User Engagement and Weight Loss Facilitated by a Mobile App: Retrospective Review of Medical Records
Notice bibliographique
Résumé
BACKGROUND: Intermittent fasting (IF) has gained popularity in recent years for its effect on weight loss and supposed additional health benefits, such as a positive effect on body composition and metabolic markers. Mobile apps can act as platforms that help deliver dietary interventions by improving adherence and motivation. Although the effect of IF on weight loss has been demonstrated in earlier trials, there is not much research about the engagement and weight loss results with IF apps. OBJECTIVE: Our main objective was to compare how a nudging platform (including smart scales) influences engagement (the extent to which users interact with the app measured by the number of active days) with the app among users who had obesity at the beginning of use. The secondary objectives were to evaluate the body weight changes among active and nonactive users and, finally, to evaluate the body composition changes of users possessing smart scales during app usage. Through this study, we hope to provide (1) more insight into how nudging (using smart scales as a nudging platform) is associated with engagement with the mobile app, (2) how engagement with the mobile app is associated with weight loss, and (3) how IF is associated with body composition. METHODS: We performed a retrospective analysis of data from 665 users with obesity (BMI≥30) who started using the IF app DoFasting. Of them, 244 used body composition scales that estimated body fat and body muscle values. Users were stratified into engagement groups in accordance with their activity ratio (number of active days divided by the total time of use). Baseline and final users' weight (in kg), body fat (in %), and body muscle (in %) were compared. RESULTS: Our findings suggest an association between the nudging platform (smart scales) and better engagement with the app. Smart scale users had a significantly higher activity ratio than regular users. Additionally, active DoFasting users lost significantly more weight. Further, body composition analysis showed that app usage might be related to body fat loss and an increase in muscle mass. CONCLUSIONS: We found a possible association between the nudging and gamified elements and higher app engagement. Additionally, increased app engagement is associated with increased weight loss. Thus, nudging and gamified elements of mobile health apps, such as interactive tools, goals, challenges, and progress tracking, are suggested to affect engagement positively and should be investigated further in future research. Finally, the IF regime delivered through the DoFasting app might be related to the body muscle mass gain and reduced fat mass.
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Comment cette classification a été obtenuedéplier
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 | 0,010 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».