Use and Acceptance of Smart Elderly Care Apps Among Chinese Medical Staff and Older Individuals: Web-Based Hybrid Survey Study
Notice bibliographique
Résumé
BACKGROUND: With the advent of China's aging population and the popularization of smartphones, there is a huge demand for smart elderly care apps. Along with older adults and their dependents, medical staff also need to use a health management platform to manage the health of patients. However, the development of health apps and the large and growing app market pose a problem of declining quality; in fact, important differences can be observed between apps, and patients currently do not have adequate information and formal evidence to discriminate among them. OBJECTIVE: The aim of this study was to investigate the cognition and usage status of smart elderly care apps among older individuals and medical staff in China. METHODS: From March 1, 2022, to March 30, 2022, we used the web survey tool Sojump to conduct snowball sampling through WeChat. The survey links were initially sent to communities in 23 representative major cities in China. We asked the medical staff of community clinics to post the survey link on their WeChat Moments. From April 1 to May 10, 2022, we contacted those who selected "Have used a smart elderly care app" in the questionnaire through WeChat for a request to participate in semistructured interviews. Participants provided informed consent in advance and interviews were scheduled. After the interviews, the audio recordings were transcribed into text and the emerging themes were analyzed and summarized. RESULTS: A total of 810 individuals participated in this study, 54.8% (n=444) of whom were medical staff, 33.1% (n=268) were older people, and the remaining participants were certified nursing assistants (CNAs) and community workers. Overall, 60.5% (490/810) of the participants had used a smart elderly care app on their smartphone. Among the 444 medical staff who participated in the study, the vast majority (n=313, 70.5%) had never used a smart elderly care app, although 34.7% of them recommended elderly care-related apps to patients. Among the 542 medical staff, CNAs, and community workers that completed the questionnaire, only 68 (12.6%) had used a smart elderly care app. We further interviewed 23 people about their feelings and opinions about smart elderly care apps. Three themes emerged with eight subthemes, including functional design, operation interface, and data security. CONCLUSIONS: In this survey, there was a huge difference in the usage rate and demand for smart elderly care apps by the participants. Respondents are mainly concerned with app function settings, interface simplicity, and data security.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».