Sleep disturbance is not created equal: latent class analysis in individuals who are alcohol dependent in recovery

Authors

• AT Brooks
• J Park
• M Krumlauf
• VA Ramchandani
• GR Wallen

Abstract

The relationship between alcohol consumption and sleep disturbances is complex and bi-directional. This study identified subgroups of individuals undergoing inpatient alcohol rehabilitation treatment in terms of sleep and psychosocial factors. Data were collected from a cohort of clinical research participants enrolled on an inpatient alcohol treatment protocol (n=164). Both objective (actigraphy) and subjective (Pittsburgh Sleep Quality Index-PSQI) measures of sleep quantity and quality were collected, along with measures of baseline anxiety and depression, withdrawal (Clinical Institute Withdrawal Assessment-CIWA), and post-traumatic stress disorder (PTSD) diagnoses. Latent class analysis was used to identify subgroups of individuals based on eight indicators: current PTSD, lifetime PTSD, withdrawal, wake after sleep onset (WASO), sleep efficiency, sleep disturbances, anxiety, and depression. Demographic characteristics of each subgroup were examined using analysis of variance (ANOVA) and chi-square tests. Three latent classes were identified: group 1 (27%) included individuals who are alcohol-dependent (AD) with sleep disruption; group 2 (52%) included AD individuals with sleep disruption and mood/anxiety disorders; and group 3 (21%) included AD individuals with PTSD, withdrawal (higher CIWA scores), sleep disruption, and mood/anxiety disorders. Among demographic factors examined, there was a statistically significant difference in gender across the three different classes (females were more likely to be in group 3). These results highlight the importance of examining homogenous subgroups of individuals undergoing alcohol rehabilitation treatment that may differ on potentially modifiable indices including withdrawal, sleep disturbance, PTSD, and mood/anxiety. If replicated, these identified classes could aid clinicians in categorizing newly-admitted patients for more focused treatment.

Scientific Focus Area: Social and Behavioral Sciences

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