Illness perceptions as a mediator between emotional distress and management self-efficacy among Chinese Americans with type 2 diabetes.
Ya-Ching HuangJulie ZuñigaAlexandra GarcíaPublished in: Ethnicity & health (2020)
Objectives: Emotional distress and illness perceptions have been linked to patients' self-efficacy for diabetes management. This study, guided by Leventhal's Self-Regulatory Model, explores the direct effects of emotional distress (diabetes distress and depressive symptoms) on diabetes management self-efficacy, and the indirect effects through illness perceptions among Chinese Americans with type 2 diabetes (T2DM).Design: Data were obtained from a cross-sectional study of Chinese Americans with T2DM recruited from health fairs and other community settings (N = 155, 47.1% male, mean age 69.07 years). Data analyses including descriptive statistics, correlation, and PROCESS mediation models were used to examine the mediation effects of illness perceptions.Results: Diabetes distress and depressive symptoms had direct negative effects on self-efficacy. Perceived treatment control mediated the association between diabetes distress and self-efficacy, while none of the illness perceptions dimensions impacted the relationship between depressive symptoms and self-efficacy.Conclusion: Improved perceptions of treatment control can ameliorate diabetes distress and improve diabetes management self-efficacy among Chinese Americans. Health providers should elicit patients' illness perceptions as a first step in evaluating their diabetes management self-efficacy and provide appropriate culturally-tailored interventions.
Keyphrases
- healthcare
- type diabetes
- depressive symptoms
- glycemic control
- cardiovascular disease
- primary care
- social support
- mental health
- end stage renal disease
- public health
- physical activity
- chronic kidney disease
- ejection fraction
- newly diagnosed
- adipose tissue
- electronic health record
- transcription factor
- skeletal muscle
- climate change
- sleep quality
- deep learning
- smoking cessation
- human health
- data analysis