Association Between Sleep Measures and Cognition Performance: Insights from a Digital Sleep Assessment Study
Bibliographic record
Abstract
Abstract Background Sleep disorders as a contributing factor to cognitive impairment have spurred growing interest. The advent of digital technology facilitates the collection of comprehensive sleep measures in a home setting. The objective of this study is to examine the association between digital sleep measures and the Montreal Cognitive Assessment (MoCA). Method This study included participants from the Boston University Alzheimer’s Disease Research Center (BU ADRC) Clinical Core, a longitudinal study of aging which includes the Uniform Dataset (UDS) and other Alzheimer’s Disease related clinical features. Participants were asked to wear a SleepImage Ring when they went to bed at night at least three times in a two week span at quarterly intervals. A variety of sleep measures, such as duration of unstable non‐rapid eye movement (NREM) sleep and percentage of time spent with SpO2 below 80%, were collected from the device and analyzed. Linear regression models were used to assess the associations between these sleep measures and the MoCA total score as well as individual MoCA scores. All models were adjusted for sex, age, and education to account for potential confounding factors. Result Our study included 75 participants from the BU ADRC (mean age: 74.9± 7.9 years; 64.0% women). On average, the SleepImage Ring was worn for 17 nights over the duration of the study (interquartile range: 6‐23 nights). As shown in Table 1, 11 sleep measures were associated with at least one MoCA item with nominal significance (P<0.05). Interestingly, the percentage of time spent with SpO2 below 80% was negatively associated with the MoCA total score (P=0.016) as well as three individual MoCA scores, including the Language – Naming (P<0.001), Delayed recall – No cue (P=0.027) and Abstraction (P=0.034). Conclusion Our analysis revealed multiple suggestive associations between digital sleep measures and MoCA test scores, highlighting the potential of sleep as a modifiable lifestyle factor to assess and improve cognitive health. Further studies with larger and independent samples are required to further validate these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".