Increased Fragmentation of Rest-Activity Patterns Is Associated With a Characteristic Pattern of Cognitive Impairment in Older Individuals
Bibliographic record
Abstract
STUDY OBJECTIVES: Aging is accompanied by changes in cognitive function, and changes in rest-activity patterns. Previous work has demonstrated associations between global rest-activity measures and cognitive performance on a number of tasks. Recently, we demonstrated that aging is associated with changes in the minute-to-minute fragmentation of rest-activity patterns in addition to changes in amounts of rest and activity. Given the body of experimental evidence linking sleep fragmentation with decrements in cognitive function in animals and humans, we hypothesized that increased fragmentation of rest-activity patterns would be associated with decreased cognitive function in older individuals. DESIGN: Cross-sectional. PARTICIPANTS: 700 community-dwelling individuals from the Rush Memory and Aging Project. MEASUREMENTS AND RESULTS: We obtained up to 11 days of actigraphic recordings in subjects' home environments and quantified the fragmentation of rest and activity using a recently developed state transition metric. We tested the associations between this metric and performance in 5 cognitive domains. Greater fragmentation of both rest and activity were associated with lower levels of cognitive performance, and this association was independent of total amounts of rest or activity. There was a characteristic pattern of cognitive deficits associated with rest and activity fragmentation, with preferential involvement of perceptual speed, semantic memory, working memory, and visuospatial abilities, and relative sparing of episodic memory. CONCLUSIONS: The fragmentation of periods of rest and activity is a clinically important characteristic of rest-activity patterns that correlates with cognitive performance in older individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".