Eating and Body Attitudes Related to Noncompetitive Bodybuilding in Military and General Hungarian Male Student Populations
Abstract: Pathological eating attitudes and extreme weight control practices occur most commonly in certain female populations. In some young male occupation groups, such as in the armed forces, the appearance of physical strength and muscularity has particular importance. We studied body and eating attitudes and the prevalence of bodybuilding and steroid abuse in 480 military college and 752 general college male students. The Eating Disorder Inventory was used for all subjects. General college students had higher body mass index values than did military students. The prevalence of bodybuilding and steroid abuse was significantly greater in the military population. Comparisons between the study groups and within groups showed significantly different scores on certain Eating Disorder Inventory subscales. The study revealed that male military college students have some protective factors against the psychopathological features of eating disorders.
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.