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Stepwise linear regression (SLR) is the most common approach to predicting activities of daily living at discharge with the Functional Independence Measure (FIM) in stroke patients, but noisy nonlinear clinical data decrease the predictive accuracies of SLR. Machine learning is gaining attention in the medical field for such nonlinear data. Previous studies reported that machine learning models, regression tree (RT), ensemble learning (EL), artificial neural networks (ANNs), support vector regression (SVR), and Gaussian process regression (GPR), are robust to such data and increase predictive accuracies. This study aimed to compare the predictive accuracies of SLR and these machine learning models for FIM scores in stroke patients. Subacute stroke patients (N = 1,046) who underwent inpatient rehabilitation participated in this study. Only patients' background characteristics and FIM scores at admission were used to build each predictive model of SLR, RT, EL, ANN, SVR, and GPR with 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) values were compared between the actual and predicted discharge FIM scores and FIM gain. Machine learning models (R2 of RT = 0.75, EL = 0.78, ANN = 0.81, SVR = 0.80, GPR = 0.81) outperformed SLR (0.70) to predict discharge FIM motor scores. The predictive accuracies of machine learning methods for FIM total gain (R2 of RT = 0.48, EL = 0.51, ANN = 0.50, SVR = 0.51, GPR = 0.54) were also better than of SLR (0.22). This study suggested that the machine learning models outperformed SLR for predicting FIM prognosis. The machine learning models used only patients' background characteristics and FIM scores at admission and more accurately predicted FIM gain than previous studies. ANN, SVR, and GPR outperformed RT and EL. GPR could have the best predictive accuracy for FIM prognosis.

Post-stroke disability affects patients’ lifestyles after discharge, and it is essential to predict functional recovery early in hospitalization to allow time for appropriate decisions. Previous studies reported important clinical indicators, but only a few clinical indicators were analyzed due to insufficient numbers of cases. Although review articles can exhaustively identify many prognostic factors, it remains impossible to compare the contribution of each predictor. This study aimed to determine which clinical indicators contribute more to predicting the functional independence measure (FIM) at discharge by comparing standardized coefficients. In this study, 980 participants were enrolled to build predictive models with 32 clinical indicators, including the stroke impairment assessment set (SIAS). Trunk function had the most significant standardized coefficient of 0.221. The predictive models also identified easy FIM sub-items, SIAS, and grip strength on the unaffected side as having positive standardized coefficients. As for the predictive accuracy of this model, R 2 was 0.741. This is the first report that included FIM sub-items separately in post-stroke predictive models with other clinical indicators. Trunk function and easy FIM sub-items were included in the predictive model with larger positive standardized coefficients. This predictive model may predict prognosis with high accuracy, fewer clinical indicators, and less effort to predict.

Background Upper extremity rehabilitation in persons with stroke should be dose-dependent and task-oriented. Virtual reality (VR) has the potential to be used safely and effectively in home-based rehabilitation. This study aimed to investigate the effects of home-based virtual reality upper extremity rehabilitation in persons with chronic stroke. Methods This was a single-blind, randomized, controlled trial conducted at two centers. The subjects were 14 outpatients with chronic stroke more than 6 months after the onset of the stroke. The participants were randomly divided into two groups. The intervention group (n = 7) performed a home rehabilitation program for the paretic hand (30 min/day, five days/week) using a VR device (RAPAEL Smart Glove™; NEOFECT Co., Yung-in, Korea) for four weeks. The control group (n = 7) participated in a conventional home rehabilitation program at the same frequency. All participants received outpatient occupational therapy once a week during the study period. The outcome measures included the Fugl-Meyer Assessment of upper extremity motor function (FMA-UE), Motor Activity Log-14 (MAL), Jebsen-Taylor Hand Function Test (JTT), and Box and Block Test (BBT) scores. Results All 14 participants completed the study. Compared to the control group, the intervention group showed more significant improvements in FMA-UE (p = 0.027), MAL (p = 0.014), JTT (p = 0.002), and BBT (p = 0.014). No adverse events were observed during or after the intervention. Conclusion Compared to a conventional home program, combining a task-oriented virtual reality home program and outpatient occupational therapy might lead to greater improvements in upper extremity function and the frequency of use of the paretic hand. Trial registration : This study was registered in the University Hospital Medical Information Network (UMIN) Clinical Trial Registry in Japan (Unique Identifier: UMIN000038469) on November 1, 2019; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000043836 .

This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms—specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms ( p  < 0.01); however, the AUC for LR (0.895) did not differ significantly from those for the other models (0.893–0.903). Other performance metrics showed no substantial differences between LR and ML algorithms. In conclusion, the DT algorithm had significantly low predictive accuracy, and LR showed no significant difference in predictive accuracy compared with the other ML algorithms. As its predictive accuracy is similar to that of ML, LR can continue to be used for predicting the prognosis of gait independence, with additional advantages of being easily understandable and manually computable.

Aim To investigate the cognitive function and its relation to the home discharge of patients following subacute stroke. Methods This retrospective cohort study included 1,229 convalescent patients experiencing their first subacute stroke. We determined discharge destination and demographic and clinical information. We recorded the following measurement scores: Mini-Mental State Examination (MMSE) score, Stroke Impairment Assessment Set score, grip strength, and Functional Independence Measure (FIM). We performed a multivariable logistic regression analysis with the forced-entry method to identify factors related to home discharge. Results Of the 1,229 participants (mean age: 68.7 ± 13.5 years), 501 (40.8%), 735 (59.8%), and 1,011 (82.3%) were female, had cerebral infarction, and were home discharged, respectively. Multivariable logistic regression analysis revealed that age (odds ratio [OR], 0.93; 95% confidence interval [CI], 0.91 – 0.96; P  < 0.001), duration from stroke onset to admission (OR, 0.98; 95% CI, 0.96 – 0.99; P  = 0.003), living situation (OR, 4.40; 95% CI, 2.69 – 7.20; P  < 0.001), MMSE score at admission (OR, 1.05; 95% CI, 1.00 – 1.09; P  = 0.035), FIM motor score at admission (OR, 1.04; 95% CI, 1.01 – 1.06; P  = 0.001), and FIM cognitive score at admission (OR, 1.08; 95% CI, 1.04 – 1.13; P  < 0.001) were significantly associated with home discharge. Conclusions MMSE at admission is significantly associated with home discharge in patients with subacute stroke.

Background: Gait recovery is one of the primary goals of stroke rehabilitation. Gait independence is a key functional component of independent activities in daily living and social participation. Therefore, early prediction of gait independence is essential for stroke rehabilitation. Trunk function is important for recovery of gait, balance, and lower extremity function. The Trunk Impairment Scale (TIS) was developed to assess trunk impairment in patients with stroke. Objective: To evaluate the predictive validity of the TIS for gait independence in patients with acute stroke. Methods: A total of 102 patients with acute stroke participated in this study. Every participant was assessed using the TIS, Stroke Impairment Assessment Set (SIAS), and Functional Independence Measure (FIM) within 48 h of stroke onset and at discharge. Gait independence was defined as FIM gait scores of 6 and 7. Multiple regression analysis was used to predict the FIM gait score, and multiple logistic regression analysis was used to predict gait independence. Cut-off values were determined using receiver operating characteristic (ROC) curves for variables considered significant in the multiple logistic regression analysis. In addition, the area under the curve (AUC), sensitivity, and specificity were calculated. Results: For the prediction of the FIM gait score at discharge, the TIS at admission showed a good-fitting adjusted coefficient of determination (R2 = 0.672, p < 0.001). The TIS and age were selected as predictors of gait independence. The ROC curve had a TIS cut-off value of 12 points (sensitivity: 81.4%, specificity: 79.7%) and an AUC of 0.911. The cut-off value for age was 75 years (sensitivity: 74.6%, specificity: 65.1%), and the AUC was 0.709. Conclusion: The TIS is a useful early predictor of gait ability in patients with acute stroke.

Background Transcutaneous spinal stimulation has been applied to gait rehabilitation for persons with neurological diseases. The authors developed electromyography-triggered transcutaneous spinal cord and hip stimulation for gait rehabilitation and called this system FAST walk. This study aimed to assess the effect of FAST walk in a randomized, controlled trial. Methods All participants were randomly allocated to three groups: FAST walk combined with treadmill gait training (FAST walk); spinal stimulation combined with treadmill gait training (spinal stim); and treadmill gait training (treadmill). Participants performed two sets of 15-min treadmill gait training with 5-min intervals in the FAST walk, spinal stim, and treadmill groups. Gait training was performed twice weekly for a total of 10 sessions. The primary outcome was 10-m walking time. The secondary outcomes were the time symmetry index (TSI) with gait analysis and spinal reciprocal inhibition on the conditioned-test H reflex study. Results Twenty persons with chronic stroke participated in this study, and 17 persons completed this study. For the primary outcome, there was no significant interaction between time and intervention in 10-m walking time on two-way analysis of covariance (ANCOVA) ( P  = 0.382, η 2  = 0.064). For the FAST walk group, 10-m walking time improved significantly at post and post-4w ( P  = 0.024 and 0.022, respectively). In the other groups, no significant improvements in 10-m walking time were seen at post and post-4w compared with before. There was also no significant between-group difference in the 10-m walking time. Conclusions The newly developed electromyography-triggered transcutaneous spinal cord and hip stimulation, FAST walk, is safe and may improve the gait speed of persons with chronic stroke. We did not, however, find a significant between-group difference among the FAST walk, spinal stim, and treadmill gait groups. Trial registration : Japan Registry of Clinical Trial (JRCT registration ID: jRCTs032180289).

As humans, we constantly change our movement strategies to adapt to changes in physical functions and the external environment. We have to walk very slowly in situations with a high risk of falling, such as walking on slippery ice, carrying an overflowing cup of water, or muscle weakness owing to aging or motor deficit. However, previous studies have shown that a normal gait pattern at low speeds results in a reduced efficiency and stability in comparison with those at a normal speed. Another possible strategy is to change the gait pattern from normal to step-to, in which the other foot is aligned with the first swing foot. However, the efficiency and stability of the step-to gait at low speeds have not been investigated yet. Therefore, in this study, we compared the efficiency and stability of the normal and step-to gaits at intermediate, low, and very low speeds. Eleven healthy participants were asked to walk with a normal gait and a step-to gait on a treadmill at five different speeds (10, 20, 30, 40, and 60 m/min), ranging from very low to normal walking speed. The efficiency parameters (percent recovery and walk ratio) and stability parameters (center of mass lateral displacement) were analyzed from the motion capture data and then compared for the two gait patterns. The results suggested that the step-to gait had a more efficient gait pattern at very low speeds of 10–30 m/min, with a larger percent recovery, and was more stable at 10–60 m/min in comparison with the normal gait. However, the efficiency of the normal gait was better than that of step-to gait at 60 m/min. Therefore, the step-to gait is effective in improving gait efficiency and stability when faced with situations that force us to walk slowly or hinder quick walking because of muscle weakness owing to aging or motor deficit along with a high risk of falling.

This retrospective cross-sectional study is aimed at investigating the prevalence and characteristics of behavioral and psychological symptoms (BPS) in subacute stroke patients with cognitive impairment. The Neuropsychiatric Inventory-Questionnaire (NPI-Q) was used to assess BPS. A total of 358 consecutive patients with first-ever stroke admitted to rehabilitation wards and with Mini-Mental State Examination (MMSE) scores<24 on admission were included. BPS was defined as a total NPI-Q Severity or Distress score≥1. Differences between the severity and presence of BPS among patients with severe cognitive impairment (MMSE scores 0–17) and those with mild cognitive impairment (MMSE scores 18–23) were analyzed using the Mann–Whitney U test and chi-squared test, respectively. Eighty-one patients (mean (standard deviation) age, 73.5 (13.1) years) were enrolled for analysis. BPS were observed in 69.1% and 74.1% of patients when assessed with NPI-Q Severity and NPI-Q Distress, respectively. The most frequently observed BPS was apathy, followed by depression (approximately 44% and 40%, respectively). The severity and frequency of delusions, euphoria, apathy, and disinhibition were significantly higher in the severe cognitive impairment group than in the mild cognitive impairment group. However, the severity, distress, and frequency of depression were not dependent on the severity of cognitive impairment. The presence of BPS, especially apathy and depression, in subacute stroke patients with cognitive impairment is high. The severity and frequency of some BPS are higher in patients with severe cognitive impairment than in those with mild cognitive impairment. However, depression is highly prevalent among the patients regardless of the severity of cognitive impairment.

Hybrid Assistive Neuromuscular Dynamic Stimulation (HANDS) therapy is one of the neurorehabilitation therapeutic approaches that facilitates the use of the paretic upper extremity (UE) in daily life by combining closed-loop electromyography- (EMG-) controlled neuromuscular electrical stimulation (NMES) with a wrist-hand splint. This closed-loop EMG-controlled NMES can change its stimulation intensity in direct proportion to the changes in voluntary generated EMG amplitudes recorded with surface electrodes placed on the target muscle. The stimulation was applied to the paretic finger extensors. Patients wore a wrist-hand splint and carried a portable stimulator in an arm holder for 8 hours during the daytime. The system was active for 8 hours, and patients were instructed to use their paretic hand as much as possible. HANDS therapy was conducted for 3 weeks. The patients were also instructed to practice bimanual activities in their daily lives. Paretic upper extremity motor function improved after 3 weeks of HANDS therapy. Functional improvement of upper extremity motor function and spasticity with HANDS therapy is based on the disinhibition of the affected hemisphere and modulation of reciprocal inhibition. HANDS therapy may offer a promising option for the management of the paretic UE in patients with stroke.