“If I want to be able to keep going, I must be active.” Supporting Remote Physical Activity Programming for Older Adults during COVID-19 and Beyond: a mixed-methods study
Notice bibliographique
Résumé
Abstract Background: Pandemic-related public health restrictions limited older adults’ physical activity programs and opportunities. Supports for older adults' physical activity shifted to remote options, including virtual programming; however, information regarding the adoption and effectiveness of these supports is limited. Thus, the purpose of this study was to investigate i) changes in physical activity of older adults during the pandemic, and ii) the uptake, perceived effectiveness, facilitators of and barriers to remote supports for physical activity among older adults during the pandemic. Methods: Community-dwelling older adults (60+) were recruited to a cross-sectional online survey and an optional semi-structured follow-up interview. Survey questions addressed demographics, physical activity behaviors, and perceived effectiveness of, and facilitators and barriers for remote supports for physical activity. Interview questions were guided by the Behaviour Change Wheel and data was analyzed via inductive and deductive thematic analysis. Results: 57 older adults (68.3±7.1 years, 43 Female) completed the survey and 15 of these (67.4±5.8 years, 12 Female) completed interviews. Most participants were Caucasian, highly educated, and lived in Canada. There was no change in older adults' total physical activity from before to during the pandemic (p=0.74); however, at-home exercise participation increased as did technology usage and adoption of new technology. Participants perceived real-time virtual exercise, recorded exercise videos, and phone/webchat check-ins to be the most effective remote supports. The greatest barriers to physical activity were lack of contact with exercise professionals, limited access to exercise equipment or space, and decreased mental wellness. Thematic analysis identified four main themes: i) Knowledge, access to equipment, and space enhance or constrain physical activity opportunities, ii) Individual and environmental factors motivate physical activity uptake, iii) Social connection and real-time support encourage physical activity engagement, and iv) Current and future considerations to support technology usage for exercise. Conclusion: Use of remote supports for physical activity increased during the pandemic, with video-based programming being most favored. Live virtual programming may be best suited to encouraging physical activity among older adults as it may provide greater motivation for exercise, increase social and mental wellness, and alleviate safety concerns.
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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 | 0,009 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».