Tai Chi and wellness interventions for Veterans with Gulf War illness: A randomized controlled feasibility trial

Abstract: Background: Gulf War Illness (GWI) is a chronic multi-symptom illness that affects up to one-third of the 700,000 American military personnel deployed to the Persian Gulf region in 1990 and 1991. We conducted a randomized controlled trial to examine feasibility and the relative efficacy of two 12-week in-person group treatments (Tai Chi and Wellness) to address GWI symptoms of chronic pain, fatigue, and changes in mood and cognitive functioning. Method: Male and female veterans were randomly assigned to Tai Chi (n = 27) or Wellness (n = 26) group interventions and assessed at four time points: baseline, post-treatment, 3-, and 9-month follow-up. Multilevel models with a treatment-by-time interaction term were utilized to evaluate treatment effects and changes in GWI-related outcomes over time. Results: Satisfaction was high, there were no adverse events, and over half the participants attended 75% or more sessions with no significant differences between groups. For pain interference, analyses revealed a significant quadratic effect of time with no differences between treatment groups. For general fatigue and a cognitive test of trail making, no significant effects were detected. For depressed mood, linear and quadratic time effects and the group x linear time interaction were significant indicating greater reductions for Tai Chi participants. For a verbal learning test, linear and quadratic time and the group x quadratic time interaction significantly predicted total recall with Tai Chi participants demonstrating more rapid initial improvements. Conclusion: Findings indicate that both Tai Chi and Wellness are feasible and acceptable. Both interventions may have a salutary impact on pain interference, depression, and verbal learning with some advantages for Tai Chi.

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.