Falls are a major public health concern in today’s aging society. Virtual reality (VR) technology is a promising method for reducing fall risk. However, the absence of representations of the user’s body in a VR environment lessens the spatial sense of presence. In terms of user experience, augmented reality (AR) can provide a higher degree of presence and embodiment than VR. We developed an AR-based exergame system that is specifically designed for the elderly to reduce fall risk. Kinect2.0 was used to capture and generate 3D models of the elderly and immerse them in an interactive virtual environment. The software included three functional modules: fall risk assessment, cognitive–motor intervention (CMI) training, and training feedback. The User Experience Questionnaire (UEQ-S) was used to evaluate user experience. Twenty-five elders were enrolled in the study. It was shown that the average scores for each aspect were: pragmatic quality score (1.652 ± 0.868); hedonic quality score (1.880 ± 0.962); and overall score was 1.776 ± 0.819. The overall score was higher than 0.8, which means that the system exhibited a positive user experience. After comparing the average score in a dataset product of UEQ-S Data Analysis Tool, it was found that the pragmatic quality aspect was categorized as good, while the hedonic quality aspect was categorized as excellent. It revealed a positive evaluation from users.

Introduction: Post-stroke depression (PSD) is a common neuropsychiatric complication that affects approximately one-third of stroke patients. The treatment and prognosis of this disease are poor. Socioeconomic status (SES) is closely related to health outcomes; however, only a few previous studies have focused on the association between SES and PSD. Given the substantial population of stroke patients in China, it is crucial to examine the potential risk factors associated with PSD. Conducting studies on this population and investigating the influence of economic conditions can provide valuable guiding theoretical insights into PSD prevention and management. Methods: We used data from the 2018 China Health and Retirement Longitudinal Study and selected appropriate samples for analysis. Depression was estimated using the Center of Epidemiologic Studies Depression Scale-10, a validated tool for assessing depression in the general population. Multiple logistic regression analysis was employed to assess the association between SES and PSD and to evaluate any urban–rural differences. Results: Of the 749 respondents, 370 (49.4%) had depression. Stroke patients with a middle school education demonstrated a greater risk of developing depression than those with a primary school education or below after adjusting for all control variables (odds ratio (OR) = 1.60, 95% confidence interval (CI): 1.03–2.51, P = 0.036). However, stroke patients with a high school education or above had a lower risk of developing depression than those with a primary school education or below (OR = 0.50, 95% CI: 0.28–0.88, P = 0.016). In rural areas, stroke patients with a high school or above education level had lower rates of depression than those with a primary school education or below (OR = 0.44, 95% CI: 0.21–0.91, P = 0.027). This difference was not significant in urban areas. Conclusions: SES significantly influences the occurrence of PSD, which is reflected by education attainment and annual household expenditures. Education attainment was an independent influence on PSD, with a more pronounced effect in rural versus urban areas. We hope to reduce the prevalence of PSD and enhance the comprehensive management of this disease by modifying the influencing factors. Sex, self-reported health status, activities of daily living, night-time sleep duration, and life satisfaction also influenced the occurrence of PSD.

Introduction: Previous studies have suggested that cognitive impairment is associated with falls in older adults. However, the consistency of results among different subgroups defined by multiple characteristics of the elderly has not yet been tested. Additionally, results are inconsistent regarding the effects of different cognitive domains on falls. Therefore, this study sought to use representative data from a nationwide study to better understand the longitudinal association between cognitive impairment and subsequent falls in older adults.Methods: The current study was conducted based on the China Health and Retirement Longitudinal Study (CHARLS) data of respondents aged ≥60 years in 2015 and the fall data in 2018. The respondents were divided into subgroups according to different demographic characteristics. Multiple logistic regression analysis was conducted to adjust for various confounding factors and evaluate the association between cognitive impairment and falls.Results: Of the 5,110 respondents included in this study, 1,093 (21.39%) had falls within the last 2 years. A significant association was found between cognitive impairment and subsequent falls (OR = 0.97, 95% CI 0.95–0.99, P = 0.001) after adjusting for all of the covariates related to falls. Analysis of different cognitive domains showed that orientation (OR = 0.94, 95% CI 0.90–0.99, P = 0.013) and memory (OR = 0.93, 95% CI 0.90–0.97, P = 0.001) were significantly associated with falls. In subgroup analysis, the ORs of people aged 60–74 years, male, with lower education level were 0.97 (95% CI 0.95–0.99, P = 0.008), 0.96 (95% CI 0.93–0.98, P = 0.001), and 0.97 (95% CI 0.95–0.99, P = 0.001), respectively, suggesting that the associations were also statistically significant in these subgroups. There was also a significant association both in urban (OR = 0.97, 95% CI 0.95–0.99, P = 0.001) and in rural residents (OR = 0.97, 95% CI 0.95–0.99, P = 0.003).Conclusions: Our results suggest that the associations between cognition and falls vary depending on the different demographic characteristics of older adults. These findings may be useful for designing more accurate identification and intervention for the fall risk for specific high-risk groups.

Introduction: Previous studies suggested that physical and cognitive function can be indicators to assess the risk of falls in the elderly. Various tests are widely used in geriatric clinical studies as assessment tools of physical and cognitive function. However, large sample studies comparing the fall predictive value of these tests are still sparse. This study was conducted to investigate the value of cognitive and physical function tests in predicting the risk of subsequent falls in the elderly, with the overarching goal of providing more evidence on fall-risk assessment.Methods: The current study was based on the data of respondents aged 60 and above from the China Health and Retirement Longitudinal Study (CHARLS). Data from the 2015 CHARLS national survey were used as the baseline data, and the fall data in 2018 were used as the follow-up data. Physical function tests included balance, walking speed, the five times sit-to-stand test (FTSST), and grip strength. The value of cognitive and physical function tests in predicting falls was evaluated by logistic regression analysis and receiver operating characteristic (ROC) curves.Results: The incidence of falls among the 4,857 subjects included in this study was 20.86%. Results showed that cognition (OR = 0.83, 95% CI: 0.70–0.98), the FTSST (OR = 3.51, 95% CI: 1.66–7.46), and grip strength (OR = 1.02, 95% CI: 1.01–1.03) were independent predictors of falls in the full sample after adjusting for various confounders. Notably, the above tests showed better predictive value for falls for the oldest-old (≥ 80 years) subjects.Conclusion: Overall, results showed that grip strength, the FTSST, and cognition tests are simple and practicable tools for identifying individuals at higher risk of falls in the community. Moreover, the fall predictive performance of physical and cognitive function tests was age-dependent, with a higher predictive value in older adults aged 80 and above.

Introduction: Post-stroke depression (PSD) is a common neuropsychiatric complication that affects approximately one-third of stroke patients. The prognosis and treatment of this disease are poor.There have been many studies exploring PSD risk factors, but previous studies have rarely focused on economic conditions and have not explored the role of urban-rural differences in PSD. Given the substantial population of stroke patients in China, it is crucial to examine the potential risk factors, including economic factors, contributing to PSD. By conducting studies on this population and investigating the influence of economic conditions, we can gain valuable theoretical insights to guide PSD prevention and management. Methods: We utilized data from the 2018 CHARLS and selected appropriate samples for analysis. Depressive symptoms were assessed using the CES-D 10 scale, a validated tool for measuring depressive symptoms in the general population. Multiple logistic regression analysis was employed to assess the association between economic status and post-stroke depression and evaluate any urban-rural differences. Results: Of the 1009 respondents in the study, 446 (44.2%) had depression. After adjusting for all covariates, we found that in the urban sample, participants with more health insurance cases had a lower risk of PSD (OR=0.19, 95% CI: 0.04-0.85, P=0.03). Conversely, in the rural sample, a significant correlation was reported between individual annual income and PSD occurrence (OR=0.19, 95% CI: 0.05-0.73, P=0.02). Conclusions: Self-reported health status, number of other disabilities, night sleep duration, ADL, and life satisfaction also influenced PSD. The research results confirm that the increase of incidence rate of post-stroke depression (PSD) is related to many factors. Higher prevalence rates were observed in individuals with poorer economic conditions. This connection is particularly evident when comparing urban and rural areas. By addressing these factors and improving the economic situation, we can reduce the prevalence of PSD and strengthen comprehensive management of this situation.