A Platform to Study the Effects of Home Environment on Health and Wellbeing of Older Adults
Bibliographic record
Abstract
Abstract While older adults’ living environment is rarely well-tuned to their specific needs, technological advances provide new opportunities to understand, and ultimately optimize, the relationship between the home environment and health outcomes. We aimed to establish proof-of-concept and feasibility of a platform enabling real-time, high-frequency, and simultaneous monitoring of environment, biological variables, and outcomes related to health and wellbeing in older adults. We recruited 7 participants (6 females, 1 male, aged 78-90, MoCA scores 14–28), installed environmental sensors measuring temperature, humidity, and CO2 inside their homes, provided them with wearables that measure sleep, activity, body temperature, and heart rhythms, and asked them to use a tablet to complete four sets of questionnaires and cognitive tests per day for three consecutive weeks. Environmental sensors collected data with no disruption or complaint from participants. Average compliance with the wearables was 81% (ring) and 60% (watch). All participants preferred the ring due to ease-of-use. Compliance was better in those with higher MoCA scores. Three participants were able to use the tablet successfully and completed 90% of prescribed questionnaires and cognitive tests. Cognitive and/or motor issues prevented the other participants from using the tablet. Exit interviews revealed that participants would prefer to complete a maximum of two sets of daily questionnaires and cognitive tests (five minutes each) in longer-term studies. These results suggest that it is feasible to study the impact of the environment on biological rhythms, cognition, and other outcomes in older adults and provide recommendations for ensuring long-term compliance with the protocol.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".