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When individuals face life pressure or significant family changes, individuals with better family adaptation can better survive the crisis. Although the influencing factors of family adaptation have been investigated, the application of potential profile analysis has yet to be found. This analytical approach can reveal different potential categories of family adaptation, providing new perspectives for theoretical development and interventions. This study used latent profile analysis to explore family adaptation levels in breast cancer patients and identify different latent categories, examining their characteristic differences. A cross-sectional study was conducted in Jinzhou, China, from July 2023 to March 2024. The questionnaire included Sociodemographic and clinical characteristics, Benefit Finding Scale (BFS), Dyadic Coping Scale (DCI), Chinese Perceived Stress Scales (PSS), and Family adaptability and cohesion evaluation scales (FACES). Mplus8.3 and SPSS26.0 software were used for data analysis. The latent profile analysis (LPA) method was used to fit the family adaptations of breast cancer patients. Three latent categories of family adaptation were identified: low-level family adaptation (21.5%), medium level family adaptation (47.8%), and high-level family adaptation (30.6%). All 14 items with high levels of family adaptation scored higher than the other two groups. In particular, out of all the categories, item 9, "The idea of educating children is sound," scored highest. Compared with the low-level group, the influential factors of family adaptation in the high-level group were BFS, DCI, PSS, relapse and personal monthly income; The factors influencing family adaptation at the middle level are DCI, BFS, breast cancer type, family history of breast cancer, and personal monthly income. Compared with the medium level group, PSS and DCI were the influential factors of family adaptation in the high-level group. Family adaptation in breast cancer patients can be divided into three categories: low-level, medium-level, and high-level. There were significant differences among different categories of family adaptation levels in "personal monthly income", "family history of breast cancer", "type of breast cancer", "recurrence", "dyadic coping", "benefit finding", and "perception stress". [ABSTRACT FROM AUTHOR]

Objectives: The Death Anxiety Beliefs and Behaviour Scale (DABBS) is a unique tool designed to assess the detrimental beliefs and avoidant behaviors linked to death anxiety. This study aimed to adapt the DABBS into Chinese and verify its psychometric characteristics within a community-dwelling older adult population., Methods: This study used a cross-sectional design and translated the DABBS into Chinese. The psychometric properties of 437 community-dwelling older persons were assessed using the Classical Test Theory (CTT) and Item Response Theory (IRT)., Results: The DABBS consisted of affect, beliefs, and behaviours, with 18 entries in 3 dimensions. The I-CVI of the DABBS ranged from 0.857 to 1.000, and the S-CVI was 0.968; Cronbach's alpha of 0.905. Rasch analysis results showed that the 3 dimensions of the scale possessed good unidimensionality, and the entries were well-fitted to the dimensions in which they were located; each entry Infit MNSQ and Outfit MNSQ were in the range of 0.50 to 1.50; the analysis of the functional differences of items in different characteristic subgroups (gender) showed that the absolute value of DIF Contrast was <0.50. The results of the Wright map showed that the ability of the participants was normally distributed, and the difficulty of the scale's entries was adapted to the average ability level of older adults., Conclusions: The present data indicate that the revised DABBS is a valid and reliable tool for assessing affect, beliefs, and behaviors associated with death anxiety in community-dwelling older individuals., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ai, Li, Ji and Zhang.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ai, Li, Dong and Zhang.)