Exploring Older Adults' Perceptions of Using Digital Health Platforms for Self-Managing Musculoskeletal Health Conditions: Focus Group Study
Notice bibliographique
Résumé
BACKGROUND: Digital technologies can assist and optimize health care processes. This is increasingly the case in the musculoskeletal health domain, where digital platforms can be used to support the self-management of musculoskeletal conditions, as well as access to services. However, given a large proportion of the population with musculoskeletal conditions are older adults (aged ≥60 years), it is important to consider the acceptability of such platforms within this demographic. OBJECTIVE: This study aims to explore participants' opinions and perceptions on the use of digital platforms for supporting the self-management of musculoskeletal conditions within older adult (aged ≥60 years) populations and to gather their opinions on real examples. METHODS: A total of 2 focus groups (focus group 1: 6/15, 40%; focus group 2: 9/15, 60%) were conducted, in which participants answered questions about their thoughts on using digital health platforms to prevent or manage musculoskeletal conditions. Participants were further presented with 2 example scenarios, which were then discussed. Interviews were audio recorded, transcribed, and analyzed thematically. Participants were aged ≥60 years and with or without current musculoskeletal conditions. Prior experience of using smartphone apps or other digital health platforms for musculoskeletal conditions was not required. Focus groups took place virtually using the Teams (Microsoft Corp) platform. RESULTS: A total of 6 themes were identified across both focus groups: "experiences of digital health platforms," "preference for human contact," "barriers to accessing clinical services," "individual differences and digital literacy," "trust in technology," and "features and benefits of digital health technologies." Each theme is discussed in detail based on the interview responses. The findings revealed that most participants had some existing experience with digital health platforms for preventing or managing musculoskeletal conditions. Overall, there was a lack of trust in and low expectations of quality for digital platforms for musculoskeletal health within this age group. While there was some concern about the use of digital platforms in place of in-person health consultations, several benefits were also identified. CONCLUSIONS: Results highlighted the need for better communication on the benefits of using digital platforms to support the self-management of musculoskeletal conditions, without the platforms replacing the role of the health care professionals. The concerns about which apps are of suitable quality and trustworthiness lead us to recommend raising public awareness around the role of organizations that verify and assess the quality of digital health platforms.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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.
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