Background. Patterns of recovery provide useful information concerning the potential of physical recovery over time and therefore the setting of realistic goals for rehabilitation programs. Objective. To compare the time course of trunk recovery with the patterns of recovery of arm, leg, and functional ability. Methods . Consecutive stroke patients were recruited in 2 acute neurology wards. Participants were evaluated at 1 week, 1 month, and 3 and 6 months after stroke. Patients were assessed with the Trunk Impairment Scale, Fugl-Meyer arm and leg test, and Barthel Index. Results. Thirty-two patients were included in the study. There were no dropouts. Repeated measures analysis of the recovery patterns of motor and functional performance revealed the most striking improvement for all measures from 1 week to 1 month (P value between .0021 and <.0001) and a significant improvement from 1 month to 3 months after stroke (P value ranges from .0008 to <.0001). No significant improvement was found between 3 and 6 months after stroke for any of the measures. Statistical analysis revealed no significant difference between time course of trunk, arm, leg, and functional recovery (P = .2565). No significant differences in level of motor and functional recovery were found at the different time points. Conclusions. Separate analyses of motor and functional recovery patterns after stroke confirm the importance of the first month for recovery. Contrary to common belief, the time course of recovery of the trunk is similar to the recovery of arm, leg, and functional ability.

To identify the best determinants of fitness to drive after stroke, following a systematic review and meta-analysis. Twenty databases were searched, from inception until May 1, 2010. Potentially relevant studies were reviewed by 2 authors for eligibility. Methodologic quality was assessed by Newcastle-Ottawa scores. The fitness-to-drive outcome was a pass-fail decision following an on-road evaluation. Differences in off-road performance between the pass and fail groups were calculated using weighted mean effect sizes (d(w)). Statistical heterogeneity was determined with the I² statistic. Random-effects models were performed when the assumption of homogeneity was not met. Cutoff scores of accurate determinants were estimated via receiver operating characteristic analyses. Thirty studies were included in the systematic review and 27 in the meta-analysis. Out of 1,728 participants, 938 (54%) passed the on-road evaluation. The best determinants were Road Sign Recognition (d(w) 1.22; 95% confidence interval [CI] 1.01-1.44; I(2), 58%), Compass (d(w) 1.06; 95% CI 0.74-1.39; I², 36%), and Trail Making Test B (TMT B; d(w) 0.81; 95% CI 0.48-1.15; I(2), 49%). Cutoff values of 8.5 points for Road Sign Recognition, 25 points for Compass, and 90 seconds for TMT B were identified to classify unsafe drivers with accuracies of 84%, 85%, and 80%, respectively. Three out of 4 studies found no increased risk of accident involvement in persons cleared to resume driving after stroke. The Road Sign Recognition, Compass, and TMT B are clinically administrable office-based tests that can be used to identify persons with stroke at risk of failing an on-road assessment.

Background Freezing of gait (FOG) is a debilitating gait disorder in Parkinson's disease (PD) with partial responsiveness to dopaminergic medication. To date, notions about the effects of subthalamic deep brain stimulation (STN-DBS) on FOG remain controversial. Objectives To compare the effects of bilateral STN-DBS and continued best medical treatment (BMT) on FOG occurrence, FOG severity and clinical outcomes in PD patients at 6 and 12 months follow-up. Methods In this prospective, controlled study, 41 PD patients with at least 5 years disease duration participated. Twenty-four subjects (20 with FOG) were treated with STN-DBS and seventeen (15 with FOG) continued BMT. The primary outcome was the New Freezing of Gait Questionnaire (NFOGQ) at 6 months postsurgery. Other outcomes were the NFOGQ at 12 months and clinical outcomes (Unified Parkinson's Disease Rating Scale III (UPDRS III), timed gait, falls and quality of life) at both time points. Results STN-DBS increased the likelihood to convert from being a freezer to a non-freezer at 6 and 12 months follow-up (relative risk reduction=0.4). However, 45% of baseline freezers still experienced FOG 6 and 12 months postsurgery although with reduced severity. Three baseline non-freezers (1/2 BMT-treated, 2/4 STN-DBS-treated) developed FOG during follow-up. STN-DBS-induced benefits on FOG were mostly mediated by baseline levodopa equivalent dose, altered medication-intake and reduced motor fluctuations. Conclusions In contrast to continued BMT, STN-DBS reduced FOG occurrence and severity at 6 months postsurgery with largely sustained effects at 12 months follow-up. Longer follow-up periods are needed to test whether FOG improvements after STN-DBS persist with disease progression.

Background: Parkinson’s disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether balance and gait variables obtained with wearable inertial motion sensors can be utilized to differentiate between PD and ET using machine learning. Additionally, we compared classification performances of several machine learning models.Methods: This retrospective study included balance and gait variables collected during the instrumented stand and walk test from people with PD (n = 524) and with ET (n = 43). Performance of several machine learning techniques including neural networks, support vector machine, k-nearest neighbor, decision tree, random forest, and gradient boosting, were compared with a dummy model or logistic regression using F1-scores.Results: Machine learning models classified PD and ET based on balance and gait characteristics better than the dummy model (F1-score = 0.48) or logistic regression (F1-score = 0.53). The highest F1-score was 0.61 of neural network, followed by 0.59 of gradient boosting, 0.56 of random forest, 0.55 of support vector machine, 0.53 of decision tree, and 0.49 of k-nearest neighbor.Conclusions: This study demonstrated the utility of machine learning models to classify different movement disorders based on balance and gait characteristics collected from wearable sensors. Future studies using a well-balanced data set are needed to confirm the potential clinical utility of machine learning models to discern between PD and ET.

Cognitive workload is increasingly recognized as an important determinant of performance in cognitive tests and daily life activities. Cognitive workload is a measure of physical and mental effort allocation to a task, which can be determined through self-report or physiological measures. However, the reliability and validity of these measures have not been established in older adults with a wide range of cognitive ability. The aim of this study was to establish the test–retest reliability of the National Aeronautics and Space Administration Task Load Index (NASA-TLX) and Index of Cognitive Activity (ICA), extracted from pupillary size. The convergent validity of these measures against event-related potentials (ERPs) was also investigated. A total of 38 individuals with scores on the Montreal Cognitive Assessment ranging between 17 and 30 completed a working memory test (n-back) with three levels of difficulty at baseline and at a two-week follow-up. The intraclass correlation coefficients (ICC) values of the NASA-TLX ranged between 0.71 and 0.81, demonstrating good to excellent reliability. The mean ICA scores showed fair to good reliability, with ICCs ranging between 0.56 and 0.73. The mean ICA and NASA-TLX scores showed significant and moderate correlations (Pearson’s r ranging between 0.30 and 0.33) with the third positive peak of the ERP at the midline channels. We conclude that ICA and NASA-TLX are reliable measures of cognitive workload in older adults. Further research is needed in dissecting the subjective and objective constructs of cognitive workload.