Association between sleep quantity, physical activity, and depression among us adults: Analysis of the nhanes 2015-2016
Bibliographic record
Abstract
Insufficient physical activity and poor sleep have been independently associated with increased risk for reduced physical and mental health. Previous research suggests that depression is associated with both long- and short-sleep duration. Although long-sleep, defined as sleeping more than 9 hours per night, may be appropriate for young adults, those recovering from sleep debt, and individuals with illnesses, less is known about the risks of long-sleep in the general population. The objective of this study was to investigate whether physical activity moderates the relationship between sleep quantity and depression. A cross-sectional, nationally representative sample from the 2015-2016 National Health and Nutrition Examination Survey (NHANES) was used. Total metabolic equivalent (METs) scores derived from the Global Physical Activity Questionnaire (GPAQ) assessed physical activity. Self-reported Patient Health Questionnaire-9 (PHQ-9) was used to classify depression severity. Sleep quantity was calculated from self-reported sleep and wake times, rounded to the nearest half hour. Analyses revealed that meeting physical activity recommendations of 150 moderate-to-vigorous minutes per week was related to lower depression scores regardless of the quantity of sleep obtained; however, the largest effect was shown in those who over sleep. For individuals who over sleep (>9 hours), those who met physical activity recommendations were less depressed than those who do not (F(2, 5) = 4.19, p = .015).These findings suggest that health benefits of extended sleep may be enhanced when sufficient amounts of physical activity are met.
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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 | 0.002 |
| 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 | 0.001 |
| 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".