Background. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. Objectives. The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. Methods. A total of 102 patients (female: 31%, age 61 ± 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R2. Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median [Formula: see text] P < .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere. Conclusion. Machine learning methods may enable clinicians to accurately predict a chronic stroke patient’s postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients’ response to therapy and, therefore, could be included in prospective studies.

Although many studies have focused on a variety of portion-related interventions, the influence of portion education with parents of young children has not been well researched. More research is needed to understand the effect of parent-focused, portion-education interventions that encourage appropriate energy intake and healthy weight attainment in young children.

BACKGROUND: Neurological injuries cause persistent upper extremity motor deficits. Device-assisted therapy is an emerging trend in neuro-rehabilitation as it offers high intensity, repetitive practice in a standardized setting. OBJECTIVE: To investigate the effects of therapy duration and staff-participant configuration on device-assisted upper limb therapy outcomes in individuals with chronic paresis. METHODS: Forty-seven participants with chronic upper extremity weakness due to neurological injury were assigned to a therapy duration (30 or 60 min) and a staff-participant configuration (1-to-1 or 1-to-2). Therapy consisted of 3 sessions a week for 6 weeks using the Armeo ® Spring device. Clinical assessments were performed at three timepoints (Pre, Post, and 3 month Follow up). RESULTS: Improvements in upper limb impairment, measured by change in Fugl-Meyer score (FM), were observed following therapy in all groups. FM improvement was comparable between 30 and 60 min sessions, but participants in the 1-to-2 group had significantly greater improvement in FM from Pre-to-Post and from Pre-to-Follow up than the 1-to-1 group. CONCLUSIONS: Device-assisted therapy can reduce upper limb impairment to a similar degree whether participants received 30 or 60 min per session. Our results suggest that delivering therapy in a 1-to-2 configuration is a feasible and more effective approach than traditional 1-to-1 staffing.

Accurate predictions of motor improvement resulting from intensive therapy in chronic stroke patients is a difficult task for clinicians, but is key in prescribing appropriate therapeutic strategies. Statistical methods, including machine learning, are a highly promising avenue with which to improve prediction accuracy in clinical practice. The first main objective of this study was to use machine learning methods to predict a chronic stroke individual's motor function improvement after 6 weeks of intervention using pre-intervention demographic, clinical, neurophysiological and imaging data. The second main objective was to identify which data elements were most important in predicting chronic stroke patients' impairment after 6 weeks of intervention. Data from one hundred and two patients (Female: 31%, age 61±11 years) who suffered first ischemic stroke 3-12 months prior were included in this study. After enrollment, patients underwent 6 weeks of intensive motor and transcranial magnetic stimulation therapy.Age, gender, handedness, time since stroke, pre-intervention Fugl-Meyer Assessment, stroke lateralization, the difference in motor threshold between the unaffected and affected hemispheres, absence or presence of motor evoked potential in the affected hemisphere and various imaging metrics were used as predictors of post-intervention Fugl-Meyer Assessment.Five machine learning methods, including Elastic-Net, Support Vector Machines, Artificial Neural Networks, Classification and Regression Trees, and Random Forest, were used to predict post-intervention Fugl-Meyer Assessment based on either demographic, clinical and neurophysiological data alone or in combination with the imaging metrics. Cross-validated Rsquared and root of mean squared error were used to assess the prediction accuracy and compare the performance of methods. Elastic-Net performed significantly better than the other methods for the model containing pre-intervention Fugl-Meyer Assessment, demographic, clinical and neurophysiological data as predictors of post-intervention Fugl-Meyer Assessment to more accurately predict a chronic stroke patient's individual response to intervention. The predictive models used in this study could assist clinicians in making treatment decisions and improve the accuracy of prognosis in chronic stroke patients.

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