The Influence of Sound-Based Interventions on Motor Behavior After Stroke: A Systematic Review
Bibliographic record
Abstract
Objective: To investigate the effects of sound-based interventions (SBI) on biomechanical parameters in stroke patients. Methods: Pubmed/Medline, Web of Science, Physiotherapy Evidence Database (PEDro), and Cochrane Library were searched until September 2019. Studies examining the effect of sound-based interventions on kinematic, kinetic and electromyographic outcome measures were included. Two independent reviewers performed the screening, and data extraction and risk of bias assessment were conducted with the PEDro and Newcastle-Ottawa scale. Disagreements were resolved by a third independent reviewer. Results: Of the 858 studies obtained from all databases, 12 studies and 240 participants met the inclusion and exclusion criteria. Six studies investigated the effect of SBI on upper limb motor tasks, while six examined walking. Concerning the quality assessment (Newcastle-Ottawa Quality Assessment Scale and PEDro), the nine cross-sectional studies had a median score of seven, while the randomized controlled trials had a median score of five (fair to good quality). In relation to upper limb motor tasks, only one study found improvements in cortical reorganization, increased central excitability and motor control during reaching after SBI (results of other 5 studies were too diverse and lacked quality to substantiate their findings). In relation to walking results were clearer: SBI led to improvements in knee flexion and gastrocnemius muscle activity. Conclusion: Due to the heterogeneity of the included studies, evidence was found demonstrating that SBI can induce biomechanical changes in motor behaviour during walking in stroke patients. No conclusions could be formulated regarding reaching tasks. Additionally, directions for future research for understanding the underlying mechanism of the clinical improvements after SBI are: 1) using actual music pieces instead of rhythmic sound sequences; and 2) examining sub-acute stroke rather than chronic stroke patients.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".