piece, for which reporting was not applicable (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Two papers reported on the same RCT, therefore demographics summarised below are taken from 58 studies (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The most frequently reported PROGRESS-Plus items related to gender (58/50: 100%) and age (58/50: 100%). The least frequently reported PROGRESS-Plus items related to non-mental health disabilities (1/58: 1.7%), religion (3/58: 5.2%) and place of residence or setting (rural vs. urban areas) (7/58: 12.1%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of studies (total n\u0026thinsp;=\u0026thinsp;58) reporting PROGRESS-Plus items\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROGRESS-Plus item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of studies reporting demographic information n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace (rural vs urban)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (39.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (70.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (43.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon mental health disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDemographic information from all included studies, disaggregated by country, is presented below. Descriptive statistics are limited to countries represented in at least two studies. Studies conducted across two or more countries in which data were not reported separately by country are excluded. A comprehensive overview of demographic information reported in each included study is available in Supplementary Material 5 (Supplementary Table\u0026nbsp;5a), alongside documentation of the source data used to acquire the representation of each sociodemographic variable by country (Supplementary Table\u0026nbsp;5b).\u003c/p\u003e \u003cp\u003e \u003cem\u003ePlace\u003c/em\u003e: Seven studies (12.1%) reported participants\u0026rsquo; place of residence categorised as rural or urban. Among the 1,211 participants reported in these seven studies, 626 (51.7%) resided in urban areas, 306 (25.3%) in rural areas, and data for 279 participants (23.0%) were either missing or not reported. One of the seven studies was in the USA (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and one in China (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) demographics for these counties are reported in Supplementary Material 5 but were insufficient for further comparison. The remaining five studies were conducted in Australia (\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), encompassing 533 participants: 260 (48.8%) lived in urban areas (population statistics: 86.5%), 160 (30.0%) in rural areas (population statistics: 13.5%), and residence data were unavailable for 113 participants (21.2%). Chi-squared tests identified there were significantly greater representation of rural based participants in these Australian studies compared to general population statistics (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRace/ethnicity\u003c/strong\u003e \u003cp\u003eEthnicity data were reported in 23 studies (39.7%), encompassing a total of 5,381 participants. Among these, 4,054 individuals (75.3%) were identified as White, 493 (9.2%) as Asian, 268 (4.9%) as Black, 208 (3.9%) as \u0026lsquo;Other\u0026rsquo;, and 152 (2.8%) as of \u0026lsquo;Multiple\u0026rsquo; ethnicities. From the 23 studies, ethnicity information was either missing or not reported for 261 participants (4.9%). Three studies were the only representation from that country (\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), and one study reported demographics aggregated across multiple countries without separating data by country (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Therefore, the tabulated papers include studies conducted in the USA (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan additionalcitationids=\"CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e), the UK (\u003cspan additionalcitationids=\"CR65 CR66 CR67\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), and China (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It was not possible to extract ethnicity of the population in China as China only officially report Han or non-Han statistics. Chi-squared test revealed a statistically significant difference between the racial representation reported in the included studies and national population statistics in the UK and USA, indicating over representation of white participants relative to their population proportions (\u003cem\u003ep\u0026rsquo;s\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEthnicity reported across the included studies and separated by countries (represented in \u0026ge;\u0026thinsp;2 studies)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal participants (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultiple\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMissing/not reported\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcross all geographic regions (n\u0026thinsp;=\u0026thinsp;23*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,381*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,054\u003c/p\u003e \u003cp\u003e(75.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e493\u003c/p\u003e \u003cp\u003e(9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e268\u003c/p\u003e \u003cp\u003e(4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e152\u003c/p\u003e \u003cp\u003e(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e208\u003c/p\u003e \u003cp\u003e(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e261\u003c/p\u003e \u003cp\u003e(4.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,564\u003c/p\u003e \u003cp\u003e(73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003cp\u003e(3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e224\u003c/p\u003e \u003cp\u003e(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003cp\u003e(2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e129\u003c/p\u003e \u003cp\u003e(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e128\u003c/p\u003e \u003cp\u003e(6.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUSA Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,011\u003c/p\u003e \u003cp\u003e(85.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003cp\u003e(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e(0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64\u003c/p\u003e \u003cp\u003e(2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72\u003c/p\u003e \u003cp\u003e(3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e130\u003c/p\u003e \u003cp\u003e(5.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUK Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e305\u003c/p\u003e \u003cp\u003e(96.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e(3.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Including four studies that are only represented by one country or combined countries\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOccupation\u003c/strong\u003e \u003cp\u003eOf the 21 studies (36.2%) that reported participants\u0026rsquo; occupational status, a total of 3,059 individuals were classified as either 'employed' (n\u0026thinsp;=\u0026thinsp;2023, 66.1%) or 'not employed' (n\u0026thinsp;=\u0026thinsp;649, 21.2%). From the 21 studies, occupational status data were missing or unreported for 387 participants (12.7%). Only one study, conducted in Hong Kong, provided detailed information on occupational type (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Nine studies were the only representation from that country (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan additionalcitationids=\"CR71 CR72 CR73 CR74\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e), and one study reported demographics aggregated across multiple countries without separating data by country (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Therefore, the tabulated papers include studies conducted in the USA (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e), the UK (\u003cspan additionalcitationids=\"CR65 CR66\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e), and Sweden (\u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Chi-squared test revealed a statistically significant difference between the representation of employed and unemployed participants reported in the included studies and national population statistics for all three countries (\u003cem\u003ep\u0026rsquo;s\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with the data indicating underrepresentation of unemployed individuals relative to their population proportions (\u003cem\u003ep\u0026rsquo;s\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOccupational status reported across the included studies and separated by countries (represented in \u0026ge;\u0026thinsp;2 studies)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal participants\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot employed\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMissing/not reported\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcross all geographic regions (n\u0026thinsp;=\u0026thinsp;21*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,059*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,023\u003c/p\u003e \u003cp\u003e(66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e649\u003c/p\u003e \u003cp\u003e(21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e387\u003c/p\u003e \u003cp\u003e(12.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438\u003c/p\u003e \u003cp\u003e(63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234\u003c/p\u003e \u003cp\u003e(33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e(3.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUSA Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e857\u003c/p\u003e \u003cp\u003e(75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174\u003c/p\u003e \u003cp\u003e(15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99\u003c/p\u003e \u003cp\u003e(8.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUK Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003cp\u003e(43.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003cp\u003e(21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103\u003c/p\u003e \u003cp\u003e(34.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSweden Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Including 10 studies that are only represented by one country or combined countries\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGender\u003c/strong\u003e \u003cp\u003eAmong studies that reported gender (n\u0026thinsp;=\u0026thinsp;58; 100%), a total of 10,401 participants were included. Of these, 6,685 (64.3%) identified as female, 2,691 (25.9%) as male, 30 (0.3%) as non-binary, and 15 (0.1%) as \u0026lsquo;other\u0026rsquo;. Gender data were missing or not reported for 980 participants (9.4%) across the included studies reporting Gender. Eight studies were the only representation from that country (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan additionalcitationids=\"CR81\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e), and two studies reported demographics aggregated across multiple countries without separating data by country (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Therefore, the tabulated papers include studies conducted in the USA (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan additionalcitationids=\"CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e), the UK (\u003cspan additionalcitationids=\"CR65 CR66 CR67\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan additionalcitationids=\"CR86\" citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e), Australia (\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e), Sweden (\u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e), China (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e), Germany (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e), Hong Kong (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e), Denmark (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e), and Spain (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Chi-squared test revealed a statistically significant difference between the representation of male and female participants reported in the included studies and national population statistics for all countries (\u003cem\u003ep\u0026rsquo;s\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) except Hong Kong (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.5) and Denmark (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.2) (both have comparatively small sample sizes) with the data indicating underrepresentation of males relative to their population proportions. It should be noted that despite the small sample size, Spain was significantly different to expected population statistics in the opposite manner to the other studies, with females being underrepresented. However, this is likely due to a reflection on poor reporting as one study (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) only reported the number of males participating, contributing to a comparably large amount of \u0026lsquo;missing/not reported\u0026rsquo; data, as we cannot assume the remaining sample are female.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGender and sex reported across the included studies and separated by countries (represented in \u0026ge;\u0026thinsp;2 studies)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal participants (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-binary\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissing/not reported\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcross all geographic regions (n\u0026thinsp;=\u0026thinsp;58*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,401*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,685\u003c/p\u003e \u003cp\u003e(64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,691 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e(0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e(0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e980\u003c/p\u003e \u003cp\u003e(9.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,542\u003c/p\u003e \u003cp\u003e(66.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e765\u003c/p\u003e \u003cp\u003e(32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e(0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUSA Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,173\u003c/p\u003e \u003cp\u003e(62.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003cp\u003e(20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e559\u003c/p\u003e \u003cp\u003e(16.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUK Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e568\u003c/p\u003e \u003cp\u003e(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e271\u003c/p\u003e \u003cp\u003e(31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAustralia Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e441\u003c/p\u003e \u003cp\u003e(77.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003cp\u003e(20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e(1.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSweden Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327\u003c/p\u003e \u003cp\u003e(68.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151\u003c/p\u003e \u003cp\u003e(31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChina Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e740\u003c/p\u003e \u003cp\u003e(75.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143\u003c/p\u003e \u003cp\u003e(14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101\u003c/p\u003e \u003cp\u003e(10.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGermany Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong Kong (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003cp\u003e(30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003cp\u003e(22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e211\u003c/p\u003e \u003cp\u003e(46.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHong Kong Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003cp\u003e(45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003cp\u003e(55.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDenmark Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e(3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003cp\u003e(51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81\u003c/p\u003e \u003cp\u003e(45.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSpain Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Including 10 studies that are only represented by one country or combined countries\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eReligion\u003c/strong\u003e \u003cp\u003eOnly three studies (5.2%), across different countries, reported data on participants\u0026rsquo; religious affiliation, comprising a total of 482 participants. One study conducted in the UK (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) included 361 participants, of whom 221 (61.2%) reported no religious affiliation, 88 (24.4%) identified as Christian, and 52 (14.4%) reported their religion as \u0026lsquo;other\u0026rsquo;. A study in South Korea (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e) included 73 participants, with 38 (52.1%) reporting no religion, 18 (24.7%) identifying as Buddhist, 13 (17.8%) as Christian, and 4 (5.5%) as Catholic. Lastly, in a study conducted in Malaysia (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), 38 of the 48 participants (79.2%) identified as Muslim, while 10 (20.8%) reported their religion as 'other'. There were no missing data on religious affiliation in any of the three studies that reported this information.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEducation\u003c/strong\u003e \u003cp\u003eForty-one studies (70.7%) reported educational attainment. However, three of these studies presented only the mean number of years of education, which could not be categorised into educational levels (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e). Therefore, 38 studies were classified based on whether participants had completed only compulsory education or education beyond the compulsory level (reporting on 5,698 participants). Of these, 4,310 (75.6%) participants had completed education beyond the compulsory level, 1,185 (20.8%) participants had completed compulsory education (e.g., primary and/or secondary school). Educational data were missing or not reported for 203 (3.6%) participants across the 41 studies.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSeven studies were the only representation from that country (\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e), and two studies reported demographics aggregated across multiple countries without separating data by country (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Therefore, the tabulated papers include studies conducted in the USA (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e), the UK (\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e), Australia (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e), Germany (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e), Hong Kong (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e), Sweden (\u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e), and China (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e). On average, across these studies, more than half of the participants had pursued education beyond the compulsory level. Only studies conducted in Germany and Sweden had samples in which the majority of participants had not continued their education beyond the compulsory level (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The chi-squared test indicated that participants who pursued education beyond the compulsory level were overrepresented compared to national population statistics in all countries (\u003cem\u003ep\u0026rsquo;s\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), except for Germany (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.3) and Sweden (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.9), where the difference not statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEducational data reported across the included studies and separated by countries (represented in \u0026ge;\u0026thinsp;2 studies)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal participants\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompulsory\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMissing/not reported\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcross all geographic regions (n\u0026thinsp;=\u0026thinsp;38*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,185\u003c/p\u003e \u003cp\u003e(20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,310\u003c/p\u003e \u003cp\u003e(75.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e203\u003c/p\u003e \u003cp\u003e(3.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152\u003c/p\u003e \u003cp\u003e(9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,461\u003c/p\u003e \u003cp\u003e(86.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003cp\u003e(4.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUSA Populations Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003cp\u003e(15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e796\u003c/p\u003e \u003cp\u003e(85.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUK Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003cp\u003e(9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e249\u003c/p\u003e \u003cp\u003e(90.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAustralia Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e602\u003c/p\u003e \u003cp\u003e(61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e377\u003c/p\u003e \u003cp\u003e(38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGermany Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong Kong (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e(7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e389\u003c/p\u003e \u003cp\u003e(86.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e(6.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHong Kong Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweden (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138\u003c/p\u003e \u003cp\u003e(46.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003cp\u003e(31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003cp\u003e(21.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSweden Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215\u003c/p\u003e \u003cp\u003e(99.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChina Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Including 9 studies that are only represented by one country or combined countries\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSocioeconomic status\u003c/em\u003e: Twenty studies reported participant income, however, one study reported frequency of financial stress and therefore was not included in this analysis (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e). Of the remaining 19 studies, there was only one study conducted in each the following countries: Canada, Germany, South Korea (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Five studies conducted in Australia (\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) are included in the table, of which two reported Socio-economic Index for Areas percentiles (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) making classification more reliable. One study in China reported income in USD (\u003cspan\u003e$\u003c/span\u003e) (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e) and one in RMB (Chinese Yuan) (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). One study in Hong Kong reported the number of participants with a monthly family income equal to or greater than the median (classified as middle) (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e) and the other reporting monthly family income in HK\u003cspan\u003e$\u003c/span\u003e (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). One UK study reported Index of Multiple Deprivation quintiles (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) and one reported annual total household income (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Of the five USA based studies, one reported the number of participants who had an income \u0026ge; \u003cspan\u003e$\u003c/span\u003e100,000 (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), these were classified as middle SES for the purpose of the table. One study only reported participants who were in poverty (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e), and these were classified as low SES in the table. The remaining three papers reported annual income (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). The data sources used for classification of income for each country is presented in Supplementary Material 6.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocioeconomic status as calculated based on area of deprivation status or income reported across the included studies and separated by countries (represented in \u0026ge;\u0026thinsp;2 studies)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Participants (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMissing/not reported n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcross all geographic regions (n\u0026thinsp;=\u0026thinsp;19*)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1087 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1203 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e999\u003c/p\u003e \u003cp\u003e(26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e509\u003c/p\u003e \u003cp\u003e(13.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAustralia (n\u0026thinsp;=\u0026thinsp;5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003cp\u003e(22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e178\u003c/p\u003e \u003cp\u003e(33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e(6.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChina (n\u0026thinsp;=\u0026thinsp;2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003cp\u003e(35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e(12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e(6.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHong Kong (n\u0026thinsp;=\u0026thinsp;2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003cp\u003e(50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003cp\u003e(27.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003cp\u003e(12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e(6.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUK (n\u0026thinsp;=\u0026thinsp;2)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e539 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e629\u003c/p\u003e \u003cp\u003e(37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e170\u003c/p\u003e \u003cp\u003e(10.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUSA (n\u0026thinsp;=\u0026thinsp;5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003cp\u003e(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e468 (56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124\u003c/p\u003e \u003cp\u003e(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e149\u003c/p\u003e \u003cp\u003e(17.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Including 3 studies that are only represented by one country\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIt was possible to extract a proxy for the number of participants potentially living in poverty as per their country\u0026rsquo;s definition for seven studies, including three in Australia (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), one in Hong Kong (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e), two in the UK (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) and one in the USA (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). This data is presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. See Supplementary Material 6 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) for information on the classifications. Chi-squared tests were not computed on this sub-selection of papers. The data indicates considerable variation amongst papers in the degree to which the sample is representative of national poverty statistics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber and % living in poverty represented in the sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%) in poverty\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarch 2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e(10.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarch 2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e(8.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAndrews 2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e(14.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAustralia Population Statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWong 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHong Kong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003cp\u003e(49.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHong Kong Population Statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreswell 2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003cp\u003e(7.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSayal 2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214\u003c/p\u003e \u003cp\u003e(17.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUK Population Statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKalmbach\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e(17.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUSA Population Statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSocial capital\u003c/strong\u003e \u003cp\u003eOf the 25 studies (43.1%) that reported on social capital, 21 categorised participants by relationship status (e.g., married or in a relationship, single, divorced, separated, or widowed), while four studies used living arrangements as the categorisation (e.g., living alone, with family, or with other adults). Among the 21 studies that reported relationship status, data were reported for 3,531 participants. Of these, 2,106 (59.6%) were married, in a civil partnership, or in a long-term relationship; 470 (13.3%) were single and/or never married; 300 (8.5%) were divorced, separated, or widowed; and 2 participants (0.1%) chose not to disclose this information. Relationship status data were missing or not reported for 653 participants (18.5%).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThree studies were the only representation from that country (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e), and two studies reported demographics aggregated across multiple countries without separating data by country (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Therefore, the tabulated papers include studies conducted in the USA (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e), China (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e), Hong Kong (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e), the UK (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e), and Germany (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). National population statistics were not possible to obtain for China and Hong Kong. For the studies in UK, USA and Germany there was a greater representation of individuals who were married than would be expected based on their countries population statistics (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001). See Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocial capital reported across the included studies and separated by countries (represented in \u0026ge;\u0026thinsp;2 studies)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal participants (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried, civil partnership or relationship\u003c/p\u003e \u003cp\u003e(n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNever married or single\u003c/p\u003e \u003cp\u003e(n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDivorced, separated or widowed\u003c/p\u003e \u003cp\u003e(n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003cp\u003e(n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissing/not reported\u003c/p\u003e \u003cp\u003e(n, %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcross all geographic regions (n\u0026thinsp;=\u0026thinsp;21*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,106\u003c/p\u003e \u003cp\u003e(59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e470\u003c/p\u003e \u003cp\u003e(13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300\u003c/p\u003e \u003cp\u003e(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e663\u003c/p\u003e \u003cp\u003e(18.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSA (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e765\u003c/p\u003e \u003cp\u003e(63.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175\u003c/p\u003e \u003cp\u003e(14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e233\u003c/p\u003e \u003cp\u003e(19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003cp\u003e(2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUSA Marriage Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChina (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285\u003c/p\u003e \u003cp\u003e(86.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003cp\u003e(11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong Kong (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003cp\u003e(19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003cp\u003e(13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e299\u003c/p\u003e \u003cp\u003e(66.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUK (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e408\u003c/p\u003e \u003cp\u003e(74.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e137\u003c/p\u003e \u003cp\u003e(25.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUK Marriage Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany (n\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e337\u003c/p\u003e \u003cp\u003e(60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003cp\u003e(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003cp\u003e(7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e143\u003c/p\u003e \u003cp\u003e(25.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGermany Marriage Population Statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e*Including 5 studies that are only represented by one country or combined countries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFour studies reported social capital in terms of living arrangements. Three of these were conducted in Australia. One study (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) reported that participants lived with a mean of 1.67 other residents. Another found that 9 of 61 participants (14.8%) were from single-parent households (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), while a third reported that 86 of 95 participants (90.5%) were from two-parent households (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The fourth study, conducted in Sweden (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e), reported that among 52 participants, 47 (90.4%) lived with a family member, 1 (1.9%) with another adult, 2 (3.8%) lived alone, and data were missing for 2 participants (3.8%).\u003c/p\u003e \u003cp\u003e \u003cem\u003eNon-mental health conditions\u003c/em\u003e: Only one study, which was conducted in South Korea (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e), reported on 'diagnosis of physical illness (Yes: n\u0026thinsp;=\u0026thinsp;9, 12.3%; No: n\u0026thinsp;=\u0026thinsp;64, 87.7%).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAge\u003c/em\u003e: Age was predominantly reported in terms of mean values therefore it was not possible to categorise the sample accurately into age-brackets. From the 60 papers included in the review, age was presented in 59 papers (one being a perspective piece), but one included the duplication of the same sample. The majority of the studies delivered interventions to adults (34/58: 58.6%), followed by children under 15 years of age (19/58: 32.5%), two studies conducted in Sweden tested the intervention in adolescence (15\u0026ndash;19 years) (2/58: 3.4%) (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e) and a third study in Sweden involved both younger adults and adolescence (16\u0026ndash;25 years) (1/58: 1.7%) (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). Two other studies included a mixed age population of adults and children (1/58: 1.7%) (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e) and children and adolescence (1/58: 1.7%) (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDigital literacy/access\u003c/strong\u003e \u003cp\u003eDigital access was a common inclusion criterion for the trials (29/58 trials), as well as being mentioned in the perspective piece (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), typically participants were required to have a computer, smartphone, or tablet with stable internet (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). Some studies specified device types (e.g., Apple/Android smartphones), specific internet speeds, or a regularly used email (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e) and a county specific (Australian) IP address (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Reliable access to both internet and phone was also required in some cases (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Technical issues were frequently reported, including connectivity problems and device limitations, which disrupted interventions and may have contributed to dropout (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe role of digital skills was only considered by the perspective piece (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) and a 7/58 trials, typically in the form of assessing digital literacy/competence through self-report or purpose-built questionnaires (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e). Some studies reported challenges when participants lacked digital familiarity, such as difficulties using computers (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e), but only one study (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e) incorporated support mechanisms, such as how to check internet connectivity. The recent perspective piece (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) highlighted the importance of structured support programs offering digital skills training both online and in person. It also identified digital navigators\u0026mdash;clinic staff trained to provide technical assistance\u0026mdash;as a promising way to support users without overburdening clinical teams. These studies highlight that digital access and literacy are critical factors for equitable access and engagement in digital mental health research and interventions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWhat are the identified barriers and enablers to participation in DCTs in a mental health context?\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHigh dropout and recruitment challenges\u003c/strong\u003e \u003cp\u003eMany studies reported challenges in keeping participants engaged and enrolled. High dropout rates were common (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan additionalcitationids=\"CR86\" citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e), and some trials struggled with recruitment, especially where stigma or low mental health literacy were factors (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e). Participants drop out was associated with feeling confused or unsupported (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). Some mistook surveys for the intervention or wanted more personal contact during onboarding (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Dropout was also linked to factors like age, education, and social support (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e), and some participants simply weren\u0026rsquo;t comfortable with digital tools (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e). However, measuring engagement itself is challenging. There is no standard way to define or track it\u0026mdash;some use clicks, others look at time spent, or pages visited\u0026mdash;making it hard to compare across studies and complicating efforts to identify best practices (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSupport/navigators\u003c/strong\u003e \u003cp\u003eSupport from therapists or trained staff consistently emerged as a key facilitator of engagement in digital mental health trials. Participants often preferred therapist-assisted formats (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), and regular check-ins, whether via email or phone, helped sustain involvement (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). Eto et al demonstrated that adapting face-to-face therapy techniques for digital delivery, such as co-creating worksheets and focusing on patient interests, could enhance engagement (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). In culturally specific contexts, like Muslim communities, directive guidance was especially valued, with unclear instructions perceived as incompetence (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eBeyond therapeutic input, practical support and project navigators played a crucial role from on-boarding, technical demonstrations, through to reminders to prevent disengagement (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). However, in resource-limited settings or among people with severe anxiety, therapist-led models may not be feasible (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Additional strategies to boost engagement were suggested included motivational interviewing, short instructional videos, and text reminders (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). However, not all digital support features, such as social networking tools, will be effective or accepted by all (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant burden\u003c/strong\u003e \u003cp\u003e The burden of participation, whether cognitive, emotional, sensory, or logistical, was another major barrier. Complex onboarding processes, long sessions, and excessive paperwork discouraged engagement (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and participants dropped out due to tedious content or lack of engagement (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). In particular, children may struggle with focus and motivation (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e) and some participants found digital formats (e.g., reading on screens) uncomfortable or inaccessible (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTo mitigate these issues, studies recommended simplifying onboarding, reducing the use of passwords, digitising forms (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and simplifying data collection methods including the numbers of measures required (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). More specific recommendations were identified in one paper, which reported that expert as opposed to caregiver/lay explanations and demonstrations supported participant engagement with the intervention and short videos (~\u0026thinsp;2 minutes) were more engaging than longer content (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). A further paper highlighted the potential of gamification and repeatable content may sustain interest, but this was not assessed directly (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e). Furthermore, online formats were beneficial to aspects of participant burden, including that associated with commuting time, reducing stigma, and supporting a more comfortable therapeutic relationship\u0026mdash;possibly due to the \u0026ldquo;online disinhibition effect (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis review of 60 papers (58 RCTs) found that, although DCTs are increasingly used in mental health research, reporting of equity-related demographics is inconsistent. Gender and age were universally reported, but items such as race/ethnicity, occupation, SES, place of residence, and religion were often missing. Evidence that DCTs enhance inclusion of underserved groups was limited. Barriers to equitable participation included digital access, literacy, and participant burden, while enablers included therapist/navigator support and simplified onboarding\u003c/p\u003e\n\u003cp\u003eThis review has several strengths. Unlike many others, it incorporated PPI from the outset, shaping the research focus and data extraction (99). To our knowledge, it is the first systematic review to apply PROGRESS-Plus to DCTs in mental health and to compare sociodemographic representation against national statistics. Previous reviews have focused on reporting quality (10, 100), whereas ours addressed representativeness. It is also uniquely specific to digital/remotely delivered mental healthcare, a global priority, and included international studies, strengthening generalisability. Our structured approach identified gaps in reporting of disadvantage-related demographics and highlighted at-risk groups for exclusion. Consistent with earlier findings, we show that despite claims that DCTs support inclusive participation, marginalised groups, particularly by ethnicity and SES, remain underreported, with religion, disability, and residence, are often overlooked (101–104). While digital literacy and comfort with telemedicine are improving (105, 106), remote/digital methods may not be sufficient to achieve inclusion in mental health research without consideration to specific methodological design. Nonetheless, the conclusions of this review should be interpreted alongside its limitations, such as that the exclusion of non-English studies and pre-2020 trials. Furthermore, inconsistent reporting of variables like socioeconomic status and social capital hindered synthesis, and dynamic demographics such as employment and education limit direct comparison with national statistics. Therefore, comparisons with national statistics should be interpreted as proxies rather than exact benchmarks.\u003c/p\u003e\n\u003cp\u003eThese findings have important implications for the design and implementation of digital mental health trials (Fig.\u0026nbsp;2). For clinicians and service providers, the lack of consistent equity-relevant data reporting limits the ability to assess whether digital interventions are reaching and benefiting those most in need. Without robust demographic data, it is difficult to tailor interventions or address disparities in access and outcomes—particularly for groups historically underserved by mental health services (107, 108). For policymakers, the findings underscore the need for clearer guidance and accountability mechanisms to ensure equity is embedded in digital trial design and reporting. This includes mandating the collection and transparent reporting of PROGRESS-Plus variables, as recommended by the \u003cem\u003ePRISMA-Equity\u003c/em\u003e extension and other equity-focused frameworks (32, 35), to monitor progress towards inclusive research and to inform equitable policy decisions.\u003c/p\u003e\n\u003cp\u003eMethodologically, equity must be embedded from the outset. Researchers need support to design trials that promote inclusion across the entire lifecycle, not just at recruitment. While our review focused on baseline representation, we found limited reporting on attrition by sociodemographic group, highlighting the need to prioritise equitable participation as well as access. DCTs can expand access to innovations in mental healthcare—but only if equity is embedded in their design and infrastructure. Realising this potential demands collaboration across disciplines, including methodologists, clinicians, industry, and patient representatives. It also important to explore how different models of decentralisation (e.g., remote recruitment, virtual intervention delivery, and digital follow-up) affect participation, engagement, and outcomes across diverse groups. Inconsistent practices and limited reporting continue to obscure representativeness and limit generalisability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion.\u003c/strong\u003e Although DCTs are promoted as a way to broaden participation in mental health research, equity in access remains unachieved. Socially disadvantaged groups are inconsistently reported and often underrepresented. Without standardised, equity-focused data collection, it is difficult to identify who is included or excluded. As digital mental health expands, the field must address structural and methodological barriers alongside technological innovation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSSH, ES, HH, CP, and JM, were responsible for the concept. SSH, ES, CLH, NR, AJ were responsible for carrying out the searches and synthesis. SSH and ES drafted the manuscript with CP conducting statical evaluations. All authors critically revised the manuscript for important intellectual content and approved the final version. SSH is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eSSH time was supported by an NIHR Development and Skills Enhancement (DSE) Fellowship (NIHR306083). The time provided by CLH was supported by an NIHR Development and Skills Enhancement Award (NIHR304774). The NIHR had no role in design of the study, data collection, analysis, or interpretation; in the writing of the report; or in the decision to submit the article for publication. The views expressed are those of the authors and not necessarily those of the NIHR or Department of Health and Social Care.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSSH, ES, HH, CP, and JM, were responsible for the concept. SSH, ES, CLH, NR, AJ were responsible for carrying out the searches and synthesis. SSH and ES drafted the manuscript with CP conducting statical evaluations. All authors critically revised the manuscript for important intellectual content and approved the final version. SSH is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank our PPI group for their input and support throughout the review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDepartment of Health and Social Care. 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The Lancet Psychiatry. 2020;7(7):567\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mental health, digital health, randomised controlled trials, minority groups","lastPublishedDoi":"10.21203/rs.3.rs-7565290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7565290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDecentralised clinical trials (DCTs) may improve access to digital mental health interventions, but equity remains limited. This review of 60 papers (58 DCTs) found key PROGRESS-Plus variables\u0026mdash;race/ethnicity, socioeconomic status, occupation, and residence\u0026mdash;were underreported. Underserved groups were consistently underrepresented. Barriers included digital exclusion and cognitive or sensory challenges, while therapist support and simplified onboarding facilitated participation. Standardised reporting and inclusive DCT design are needed.\u003c/p\u003e","manuscriptTitle":"Who Gets Included? Equity in Digital and Decentralised Mental Health and Neurodevelopmental Trials: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 11:39:35","doi":"10.21203/rs.3.rs-7565290/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ae8299e-51cd-4f8a-882d-39975f5c9bbc","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61848622,"name":"Health sciences/Health care"},{"id":61848623,"name":"Health sciences/Medical research"},{"id":61848624,"name":"Biological sciences/Psychology"},{"id":61848625,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-01-29T11:39:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 11:39:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7565290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7565290","identity":"rs-7565290","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}