Capturing step counts at slow walking speeds in older adults: Comparison of ankle and waist placement of measuring device
Bibliographic record
Abstract
OBJECTIVE: It is important for older adults to be physically active, but many older adults walk slowly. This study examined the accuracy of a commercially available step-count device (Fitbit One) at slow speeds and compared the accuracy of the device when worn at the ankle and waist in older adults. METHODS: The Fitbit One was placed at the ankle and waist of participants (n=42; mean age 73 years) while they performed walking trials at 7 different speeds (0.3-0.9 m/s). Step counts obtained from video recordings were used as the gold standard comparison to determine the accuracy of the device. RESULTS: The ankle-worn device had significantly less error than the waist-worn device at all speeds. The percentage error of the ankle-worn device was less than 10% at speeds of 0.4-0.9 m/s and did not record zero steps at any speed. The percentage error of the waist-worn device was below 10% at only the 2 fastest speeds (0.8 and 0.9 m/s) and recorded zero steps for numerous participants at speeds of 0.3-0.5 m/s. CONCLUSION: The Fitbit One can accurately capture steps at slow speeds when placed at the ankle and thus may be appropriate for capturing physical activity in slow-walking older adults.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".