CO-DESIGNING A HIGH-ACCURACY HOME MONITORING SYSTEM FOR MANAGING FRAILTY IN OLDER ADULTS
Bibliographic record
Abstract
Abstract Background Frailty, a condition often affecting older adults, increases vulnerability and diminishes physical abilities across bodily systems. Current non-routine frailty screening in primary care or clinical settings fails to detect “hidden health vulnerabilities” in a timely manner. Smart home technologies offer an affordable and effective solution for continuous frailty tracking and prevention. However, existing home monitoring technologies typically require users to acquire new skills or are invasive, such as camera-based systems. Objective: Our goal is to create a high-accuracy home monitoring system coupled with Internet of Things devices to identify potential frailty indicators. Methods This qualitative description study involves 4 to 8 participants, including older adults with and without Mild Cognitive Impairments/frailty, caregivers, and clinicians, in a group interview. Using card sorting and task mapping, the interview seeks to identify and define the features and challenges of a smart home system to monitor frailty in older adults. Results The study is registered in clinical trials, with data collection commencing soon. At the conference, we will present the research protocol and the findings from the analysis of the interviews. Conclusion Through the early engagement of older adults and caregivers, we strive to design a valuable and meaningful system that (1) uses zero-effort technologies so frail older adults do not need to develop new skills in order to use the system; (2) is a non-camera-based tracking technology preserving autonomy and privacy; (3) generates the frailty data meaningful for older adults, caregivers and the health system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".