[Purpose] The purpose of this study was to examine changes in vagal tone during passive exercise while supine. [Subjects and Methods] Eleven healthy males lay supine for 5 min and then performed passive cycling for 10 min using a passive cycling machine. The lower legs moved through a range of motion defined by 90° and 180° knee joint angles at 60 rpm. Respiratory rates were maintained at 0.25 Hz to elicit respiratory sinus arrhythmia. Heart rate variability was analyzed using the time domain analysis, as the root mean squared standard differences between adjacent R-R intervals (rMSSD), and spectrum domain analysis of the high frequency (HF) component. [Results] Compared to rest, passive cycling decreased rMSSD (rest, 66.6 ± 92.6 ms; passive exercise, 53.5 ± 32.5 ms). However, no significant changes in HR or HF were observed (rest, 68.2 ± 6.9 bpm, 65.6 ± 12.0 n.u.; passive exercise, 70.2 ± 7.2 bpm, 67.9 ± 10.0 n.u.). [Conclusion] These results suggest that passive exercise decreases rMMSD through supine-stimulated mechanoreceptors with no effect on HR or HF. Therefore, rMSSD is not affected by hydrostatic pressure during passive cycling in the supine position.

Background While in an acute hospital setting, it is challenging for clinicians to make discharge decisions due to the multifactorial process and individual patient characteristics. Objective To evaluate and develop a scoring system utilizing the Japan Rehabilitation Database (JRD) to identify acute stroke patients with a high possibility of discharge to home. Design Retrospective observational cohort study. Setting Thirty‐seven acute hospitals in Japan. Participants A total of 10 270 patients admitted to the acute hospitals with stroke were identified. The inclusion criteria were (1) admission within 3 days of onset and (2) admission from home. Exclusion criteria were (1) incomplete data and (2) death in hospital. A total of 4216 patients were included in the study. Intervention Not applicable. Main Outcome Measurements Discharge destination from acute care hospital, that is, home versus other locations. Results In the multivariate logistic regression, age, stroke type, Barthel Index on admission, modified Rankin scale on admission, the National Institutes of Health Stroke Scale on admission, and extent of paralysis were retained. When the cutoff point was 33, the scoring system showed a sensitivity of 80.4% and specificity of 80.3%. The area under curve (AUC) was 0.88. On evaluating the predictive performance, the model showed a sensitivity of 78.2% and a specificity of 78.5% with an AUC = 0.86. Conclusions We developed a point system to identify acute stroke patients with a high possibility of discharge to home using the JRD. This point system may provide useful information for clinicians to plan the discharge of acute stroke patients.

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