Biofeedback vs. game scores for reducing trunk compensation after stroke: a randomized crossover trial
Bibliographic record
Abstract
Background Compensatory movements are commonly employed by stroke survivors, and their use can have negative effects on motor recovery. Current practices to reduce them rely on strapping a person to a chair. The use of technology to substitute or supplement this methodology has not being thoroughly investigated. Objective To compare the use of Scores + Visual + Force and Visual + Force feedback for reducing trunk compensation. Methods Fourteen hemiparetic stroke survivors performed bimanual reaching movements while receiving feedback on trunk compensation. Participants held onto two robotic arms and performed movements in the anterior/posterior direction toward a target displayed on a monitor. A motion-tracking camera tracked trunk compensation; the robots provided force feedback; the monitor displayed the visual feedback and scores. Kinematic variables, a post-test questionnaire, and system usability were analyzed. Results Both conditions reduced trunk compensation from baseline: Scores + Visual + Force: 51.7% (40.8), p = 0.000; Visual + Force: 55.2% (40.9), p = 0.000. No statistically significant difference was found between modalities. Secondary outcome measures were not improved. Most participants would like to receive game scores to reduce trunk compensation, and the usability of the system was rated "Good." Conclusions Multimodal feedback about stroke survivors' trunk compensation levels resulted in reduced trunk displacement. No difference between feedback modalities was obtained. The positive effects of including game scores might not have been observed in a short-term intervention. Longer studies should investigate if the use of game scores could result in trunk compensation improvements when compared to trunk restraint strategies. Clinical Trial Registration Clinicaltrials.gov, NCT02912923, https://clinicaltrials.gov/ct2/show/NCT02912923?term=reaching+in+stroke&rank=2 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".