Functions of Smartphone Apps and Wearable Devices Promoting Physical Activity: Six-Month Longitudinal Study on Japanese-Speaking Adults
Notice bibliographique
Résumé
BACKGROUND: Smartphone apps and wearable activity trackers are increasingly recognized for their potential to promote physical activity (PA). While studies suggest that the use of commercial mobile health tools is associated with higher PA levels, most existing evidence is cross-sectional, leaving a gap in longitudinal data. OBJECTIVE: This study aims to identify app-use patterns that are prospectively associated with increases in and maintenance of PA. The primary objective was to test whether continued app use is linked to adherence to the recommended PA levels (ie, 23 metabolic equivalent task [MET] hours per week for adults or 10 MET hours/week for individuals aged >65 years) during a follow-up assessment. The secondary objective was to explore which functions and features of PA apps predict changes in PA levels. METHODS: A 2-wave longitudinal survey was conducted, with baseline and follow-up assessments separated by 6 months. A total of 20,573 Japanese-speaking online respondents participated in the baseline survey, and 16,286 (8289 women; mean age 54.7 years, SD 16.8 years) completed the follow-up. At both time points, participants reported their current PA levels and whether they were using any PA apps or wearables. Each participant was classified into 1 of the following 4 categories: continued users (those using apps at both the baseline and follow-up; n=2150, 13.20%), new users (those who started using apps before the follow-up; n=1462, 8.98%), discontinued users (those who had used apps at baseline but not at follow-up; n=1899, 11.66%), and continued nonusers (those who had never used apps; n=10,775, 66.16%). RESULTS: The majority of continued users (1538/2150, 71.53%) either improved or maintained their PA at the recommended levels over 6 months. By contrast, discontinued users experienced the largest reduction in PA (-7.95 MET hours/week on average), with more than half failing to meet the recommended levels at the follow-up (n=968, 50.97%). Analyses of individual app functions revealed that both energy analysis (eg, app calculation of daily energy expenditure) and journaling (eg, users manually entering notes and maintaining an exercise diary) were significantly associated with increases in PA. Specifically, energy analysis was associated with an odds ratio (OR) of 1.67 (95% CI 1.05-2.64, P=.03), and journaling had an OR of 1.76 (95% CI 1.12-2.76, P=.01). By contrast, individuals who maintained the recommended PA levels at the follow-up were more likely to use the goal setting (OR 1.73, 95% CI 1.21-2.48, P=.003), sleep information (OR 1.66, 95% CI 1.03-2.68, P=.04), and blood pressure recording (OR 2.05, 95% CI 1.10-3.83, P=.02) functions. CONCLUSIONS: The results highlight the importance of continued app use in both increasing and maintaining PA levels. Different app functions may contribute to these outcomes, with features such as goal setting and journaling playing a key role in increasing PA, while functions related to overall health, such as sleep tracking and blood pressure monitoring, are more associated with maintaining high PA levels.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 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 ».