Multiple sclerosis is a chronic, autoimmune and neurodegenerative disease affecting multiple functional systems and resulting in motor impairments associated with muscle weakness and lack of movement coordination. We quantified upper limb motor deficits with a robot-based assessment including behavioral and muscle synergy analysis in 11 multiple sclerosis subjects with mild to moderate upper limb impairment (9 female; 50 ± 10 years) compared to 11 age- and gender- matched controls (9 female; 50 ± 9 years). All subjects performed planar reaching tasks by moving their upper limb or applying force while grasping the handle of a robotic manipulandum that generated four different environments: free space, assistive or resistive forces, and rigid constraint. We recorded the activity of 15 upper body muscles. Multiple sclerosis subjects generated irregular trajectories. While activities in isolated arm muscles appeared generally normal, shoulder muscle coordination with arm motions was impaired and there was a marked co-activation of the biceps and triceps in extension movements. Systematic differences in timing and organization of muscle synergies have also been observed. This study supports the definition of new biomarkers and rehabilitative treatments for improving upper limb motor coordination in multiple sclerosis.

Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease resulting in motor impairments associated with muscle weakness and lack of movement coordination. The goal of this work was to quantify upper limb motor deficits in asymptomatic MS subjects with a robot-based assessment including performance and muscle synergies analysis. A total of 7 subjects (MS: 3 M−4 F; 42 ± 10 years) with clinically definite MS according to McDonald criteria, but with no clinical disability, and 7 age- and sex-matched subjects without a history of neurological disorders participated in the study. All subjects controlled a cursor on the computer screen by moving their hand or applying forces in 8 coplanar directions at their self-selected speed. They grasped the handle of a robotic planar manipulandum that generated four different environments: null, assistive or resistive forces, and rigid constraint. Simultaneously, the activity of 15 upper body muscles was recorded. Asymptomatic MS subjects generated less smooth and less accurate cursor trajectories than control subjects in controlling a force profile, while the end-point error was significantly different also in the other environments. The EMG analysis revealed different muscle activation patterns in MS subjects when exerting isometric forces or when moving in presence of external forces generated by a robot. While the two populations had the same number and similar structure of muscle synergies, they had different activation profiles. These results suggested that a task requiring to control forces against a rigid environment allows better than movement tasks to detect early sensory-motor signs related to the onset of symptoms of multiple sclerosis and to differentiate between stages of the disease.

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IntroductionAlcohol use disorders have been associated with an increased risk of frequent readmissions. This study aimed to examine factors that contribute to the risk for readmission within one year after discharge from an alcohol rehabilitation program.MethodsRehospitalization status was assessed for all patients with an alcohol use disorder as primary diagnosis (n = 468) admitted to our inpatient unit between July 1, 2012, and June 30, 2014. All patients were followed up for one year after their first hospitalization (index hospitalization) within this period. Time to readmission within one year after discharge was measured using the Kaplan–Meier method. Risk factors for readmission were examined using Cox proportional hazard regression models. Three set of variables were selected to be included in the analyses:– demographic features at time of admission of index hospitalization;– comorbid conditions at time of admission of index hospitalization;– treatment-related variables in relation to the index hospitalization including observer-rated outcome measures.ResultsReadmissions within one year after discharge from an alcohol rehabilitation program as well as the corresponding time to readmission were linked to higher numbers of previous hospitalizations and the presence of comorbid opioid use disorders.ConclusionHigher numbers of past treatments for AUD are indicators for a chronic course of the disorder, which, in turn, increase the risk of further relapses. Our findings further confirmed previous findings suggesting high rates of comorbidity among alcohol and opioid use disorders, and their link with poorer clinical outcomes.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Multiple sclerosis is a complex neurological disease that results in motor impairment associated with muscle weakness and lack of motor coordination. Indeed, previous studies showed that, while activities in isolated arm muscles appeared generally similar to those of unimpaired subjects, shoulder muscle coordination with arm motions was affected by MS and there was a marked co-activation of the biceps and triceps in the extension movements. This inability to activate muscles independently has a significant impact in motor function therefore reducing the co-contraction could improve the overall arm function. In this pilot study, we developed a body-machine interface based on muscle activities with the goal of 'breaking' the abnormal triceps-biceps co-activation during planar flexion-extension movements of people with multiple sclerosis during a robot-based task. The task consisted in 2D center-out reaching movements with the assistance of a robotic manipulandum. When the subject was not exhibiting the abnormal triceps-biceps co-activation for three consecutive movements the robot was decreasing the assistance. Subjects trained for up to six 1-hour sessions in three weeks. Results showed that the assistance from the robot decreased within each session for most of the subjects, while the movement became faster and straighter. The comparison between muscle activity before and after the training with this body-machine interface demonstrated that subjects learned how to reduce the tricepsbiceps co-activation.