Sleep disturbances and PTSD: identifying baseline predictors of insomnia response in an intensive treatment programme

Objective: This study examined whether baseline demographic and clinical variables could predict clinically significant reductions in insomnia symptoms among veterans receiving a 2-week Cognitive Processing Therapy (CPT)-based intensive PTSD treatment programme (ITP). A key aim was to identify individuals likely to benefit from additional, sleep-focused interventions.Method: A total of 449 veterans completed the Insomnia Severity Index (ISI) at baseline, post-treatment, and at 1- and 3-month follow-up. Linear mixed models were used to analyse insomnia trajectories and clinical predictors (e.g. PTSD severity, depression, posttrauma cognitions, neurobehavioral symptoms). Machine learning models (neural net, random forest, elastic net, and ensemble) were trained to classify participants with clinically meaningful insomnia improvements.Results: Veterans reported large average PTSD severity reductions (d = 0.96), whereas depression and insomnia symptoms reduced moderately (d = 0.57) and modestly (d = 0.34), respectively. Higher PTSD severity, depression severity, negative posttrauma cognitions, and neurobehavioral symptoms were linked to poorer insomnia outcomes. None of the demographic or military service factors significantly predicted insomnia trajectories. Machine learning models performed only slightly better than chance (AUC range = 0.52-0.62) in predicting insomnia severity reductions.Conclusions: Although insomnia symptoms improved during the ITP and improvements persisted up to 3-month follow-up, a substantial number of veterans continued to experience significant sleep problems post-treatment. Given the limited predictive accuracy of machine learning models using self-reported variables, incorporating additional biological or psychosocial factors may be necessary to identify veterans with PTSD who need more specialised sleep interventions.