Sleep quality and treatment of social anxiety disorder

Background and Objectives: Poor sleep is prevalent among individuals with social anxiety disorder (SAD) and may affect treatment outcome. We examined whether: (1) individuals with SAD differed from healthy controls (HCs) in sleep quality, (2) baseline sleep quality moderated the effects of treatment (Cognitive–behavioral group therapy [CBGT] vs. mindfulness-based stress reduction [MBSR] vs. waitlist [WL]) on social anxiety, (3) sleep quality changed over treatment, and (4) changes in sleep quality predicted anxiety 12-months post-treatment.

Design: Participants were 108 adults with SAD from a randomized controlled trial of CBGT vs. MBSR vs. WL and 38 HCs.

Methods: SAD and sleep quality were assessed pre-treatment and post-treatment; SAD was assessed again 12-months post-treatment.

Results: Participants with SAD reported poorer sleep quality than HCs. The effect of treatment condition on post-treatment social anxiety did not differ as a function of baseline sleep quality. Sleep quality improved in MBSR, significantly more than WL, but not CBGT. Sleep quality change from pre- to post-treatment in CBGT or MBSR did not predict later social anxiety.

Conclusions: MBSR, and not CBGT, improved sleep quality among participants. Other results were inconsistent with prior research; possible explanations, limitations, and implications for future research are discussed. ClinicalTrials.gov identifier: NCT02036658.     

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects’ words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.     

Self-dehumanisation in severe alcohol use disorder: Links with self-stigma and environmental satisfaction

Metadehumanisation (i.e., the perception of being considered as less than human by others) is proposed to be widespread in stigmatised populations, such as people with severe alcohol use disorder (SAUD). However, the relations between metadehumanisation, self-dehumanisation (i.e., the self-perception of being less than human), and stigmatisation (i.e., the negative taint applied to some groups) remain unexplored. The aim of this research is thus to investigate the relations between these processes. Metadehumanisation, self-dehumanisation, self-stigma (and its subdimensions) and environmental satisfaction were assessed in 120 inpatients with SAUD and analysed in a mediational model. Stigma awareness was positively associated with metadehumanisation, whereas environmental satisfaction was negatively associated with metadehumanisation. Stigma's application to the self was associated with increased self-dehumanisation. Self-stigma and self-dehumanisation are closely intertwined phenomena. Self-dehumanisation seems to follow a multi-step process suggesting that some steps, such as dehumanisation awareness, are missing from current models of dehumanisation.