Healthcare

Background: Despite growing concerns about the impact of excessive digital media use on adolescents’ health, few studies have examined how digital detox practices and physical activity interact to influence it, particularly from a gender perspective. Purpose: This study investigated the effects of digital detox and physical activity on adolescents’ health habits, focusing on gender differences and sociocultural implications. Methods: In February 2025, a self-reported survey was conducted among 652 adolescents (mean age = 15.6, SD = 1.4) residing in Seoul, South Korea, using a quota sampling method. The survey measured four domains: demographic characteristics, digital detox practices, physical activity, and perceptions of health habit improvement. Results: Gender-based analyses revealed that female students reported higher engagement in digital detox practices, whereas male students showed greater participation in physical activity and higher levels of health efficacy. Digital detox had a significant positive effect on adolescents’ health habit improvement; however, its effect on physical activity and the effect of physical activity on health habit improvement were not statistically significant. Conclusions: These findings suggest that the complex interplay among digital engagement, physical activity, and gender-based social norms shapes adolescents’ health behaviors. To effectively improve adolescent health, strategies should be tailored to address sociocultural dynamics and gender-specific needs and experiences.

Background/Objectives: Older individuals are more vulnerable to stress and trauma. Although pressure injuries (PIs) are recognized as a significant complication, the specific impact of frailty on PI development in older Asian trauma patients remains insufficiently explored. This study aims to investigate the factors associated with the occurrence of hospital-acquired pressure injuries (HAPU) in older patients aged ≥65 years, including frailty. Methods: This study is a retrospective secondary data analysis of 3418 older trauma patients admitted to a regional trauma center (including ICU and trauma ward) from 1 January 2020 to 31 December 2023. Patients with PIs present on admission (POA) were excluded to strictly analyze new PI occurrence. Frailty was assessed using the mFI-5. Results: The mean age of participants was 77.33 years. During hospitalization, 2.5% (n = 84) of patients developed new PIs. Multivariate logistic regression identified that higher frailty score (Odds Ratio [OR] = 1.59, 95% Confidence Interval [CI]: 1.26–2.02), lower BMI (OR = 0.93, 95% CI: 0.86–0.99), hypoalbuminemia (OR = 0.55, 95% CI: 0.36–0.84), and prolonged hospital stay (OR = 1.05, 95% CI: 1.04–1.06) were independently associated with PI occurrence. Chronological age was not a significant predictor in the multivariate model. Conclusions: Frailty, nutritional status (BMI, albumin), and prolonged hospital stay are significant factors associated with HAPU in older trauma patients.

Background: In the era of Health 4.0, Emergency Departments (EDs) face increasing crowding and complexity, necessitating smart management solutions to balance efficiency with equitable care. Effective scheduling is critical for optimizing patient throughput and mitigating congestion. Methods: This paper constructs a decision support framework using Discrete Event Simulation (DES) to evaluate three patient scheduling strategies, including the Initial-First policy, Alternating 1:1 policy and a Slack-Based dynamic policy. The simulation framework has been conducted using a standardized operational dataset representing typical ED dynamics. The threshold of SBP was optimized by a grid search method to guarantee an objective comparison. Results: The simulation results show that when adopting the optimized SBP policy, the mean waiting time was shortened by around 23.8%, thus meeting all triage service level targets. Also, it could be seen that Slack-Based dynamic policy was robust under different arrival rates and physician staffing levels. Conclusions: This proposed model can provide a real-time and dynamic solution for ED resource allocation, meeting the demand of modern smart hospitals management.