Correlation of sleep quality with anxiety and depression in elderly patients with functional dyspepsia in a tertiary hospital

Abstract

Background: Functional dyspepsia (FD) is a common disease affecting the sleep quality of elderly patients, and patients with long-term sleep disorders are accompanied by different degrees of anxiety and depression. Aim: To observe the advantages of sleep quality and anxiety and depression in elderly patients with FD in a tertiary hospital, and to explore the importance of sleep quality factors in predicting anxiety and depression. Methods: One hundred and twelve elderly patients with FD who were hospitalized at The Second Hospital of Jinhua, Zhejiang Province from September 2017 to September 2019 were selected as research subjects. Pittsburgh sleep quality index (PSQI) scale and anxiety/depression selfrating scale (SAS/SDS) were used for investigation. Results: In elderly patients with FD, the scores of sleep quality, sleep time, sleep efficiency, use of hypnotic drugs, and PSQI and SAS scores were all significantly lower in women than in men (P < 0.05), and the score of each dimension in the overall population was significantly higher than the domestic norm (P < 0.05). Through regression analysis, it was found that the prediction performance of daytime function, sleep quality, sleep time, and sleep efficiency for anxiety was higher (P < 0.05), and sleep efficiency and daytime function performed significantly better in predicting depression (P < 0.05). The regression analysis suggested that X1 and X2 accounted for 61.54% and 38.46% of the predicted variance, respectively. After stratification and gradual regression of sleep quality and anxiety and depression, four variables (X1, X2, X3, and X4) were entered into the regression equation. X1, X2, X3, and X4 accounted for 38.26%, 22.86%, 17.63%, and 20.85% of the predicted variance. Conclusion: Among all factors of sleep quality, daytime function, sleep quality, sleep efficiency, and sleep time can predict anxiety, while sleep efficiency and daytime function can predict depression. Therefore, targeted treatment and nursing intervention should be adopted in clinical work according to each factor of sleep quality.