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In current years, digital well being interventions, significantly eHealth and cellular well being (mHealth) platforms, have proven promise in selling wholesome behaviors []. eHealth refers to the usage of digital applied sciences, reminiscent of web sites and on-line platforms, to ship well being providers and knowledge, whereas mHealth particularly refers to mobile-based well being applied sciences, reminiscent of smartphone apps, SMS textual content messages, and wearable units []. These interventions use digital instruments, reminiscent of smartphones, apps, wearable exercise trackers, and web sites, to ship health-related data, social help, engagement methods, and habits change applications []. Key goal behaviors for well being promotion in youngsters and adolescents embody bodily exercise, sedentary habits, sleep, and wholesome consuming. These behaviors are thought of important for general well being and continual illness prevention, as they’re modifiable danger components with a considerable influence on long-term well being outcomes [-]. Higher ranges of bodily exercise, particularly average to vigorous bodily exercise (MVPA), are related to a diminished danger of continual ailments, reminiscent of heart problems and kind 2 diabetes [,]. In distinction, excessive ranges of sedentary habits are linked to weight problems and metabolic syndrome [,]. Adequate sleep in each high quality and length is important for cognitive perform and psychological well being [], whereas a balanced weight loss program is crucial for sustaining a wholesome weight and supporting optimum development and improvement []. It is necessary to acknowledge that, along with bodily exercise, sedentary habits, sleep, and wholesome consuming, different life-style components—reminiscent of stress administration, social engagement, and substance use—additionally play important roles in general well being. In addition, weight-related outcomes, reminiscent of BMI, are generally used indicators of lifestyle-related well being standing as they mirror the cumulative influence of a number of well being behaviors []. By focusing on these behaviors early, eHealth and mHealth interventions provide useful alternatives to determine lifelong wholesome habits, which may have a profound influence on decreasing the danger of continual ailments and enhancing general well-being in the long run.
Despite recognized advantages, nonadherence to advisable ranges of bodily exercise, sedentary habits, sleep, and wholesome consuming stays prevalent amongst youngsters and adolescents [,], highlighting the necessity for cost-effective and scalable interventions. Widespread web entry has enabled the usage of eHealth and mHealth interventions that use habits change methods, reminiscent of aim setting, self-monitoring, suggestions, and social help []. They additionally leverage gamification, tailor-made messaging, and machine studying to boost engagement []. Addressing well being behaviors in youngsters and adolescents is crucial as a result of important influence on their instant well-being and long-term well being outcomes []. Early intervention throughout these adolescence can set up a basis for a wholesome lifespan []. Moreover, youngsters and adolescents are significantly receptive to digital expertise, rendering eHealth and mHealth interventions promising for this demographic [].
The proof base for kids’s eHealth and mHealth interventions has advanced considerably. Early research have been typically small, with decrease high quality designs, however the subject has advanced to include larger high quality research, significantly randomized managed trials (RCTs), and statistically powered research, which supply extra strong proof of effectiveness []. This development has been accompanied by quite a few systematic evaluations, every with various foci and inclusion standards [,]. Despite the enlargement of the proof base, the sheer quantity and heterogeneity of research make it difficult to discern the general effectiveness of those interventions. Umbrella evaluate methodology gives the flexibility to consolidate proof and make clear which eHealth and mHealth intervention approaches are efficient and most promising. To date, there have been 2 umbrella evaluations, however with a restricted scope. Rodríguez-González et al [] centered on app-based interventions, discovering that apps can enhance bodily exercise, cut back sedentary habits, and improve weight loss program high quality amongst youngsters and adults. Prowse and Carsley [], specializing in youngsters’s diet, revealed small and inconsistent results of digital interventions on general dietary outcomes, with promising outcomes restricted to rising fruit and vegetable consumption. These evaluations had slim scopes, focusing completely on diet interventions [] or solely on apps [] or evaluating youngsters and adults collectively []. Neither evaluate quantitatively pooled outcomes by means of meta-analysis [,].
A complete umbrella evaluate is required to information analysis, follow, and coverage on this evolving subject. This evaluate goals to offer a radical overview of the effectiveness of eHealth and mHealth interventions on bodily exercise, sedentary habits, sleep, and dietary outcomes in youngsters and adolescents, utilizing high-quality analysis designs and quantitative meta-analysis. These 4 outcomes (bodily exercise, sedentary habits, sleep, and weight loss program) have been chosen as a result of they’re important, modifiable behaviors that considerably affect each instant and long-term well being, significantly in youngsters and adolescents [-]. Subgroup analyses deal with age, intercourse, inhabitants kind, intervention kind, and examine high quality. Our evaluate gives a complete evaluation throughout numerous subgroups, addressing gaps within the present literature and offering insights to tell future analysis and scientific follow.
The protocol was preregistered on PROSPERO (CRD42024537019) and is reported in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) tips () [].
The inclusion and exclusion standards have been formulated utilizing the contributors, intervention, comparability, outcomes, and examine design framework, as proven in [].
Inclusion standards
Exclusion standards
Nine databases (CINAHL, Cochrane Library, Embase through OVID, MEDLINE through OVID, Emcare through OVID, ProQuest Central, ProQuest Nursing and Allied Health Source, PsycINFO, and Scopus) have been systematically searched utilizing topic headings, key phrases, and Medical Subject Headings (MeSH) phrases associated to “eHealth,” “mHealth,” “physical activity,” “sedentary behaviour,” “sleep,” “diet,” “children and adolescents,” and “systematic review” (confer with for the complete search technique). The database searches have been restricted to peer-reviewed journal articles printed within the English language till April 6, 2024.
Search outcomes have been imported into EndNote (Version 20; Clarivate), and duplicates have been eliminated. Results have been then exported to Covidence (Veritas Health Innovation Ltd), and title and summary screening, full-text screening, information extraction, and examine high quality scoring have been carried out. All screening, information extraction, and examine high quality assessments have been accomplished independently in duplicate by 2 reviewers (BS, MA, AES, MFV, CG, JMP, ZY, CV, CK, JF, IT, JD, BS, HB, CH, KW, MS-R, SS, AMB, KS, and CM), and discrepancies have been resolved by a 3rd reviewer. A standardized Covidence information extraction kind was used to extract data on examine traits, inhabitants traits, intervention traits, outcomes of curiosity, and outcomes.
Study high quality of the included evaluations was evaluated utilizing the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews-2) guidelines [] by 2 reviewers (BS, MA, AES, MFV, CG, JMP, ZY, CV, CK, JF, IT, JD, BS, HB, CH, KW, MS-R, SS, AMB, KS, and CM), and discrepancies have been resolved by a 3rd reviewer. This instrument entails 16 objects which might be scored as both sure, partial sure, or no. Seven of the AMSTAR-2 objects are thought of important, and 9 objects are noncritical []. The important objects embody protocol registration, search technique, examine exclusions, examine high quality evaluation, meta-analysis strategies, examine high quality interpretation, and publication bias. Reviews have been scored as both critically low confidence (>1 important weak spot and ≥3 noncritical weaknesses), low confidence (>1 important weak spot and <3 noncritical weaknesses), average confidence (1 important weak spot and <3 noncritical weaknesses), or excessive confidence (no important weak spot and <3 noncritical weaknesses) [].
The evaluation of overlap between the research included within the evaluations was carried out utilizing the corrected lined space (CCA) []. The CCA was calculated utilizing the next equation:
CCA = (N – r) ÷ (r × c) – r
the place N represents the whole variety of RCTs throughout all evaluations (together with duplicates), r is the variety of distinctive RCTs (excluding duplicates), and c is the variety of evaluations included within the evaluation []. The classes used to categorise the overlap have been 0% to five% (slight), 6% to 10% (average), 11% to fifteen% (excessive), and greater than 15% (very excessive overlap) [].
Meta-analyses of the outcomes of curiosity have been performed by pooling the impact sizes and 95% CIs reported in every meta-analysis, utilizing a random-effects mannequin for outcomes that have been reported in not less than 2 research. The outcomes of all meta-analyses have been introduced visually utilizing forest plots. Separate meta-analyses have been carried out for standardized impact sizes (eg, standardized imply distinction [SMD]) and unstandardized impact sizes (eg, imply distinction [MD]). The meta-analyzed impact sizes (SMDs or MDs) have been reported with their corresponding 95% CIs. For meta-analyses of SMD, constructive impact sizes point out that the results favor the intervention. Subgroup analyses have been carried out for age (people aged <13 years or ≥13 years), intercourse (feminine, male, or not reported), inhabitants (common inhabitants and continual illness), intervention kind (cellular apps, net primarily based, SMS textual content messages, and blended [which included combinations of at least 3 of the other modes]), and AMSTAR-2 score (critically low, low, average, or excessive) if greater than 1 eligible meta-analysis was included in not less than 2 of the teams. The classification of eHealth and mHealth interventions was knowledgeable by established definitions and frameworks within the digital well being literature, distinguishing eHealth as primarily web-based interventions and mHealth as mobile-based (eg, apps, SMS textual content messages, and wearable units) []. This method aligned with earlier umbrella evaluations and systematic evaluations evaluating digital well being instruments for habits change in youth populations [,]. The I2 statistic was used to quantify the proportion of the general final result variability [], with the next values used to find out the extent of heterogeneity: low heterogeneity: I2=0% to 25%; average heterogeneity: I2 ≥25% to 50%; and excessive heterogeneity: I2 ≥75% to 100% []. To consider potential publication bias, funnel plots have been generated, and the presence of asymmetries or lacking information sections was visually inspected for meta-analyses that included not less than 10 research []. The magnitude of impact was labeled utilizing the next standards: small impact: lower than 0.20, medium impact: 0.20 to 0.50, and enormous impact: higher than 0.50 []. A P worth of <.05 was thought of statistically important. All meta-analyses have been carried out utilizing Stata (model 16, StataCorp) software program.
Levels of proof and grades for suggestions [] have been used to categorise the general stage of proof as grade A—constant stage 1 research (ie, systematic evaluations of RCTs or particular person RCTs), grade B—constant stage 2 (ie, systematic evaluations of cohort research or particular person cohort research) or 3 research (ie, systematic evaluations of case-control research or particular person case-control research) or extrapolations from stage 1 research, grade C—stage 4 research (ie, case sequence) or extrapolations from stage 2 or 3 research, or grade D—stage 5 proof (ie, professional opinion with out express important appraisal) or troublingly inconsistent or inconclusive research of any stage [].
The database search recognized 6056 outcomes. After screening, 25 (0.4%) systematic evaluations and meta-analyses met the eligibility standards and have been included within the evaluation. The PRISMA flowchart, exhibiting the explanations for exclusions, is introduced in . A listing of causes for excluding research after the full-text evaluate might be present in [-]. The 25 included evaluations comprised 440 trials involving 133,501 contributors. The general CCA was 3.28%, indicating a slight overlap throughout the systematic evaluations.
A abstract of the participant demographics, reminiscent of age and inhabitants teams, in addition to the traits of the interventions, is proven in [-]. Of the 25 included research, imply participant age in most (n=23, 92%) systematic evaluations ranged between 3 and 18 years, and all (n=25, 100%) evaluations included each feminine and male contributors. Most (n=8, 32%) research within the systematic evaluations concerned wholesome youngsters and adolescents [-]), 4(16%) systematic evaluations concerned youngsters and adolescents who have been chubby or with weight problems [-], 1 (4%) concerned infants and kids [], 1 (4%) concerned youngsters with a continual illness [], and 1 (4%) concerned youngsters and younger folks residing with juvenile idiopathic arthritis []. The interventions within the systematic evaluations concerned lively video video games or severe video games (n=8, 32%) [,,,,,,,], numerous mHealth interventions (n=6, 24%) [,,,,,], numerous eHealth interventions (eg, web sites and social media teams; n=5, 20%) [,,,,], numerous eHealth and mHealth interventions mixed (eg, cellular apps and SMS textual content message interventions; n=4, 16%) [,,,], wearables (n=1, 4%) [], and computer-based interventions (n=1, 4%) []. The systematic evaluations included interventions that focused weight problems and weight administration (n=11, 44%) [,,,-,-], bodily exercise ranges (n=6, 24%) [,,,,,], a number of life-style behaviors or well being promotion (n=3, 12%) [,,], 24-hour motion behaviors (n=1, 4%) [], weight loss program (n=1, 4%) [], sleep (n=1, 4%) [], juvenile idiopathic arthritis administration (n=1, 4%) [], and health-related bodily health and motor competence (n=1, 4%) [].
The included evaluations had both a average (2/25, 8%) [,], low (5/25, 20%) [,,,,], or critically low (18/25, 72%) [-,,-,-,-] AMSTAR-2 rating (confer with [-] for full examine high quality scoring). Common limitations included not offering an inventory of full-text exclusions (22/25, 88%) and never describing the funding sources of the included research (25/25, 100%).
Results of meta-analyses primarily based on SMD confirmed a major impact in favor of eHealth and mHealth interventions on MVPA (SMD 0.18, 95% CI 0.09-0.27; I2=0%; P<.001; 6/25, 24%) and complete bodily exercise (mixed mild, average, and vigorous; SMD 0.24, 95% CI 0.13-0.35; I2=28.8%; P<.001; 9/25, 36%; [,-,,,]). Results primarily based on MD confirmed no important impact on MVPA (MD 1.78 minutes/day, 95% CI –3.79 to 7.35; I2=55.4%; P=.53; 2/25, 8%; [,]; there have been inadequate MD information for complete bodily exercise).
Evidence for enhancements in MVPA and complete bodily exercise was primarily based on stage 1 research (systematic evaluations of RCTs) with constant results. Due to the low methodological high quality of many evaluations, the grade of advice was grade B.
Results of meta-analyses primarily based on SMD confirmed a major impact of eHealth and mHealth interventions on fats consumption (SMD 0.10, 95% CI 0.02-0.18; I2=30.3%; P=.01; 2/25, 8%) and fruit and vegetable consumption (SMD 0.11, 95% CI 0.00-0.22; I2=0%; P=.05; 2/25, 8%; [,]). There have been inadequate MD information for weight loss program outcomes.
Improvements in fats, fruit, and vegetable consumption have been supported by stage 1 proof. However, the restricted variety of meta-analyses and low evaluate high quality supported a grade B advice.
Results of meta-analyses primarily based on SMD confirmed no impact of eHealth and mHealth interventions on sedentary habits (SMD 0.12, 95% CI –0.11 to 0.35; I2=90.8%; P=.31; 4/25, 16%; [,,,]). Meta-analyses primarily based on MD confirmed important reductions in general sedentary habits (MD 24.08 minutes/day, 95% CI –37.97 to –10.20; I2=0%, P<.001; 2/25, 8%; [,]) and display time (MD 21.83 minutes/day, 95% CI –42.77 to –0.89; I2=0%; P=.04; 2/25, 8%; [,]).
Mixed findings with excessive heterogeneity and inconsistent outcomes throughout SMD and MD analyses resulted in decrease confidence on this final result. Despite being stage 1 proof, the advice was grade C.
Results of meta-analyses primarily based on SMD confirmed no impact of eHealth and mHealth interventions on sleep length (SMD 0.27, 95% CI –0.09 to 0.63; I2=78.2%; P=.14; 2/25, 8%; [,]). There have been inadequate MD information for sleep outcomes.
Findings for sleep length have been nonsignificant and primarily based on restricted, heterogeneous information from low-quality evaluations. This final result was supported by stage 1 proof however was graded as grade C.
Results of meta-analyses primarily based on SMD confirmed a major impact in favor of eHealth and mHealth interventions on BMI (SMD 0.19, 95% CI 0.11-0.27; I2=42.4%; P<.001; 7/25, 28%) and physique weight (SMD 0.15, 95% CI 0.01-0.30; I2=0%; P=.04; 2/25, 8%; [,,,,-,]). Meta-analysis outcomes primarily based on MD confirmed a major discount in BMI (MD –0.22 kg/m2, 95% CI –0.33 to –0.10; I2=36.99%; P<.001; 7, 28%; [,,,,]) and physique weight (MD –0.99 kg, 95% CI=–1.51 to –0.47; I2=0%; P<.001; 9/25, 36%; [,]). Results of meta-analyses primarily based on MD additionally confirmed a major discount in physique fats proportion (MD –0.47%, 95% CI –0.66 to –0.29; I2=0%; P<.001; 4/25, 16%; [,,,]) and no important change in waist circumference (MD –0.15, 95% CI –0.87 to 0.57; I2=56.05%; P=.68; 4/25, 16%; [,,,]).
BMI, physique weight, and physique fats confirmed constant and important enhancements throughout stage 1 research. Due to average heterogeneity and evaluate high quality issues, the advice was grade B. Waist circumference confirmed no impact.
Subgroup analyses have been performed to discover whether or not intervention effectiveness assorted in keeping with age, intervention length, intervention kind, and examine high quality rating, primarily based on the provision of knowledge from not less than 2 eligible meta-analyses per subgroup.
There have been no important subgroup results for age (<13 or ≥13 years) on MVPA (Qb (1)=0.02; P=.88; ), complete bodily exercise (Qb (1)=3.21; P=.38; ), BMI (Qb (1)=0.35; P=.55; ), and sedentary habits (Qb (1)=2.25; P=.13; ).
Interventions that lasted lower than 8 weeks had a higher impact on MVPA in contrast with those who lasted 8 weeks or longer (SMD 0.86 vs 0.19; Qb (1)=9.03; P<.001; ). In distinction, longer interventions (≥12 weeks) had a higher impact on BMI in contrast with shorter interventions (SMD 0.46 vs –0.07; Qb (1)=6.08; P<.001; ). There was no distinction between shorter and longer interventions for complete bodily exercise (Qb (1)=1.66; P=.20; ) and sedentary habits (Qb (1)=2.25; P=.13; ).
There have been no important subgroup results for intervention kind (eHealth solely, mHealth solely, exergames, eHealth and mHealth blended, net primarily based solely, app solely, SMS textual content message solely, or SMS textual content message plus app) on MVPA (Qb (7)=7.08; P=.42; ) and complete bodily exercise (Qb (3)=1.28; P=.73; ).
Wearable-only interventions confirmed bigger results on sedentary habits (SMD 0.97) in comparison with numerous eHealth (SMD 0.11) and mHealth (SMD –0.11) interventions (Qb (2)=16.2; P<.001; ).
App-only interventions (SMD 0.78) confirmed bigger results in comparison with numerous eHealth and mHealth interventions, exergames, and web-based solely interventions on BMI (SMD vary 0.00-0.31; Qb (5)=38.00; P<.001; ).
There have been no important subgroup results for examine high quality rating (average, low, or critically low on MVPA; Qb (2)=1.06; P=.59; ), complete bodily exercise (Qb (2)=4.48; P=.11; ), and BMI (Qb (1)=2.09; P=.15; ).
Studies rated as critically low (ie, highest danger of bias) confirmed the best results on sedentary habits (SMD 0.97) in contrast with low- (SMD 0.02) and moderate-rated research (SMD 0.09; Qb (2)=6.44; P=.04; ).
A visible examination of funnel plots for bodily exercise outcomes () revealed some asymmetry, with a niche within the backside left quadrant. This advised a possible lack of smaller research reporting adverse impact sizes. The estimated true impact measurement (Cohen d or θ) was –0.17. For different outcomes, there was an inadequate variety of systematic evaluations (<10) to create funnel plots.
This complete umbrella evaluate synthesized proof from 25 systematic evaluations and meta-analyses, encompassing 440 trials involving 133,501 contributors, to judge the effectiveness of eHealth and mHealth interventions for selling wholesome life-style behaviors in youngsters and adolescents. eHealth and mHealth interventions demonstrated small however constructive results on rising MVPA (SMD 0.18) and complete bodily exercise (SMD 0.24); enhancing dietary habits, together with fats consumption (SMD –0.10) and fruit and vegetable consumption (SMD 0.11); and decreasing BMI (SMD –0.19; MD –0.22 kg/m2) and physique weight (SMD –0.15; MD –0.99 kg). While SMD confirmed no important impact on sedentary habits, MDs indicated reductions in general sedentary time (–24 minutes/day) and display time (–22 minutes/day). However, the interventions didn’t considerably influence sleep length. Although the impact sizes for well being behaviors have been small, even modest enhancements in bodily exercise, sedentary habits, and weight loss program can contribute to important long-term well being advantages, significantly when sustained over time. These small modifications are significantly necessary throughout childhood and adolescence when wholesome habits are shaped and might have an enduring influence on future well being [,]. Moreover, the scalability of digital interventions implies that even small particular person results can translate into substantial population-level influence when delivered to massive numbers of individuals. These findings counsel that digital well being interventions can play a useful function in selling more healthy life amongst youth, though with various levels of effectiveness throughout completely different well being behaviors.
Our findings reveal important enhancements in bodily exercise, dietary behaviors, and weight-related outcomes following eHealth and mHealth interventions. While the noticed impact sizes have been usually small, they’re comparable and even favorable in comparison with these present in conventional face-to-face interventions focusing on well being behaviors in youngsters and adolescents. For occasion, our examine confirmed extra favorable results on BMI (SMD –0.19) and physique weight (SMD –0.15) in comparison with the meta-analysis by Guerra et al [] of 12 school-based bodily exercise interventions (BMI: SMD –0.02; physique weight: SMD –0.07). Similarly, our outcomes for MVPA have been extra promising than these reported within the meta-analysis by Love et al [] of 17 school-based bodily exercise interventions. Regarding dietary outcomes, our findings for fruit and vegetable consumption (SMD 0.11) have been corresponding to these reported in school-based diet research (SMD 0.23) [], though barely smaller. This distinction is likely to be attributed to the flexibility of school-based interventions to straight supervise or present fruit and vegetable consumption, which isn’t possible with eHealth and mHealth approaches. Overall, these comparisons counsel that digital well being interventions might be as efficient, if no more so, than conventional intervention strategies.
Our evaluation revealed blended outcomes for sedentary habits and sleep outcomes. While SMD results confirmed no important impact on general sedentary habits, MD results indicated important reductions in sedentary habits (24 minutes/day) and display time (22 minutes/day) outcomes. This discrepancy is likely to be because of measurement heterogeneity throughout research or the problem of capturing nuanced modifications in sedentary patterns. Regarding sleep, our evaluate discovered no statistically important influence on sleep length; nevertheless, this conclusion is predicated on restricted information from a single meta-analysis and will due to this fact be interpreted with warning. This highlights a important hole within the present analysis panorama, as optimum sleep is essential for kids’s development, cognitive improvement, and psychological well being []. The lack of great results on sleep and the blended outcomes for sedentary habits underscore the necessity for extra focused, high-quality analysis in these areas.
Our subgroup analyses supplied useful insights relating to components that will optimize intervention results. Wearable-only interventions demonstrated considerably bigger results on decreasing sedentary habits (SMD 0.97) in comparison with numerous eHealth (SMD 0.11) and mHealth (SMD –0.11) interventions. This discovering means that wearable units could also be significantly efficient in selling motion and decreasing sedentary time amongst youngsters and adolescents. Furthermore, intervention length emerged as a major moderator of outcomes. Shorter interventions (<8 weeks) demonstrated bigger results for rising bodily exercise, whereas longer durations (≥12 weeks) have been simpler for decreasing BMI. This sample aligns with earlier analysis, demonstrating that folks’s motivation typically declines throughout longer bodily exercise interventions, highlighting the necessity for methods to maintain long-term engagement []. In distinction, the higher effectiveness of longer interventions for BMI discount is per the understanding that sustained caloric deficits and life-style modifications are needed for significant weight administration []. Regarding intervention kind, app-based interventions confirmed probably the most promising outcomes for enhancing BMI in comparison with different digital codecs. This could also be attributed to the distinctive capabilities of apps, reminiscent of frequent self-monitoring, aim monitoring, tailor-made suggestions, and gamification parts, which have been proven to be efficient habits change methods for profitable weight reduction intervention [-]. These findings counsel that tailoring intervention length to particular well being outcomes and leveraging the interactive options of cellular apps may improve the influence of digital well being initiatives for kids and adolescents. Although no important subgroup results have been discovered for age, it is very important acknowledge that developmental variations between youngsters and adolescents might affect how interventions are designed, delivered, and acquired. Factors reminiscent of cognitive maturity, autonomy, and expertise use habits range throughout age teams and will have an effect on engagement and outcomes. The lack of age results in our analyses might mirror restricted energy in subgroup comparisons or inadequate age-specific tailoring in lots of interventions. Future analysis ought to discover age-tailored methods to boost relevance and effectiveness. Due to the inconsistency in reporting adherence and engagement throughout research, we have been unable to conduct a complete evaluation of their function on this evaluate. Future analysis ought to examine adherence and engagement as key mediators of the effectiveness of eHealth and mHealth interventions. A greater understanding of how these components affect habits change may assist optimize interventions and improve their real-world applicability. Furthermore, to make sure long-term well being advantages, future eHealth and mHealth interventions ought to incorporate methods to keep up participant engagement, reminiscent of common progress monitoring, customized suggestions, gamification, and social help options. These methods may help people maintain wholesome behaviors over time and forestall dropout.
This umbrella evaluate synthesizes proof from 25 systematic evaluations and meta-analyses, comprising 440 trials with 133,501 contributors, offering a complete evaluation of eHealth and mHealth interventions for kids and adolescents. Adhering to PRISMA tips ensures transparency and reproducibility, and utilizing rigorous screening and extraction processes in duplicate minimizes examine overlap (CCA=3.28%), enhancing reliability and deal with digital well being interventions for youth.
This umbrella evaluate has a number of limitations, primarily associated to the various methodological high quality of included evaluations. Many (23/25, 92%) research have been rated as low or critically low confidence per AMSTAR-2 standards, typically because of inconsistent reporting, lack of preregistration, and restricted assessments of bias and heterogeneity. These high quality points might cut back confidence within the reported results, significantly for bodily exercise outcomes, and counsel that some results might be overestimated because of potential publication bias favoring constructive outcomes. Future analysis ought to enhance rigor by adopting standardized protocols, thorough bias evaluations, and constant subgroup analyses. In addition, inadequate information restricted subgroup analyses throughout key populations and intervention traits, stopping an in depth understanding of intervention effectiveness throughout numerous teams and settings. Furthermore, the kind of final result measurement (goal vs self-reported) and the id of the respondent (eg, youngster, adolescent, or father or mother) weren’t persistently reported throughout evaluations, limiting our capacity to discover these components in subgroup analyses. More granular information reporting in future research would allow extra tailor-made and dependable insights for numerous demographic contexts. Although many research included on this evaluate had low-quality rankings, our subgroup analyses point out that these research had minimal influence on the general findings for many outcomes, aside from sedentary habits. This means that whereas examine high quality is a important issue, the tendencies recognized within the meta-analyses stay related. It is necessary to notice that the strict standards of the AMSTAR 2 instrument, significantly for objects reminiscent of complete search methods, meant that even well-conducted evaluations typically didn’t meet all necessities for a full “yes” score, doubtlessly underestimating their methodological high quality.
The findings spotlight important scientific implications for youngster and adolescent well being stakeholders. eHealth and mHealth interventions present promising however modest enhancements in bodily exercise, dietary habits, and BMI. Stakeholders, together with well being care suppliers and educators, ought to take into account integrating these digital instruments, significantly app-based platforms, into youth well being promotion methods. Tailoring intervention durations to particular outcomes, reminiscent of shorter durations for rising bodily exercise and longer durations for decreasing BMI, is advisable. Early intervention is essential, with youthful youngsters exhibiting higher reductions in sedentary habits in comparison with adolescents, suggesting potential roles for colleges and neighborhood organizations in integrating these interventions into present applications.
In the quickly evolving digital well being panorama, curated app libraries and systematic frameworks are urgently wanted to help professionals in deciding on efficient habits change apps confidently. Public well being companies can contribute by creating tips and sources for evidence-based digital well being instruments. Future analysis ought to deal with enhancing the effectiveness of eHealth and mHealth interventions for kids and adolescents by conducting extra high-quality main RCTs, exploring revolutionary methods for sustaining long-term engagement, assessing long-term intervention results, investigating digital interventions for sleep and sedentary habits, and optimizing integration with different well being promotion methods. These outcomes have necessary implications for analysis, follow, and coverage within the subject of digital well being interventions for youth. Researchers ought to deal with additional investigating the precise options of wearable units and cellular apps that contribute to their effectiveness in decreasing sedentary habits and BMI, respectively. This may contain systematic evaluations of intervention elements and exploring combos of those promising approaches with different digital instruments. In follow, well being care suppliers and educators ought to take into account prioritizing the combination of evidence-based wearable units and cellular apps into their well being promotion methods for kids and adolescents, significantly when focusing on sedentary habits and weight administration. Policymakers ought to be aware of these findings when creating tips for digital well being interventions and allocating sources for his or her implementation in numerous settings, together with colleges and neighborhood organizations. Future analysis must also discover improve the long-term effectiveness of those interventions, presumably by combining the simplest digital approaches with conventional face-to-face elements to create complete, multilevel interventions that tackle numerous determinants of well being behaviors in youth.
In conclusion, this umbrella evaluate reveals that eHealth and mHealth interventions provide important advantages for enhancing bodily exercise, weight loss program high quality, and weight administration in youngsters and adolescents. While results on sedentary habits have been blended and sleep outcomes confirmed no important enchancment, digital applied sciences have potential for selling more healthy life amongst youth, proving comparable or superior to conventional interventions in some features. On the premise of our findings of small however constructive results throughout these domains, we advise that future analysis ought to deal with how greatest to translate these evidence-based interventions into real-world settings at scale. Particularly, research exploring implementation methods; fairness of entry; and integration into faculty, main care, or neighborhood settings can be useful. In addition, whereas our evaluate didn’t assess engagement or adherence straight, future umbrella or systematic evaluations focusing particularly on these components might assist complement and prolong our findings.
This mission acquired no particular funding. CM is supported by a Medical Research Future Fund Emerging Leader Grant (GNT1193862). CV is supported by an Australian Research Council Future Fellowship (FT210100234). CK was supported by National Institute of General Medical Sciences (NIGMS), part of the National Institutes of Health (NIH).
(P20GM144269). The content material is solely the accountability of the authors and doesn’t essentially signify the official views of the National Institutes of Health. The funders had no function within the design and conduct of the examine; assortment, administration, evaluation, and interpretation of the info; preparation, evaluate, or approval of the manuscript; and resolution to submit the manuscript for publication. The different authors acquired no extra funding.
BS and CM designed, conceptualized, and outlined the strategies on this examine and contributed to information curation, visualization, information validation, and drafting the manuscript and its closing model. BS contributed to the formal information evaluation and carried out software-based assessments. CM supervised the examine. All authors contributed to screening, information extraction, and danger of bias scoring, and penning this paper and authorised the submitted model. All authors had entry to all the info, and BS and CM have verified the info.
None declared.
Edited by G Greco; submitted 21.Nov.2024; peer-reviewed by S Jiang, J Lee, W-F Khaw, R Raeside; feedback to writer 30.Mar.2025; revised model acquired 14.Apr.2025; accepted 19.Jun.2025; printed 17.Oct.2025.
©Ben Singh, Mavra Ahmed, Amanda E Staiano, Maria F Vasiloglou, Claire Gough, Jasmine M Petersen, Zenong Yin, Corneel Vandelanotte, Chelsea Kracht, Janis Fiedler, Irina Timm, Joan Dallinga, Bridve Sivakumar, Hannes Baumann, Christopher Huong, Kathrin Wunsch, Mónica Suárez-Reyes, Stephanie Schoeppe, Alyssa M Button, Katherine Spring, Carol Maher. Originally printed within the Journal of Medical Internet Research ( 17.Oct.2025.
This is an open-access article distributed beneath the phrases of the Creative Commons Attribution License ( which allows unrestricted use, distribution, and replica in any medium, supplied the unique work, first printed within the Journal of Medical Internet Research (ISSN 1438-8871), is correctly cited. The full bibliographic data, a hyperlink to the unique publication on in addition to this copyright and license data have to be included.
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This web page was created programmatically, to learn the article in its authentic location you…
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