IN-HOME ASSISTIVE TECHNOLOGY PROTECTS DEMENTIA CAREGIVERS FROM WORSENING SLEEP EFFICIENCY
Bibliographic record
Abstract
Abstract Caregivers for people with dementia or mild cognitive impairment often report sleep problems due to heightened vigilance concerning worrisome behaviors (e.g., falls, wandering) by their care recipients (CRs). Interventions are needed to help alleviate these issues and the associated sleep troubles for caregivers. One promising approach is to utilize scalable and affordable in-home technologies that monitor CR behaviors round-the-clock and alert caregivers to potentially dangerous situations. We conducted two randomized controlled trials with independent samples to determine whether People Power Caregiver (PPCg), a newly developed in-home monitoring and alerting system, benefitted caregivers’ sleep over a six-month period. Combining the two studies, a total of 162 primary caregivers of the CRs were randomly assigned to either an active condition (PPCg system fully activated) or a control condition (Study 1: water leak detection only; Study 2: waitlist control procedure). Caregivers self-reported their sleep quality using the Pittsburgh Sleep Quality Index at baseline, three-months, and six-months. Using latent growth modeling, caregivers in the control conditions reported significantly worsening sleep efficiency compared to caregivers in the active condition (Active: B = -0.30, SE(B) = 0.14; Control: B = 0.74, SE(B) = 0.12; Condition Effect on Linear Slope: p =.038). We observed a similar pattern with PPCg benefitting caregivers’ sleep duration; however, this result was not statistically significant (Condition Effect on Linear Slope: p =.105). These results suggest that in-home technologies such as PPCg may offer an effective way to help caregivers achieve healthy sleep as they contend with the demands of caregiving.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".