Attitudes and Perspectives of Older Adults on Technologies for Assessing Frailty in Home Settings: A Focus Group Study
Notice bibliographique
Résumé
Abstract Background The rapid development of technology such as sensors and artificial intelligence in recent years enables monitoring frailty criteria to assess frailty early and accurately from a remote location such as a home. However, research shows technologies being abandoned or rejected by users due to a lack of compatibility and consumer involvement with the technologies. This study aims to understand older adult’s perceptions and preferences of technologies that can be potentially used to assess frailty in home settings. Methods This study is a qualitative study in which data were collected through focus group interviews. We recruited 15 older participants. Questions were asked to achieve the goal of understanding their attitudes on the technologies. These questions include 1) the concerns or barriers of installing and using the presented technology in daily life at home, 2) the reasons participants like or dislike a particular technology, 3) what makes a particular technology more acceptable, and 4) participants’ preferences in choosing technologies. Data were transcribed, coded and categorized, and finally synthesized to understand the attitudes towards presented technologies. Results A total of 15 older adults aged 65 and older were recruited. Three focus group sessions were conducted with five participants in each session. In the findings, the attitudes and perspectives of participants on the technologies for assessing frailty were presented in four areas: A) general attitude towards using the technologies, B) concerns about the technologies, C) existing living habits or patterns related to using the technologies, and D) constructive suggestions related to the technologies. Conclusions This study focuses on understanding the attitudes and perceptions of older adults on several technologies that could potentially be used to assess frailty in home settings. Participants generally have positive attitudes towards allowing the technologies to be installed and used at their home. Some technologies were found to be more acceptable if used under certain conditions. However, questions and concerns still remain. The study also found the living habits or patterns of older adults could affect the design and use of technology. Lastly, many valuable suggestions have been made by participants.
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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 | 0,003 | 0,007 |
| 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 | 0,001 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».