Factors associated with life satisfaction among Veterans enrolled in the Healthy Aging Project-Brain (HAP-B) psychoeducational class

Abstract: Healthy Aging Project-Brain (HAP-B) is a novel clinical psychoeducation offering developed to encourage engagement in activities associated with successful aging. HAP-B targets sleep, socialization, physical, and cognitive activity through myth-busting, developing SMART goals, and tracking behavioral change. Study aims: (1) assess feasibility/acceptability in a Veteran population; (2) analyze pre- and post-intervention ratings to examine health and well-being; (3) explore associations between health factors and life satisfaction. The 50 participants (mean age = 70.6 years) were predominantly male-identity (88%) and White (76%). Findings, based on independent t tests, revealed improvements in life satisfaction, emotional well-being, and energy levels post-intervention. Linear regression results found higher life satisfaction was associated with lower depressive symptoms, higher emotional well-being, and higher self-efficacy. This easily implementable education intervention can result in more positive self-appraisal with encouraging downstream effects. Healthcare providers are well-positioned to utilize classes such as HAP-B to promote patient-centric approaches to brain health.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    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.