A phenomenological inquiry into the costs and consequences of insomnia for Veterans with serious mental illness

Abstract: Many individuals with serious mental illness (i.e. schizophrenia spectrum, bipolar or major depressive disorders, with serious functional impairments) have insomnia symptoms. Insomnia is a common reason for mental health referrals in the Veterans Health Administration. The primary aim of this study was to explore the costs (what participants lose or what trade-offs they make due to insomnia) and consequences (how insomnia impacts functioning) of insomnia for veterans with serious mental illness. Semi-structured interviews of 20 veterans with insomnia and serious mental illness were collected as data using an inductive phenomenological approach. Two main themes were identified: Sleep Affects Mental Health and Functioning; and Compromising to Cope. Results illuminate pathways by which sleep effort destabilizes functional recovery, and illustrate how sleep has multiplicative positive impacts on functioning and mood. Researchers and clinicians alike must explore supporting people with serious mental illness in replacing sleep effort with the recovery of meaningful identity-driven, values-based experiences formerly conceded due to serious mental illness, insomnia or both.

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.

    Mental Health Peer reviewed