In-Home Assistive Technology May Help Protect Dementia Caregivers from Declining Sleep Efficiency: A Randomized Control Trial
Bibliographic record
Abstract
OBJECTIVES: Caregivers for people with dementia (PWDs) often experience sleep problems due to stressors associated with their role (e.g. concern about PWDs' nighttime wandering). We investigated whether a technology system, People Power Caregiver (PPCg), that helps monitor the caregiver's home would benefit caregivers' sleep. METHODS: = 62.73, SD = 11.10, range = 32-89) were assigned to a fully activated PPCg condition or control condition (Study 1: partially active PPCg; Study 2: waitlist control). Caregivers completed the Pittsburgh Sleep Quality Index at baseline, three-months, and six-months. RESULTS: Caregivers in the control conditions reported significantly worsening sleep efficiency whereas in comparison, those in the active conditions reported improving sleep efficiency. CONCLUSIONS: Given how critical sleep is both for caregivers' health and the care they provide, these findings underscore potential benefits of in-home technologies for protecting caregivers' sleep. CLINICAL IMPLICATIONS: Technology-based interventions that help monitor the home may support caregivers' sleep. Protecting caregivers' sleep may also preserve their ability to provide high-quality care as their loved one's disease and associated functional decline progresses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".