Technology-Assisted Physical Activity Interventions for Older People in Their Home-Based Environment: Scoping Review
Notice bibliographique
Résumé
Background: Technology-assisted physical activity interventions for older adults in their home-based environment have been used to promote physical activity. Previous research has reported that such interventions benefit body composition, aerobic fitness, cognitive abilities, and postural control, reducing the risk of falls and maintaining regular physical activity among the older population. Objective: While previous reviews on technology-assisted physical activity interventions focused on health-related outcomes, this scoping review explores the characteristics of the technology in relation to the characteristics of populations, purpose of the activity, and usability in terms of adverse events, drop-outs, adherence, and user experience. Methods: A full search was performed in Medline, Embase, CINAHL, SportDiscus, and Web of Science. Sources were considered for inclusion if the participants aged on average 60 years and older, if the physical activity intervention was assisted by technology, and if performed within home-based environments. Results: We identified 8496 sources. After title and abstract screening, 455 full texts were assessed, and 148 were included, representing 12,717 participants aged 74 (SD 6) years. In total, 63% (93/148) of the sources reported on the population's health status. The main purpose of the interventions was balance (75/148, 51%), and strength and power (64/148, 43%) and the intervention purposes were not related to the embedded technology. In studies where the participant's health status was reported as healthy, 53% (78/148) implemented exergames compared to only 27% (40/148) in studies with participants with a clinical condition. Mobile apps (30/148, 20%) and trackers (16/148, 11%) were implemented likewise in both groups. The technology was embedded to provide continuous exercise information (40/148, 27%) and exercise feedback (40/148, 27%) or to record real-time movement data (38/148, 26%). Adverse events were reported in 46% (68/148) of the sources with three quarters (49/68) reporting no adverse events. Only two mild events were related to technology. Dropout rates were reported in 68% (100/148) of the sources, with no differences between intervention (16 SD 16%) and control (14 SD 12%) groups. Dropout reasons related to technology are only 3% (3/100). Adherence was reported in 53% (79/148) sources and was slightly higher in the intervention group (80 SD 18%) compared to the control group (71 SD 25%). A significantly higher adherence was found between interventions that were tailored (83 SD 15%) versus those that were not (75 SD 21%). General enjoyment of the technology was captured in 37% (55/148) of the sources. Within those sources, 91% rated positive (91/100), 7% neutral (7/100), and 2% negative (2/100). Occasionally reported wishes were related to goal setting, feedback, technical support, exercise variation, and social setting. Conclusions: Various technologies were successfully used in healthy and clinical older populations, though evidence regarding their implementation in physical activity interventions in hospital settings remains limited. The embedded technology was not a reason for additional dropouts, led to slightly better adherence, and adverse events were rarely related to technology. When assessed, the technology was well accepted and positively enjoyed.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».