Huijun Zhang’s research while affiliated with Jinzhou Medical University and other places

Background Depression among rural empty-nest older adults has emerged as a significant global and Chinese concern in recent years. Studies have identified associations between depression in rural empty-nest older adults and factors such as social alienation, attitudes towards aging, and mental health. However, the intricate mechanisms underlying these relationships remain incompletely understood. Therefore, this study seeks to investigate the intricate connections between social alienation and depression among Chinese rural empty-nest older adults, aiming to shed light on the underlying mechanisms contributing to depression in this demographic. Methods The study data were gathered from a sample of rural empty-nest older adults (N = 1025) residing in the cities of Chaoyang, Liaoning Province, China. Depression served as the primary outcome variable, with social alienation as the predictor variable. Attitude towards aging and anxiety symptoms were examined as potential mediators in the relationship between social alienation and depression. Mediation and chained mediation modeling analyses were conducted to analyze the data and explore the proposed relationships. Results (1) Social alienation positively predicted attitude towards aging (β = 0.109, P < 0.001), anxiety symptoms (β = 0.076, P < 0.001), and depression (β = 0.021, P < 0.001). Attitude towards aging positively predicted anxiety symptoms (β = 0.390, P < 0.001) and depression (β = 0.006, P < 0.001). Anxiety symptoms also positively predicted depression (β = 1.207, P < 0.001). (2) Social alienation mainly affects depression through three indirect effects: (1) Social alienation → attitude towards aging → depression; (2) Social alienation → anxiety symptoms → depression; (3) Social alienation → attitude towards aging → anxiety symptoms → depression. Conclusion Our findings suggest that social alienation impacts depression among Chinese rural empty-nest older adults through the pathways of attitude towards aging and anxiety symptoms. Consequently, efforts aimed at preventing depression rooted in social alienation among this demographic should address both attitudes towards aging and mental health concerns.

Background Depression is a prevalent psychological issue among rural elderly individuals living alone, severely impacting their physical and mental health. Objective To develop and validate a depression risk prediction model for rural elderly living alone based on the health ecological model, providing a scientific basis for early intervention. Methods Using data from the 2011 China Health and Retirement Longitudinal Study (CHARLS), we included 1,221 participants. Thedataset was randomly stratified into a training set (70%) and a validation set (30%). Predictors were screened via univariate analysis, followed by multivariate logistic regression to construct the nomogram model. Statistical analysis was performed using R Studio 4.4.1.Ten-fold cross-validation was used to assess the model’s stability. Model performance was evaluated using the Receiver Operating Characteristic (ROC) curve, with the Area Under the Curve (AUC) calculated, along with calibration plots, the Hosmer-Lemeshow test, and Decision Curve Analysis (DCA). Results Self-rated health, pain, frailty, nighttime sleep duration, poor sleep quality, life satisfaction, and visit frequency were identified as independent predictors of depressive symptoms. The model demonstrated excellent discrimination (AUC = 0.85 [95% CI: 0.83–0.88] in the training set and 0.83 [95% CI: 0.78–0.87] in validation), good calibration (Hosmer-Lemeshow test p = 0.47), and high clinical utility (net benefit > 10% in DCA). Conclusion The nomogram provides a reliable and intuitive tool for early screening of depressive symptoms in rural elderly individuals living alone, supporting targeted interventions. Clinical trial number Not applicable.

Self-Reported Health (SRH) is an important covariate in health and gerontology research and may be related to dietary diversity. Therefore, this study aimed to analyze this association in a large-scale general population of rural older adults from China. It also investigates the mediating effect of the psychological equilibrium (PE) in the association between dietary variety and SRH in this population. Based on data from the 2018 Chinese Longitudinal Healthy Longevity Study (CLHLS), 4,643 rural older adults were included in the study. The Dietary Variety Index was found across seven food classes using latent class analysis (LCA). We examined the factors impacting self-reported health using unordered multiple logistic regression analysis. Model 4 of the PROCESS version 4.1 software was used to evaluate the mediation impact of psychological balance. 4,643 rural seniors from the CLHLS were included in the study. Four distinct dietary variation indices (DVIs) were identified for each of the seven food categories through Latent Class Analysis. Fruit, vegetables, meat, fish, eggs, nuts, and milk significantly influenced self-reported health (SRH), according to unordered multinomial logistic regression. Positive correlations between DVI and psychological equilibrium (PE) (r = 0.052, P < 0.001), DVI and SRH (r = 0.054, P < 0.001), and PE and SRH (r = 0.220, P < 0.001) were revealed by Pearson correlation analysis. PE modifies the connection between DVI and SRH, with a significant indirect effect (β = 0.002, 95% CI = 0.001 to 0.003), according to mediation analysis. The self-reported health (SRH) of older people living in rural areas has unique classification features; fruits, vegetables, meat, fish, eggs, nuts, and milk have all been found to be important influencing factors. The relationship between psychological equilibrium (PE) and SRH is indirect. Our results emphasize how critical it is to support a varied diet in order to improve SRH in older adults living in rural areas. Furthermore, this population’s psychological balance needs to be taken into consideration.

Background Physical resilience is known to minimize the adverse outcomes of health stressors for older people. However, validated instruments that assess physical resilience in older adults are rare. Therefore, the purpose of this study was to translate the Physical Resilience Scale (PRS) into Chinese and to validate its psychometric properties in a population of community-dwelling older adults following SARS-CoV-2 infection. Methods This study used a cross-sectional design and translated the Physical Resilience Scale into Chinese. A total of 426 older adults who had recovered from SARS-CoV-2 infection were chosen for assessment through convenience sampling. The measurement data were analyzed using the Rasch analysis. Results Rasch analysis indicates that the Physical Resilience Scale demonstrates excellent reliability, validity, and unidimensionality. The Infit MNSQ and Outfit MNSQ of each entry were 0.77 ~ 1.19, and the degree of fit of each entry to the scale was good. Person and item separation reliability support the internal consistency of the studied samples and PRS items. Conclusions The Physical Resilience Scale has good reliability and is suitable for the assessment of physical resilience tests in older people. However, the overall difficulty of the scale is not suitable for older adults of all ability ranges, and it is possible to add higher and lower difficulty items and adjust the difficulty spacing between items in a later study.

Background Hypertension is a common chronic disease that can trigger symptoms such as anxiety and depression. Therefore, it is essential to predict their risk of depression. The aim of this study is to find the best prediction model and provide effective intervention strategies for health professionals. Methods The study subjects were 2733 middle-aged and older adults who participated in the China Health and Retirement Longitudinal Study (CHARLS) between 2018 and 2020. R software was used for Lasso regression analysis to screen the best predictor variables, and logistic regression, random forest and XGBoost models were constructed. Finally, the prediction efficiency of the three models was compared. Results In this study, 18 variables were included, and LASSO regression screened out 10 variables that were important for the establishment of the model. Among the three models, Logistic Regression model showed the best performance in various evaluation indicators. Conclusion The prediction model based on machine learning can accurately assess the likelihood of depression in middle-aged and elderly patients with hypertension in the next three years. And by combining Logistic regression and nomograms, we were able to provide a clear interpretation of personalized risk predictions.

Objective Many previous studies have found that disability leads to cognitive impairment, and in order to better understand the underlying mechanisms between disability and cognitive impairment, the present study aimed to investigate the moderating role of social relationships, including their role as mediators between disability and cognitive impairment in depressive symptoms. Study design This is a cross-sectional study. Methods A total of 5,699 Chinese older adults from the 2018 China Longitudinal Healthy Longevity Survey (CLHLS) were included in this study, and PROCESS macro was used to perform simple mediator and moderator mediator analyses, which were used to analyze the relationship between depressive symptoms and social relationships between disability and cognitive impairment. Results The results of this study showed significant correlations between disability, cognitive impairment, depressive symptoms, and social relationships, and that depressive symptoms mediated the relationship between disability and cognitive functioning [B = −0.232; 95% CI: (−0.304, −0.164)], and that social relationships mediated disability and cognitive functioning through pathway a (Disability-Depressive Symptoms) [B = 0.190; 95% CI: (0.020, 0.036)], path b (depressive symptoms-cognitive impairment) [B = 0.029; 95% CI: (0.015, 0.042)], and path c’ (incapacitation-cognitive impairment) [B = 0.492; 95% CI: (0.298, 0.685)] to modulate the effect of incapacitation on cognitive impairment. In addition, social activities and social networks moderated the mediation model directly or indirectly, whereas social support moderated only the direct effect. Conclusion This study explains the intrinsic link between incapacitation and cognitive impairment in Chinese older adults, and that social relationships and depressive symptoms can directly or indirectly modulate the effects between them. This provides a basis for healthcare professionals to be able to better develop interventions that can be used to improve the level of cognitive functioning and mental health of older adults.

Background This study aims to explore the roles of psychological resilience and subjective well-being as multiple mediators in the relationship between social alienation and the ability for self-care in elderly individuals living alone in rural areas, particularly focusing on the empty-nest segment of this population. Methods From February 17, 2021, to April 20, 2023, A multistage stratified sampling approach was utilized to conduct a survey among 425 elderly individuals (aged 60 and above) living in rural areas without the company of their children, commonly referred to as 'empty-nesters'. These participants were recruited from rural areas of Liaoning Province, China. The questionnaire included the Generalized Social Alienation Scale, the Psychological Resilience Scale, the Subjective Well-being Scale, and the Self-care Ability In Old Age Scale. The analysis was designed to define the characteristics of the sample. We employed linear regression to examine how social alienation influences self-care capabilities in the elderly. To assess the compound mediating effects of psychological resilience and subjective well-being, the PROCESS macro (Model 6) was utilized. Results Social alienation significantly impacts self-care ability in the elderly living alone in rural areas, with a direct effect size of β = -0.678 (95% CI = -0.750–0.607), accounting for 66.67% of the total influence. Additionally, three indirect pathways play a role in shaping self-care ability in old age: 1) The psychological resilience route, contributing 42.37% of the total effect (β=-0.431, 95% CI=-0.515–-0.350); 2) The subjective well-being route, adding 4.32% to the total effect (β = 0.044, 95% CI = 0.010–0.089); and 3) A combined pathway involving both psychological resilience and subjective well-being, which adds 4.71% to the overall effect (β = 0.048, 95% CI = 0.029–0.073). The cumulative mediating effect of these pathways is 33.33%. Conclusions In rural areas, the well-being and self-care capabilities of older adults living alone are significantly influenced by their psychological resilience and sense of subjective well-being. As such, it's imperative for healthcare professionals and relevant stakeholders to focus on the mental health and psychological resilience of these individuals. By doing so, and by prioritizing their subjective well-being, targeted interventions can be effectively implemented to bolster the resilience and overall happiness of these elderly members of our rural communities.

Objective The purpose of this study was to translate the Geriatric Anxiety Scale-Long-Term Care into Chinese and to assess its reliability and validity in a long-term care population, as well as to explore factors contributing to anxiety in older adults requiring long-term care. Methods The study recruited 399 older adults residents requiring long-term care and used the Brislin double translation-back-translation method to create the initial Chinese version of the Geriatric Anxiety Scale-Long-Term Care. The study used internal consistency and split-half reliability to assess the reliability of the scale, as well as exploratory factor analysis, validation factor analysis, and content validity to assess the validity of the scale. Linear regression was used to analyze the relationship between the independent variables and anxiety levels in the long-term care population. Results The Cronbach’s coefficient value of the Chinese version of the Geriatric Anxiety Scale-Long-Term Care was 0.81, and the split-half reliability was 0.80. The results of exploratory factor analysis showed support for a one-dimensional factor structure. The results of the validation factor analysis indicated a good fit for the one-factor model. Gender (β = 0.190, 95% CI:0.540 ~ 1.546, p < 0.001), self-rated health (β = 0.220, 95% CI:0.379 ~ 0.953, p < 0.001), life satisfaction (β = −0.315, 95% CI: −1.355 ~ −0.734, p < 0.001) and participation in activities (β = −0.106, 95% CI: −1.122 ~ −0.084, p < 0.05) were significant predictors of anxiety levels in the long-term care population. Conclusion The Chinese version of the Geriatric Anxiety Scale-Long-Term Care has good reliability and validity in the long-term care population. The Geriatric Anxiety Scale-Long-Term Care is effective in assessing the anxiety level of the Chinese long-term care older adults population and provides an opportunity to detect and observe anxiety disorders in the long-term care population.