Purpose: Sleep disturbance is one of the major complaints among patients with diabetes. The status of diabetes control and associated complications may contribute to sleep disturbance. This study explored night time sleep and excessive daytime sleepiness in adults with type 2 diabetes and examined the association of diabetes control and associated complications on their sleep quality. Methods: A retrospective cohort study design was used. Type 2 diabetic patients (87 females and 79 males, aged 63.1 ± 10.5 years) were recruited from the outpatient clinics of the endocrine department. Sleep quality was assessed by the Pittsburg Sleep Quality Index and the Epworth Sleepiness Scale. Diabetes control and complications were obtained by retrospectively reviewing patients’ medical records over 1 year prior to study enrollment. Results: 72.3% of recruited patients had poor glycemic control, and 71.1% had at least one diabetic complication. 56.0% of patients experienced poor sleep quality, and 24.1% had excessive daytime sleepiness. Those who were female (OR = 3.45) and who had ophthalmological problems (OR = 3.17) were associated with poor night time sleep quality, but if they did exercise to the point of sweating (OR = 0.48) reduced the risk of poor sleep quality. Furthermore, poor sleep quality (OR = 4.35) and having nephropathy (OR = 3.78) were associated with a higher risk of excessive daytime sleepiness. Conclusions: Sex, ophthalmological problems, nephropathy, and no exercise to the point of sweating are associated with sleep problems in patients with type 2 diabetes. Both lifestyle behaviors and diabetic complications affect sleep disturbances in patients with diabetes.

Background: Sedentary employees face increased chronic health risks due to physical inactivity, immobility, and unhealthy eating behavior. Although mobile health (mHealth) interventions show promise in improving lifestyle behaviors, their effectiveness in occupational settings remains underexplored. Building on previous workplace interventions, this study developed and evaluated a mobile-enabled web app, SIMPLE HEALTH, developed with Din-J Design Co, Ltd, integrating activity tracking, healthy eating, and behavioral support for sedentary employees. Objective: This study evaluated the short-term effects of a 12-week mHealth intervention on physical activity, sedentary behavior, dietary habits, and cardiometabolic health indicators among sedentary employees in Taiwan. Methods: A 2-arm quasi-experimental study was conducted at 2 aerospace industrial workplaces. A total of 101 sedentary employees (mean age 46.9, SD 12.2 years; 52/101, 51.5% female) were enrolled from 2 worksites that were assigned by coin toss to either the intervention condition (n=50) or the control condition (n=51). The intervention group participated in the SIMPLE HEALTH program, an mHealth intervention grounded in Social Cognitive Theory and the Ecological Model, consisting of 8 components: activity tracking, goal setting, behavior logging, reminders, personalized advice, educational and motivational electronic booklets, and individual and team challenges. The control group received 6 print educational booklets. Cardiometabolic biomarkers, objectively measured physical activity (Fitbit Charge 3; Fitbit Inc), occupational sitting (occupational sitting and physical activity questionnaire), and dietary behavior (3-day photographic food records and the healthy eating behavior inventory) were assessed at baseline and 12 weeks. Data were analyzed using generalized estimating equations following the intention-to-treat principle. Results: At 12 weeks, the intervention group showed a significant increase in step counts (adjusted mean difference, MD 1227.13, 95% CI 2.90-2451.36; P =.049), a more favorable between-group change in moderate physical activity (adjusted MD 0.17, 95% CI 0.01-0.33; P =.04), and favorable dietary behaviors, including reduced intake of calories (adjusted MD −144.59, 95% CI −276.57 to −12.60; P =.03), carbohydrates (adjusted MD −19.88, 95% CI −37.99 to −1.78; P =.03), fats (adjusted MD −6.99, 95% CI −13.69 to −0.29; P =.04), and grains (adjusted MD −1.46, 95% CI −2.43 to −0.50; P =.003), and increased vegetable intake (adjusted MD 0.47, 95% CI 0.06-0.88; P =.02), compared to the control group. Favorable trends were noted in diastolic blood pressure (adjusted MD −2.38, 95% CI −4.99 to 0.22; P =.07) and soft lean mass (adjusted MD 0.34, 95% CI −0.06 to 0.75; P =.10). Both groups showed significant within-group improvements in low-density lipoprotein cholesterol (intervention: P =.01; control: P =.03), body fat percentage (intervention: P <.001; control: P =.01), waist circumference (intervention: P =.001; control: P =.002), and occupational sitting (intervention: P <.001; control: P =.03), and occupational walking (intervention: P =.01; control: P =.046), but between-group differences were nonsignificant. Conclusions: The 12-week mHealth intervention improved physical activity and dietary behaviors and showed favorable trends in cardiometabolic indicators among sedentary employees. These findings support integrating mHealth programs into employee wellness initiatives to promote healthy behaviors, mitigate productivity loss, and reduce chronic disease burden. Further research should assess long-term sustainability, scalability, and cost-effectiveness in diverse occupational settings.

Background: Functional decline and increased dependence on others are common health issues among hospitalized elderly patients. However, a well-validated screening tool for predicting functional decline in elderly patients is still lacking. The current study therefore aimed to evaluate and compare the diagnostic accuracy of the Identification of Seniors at Risk—Hospitalized Patients (ISAR-HP), Variable Indicative of Placement Risk (VIP), and Score Hospitalier d’ Evaluation du Risque de Perte d’Autonomie (SHERPA) in predicting functional decline 30 days after discharge in older patients admitted to an acute hospital ward. Methods: A prospective, longitudinal study was conducted in 197 elderly inpatients at the internal medicine ward of a teaching hospital in central Taiwan. Data were collected twice, first within 48 h after hospitalization and second via a telephone interview 30 days after hospital discharge. Variables included demographic data, Barthel Index of activities of daily living (ADL), and screening instruments. The Barthel Index was used to measure functional disability. Functional decline was defined as a decline of at least five points on the Barthel Index 30 days after discharge compared to that at pre-admission. Results: Patients had a mean age of 77.7 years, with 55.7% being female. Functional decline was observed in 39.1% of all patients. The best cutoff point, sensitivity, specificity, and area under the receiver operating characteristic curve were 2.5, 96.1%, 52.5%, and 0.751 for ISAR-HP; 1.5, 83.1%, 62.5%, and 0.761 for VIP; and 4.75, 89.6%, 54.2%, and 0.758 for SHERPA, respectively. Conclusions: All three instruments showed moderate diagnostic accuracy as indicated by their best cutoff points. Therefore, the results presented herein can guide health care professionals in selecting the appropriate assessment tool for predicting functional decline among hospitalized elderly patients in a clinical setting.

Prolonged sedentary behavior in workplace settings is associated with increased health risks, including chronic diseases and reduced productivity. While various mobile health (mHealth) interventions exist, most focus on single behaviors such as physical activity (PA) or sedentary time, with limited solutions integrating diet, PA, and sedentary behavior for sedentary employees. This study aimed to develop Simple Health, an evidence- and theory-based mHealth web app designed to promote healthy eating, increase PA, and reduce sedentary time among sedentary employees. The study also evaluated the app’s usability. The study followed a two-phase approach. In Phase 1, Simple Health was developed by a multidisciplinary team using social cognitive theory and the ecological model as theoretical foundations. Behavior change techniques such as goal-setting, self-monitoring, and personalized feedback were incorporated. In Phase 2, usability testing was conducted with eight sedentary employees. Participants used Simple Health for five days and completed the mHealth app usability questionnaire (MAUQ) to assess ease of use, interface satisfaction, and perceived usefulness. Open-ended feedback was also collected to identify areas for improvement. Most participants (87.5%) found Simple Health easy to use, with usability scores indicating favorable responses across ease of use (mean: 2.20/7), interface satisfaction (mean: 1.91/7), and usefulness (mean: 1.98/7). Participants valued the simplicity of behavior logging and the actionability of personalized advice. Suggestions for improvement included increasing goal-setting flexibility and refining navigation to enhance user experience. The development and usability evaluation of Simple Health demonstrate its potential as a workplace mHealth intervention that integrates PA promotion, sedentary behavior reduction, and dietary tracking. While usability feedback was generally positive, refinements such as improved navigation and enhanced goal-setting flexibility could further optimize engagement. Future iterations should explore automatic syncing with wearable devices and expanded implementation in workplace wellness programs to assess long-term adoption and effectiveness.