Mobile Apps for Weight Management: A Scoping Review
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Notice bibliographique
Résumé
BACKGROUND: Obesity remains a major public health concern. Mobile apps for weight loss/management are found to be effective for improving health outcomes in adults and adolescents, and are pursued as a cost-effective and scalable intervention for combating overweight and obesity. In recent years, the commercial market for 'weight loss apps' has expanded at rapid pace, yet little is known regarding the evidence-based quality of these tools for weight control. OBJECTIVE: To characterize the inclusion of evidence-based strategies, health care expert involvement, and scientific evaluation of commercial mobile apps for weight loss/management. METHODS: An electronic search was conducted between July 2014 and July 2015 of the official app stores for four major mobile operating systems. Three raters independently identified apps with a stated goal of weight loss/management, as well as weight loss/management apps targeted to pediatric users. All discrepancies regarding selection were resolved through discussion with a fourth rater. Metadata from all included apps were abstracted into a standard assessment criteria form and the evidence-based strategies, health care expert involvement, and scientific evaluation of included apps was assessed. Evidence-based strategies included: self-monitoring, goal-setting, physical activity support, healthy eating support, weight and/or health assessment, personalized feedback, motivational strategies, and social support. RESULTS: A total of 393 apps were included in this review. Self-monitoring was most common (139/393, 35.3%), followed by physical activity support (108/393, 27.5%), weight assessment (100/393, 25.4%), healthy eating support (91/393, 23.2%), goal-setting (84/393, 21.4%), motivational strategies (28/393, 7.1%), social support (21/393, 5.3%), and personalized feedback (7/393, 1.8%). Of apps, 0.8% (3/393) underwent scientific evaluation and 0.3% (1/393) reported health care expert involvement. No apps were comprehensive in the assessment criteria, with the majority of apps meeting less than two criteria. CONCLUSIONS: Commercial mobile apps for weight loss/management lack important evidence-based features, do not involve health care experts in their development process, and have not undergone rigorous scientific testing. This calls into question the validity of apps' claims regarding their effectiveness and safety, at a time when the availability and growth in adoption of these tools is rapidly increasing. Collaborative efforts between developers, researchers, clinicians, and patients are needed to develop and test high-quality, evidence-based mobile apps for weight loss/management before they are widely disseminated in commercial markets.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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 découle