ABSTRACT
Background: The prevalence of diabetes has increased rapidly, and comorbid chronic conditions are common among diabetes patients. However, little is known about the pattern of multimorbidity in diabetes patients and the effect on physical and cognitive function. This study aimed to assess the disease clusters and patterns of multimorbidity in diabetes patients using a novel latent class analysis (LCA) approach in middle-aged and older adults and explore the association between different clusters of multimorbidity in diabetes and the effect on physical and cognitive function. Methods: This national observational study included 1,985 diabetes patients from the four waves of the China Health and Retirement Longitudinal Study (CHARLS) in 2011 to 2018. Thirteen chronic diseases were used in latent class analysis to identify the patterns of multimorbidity in diabetes, which span the cardiovascular, physical, psychological, and metabolic systems. Cognitive function is assessed via a structured questionnaire in three domains: memory, executive function, and orientation. We combined activities of daily living (ADL) with instrumental activities of daily living (IADL) to measure physical function. Linear mixed models and negative binomial regression models were used to analyze the association between patterns of multimorbidity in diabetes and the effect on cognitive function and disability, respectively. Results: A sample of 1,985 diabetic patients was identified, of which 1,889 (95.2%) had multimorbidity; their average age was 60.6 years (standard deviation (SD) = 9.5), and 53.1% were women. Three clusters were identified: "cardio-metabolic" (n = 972, 51.5%), "mental-dyslipidemia-arthritis" (n = 584, 30.9%), and "multisystem morbidity" (n = 333, 17.6%). Compared with diabetes alone, the "multisystem morbidity" class had an increased association with global cognitive decline. All patterns of multimorbidity were associated with an increased risk of memory decline and disability; however, the "multisystem morbidity" group also had the strongest association and presented a higher ADL-IADL disability (ratio = 4.22, 95% CI = 2.52, 7.08) and decline in memory Z scores (ß = -0.322, 95% CI = -0.550, -0.095, p = 0.0058). Conclusion: Significant longitudinal associations between different patterns of multimorbidity in diabetes patients and memory decline and disability were observed in this study. Future studies are needed to understand the underlying mechanisms and common risk factors for multimorbidity in diabetes patients and to propose treatments that are more effective.
ABSTRACT
AIMS: To explore the relationship between health personality and quality of life among community-dwelling older adults and to examine the mediating effect of eHealth literacy on this relationship. METHODS: A total of 413 community-dwelling older adults from central China were recruited from September 2022 to January 2023. A cross-sectional investigation was conducted using the Chinese versions of the Health Personality Assessment (HPA), eHealth Literacy Scale (eHEALS), and 12-item Short Form Health Survey (SF-12). Correlations between the three variables were examined by Pearson analysis, and mediation analysis was conducted to explore the direct, indirect, and total effects of the health personality on quality of life vis-à-vis eHealth literacy. RESULTS: Health personality factors (including health neuroticism, health openness, and health conscientiousness), eHealth literacy, and quality of life are significantly correlated (P<0.05); eHealth literacy can play a significant mediating role in the relationship between health neuroticism (ß= -0.256, 95 %CI: [-0.405, -0.119]), health openness (ß = 0.488, 95 % CI: [0.343,0.652]), health conscientiousness (ß= 0.489, 95 % CI: [0.354, 0.634]) and quality of life. CONCLUSION: This study revealed that the effect of health personality factors (including health neuroticism, health openness, and health conscientiousness) on quality of life in older adults was mediated through eHealth literacy. IMPACT: Individual personality is essential to understanding eHealth literacy and quality of life among community-dwelling older adults. It may be helpful to identify the health personality traits of older adults first, then implement targeted interventions accordingly to enhance eHealth literacy and ultimately improve quality of life.