Can robot-based measurements improve prediction of motor performance after robot-assisted upper-limb rehabilitation in patients with moderate-to-severe sub-acute stroke?
Affiliations: [a] CRF Les Trois Soleils, Médecine Physique et de Réadaptation, Unité de Rééducation Neurologique, Boissise-Le-Roi (77), France
| [b]
Centre Hospitalier Sud Francilien, Neurologie, Corbeil-Essonnes (91), France
| [c] EA 7377 BIOTN, Laboratoire Analyse et Restauration du Mouvement (ARM), Université Paris-Est Créteil, Hôpitaux Universitaires Henri Mondor, Assistance Publique - Hôpitaux de Paris, Créteil (94), France
Correspondence:
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Corresponding author: Dr Christophe Duret, CRF Les Trois Soleils, 19 rue du Château, 77310 Boissise-Le-Roi, France. Tel.: +33164718082; Fax: +33160657625; E-mail: [email protected].
Abstract: Purpose:Patients with moderate-to-severe stroke-related upper limb impairment can benefit from repetitive robot-assisted training. However, predicting motor performance in these patients from baseline measurements, including robot-based parameters would help clinicians to provide optimal treatments for each individual. Methods:Forty-six patients with sub-acute stroke underwent a 16-session upper limb rehabilitation combining usual care and robotic therapy. Motor outcomes (Fugl-Meyer Assessment Upper Extremity (FMA) score) were retrospectively analysed and potential predictors of motor outcome (including baseline FMA scores, kinematics and number of repetitions performed in the first session etc.) were determined. Results:The 16-sessions upper limb combined training program led to significantly improved clinical outcomes (gains of 13.8±11.2 for total FMA score and 7.3±6.7 for FMA Shoulder/Elbow score). For the prediction model, time since stroke poorly explained the FMA total score (R2 < 35%). The model however found that time since stroke and initial value of FMA Shoulder/Elbow score were predictors of the FMA Shoulder/Elbow score: (R2 = 59.6%). Conclusion:This study found that clinical prediction of motor outcomes after moderate-to-severe upper-limb paresis is limited. However, initial proximal motor impairment severity predicted proximal motor performance. The value of baselines kinematics and of the number of repeated movements at initiation in the prediction would need further studies.
Keywords: Robotics, predictors, motor outcomes, kinematics, stroke
