Habitual physical activity and health in the elderly: The Nakanojo Study
Bibliographic record
Abstract
This article provides a detailed overview of both factors influencing habitual physical activity, and relationships between such activity and health in the elderly. Current cross-sectional data from the Nakanojo Study, which we have been carrying out since 2000, indicate substantial associations between the overall health of participants, and both the year-averaged daily step count and the year-averaged daily duration of effort undertaken at an intensity >3 metabolic equivalents (MET). In men, the extent of health is associated more closely with the daily duration of activity >3 MET than with the daily step count, whereas in women the association is closer for the step count than for the duration of activity >3 MET. In both sexes, the threshold amount of physical activity associated with better health is greater for physical than for mental benefits: >8000 vs >4000 steps/day and/or >20 vs >5 min/day at >3 MET, respectively. In other words, physical health is better in those spending at least 20 min/day in moderate walking (at a pace of around 1.4 m/s [5 km/h]) and a further >60 min of light activity per day. In contrast, better mental health is associated with much smaller amounts of deliberate physical activity. Both the intensity and the total volume of physical activity are influenced by meteorological factors, particularly precipitation and mean ambient temperature. Activity decreases exponentially to about 4000 steps/day as precipitation increases. Excluding the influence of rainfall, the daily step count peaks at a mean outdoor temperature of around 17 degrees C; above and especially below such readings, physical activity decreases as a quadratic function of temperature. Seasonal changes in the microclimate should thus be considered when designing interventions intended to increase the habitual physical activity of older adults. Based on these findings, we are now developing preventive tactics that should contribute to health promotion, disease prevention and thus a reduction in medical expenses for elderly people.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".