IMPORTANCE Many patients receive suboptimal rehabilitation therapy doses after stroke owing to limited access to therapists and difficulty with transportation, and their knowledge about stroke is often limited. Telehealth can potentially address these issues.OBJECTIVES To determine whether treatment targeting arm movement delivered via a home-based telerehabilitation (TR) system has comparable efficacy with dose-matched, intensity-matched therapy delivered in a traditional in-clinic (IC) setting, and to examine whether this system has comparable efficacy for providing stroke education. DESIGN, SETTING, AND PARTICIPANTSIn this randomized, assessor-blinded, noninferiority trial across 11 US sites, 124 patients who had experienced stroke 4 to 36 weeks prior and had arm motor deficits (Fugl-Meyer [FM] score, 22-56 of 66) were enrolled between September 18, 2015, and December 28, 2017, to receive telerehabilitation therapy in the home (TR group) or therapy at an outpatient rehabilitation therapy clinic (IC group). Primary efficacy analysis used the intent-to-treat population.INTERVENTIONS Participants received 36 sessions (70 minutes each) of arm motor therapy plus stroke education, with therapy intensity, duration, and frequency matched across groups.MAIN OUTCOMES AND MEASURES Change in FM score from baseline to 4 weeks after end of therapy and change in stroke knowledge from baseline to end of therapy.RESULTS A total of 124 participants (34 women and 90 men) had a mean (SD) age of 61 ( 14) years, a mean (SD) baseline FM score of 43 (8) points, and were enrolled a mean (SD) of 18.7 (8.9) weeks after experiencing a stroke. Among those treated, patients in the IC group were adherent to 33.6 of the 36 therapy sessions (93.3%) and patients in the TR group were adherent to 35.4 of the 36 assigned therapy sessions (98.3%). Patients in the IC group had a mean (SD) FM score change of 8.36 (7.04) points from baseline to 30 days after therapy (P < .001), while those in the TR group had a mean (SD) change of 7.86 (6.68) points (P < .001). The covariate-adjusted mean FM score change was 0.06 (95% CI, -2.14 to 2.26) points higher in the TR group (P = .96). The noninferiority margin was 2.47 and fell outside the 95% CI, indicating that TR is not inferior to IC therapy. Motor gains remained significant when patients enrolled early (<90 days) or late (Ն90 days) after stroke were examined separately.CONCLUSIONS AND RELEVANCE Activity-based training produced substantial gains in arm motor function regardless of whether it was provided via home-based telerehabilitation or traditional in-clinic rehabilitation. The findings of this study suggest that telerehabilitation has the potential to substantially increase access to rehabilitation therapy on a large scale.

Background and Purpose-Many therapies are emerging that aim to improve motor function in people with stroke.Identifying key biological substrates needed for treatment gains would help to predict treatment effects and to maximize treatment impact. The current study addressed the hypothesis that behavioral gains from therapy targeting distal upper extremity are predicted by the structural integrity of key motor system white matter tracts. Methods-Twenty-three subjects with chronic left-sided stroke underwent robotic therapy targeting the distal right upper extremity. MRI was obtained at baseline and used to outline the infarct. For each subject, the degree to which stroke injured each of 4 descending white matter tracts (from the primary motor cortex, supplementary motor area, dorsal premotor cortex, and ventral premotor cortex, respectively) was determined. Correlations between tract-specific injury and behavioral gains from therapy were then examined. Results-Numerous examples were found whereby tract-specific injury predicted treatment gains. The strongest correlations pertained to stroke injury to tracts descending from the primary motor cortex and dorsal premotor cortex. Infarct volume and baseline behavior were weak predictors of treatment gains. Conclusions-Extent of injury to specific motor tracts predicts behavioral gains from treatment in subjects with chronic stroke. This supports a role for these tracts in mediating treatment effects and reinforces the importance of lesion location in stroke. Tract-specific injury was stronger than infarct volume or baseline clinical status at predicting gains, identifies subjects with sufficient biological substrate to improve from therapy, and so might be useful as an entry criterion in repair-based trials. (Stroke. 2011;42:421-426.)

Valid biomarkers of motor system function after stroke could improve clinical decision-making. Electroencephalography-based measures are safe, inexpensive, and accessible in complex medical settings and so are attractive candidates. This study examined specific electroencephalography cortical connectivity measures as biomarkers by assessing their relationship with motor deficits across 28 days of intensive therapy. Resting-state connectivity measures were acquired four times using dense array (256 leads) electroencephalography in 12 hemiparetic patients (7.3 ± 4.0 months post-stroke, age 26-75 years, six male/six female) across 28 days of intensive therapy targeting arm motor deficits. Structural magnetic resonance imaging measured corticospinal tract injury and infarct volume. At baseline, connectivity with leads overlying ipsilesional primary motor cortex (M1) was a robust and specific marker of motor status, accounting for 78% of variance in impairment; ipsilesional M1 connectivity with leads overlying ipsilesional frontal-premotor (PM) regions accounted for most of this (R(2) = 0.51) and remained significant after controlling for injury. Baseline impairment also correlated with corticospinal tract injury (R(2) = 0.52), though not infarct volume. A model that combined a functional measure of connectivity with a structural measure of injury (corticospinal tract injury) performed better than either measure alone (R(2) = 0.93). Across the 28 days of therapy, change in connectivity with ipsilesional M1 was a good biomarker of motor gains (R(2) = 0.61). Ipsilesional M1-PM connectivity increased in parallel with motor gains, with greater gains associated with larger increases in ipsilesional M1-PM connectivity (R(2) = 0.34); greater gains were also associated with larger decreases in M1-parietal connectivity (R(2) = 0.36). In sum, electroencephalography measures of motor cortical connectivity-particularly between ipsilesional M1 and ipsilesional premotor-are strongly related to motor deficits and their improvement with therapy after stroke and so may be useful biomarkers of cortical function and plasticity. Such measures might provide a biological approach to distinguishing patient subgroups after stroke.

Training with the current method improved accuracy, and reduced variance, of FMA scoring; the 20% FMA variance reduction with training would decrease sample size requirements from 137 to 88 in a theoretical trial aiming to detect a 7-point FMA difference. Minimal detectable change was much smaller than FMA minimal clinically important difference. The variation in FMA gains in relation to baseline FMA suggests that future trials consider a sliding outcome approach when FMA is an outcome measure. The current training approach may be useful for assessing motor outcomes in restorative stroke trials.

Objective To better understand the high variability in response seen when treating human subjects with restorative therapies post-stroke. Preclinical studies suggest that neural function, neural injury, and clinical status each influence treatment gains, therefore the current study hypothesized that a multivariate approach incorporating these three measures would have the greatest predictive value. Methods Patients 3-6 months post-stroke underwent a battery of assessments before receiving 3-weeks of standardized upper extremity robotic therapy. Candidate predictors included measures of brain injury (including to gray and white matter), neural function (cortical function and cortical connectivity), and clinical status (demographics/medical history, cognitive/mood, and impairment). Results Among all 29 patients, predictors of treatment gains identified measures of brain injury (smaller corticospinal tract (CST) injury), cortical function (greater ipsilesional motor cortex (M1) activation), and cortical connectivity (greater inter-hemispheric M1-M1 connectivity). Multivariate modeling found that best prediction was achieved using both CST injury and M1-M1 connectivity (r2=0.44, p=0.002), a result confirmed using Lasso regression. A threshold was defined whereby no subject with >63% CST injury achieved clinically significant gains. Results differed according to stroke subtype: gains in patients with lacunar stroke were exclusively predicted by a measure of intra-hemispheric connectivity. Interpretation Response to a restorative therapy after stroke is best predicted by a model that includes measures of both neural injury and function. Neuroimaging measures were the best predictors and may have an ascendant role in clinical decision-making for post-stroke rehabilitation, which remains largely reliant on behavioral assessments. Results differed across stroke subtypes, suggesting utility of lesion-specific strategies.