Attitudes and Perspectives of Older Adults on Technologies for Assessing Frailty in Home Settings: A Focus Group Study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".