Feasibility of delivering a virtual 1-day acceptance and commitment therapy workshop to rural veterans through community partnerships
Abstract: This single-arm, open pilot study examined the feasibility and initial efficacy of a 1-day virtual Acceptance and Commitment Therapy (ACT) group workshop for distressed veterans. We collaborated with veteran-serving community-based organizations to enhance outreach to veterans, especially those in rural areas. Veterans completed a baseline assessment and two follow-up assessments (1 month, 3 months) after workshop participation. Feasibility outcomes included reach (workshop recruitment and completion rates; veteran characteristics) and acceptability (open-ended survey question about satisfaction). Clinical outcomes included psychological distress (Outcome Questionnaire-45), stressor-related distress (PTSD Checklist-5), community reintegration (Military to Civilian Questionnaire), and meaning and purpose (PROMIS Short Form). Psychological flexibility (Action and Acceptance Questionnaire-II) – the proposed change mechanism underlying ACT – was also measured. Sixty-four veterans (50% rural, 39% self-identified as female) participated in a virtual workshop (97.1% completion rate). Overall, veterans liked the format and interactive nature of workshops. Convenience was noted as a benefit, while connectivity issues were highlighted as a drawback. Veterans showed improvements in psychological distress (F(2,109) = 3.30; p = 0.041), stressor-related distress (F(2,110) = 9.50; p = 0.0002), community reintegration (F(2,108) = 4.34; p = 0.015), and meaning and purpose (F(2,100) = 4.06; p = 0.020) over time. No between-group differences were detected, based on rurality or gender. Pilot findings were promising and warrant a larger randomized trial to assess the efficacy of the 1-day virtual ACT workshop. Integrating community-engaged and participatory-research designs can enhance the external validity of these future studies and promote greater health equity.
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.