Rhythm Training for People with Stroke: A Tool to Enhance Neurorehabilitation
Bibliographic record
Abstract
Rhythmic auditory stimulation (RAS) is a promising therapy to improve spatiotemporal gait parameters after stroke. However, RAS is not equally effective for everyone; an individual’s response to RAS is likely partially mediated by rhythm abilities, specifically beat perception and production. If rhythm can be trained post-stroke, there is potential to enhance response to RAS among people with poor rhythm, which could make RAS more effective for a wider range of people. Furthermore, there are many stroke therapies implemented using telerehabilitation. If rhythm is important for some of the therapies being conducted online, it is necessary to determine if it is feasible to test and train rhythm abilities online as well. This work aims to answer these queries with three studies. Based on the framework of co-design, Study 1 involved collaborating with relevant stakeholders (i.e., people with stroke, musicians/music therapists/teachers, and physiotherapist specializing in neurorehabilitation) to gain insight on how to develop music programs for people with stroke. Four topics were identified as important to consider when designing music programs for people with stroke including: 1) goals of music in stroke rehabilitation, 2) therapeutic interactions with music, 3) individualizing components of music programs, and 4) logistics of music program development and delivery. Study 2 involved evaluating the feasibility of testing rhythm online with the Beat Alignment Test (BAT), a rhythm assessment often used in the stroke population. Completing the BAT online was more feasible for neurotypical adults than people with stroke. BAT components had poor to good test-retest reliability and there were no learning effects when the BAT was completed three times online. Lastly, Study 3 was a pilot study of a rhythm training program for people with stroke that was delivered in-person and online. Rhythm training was well tolerated regardless of delivery method, with minimal changes in BAT scores pre-post intervention. The lack of consistent pre-post changes in rhythm abilities may explain why there were no changes in immediate gait response to RAS pre-post intervention. Future work is needed to investigate dose of rhythm training required to cause meaningful changes in rhythm abilities after stroke.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".