# Prevalence and determinants of insomnia in patients with chronic heart failure: a cross-sectional survey in Jiaxing, China

## TL;DR

This study found that over two-thirds of patients with chronic heart failure in Jiaxing, China, suffer from insomnia, with factors like gender, mood, and income playing a role.

## Contribution

The study identifies specific sociodemographic and clinical factors associated with insomnia in CHF patients in a Chinese population.

## Key findings

- The prevalence of insomnia among 261 CHF patients was 66.7%.
- Factors like gender, mood, and income were significantly associated with insomnia in CHF patients.

## Abstract

To investigate the current status of insomnia in patients with chronic heart failure (CHF) and its influencing factors.

Cross-sectional study,261 CHF patients who were hospitalized in the cardiovascular department of a tertiary-level hospital in Jiaxing City from December 2023 to August 2024 were included in the cross-sectional study, and were investigated by using a general information questionnaire, the Pittsburgh Sleep Quality Index (PSQI). The influencing factors were analyzed by univariate analysis and logistic regression using SPSS 26.0 software.

The prevalence of insomnia was 66.7% (174 patients) among 261 CHF patients.Logistic regression analysis showed that gender (OR = 13.566, 95%CI: 3.479∼52.899), mood (OR = 4.616, 95%CI: 1.266∼16.823), NYHA cardiac function classification (OR = 6.514, 95%CI: 2.475∼0.40417.142), chronic pain (OR = 6.334, 95%CI: 1.070∼37.475), per capita monthly household income (OR = 8.078, 95%CI: 2.870∼22.741), socialization range (OR = 4.312, 95%CI: 2.404∼7.732), smoking (OR =  3.673, 95%CI: 1.590∼8.317), number of comorbidities (OR = 3.698, 95%CI: 1.586∼8.622), and healthcare payment method (OR = 2.605, 95%CI: 1.290∼5.259) were the influential factor of insomnia in CHF patients.

The prevalence of insomnia in CHF patients was high, and gender, mood, NYHA cardiac function class, chronic pain, per capita monthly household income, socialization range, smoking, number of comorbidities, and mode of healthcare payment were the influencing factors. It is important to note that cross-sectional study designs cannot establish causal relationships, but they can identify associated or coexisting risk factors.