Design and Evaluation of a Digital Health App (SingaporeWALK) for Active Aging: Pre-Post Intervention Study
Notice bibliographique
Résumé
Background: The global trend toward population aging poses significant challenges for maintaining older adults' health and well-being, particularly in multicultural urban environments like Singapore. Despite the potential of digital health interventions, older adults face substantial barriers to technology adoption, including complex interfaces and culturally inappropriate content. Existing mobile health apps often fail to integrate physical, nutritional, and mental health components or accommodate the needs of multicultural older adult populations. Objective: To address gaps in mobile app design for older adults and bridge the digital divide, this research aimed to create and evaluate the SingaporeWALK (SGWALK) app, a culturally inclusive digital health solution promoting active aging through community-based interventions among Singapore's older adults. Methods: The SGWALK app was developed using participatory design methodology involving iterative testing with older adults to ensure appropriateness and usability. The app integrates 3 core components: exergames (Fruit Ninja, Piano Step, and Arctic Punch) aligned with Singapore's exercise guidelines for older adults, nutrition tracking based on local dietary recommendations, and mental well-being assessment using the Mental Health Continuum-Short Form. Following development, a 4-week pre-post intervention study was conducted with 48 participants (aged 60-85 y) randomly allocated to 4 conditions: conventional exercise, exergames only, exergames with health coach support, or exergames with peer support. In total, 5 wearable inertial measurement unit sensors captured movement data during weekly 30-minute supervised sessions at community centers. Primary outcomes included changes in physical activity metrics, technology acceptance, and mental well-being measured through pre- and postintervention assessments. Results: The 4-week intervention demonstrated significant improvements across multiple health domains. Physical activity measures showed a 6.5% increase in maximum acceleration (t47=3.82, P<.001), while nutritional tracking revealed steady improvements in healthy eating patterns throughout the intervention period. Mental health assessments indicated that participants classified as "mentally well" consistently outperformed the "moderate" group across physical activity measures. Technology acceptance showed substantial enhancement, with willingness to use health apps increasing from mean 3.18 (SD .79) to mean 3.95 (SD .82; t29=-3.63, P<.001), and perceived ease of use improving from mean 3.01 (SD .70) to mean 3.76 (SD .68; t29=-4.08, P<.001). Additionally, participants developed more efficient movement patterns over time and formed supportive social relationships during the community-based implementation. Conclusions: The SGWALK app shows promise for promoting active aging and reducing technology barriers among Singapore's older adults. The community-based implementation model, bilingual interface, integrated health monitoring approach, and sensor-based movement tracking offer potential advantages over existing solutions. These findings provide useful insights for researchers and practitioners developing digital health interventions for older adult populations in multicultural urban settings.
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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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
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 ».