Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices.Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies.Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman’s rank correlation coefficient (R) of 0.56 and −0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics.Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements

The Effect of Medical Cooperation in the CKD Patients: 10-Year Multicenter Cohort Study

Introduction: While chronic kidney disease (CKD) is one of the most important contributors to mortality from non-communicable diseases, the number of nephrologists is limited worldwide. Medical cooperation is a system of cooperation between primary care physicians and nephrological institutions, consisting of nephrologists and multidisciplinary care teams. Although it has been reported that multidisciplinary care teams contribute to the prevention of worsening renal functions and cardiovascular events, there are few studies on the effect of a medical cooperation system. Methods: We aimed to evaluate the effect of medical cooperation on all-cause mortality and renal prognosis in patients with CKD. One hundred and sixty-eight patients who visited the one hundred and sixty-three clinics and seven general hospitals of Okayama city were recruited between December 2009 and September 2016, and one hundred twenty-three patients were classified into a medical cooperation group. The outcome was defined as the incidence of all-cause mortality, or renal composite outcome (end-stage renal disease or 50% eGFR decline). We evaluated the effects on renal composite outcome and pre-ESRD mortality while incorporating the competing risk for the alternate outcome into a Fine-Gray subdistribution hazard model. Results: The medical cooperation group had more patients with glomerulonephritis (35.0% vs. 2.2%) and less nephrosclerosis (35.0% vs. 64.5%) than the primary care group. Throughout the follow-up period of 5.59 +/- 2.78 years, 23 participants (13.7%) died, 41 participants (24.4%) reached 50% decline in eGFR, and 37 participants (22.0%) developed end-stage renal disease (ESRD). All-cause mortality was significantly reduced by medical cooperation (sHR 0.297, 95% CI 0.105-0.835, p = 0.021). However, there was a significant association between medical cooperation and CKD progression (sHR 3.069, 95% CI 1.225-7.687, p = 0.017). Conclusion: We evaluated mortality and ESRD using a CKD cohort with a long-term observation period and concluded that medical cooperation might be expected to influence the quality of medical care in the patients with CKD

Effect of gait training using Welwalk on gait pattern in individuals with hemiparetic stroke: a cross-sectional study

IntroductionWe aimed to explore the effect of gait training using Welwalk on gait patterns by comparing differences in gait patterns between robotic-assisted gait training using Welwalk and gait training using an orthosis in individuals with hemiparetic stroke.MethodsThis study included 23 individuals with hemiparetic stroke who underwent gait training with Welwalk combined with overground gait training using an orthosis. Three-dimensional motion analysis on a treadmill was performed under two conditions for each participant: during gait training with Welwalk and with the ankle-foot orthosis. The spatiotemporal parameters and gait patterns were compared between the two conditions.ResultsThe affected step length was significantly longer, the step width was significantly wider, and the affected single support phase ratio was significantly higher in the Welwalk condition than in the orthosis condition. The index values of abnormal gait patterns were significantly lower while using Welwalk than in the orthosis condition. The following four indices were lower in the Welwalk condition: contralateral vaulting, insufficient knee flexion, excessive hip external rotation during the paretic swing phase, and paretic forefoot contact.DiscussionGait training using Welwalk increased the affected step length, step width, and single support phase while suppressing abnormal gait patterns as compared to gait training using the ankle-foot orthosis. This study suggests that gait training using Welwalk may promote a more efficient gait pattern reacquisition that suppresses abnormal gait patterns.Trial registrationProspectively registered in the Japan Registry of Clinical Trials (https://jrct.niph.go.jp; jRCTs042180152)

Reliability of the thumb localizing test and its validity against quantitative measures with a robotic device in patients with hemiparetic stroke.

ObjectivesTo examine the inter-rater reliability of the thumb localizing test (TLT) and its validity against quantitative measures of proprioception.MethodsThe TLT was assessed by two raters in a standardized manner in 40 individuals with hemiparetic stroke. Inter-rater reliability was examined with weighted Kappa. For the quantitative measures, a bimanual matching task in a planar robotic device was performed. Without vision, each participant moved the unaffected hand to the perceived mirrored location of the affected hand, which was passively moved by the robot. Three measures were taken after 54 trials: Variability, trial-to-trial variability of the mirrored-matched locations; Area, the ratio of the area enclosed by the active hand relative to the passive hand; and Shift, systematic shifts between the passive and active hands. The correlation between the TLT and each robotic measure was examined with Spearman's rank correlation coefficient.ResultsThe overall weighted kappa of the TLT was 0.84 (PConclusionsThe TLT had a high inter-rater reliability, and was validated against quantitative measures of proprioception reflecting the perceived area of movement and variability of the limb location

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