Real-time Auditory Feedback for Improving Gait and Walking in People with Parkinson’s Disease: A Pilot and Feasibility Trial
Bibliographic record
Abstract
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 TM , 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 TM 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 Six 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. 40% 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. Key messages regarding feasibility 1) What uncertainties existed regarding the feasibility? The Heel2Toe sensor had been used in clinical research as an assessment tool and in two small proof-of-concept studies with short-term supervised use to detect change and get user feedback on their experience. There was a need to test the sensor for home use and include a control group as perhaps the attention and exercise recommendations could alone have benefit. Therefore, we designed this pilot and feasibility study. 2) What are the key feasibility findings? Dropouts from the trial were mainly related to the COVID situation. There were no adverse events in either group. Challenges with using the Heel2Toe sensor related to functionality of the app which were addressed immediately; hardware challenges were addressed in revisions including ease of charging and Bluetooth connectivity; there were challenges for people to use the smart-phone app optimally. Our current revision has removed need for the smartphone. The results also showed that people were able to use the sensor on their own at home with some technical support (average 22 minutes per person) which diminished over time and that, despite technical challenges, the majority of people were satisfied with their experience with the technology, some very much so. There was a strong response in the Heel2Toe group and a near nil response in the control group demonstrating efficacy potential. 3) What are the implications of the feasibility findings for the design of the main study? The main study will use the revised version of the Heel2Toe sensor which has eliminated the challenges with connectivity and smartphone skills. Using the 6MWT as the outcome and based on conservative estimates of effect size (0.5), a sample size of 64 per group would be supported. This sample size would also be sufficient for estimating effects on other explanatory and downstream outcomes. Participants would keep the sensor after the study.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".