Cognitive behavior therapy (CBT) is effective for most patients with a social anxiety disorder (SAD) but a substantial proportion fails to remit. Experimental and clinical research suggests that enhancing CBT using imagery-based techniques could improve outcomes. It was hypothesized that imagery-enhanced CBT (IE-CBT) would be superior to verbally-based CBT (VB-CBT) on pre-registered outcomes.

A randomized controlled trial of IE-CBT v. VB-CBT for social anxiety was completed in a community mental health clinic setting. Participants were randomized to IE (n = 53) or VB (n = 54) CBT, with 1-month (primary end point) and 6-month follow-up assessments. Participants completed 12, 2-hour, weekly sessions of IE-CBT or VB-CBT plus 1-month follow-up.

Intention to treat analyses showed very large within-treatment effect sizes on the social interaction anxiety at all time points (ds = 2.09–2.62), with no between-treatment differences on this outcome or clinician-rated severity [1-month OR = 1.45 (0.45, 4.62), p = 0.53; 6-month OR = 1.31 (0.42, 4.08), p = 0.65], SAD remission (1-month: IE = 61.04%, VB = 55.09%, p = 0.59); 6-month: IE = 58.73%, VB = 61.89%, p = 0.77), or secondary outcomes. Three adverse events were noted (substance abuse, n = 1 in IE-CBT; temporary increase in suicide risk, n = 1 in each condition, with one being withdrawn at 1-month follow-up).

Group IE-CBT and VB-CBT were safe and there were no significant differences in outcomes. Both treatments were associated with very large within-group effect sizes and the majority of patients remitted following treatment.

Self-harm is a significant public health issue, and both our understanding and ability to predict adverse outcomes are currently inadequate. The current study explores how preventative efforts could be aided through short-term prediction and modelling of risk factors for self-harm.

Patients (72% female, Mage = 40.3 years) within an inpatient psychiatric facility self-reported their psychological distress, interpersonal circumstances, and wish to live and die on a daily basis during 3690 unique admissions. Hierarchical logistic regressions assessed whether daily changes in self-report and history of self-harm could predict self-harm, with machine learning used to train and test the model. To assess interrelationships between predictors, network and cross-lagged panel models were performed.

Increases in a wish to die (β = 1.34) and psychological distress (β = 1.07) on a daily basis were associated with increased rates of self-harm, while a wish to die on the day prior [odds ratio (OR) 3.02] and a history of self-harm (OR 3.02) was also associated with self-harm. The model detected 77.7% of self-harm incidents (positive predictive value = 26.6%, specificity = 79.1%). Psychological distress, wish to live and die, and interpersonal factors were reciprocally related over the prior day.

Short-term fluctuations in self-reported mental health may provide an indication of when an individual is at-risk of self-harm. Routine monitoring may provide useful feedback to clinical staff to reduce risk of self-harm. Modifiable risk factors identified in the current study may be targeted during interventions to minimise risk of self-harm.