Variability in sleep disturbance, physical activity and quality of life by level of depressive symptoms in women with Type 2 diabetes

Author(s): Danhauer, SC; Brenes, GA; Levine, BJ; Young, L; Tindle, HA; Addington, EL; Wallace, RB; Naughton, MJ; Garcia, L; Safford, M; Kim, MM; LeBlanc, ES; Snively, BM; Snetselaar, LG; Shumaker, S | Abstract: AIMS:To examine (1) the prevalence of depressive symptoms in women with Type 2 diabetes, (2) the associations between depressive symptoms and the following dependent variables: sleep disturbance; physical activity; physical health-related; and global quality of life, and (3) the potential moderating effects of antidepressants and optimism on the relationship between depressive symptoms and dependent variables. METHODS:Participants in the Women's Health Initiative who had Type 2 diabetes and data on depressive symptoms (N=8895) were included in the analyses. In multivariable linear regression models controlling for sociodemographic, medical and psychosocial covariates, we examined the main effect of depressive symptoms, as well as the interactions between depressive symptoms and antidepressant use, and between depressive symptoms and optimism, on sleep disturbance, physical activity, physical health-related quality of life; and global quality of life. RESULTS:In all, 16% of women with Type 2 diabetes reported elevated depressive symptoms. In multivariable analyses, women with depressive symptoms had greater sleep disturbance (Pl0.0001) and lower global quality of life (Pl.0001). We found evidence of significant statistical interaction in the models for quality-of-life outcomes: the increased risk of poor physical health-related quality of life associated with antidepressant use was stronger in women without vs with depressive symptoms, and the association between greater optimism and higher global quality of life was stronger in women with vs without depressive symptoms. CONCLUSIONS:To improve health behaviours and quality of life in women with Type 2 diabetes, sociodemographic and medical characteristics may identify at-risk populations, while psychosocial factors including depression and optimism may be important targets for non-pharmacological intervention.