Preliminary trial of an online acceptance-based behavioural treatment for military, police, and Veterans with chronic pain
Abstract: Introduction: Chronic pain is a serious health issue in Canada but an even greater issue in military populations. Individuals experiencing chronic pain frequently find attending in-person treatment sessions difficult because of pain flare-ups, discomfort when travelling, and pain-related avoidance behaviours. These challenges function to maintain the pain cycle and prevent engagement in previously enjoyed activities. The purpose of this study was to gather preliminary evidence for the effectiveness of an online acceptance-based behavioural treatment of chronic pain designed specifically for military, police, and Veterans of these forces. Methods: In this preliminary trial, 15 participants engaged in an 8-week online treatment of chronic pain supplemented with optional biweekly group sessions. Participants completed pre- and post-treatment measures relating to key facets of the fear–avoidance model of chronic pain. Results: Participants' scores improved following treatment on measures of pain acceptance, kinesiophobia, and pain catastrophizing, and pain intensity ratings trended in the expected direction. Discussion: These preliminary results support the feasibility of our online acceptance-based treatment of chronic pain when combined with optional biweekly in-person group sessions.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.