An Interactive Home-Based Cognitive-Motor Step Training Program to Reduce Fall Risk in Older Adults: Qualitative Descriptive Study of Older Adults’ Experiences and Requirements
Notice bibliographique
Résumé
BACKGROUND: Falls are a major contributor to the burden of disease in older adults. Home-based exercise programs are effective in reducing the rate and risk of falls in older adults. However, adherence to home-based exercise programs is low, limiting the efficacy of interventions. The implementation of technology-based exercise programs for older adults to use at home may increase exercise adherence and, thus, the effectiveness of fall prevention interventions. More information about older adults' experiences when using technologies at home is needed to enable the design of programs that are tailored to older adults' needs. OBJECTIVE: This study aimed to (1) explore older adults' experiences using SureStep, an interactive cognitive-motor step training program to reduce fall risk unsupervised at home; (2) explore program features that older adults found encouraged program uptake and adherence; (3) identify usability issues encountered by older adults when using the program; and (4) provide guidance for the design of a future technology-based exercise program tailored to older adults to use at home as a fall prevention strategy. METHODS: This study was part of a larger randomized controlled trial. The qualitative portion of the study and the focus of this paper used a qualitative descriptive design. Data collectors conducted structured, open-ended in-person interviews with study participants who were randomly allocated to use SureStep at home for 4 months. All interviews were audiotaped and ranged from 45 to 60 min. Thematic analysis was used to analyze collected data. This study was guided by Pender's Health Promotion Model. RESULTS: Overall, 24 older adults aged 70 to 97 years were interviewed. Findings suggest older adults are open to use technology-based exercise programs at home, and in the context of optimizing adherence to home-based exercise programs for the prevention of falls, findings suggest that program developers should develop exercise programs in ways that provide older adults with a fun and enjoyable experience (thus increasing intrinsic motivation to exercise), focus on improving outcomes that are significant to older adults (thus increasing self-determined extrinsic motivation), offer challenging yet attainable exercises (thus increasing perceived self-competence), provide positive feedback on performance (thus increasing self-efficacy), and are easy to use (thus reducing perceived barriers to technology use). CONCLUSIONS: This study provides important considerations when designing technology-based programs so they are tailored to the needs of older adults, increasing both usability and acceptability of programs and potentially enhancing exercise participation and long-term adherence to fall prevention interventions. Program uptake and adherence seem to be influenced by (1) older adults' perceived benefits of undertaking the program, (2) whether the program is stimulating, and (3) the perceived barriers to exercise and technology use. Older adults shared important recommendations for future development of technologies for older adults to use at home.
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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 | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».