Digital Health Interventions for Managing Pediatric Obesity: A Systematic Review of Mobile Apps and Telehealth Strategies
Bibliographic record
Abstract
Background: The growing number of children and adolescents with obesity has sparked growing interest into innovative digital health solutions, including mobile applications and telehealth approaches. These methods feature the possibility of remote monitoring and feedback, personalized guidance, along with support that can augment improving dietary habits, increase exercise, and maintain a healthy weight over time in the young population. However, despite the increase in their use, there is no complete summary of their effectiveness and implementation barriers. Objective: Develop a systematic review of the literature on the effectiveness of obesity digital health interventions focusing on mobile health (mHealth) applications and telehealth in order to evaluate the behavioral outcomes, adherence to the interventions, and the technology’s ease of use of those strategies. Methods: A systematic search of the literature was conducted through peer-reviewed publications using PubMed, Scopus, Web of Science, and Google Scholar for the years 2010 to 2025. The criteria looked for articles engaging children and adolescents aged between 2 to 18 years undergo digital intervention for weight management. Data captured included the design of the intervention and its duration, population of the study, outcomes based on behaviors, changes in BMI, satisfaction, and user satisfaction. Quality of included studies was measured with the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias Tool for randomized controlled trials. The effectiveness and intervention outcomes of complex public health challenges were analyzed using descriptive synthesis, evaluation of patterned intervention outcomes, and correlation analysis. Results: The final synthesis yielded 120 responses and relevant studies. It is notable that mobile apps and telehealth services are moderately to highly effective at fostering increased physical activity, improved dietary habits, and lower body mass index (BMI) among children and adolescents within the age range of 6-17 years. Positive behavioral outcomes along with high user satisfaction were reported by most studies, especially when caregivers provided support during interactive interventions. However, limited engagement, usability, and low levels of digital literacy are frequently presented as challenges. A correlational analysis further identified a strong positive correlation between the perceived effectiveness of the intervention and its frequency. Conclusions: This systematic review highlights the ability of health technology to transform pediatric obesity through remote and self-directed care. Significant improvements were observed concerning mobile applications and telehealth services, especially regarding self-monitoring and boosting active behavioral changes. However, sustained engagement, motivation from the children, and equitable access remain critical concerns. It is important to assess the primary modifying factors for sustained engagement and motivation in future research on pediatric populations of diverse socio-economic backgrounds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".