Association of step counts with cognitive function in apparently healthy middle-aged and older Japanese men
Bibliographic record
Abstract
Background: Increasing physical activity may prevent cognitive decline. Previous studies primarily focused on older adults and used self-reported questionnaires to assess physical activity. We examined the relationship between step count, an objective measure of physical activity, and cognitive function in community-based middle-aged and older Japanese men. Methods: The Shiga Epidemiological Study of Subclinical Atherosclerosis randomly recruited community-dwelling healthy men aged 40-79 years from Shiga, Japan, and measured their step counts over 7 consecutive days using a pedometer at baseline (2006-2008). Among men who returned for follow-up (2009-2014), we assessed their cognitive function using the Cognitive Abilities Screening Instrument (CASI) score. We restricted our analyses to those with valid 7-day average step counts at baseline and those who remained free of stroke at follow-up (n = 676). Using analysis of covariance, we calculated the adjusted means of the CASI score according to the quartiles of the average step counts. Results: The mean (standard deviation) of age and unadjusted CASI score were 63.8 (9.1) years and 90.8 (5.8), respectively. The CASI score was elevated in higher quartiles of step counts (90.2, 90.4, 90.6, and 91.8 from the lowest to the highest quartile, respectively, [p for trend = 0.004]) in a model adjusted for age and education. Further adjustment for smoking, drinking, and other cardiovascular risk factors resulted in a similar pattern of association (p for trend = 0.005). Conclusion: In apparently healthy middle-aged and older Japanese men, a greater 7-day average step count at baseline was associated with significantly higher cognitive function score.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".