Background: This study aimed to examine whether robotic self-training improved upper-extremity function versus conventional self-training in mild-to-moderate hemiplegic chronic stroke patients.Methods: Study design was a multi-center, prospective, randomized, parallel-group study comparing three therapist-guided interventions (1-hour sessions, 3×/wk, 10 weeks). We identified 161 prospective patients with chronic, poststroke, upper-limb hemiplegia treated at participating rehabilitation centers. Patients were enrolled between November 29, 2016, and November 12, 2018 in Japan. A blinded web-based allocation system was used to randomly assign 129 qualifying patients into 3 groups: (1) conventional self-training plus conventional therapy (control, N=42); (2) robotic self-training (ReoGo-J) plus conventional therapy (robotic therapy [RT], N=44); or (3) robotic self-training plus constraint-induced movement therapy (N=43). Primary outcome: Fugl-Meyer Assessment for upper-extremity. Secondary outcomes: Motor Activity Log-14 amount of use and quality of movement; Fugl-Meyer Assessment shoulder/elbow/forearm, wrist, finger, and coordination scores; Action Research Arm Test Score; Motricity Index; Modified Ashworth Scale; shoulder, elbow, forearm, wrist, and finger range of motion; and Stroke Impact Scale (the assessors were blinded). Safety outcomes were adverse events.Results: Safety was assessed in 127 patients. An intention-to-treat full analysis set (N=121), and a per-protocol set (N=115) of patients who attended 80% of sessions were assessed. One severe adverse event was recorded, unrelated to the robotic device. No significant differences in Fugl-Meyer Assessment for upper-extremity scores were observed between groups (RT versus control: −1.04 [95% CI, −2.79 to 0.71],P=0.40; RT versus movement therapy: −0.33 [95% CI, −2.02 to 1.36],P=0.90). The RT in the per-protocol set improved significantly in the Fugl-Meyer Assessment for upper-extremity shoulder/elbow/forearm score (RT versus control: −1.46 [95% CI, −2.63 to −0.29];P=0.037).Conclusions: Robotic self-training did not improve upper-limb function versus usual self-training, but may be effective combined with conventional therapy in some populations (per-protocol set).Registration: URL:https://www.umin.ac.jp/ctr; Unique identifier: UMIN000022509.

Clinical application of platelet-rich-plasma (PRP) has been accelerated to investigate early recovery from various musculoskeletal conditions. It involves the promotion of tissue damage repair through the action of multiple growth factors at physiological concentrations. The composition of PRP differs based on many factors, which may include age and gender. Therefore, we analyzed correlations between age, gender, and platelet counts in PRP with growth factors in Japanese subjects. Peripheral blood was drawn from 39 healthy volunteers between 20 and 49 years of age (age, mean ± standard deviation = 33 ± 8.7 years; gender ratio, male:female = 19:20; BMI, mean ± standard deviation = 22 ± 4.0) and prepared through centrifugation (volume, 6 mL per sample). After being activated with CaCl2, the supernatant was stored. The mean platelet count in PRP was 41.4 ± 12.2 × 104/μL. PRP concentration rate (i.e., PRP/peripheral platelet counts) was 1.8 ± 0.4 times. Growth factor levels (platelet-derived growth factor-BB, transforming growth factor-β1, vascular endothelial growth factor, epidermal growth factor, fibroblast growth factor, insulin-like growth factor-1, and hepatocyte growth factor) were measured using enzyme-linked immunosorbent assay (ELISA), and correlations with age, gender, and PRP platelet counts were statistically analyzed by calculating Spearman’s rank correlation coefficients (r). Age was negatively correlated with platelet-derived growth factor-BB and insulin-like growth factor-1 (r = − 0.32, − 0.39), and gender had no influence on growth factors. Platelet counts in PRP positively correlated with platelet-derived growth factor-BB, transforming growth factor-β1, epidermal growth factor, and hepatocyte growth factor (r = 0.39, 0.75, 0.71, and 0.48, respectively). This clinical study shows a significant variation of PRP among individual patients and that this variation is influenced by the age and the platelet counts of the subjects. Our data demonstrate that patient characteristics account for the differences in PRP physiological activity.

Background: Logistic regression models are widely used to evaluate the association between a binary outcome and a set of covariates. However, when there are few study participants at the outcome and covariate levels, the models lead to bias of the odds ratio (OR) estimated using the maximum likelihood (ML) method. This bias is known as sparse data bias, and the estimated OR can yield impossibly large values because of data sparsity. However, this bias has been ignored in most epidemiological studies.Methods: We review several methods for reducing sparse data bias in logistic regression. The primary aim is to evaluate the Bayesian methods in comparison with the classical methods, such as the ML, Firth’s, and exact methods using a simulation study. We also apply these methods to a real data set.Results: Our simulation results indicate that the bias of the OR from the ML, Firth’s, and exact methods is considerable. Furthermore, the Bayesian methods with hyper-g prior modeling of the prior covariance matrix for regression coefficients reduced the bias under the null hypothesis, whereas the Bayesian methods with log F-type priors reduced the bias under the alternative hypothesis.Conclusion: The Bayesian methods using log F-type priors and hyper-g prior are superior to the ML, Firth’s, and exact methods when fitting logistic models to sparse data sets. The choice of a preferable method depends on the null and alternative hypothesis. Sensitivity analysis is important to understand the robustness of the results in sparse data analysis.

Mobile applications are increasingly used in healthcare. We have developed a smartphone healthcare application, CALO mama Plus, that can register daily diet, exercise, mood, and sleep quality, calculate dietary intake, and provide advice using artificial intelligence technology. This 3-month randomized controlled trial tested the hypothesis that CALO mama Plus could promote body weight reduction in Japanese adults with overweight or obesity. We recruited office workers as participants. The key eligibility criteria were an age of 20–65 years and a body mass index of 23–40 kg/m². The primary outcome was body weight change over 3 months. We enrolled 141 participants and randomly assigned them to the intervention (n = 72) and control (n = 69) groups. The intervention group used CALO mama Plus, and the control group did not receive any intervention. The change in body weight was −2.4 ± 4.0 kg and −0.7 ± 3.3 kg in the intervention and control groups, respectively. An analysis of covariance adjusted for related variables showed a significant between-group difference in body weight change (−1.60 kg; 95% confidence interval −2.83 to −0.38; p = 0.011). The present study suggests that CALO mama Plus effectively promotes weight loss.