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Digital Health Interventions for Managing Pediatric Obesity: A Systematic Review of Mobile Apps and Telehealth Strategies

2025· review· en· W4414126215 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Carcinogenesis · 2025
Typereview
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthPsychological interventionObservational studymHealthDigital healthIntervention (counseling)TelemedicinePopulationBehavior change methodsSystematic review

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.164
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.468
Teacher spread0.403 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it