Real‐world sleep in Parkinson’s disease predicts cognitive dysfunction.
Bibliographic record
Abstract
Abstract Background Sleep dysfunction is signature of Alzheimer’s disease and Parkinson’s disease (PD), and can signify incipient disease, disease risk, and worsen symptoms over time. How real‐world sleep dysfunction relates to patient self‐report of sleep and clinical cognitive dysfunction is poorly understood, partly because self ‐report is impaired in patients with cognitive decline. We monitored real‐world sleep with wearable actigraphy devices in patients with PD to test the hypothesis that objective patterns of sleep dysfunction are associated with worse cognitive performance. Method Twenty‐nine participants with idiopathic PD (age = 67.44 ± 5.79, 20 males) completed the Montreal Cognitive Assessment (MoCA) and Epworth Sleepiness Scale (ESS) to capture clinical cognitive decline and self‐reported daytime sleepiness. Sleep was monitored for 4‐weeks using wrist‐worn ActiGraphs. Sub‐scores and total scores of MoCA were compared with Total Sleep Time (TST), Sleep Efficiency (SE), Wakefulness After Sleep Onset (WASO), and Sleep Fragmentation Index (SFI) measured by actigraphy, as well as ESS total using a Pearson correlation. Result Worse sleep fragmentation (SFI), the percentage of awakenings and movements during sleep, predicted worse cognitive impairment overall (MoCA score: r = ‐0.38, p < .05) and delayed recall (r = ‐0.44, p < .05). Reduced sleep time (TST) and worse sleep quality (SE, WASO) did not worsen patient cognitive impairment. Patient self‐report of sleepiness (ESS) did not associate with worse cognitive outcomes. Conclusion This pilot analysis identifies sleep fragmentation as a key risk factor for cognitive dysfunction in PD. Patient self‐report of sleep may not reliably reflect chronic sleep disruption and related cognitive dysfunction. Results underscore that objective measures of real‐world dysfunction can help inform clinical care and intervention for patients at risk for cognitive decline and dementia.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".