Abstract Background Across Japan, there are “non-physician areas” where medical institutions are not present and health care access is difficult. Although these areas are defined as a single administrative category, they may vary according to regional demographic characteristics. Understanding this possible heterogeneity may be important for developing more context-sensitive strategies to improve health care accessibility. Objective This study examined whether non-physician areas can be classified based on regional demographic characteristics using an unsupervised machine learning model. Methods A total of 590 non-physician areas were identified from the national survey conducted by the Ministry of Health, Labour and Welfare of Japan (2019). Data on regional demographic characteristics were also obtained. After z score standardization, k-means clustering was used for classification. The optimal number of clusters was determined using silhouette scores. The data on non-physician areas were divided into training (354/590, 60.0% areas) and validation (236/590, 40.0% areas) datasets. The reproducibility of the cluster structure was evaluated using Jensen-Shannon distance and chi-square tests, and principal component analysis (PCA) was used to examine the overall structure of the clusters. Results The optimal number of clusters was 3 (silhouette score=0.266), indicating that non-physician areas could be classified into 3 types. The cluster distributions of the training and validation datasets were consistent (Jensen-Shannon distance=0.017; P =.76), supporting the reproducibility of the cluster structure. The PCA suggested that differences among non-physician areas could be interpreted along 2 axes, including population age structure and settlement size. The 3 clusters represented younger, intermediate, and older types, characterized by differences in demographic and settlement characteristics. Conclusions The present study demonstrated that administratively defined non-physician areas in Japan are not uniform and can be classified into 3 demographically distinct types. The typology may provide a framework for understanding differences not captured by the current administrative designation and for considering more context-sensitive health care strategies.
Epilepsy affects multiple dimensions of health. Exercise may serve as a nonpharmacological strategy to improve health outcomes among adults with epilepsy; however, translating this potential into effective and scalable interventions remains challenging. Before advancing to efficacy trials, early-stage research is needed to establish whether exercise interventions are feasible, acceptable, and appropriate for this population. This study aimed to evaluate the feasibility, acceptability, and appropriateness of an 8-week supervised aerobic exercise intervention for adults with epilepsy, guided by the Obesity-Related Behavioral Intervention Trials (ORBIT) model. Adults with epilepsy aged 18-65 years were recruited between April 2024 and December 2024 and randomized to either a supervised aerobic exercise intervention (n=4) or a control group (n=2). The exercise group completed an 8-week supervised aerobic exercise program consisting of 50-minute sessions performed 3 days per week. The intervention incorporated behavior change techniques, including goal setting, action planning, habit formation, and commitment contracts to support exercise adherence. Process feasibility outcomes included recruitment, retention, adherence, survey completion, protocol compliance, and safety. Self-reported feasibility, acceptability, and appropriateness were assessed using the Feasibility of Intervention Measure, Acceptability of Intervention Measure, and Intervention Appropriateness Measure. Overall, 34 individuals expressed interest in the study, 25 completed the screening survey, 10 were preliminarily eligible, and 6 were enrolled in the study over 8 months. Recruitment was low, with a recruitment rate of 0.75 participants per month and enrollment achievement of 16.7% of the target sample (target n=36). Retention and adherence were 100%. All exercise participants completed 24/24 sessions, with 100% weekly survey completion. Protocol compliance was high. No seizures occurred during exercise sessions. Mean scores for self-reported feasibility, acceptability, and appropriateness were high (>4 out of 5). The results of this Phase IIb pilot study suggest that a supervised aerobic exercise intervention for adults with epilepsy is feasible, acceptable, and appropriate to implement among participants who enroll. Recruitment remains the primary challenge to progressing this line of research and may represent a broader field-level barrier. Future trials should focus on improving how adults with epilepsy are reached and engaged in exercise research while preserving intervention strategies that support strong engagement among those who enroll.
Abstract Background Older outpatients with chronic diseases are susceptible to muscle weakness and functional disabilities. Recently, various wearable devices have been developed to generate health lifelogs in daily life; however, data from patients with chronic diseases remain scarce. Previous studies have shown that subjective sleep efficiency assessed by self-questionnaires is associated with grip strength, but whether objective sleep efficiency detected by wearable devices is associated with physical function and body composition remains unclear. Objective The aim of this study is to clarify the association between sleep duration, sleep efficiency estimated by wearable watch, grip strength, frailty, and sarcopenia in outpatients with predominantly cardiometabolic diseases and the influence of diabetes as part of the Smart Watch Innovation for Geriatrics and Gerontology (SWING-Japan) project. Methods In this cross-sectional study, the wrist-worn device Silmee 22 was used in an outpatient clinic of an acute care facility for 7‐90 nights for the analyses, and individual average sleep duration and efficiency (wearable-estimated sleep efficiency: total sleep duration/time spent in bed) were calculated. Low grip strength was defined as <28 kg for men and <18 kg for women. Sarcopenia was defined based on the Asian criteria and frailty based on the Japanese version of Cardiovascular Health Study and Kihon Checklist criteria. Sleep indices were compared between individuals with and without these conditions. Next, logistic regression analysis was performed, with lower grip strength as the dependent variable and wearable-estimated sleep efficiency as the independent variable, adjusted for age, sex, BMI, glycohemoglobin, cognitive function (Mini-Mental State Examination), Geriatric Depression Scale-15 score, and daily step count measured using the ankle-worn device WalkX. Subgroup analyses were performed and stratified according to the status of diabetes mellitus. Results Data from 165 patients (median age: 77 y; duration of device use: 65 d; diabetes prevalence: 55%) were used for analyses. Wearable-estimated sleep efficiency and sleep duration were significantly lower in patients with low grip strength than in those without (58.0% vs 62.7%, P =.006, 245 vs 276 min, P =.04). Wearable-estimated sleep efficiency was not associated with frailty, low muscle mass, or slow walking speed. After adjusting for the above variables, low wearable-estimated sleep efficiency, but not low sleep duration, was associated with a risk of lower grip strength (odds ratio 1.04, 95% CI 1.01‐1.08; P =.02) by 1 percentage point decrease in wearable-estimated sleep efficiency. This association was observed in patients with diabetes (odds ratio 1.05, 95% CI 1.002‐1.093; P =.04). However, no significant interaction was observed. Conclusions In older patients with cardiometabolic diseases, objective low wearable-estimated sleep efficiency evaluated with wrist-worn wearable devices was associated with low muscle strength. No evidence was found to suggest that the strength of this association differed according to diabetes status. Longitudinal studies are needed to clarify the long-term association between low wearable-estimated sleep efficiency and muscle strength in this population.
Abstract Background Emergency-to–intensive care unit (ICU) admissions are high-stakes transitions in care, where delays or documentation errors can compromise patient safety and disrupt operational efficiency. Although digital order systems can support these workflows, traditional platforms often lack real-time traceability and structured input logic, leaving them vulnerable to duplicate submissions, lost orders, and misaligned ICU bed reservations. Objective The study aimed to examine whether an updated digital order system was associated with differences in emergency-to-ICU admission workflows, including order validity, documentation-error frequencies, and ICU reservation alignment, compared with those of the traditional platform. Methods We conducted a single-center retrospective historical-control formative evaluation of emergency-to-ICU admissions processed through traditional (2023) and updated (2024) digital order systems at a tertiary medical center. Prospectively generated system log and ICU-reservation data were analyzed to quantify order validity, error subtypes (duplicate, lost, and tracking), and admission-to-reservation alignment. Proportions were compared using chi-square tests, with risk ratios (RRs) and absolute differences in proportions reported. Statistical significance was defined as P <.05. Results The updated system was associated with higher admission-order validity (54.1%-75.4%; RR 1.39, 95% CI 1.25‐1.54; P <.001) and lower erroneous-order frequency (45.9%-24.6%; RR 0.54, 95% CI 0.44‐0.66; P <.001). Duplicate and lost orders declined, whereas tracking-error frequency showed no meaningful change. ICU reservation alignment also differed, with the admission-to-reservation ratio rising from 0.68 to 0.78 (RR 0.88, 95% CI 0.77-0.99; P =.03). Conclusions The updated digital order system was associated with more consistent emergency-to-ICU workflow patterns, including higher order validity, fewer documentation errors, and closer alignment between admission intent and ICU reservations. These findings are consistent with literature on structured electronic order systems, digital-workflow standardization, and timestamp integrity, and they highlight the potential value of structured, timestamp-driven digital infrastructures in supporting more reliable high-acuity workflows. Further evaluation using time-motion analysis, interaction-log metrics, or mixed methods assessment is warranted to examine the robustness and generalizability of these patterns.
