Susan S. Conroy

Robot-Assisted Therapy for Long-Term Upper-Limb Impairment after Stroke

BACKGROUND Effective rehabilitative therapies are needed for patients with long-term deficits after stroke. METHODS In this multicenter, randomized, controlled trial involving 127 patients with moderate-to-severe upper-limb impairment 6 months or more after a stroke, we randomly assigned 49 patients to receive intensive robot-assisted therapy, 50 to receive intensive comparison therapy, and 28 to receive usual care. Therapy consisted of 36 1-hour sessions over a period of 12 weeks. The primary outcome was a change in motor function, as measured on the Fugl-Meyer Assessment of Sensorimotor Recovery after Stroke, at 12 weeks. Secondary outcomes were scores on the Wolf Motor Function Test and the Stroke Impact Scale. Secondary analyses assessed the treatment effect at 36 weeks. RESULTS At 12 weeks, the mean Fugl-Meyer score for patients receiving robot-assisted therapy was better than that for patients receiving usual care (difference, 2.17 points; 95% confidence interval [CI], -0.23 to 4.58) and worse than that for patients receiving intensive comparison therapy (difference, -0.14 points; 95% CI, -2.94 to 2.65), but the differences were not significant. The results on the Stroke Impact Scale were significantly better for patients receiving robot-assisted therapy than for those receiving usual care (difference, 7.64 points; 95% CI, 2.03 to 13.24). No other treatment comparisons were significant at 12 weeks. Secondary analyses showed that at 36 weeks, robot-assisted therapy significantly improved the Fugl-Meyer score (difference, 2.88 points; 95% CI, 0.57 to 5.18) and the time on the Wolf Motor Function Test (difference, -8.10 seconds; 95% CI, -13.61 to -2.60) as compared with usual care but not with intensive therapy. No serious adverse events were reported. CONCLUSIONS In patients with long-term upper-limb deficits after stroke, robot-assisted therapy did not significantly improve motor function at 12 weeks, as compared with usual care or intensive therapy. In secondary analyses, robot-assisted therapy improved outcomes over 36 weeks as compared with usual care but not with intensive therapy. (ClinicalTrials.gov number, NCT00372411.)

Effect of Gravity on Robot-Assisted Motor Training After Chronic Stroke: A Randomized Trial

OBJECTIVES To determine the efficacy of 2 distinct 6-week robot-assisted reaching programs compared with an intensive conventional arm exercise program (ICAE) for chronic, stroke-related upper-extremity (UE) impairment. To examine whether the addition of robot-assisted training out of the horizontal plane leads to improved outcomes. DESIGN Randomized controlled trial, single-blinded, with 12-week follow-up. SETTING Research setting in a large medical center. PARTICIPANTS Adults (N=62) with chronic, stroke-related arm weakness stratified by impairment severity using baseline UE motor assessments. INTERVENTIONS Sixty minutes, 3 times a week for 6 weeks of robot-assisted planar reaching (gravity compensated), combined planar with vertical robot-assisted reaching, or intensive conventional arm exercise program. MAIN OUTCOME MEASURE UE Fugl-Meyer Assessment (FMA) mean change from baseline to final training. RESULTS All groups showed modest gains in the FMA from baseline to final with no significant between group differences. Most change occurred in the planar robot group (mean change ± SD, 2.94 ± 0.77; 95% confidence interval [CI], 1.40-4.47). Participants with greater motor impairment (n=41) demonstrated a larger difference in response (mean change ± SD, 2.29 ± 0.72; 95% CI, 0.85-3.72) for planar robot-assisted exercise compared with the intensive conventional arm exercise program (mean change ± SD, 0.43 ± 0.72; 95% CI, -1.00 to 1.86). CONCLUSIONS Chronic UE deficits because of stroke are responsive to intensive motor task training. However, training outside the horizontal plane in a gravity present environment using a combination of vertical with planar robots was not superior to training with the planar robot alone.

Determining Levels of Upper Extremity Movement Impairment by Applying a Cluster Analysis to the Fugl-Meyer Assessment of the Upper Extremity in Chronic Stroke

OBJECTIVE To quantitatively determine levels of upper extremity movement impairment by using a cluster analysis of the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) with and without reflex items. DESIGN Secondary analysis. SETTING University and research centers. PARTICIPANTS Individuals (N=247) with chronic stroke (>6mo poststroke). INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES Cutoff scores defined by FMA-UE total scores of clusters identified by 2 hierarchical cluster analyses performed on the full sample of FMA-UE individual item scores (with and without reflexes). Patterns of motor function defined by aggregate item scores of clusters. RESULTS FMA-UE scores ranged from 2 to 63 (mean, 26.9±15.7) with reflex items and from 0 to 57 (mean, 22.1±15.3) without reflex items. Three clusters were identified. The distributions of the FMA-UE scores revealed considerable overlap between the clusters; therefore, 4 distinct stroke impairment levels were derived. CONCLUSIONS For chronic stroke, the cluster analysis of the FMA-UE supports either a 3- or a 4-impairment level classification scheme.

A Clinically Relevant Method of Analyzing Continuous Change in Robotic Upper Extremity Chronic Stroke Rehabilitation.

Background. Robots designed for rehabilitation of the upper extremity after stroke facilitate high rates of repetition during practice of movements and record precise kinematic data, providing a method to investigate motor recovery profiles over time. Objective. To determine how motor recovery profiles during robotic interventions provide insight into improving clinical gains. Methods. A convenience sample (n = 22), from a larger randomized control trial, was taken of chronic stroke participants completing 12 sessions of arm therapy. One group received 60 minutes of robotic therapy (Robot only) and the other group received 45 minutes on the robot plus 15 minutes of translation-to-task practice (Robot + TTT). Movement time was assessed using the robot without powered assistance. Analyses (ANOVA, random coefficient modeling [RCM] with 2-term exponential function) were completed to investigate changes across the intervention, between sessions, and within a session. Results. Significant improvement (P < .05) in movement time across the intervention (pre vs post) was similar between the groups but there were group differences for changes between and within sessions (P < .05). The 2-term exponential function revealed a fast and slow component of learning that described performance across consecutive blocks. The RCM identified individuals who were above or below the marginal model. Conclusions. The expanded analyses indicated that changes across time can occur in different ways but achieve similar goals and may be influenced by individual factors such as initial movement time. These findings will guide decisions regarding treatment planning based on rates of motor relearning during upper extremity stroke robotic interventions.

Robotic assay of arm reaching movements in diverse neurologic populations: Can movement features be reliable, disease-specific diagnostic biomarkers?

In this paper, we compared robotic measures of point-to-point arm reaching movements in those without impairment to those with impairment due to different neurological diseases. The goal was to determine if robot-derived performance metrics (robotic assay) are capable of discerning movement features unique to underlying pathology. Subjects without neurological impairment and those with clinically defined neurological movement disorders, including multiple sclerosis, Parkinsons disease, Huntingtons disease and cerebellar ataxia performed unassisted, visually evoked center-out movements (14 cm out and back) using a 2 degree-of-freedom planar robot. Kinematic robot-derived measures of movement quality (speed, smoothness) and amount (path length) were computed. Analysis showed that metrics related to reaching speed-specifically, mean speed emerged as the strongest movement biomarker distinguishing between those with and without neurological impairment. Relative time-to-first speed peak, also a metric for reaching speed, emerged as the strongest differentiator between the disease groups. Overall, our results provide a starting point for analysis of functional arm reaching movements in diverse neurologic diseases to establish validity of these trends in larger disease-specific cohorts and afford comparison of robotic assay to clinical scales.