Use and Acceptance of Smart Elderly Care Apps Among Chinese Medical Staff and Older Individuals: Web-Based Hybrid Survey Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".