IS PHYSICAL ACTIVITY INVERSELY RELATED TO TIME SPENT IN HOMEWORK, TELEVISION, VIDEO GAMES, OR COMPUTER?
Bibliographic record
Abstract
Physical activity is an important component of a healthy lifestyle. Recently, many authors have suggested that the increased amount of time adolescents spend on television watching, and computer/video games takes away from the amount of time spent doing physical activity. However, before the advent of television and computers, adolescents still participated in other non-physical leisure-time activities (e.g. board games, reading, chess, etc). Therefore, the objective of this study was to determine if there was an inverse relationship between the time spent during physical activity and the time spent during each of the following: television, homework, video games, and computers. We surveyed 743 high school students on their participation in sports and time spent doing the various non-physical leisure-time activities mentioned above, split by weekdays and weekends. We defined inactivity as no sports participation, and slight activity as < 5 hours per week in each of 1–2 sport activities. We defined moderate activity as participation in one activity for 5–10 hours/week, or < 5 hours participation per week in each of 3 or more activities. High activity was defined as > 10 hours/wk in at least one activity. Time spent during non-physical activities was categorized as none, 1 hour, 2–3 hours, 4–5 hours, and > 5hours. Overall, 12% of our respondents were inactive, 11% were slightly active, 56% were moderately active, and 20% were highly active. We employed logistic regression, dichotomizing physical activity into two groups: inactive or slightly active vs. moderately or highly active. The analysis revealed an inverse association with weekday television watching (odds ratio: 0.7, 95% CI: 0.5, 1.0). Time spent on the computer was positively associated with physical activity (odds ratio: 3.17, 95% CI: 1.6, 6.4). We conclude that in our cohort, not all sedentary pursuits lead to decreased physical activity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".