Meeting 24-hour Movement Guidelines: Prevalence, Correlates, and Associations with Physical Fitness and Depression Among Adults: A Provincial Surveillance in China (Preprint)
Bibliographic record
Abstract
BACKGROUND 24-hour movement behaviors integrating physical activity (PA), sedentary behavior (SB) and sleep are important components influencing adults’ health. Canadian 24-hour movement guidelines for adults were launched in 2020. However, there is little evidence on the prevalence and correlates of guideline compliance and its associations with health outcomes among Chinese adults. OBJECTIVE The purposes of this study were to investigate the prevalence and correlates of meeting 24-hour movement guidelines among Chinese adults and examine the association of meeting guidelines with physical fitness and depression among Chinese adults. METHODS A total of 7059 adults (45.73 ± 14.56 years, age range: 20-79 years, 52% female) were recruited by stratified cluster random sampling from the latest provincial health surveillance of Hubei China between 25-Jul and 19-Nov 2020. Participants completed a self-reported questionnaire including movement behaviors (PA, SB, and sleep), depressive symptoms, and demographic information. In addition, eight objectively measured physical fitness tests (body mass index, waist-hip ratio, body fat percentage, vital capacity, handgrip strength, flexibility, balance, and choice reaction time) were conducted. SPSS 28.0 was used to perform Generalized Linear Mixed Models analysis to examine the correlates and associations. RESULTS 25.54% of participants met all three movement guidelines, while 48.62% met only two of them, 23.10% met one of them and 2.75% met none. Participants, who were older adults, unmarried, and living near PA facilities were more likely to meet all three movement guidelines. Meeting more movement guidelines was associated with less likelihood of depression, while no significant associations were observed between meeting 24-hour movement guidelines and physical fitness indicators. CONCLUSIONS This is the first study to investigate the prevalence and correlates of compliance with 24-hour movement guidelines among Chinese adults, and its associations with physical fitness and depression. Future efforts aiming to encourage adults to meet movement guidelines should consider detailed demographic differences. Future interventions should be applied to enhance adults’ overall 24-hour movement behaviors further mitigating their depressive symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".