Long-Term Links between Physical Activity and Sleep Quality
Bibliographic record
Abstract
PURPOSE: Findings from cross-sectional research indicate that the relationship between sleep quality and physical activity is mixed. For research that does indicate a significant association, the interpretation of the finding most often is that physical activity leads to better sleep, or less frequently, that better sleep leads to more involvement in physical activity (see sleep deprivation studies). Cross-sectional studies, however, are not able to assess the direction of these effects, and experimental studies have tested only one direction of the effects. Longitudinal studies, with their focus on temporal order, are needed to specifically examine the link between sleep and physical activity as well as the direction of effects. The current study had three goals: to examine 1) the longitudinal relationship between sleep and physical activity, 2) the direction of effects, and 3) whether emotion regulation mediates the relationship between sleep and physical activity. METHODS: Participants included a sample of 827 (Mage at baseline = 19.04 yr, SD = 0.92 yr, 73.88% female) students at a university in Southwestern Ontario, who took part in a larger longitudinal survey that started in their first year of university. Participants were surveyed annually for 3 yr (2011, 2012, 2013; retention, 83.9%). Measures assessed sleep quality, physical activity, emotion regulation, and involvement in sports clubs. RESULTS: A cross-lagged autoregressive path analysis revealed that sleep quality indirectly predicted increased high-, moderate-, and low-intensity physical activity over time through its positive effect on emotion regulation. Moderate levels of physical activity indirectly predicted sleep quality over time through emotion regulation. CONCLUSIONS: Overall, there appears to be support for a bidirectional relationship between sleep and physical activity over time (at least for moderate physical activity) but only indirectly through emotion regulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| 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 teacher head, 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".