The Efficacy of an mHealth App in Facilitating Weight Loss Among Japanese Fitness Center Members: Regression Analysis Study
Notice bibliographique
Résumé
BACKGROUND: Self-tracking smartphone apps have emerged as promising tools to encourage healthy behaviors. In this longitudinal study, we used gym use data from members of a major fitness club that operates gyms throughout Japan from January 2014 to December 2019. OBJECTIVE: Our objective was to assess the extent to which a health and fitness self-tracking mobile app introduced to gym members on January 1, 2018, contributed to their weight loss. The app allows users to input information regarding diet, sleep, weight, and gym exercise so that they can receive personalized feedback from an artificial intelligence chatbot to improve their health behaviors. METHODS: We used linear regression to quantify the association between app use and weight loss. The primary outcome of the study was the weight loss achieved by each gym user, which was calculated as the difference between their initial and final weights in kilograms, as recorded in the app. Individuals who did not attend the gym or failed to use the mobile app at least twice during the study period were excluded from the analysis. The model accounted for age, gender, distance between the gym and the member's residence, average weekly number of times a member used the gym, user's gym membership length in weeks, average weekly number of times a member input information into the app, and the number of weeks that the app was used at least once. RESULTS: Data from 26,589 participants were analyzed. Statistically significant associations were detected between weight loss and 2 metrics related to app use: the average weekly frequency of use and the total number of weeks in which the app was used at least once. One input per week was found to be associated with a loss of 62.1 (95% CI 53.8-70.5) g, and 1 week of app use was associated with 21.7 (95% CI 20.5-22.9) g of weight loss from the day of the first input to that of the final input to the app. Furthermore, the average number of times that a member used the gym weekly was also shown to be statistically significantly associated with weight loss: 1 use per week was associated with 255.5 (95% CI 228.5-282.6) g of weight loss. CONCLUSIONS: This empirical study demonstrated a significant association between weight loss among gym members and not only the frequency of weekly gym use but also the use of a health and fitness self-tracking app. However, further work is needed to examine the mechanisms through which mobile apps affect health behaviors and to identify the specific app features that are most effective in promoting weight loss.
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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 | 0,007 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,002 | 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 ».