Real-time auditory feedback for improving gait and walking in people with Parkinson’s disease: a pilot and feasibility trial
Bibliographic record
Abstract
BACKGROUND: Technology is poised to bridge the gap between demand for therapies to improve gait in people with Parkinson's and available resources. A wearable sensor, Heel2Toe™, a small device that attaches to the side of the shoe and gives a sound each time the person starts their step with a strong heel strike, has been developed and pre-tested by a team at McGill University. The objective of this study was to estimate feasibility and efficacy potential of the Heel2Toe™ sensor in changing walking capacity and gait pattern in people with Parkinson's. METHODS: A pilot study was carried out involving 27 people with Parkinson's randomized 2:1 to train with the Heel2Toe[TM] sensor and or to train with recommendations from a gait-related workbook. RESULTS: A total of 21 completed the 3-month evaluation, 14 trained with the Heel2Toe[TM] sensor, and 7 trained with the workbook. Thirteen of 14 people in the Heel2Toe group improved over measurement error on the primary outcome, the 6-Minute Walk Test, (mean change 66.4 m) and 0 of the 7 in the Workbook group (mean change - 19.4 m): 4 of 14 in the Heel2Toe group made reliable change and 0 of 7 in the Workbook group. Improvements in walking distance were accompanied by improvements in gait quality. Forty percent of participants in the intervention group were strongly satisfied with their technology experience and an additional 37% were satisfied. CONCLUSIONS: Despite some technological difficulties, feasibility and efficacy potential of the Heel2Toe sensor in improving gait in people with Parkinson's was supported.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".