Associations Between Type and Timing of Physical Activity and Sedentary Behavior With Mental Health in Adolescents and Young Adults
Bibliographic record
Abstract
BACKGROUND: This cross-sectional study analyzed the association of leisure-time physical activity (PA) and sedentary behavior (SB), nonleisure PA and SB, and total PA and SB in different time segments of the day with mental health among Dutch adolescents and young adults. METHODS: A total of 881 participants aged 16-25 years completed an online survey. Mental health was assessed using the Mental Health Inventory-5, and participants also reported sex, age, and income. They filled out a questionnaire of types of PA and SB for each hour of the day. Activities were categorized into nonleisure and leisure, during the morning, afternoon, evening, and for the whole day. RESULTS: Participants (52.8% female, on average 20.8 y) generally engaged in more leisure-time PA and SB during weekends compared with weekdays, and more nonleisure activities on weekdays. Associations varied between time segments and days of the week. Positive associations of leisure-time and total PA during the whole day and evenings with mental health were observed on weekdays. Total, leisure-time, and nonleisure-time SB were associated with worse mental health. Nonleisure PA was not associated with mental health. CONCLUSIONS: Leisure-time PA was found to have a favorable association with mental health, particularly in the evenings of weekdays and afternoons of weekend days. On the other hand, leisure SB was associated with poorer mental health in most of the time segments analyzed, and nonleisure SB in the evenings was also related to worse mental health. The type and timing of PA and SB behaviors play an important role in the relationship with mental 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".