Prevalence, Clinical Correlates, Gender Differences, and Predictive Factors of Paradoxical Insomnia in Chinese Adults...

This study aimed to determine the prevalence of paradoxical insomnia (PI) in Chinese adults with chronic insomnia, compare clinical and sleep-related characteristics between PI and non-paradoxical insomnia (non-PI) groups, explore gender-specific differences, and identify factors associated with PI, with particular focus on anxiety and depression.

A total of 862 adults with chronic insomnia were involved. All participants completed 7-day sleep diaries and validated questionnaires, including the Hamilton Depression Rating Scale (HAMD; clinician-rated depression), Hamilton Anxiety Rating Scale (HAMA; clinician-rated anxiety), and Insomnia Severity Index (ISI; subjective insomnia severity). All participants underwent two consecutive nights of polysomnography (PSG), and biochemical testing. Group differences were tested using independent samples t-tests or chi-square tests. Subjective-objective sleep associations were examined using Pearson correlations. Multivariable logistic regression was conducted to identify factors associated with PI.

PI was present in 30.5% of participants (n = 263). Compared with non-PI, the PI group showed a higher proportion of females and exhibited greater depressive symptom severity on the HAMD and somatic anxiety on the HAMA, alongside lower perceived insomnia severity on the ISI (all p < 0.05). Gender-stratified analyses suggested distinct profiles, including higher perceived insomnia severity among females with PI and higher apnea-hypopnea index (AHI) among males with non-PI. In multivariable analyses, older age, male gender, heightened somatic symptoms (one subscale of HAMD), and lower subjective insomnia severity remained independently associated with PI (all p < 0.05).

Nearly one-third of Chinese adults with chronic insomnia meet the criteria for PI. Depression, particularly somatic symptoms, male gender, older age, and lower subjective insomnia severity are key predictors of PI. The findings highlight that incorporating objective sleep assessment and considering gender-specific clinical features may reduce diagnostic misclassification and facilitate more individualized management.