Augmented visual feedback-aided interventions for motor rehabilitation in Parkinson’s disease: a systematic review
Bibliographic record
Abstract
PURPOSE: A systematic review was performed to (1) evaluate the effectiveness of augmented visual feedback-based treatments for motor rehabilitation in Parkinson's disease, and (2) examine treatment design factors associated with enhanced outcomes following these treatments. METHODS: Eight databases were searched from their start-date up to January 2017 using the key terms Parkinson's Disease and augmented visual feedback. Two independent raters screened the abstracts and full articles for inclusion. Relevant data were extracted and summarized, and methodological quality of accepted articles was assessed. RESULTS: Eight single-group studies and 10 randomized control trials were included in the review. Augmented visual feedback-based treatments resulted in improved outcomes with small to large effect sizes post-treatment for the majority of impairment, activity, participation, and global motor function measures, and these improvements were often superior to traditional rehabilitation/education programs. Enhanced treatment outcomes were observed in studies that provided large amounts and high intensities of treatment; gamified feedback; and provided knowledge of performance feedback in real-time on 100% of practice trials. CONCLUSION: Augmented visual feedback appears to be a useful motor rehabilitation tool in Parkinson's disease; however, high-quality, rigorous studies remain limited. Future studies should consider factors that enhance rehabilitation outcomes when designing augmented visual feedback-based interventions. Implications for rehabilitation Augmented visual feedback is a useful tool for motor rehabilitation in Parkinson's disease; augmented visual feedback-based treatments are often superior to traditional programs. These treatments are associated with improved outcomes in impairment, activity, participation, and global motor function domains. Rehabilitation professionals can optimize their use of augmented visual feedback-based treatments by providing large amounts and a high intensity of treatment, gamifying feedback, and providing knowledge of performance feedback in real-time and at a high frequency.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".