Using smart supportive technology to explore nighttime rest in persons living with dementia
Bibliographic record
Abstract
Abstract Background Sleep disturbances affect 60‐70% of persons living with dementia (PLWD) affecting prognosis and care burden. Sleep studies typically employ actigraphy, however, it is intrusive and lacks information on location. This study explores the use of ambient sensors, to compare nighttime rest patterns of higher and lower functioning PLWD. Method Study participants were recruited in a 16‐bed residential care setting for PLWD. Research ethics and consent were obtained. Pressure‐sensing bed mats, motion and door sensors were installed. The sensors were connected to a hub and the cloud. Participants were divided into lower‐ and higher‐functioning groups based on interRAI Home Care scores. An AI program created nightly summaries for each resident using the sensor: evening rest start / end time, number / duration of in room movements (iRM), and actual resting time (aRT) per night. Result The higher functioning residents (n = 7) had a mean age of 83.1 ± 11.0 years, as compared to the lower functioning group (n = 5): 85.0 ± 6.7 years. The lower functioning group had consistently higher mean interRAI scores and tended to have a greater number of medications. Preliminary data from 407 nights suggest that the total rest time was longer for the lower functioning group (12.3+/‐0.97 hr) than the higher functioning group (11.9 +/‐ 1.24 hrs). The lower functioning group had 1.83 +/‐ 1.86 iRMs per night, with an average duration of 13.8 +/‐ 11.0 mins. The higher functioning group had 3.34 +/‐ 2.12 iRMs per night, with an average duration of 15.2 +/‐ 9.3 mins. This resulted in an aRT of 11.8 +/‐ 1.04 hrs for the lower functioning group and 11.1 +/‐ 1.25 hrs for the higher functioning group. Conclusion Ambient sensors were used to collect longitudinal data on nighttime activities in PLWD within a communal living setting. The findings suggest evening rest differences between higher‐ and lower‐functioning PLWD. Residents with more advanced dementia appear to be resting more, and those with less impairment seem to have more and longer rest interruptions. This technology could be used to explore the usefulness of various interventions on rest quality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".