Evaluation of the Effect of Gait Aids, Such as Canes, Crutches, and Walkers, on the Accuracy of Step Counters in Healthy Individuals
Bibliographic record
Abstract
BACKGROUND: Wearable step-counter devices have become inexpensive tools that enable patients, researchers, and clinicians to objectively monitor physical activity. It is unknown how the use of gait aids, such as canes, crutches, and walkers, affects the accuracy of these devices. Such gait aids are commonly used by patients with chronic physical impairment and after joint-replacement surgery. The aim of this study was to determine the effect of gait aids on the accuracy of wearable step counters. METHODS: Nine healthy participants wore a Fitbit step counter on their wrist and hip and performed eight walking tests with canes, crutches, and walkers. Bland-Altman analyses were performed for all eight walking tests in order to compare agreement between measurement techniques. RESULTS: Mean overall agreement for subjects walking without gait aids in the hip group was excellent, showing a bias of -2.9, with limits of agreement (LOAs) between -8.72 and 2.95. For use of canes or crutches, the Bland-Altman plots had a range of bias values between the hip and wrist counters from -7.22 to 33.56, with LOAs from -98.55 to 124.2. The wrist counter during the four-wheeled walking test showed very little agreement with the actual step count, with a bias value of 91.33 and LOAs of 64.1-118.6, illustrating exceptionally unreliable step counts. CONCLUSION: This study suggests that these widely commercially available step counters have poor reliability with gait aids, especially walkers, which should be taken into account in research and clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".