Investigating the experience of individuals with comorbid posttraumatic stress disorder and substance misuse attending a Seeking Safety group

Purpose There is no specific recommended treatment for the co-morbid presentation of post-traumatic stress disorder (PTSD) and substance use disorder in the United Kingdom (UK). Seeking Safety (SS), a group-based treatment that targets symptoms of both disorder, has emerging evidence in the USA but lacks evidence from UK-based samples. The purpose of this study was to explore UK service users’ experience of attending SS and evaluate its impact on mental health symptomology and substance misuse. Design/methodology/approach A mixed method approach was used to evaluate the acceptability of SS for a small sample (n = 7) of adult users of a substance misuse service in the UK. Thematic analysis was used to explore their experiences, derived from individual semi-structured interviews. The authors also calculated the number of participants who achieved reliable and/or clinically significant change in mental health symptomology and substance misuse from data routinely collected by the service. Findings Seven overarching themes emerged: strengthening the foundations of the self, the evocation and management of emotions, safety and validation provided relationally, readiness and commitment, content and delivery, Seeking Safety is Not an Island and ending. Most participants with data available both before and after the group made reliable (three out of four) and clinically significant (two out of three) change for depression and anxiety symptomology; however, this was less evident for PTSD symptomology with two out of three making reliable change and one out of three making clinically significant change. Originality/value To the best of the authors’ knowledge, this was the first study exploring the experiences of UK attendees of a SS group as an approach to treating comorbid PTSD and substance misuse.

Predictive Modeling Approach to Evaluate Individual Response to a Physical Activity Digital Intervention for Subjects with Major Depressive Disorder

Worldwide, there is roughly one mental health care provider for every 400 people with major depressive disorder (MDD). Without including other disorders, it would be impossible for everyone suffering from MDD to get clinical assistance. One step towards closing this gap may be the development of digital interventions. These can be delivered via smartphone, personal computer or tablet and require a significantly decreased time commitment from a provider. Given these benefits, there has been an increasing number of new digital interventions being studied with varying results. This presents a need for evidence-based processes that select the right treatment for a given person. One digital intervention that has been widely studied is a physical activity intervention where subjects are encouraged, via the internet, to become more active as a method of reducing depressive symptoms. The goal of the present study was to evaluate whether baseline characteristics could be leveraged to determine whether individuals would be likely to respond to this form of digital intervention. Machine learning models were trained to predict all individuals’ changes in Beck Depression Inventory-II (BDI-II) score and whether or not an individual had clinically significant change in depression. The correlation between predicted values and true values for change in BDI-II was r = 0.399 and the AUC for predicting clinically significant change was 0.75. Important predictors included marital status, gender, and pre-intervention anxiety and depression severity. These models may facilitate precision medicine in the digital era by enabling personalized treatment planning of digital interventions.

Self-Reported Change in Antisocial Attitudes and Reoffending Among a Sample of 2,337 Males Convicted of Violent Offenses

Antisocial attitudes are among the strongest predictors of reoffending; however, there is little evidence to show that treatment-induced changes in antisocial attitudes correspond to changes in individuals’ risk of recidivism. This study examined relationships between within-treatment change in antisocial attitudes derived from the Measures of Criminal Attitudes and Associates (MCAA) and reoffending among a large sample of males convicted of violent offenses ( N = 2,337). Residual change scores (RCS) and categories of clinically significant change (CSC) were used as indices of within-treatment change. A number of MCAA factor scores significantly predicted general and violent reoffending when assessed before and after treatment. RCS calculations of within-treatment change on the Violence and Antisocial Intent factors were also significantly associated with general reoffending outcomes. There was no evidence that within-treatment change on any measure had predictive validity for violent reoffending.

A Theoretically Derived Minimal Clinically Important Difference for Clinical Trials in Patients with Disorders of Consciousness

Assessing the minimal clinical change that a patient would perceive as beneficial is impossible in unconscious and non-communicative patients. We propose a novel probability-based approach to developing a minimal clinically significant change for the two most commonly used scales in patients with disorders of consciousness.

Heart Rate Variability Analysis: How Much Artifact Can We Remove?

Objective Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology.Methods This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject’s HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered.Results Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed.Conclusion Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.