Real-time Auditory Feedback for Improving Gait and Walking in People with Parkinson’s Disease: A Pilot and Feasibility Trial
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».