Perceptions of In-home Monitoring Technology for Activities of Daily Living: Semistructured Interview Study With Community-Dwelling Older Adults
Bibliographic record
Abstract
BACKGROUND: Many older adults prefer to remain in their own homes for as long as possible. However, there are still questions surrounding how best to ensure that an individual can cope with autonomous living. Technological monitoring systems are an attractive solution; however, there is disagreement regarding activities of daily living (ADL) and the optimal technologies that should be used to monitor them. OBJECTIVE: This study aimed to understand older adults' perceptions of important ADL and the types of technologies they would be willing to use within their own homes. METHODS: Semistructured interviews were conducted on the web with 32 UK adults, divided equally into a younger group (aged 55-69 years) and an older group (≥70 years). RESULTS: Both groups agreed that ADL related to personal hygiene and feeding were the most important and highlighted the value of socializing. The older group considered several activities to be more important than their younger counterparts, including stair use and foot care. The older group had less existing knowledge of monitoring technology but was more willing to accept wearable sensors than the younger group. The younger group preferred sensors placed within the home but highlighted that they would not have them until they felt that daily life was becoming a struggle. CONCLUSIONS: Overall, technological monitoring systems were perceived as an acceptable method for monitoring ADL. However, developers and carers must be aware that individuals may express differences in their willingness to engage with certain types of technology depending on their age and circumstances.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".