The Independent Associations of Physical Activity and Sleep with Cognitive Function in Older Adults
Bibliographic record
Abstract
BACKGROUND: Current evidence suggests physical activity (PA) and sleep are important for cognitive health; however, few studies examining the role of PA and sleep for cognitive health have measured these behaviors objectively. OBJECTIVE: We cross-sectionally examined whether 1) higher PA is associated with better cognitive performance independently of sleep quality; 2) higher sleep quality is associated with better cognitive performance independently of PA; and 3) whether higher PA is associated with better sleep quality. METHODS: We measured PA, subjective sleep quality using the Pittsburgh Sleep Quality Index (PSQI), and objective sleep quality (i.e., fragmentation, efficiency, duration, and latency) using the MotionWatch8© in community-dwelling adults (N = 137; aged 55+). Cognitive function was indexed using the Alzheimer's Disease Assessment Scale-Plus. Correlation analyses were performed to determine relationships between PA, sleep quality, and cognitive function. We then used latent variable modelling to examine the relationships of PA with cognitive function independently of sleep quality, sleep quality with cognitive function independently of PA, and PA with sleep quality. RESULTS: We found greater PA was associated with better cognitive performance independently of 1) PSQI (β= -0.03; p < 0.01); 2) sleep fragmentation (β= -0.02; p < 0.01); 3) sleep duration (β= -0.02; p < 0.01); and 4) sleep latency (β= -0.02; p < 0.01). In addition, better sleep efficiency was associated with better cognitive performance independently of PA (β= -0.01; p = 0.04). We did not find any associations between PA and sleep quality. CONCLUSIONS: PA is associated with better cognitive performance independently of sleep quality, and sleep efficiency is associated with better cognitive performance independently of PA. However, PA is not associated with sleep quality and thus PA and sleep quality may be related to cognitive performance through independent mechanisms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".