This study was intended to determine the applicability of an augmented-reality-based muscle reduction prevention exercise program for elderly Korean women by observing changes in exercise self-efficacy and verifying the effectiveness of the program in the elderly after the application of the program. A total of 27 participants, who were elderly women aged 65+ and had not participated in any exercise programs until this study, were recruited for this study. They were divided into an experimental group (13 people) and a control group (14 people), and then the augmented-reality-based muscle reduction prevention exercise program was applied. This was a 30-min program, which included regular, aerobic, and flexibility exercises, and it was applied 5 times a week for 12 weeks. As a result of observing changes, it was found that the appendicular skeletal muscle mass (ASM) (F = 11.222, p < 0.002) and the skeletal muscle index (SMI) (kg/m²) (F = 10.874, p < 0.003) muscle parameters increased more in the experimental group compared to the control group, and there was a significant increase in gait speed (m/s) (F = 7.221, p < 0.005). For physical performance, as a result of conducting the Senior Fitness Test (SFT), a significant change was observed in the chair stand test (F = 5.110, p < 0.033), 2-min step test (2MST) (F = 6.621, p < 0.020), and the timed up-and-go test (TUG) (F = 5.110, p < 0.032) and a significant increase was also observed for exercise self-efficacy (F = 20.464, p < 0.001). Finally, the augmented-reality-based exercise program in this study was found to be effective in inducing physical activity in the elderly. Therefore, the augmented-reality-based muscle reduction prevention exercise program is considered to be effective in increasing the sustainability of exercise, thus preventing muscle reduction in the elderly.

Driver drowsiness is a major cause of fatal accidents throughout the world. Recently, some studies have investigated steering wheel grip force-based alternative methods for detecting driver drowsiness. In this study, a driver drowsiness detection system was developed by investigating the electromyography (EMG) signal of the muscles involved in steering wheel grip during driving. The EMG signal was measured from the forearm position of the driver during a one-hour interactive driving task. Additionally, the participant’s drowsiness level was also measured to investigate the relationship between muscle activity and driver’s drowsiness level. Frequency domain analysis was performed using the short-time Fourier transform (STFT) and spectrogram to assess the frequency response of the resultant signal. An EMG signal magnitude-based driver drowsiness detection and alertness algorithm is also proposed. The algorithm detects weak muscle activity by detecting the fall in EMG signal magnitude due to an increase in driver drowsiness. The previously presented microneedle electrode (MNE) was used to acquire the EMG signal and compared with the signal obtained using silver-silver chloride (Ag/AgCl) wet electrodes. The results indicated that during the driving task, participants’ drowsiness level increased while the activity of the muscles involved in steering wheel grip decreased concurrently over time. Frequency domain analysis showed that the frequency components shifted from the high to low-frequency spectrum during the one-hour driving task. The proposed algorithm showed good performance for the detection of low muscle activity in real time. MNE showed highly comparable results with dry Ag/AgCl electrodes, which confirm its use for EMG signal monitoring. The overall results indicate that the presented method has good potential to be used as a driver’s drowsiness detection and alertness system.

This study comparatively analyzed obesity, lean balance, and physical fitness related to growth and development in female middle school students to investigate the importance of regular physical activity on body composition and physical fitness in adolescence. The subjects were 31 elementary school students in grades 5-6 in Seoul, consisting of 16 students in the regularly active group and 15 students in the inactive group. Body composition and physical fitness factors were measured. Height, weight, body mass index (BMI), fat mass, and fat-free mass were measured using Inbody 3.0. Physical fitness factors such as muscle strength (grip strength), muscle endurance (situps), flexibility (body flexion), agility (side stepping), and lean balance (balancing with eyes closed), were measured as described herein. The results of comparing the two groups' obesity-related body composition, the weight (P<0.001), body fat mass (P<0.001), BMI (P<0.002), and % body fat (P<0.033) were significantly higher in the inactive group compared to the physically active group. Comparison of lean balance was there was a significant difference in muscle mass between the left and right Arm (P<0.001), left and right leg (P<0.002) in the inactive group. comparing physical fitness between groups was trunk flexion (P<0.001) and side stepping (P<0.001) were higher in the physically active than the inactive group. This study found significant differences in weight, body fat, BMI, flexibility, and side stepping between the physically active and inactive groups, which may negatively affect health indicators related to adolescents' growth, development, and obesity. Therefore, attempts to promote physical activity in growing adolescents are crucial.

The COVID-19 pandemic has increased the importance of health literacy in disseminating information on health in a non-contact society. This study focused on examining the acceptance capacity by older adults of smart devices in Korea and investigating the potential differences between men and women in terms of e-health literacy and technology-use anxiety. The study included 1369 respondents who were adults over 50 years of age and used welfare centers, public health centers, senior citizen centers, and exercise centers in Seoul and Incheon. An online survey was conducted from 1 June 2021 to 24 June 2021. The study found that the older adults’ low levels of digital literacy could limit their access to health information and negatively impact their health. The difference between men and women in terms of technology-use anxiety was statistically significant, with the latent mean for men being higher than that for women. The effect sizes of the potential mean differences were found to be at a medium level for e-health literacy and a significant level for technology-use anxiety. With Korea’s aging population and the need for the continuous management of chronic diseases among older adults, it is essential to discuss internet-based health information for disease maintenance and treatment.

Since the WHO’s 2021 aging redefinition emphasizes “healthy aging” by focusing on the elderly’s ability to perform daily activities, sarcopenia, which is defined as the loss of skeletal muscle mass, is now becoming a critical health concern, especially in South Korea with a rapidly aging population. Therefore, we develop a prediction model for sarcopenia by using machine learning (ML) techniques based on the Korea National Health and Nutrition Examination Survey (KNHANES) data 2008–2011, in which we focus on the role of socioeconomic status (SES), social infrastructure, and quality of life (QoL) in the prevalence of sarcopenia. We successfully identify sarcopenia with approximately 80% accuracy by using random forest (RF) and LightGBM (LGB), CatBoost (CAT), and a deep neural network (DNN). For prediction reliability, we achieve area under curve (AUC) values of 0.831, 0.868, and 0.773 for both genders, males, and females, respectively. Especially when using only male data, all the models consistently exhibit better performance overall. Furthermore, using the SHapley Additive exPlanations (SHAP) analysis, we find several common key features, which mainly contribute to model building. These include SES features, such as monthly household income, housing type, marriage status, and social infrastructure accessibility. Furthermore, the causal relationships of household income, per capita neighborhood sports facility area, and life satisfaction are analyzed to establish an effective prediction model for sarcopenia management in an aging population.

Objective: We investigated the effectiveness of a 12-week SaBang-DolGi walking exercise program on the physical and mental health of menopausal women and aimed to provide the basic data needed to develop health promotion programs for the active and healthy aging of menopausal women. Materials and methods: The participants comprised 40 women aged 50–65 years who were divided into two randomly selected groups in training sessions (exercising group, n = 21 and control group, n = 19). A physical (grip, muscle and endurance) test and mental health test (simple mental health test II) were conducted using questionnaires with the aim of examining subjects’ physical and mental health before and after exercise. Results: After the intervention, the participants experienced positive changes in the physical dimension, with significant enhancements particularly in mental well-being and menopause-related health and subdomains. Controlled and regular exercise for 12 weeks was significantly correlated with a positive change in vitality and mental health. Conclusions: We found that the SaBang-DolGi walking exercise program helps to promote the physical and mental health of menopausal women who are exposed to the various stresses and depressions that accompany physical deterioration; the program was found to encourage active and healthy aging.