Is Physical Activity Differentially Associated With Different Types of Sedentary Pursuits?
Bibliographic record
Abstract
OBJECTIVE: To determine whether there is a relationship between the time adolescents spend in physical activity and time they spend in different sedentary pursuits: watching television, playing video games, working on computers, doing homework, and reading, taking into account the effect of part-time work on students' residual time. DESIGN: Cross-sectional cohort design. PARTICIPANTS AND SETTING: Seven hundred forty-three high school students from 2 inner-city public schools and 1 private school. METHODS: Students completed a self-administered questionnaire that addressed time spent in physical activity, time spent in sedentary pursuits, musculoskeletal pain, and psychosocial issues and were also measured for height and weight. Main Outcome Measure Level of physical activity (low, moderate, high). RESULTS: There were more girls than boys in the low and moderate physical activity groups and more boys than girls in the high activity group. Ordinal logistic regression showed that increased time spent in "productive sedentary behavior" (reading or doing homework and working on computers) was associated with increased physical activity (odds ratio, 1.7; 95% confidence interval, 1.2-2.4), as was time spent working (odds ratio, 1.3; 95% confidence interval, 1.2-1.4). Time spent watching television and playing video games was not associated with decreased physical activity. CONCLUSIONS: Physical activity was not inversely associated with watching television or playing video games, but was positively associated with productive sedentary behavior and part-time work. Some students appear capable of managing their time better than others. Future studies should explore the ability of students to manage their time and also determine what characteristics are conducive to better time management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".