How Many Days of Pedometer Use Predict the Annual Activity of the Elderly Reliably?
Bibliographic record
Abstract
PURPOSE: Daily variations of physical activity in the elderly remain unclear. We thus used a uniaxial accelerometer/pedometer to examine the variability of step counts for 1 yr, determining the minimum number of days observation needed to obtain reliable estimates of annual physical activity. METHODS: Subjects were 37 males and 44 females, healthy Japanese, aged 65-83 yr. The pedometer was worn on the waistband throughout 1 yr, accumulating information on the individual's daily step count. RESULTS: The step count spectrum showed peaks with periods of 2.3, 3.5, and 7.0 d and an aperiodic component that had a greater power at low frequencies (i.e., non-white noise). These characteristics were absent in randomly resequenced data. To ensure that 80% of total variance was attributable to between-subjects variance, 25 and 8 consecutive days of observation were needed in male and female subjects, respectively. To achieve 90% on this same measure of reliability, 105 and 37 consecutive days of observation were required. In contrast, 4 d of randomly timed observations yielded 80% reliability for both men and women, and 11 and 9 d gave 90% reliability in men and women, respectively. If sampling also took account of season and day of the week, the respective observation periods for men and women were reduced to 8 and 4 d (i.e., 2 and 1 consecutive days of sampling every 89 d) for 80% and to 16 and 12 d (i.e., 4 and 3 consecutive days every 89 d) for 90% reliability. CONCLUSION: When estimating annual step counts, seasonal and/or random sampling of data allows collection of reliable data during substantially fewer days than needed for consecutive observations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".