Robotic Kinematic measures of the arm in chronic Stroke: part 1 - Motor Recovery patterns from tDCS preceding intensive training

Abstract

Background: Effectiveness of robotic therapy and transcranial direct current stimulation is conventionally assessed with clinical measures. Robotic metrics may be more objective and sensitive for measuring the efficacy of interventions on stroke survivor's motor recovery. This study investigated if robotic metrics detect a difference in outcomes, not seen in clinical measures, in a study of transcranial direct current stimulation (tDCS) preceding robotic therapy. Impact of impairment severity on intervention response was also analyzed to explore optimization of outcomes by targeting patient sub-groups.

Methods: This 2020 study analyzed data from a double-blind, sham-controlled, randomized multi-center trial conducted from 2012 to 2016, including a six-month follow-up. 82 volunteers with single chronic ischemic stroke and right hemiparesis received anodal tDCS or sham stimulation, prior to robotic therapy. Robotic therapy involved 1024 repetitions, alternating shoulder-elbow and wrist robots, for a total of 36 sessions. Shoulder-elbow and wrist kinematic and kinetic metrics were collected at admission, discharge, and follow-up.

Results: No difference was detected between the tDCS or sham stimulation groups in the analysis of robotic shoulder-elbow or wrist metrics. Significant improvements in all metrics were found for the combined group analysis. Novel wrist data showed smoothness significantly improved (P < ·001) while submovement number trended down, overlap increased, and interpeak interval decreased. Post-hoc analysis showed only patients with severe impairment demonstrated a significant difference in kinematics, greater for patients receiving sham stimulation.

Conclusions: Robotic data confirmed results of clinical measures, showing intensive robotic therapy is beneficial, but no additional gain from tDCS. Patients with severe impairment did not benefit from the combined intervention. Wrist submovement characteristics showed a delayed pattern of motor recovery compared to the shoulder-elbow, relevant to intensive intervention-related recovery of upper extremity function in chronic stroke.

Trial registration: http://www.clinicaltrials.gov . Actual study start date September 2012. First registered on 15 November 2012. Retrospectively registered. Unique identifiers: NCT01726673 and NCT03562663 .

Keywords: Kinematics; Outcome measures; Robotics; Stroke; tDCS.

Conflict of interest statement

TH reports personal fees from Bionik Laboratories, outside the submitted work; In addition, TH has a patent “An apparatus and/or method for positioning a hand for rehabilitation” pending to Bionik Laboratories. HIK declares he was the founder of Interactive Motion Technologies and Chairman of the Board (1998–2016). He successfully sold Interactive Motion Technologies on April 2016 to Bionik Laboratories, where he served as Chief Science Officer and Board Member until July 2017. HIK was the founder of 4Motion Robotics. HIK has patents; Interactive Robotic Therapist; US Patent 5,466,213; 1995; Massachusetts Institute of Technology issued, and a patent Wrist And Upper Extremity Motion; US Patent No. 7,618,381; 2009; Massachusetts Institute of Technology licensed to Bionik Laboratories.

© 2021. The Author(s).

Figures

Fig. 1

CONSORT flow diagram

Fig. 2

Combined RobottDCS and RobotSham mean and standard error of kinematic and kinetic outcome metrics for the shoulder-elbow (S/E.) Significant changes (P < .0167) between admission (ad), discharge (dc), and follow-up (fu.) are marked with an * Note: The bar-graphs in white background represent the unconstrained trained reaching macro-metrics, the lightest grey shading background represents the unconstrained trained reaching micro-metrics (sm= sub-movement), the slightly darker grey represents the unconstrained untrained circle metrics, and the darkest grey represents the reaching against resistance, isometric stabilization, and kinetic metrics (respectively, see Additional file 1 for further details on the metrics.)

Fig. 3

Combined RobottDCS and RobotSham mean and standard error of kinematic and kinetic outcome metrics for the wrist. Significant changes (P < .0167) between admission (ad), discharge (dc), and follow-up (fu.) are marked with an * Note: The bar-graphs in white background represent the unconstrained trained pointing macro-metrics, the lightest grey shading background represents the unconstrained trained pointing micro-metrics (sm= sub-movement), and the darkest grey represents the reaching against resistance, isometric stabilization, and kinetic metrics (respectively, see Additional file 1 for further details on the metrics.)

Fig. 4

Improvements in joint independence over time. To afford direct comparison with Figs. 4 and 5 of (Dipietro et al., 2007), here we employed the same non-corrected significance at P < .05 level. Individual values of joint independence metric (a and b for all patients), sorted by their performance at admission (squares) in comparison to discharge (diamonds), and follow-up (circles). A lower number indicates greater/improving joint independence. Figures c and d represent changes in the joint independence metric, where filled circles indicate significance at discharge (c) and follow-up (d)