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.
Acknowledgments
The authors would like to thank the participants and their families, the Stroke Recovery Association of BC, and colleagues Keith Lohse, Leia Shum, and Yi Jui Lee.