Digital Health Interventions for Managing Pediatric Obesity: A Systematic Review of Mobile Apps and Telehealth Strategies
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,008 |
| Bibliométrie | 0,012 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».