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Sleep disturbances, glycaemic control, stress, and coping among diabetic patients: A structural equation modelling approach.
Applied Nursing Research : ANR 2023 April
BACKGROUND: Sleep disturbances are more prevalent in diabetic patients than in the general population and may consequently be comorbid with hyperglycaemia.
OBJECTIVE: The two study aims were to (1) verify the factors associated with sleep disturbances and glycaemic control and (2) further understand the mediation effects of coping and social support in the relationship among stress, sleep disturbances, and glycaemic control.
METHODS: A cross-sectional study design was used. Data were collected at two metabolic clinics in southern Taiwan. The study recruited 210 patients with type II diabetes mellitus who were aged 20 years or above. Demographic information and data on stress, coping, social support, sleep disturbances, and glycaemic control were collected. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality, and PSQI scores >5 were considered to indicate sleep disturbances. Structural equation modelling (SEM) approaches were employed to analyse the path association for sleep disturbances in diabetic patients.
RESULTS: The mean age of the 210 participants was 61.43 (standard deviation, SD 11.41) years old, and 71.9 % reported sleep disturbances. The final path model had acceptable model fit indices. Stress perception was divided into stress perceived positively and negatively. Stress perceived positively was associated with coping (β = 0.46, p < .01) and social support (β = 0.31, p < .01), whereas stress perceived negatively was significantly associated with sleep disturbances (β = 0.40, p < .001).
CONCLUSIONS: The study shows that sleep quality is essential to glycaemic control, and stress perceived negatively might play a critical role to sleep quality.
OBJECTIVE: The two study aims were to (1) verify the factors associated with sleep disturbances and glycaemic control and (2) further understand the mediation effects of coping and social support in the relationship among stress, sleep disturbances, and glycaemic control.
METHODS: A cross-sectional study design was used. Data were collected at two metabolic clinics in southern Taiwan. The study recruited 210 patients with type II diabetes mellitus who were aged 20 years or above. Demographic information and data on stress, coping, social support, sleep disturbances, and glycaemic control were collected. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality, and PSQI scores >5 were considered to indicate sleep disturbances. Structural equation modelling (SEM) approaches were employed to analyse the path association for sleep disturbances in diabetic patients.
RESULTS: The mean age of the 210 participants was 61.43 (standard deviation, SD 11.41) years old, and 71.9 % reported sleep disturbances. The final path model had acceptable model fit indices. Stress perception was divided into stress perceived positively and negatively. Stress perceived positively was associated with coping (β = 0.46, p < .01) and social support (β = 0.31, p < .01), whereas stress perceived negatively was significantly associated with sleep disturbances (β = 0.40, p < .001).
CONCLUSIONS: The study shows that sleep quality is essential to glycaemic control, and stress perceived negatively might play a critical role to sleep quality.
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