PEDOMETER ACCURACY IN INSTITUTIONAL AND COMMUNITY OLDER ADULTS
Bibliographic record
Abstract
Pedometers are simple, inexpensive motion sensors used for measurement and motivation of physical activity. Under controlled conditions pedometers are known to be less accurate during slow walking (i.e., < 60m/min). Their utility for assessing physical activity in frail institutionalized elderly (characterized by slow and shuffling gaits) has been questioned. PURPOSE To determine pedometer accuracy in older adults stratified by living situation (nursing home residents vs. community dwelling). METHODS 26 residents (5 males, 21 females; 79.4±8.2 years) were recruited from nursing home facilities and 28 older adults (5 males, 23 females; 70.6±5.5 years) from a senior's recreation center. All participants walked 13 meters at three speeds (slow, normal, fast) wearing a pedometer (Model DW200, Yamasa Corporation, Tokyo, Japan). Actual steps taken were observed and recorded using a hand-tally counter. To determine walking speed, the middle 7m of walk was timed (using photoelectric cells) to exclude the effects of acceleration and deceleration. Percent error was calculated as [(pedometer steps-actual steps)/actual steps)* 100. Independent sample t-tests were used to examine the difference in age and gait speeds between the nursing home and community samples. RESULTS The residents walked slow = 25.0±9.9 m/min, normal = 38.2±16.6 m/min, and fast = 48.1±21.4 m/min. The corresponding values for the community sample were: slow = 56.7±11.9 m/min, normal = 76.4±11.9 m/min, and fast = 96.4±12.7 m/min. The community sample was significantly younger and walked faster (p < .0001) at all three paces, even adjusting for age. Pedometers underestimated actual steps taken in residents by 74% (slow) to 46% (fast). The percent error for the community sample ranged from 25% (slow) to 7% (fast). CONCLUSIONS The magnitude of the error observed indicates that pedometers are not acceptably accurate for assessing physical activity in frail institutionalized elderly whose self-selected normal pace of walking is < 60m/min.
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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 | 0.012 |
| 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 | 0.001 | 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".