Risk Factors and Coping Style Affect Health Outcomes in Adults With Type 2 Diabetes

2015 ◽  
Vol 18 (1) ◽  
pp. 82-89 ◽  
Author(s):  
Chiung-Yu Huang ◽  
Hui-Ling Lai ◽  
Yung-Chuan Lu ◽  
Wen-Kuei Chen ◽  
Shu-Ching Chi ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Coping Style ◽  
Coping Styles ◽  
Equation Modeling ◽  
Duration Of Diabetes ◽  
And Coping

Objective: Most psychosocial interventions among individuals with Type 2 diabetes mellitus (T2DM) target depressive symptoms (DSs) rather than causal antecedents that lead to DSs or affect health-related quality of life (HrQoL). This research investigated a conceptual model of the effects of risk factors and coping styles on HrQoL and DSs in patients with T2DM. Method: A descriptive, correlational design was used with a convenience sample of 241 adults with T2DM aged ≥ 20 years recruited from a hospital metabolic outpatient department. Data were collected using a demographic questionnaire, the modified Ways of Coping Checklist, the Center for Epidemiological Studies Depression Scale, the Short Form 36 Health Survey, and physiological examination. HbA1C was collected from participants’ medical records. Structural equation modeling techniques were used to analyze relationships among risk factors, mediators, and HrQoL. Results: Younger age, more education, and longer duration of diabetes predicted better physical quality of life. Duration of diabetes and three coping styles predicted DSs. Longer duration of diabetes and lower fasting glucose predicted better mental quality of life. Three coping styles acted as mediators between risk factors and health, that is, active and minimizing styles promoted positive outcomes, while avoidance promoted negative outcomes. Conclusions: This integrated model provides a holistic picture of how risk factors and coping style influence HrQoL and DSs in individuals with T2DM. Nurses could use active coping strategies in cognitive behavioral therapy to enhance glycemic control in patients with T2DM.

scholarly journals Investigating Factors Associated with Depressive Symptoms of Chronic Kidney Diseases in China with Type 2 Diabetes

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xu Wang ◽  
Biyu Shen ◽  
Xun Zhuang ◽  
Xueqin Wang ◽  
Weiqun Weng
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Regression Analysis ◽  
Depressive Symptoms ◽  
Kidney Diseases ◽  
Chronic Kidney Diseases ◽  
Factors Associated

Aim.To assess the depressive symptoms status of chronic kidney diseases in Nantong, China, with type 2 diabetes and to identify factors associated with depressive symptoms.Methods.In this cross-sectional analytic study, 210 type 2 diabetic patients were recruited from the Second Affiliated Hospital of Nantong University. Depressive symptoms were assessed with the depression subscale of the Hospital Anxiety and Depression Scale (HAD-D). The quality of life was measured with the RAND 36-Item Health Survey (SF-36). And the independent risk factors of depressive symptoms were assessed by using a stepwise forward model of logistic regression analysis.Results.The mean age of the study subjects was 57.66 years (SD: 11.68). Approximately 21.4% of subjects reported depressive symptoms (n=45). Forward stepwise logistic regression analysis showed that female gender (P=0.010), hypertension (P=0.022), Stage IV (P=0.003), and Stage V (P<0.001) were significant risk factors for depressive symptoms. The quality of life of individuals with HAD-D score <11 was significantly better compared with individuals with HAD-D score ≥ 11.Conclusions.These results indicate that clinicians should be aware that female patients with chronic kidney diseases with T2DM in their late stage with hypertension are at a marked increased risk of depressive symptoms. Providing optimal care for the psychological health of this population is vital.

scholarly journals Common sense model of self-regulation for understanding adherence and quality of life in type 2 diabetes with structural equation modeling

2021 ◽  
Vol 104 (1) ◽  
pp. 171-178
Author(s):  
Estelle Fall ◽  
Nadia Chakroun-Baggioni ◽  
Philip Böhme ◽  
Salwan Maqdasy ◽  
Marie Izaute ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Type 2 Diabetes ◽  
Common Sense ◽  
Structural Equation ◽  
Self Regulation ◽  
Equation Modeling ◽  
Common Sense Model ◽  
Sense Model

Simulation of Quality-Adjusted Survival in Chronic Diseases

2011 ◽  
Vol 31 (4) ◽  
pp. 559-570 ◽  
Author(s):  
Alison J. Hayes ◽  
Philip M. Clarke ◽  
Merryn Voysey ◽  
Anthony Keech
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Health Utility ◽  
Clinical Risk Factors ◽  
Patient Level ◽  
Clinical Risk ◽  
Life Years

Background. Recent studies have demonstrated that measures of health-related quality of life can predict complications and mortality in patients with diabetes, even after adjustment for clinical risk factors. Methods. The authors developed a simulation model of disease progression in type 2 diabetes to investigate the impact of patient quality of life on lifetime outcomes and its potential response to therapy. Changes in health utility over time are captured as a result of complications and aging. All risk equations, model parameter estimates, and input data were derived from patient-level data from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial. Results. Healthier patients with type 2 diabetes enjoy more life years, quality-adjusted life years (QALYs), and more life years free of complications. A 65-year-old patient at full health (utility = 1) can expect to live approximately 2 years longer and achieve 6 more QALYs than a patient at average health (utility = 0.8), given similar clinical risk factors. For patients with higher EQ-5D utility, the additional years lived without complications contribute more to longer life expectancy than years lived with complications. Conclusions. The authors have developed a model for progression of disease in diabetes that has a number of novel features; it captures the observed relationships between measures of quality of life and future outcomes, the number of states have been minimized, and it can be parameterized with just 4 risk equations. Underlying the simple model structure is important patient-level heterogeneity in health and outcomes. The simulations suggest that differences in patients’ EQ-5D utility can account for large differences in QALYs, which could be relevant in cost-utility analyses.

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