Nutrition knowledge is associated with diet quality among US Army soldiers
Abstract: Objective: Examine the relationship between nutrition knowledge, diet quality, and eating behavior among active-duty US Army Soldiers. Methods: Cross-sectional study with data collection in February 2018 via paper surveys during the validation of the Military Eating Behavior Survey. Results: Among 440 US Army Soldiers, nutrition knowledge was positively and significantly associated with diet quality (b = 0.29, P < 0.001). For every 1-point increase in nutrition knowledge, the Healthy Eating Index-2015 score was expected to increase by 0.29 points. Nutrition knowledge was not significantly associated with skipping breakfast (odds ratio, 1.01; 95% confidence interval, 0.98–1.04) or dining out (odds ratio, 1.01; 95% confidence interval, 0.98–1.03). Conclusions and Implications: The outcomes of this study warrant further investigation to determine what interventions provide the strongest outcomes for improving nutrition knowledge and diet quality, as well as create and support an environment that enhances healthy behaviors regarding nutrition that lead to improved diet quality among active-duty Soldiers.
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.