Accuracy of Pedometry for Ambulatory Adults with Neurological Disabilities
Bibliographic record
Abstract
Purpose: The purpose of the study was to determine the contribution of gait variability to pedometer accuracy. Method: Participants completed one 100 m walking trial wearing a Yamax SW-200 pedometer (New Lifestyles Canada, Deep River, Ontario). Error scores and percent error scores were calculated. Gait speed was measured over the 100m distance. Gait variability was measured by walking on a 10 m paper walkway with ink pads on the bottoms of the participants' shoes. The ink marks were used to calculate average step length and step width. A stepwise linear regression analysis determined the contribution of step length (SL) variability, step width variability, and gait speed to error score. Results: Forty-five ambulatory volunteers (27 males, 18 females; age 54 6 14 years) with neurological disabilities participated in the study. Mean SL variability and gait speed were 6.6 6 3.4% and 53.8 6 21.1 m/min, respectively. On average, the pedometer underestimated the number of actual steps taken by 11.2%. Both SL variability and gait speed were significant predictors of error score, with gait speed accounting for 41% and SL variability accounting for 8% of the variance in error score. Conclusions: In a sample of ambulatory persons with neurological disabilities, gait speed was the most important determinant of pedometer accuracy, but SL variability also made a significant contribution to error 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".