Physical Activity Contributes to Several Sleep–Cardiometabolic Health Relationships
Bibliographic record
Abstract
PURPOSE: To estimate the contribution of accelerometer-derived physical activity to the relationship between sleep and cardiometabolic health. METHODS: Data from the 2005 to 2006 US National Health and Nutritional Examination Survey were used (N = 1226; 20 years+). Metabolic syndrome (MetS) was defined by the Joint Interim Statement, and sleep quality and quantity by the Sleep Disorders Questionnaire. Physical activity intensities were defined by activity thresholds (counts per minute) as sedentary activity (0-99), light intensity (100-759), lifestyle activity (760-2019), moderate intensity (2020-5996), and vigorous intensity (≥5999). Outcomes were MetS, number of MetS components, waist circumference (WC), systolic and diastolic blood pressure (BP), triglycerides, HDL-cholesterol, fasting plasma glucose, and fasting insulin concentration. The bootstrap method was used to estimate the amount of mediation or contribution of activity intensities (ab) to the sleep-cardiometabolic health relationships, which were quantified as large (≥0.25) or moderate (≥0.09). RESULTS: Lifestyle activity level contributes to several sleep duration and cardiometabolic health relationships, most notably for WC (ab: 0.28), systolic BP (0.39), and fasting insulin concentration (0.85). While moderate intensity and lifestyle activity intensities were large contributors to the sleep quality-fasting insulin concentration relationship (0.47 and 0.48, respectively), light intensity activity only moderately contributed to the relationship between sleep duration and quality with abdominal obesity (0.15). CONCLUSION: Lifestyle and moderate intensity physical activity have a large effect on the relationship between sleep and cardiometabolic health, including WC, BP, and fasting insulin concentration. Appropriate sleep hygiene, in combination with regular physical activity should be considered mutually beneficial targets for cardiometabolic health.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".