MEASURING PHYSICAL ACTIVITY IN OLDER ADULT EXERCISERS WITH PEDOMETERS
Bibliographic record
Abstract
The pedometer is reported to be a reliable and inexpensive tool to measure physical activity in independent living populations, however, previous studies have reported a large degree of variability between the accumulated steps on weekdays and weekends. An important question that arises concerning the use of pedometers is whether weekdays, weekends, or a combination of these days should be utilized to monitor physical activity. The purpose of this study was to determine on which days community-dwelling older adults should be monitored to provide an estimate of their daily physical activity. Eighteen subjects (6 males,12 females; mean age 69 ± SD 9, 95%CI = 65–73), self-monitored their physical activity by wearing a Digi-walker sw-200, during waking hours, for 9 consecutive days (two weekends framing five weekdays). The average number of steps/day were 6,559 ± 3,765, 95% CI = 4,820 - 8,299. A one-way analysis of variance indicated that there was a significant difference (p = 0.02) in the accumulated steps between days. Post-Hoc analysis revealed that pedometer values were higher on weekdays (7,463 ± 3,393, 95% CI = 5,880 - 9,046) than weekends (5,430 ± 3,922, 95% CI = 3,600 - 7,060) and highest on days attending exercise class (8,119 ± 2,912, 95% CI = 6,760 - 9,478), which were Monday, Wednesday and Friday. The results suggests that when using pedometers to assess physical activity in exercising, community-dwelling older adults both weekdays and weekends should be sampled.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".