Dyad motor learning in a wrist-robotic environment: Learning together is better than learning alone

Objective

Dyad motor practice is characterized by two learners alternating between physical and observational practice, which can lead to better motor outcomes and reduce practice time compared to physical practice alone. Robot-assisted therapy has become an established neurorehabilitation tool but is limited by high therapy cost and access. Implementing dyad practice in robot-assisted rehabilitation has the potential to improve therapeutic outcomes and/or to achieve them faster. This study aims to determine the effects of dyad practice on motor performance in a wrist-robotic environment to evaluate its potential use in robotic rehabilitation settings.

Methods

Forty-two healthy participants (18–35 years) were randomized into three groups (n = 14): Dyad practice, physical practice with rest and physical practice without rest. Participants practiced a 2 degree-of-freedom gamified wrist movement task for 20 trials using a custom-made wrist robotic device. A motor performance score (MPS) that captured temporal and spatial time-series kinematics was computed at baseline, the end of training and 24 h later to assess retention.

Results

MPS did not differ between groups at baseline. All groups revealed significant performance gains by the end of training. However, dyads outperformed the other groups at the end of training (p < 0.001) and showed higher retention after 24-h (p = 0.02). Median MPS improved by 46.5% in dyads, 25.3% in physical practice-rest, and 33.6% in physical practice-no rest at the end of training compared to baseline.

Conclusion

Compared to physical practice alone, dyad practice leads to superior motor outcomes in a robot-assisted motor learning task. Dyads still outperformed their counterparts 24-h after practice.

Impact statement

Improving motor function in complex motor tasks without increasing required practice time, dyad practice can optimize therapeutic resources. This is particularly impactful in robot-assisted rehabilitation regimens as it would help to improve patients' outcomes and increase care efficiency.