Analysis of dynamic, bidirectional associations in older adult physical activity and sleep quality
Bibliographic record
Abstract
Sleep quality and physical activity (PA) appear to be interrelated; thus, by promoting one behaviour, it may be possible to improve the other in older adults. Examination of the within-person day-to-day variation in PA and sleep quality could potentially elucidate the directionality of the association of these behaviours. We measured sleep quality (i.e. fragmentation, efficiency, duration and latency) and moderate-to-vigorous PA using the MotionWatch8© over 14 consecutive days and nights in community-dwelling adults (n = 152; age range 53-101 years). Multilevel modelling estimated within-subject autoregressive and cross-lagged effects and between-subject associations between PA and sleep quality. On days when individuals engaged in a high amount of PA on one day (relative to their averages), they were more likely to engage in a high amount of PA on the next day (estimate, 0.19; 95% CI, 0.14, 0.24). Nights in which individuals had a long sleep latency were followed by nights in which they also had a long sleep latency (estimate, 0.09; 95% CI, 0.03, 0.14). In contrast, nights in which individuals slept for a long period of time were followed by nights in which they slept relatively less than their averages (estimate, -0.09; 95% CI, -0.13, -0.04). When individuals engaged in a large amount of PA during the day, they tended to sleep longer that following night (estimate, 0.01; 95% CI, 0.001, 0.02). All other associations between PA and sleep quality were not significant. Increasing PA therefore might increase sleep duration in older adults.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".