Statistical analysis Data were presented as the mean ± standard deviation for normally distributed variables, or median values (P25, P75) for non-normally distributed variables. Student's t-tests or nonparametric Mann-Whitney tests were performed to compare the differences between normally distributed variables or non-normally distributed variables. For the analysis of biochemical test results, data were transferred to normal distribution and Hotelling's T2 tests were performed. Before entering variables into the regression model, centering predictor variables were performed to avoid nonessential collinearity. Binary logistic regression analysis was conducted to identify the association between variables or variables interaction and insomnia diagnosis after infarction. Multiple linear regression analysis was carried out to determine the correlation between variables or variables interaction and sleep-related scores, such as Epworth Sleepiness Scale scores, Pittsburgh Sleep Quality Index scores, Insomnia Severity Index scores, Morningness-Eveningness Questionnaire (Chinese version) scores and Fatigue Severity Scale scores by using backward method. P values < 0.05 were considered statistically significant. SPSS 22.0 software was used for statistical analysis.