Can We Make Time for Physical Activity? Simulating Effects of Daily Physical Activity on Mortality
Bibliographic record
Abstract
Background. The link between physical activity and health outcomes is well established, yet levels of physical activity remain low. This study quantifies effects on mortality of the substitution of low activity episodes by higher activity alternatives using time-use data. Methods. Sample time profiles are representative of the Canadian population ( n=19,597 ). Activity time and mortality are linked using metabolic equivalents(METs). Mortality risk is determined by peak daily METs and hours spent sedentary. The impact of altering activity patterns is assessed using simulated life expectancy. Results. If all leisure screen time was replaced with an equal amount of time spent going for a walk, an increase in life expectancy of about 2.5 years (95% CI, 1.4 to 3.8) would be expected. No other activity category would have as large as an effect. Conclusions. Reducing leisure screen time has a large effect, because seniors particularly have a large potential for mortality reduction and watch more television than other age groups. The general problem of inactivity cannot be solved simply by reallocating time to more active pursuits, because daily activity patterns can be heterogeneous or fragmented and activities may be nondiscretionary (e.g., work or childcare).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".