BACKGROUND AND OBJECTIVES

Digital media has been used mostly to deliver clinical treatments and therapies; however limited evidence evaluates digital interventions for health promotion. The objective of this review is to identify digital interventions for universal health promotion in school-aged children and adolescents globally.

METHODS

Eligible articles were searched in PubMed, Embase, Medline, Ovid SP, The Cochrane Library, Cochrane Central Register of Controlled Trials, WHO regional databases, Google Scholar, and reference lists from 2000 to March 2021. Randomized controlled trials and quasi-experimental studies evaluating interventions that promote health in school-aged children and adolescents (5–19.9 years) were included. Methods were conducted in duplicate. Where possible, data were pooled with a random-effects model.

RESULTS

Seventy-four studies were included (46 998 participants), of which 37 were meta-analyzed (19 312 participants). Interventions increased fruit and vegetable consumption (servings per day) (mean difference [MD] 0.63, 95% confidence interval [CI] 0.21 to 1.04; studies = 6; P = .003; high quality of evidence), and probably reduced sedentary behavior (MD −19.62, 95% CI −36.60 to −2.65; studies = 6; P = .02; moderate quality of evidence), and body fat percentage (MD −0.35%, 95% CI −0.63 to −0.06; studies = 5; P = .02; low quality of evidence). The majority of studies were conducted in high-income countries and significant heterogeneity in design and methodology limit generalizability of results.

CONCLUSIONS

There is great potential in digital platforms for universal health promotion; however, more robust methods and study designs are necessitated. Continued research should assess factors that limit research and program implementation in low- to middle-income countries.

Over recent years, the amount of time spent in front of screens by school-aged children and adolescents (SACA), such as televisions, computers, and mobile phones through apps and social media has increased considerably.1  SACA account for about one-third of internet users globally, with increasing evidence that they are accessing it at increasingly younger ages.2  The landscape has also changed considerably over the years in low- and middle- income countries (LMICs). Not only are mobile phones the primary means of internet access in LMICs, but also possession and uptake of mobile phones by young people have greatly increased.3,4 

It is known that excessive immersion and screen use have the potential to harm child growth, health, and development, increasing the risk of poorer health outcomes in adulthood. As reported in the State of the World Children’s 2019 Report, unlimited and unsupervised connectivity can lend to issues of “digital dependency” or screen addiction.1  Increased leisure screen use is also associated with depressive symptoms in younger children; risk of exposure to age-inappropriate, violent content, or sexual exploitation; and increased sedentary behavior and exposure to digital marketing of unhealthy foods and beverages, which may be contributing to rising rates of obesity globally.1,2,57  However, there is also growing evidence of positive benefits in health and development with appropriate use of digital platforms, such as mental well-being, cognitive, and psychosocial benefits.2,812  Digital media can provide social support that otherwise may not be received elsewhere, and promote development of friendships that extend beyond physical distances.9  Among adolescents, social media and networking sites increase feelings of connectedness among peers and promote more diverse and gender-inclusive friendships.5,10,13  Studies also show that it is easier for adolescents, especially boys, to share sensitive and personal topics online than in person, providing opportunities for counseling and communication on digital platforms.14,15  Other benefits include opportunities for learning and education, entertainment, and communication.10,13  Digital media can provide immersive and informative experiences, such as eSports, and may improve academic performance by enhancing knowledge and literacy skills. It should be noted that these experiences depend on age, type of media, purpose of media use, and timing of screen use (weekend or weekday).5,8,16 

The use of digital platforms presents unique opportunities for universal health promotion, such as physical activity, healthy nutrition and diets, and chronic disease prevention.1724  A variety of interventions are used to reach and engage SACA, including smartphone apps19 ; text-messaging for universal health promotion; social media and networking sites for peer-to-peer connectivity, counseling, and/or mental health and well-being support.17,23,25,26  Platforms like video games and “exergames” or “active video games” have become increasingly popular to promote physical activity and reduce sedentary behavior.21,27 

A previous scoping exercise conducted by the review authors highlighted the need to evaluate and compare different types of digital-based interventions. Previous reviews typically focused on one type of digital platform, such as text-messaging,18  preventing possible comparisons of effectiveness between different platforms. Most reviews also evaluated treatment, therapy or management-based interventions for existing or chronic conditions, which is not the focus of this review. If reviews did include interventions promoting healthy behaviors, intervention effects were not disaggregated from their treatment or therapy counterparts. This review aims to add to the growing knowledge base about digital-based interventions and to evaluate the use of digital platforms for universal health promotion in SACA, which include healthy living, physical activity, nutrition, reduction in screen use, sedentary behaviors, online risk exposure, and mental health globally.

This systematic review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria and registered within the International Prospective Register of Systematic Reviews (CRD42020213361).28  This review was initially designed to evaluate the effectiveness of both (1) nondigital interventions to reduce screen use and sedentary behavior, and (2) digital-based interventions for universal health promotion, in SACA. A single search strategy was used (Supplemental Information) and eligible studies were screened together until the abstraction stage, at which studies were abstracted and analyzed separately. Given the large number of studies, the review authors chose to report the evidence synthesis separately. For the synthesis on nondigital interventions for physical activity and screen reduction, please refer to the Supplemental Information.29  As guidance, a socio-ecological conceptual framework for nondigital and digital interventions on health outcomes was proposed (Fig 1).

The search strategy (Supplemental Information) was developed according to population, intervention, comparator, and outcome criteria and conducted in PubMed, Embase, Medline, PsycINFO, The Cochrane Library, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register, and the World Health Organization regional databases. Gray literature searches were also conducted. There were no limitations on geographical settings, publication language, or duration of intervention follow-up. The date of the final search was March 16, 2021. Additional details about search strategy development and other information sources are included in another supplement article.30 

FIGURE 1

Conceptual framework. Child and adolescent screen time and sedentary behaviors are influenced by microenvironments, as well as mediation (individual-level) and moderation (biological and demographics) factors, leading to intermediate benefits or risks, long-term morbidity, and mortality. Such a framework helps illustrate the complexity of these behaviors, guides research, and supports intervention and policy development.

FIGURE 1

Conceptual framework. Child and adolescent screen time and sedentary behaviors are influenced by microenvironments, as well as mediation (individual-level) and moderation (biological and demographics) factors, leading to intermediate benefits or risks, long-term morbidity, and mortality. Such a framework helps illustrate the complexity of these behaviors, guides research, and supports intervention and policy development.

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A multistage screening process was conducted. Title-abstract screening was done by a single reviewer. Full-text review and data abstraction were conducted in duplicate. Further details about the screening and selection process can be found in a supplement article.30  Studies were screened and selected according to specific eligibility criteria (Table 1). Eligible study designs included randomized controlled trials, quasi-experimental studies (QES), and nonrandomized trials that already assessed the feasibility of the intervention to evaluate the review question. Small pilot or feasibility trials without any follow-up trial were excluded.31 

TABLE 1

Population, Intervention, Comparator, and Outcomes

InclusionExclusion
Population Healthy, male and female children (5–9.9 y) and adolescents (10–19.9 y) with no chronic or existing medical condition, living in a low, middle or high- income country Unhealthy population, including but not limited to acute or chronic conditions and diseases, genetic diseases 
  Mean age of participants <5 y or >19.9 y 
Intervention Digital-based interventions that aim to promote healthy lifestyle habits and behaviors, prevent chronic conditions (e.g. overweight and obesity), and reduce leisure screen time and sedentary behavior, with data collected in or after the year 2000 Irrelevant study designs: observational and cross-sectional studies, feasibility studies, reviews 
Eligible Study Designs: Randomized controlled trials (RCTs) — 
 Quasi-experimental studies (QES) and nonrandomized trials: — 
  Natural experiment designs — 
  Controlled before-after — 
  Regression discontinuity designs — 
  Interrupted time series — 
Comparator No intervention (placebo) — 
 Standard arm of care (eg, existing school programs, activities, or initiatives) — 
 Other intervention arms in the case of a multicomponent intervention (eg, nutrition education arm versus nutrition education + digital component) — 
Outcomes   
 Primary Outcomes:  — 
 Screen time or screen use, as author defined, including digital dependency, screen addiction or excessive screen use (continuous and dichotomous outcomes) — 
 Sedentary behavior — 
 Physical activity: all outcomes as author defined pertaining to the measurement of physical activity and energy expenditure — 
 Secondary Outcomes:   
 Anthropometric outcomes — 
 Prevalence of overweight, obesity, or overweight and obesity — 
 Dietary intake and eating patterns, including types of food consumed, snacking patterns, skipping meals — 
 Mental health and well-being: prevention of mental health conditions before onset or diagnosis, promotion, and education of positive mental health reported metrics of knowledge pertaining to nutrition and healthy lifestyle habits — 
 Online risk exposure (violent or hateful content, pornographic or unwanted sexual content, spam, unhealthy advertising, harassment and stalking, and sexual solicitation and exploitation)a — 
InclusionExclusion
Population Healthy, male and female children (5–9.9 y) and adolescents (10–19.9 y) with no chronic or existing medical condition, living in a low, middle or high- income country Unhealthy population, including but not limited to acute or chronic conditions and diseases, genetic diseases 
  Mean age of participants <5 y or >19.9 y 
Intervention Digital-based interventions that aim to promote healthy lifestyle habits and behaviors, prevent chronic conditions (e.g. overweight and obesity), and reduce leisure screen time and sedentary behavior, with data collected in or after the year 2000 Irrelevant study designs: observational and cross-sectional studies, feasibility studies, reviews 
Eligible Study Designs: Randomized controlled trials (RCTs) — 
 Quasi-experimental studies (QES) and nonrandomized trials: — 
  Natural experiment designs — 
  Controlled before-after — 
  Regression discontinuity designs — 
  Interrupted time series — 
Comparator No intervention (placebo) — 
 Standard arm of care (eg, existing school programs, activities, or initiatives) — 
 Other intervention arms in the case of a multicomponent intervention (eg, nutrition education arm versus nutrition education + digital component) — 
Outcomes   
 Primary Outcomes:  — 
 Screen time or screen use, as author defined, including digital dependency, screen addiction or excessive screen use (continuous and dichotomous outcomes) — 
 Sedentary behavior — 
 Physical activity: all outcomes as author defined pertaining to the measurement of physical activity and energy expenditure — 
 Secondary Outcomes:   
 Anthropometric outcomes — 
 Prevalence of overweight, obesity, or overweight and obesity — 
 Dietary intake and eating patterns, including types of food consumed, snacking patterns, skipping meals — 
 Mental health and well-being: prevention of mental health conditions before onset or diagnosis, promotion, and education of positive mental health reported metrics of knowledge pertaining to nutrition and healthy lifestyle habits — 
 Online risk exposure (violent or hateful content, pornographic or unwanted sexual content, spam, unhealthy advertising, harassment and stalking, and sexual solicitation and exploitation)a — 

—, XXX.

a

Online (cyber) bullying is considered an online risk exposure; however, this outcome was covered by another review (mental health interventions) of the same series.

Interventions were defined as any planned action, program, or policy aiming to promote healthy lifestyle behaviors. Eligible comparisons were no intervention (placebo), standard arm of care (eg, existing school programs), or other intervention arms in the case of a multi-arm intervention. Studies comparing two types of digital platforms were excluded (ie, the comparator intervention is also implemented digitally). Interventions whose focus was treatment, therapy and/or management of existing chronic disease (eg, weight loss, or treatment of overweight and obesity, including mental health) were excluded. Interventions promoting sexual reproductive health (eg, human immunodeficiency virus prevention, and safe sex practices) and addressing substance use and addiction were not included as these topics were covered by other reviews in the current supplement.

Statistical analyses were conducted using Review Manager 5.4. Analyses for randomized controlled trials and QES were separated. A random effects model was used to mitigate heterogeneity. Meta-analyses were conducted for each outcome of interest, only where there was data from, at the minimum, 3 studies. Overall effect estimates were interpreted as statistically significant where the associated P value was <.05. Where multiple follow-up timepoints were reported, the timepoint immediately following completion of the intervention was used for analysis across all studies to minimize heterogeneity and increase generalizability. Estimates were not adjusted for clustering if cluster-randomized controlled trials reported adjusted estimates. Where appropriate, unit conversions were conducted. Sensitivity analyses were conducted for intervention type (computer-based, internet-based, mobile app, messaging platform, or active video games).

Risk of bias assessments were conducted according to Cochrane Effective Practice and Organization of Care guidelines for randomized trials, nonrandomized trials, controlled before-after and interrupted time series,32  and the Cochrane Handbook for Systematic Reviews of Interventions.31  The authors independently assessed risk of bias for each study. These scores were compared, and a final decision was made.

The Cochrane Risk of Bias-2 tool was used to assess risk of bias in randomized controlled trials per the following domains: randomization process, deviations from the intended interventions (blinding of personnel, participants, and outcome assessment), missing outcome data, outcome measurement, the selection of the reported result, disclosure of funding, and conflicts of interest.31,33  Studies were assigned an overall risk of bias judgement accordingly (low risk, high risk, some concerns).

Quasi-experimental study designs were assessed using the Risk of Bias Tool for Nonrandomized Studies of Interventions (ROBINS-I) tool.31,34  Assessments were made per the following domains: bias due to confounding, bias in selection of study participants, bias in classification of interventions, bias due to deviations from intended interventions, bias due to missing data, bias in measurement of outcomes, bias in selection of the reported result. An overall judgement (low, moderate, serious, and critical risk) was assigned to each study.

A summary of the intervention effect and quality assessment for all pooled outcomes were produced using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach across 5 domains: study limitations, consistency of effect, imprecision, indirectness, and publication bias.35  Evidence was downgraded from “high quality” by one level for serious (or by two levels for very serious) limitations, depending on assessments for risk of bias, indirectness of evidence, serious inconsistency, imprecision of effect estimates, or potential publication bias.

A database search produced 29 301 records; hand searching revealed another 168 records. Following removal of 9132 duplicates, 20 337 records were screened at the title-abstract stage, which identified 680 records for full-text review. Four hundred and seven records were excluded at the full-text review stage for reasons including wrong intervention type, wrong study design, wrong comparator, wrong patient population, or wrong outcomes (Supplemental Information). In total, 51 studies (146 articles) were included for nondigital interventions and 74 studies (127 articles) were included for digital-based interventions. This article will solely focus on digital-based interventions. For the synthesis on nondigital interventions for screen reduction, please refer to the other supplement.29  Of the 74 studies evaluating digital interventions, 37 were included in meta-analysis. Where possible, the remaining studies were narratively synthesized. See Fig 2 for the study breakdown across exclusion reasons.

FIGURE 2

PRISMA diagram.

Of the 74 included studies, 46 were RCTs,3681  3 nonrandomized controlled trials8284  and 2585109  were QES (Table 2). Across all study designs, an overwhelming majority of studies (n = 61, 82%) were conducted in high-income countries: Australia (n = 8),37,40,61,62,66,81,83,84  Belgium (n = 2),57,58  Canada (n = 3),43,52,78  France (n = 2),69,100  England (n = 3),54,82,86  Germany (n = 2),74,80  Hong Kong (n = 3),94,107,109  the Netherlands (n = 3),49,51,72  New Zealand (n = 4),48,75,79,93  Portugal (n = 2),50,99  and the United States (n = 21),38,42, 44,46,53,55,56,63,64,67,70,85,87,8992,96, 97,101,105  and one each in Denmark,95  Finland,36  Italy,41  Japan,68  South Korea,102  Spain,104  Norway.39  One study was a multicountry study, comprised of only high-income countries (Austria, Belgium, Germany, Crete and Greece, and Sweden).88  Thirteen studies were conducted in LMICs: Brazil (n = 5),47,60,65,76,77  Thailand (n = 2),71,73  China (n = 2),59,108  Ghana,106  Indonesia,103  Malaysia,98  and Turkey.45 

TABLE 2

Characteristics of Included Studies (Digital Interventions)

First Author, yearCountry, World Bank RegionStudy DesignParticipantsInterventionReported Outcomes
Duration, FrequencyDescriptionANDISBSTPAOther
Aittasalo 201936  Finland, ECA cRCT N = 1550; mean age 13.9 (SD 0.5) y; grade 8 students; 48% female 2 mo, weekly ‘Kids Out’; Health education and physical activity promotion through internet-based assessment, material and YouTube videos. Other intervention components include leaflets for students and parents, classroom discussion, school posters — — ✓ — — — 
Andrade 2020,76,a Brazil, LAC cRCT N = 213; 7–11 y; mean age 9.41 (SD 0.48) y; grades 4 and 5 students 1 school year Exergames to promote physical activity compared with routine curricular physical education classes — — — — — ✓ 
Angkasa 2020103,a Indonesia, EAP QES N = 228; 9–12 y; urban schools; ∼55% female 1 time for 2 d ‘MAPAGI’ (Makan Pagi Bergizi) interactive, internet-based video game to promote nutritional knowledge — — — — — ✓ 
Azevedo 201486  England, ECA QES N = 497; 11–13 y; ∼64% female 1 school year Dance mats exergaming system to improve physical activity ✓ — ✓ — ✓ — 
Babic 201537  Australia, EAP cRCT N = 322; mean age 14 (SD 0.6) y; 66% female; secondary school students who exceed recreational screen-time (ie, >2 h per day) 6 mo, twice weekly ‘Switch-Off 4 Healthy Minds’; e-Health messaging sent to participants via social media and messaging platforms (Twitter, Facebook, Kik, e-mail or text) ✓ — — ✓ ✓ — 
Baños 2013104,a Spain, ECA QES N = 228; 10–13 y; 11.22 (SD 0.92) y; grades 4–6 students of similar socioeconomic background 2 wk ETIOBE mates'; online video game to improve nutritional knowledge — — — — — ✓ 
Baranowski 201138  United States, NA RCT N = 153; 10–12 y; 43.8% female; students between the 50th and 95th percentile for BMI, allowed to play video games and with internet access 9 sessions, completion Video games (‘Escape from Diab’ and ‘Nanoswarm: Invasion from Inner Space’) to promote physical activity ✓ ✓ — — ✓ — 
Bjelland 201139  Norway, ECA cRCT N = 2165; 11–13 y; mean age 11.2 (SD 0.27) y; 50% female 20 mo, mixed ‘HEIA (HEalth In Adolescents)’; computer-based education on dietary intake, screen time and physical activity — ✓ — ✓ — — 
Byrne 201287,a United States, NA QES N = 39; 12–14 y; mean age 13.1 (SD 0.70) ys; grades 7 and 8; 43.6% females 9 d Mobile phone game to promote healthy eating — ✓ — — — — 
Calear 200940,a Australia, EAP cRCT N = 1477; 12–17 y; mean age 14.34 (SD 0.75) y; 55.9% female; 5 wk; 1 module per wk ‘MoodGYM’; An interactive, internet-based program to prevent or decrease symptoms of anxiety and depression — — — — — ✓ 
Carfora 201641  Italy, ECA RCT N = 1348; 13–19 y; adolescents with a personal mobile phone with an internet connection 2 wk, daily SMS messages sent via WhatsApp focusing on positive affective consequences of fruit and vegetable intake — ✓ — — — — 
Casazza 200785,a USA, NA QES N = 6737; grades 9–12; 13–18 y; mean age 15.79 y; 65.8% female 16 wk Computer-based nutrition education and health promotion ✓ ✓ — — ✓ — 
Catenacci 201442  USA, NA RCT N = 131; 8–12 y; with access to the internet at home; no target range for percentile BMI-for-age 12 wk, weekly ‘America on the Move Family Intervention’; Interactive, Web site-based education program — — ✓ ✓ ✓ — 
Chagas 202077,a Brazil, LAC cRCT N = 319; 13–16 y; mean age 15.8 (SD 0.7) y; high school students from private schools; 57.4% female 7 to 17 d Rango Cards'; digital game app to promote health eating and improve nutritional knowledge — ✓ — — — ✓ 
Chamberland 201743  Canada, NA cRCT N = 282; 13–14 y; grades 7 and 8 students; 61% female 6 wk, daily ‘Team Nutriathlon’; Web-based platform that records daily consumption of fruits, vegetables, milk, dairy alternative products ✓ ✓ — — — — 
Chen 201144  United States, NA RCT N = 54; 12–15 y; mean age 12.52 (SD 3.15) y; self-identified as Chinese or of Chinese origin by both subject and parent 8 wk, weekly ‘Web ABC’; Web-based, interactive program to enhance problem solving and knowledge about nutrition and physical activity ✓ ✓ — — ✓ — 
Coknaz 201945  Turkey, ECA RCT N = 106; grades 3–6; 56% female; students form urban elementary schools who are preoccupied with technology and physically inactive 12 wk, 3 d per week Active video games on Nintendo Wii (eg, sports, balance, aerobics, etc.) ✓ — — — — — 
Cullen 201346,a United States, NA RCT N = 390; 12–17 y; adolescents with internet access; 54% female 8 wk, weekly ‘Teen Choice: Food & Fitness’; Website with 12 short role model videos addressing barriers to healthy eating and physical activity; blog with information and online tracking function — ✓ — ✓ ✓ — 
Brito Beck DaSilva 201947  Brazil, LAC cRCT N = 895; grades 7–9 students; mean age 14.5 (SD 1.42) y; 48.4% female 12 mo, weekly ‘Stayingfit‘;Online program to guide health eating habits and behaviors ✓ ✓ ✓ — — — 
De Bourdeaudhuij 201088  Multicountry, ECA QES N = 1050; 12–17 y; mean age 14.5 (SD 1.4) y; 49% female 2 mo Activ-O-Meter, an internet-based computer-tailored physical activity intervention — — — — ✓ — 
Direito 201548  New Zealand, EAP RCT N = 51; 14–17 y; mean age 15.7 (SD 1.2) y; 57% female 8 wk ‘AIMFIT’; Smartphone apps (nonimmersive and immersive apps) on improving fitness and physical activity — — — — ✓ — 
Ezendam 201449  Netherlands, ECA cRCT N = 883; 12–13 y; students from 20 selected schools; 45.1% female 10 wk ‘FATaintPHAT’; Web-based computer program that focuses on physical activity and healthy eating promotion ✓ ✓ — ✓ ✓ — 
Fassnacht 201550  Portugal, ECA RCT N = 49; 8–10 y; mean age 9.6 (SD 0.4) y; of any wt and ethnicity; 53.1% female 8 wk SMS reporting and monitoring by participants in response to education sessions on physical activity and nutrition — ✓ — ✓ — — 
Flett 202079,a New Zealand, EAP RCT N = 250; mean age 17.87 (SD 0.47) y; 67.6% female 3 mo HeadSpace'; mobile app to promote mental wellbeing and mindfulness meditation — — — — — ✓ 
Folkvord 201351,a Netherlands, ECA RCT N = 277; 8–10 y; grades 3–4 from primary schools  Online game that discussed healthy and non healthy food products and consumption — ✓ — — — — 
Frenn 200589,a United States, NA QES N = 132; 12–14 y; grade 7 students 8 sessions Internet based program and videos to promote physical activity and health eating — ✓ — — ✓ — 
Garde 201852,a Canada, NA RCT N = 37; 10–11 y; elementary school students; 56.8% female 2 wk ‘MobileKids Monster Manor’; Active video game — — — — ✓ — 
Goran 200553  United States, NA RCT N = 209; fourth graders; ages 8.8–11.1; mean age 9.5 (SD 0.4) y; 51% females 8 wk Interactive CD-ROM game;12 h of intervention 8 CD-ROM interactive lessons (45 min per lesson); supplemented classroom assignments and homework ✓ — — — — — 
Gorely 200982,a England, ECA nRCT N = 589; 7–11 y; from primary schools 10 mo ‘GreatFun2Run’; Interactive Web site to raise about physical activity and healthy eating ✓ — — — ✓ — 
Graves 201054  England, ECA RCT N = 58; 8–10 y; grades 4–5 from schools in low socioeconomic area; owned PS2 of PS3 video game console and self reported playing these for ≥ 2 h per week 12 wk Active video games ✓ — ✓ — ✓ — 
Greene 201255  United States, NA RCT N = 1689; mean age 19.1 (SD 1.1) y; 18-24 y; 62% female 3 mo Project Webhealth'; online curriculum (10 lessons) that focuses on fruit and vegetable intakes and physical activity — ✓ — — ✓ — 
Gribbon 201578,a Canada, NA RCT N = 26; 13–17 y; mean age 14.5 (SD 1.3) y; 100% male 3 d Active video games to promote physical activity — — — — ✓ — 
Gustafson 201956  United States, NA RCT N = 411; 14–16 y; adolescents from rural areas 8 wk, twice weekly ‘Go Big and Bring It Home’; Weekly SMS messaging covering nutrition related content sent to students ✓ ✓ — — — — 
Haerens 2007A57  Belgium, ECA cRCT N = 304; grade 7 students; mean age 13.2 (SD 0.5) y; 70.4% female 4 mo, 1 time Computer based intervention to reduce dietary fat intake — ✓ — — — — 
Haerens 2007B58  Belgium, ECA cRCT N = 2840; grades 7 and 8 from technical and vocational education schools; mean age 13.1 (SD 0.81 y); 36.6% female 9 mo (1 school year) Computer based intervention to reduce dietary fat intake and increase fruit intake — ✓ — — — — 
Hieftje 2021105,a United States, NA QES N = 560; 10–16 y; but no participants between 14 and 16 y were enrolled; several program and class sessions smokeSCREEN'; a web-based videogame to promote tobacco product use prevention, beliefs, and knowledge about tobacco use — — — — — ✓ 
Hutchinson 2020106,a Ghana, AFRICA QES N = 2625; 13–16 y; 100% female; only 2/3 of participants enrolled had access to cellphone 1.5 y SKY Girls Ghana'; a multimedia youth antismoking and girl's empowerment — — — — — ✓ 
Lanningham-Foster 200691,a USA, NA QES N = 25; 8–12 y; mean age 9.7 (SD 1.6) y; 52% female; 50% overweight and 50% were normal wt 1 time Active video games compared with a seated video game to promote physical activity and energy expenditure — — — — ✓ — 
Lanningham-Foster 200990,a United States, NA QES N = 22; 9–15 y; mean age 12 (SD 2.0) y; 50% female; 50% overweight and 50% were normal wt 1 time Active video games to promote physical activity and energy expenditure compared with traditional sedentary video games — — — — ✓ — 
Lau 201959,a China, EAP RCT N = 69; 12–16 y; mean age 13.75 (SD 0.90) y; students from government-subsidized schools and owned personal mobile phone 4 wk, weekly SMS messaging promoting healthy behaviors — — — — ✓ — 
Leme 201660  Brazil, LAC cRCT N = 253; 14–18 y; adolescent girls from government secondary schools; 100% female 7 mo, daily ‘Healthy Habits, Healthy Girls’; Weekly health messages (via WhatsApp) to students and parents. Other components include handbooks, interactive seminars and workshops, and physical activity sessions ✓ ✓ ✓ ✓ — — 
Liang 2020107  Hong Kong, EAP QES N = 87; 9–12 y; grades 4–6 students who did not participate on any school teams or extracurricular exercise classes 8 wk Active video games ✓ — ✓ — ✓ — 
Long 200492,a United States, NA QES N = 121; 12–16 y; grades 7–9; 52.1% female 1 mo Web-based curriculum on healthy behaviors — ✓ — — — — 
Lubans 201262  Australia, EAP RCT N = 357; 12–14 y; mean age 13.18 (SD 0.45) y; grade 8 students from schools in low-income areas; 100% female 12 mo (4 school terms) ‘NEAT’; Text messaging to support students through increasing physical activity, reducing sedentary behaviors and healthy eating ✓ ✓ — ✓ ✓ — 
Lubans 201661  Australia, EAP cRCT N = 361; 12–14 y; students from low-income areas who failed to meet national guideline for physical activity and/or recreational screen time, and did not have a physical impairment that would prevent them from participating in physical activity 20 wk ‘ATLAS’; Web-based, smart phone app to promote healthy behaviors; pedometers for self-monitoring. Other components include fitness equipment provision, physical activity sessions, student seminars, newsletters (parents) ✓ ✓ — ✓ ✓ — 
Mack 202080,a Germany, ECA cRCT N = 82; 9–12 y; grade 4 students 2 wk The Kids Obesity Prevention Program; active video games to promote healthy eating habits, media consumption and knowledge — ✓ — ✓ ✓ ✓ 
Maddison 200753,a New Zealand, EAP QES N = 21; 10–14 y; mean age 12.4 (SD 1.1) y; 47.6% female 1 time Active video games to increase physical activity and energy expenditure, compared with rest and nonactive video games — — — — ✓ — 
Marks 200663,a United States, NA RCT N = 319; mean age 12.15 (SD 0.95) y; grades 6–8; 100% female 2 wk Web-based physical activity intervention, compared with print workbook with the same information — — — — ✓ — 
Mauriello 201064,a United States, NA RCT N = 1800; mean age 15.97 y; from 8 high schools; 50.8% female; 7.6% at risk for overweight and 15.5% overweight 14 mo Computer-based program to promote physical activity, fruit and vegetable consumption, and TV viewing — ✓ — ✓ ✓ — 
Mellecker 200894,a Hong Kong, EAP QES N = 18; 6–12 y 1 time Active video games to increase physical activity and energy expenditure compared with rest and seated computer games — — — — ✓ — 
Hardman 201465,a Brazil, LAC RCT N = 2155; mean age 18.4 (SD 2.3) y; 15–24 y; evening-class students from public schools 10 mo ‘Saúde na Boa’; Website provides health promotion information and resources; other components include posters, newsletters, fruit distribution, physical education kit — — — ✓ — — 
Newton 200966,a Australia, EAP cRCT N = 764; 13 y olds; mean age 13.08 (SD 0.58) y; students from urban independent schools; 40% female 12 mo Online, internet-based modules on healthy behaviors, including alcohol and cannabis — ✓ — — — ✓ 
Nollen 201467  United States, NA RCT N = 51; 9–14 y; mean age 11.3 (SD 1.6) y; low-income, racial and ethnic minority girls recruited through afterschool programs 12 wk, weekly Mobile app for goal setting, self-monitoring, positive feedback, and information on healthy behaviors ✓ ✓ — ✓ — — 
O’Kearney 200684,a Australia, EAP nRCT N = 78; 15–16 y; 100% male; any student at the school within the age range regardless of history and/or risk of anxiety and depression 5 wk; one module per week ‘MoodGYM’; An interactive, internet-based program to prevent or decrease symptoms of anxiety and depression — — — — — ✓ 
O’Kearney 200983,a Australia, EAP nRCT N = 157; 15–16 y; 100% female; any student at the school within the age range regardless of history and/or risk of anxiety and depression 6 wk; one module per week ‘MoodGYM’; An interactive, internet-based program to prevent or decrease symptoms of anxiety and depression — — — — — ✓ 
Okazaki 201068,a Japan, EAP RCT N = 81; mean age 19.25 (SD 1.25) y; 35.1% female; healthy university students 4 mo, weekly Internet-based physical activity program — — — — ✓ — 
Paineau 200869  France, ECA RCT N = 1013; from 54 elementary schools in Paris; mean age 7.7 y; 52.2% female; 18% overweight 8 mo Internet-based programs monitoring and promoting dietary intake physical activity, meal preparation and quality of life ✓ ✓ — — — — 
Pedersen 201695,a Denmark, ECA QES N = 1488; 11–16 y; mean age 12.9 y; 50/50 male and female 11 wk Feedback text messaging intervention on behavior, self-efficacy, outcome expectations on fruit and vegetable intake — ✓ — — — — 
Pfeiffer 201970,a United States, NA RCT N = 1519; 10–14 y; grades 5–8; racially diverse, urban students from low SES areas; 100% female 17 wk ‘Girls on the Move’; Internet-based sessions; students receive motivational and feedback messages via iPad; other components include physical activity and motivational interviews ✓ — — — ✓ — 
Pittman 202096  United States, NA QES N = 98; 11–15 y; grades 7–8; able to read English, access to cellphone with unlimited texting (for the intervention groups) 10 wk, weekly Website and social media and text messaging platform to promote physical activity and healthy eating. Activity trackers were also provided to monitor physical activity ✓ — — — — — 
Rerksuppaphol 201771  Thailand, EAP RCT N = 218; mean age 10.7 (SD 3.1) y; grades1-6 from public elementary schools; 51% female 4 mo, encouraged daily use Internet-based education on healthy eating and physical activity ✓ — — — — — 
Schwinn 201497,a United States, NA QES N = 67; 10–12 y; mean age 11.85 (SD 0.88) y; lived in public housing with their mothers; from 27 US states; 100% female 3 wk, weekly Web-based program to focus on healthy behaviors and mental health, and positive relationships between mothers and daughters — ✓ — — ✓ — 
Shen 2020108  China, EAP QES N = 573; mean age 13.1 (SD 0.4) y 3 mo Smartphone app to provide health education and feedback, and promote nutrition knowledge, perceptions, and nutritional status ✓ — — — — ✓ 
Shukri 201998,a Malaysia, EAP QES N = 201; mean age 10.5 y; 13.9% underweight, 13.4% overweight, and 8% were obese 3 wk Computer-based program with interactive games and animated presentations to promote healthy eating, nutrition, and physical activity — ✓ — — — — 
Shum 2019109,a Hong Kong, EAP QES N = 459; 8–12 y; grades 4 and 5 students 2 wk The Adventures of DoReMiFa, digital game-based intervention; mental health and wellbeing enhancement — — — — — ✓ 
Simons 201572  Netherlands, ECA RCT N = 260; 12–17 y; 10% females 10 mo Active video games ✓ ✓ — ✓ ✓ — 
Sousa 202099,a Portugal, ECA QES N = 353; 12–16 y; mean age 12.43 (SD 0.87) y; with easy access to internet, smartphone or tablet; 57.8% female 6 mo ‘TeenPower’; Mobile app for adolescents to provide educational resources for healthy behaviors — ✓ — — ✓ ✓ 
Sriramatr 201473  Thailand, EAP RCT N = 220; 100% female; mean age 19 y; university-aged students 3 mo Internet-based program to promote and maintain leisure-time physical activity — — — — ✓ — 
Straker 201381  Australia, EAP RCT N = 56; 10–12 y; 51.8% female 8 wk Active video games, compared with traditional sedentary video games and no games at all to promote physical activity and fitness — — — — ✓ — 
Turnin 2015100,a France, ECA QES N = 580; 11–16 y 6 mo, daily ‘NutriAdvice’; Freestanding computer kiosks that allow students to select the type of foods they want to eat, and how much ✓ — — — —  
Unnithan 2006101,a United States, NA QES N = 22; 11–17 y; 27.3% female; 50% overweight; 50% normal wt 1 time Active video games (Dance Dance Revolution) to increase physical activity and energy expenditure compared with rest and seated computer games — — — — ✓ — 
Walther 201474,a Germany, ECA cRCT N = 2303; 10–14 y; mean age 12.0 (SD 0.83) y; grades 6–7 3 mo Web-based platform to raise awareness about internet and electronic media use — — — ✓ — — 
Whittaker 201775,a New Zealand, EAP RCT N = 855; grades 9–12; mean age 14.3 (SD 0.9) y; owns/use a Vodafone mobile phone; 68.3% female 9 d, daily ‘MEMO’; Video diary messages and text messages sent via mobile phones to promote healthy behaviors and prevent depression — — — — — ✓ 
Yang 2017102  South Korea, EAP QES N = 768; grade 4 (9–10 y) and grade 7(12–13 y) 1 school year, daily Online based, video curriculum to promote physical activity and healthy eating ✓ — — — — — 
First Author, yearCountry, World Bank RegionStudy DesignParticipantsInterventionReported Outcomes
Duration, FrequencyDescriptionANDISBSTPAOther
Aittasalo 201936  Finland, ECA cRCT N = 1550; mean age 13.9 (SD 0.5) y; grade 8 students; 48% female 2 mo, weekly ‘Kids Out’; Health education and physical activity promotion through internet-based assessment, material and YouTube videos. Other intervention components include leaflets for students and parents, classroom discussion, school posters — — ✓ — — — 
Andrade 2020,76,a Brazil, LAC cRCT N = 213; 7–11 y; mean age 9.41 (SD 0.48) y; grades 4 and 5 students 1 school year Exergames to promote physical activity compared with routine curricular physical education classes — — — — — ✓ 
Angkasa 2020103,a Indonesia, EAP QES N = 228; 9–12 y; urban schools; ∼55% female 1 time for 2 d ‘MAPAGI’ (Makan Pagi Bergizi) interactive, internet-based video game to promote nutritional knowledge — — — — — ✓ 
Azevedo 201486  England, ECA QES N = 497; 11–13 y; ∼64% female 1 school year Dance mats exergaming system to improve physical activity ✓ — ✓ — ✓ — 
Babic 201537  Australia, EAP cRCT N = 322; mean age 14 (SD 0.6) y; 66% female; secondary school students who exceed recreational screen-time (ie, >2 h per day) 6 mo, twice weekly ‘Switch-Off 4 Healthy Minds’; e-Health messaging sent to participants via social media and messaging platforms (Twitter, Facebook, Kik, e-mail or text) ✓ — — ✓ ✓ — 
Baños 2013104,a Spain, ECA QES N = 228; 10–13 y; 11.22 (SD 0.92) y; grades 4–6 students of similar socioeconomic background 2 wk ETIOBE mates'; online video game to improve nutritional knowledge — — — — — ✓ 
Baranowski 201138  United States, NA RCT N = 153; 10–12 y; 43.8% female; students between the 50th and 95th percentile for BMI, allowed to play video games and with internet access 9 sessions, completion Video games (‘Escape from Diab’ and ‘Nanoswarm: Invasion from Inner Space’) to promote physical activity ✓ ✓ — — ✓ — 
Bjelland 201139  Norway, ECA cRCT N = 2165; 11–13 y; mean age 11.2 (SD 0.27) y; 50% female 20 mo, mixed ‘HEIA (HEalth In Adolescents)’; computer-based education on dietary intake, screen time and physical activity — ✓ — ✓ — — 
Byrne 201287,a United States, NA QES N = 39; 12–14 y; mean age 13.1 (SD 0.70) ys; grades 7 and 8; 43.6% females 9 d Mobile phone game to promote healthy eating — ✓ — — — — 
Calear 200940,a Australia, EAP cRCT N = 1477; 12–17 y; mean age 14.34 (SD 0.75) y; 55.9% female; 5 wk; 1 module per wk ‘MoodGYM’; An interactive, internet-based program to prevent or decrease symptoms of anxiety and depression — — — — — ✓ 
Carfora 201641  Italy, ECA RCT N = 1348; 13–19 y; adolescents with a personal mobile phone with an internet connection 2 wk, daily SMS messages sent via WhatsApp focusing on positive affective consequences of fruit and vegetable intake — ✓ — — — — 
Casazza 200785,a USA, NA QES N = 6737; grades 9–12; 13–18 y; mean age 15.79 y; 65.8% female 16 wk Computer-based nutrition education and health promotion ✓ ✓ — — ✓ — 
Catenacci 201442  USA, NA RCT N = 131; 8–12 y; with access to the internet at home; no target range for percentile BMI-for-age 12 wk, weekly ‘America on the Move Family Intervention’; Interactive, Web site-based education program — — ✓ ✓ ✓ — 
Chagas 202077,a Brazil, LAC cRCT N = 319; 13–16 y; mean age 15.8 (SD 0.7) y; high school students from private schools; 57.4% female 7 to 17 d Rango Cards'; digital game app to promote health eating and improve nutritional knowledge — ✓ — — — ✓ 
Chamberland 201743  Canada, NA cRCT N = 282; 13–14 y; grades 7 and 8 students; 61% female 6 wk, daily ‘Team Nutriathlon’; Web-based platform that records daily consumption of fruits, vegetables, milk, dairy alternative products ✓ ✓ — — — — 
Chen 201144  United States, NA RCT N = 54; 12–15 y; mean age 12.52 (SD 3.15) y; self-identified as Chinese or of Chinese origin by both subject and parent 8 wk, weekly ‘Web ABC’; Web-based, interactive program to enhance problem solving and knowledge about nutrition and physical activity ✓ ✓ — — ✓ — 
Coknaz 201945  Turkey, ECA RCT N = 106; grades 3–6; 56% female; students form urban elementary schools who are preoccupied with technology and physically inactive 12 wk, 3 d per week Active video games on Nintendo Wii (eg, sports, balance, aerobics, etc.) ✓ — — — — — 
Cullen 201346,a United States, NA RCT N = 390; 12–17 y; adolescents with internet access; 54% female 8 wk, weekly ‘Teen Choice: Food & Fitness’; Website with 12 short role model videos addressing barriers to healthy eating and physical activity; blog with information and online tracking function — ✓ — ✓ ✓ — 
Brito Beck DaSilva 201947  Brazil, LAC cRCT N = 895; grades 7–9 students; mean age 14.5 (SD 1.42) y; 48.4% female 12 mo, weekly ‘Stayingfit‘;Online program to guide health eating habits and behaviors ✓ ✓ ✓ — — — 
De Bourdeaudhuij 201088  Multicountry, ECA QES N = 1050; 12–17 y; mean age 14.5 (SD 1.4) y; 49% female 2 mo Activ-O-Meter, an internet-based computer-tailored physical activity intervention — — — — ✓ — 
Direito 201548  New Zealand, EAP RCT N = 51; 14–17 y; mean age 15.7 (SD 1.2) y; 57% female 8 wk ‘AIMFIT’; Smartphone apps (nonimmersive and immersive apps) on improving fitness and physical activity — — — — ✓ — 
Ezendam 201449  Netherlands, ECA cRCT N = 883; 12–13 y; students from 20 selected schools; 45.1% female 10 wk ‘FATaintPHAT’; Web-based computer program that focuses on physical activity and healthy eating promotion ✓ ✓ — ✓ ✓ — 
Fassnacht 201550  Portugal, ECA RCT N = 49; 8–10 y; mean age 9.6 (SD 0.4) y; of any wt and ethnicity; 53.1% female 8 wk SMS reporting and monitoring by participants in response to education sessions on physical activity and nutrition — ✓ — ✓ — — 
Flett 202079,a New Zealand, EAP RCT N = 250; mean age 17.87 (SD 0.47) y; 67.6% female 3 mo HeadSpace'; mobile app to promote mental wellbeing and mindfulness meditation — — — — — ✓ 
Folkvord 201351,a Netherlands, ECA RCT N = 277; 8–10 y; grades 3–4 from primary schools  Online game that discussed healthy and non healthy food products and consumption — ✓ — — — — 
Frenn 200589,a United States, NA QES N = 132; 12–14 y; grade 7 students 8 sessions Internet based program and videos to promote physical activity and health eating — ✓ — — ✓ — 
Garde 201852,a Canada, NA RCT N = 37; 10–11 y; elementary school students; 56.8% female 2 wk ‘MobileKids Monster Manor’; Active video game — — — — ✓ — 
Goran 200553  United States, NA RCT N = 209; fourth graders; ages 8.8–11.1; mean age 9.5 (SD 0.4) y; 51% females 8 wk Interactive CD-ROM game;12 h of intervention 8 CD-ROM interactive lessons (45 min per lesson); supplemented classroom assignments and homework ✓ — — — — — 
Gorely 200982,a England, ECA nRCT N = 589; 7–11 y; from primary schools 10 mo ‘GreatFun2Run’; Interactive Web site to raise about physical activity and healthy eating ✓ — — — ✓ — 
Graves 201054  England, ECA RCT N = 58; 8–10 y; grades 4–5 from schools in low socioeconomic area; owned PS2 of PS3 video game console and self reported playing these for ≥ 2 h per week 12 wk Active video games ✓ — ✓ — ✓ — 
Greene 201255  United States, NA RCT N = 1689; mean age 19.1 (SD 1.1) y; 18-24 y; 62% female 3 mo Project Webhealth'; online curriculum (10 lessons) that focuses on fruit and vegetable intakes and physical activity — ✓ — — ✓ — 
Gribbon 201578,a Canada, NA RCT N = 26; 13–17 y; mean age 14.5 (SD 1.3) y; 100% male 3 d Active video games to promote physical activity — — — — ✓ — 
Gustafson 201956  United States, NA RCT N = 411; 14–16 y; adolescents from rural areas 8 wk, twice weekly ‘Go Big and Bring It Home’; Weekly SMS messaging covering nutrition related content sent to students ✓ ✓ — — — — 
Haerens 2007A57  Belgium, ECA cRCT N = 304; grade 7 students; mean age 13.2 (SD 0.5) y; 70.4% female 4 mo, 1 time Computer based intervention to reduce dietary fat intake — ✓ — — — — 
Haerens 2007B58  Belgium, ECA cRCT N = 2840; grades 7 and 8 from technical and vocational education schools; mean age 13.1 (SD 0.81 y); 36.6% female 9 mo (1 school year) Computer based intervention to reduce dietary fat intake and increase fruit intake — ✓ — — — — 
Hieftje 2021105,a United States, NA QES N = 560; 10–16 y; but no participants between 14 and 16 y were enrolled; several program and class sessions smokeSCREEN'; a web-based videogame to promote tobacco product use prevention, beliefs, and knowledge about tobacco use — — — — — ✓ 
Hutchinson 2020106,a Ghana, AFRICA QES N = 2625; 13–16 y; 100% female; only 2/3 of participants enrolled had access to cellphone 1.5 y SKY Girls Ghana'; a multimedia youth antismoking and girl's empowerment — — — — — ✓ 
Lanningham-Foster 200691,a USA, NA QES N = 25; 8–12 y; mean age 9.7 (SD 1.6) y; 52% female; 50% overweight and 50% were normal wt 1 time Active video games compared with a seated video game to promote physical activity and energy expenditure — — — — ✓ — 
Lanningham-Foster 200990,a United States, NA QES N = 22; 9–15 y; mean age 12 (SD 2.0) y; 50% female; 50% overweight and 50% were normal wt 1 time Active video games to promote physical activity and energy expenditure compared with traditional sedentary video games — — — — ✓ — 
Lau 201959,a China, EAP RCT N = 69; 12–16 y; mean age 13.75 (SD 0.90) y; students from government-subsidized schools and owned personal mobile phone 4 wk, weekly SMS messaging promoting healthy behaviors — — — — ✓ — 
Leme 201660  Brazil, LAC cRCT N = 253; 14–18 y; adolescent girls from government secondary schools; 100% female 7 mo, daily ‘Healthy Habits, Healthy Girls’; Weekly health messages (via WhatsApp) to students and parents. Other components include handbooks, interactive seminars and workshops, and physical activity sessions ✓ ✓ ✓ ✓ — — 
Liang 2020107  Hong Kong, EAP QES N = 87; 9–12 y; grades 4–6 students who did not participate on any school teams or extracurricular exercise classes 8 wk Active video games ✓ — ✓ — ✓ — 
Long 200492,a United States, NA QES N = 121; 12–16 y; grades 7–9; 52.1% female 1 mo Web-based curriculum on healthy behaviors — ✓ — — — — 
Lubans 201262  Australia, EAP RCT N = 357; 12–14 y; mean age 13.18 (SD 0.45) y; grade 8 students from schools in low-income areas; 100% female 12 mo (4 school terms) ‘NEAT’; Text messaging to support students through increasing physical activity, reducing sedentary behaviors and healthy eating ✓ ✓ — ✓ ✓ — 
Lubans 201661  Australia, EAP cRCT N = 361; 12–14 y; students from low-income areas who failed to meet national guideline for physical activity and/or recreational screen time, and did not have a physical impairment that would prevent them from participating in physical activity 20 wk ‘ATLAS’; Web-based, smart phone app to promote healthy behaviors; pedometers for self-monitoring. Other components include fitness equipment provision, physical activity sessions, student seminars, newsletters (parents) ✓ ✓ — ✓ ✓ — 
Mack 202080,a Germany, ECA cRCT N = 82; 9–12 y; grade 4 students 2 wk The Kids Obesity Prevention Program; active video games to promote healthy eating habits, media consumption and knowledge — ✓ — ✓ ✓ ✓ 
Maddison 200753,a New Zealand, EAP QES N = 21; 10–14 y; mean age 12.4 (SD 1.1) y; 47.6% female 1 time Active video games to increase physical activity and energy expenditure, compared with rest and nonactive video games — — — — ✓ — 
Marks 200663,a United States, NA RCT N = 319; mean age 12.15 (SD 0.95) y; grades 6–8; 100% female 2 wk Web-based physical activity intervention, compared with print workbook with the same information — — — — ✓ — 
Mauriello 201064,a United States, NA RCT N = 1800; mean age 15.97 y; from 8 high schools; 50.8% female; 7.6% at risk for overweight and 15.5% overweight 14 mo Computer-based program to promote physical activity, fruit and vegetable consumption, and TV viewing — ✓ — ✓ ✓ — 
Mellecker 200894,a Hong Kong, EAP QES N = 18; 6–12 y 1 time Active video games to increase physical activity and energy expenditure compared with rest and seated computer games — — — — ✓ — 
Hardman 201465,a Brazil, LAC RCT N = 2155; mean age 18.4 (SD 2.3) y; 15–24 y; evening-class students from public schools 10 mo ‘Saúde na Boa’; Website provides health promotion information and resources; other components include posters, newsletters, fruit distribution, physical education kit — — — ✓ — — 
Newton 200966,a Australia, EAP cRCT N = 764; 13 y olds; mean age 13.08 (SD 0.58) y; students from urban independent schools; 40% female 12 mo Online, internet-based modules on healthy behaviors, including alcohol and cannabis — ✓ — — — ✓ 
Nollen 201467  United States, NA RCT N = 51; 9–14 y; mean age 11.3 (SD 1.6) y; low-income, racial and ethnic minority girls recruited through afterschool programs 12 wk, weekly Mobile app for goal setting, self-monitoring, positive feedback, and information on healthy behaviors ✓ ✓ — ✓ — — 
O’Kearney 200684,a Australia, EAP nRCT N = 78; 15–16 y; 100% male; any student at the school within the age range regardless of history and/or risk of anxiety and depression 5 wk; one module per week ‘MoodGYM’; An interactive, internet-based program to prevent or decrease symptoms of anxiety and depression — — — — — ✓ 
O’Kearney 200983,a Australia, EAP nRCT N = 157; 15–16 y; 100% female; any student at the school within the age range regardless of history and/or risk of anxiety and depression 6 wk; one module per week ‘MoodGYM’; An interactive, internet-based program to prevent or decrease symptoms of anxiety and depression — — — — — ✓ 
Okazaki 201068,a Japan, EAP RCT N = 81; mean age 19.25 (SD 1.25) y; 35.1% female; healthy university students 4 mo, weekly Internet-based physical activity program — — — — ✓ — 
Paineau 200869  France, ECA RCT N = 1013; from 54 elementary schools in Paris; mean age 7.7 y; 52.2% female; 18% overweight 8 mo Internet-based programs monitoring and promoting dietary intake physical activity, meal preparation and quality of life ✓ ✓ — — — — 
Pedersen 201695,a Denmark, ECA QES N = 1488; 11–16 y; mean age 12.9 y; 50/50 male and female 11 wk Feedback text messaging intervention on behavior, self-efficacy, outcome expectations on fruit and vegetable intake — ✓ — — — — 
Pfeiffer 201970,a United States, NA RCT N = 1519; 10–14 y; grades 5–8; racially diverse, urban students from low SES areas; 100% female 17 wk ‘Girls on the Move’; Internet-based sessions; students receive motivational and feedback messages via iPad; other components include physical activity and motivational interviews ✓ — — — ✓ — 
Pittman 202096  United States, NA QES N = 98; 11–15 y; grades 7–8; able to read English, access to cellphone with unlimited texting (for the intervention groups) 10 wk, weekly Website and social media and text messaging platform to promote physical activity and healthy eating. Activity trackers were also provided to monitor physical activity ✓ — — — — — 
Rerksuppaphol 201771  Thailand, EAP RCT N = 218; mean age 10.7 (SD 3.1) y; grades1-6 from public elementary schools; 51% female 4 mo, encouraged daily use Internet-based education on healthy eating and physical activity ✓ — — — — — 
Schwinn 201497,a United States, NA QES N = 67; 10–12 y; mean age 11.85 (SD 0.88) y; lived in public housing with their mothers; from 27 US states; 100% female 3 wk, weekly Web-based program to focus on healthy behaviors and mental health, and positive relationships between mothers and daughters — ✓ — — ✓ — 
Shen 2020108  China, EAP QES N = 573; mean age 13.1 (SD 0.4) y 3 mo Smartphone app to provide health education and feedback, and promote nutrition knowledge, perceptions, and nutritional status ✓ — — — — ✓ 
Shukri 201998,a Malaysia, EAP QES N = 201; mean age 10.5 y; 13.9% underweight, 13.4% overweight, and 8% were obese 3 wk Computer-based program with interactive games and animated presentations to promote healthy eating, nutrition, and physical activity — ✓ — — — — 
Shum 2019109,a Hong Kong, EAP QES N = 459; 8–12 y; grades 4 and 5 students 2 wk The Adventures of DoReMiFa, digital game-based intervention; mental health and wellbeing enhancement — — — — — ✓ 
Simons 201572  Netherlands, ECA RCT N = 260; 12–17 y; 10% females 10 mo Active video games ✓ ✓ — ✓ ✓ — 
Sousa 202099,a Portugal, ECA QES N = 353; 12–16 y; mean age 12.43 (SD 0.87) y; with easy access to internet, smartphone or tablet; 57.8% female 6 mo ‘TeenPower’; Mobile app for adolescents to provide educational resources for healthy behaviors — ✓ — — ✓ ✓ 
Sriramatr 201473  Thailand, EAP RCT N = 220; 100% female; mean age 19 y; university-aged students 3 mo Internet-based program to promote and maintain leisure-time physical activity — — — — ✓ — 
Straker 201381  Australia, EAP RCT N = 56; 10–12 y; 51.8% female 8 wk Active video games, compared with traditional sedentary video games and no games at all to promote physical activity and fitness — — — — ✓ — 
Turnin 2015100,a France, ECA QES N = 580; 11–16 y 6 mo, daily ‘NutriAdvice’; Freestanding computer kiosks that allow students to select the type of foods they want to eat, and how much ✓ — — — —  
Unnithan 2006101,a United States, NA QES N = 22; 11–17 y; 27.3% female; 50% overweight; 50% normal wt 1 time Active video games (Dance Dance Revolution) to increase physical activity and energy expenditure compared with rest and seated computer games — — — — ✓ — 
Walther 201474,a Germany, ECA cRCT N = 2303; 10–14 y; mean age 12.0 (SD 0.83) y; grades 6–7 3 mo Web-based platform to raise awareness about internet and electronic media use — — — ✓ — — 
Whittaker 201775,a New Zealand, EAP RCT N = 855; grades 9–12; mean age 14.3 (SD 0.9) y; owns/use a Vodafone mobile phone; 68.3% female 9 d, daily ‘MEMO’; Video diary messages and text messages sent via mobile phones to promote healthy behaviors and prevent depression — — — — — ✓ 
Yang 2017102  South Korea, EAP QES N = 768; grade 4 (9–10 y) and grade 7(12–13 y) 1 school year, daily Online based, video curriculum to promote physical activity and healthy eating ✓ — — — — — 

AN, anthropometry; cRCT, cluster randomized controlled trial; DI, dietary intake; EAP, East Asia Pacific; ECA, Europe and Central Asia; LAC, Latin America and Caribbean; MENA, Middle East and North Africa; NA, North America; PA, physical activity; QES, quasi-experimental study; RCT, randomized controlled trial; SA, South Asia; SB, sedentary behavior; SSA, SubSaharan Africa; ST, screen time; —, not applicable; ✓, present.

a

Studies were excluded from analysis for reasons including, unclear sample sizes at follow-up or postintervention, lack of disaggregation of data between intervention and control groups, no outcomes of interest.

Most interventions were conducted in school settings (n = 46). Fourteen studies were conducted in the home environment,38,42,44,48,51,52, 54,63,72,73,81,87,97  and 5 studies were conducted in a laboratory clinic setting.90,91,93,94,101  The duration of most interventions ranged from a minimum of 2 weeks (daily frequency) to 20 months. Five studies conducted the intervention in a single day period, evaluating the use of exergames and active video games on physical activity measures compared with a rest state.90,91,93,94,101 

The mean age of study participants ranged from 7.7 to 19.25 years. Twenty-nine studies recruited participants whose collective mean age or age range fell below 13 years of age.38,39,42,45,5054,63,67,69,71,74, 76,8082,90,91,93,94,97,102104,107,109  The remaining studies recruited participants in their teenage years, with the majority conducted with participants between the ages of 13 and 16 years. Of these, five recruited participants with a mean age between 17 and 19.9 years.55,65,68,73,79 

Among the RCTs, 19 studies used an internet-based platform36,40,4244,46, 47,49,51,55,63,65,66,6871,73,74  to conduct the intervention; 5 studies computer-based interventions,39,53, 57,58,64  mostly CD-ROMs and computer programs; 8 studies used text messaging, SMS or other messaging platforms (eg, WhatsApp) to interact with the students and parents37,41,50,56,59,60,62,75 ; 5 studies developed or used a mobile app to deliver the interventions48,61,67,77,79 ; and 9 studies used active video games or exergames to reduce sedentary behavior and promote physical activity and healthy behaviors.38,45,52,54,72,76,78,80,81  Of the QES studies, three studies used a computer-based intervention 85,98,100 ; nine studies used an internet-based platform8284,88,89,92,97,102,109 ; 10 studies used active video games or exergames86,90,91,93,94,101, 103105,107 ; and two studies used messaging platforms95,96  and three, mobile apps.87,99,108  One study was multimedia and incorporated elements of internet-based, mobile, and messaging platforms.106 

Of the 46 RCTs, the majority of studies (n = 24) were rated with a risk of bias of some concerns.36,3947,49,50,53,54,5658, 6568,71,77,80  Seven studies were rated with a high risk of bias.38,51, 55,59,64,76,78  The main concerns arose from selective reporting, incomplete outcome data, and high attrition. Fifteen studies were assessed with a low risk of bias.37,48, 52,6063,69,70,7275,79,81  Random sequence generation was adequately reported by 29 of 46 studies; however, allocation concealment was unclear or not reported in most studies (n = 29). Only 17 studies reported methods of allocation concealment. By nature of the intervention design, authors of most studies reported that blinding of participants was not possible; only one study reported blinding of participants.60  Blinding of personnel or outcome assessors was reported in 12 studies. Attrition bias (defined as a loss to follow up of >20%) was assessed as high risk in 19 studies; reasons provided by study authors include absence in school on day of post intervention assessment, refusal to be measured, left the school or program, and pregnancy.

Of the 28 QES and nonrandomized trials, all were assessed with a moderate risk of bias8385, 8792,94,96101,103106,108,109  with the exception of 6 studies that were rated with a low risk of bias.82,86,93,95,102,107  Main reasons for downgrading study quality to a moderate risk of bias include poor adjustment of confounding variables and bias due to deviations from intended interventions.

When compared with the control, digital-based interventions may result in a slight reduction of body fat percentage (MD −0.35%, 95% CI −0.63 to −0.06; participants = 615; studies = 5; I2 = 0%; P = .02; low quality of evidence) (Fig 3). Of the five studies, two were online or web-based interventions,44,53  two were active video games,45,54  and one study used a specific messaging platform.62  No major differences were indicated when analyzed by intervention type. The analysis for waist circumference measured in centimeters showed that the control group had a positive effect compared with the intervention (MD 0.35, 95% CI 0.08 to 0.62; participants = 3102; studies = 7; I2 = 28%; P = .01; low quality of evidence) (Supplemental Information). Six of the 7 studies implemented online or web-based interventions on the phone or computer.47,49,60,61,69,71  The interventions likely resulted in little to no difference in body mass index (BMI) as measured as kg/m2 (MD −0.01, 95% CI −0.16 to 0.13; participants = 5285; studies = 14; I2 = 33%; P = .88; moderate quality of evidence) (Supplemental Information) and may have resulted in little to no difference in BMI z-scores (MD −0.02, 95% CI −0.06 to 0.03; participants = 3306; studies = 11; I2 = 95%; P = .47; low quality of evidence) (Supplemental Information). Sensitivity analyses were conducted to determine effects by intervention type (computer, internet-based, messaging platforms, mobile apps, and video games); no significant difference was noted across intervention types.

FIGURE 3

Forest plot of body fat percentage. aT1 values reported (immediate post intervention). bPostintervention (12 weeks) timepoint used.

FIGURE 3

Forest plot of body fat percentage. aT1 values reported (immediate post intervention). bPostintervention (12 weeks) timepoint used.

Close modal

Eight QES reported anthropometric outcomes,82,85,86,96,100,102,107,108  of which two studies82,85  were excluded from analysis due to incomplete and missing data. Digital-based interventions likely resulted in little to no difference in BMI z-scores (MD −0.10, 95%CI −0.15 to −0.04; participants = 1398; studies = 3; I2 = 0%; P = .0004; moderate quality of evidence) (Fig 4).

FIGURE 4

Forest plot of BMI z-score.

FIGURE 4

Forest plot of BMI z-score.

Close modal

The evidence is very uncertain about effects on body fat percentage (MD −0.85, 95% CI: −1.33 to −0.37; participants = 1111; studies = 4; I2 = 0%; P = .0006; very low quality of evidence) (Supplemental Information). Given the limited number of studies per anthropometric outcome, sensitivity analyses per intervention type were not possible. Other anthropometric outcomes can be found in the Supplemental Information.

Seventeen RCTs reported various outcomes on screen time and screen use,36,37,39,42,46,47,49,50,54,6062,64, 65,67,72,74  of which 5 were excluded46,47,64,65,72  due to incomplete or missing data, no poolable outcomes, and different author-defined metrics of similar outcomes. When compared with control or no treatment groups, digital-based interventions probably result in little to no difference on screen time on all types of media, measured as minutes per day (MD −17.32, 95% CI −42.69 to 8.05; participants = 1894; studies = 8; I2 = 90%; P = .18; moderate quality of evidence) (Fig 5). Sensitivity analysis by intervention type showed a possible improvement of mobile app interventions on screen time (MD −47.11, 95%CI −77.06 to −17.15; studies = 2), although the sample size is limited. Overall, intervention effects likely did not reduce screen time in terms of computer use and video gaming and TV and DVD use (Supplemental Information).

FIGURE 5

Forest plot of screen time all media (author defined; minutes per day). aFour-month timepoint used (end of intervention). bReported as hours per day, converted to minutes per day. cPostintervention (12 weeks) timepoint used. dPostintervention (8 months) timepoint used. eReported as hours per day, converted to minutes per day.

FIGURE 5

Forest plot of screen time all media (author defined; minutes per day). aFour-month timepoint used (end of intervention). bReported as hours per day, converted to minutes per day. cPostintervention (12 weeks) timepoint used. dPostintervention (8 months) timepoint used. eReported as hours per day, converted to minutes per day.

Close modal

A total of eight RCTs36,42, 47,48,54,60,62,81 reported leisure-based sedentary behavior as defined by study authors. Of these, two studies were excluded from meta-analysis due to incomplete or missing data and no poolable outcomes.36,47  Digital-based interventions, compared with the control or no treatment group, likely reduces sedentary behavior (mins per day) (MD −19.62, 95% CI −36.60 to −2.65; participants = 872; studies = 6; I2 = 49%; P = .02; moderate quality of evidence) (Fig 6). Sensitivity analysis by intervention type showed that messaging-based interventions had a significant effect on sedentary behavior (MD −37.45; 95% CI −51.76 to −23.15; studies = 2), compared with internet-based platforms42  or active video games.54,81 

FIGURE 6

Forest plot of sedentary behavior (author-defined; minutes per day). aPostintervention (12 weeks) timepoint used. bReported as hours per day, converted to minutes per day; only weekend values reported.

FIGURE 6

Forest plot of sedentary behavior (author-defined; minutes per day). aPostintervention (12 weeks) timepoint used. bReported as hours per day, converted to minutes per day; only weekend values reported.

Close modal

Twenty-four RCTs3638,42,44,46,48,49,5255,59,6164,68, 70,72,73,78,80,81 reported physical activity outcomes. Eleven studies were excluded from the meta-analysis due to incomplete or missing data or no poolable outcomes.46,52,55,59,63,64, 68,70,72,78,80  Overall, the evidence is very uncertain on the effect of digital-based interventions on moderate and vigorous physical activity (MVPA) (minutes per day) and daily step count (Supplemental Information). Sensitivity analysis indicated no significant effect for any particular type of intervention for both outcomes. Interventions had little to no effect on counts per minute, but the evidence is very uncertain (MD 51.45, 95% CI −17.08 to −117.98; participants = 1071; studies = 8; I2 = 94%; P = .14; very low quality of evidence) (Supplemental Information). Sensitivity analysis showed that video games or exergames had a significant effect in the intervention group (MD 19.88, 95%CI 13.91 to 25.84; studies = 2), although the sample size is limited.

Twelve QES82,85,86,8891,93,94,97,101,107  reported physical activity outcomes. Of these, three studies were included in the analysis for MVPA (mins per day). The interventions likely increased MVPA among participants compared with control (MD 3.92, 95% CI 0.54 to 7.30; participants = 1210; studies = 3; I2 = 1%; P = .02; low quality of evidence) (Fig 7).

FIGURE 7

Forest plot of moderate-to-vigorous physical activity (minutes per day). aReported as minutes per week, converted to minutes per day. bMean change (minutes per day) from baseline to postintervention assessment.

FIGURE 7

Forest plot of moderate-to-vigorous physical activity (minutes per day). aReported as minutes per week, converted to minutes per day. bMean change (minutes per day) from baseline to postintervention assessment.

Close modal

Energy expenditure was another commonly reported outcome; however, high heterogeneity in used metrics and units restricted any pooled analysis. Overall, evidence suggests that energy expenditure increased during active video game or active computer play, compared with sedentary video games, leisure computer use, or rest.

Twenty-four RCTs38,39,41,43,44,46, 47,4951,5558,6062,64,66,67,69,72,77,80  reported outcomes for dietary intake, such as intake of macronutrients, total energy, and specific food groups. Of these, 12 studies were not included in the meta-analysis due to incomplete and missing data, and no poolable outcomes.39,43,46,47,51,55,61,64,66,72,77,80  No significant effects were observed on fruit consumption measured as servings or pieces per day, daily energy intake, or fat intake (Supplemental Information). However, interventions did show a slight increase in the consumption of both fruit and vegetables, measured as servings or pieces per day (MD 0.63, 95% CI 0.21 to 1.04; participants = 1328; studies = 6; I2 = 70%; P = .003; high quality of evidence) (Fig 8). Sensitivity analysis indicated that messaging-type interventions had a significant effect on fruit and vegetable intake (MD 0.98 servings per day, 95% CI 0.28 to 1.68, participants = 1093; studies = 3), compared with other media types.

FIGURE 8

Forest plot of fruit and vegetable intake (servings or portions per day). aPost 2 (immediate post intervention) values used. bTwo arms combined in intervention group. cT1 postintervention used.

FIGURE 8

Forest plot of fruit and vegetable intake (servings or portions per day). aPost 2 (immediate post intervention) values used. bTwo arms combined in intervention group. cT1 postintervention used.

Close modal

Several RCTs also reported intake of snacks47,49,51,72  and unhealthy food products, and sugar-sweetened beverages39,47,49,56,58,67,72 ; however, the majority of studies reported no significant effect of the intervention.

Fifteen studies assessed knowledge, intentions and perceptions of school-aged children and adolescents in nutrition, dietary intake and physical activity, with a few focused on sexual health and substance use education and prevention, targeting older adolescents.44,63, 66,76,77,80, 85,92,99,103106,108,109  Four RCT studies showed that overall knowledge acquisition in physical activity and nutrition recommenda tions and sexual health was greater in the intervention group, compared with the control group, as indicated by higher mean posttest scores.44,63,66,80  Three of the 4 studies used a web-based platform44,63,66  and one study used a gaming platform80  to provide the education. No difference between intervention and control groups was noted for Chagas et al.77  Similar findings were noted in the QES findings, whereby knowledge acquisition in nutrition, healthy lifestyles, and positive life perspectives was greatest in the intervention group compared with the control for the majority of studies.85,92,99,103105,109 

Eight studies examined mental health wellbeing, prevention of mental health illness, and promotion of mindfulness, self-esteem, and mood.40,75,76,79,83,84,109  Two RCTs40,75  and two QES studies83,84  used interventions to prevent anxiety and depression in healthy SACA. One RCT40  and both QES83,84  implemented the MoodGYM intervention, an internet-based, self-paced interactive program to reduce the onset of anxiety and depressive symptoms. Calear et al40  found the MoodGYM intervention condition reduced anxiety symptoms compared with the control group (P < .001). Another study also saw a significant rate of decline in depression scores in their female participants across the intervention period.83  A similar study examined the same intervention with only male participants, and showed no significant change in scores from preintervention to immediate postintervention.84  Whittaker et al,75  also found no difference between intervention and control groups.

Among the included studies, there were no studies that assessed specific intervention effects on online risk exposure including, violent, hateful, and pornographic and/or unwanted sexual content; bullying, harassing or stalking, or sexual exploitation of SACA.

This review provides a comprehensive review of 74 studies across 25 countries, evaluating digital-based interventions for universal health promotion in healthy, normative BMI children and adolescents, and included 25 new studies conducted over the last 5 years.36,37,41,43,45,47,50,52,56,5961,71, 72,7580,103,105109  This review found that digital-based interventions resulted in a small but significant uptake in fruit and vegetable consumption, and a small but significant reduction in both body fat percentage and sedentary behavior. There is moderate to low certainty of evidence to suggest improvements in outcomes for physical activity and other dietary and anthropometric outcomes. As well, this review found that certain media types of intervention delivery had better effects on specific outcomes. Mobile apps showed improvements in screen time compared with other media platforms, while messaging platforms showed clear improvements in fruit and vegetable intake and sedentary behavior. Unsurprisingly, active video games showed improvements in counts per minute, compared with other media.

These findings support existing evidence of digital-based interventions for health promotion and behavior change.20,110112  Some reviews showed the effectiveness of active video games and exergames to encourage and promote physical activity and energy expenditure during leisure play time.113116  Others have noted the benefits of mobile phones to deliver short messages and engage with participants through apps and social media.20,112  Mobile phones or internet-based apps are more portable, which may lead to greater potential in intervening and influencing behaviors regardless of setting.110  This portability can help to deliver interventions more effectively and consistently, leading to longer-lasting results. This review also noted that the type of digital platform varied depending on the age of participants. Interventions delivered through mobile phones, computers, and other internet platforms were largely implemented with older participants in their early to late teenage years. Comparatively, active video games were used among younger populations, typically 5 to 13 years of age. These observations suggest that older participants were selected due to the greater likelihood of possessing a mobile phone or having the capacity and knowledge to use phones, social media apps, or computer programs, thereby reducing possible confounders or the need for additional training. Active video and interactive games are considered more appropriate and fun for younger populations, possibly in an effort for greater attention, compliance, and adherence to the intervention.

Most interventions were multicomponent, involving parents or primary caregivers, especially when the mean participant age fell below the age of majority. Previous systematic reviews report that interventions may be more effective when caregivers were involved.20,110  Caregiver involvement may be an important moderator, especially in early childhood during which they have less autonomy and control over their environments and are guided in their behaviors.110  However, all studies in this review did not isolate the effects of different intervention components. While this makes it difficult to evaluate the true effect of the digital component, this observation suggests that universal health promotion and behavioral change in school-aged children and adolescents may be most effective when designed as a multifaceted strategy. These may be considerations for future research when designing age-appropriate interventions.

It is important to note that there is growing evidence highlighting increased online risk, such as inappropriate content, unhealthy advertising, bullying, harassment and stalking, and sexual solicitation and exploitation, with increased use and interaction with online platforms.6  This review found no intervention study that specifically focused on preventing this online risk exposure. While cyberbullying is an acknowledged online risk, these studies were included in the mental health review of the same series.117  A recent rapid review showed that the majority of existing data assessing online risk exposure comes from survey and cross-sectional studies, and pointed to a substantial research gap in assessing the differences between online risk and harm, the long-term effects on development and well-being, and strategies and protective factors that can mitigate online risk exposure.6 

To our knowledge, this is the first comprehensive systematic review to examine and compare digital media interventions through various mediums for universal health promotion in healthy SACA. However, this review unfortunately presents similar findings to an earlier scoping review conducted by the authors and related systematic reviews, pertaining to gaps in the evidence and methodologies. An overwhelming majority of studies are conducted in high-income settings; in this review, 82% of the interventions. Despite current knowledge that over the last decade, the number of people worldwide possessing a mobile phone and accessing the internet has increased significantly, there remains a stark “digital divide” between those living in LMICs and those in high-income countries.6,118 

The heterogeneity of available data due to diverse interventions, a lack of standardization of outcome metrics, vague and diverse methodologies, and the use of subjective tools, such as self-reporting, also limit the findings of this review. Thus, generalizability of these findings proves difficult. Despite a large number of included studies, few were of high-quality, with the majority lacking description and/or implementation of robust methodologies. Consistent with existing literature,20,112  risk of bias was notable across studies; the most frequent bias was failure to blind participants and personnel, attrition bias, and selective reporting. This limits and introduces a level of uncertainty regarding the effectiveness of these interventions. In view of these quality issues, the findings should be interpreted with caution.

Digital interventions offer great potential to promote universal health, and address health system shortcomings such as lack of access to services and resources, and education. This may have a significant impact on behaviors and lifestyles of our growing generations, lending to long-term, positive benefits in human health and development. Although this remains a relatively new research area with the majority of studies having been published in the last 5 to 10 years, largely in North America and Europe, continued efforts are needed to evaluate digital media as effective and accessible intervention platforms for health promotion in SACA globally. It is recommended that future research considers standardization of study design methodology and reporting of results and assesses the barriers and other factors that limit this type of research and program implementation in low- and middle-income populations and settings. Greater attention to interventions which assess the risk of online media including cyberbullying, sexual content, or exploitation and violence are needed to better support and inform universal health promotion efforts for SACA worldwide and inform policies and programs.

Dr Bhutta and Mr Vaivada conceptualized and designed the study; Ms Oh conceptualized and designed the study, screened the search results, screened the retrieved papers against the inclusion criteria, appraised the quality of papers, extracted the data, completed the data analysis, and drafted the initial manuscript; Dr Carducci conceptualized and designed the study, screened the search results, screened the retrieved papers against the inclusion criteria, appraised the quality of papers, extracted the data, completed the data analysis, and drafted the initial manuscript; and all authors reviewed, revised, and approved the final manuscript as submitted and agreed to be accountable for all aspects of the work.

FUNDING: This work was supported by a grant from the International Development Research Centre (#109010-001). The funder did not participate in the work. Core funding support was also provided by the SickKids Centre for Global Child Health in Toronto.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no financial relationships relevant to this article to disclose.

This systematic review was registered within the International Prospective Register of Systematic Reviews (www.crd.york.ac.uk/prospero/) (identifier, CRD42020213361).

     
  • LMICs

    low- and middle-income countries

  •  
  • SACA

    school-aged children and adolescents

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Supplementary data