Physical activity and cardiometabolic health across an extreme lifestyle gradient
Bibliographic record
Abstract
Abstract Background and objectives Cardiometabolic health in many small-scale subsistence populations has been shown to be substantially better than populations living in industrialized, urban environments. This disparity is often partially attributed to the characteristically high levels of physical activity observed in non-industrialized societies, yet it remains unclear how much of the difference in cardiometabolic health between non-industrialized and industrialized populations is due specifically to physical activity rather than to other lifestyle factors that covary with industrialization. Methodology To address this question, we collected objective data on physical activity (as characterized by daily step counts, time spent inactive, and intensity gradient, among other measures), cardiometabolic biomarker profiles (16 measures: anthropometrics, blood lipids, blood pressure, obesity, hypertension, pre-diabetes, and diabetes), and detailed lifestyle information from 1075 Orang Asli adults in Peninsular Malaysia, who span an exceptionally wide lifestyle gradient from small-scale subsistence communities to urban, industrialized settings. Results More urban and market-integrated lifestyles were associated with marked reductions in physical activity, particularly among older individuals, and with poorer cardiometabolic health. At the individual level, greater physical activity was directly associated with better cardiometabolic health. However, physical activity accounted for only a small portion of urbanization’s association with cardiometabolic health, indicating that other industrialization-related factors play substantial roles in shaping health outcomes. Conclusions and implications These findings suggest that declining physical activity represents an evolutionarily novel challenge that exacerbates the burden of cardiometabolic disease, especially among older adults, in contemporary industrialized environments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".