A 12-Month Digital Peer-Supported App Intervention to Promote Physical Activity Among Community-Dwelling Older Adults: Follow-Up Study of a Nonrandomized Controlled Trial
Notice bibliographique
Résumé
Background: Mobile apps and peer support are known to effectively promote physical activity in older adults, which, in turn, improves physical function. Previously, we investigated the feasibility and impact of using digital peer-supported apps (DPSAs) to increase physical activity among older adults over a 3-month period. However, the long-term feasibility and impact on sustainable behavior change remain unknown. Objective: This study aims to evaluate the 12-month feasibility of the DPSA and to obtain preliminary estimates of its effects on physical activity and physical function among older Japanese adults. Methods: This nonrandomized controlled trial recruited older adults aged 65 years or older from 2 physical activity programs. Participants chose either the intervention (app program + exercise instruction) group or the control (exercise instruction only) group. Only those participants who had completed the 3-month intervention and wished to continue in the 12-month follow-up intervention study were included. DPSA feasibility was assessed using retention and adherence rates. Physical activity was assessed using accelerometers, capturing daily step count, light-intensity activity, moderate to vigorous intensity activity, and sedentary behavior. Physical function was evaluated using grip strength and the 30-second chair stand test (CS-30). Accelerometer measurements were collected every 3 months over 12 months (5 time points, including baseline), whereas physical function was measured at baseline, 3 months, and 12 months. Results: The follow-up study included 44 of 66 participants from the 3-month intervention study, with 26 participants in the intervention group and 18 participants in the control group. The 12-month retention rate for participants in the DPSA intervention group was 73% (19/26), whereas the retention rate among all 41 participants, including those who chose not to participate in the follow-up study, was 46% (19/41). The adherence rate was 85.9%. The average number of steps per day (95% CI) in the intervention group changed before and after DPSA use (P=.048). We observed an increase of 1736 (β=1736, 95% CI 232-3241) steps per day compared with baseline. No significant change was observed in the control group. There were significant within-group differences in CS-30 scores for both intervention (P<.001) and control (P=.03) groups over the 12-month period. Specifically, there was a significant change in CS-30 scores (95% CI) between the baseline and 12-month assessments for the intervention (β=6.5, 95% CI 3.8-9.1; P<.001) and control (β=3.8, 95% CI 0.6-7.1; P=.02) groups. Conclusions: Participants with long-term DPSA use observed increases in average daily steps and CS-30 scores before and after DPSA use, although only a limited number of older adults had long-term access to the DPSA. Identifying ways to expand long-term DPSA use among older adults is necessary. Additionally, randomized controlled trials should be conducted to determine the long-term effects of DPSAs on physical activity and function in older adults.
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 | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».