Effect of Adherence to Smartphone App Use on the Long-term Effectiveness of Weight Loss in Developing and OECD Countries: Retrospective Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Globally, 71% of deaths occur due to noncommunicable diseases (NCDs). Poor diet quality and physical activity have a significant impact on NCDs. At present, behavior change interventions using smartphone apps have rapidly increased worldwide to prevent NCDs. However, most previous studies on the use and effectiveness of apps have been conducted in Organization for Economic Co-operation and Development (OECD) countries. As such, relevant research in low-income countries is scarce. OBJECTIVE: This retrospective cohort study aims to investigate the characteristics of adherence to the use of the Noom app. We also aim to compare the effects of using the app on body weight changes over time according to adherence to the use of the app between users in low-income and OECD countries. In addition, the differences in weight loss are compared among users who use the free and paid versions of the app. METHODS: A secondary data analysis was conducted using repeated measures. The data were collected from users in low-income countries (n=312) and OECD countries (n=8041) who used the app for 12 months. The app provided programs for the self-monitoring of physical activity, dietary intake, and body weight. Descriptive statistics, independent two-tailed t tests, chi-square tests, and linear mixed models were used for the analysis. RESULTS: During the first 3 months of using the Noom app, users from OECD countries entered data into the app more frequently; however, users in low-income countries entered data more frequently from 3 months to 12 months. Users in OECD countries consumed significantly more calories than those in low-income countries for 12 months. The body weight of all users significantly decreased over time (-1.8 kg; P<.001); however, no statistically significant differences in the change in body weight for 12 months were observed between users from low-income and OECD countries (β=-.2; P=.19). The users who frequently monitored their lunch (β=-.1; P<.001), dinner (β=-.1; P<.001), body weight (β=-.1; P<.001), evening snack (β=-.1; P<.001), and exercise (β=-.03; P<.001) exhibited significant weight loss over time. We found no significant differences in the body weight changes between users who used the free and paid versions of the app (β=-.2; P=.19). CONCLUSIONS: This study found that using the app has a significant effect on weight loss regardless of users' country of residence. The results of this study suggest that the frequency of monitoring health-related behaviors by entering data into the app plays a pivotal role in losing weight. In conclusion, regardless of where users live and what versions of the app they use, it is important to monitor health-related behaviors by frequently entering data into the app to efficiently lose weight.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 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 ».