Impact of military culture and experiences on eating and weight-related behavior
Abstract: Disordered eating behaviors and obesity are becoming increasingly common among United States military personnel. However, little research has explored the role of the military environment as it may influence the development of disordered eating among personnel. The present qualitative analysis examined beliefs about how military experiences affected eating and weight-related behaviors. Military personnel who served within the last year and a year or more ago (n = 250) were recruited using Amazon's Mechanical Turk (mTurk). Data included in the present study consisted of participant responses to three open-ended questions, analyzed by means of content and thematic analysis. Analyses yielded eight themes: eating extremely quickly, strict mealtime regimens, the pressure to “make weight,” food insecurity, difficulty after military, food quality/content, overeating behavior, and military superior maltreatment. The current study provides a preliminary examination of the role of the military culture and experiences in the development of unhealthy eating and weight-related behaviors and offers suggestions for future research and interventions.
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.