Attitudes and perspectives of older adults on technologies for assessing frailty in home settings: a focus group study
Bibliographic record
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 in selecting their assistive technology devices. This study aims to understand older adults' perceptions and preferences of technologies that can potentially assess frailty at home. METHODS: This study collected qualitative data through focus group meetings with 15 participants ages 65 and older. Researchers asked participants questions 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 specific 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: 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 categorized into four themes: (A) general attitude towards using the technologies, (B) conditions for accepting certain technologies, (C) existing living habits or patterns related to using the technologies, and (D) constructive suggestions related to the technologies. CONCLUSIONS: Participants generally had positive attitudes towards allowing the technologies to be installed and used at their homes. They would accept some technologies if used under certain conditions. However, questions and concerns remain, such as concerns about privacy, functionality, and aesthetics. The study also found that older adults' living habits or patterns could affect the design and use of technology. Lastly, many valuable suggestions have been made by participants. These perspectives and insights can help improve the design and adoption of home-based frailty assessment technologies among older adults.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".