Recommendations for physical activity within the general population: is this all what we need to keep us healthy?
Bibliographic record
Abstract
In the natural habitat of our ancestors, physical activity was not a preventive intervention but a matter of survival. From our genetic pool genes were selected which were very effective in regulating food intake and utilization.1,2 This was a clear survival advantage in periods of food deprivation, because they could rely on larger previously generated energy stores. Unfortunately, our selected gene pool has not changed dramatically since then, but we are no longer faced with long periods of food deprivation, and we are no longer forced to run several miles to chase our food. In addition to this ‘old’ genotype, physical inactivity is one important risk factor for the development of several diseases. Several landmark studies around 1990 clearly documented that physical inactivity is a modifiable risk factor for cardiovascular disease and several other chronic diseases, including diabetes mellitus, cancer, obesity, hypertension, bone and joint diseases, and depression.3–5 Immediately it became evident that increasing physical activity and exercise would be a powerful tool to reduce the overall incidence of cardiovascular and even all-cause mortality. Over the years many studies in more than 100,000 individuals have clearly documented that the higher the level of physical fitness, the less likely an individual will suffer premature cardiovascular death. The importance of fitness for the risk of overall death becomes very clear in one of the landmark studies of Jonathan Myers and colleagues in 2002.6 In this trial the authors studied a total of 6213 consecutive men referred for treadmill exercise testing. Among these individuals 3679 individuals had an abnormal exercise test result or a history of cardiovascular events. The rest were classified as healthy. Comparing the relative risk of a healthy but unfit individual with a fit subject but having signs for cardiovascular disease, the risk to die from any cause was much higher for the unfit healthy individual. This clearly shows that being unfit is even worse than being overweight, having diabetes or other risk factors for premature death. Therefore, improving fitness warrants at least as much attention as other risk factors, such as body mass index (BMI), smoking, diabetes or hypertension. Moreover, improving physical activity reduces these common risk factors and therefore may potentiate its beneficial effects.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 0.021 |
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".