Low resilience is associated with worse health-related quality of life in caregivers of service members and Veterans with traumatic brain injury: A longitudinal study

Abstract: Purpose: To examine [a] the association of caregiver health-related quality of life (HRQOL) and service member/veteran (SMV) neurobehavioral outcomes with caregiver resilience; [b] longitudinal change in resilience at the group and individual level; and [c] the magnitude of change at the individual level. Methods: Caregivers (N = 232) of SMVs with traumatic brain injury completed a resilience measure, and 18 caregiver HRQOL and SMV neurobehavioral outcome measures at a baseline evaluation and follow-up evaluation three years later. Caregivers were divided into two resilience groups at baseline and follow-up: [1] Low Resilience (<= 45 T, baseline n = 99, follow-up n = 93) and [2] High Resilience (> 45 T, baseline n = 133, follow-up n = 139). Results: At baseline and follow-up, significant effects were found between Low and High Resilience groups for the majority of outcome measures. There were no significant differences in resilience from baseline to follow-up at the group-mean level. At the individual level, caregivers were classified into four longitudinal resilience groups: [1] Persistently Low Resilience (Baseline + Follow-up = Low Resilience, n = 60), [2] Reduced Resilience (Baseline = High Resilience + Follow-up = Low Resilience, n = 33), [3] Improved Resilience (Baseline = Low Resilience + Follow-up = High Resilience, n = 39), and [4] Persistently High Resilience (Baseline + Follow-up = High Resilience, n = 100). From baseline to follow-up, approximately a third of the Reduced and Improved Resilience groups reported a meaningful change in resilience (>= 10 T). Nearly all of the Persistently High and Persistently Low Resilience groups did not report meaningful change in resilience (< 10 T). Conclusion: Resilience was not a fixed state for all caregivers. Early intervention may stall the negative caregiving stress-health trajectory and improve caregiver resilience.

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.