Promoting adherence to a yoga intervention for Veterans with chronic low back pain

Abstract: Background: Research demonstrates that yoga can be effective for improving chronic low back pain (cLBP) among military veterans and non-veterans. Attendance of yoga interventions is necessary to obtain benefits, yet yoga class attendance can be a challenge both within and outside of research, especially for persons who lack resources. Objective: Our objective was to describe efforts to boost attendance within a randomized trial of yoga for cLBP, and to examine factors related to attendance. Methods: A previous trial of yoga for cLBP among military veterans randomly assigned participants to 2x weekly yoga for 12 weeks, or delayed treatment. After the second of 6 intervention cohorts, efforts were made to improve participant attendance. Attendance and reasons for missing yoga sessions were tracked using sign-in logs and phone calls. Regression analysis was used to examine factors related to attendance. Results: After efforts to boost attendance, mean attendance increased from 10.2/24 sessions, (42% attending at least half of sessions), to 13.3/24 sessions, (df (1,74), t = -1.44; P = 0.15) (59% attending half of the sessions). The most common reasons for non-attendance were transportation, financial problems, other health issues, and work or school conflicts. Living status and back pain-related disability at baseline were significantly associated with attendance (P= < .001 and P = .038 respectively). When including all participants, yoga session attendance was significantly associated with reduced pain severity (P = 0.01). Conclusions: Efforts to boost attendance appeared meaningful but the changes were not statistically significant. Attendance rate in later cohorts were comparable to those in other studies. Reasons provided for non-attendance by participants, and the regression results suggest that resources such as transportation, a stable living situation, and disability levels at baseline were related to attendance rates for this in-person intervention. Remotely delivered yoga may address some of these barriers but hybrid interventions that bring in-person yoga closer to participants may be the best option.

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.