Gender Differences of Sleeping Problems in Chinese Illicit Drug Dependent Subjects

Introduction: Illicit drug use/dependence has been recognized as a major problem. Clinical studies demonstrated that poor sleep quality was associated with increased frequency of drug use and relapse. However, few studies addressed the issue of sleep quality and gender differences in illicit drug dependent subjects.

Methods: This cross-sectional study explored sleep quality in those subjects in China. 1875 male and 303 female illicit drug users from drug rehabilitation centers in Changsha completed the self-report Pittsburgh Sleep Quality Index (PSQI). The mean age were 30.0 ±7.13 and 30.1±7.16, the mean duration (month) of drug use were 59.0 ± 51.73 and 62.3 ± 55.38 for male and female users.

Results: 67.4% male and 75.25% female drug users showed suggestive sleep disturbance. The mean of PSQI total score were 7.82 ±4.42 and 8.91 ±4.15 (p<0.001) for male and female users. The mean of 7 items for male and female were: sleep duration 1.4 ± 0.90 and 1.6 ± 0.84 (p<0.001), sleep efficiency 1.4 ±1.13 and 1.6 ±1.08 (p=0.003), sleep latency 0.82 ± 0.99 and 0.98 ±1.06 (p=0.007), sleep disturbance 0.64 ±1.03 and 0.80 ±1.14 (p=0.014), daytime dysfunction 1.10 ± 0.72 and 1.21 ± 0.79 (p=0.019), frequency of sleep medications 0.87 ±1.23 and 0.98 ±1.30 (p=0.134), subjective sleep quality 1.61 ±1.10 and 1.73 ±1.14 (p=0.091). The mean sleep duration (minutes) were 448.9 ±126.68 and 428 ±137.56 for male and female users.

Conclusions: Poor sleep quality is common among illicit drug dependent subjects. In this study, female illicit drug users reported higher PSQI total score, showed higher suggestive sleep disturbance and shorter sleep duration than that in male users. Future researches aim at quantifying the benefits of treatment interventions should not neglect the influence of sleeping problems and its gender differences. Gaining more insight into the impact of gender differences of sleep quality on the addiction treatment could also help to target future intervention measures more effectively for female users.