BACKGROUND: Chronic pain is one of the most common reasons adults seek medical care in the US, with prevalence estimates ranging from 11% to 40%. Mindfulness meditation has been associated with significant improvements in pain, depression, physical and mental health, sleep, and overall quality of life. Group medical visits are increasingly common and are effective at treating myriad illnesses, including chronic pain. Integrative Medical Group Visits (IMGV) combine mindfulness techniques, evidence based integrative medicine, and medical group visits and can be used as adjuncts to medications, particularly in diverse underserved populations with limited access to non-pharmacological therapies. OBJECTIVE AND DESIGN: The objective of the present study was to use a blended analytical approach of machine learning and regression analyses to evaluate the potential relationship between depression and chronic pain in data from a randomized clinical trial of IMGV in diverse, income disadvantaged patients suffering from chronic pain and depression. METHODS: The analytical approach used machine learning to assess the predictive relationship between depression and pain and identify and select key mediators, which were then assessed with regression analyses. It was hypothesized that depression would predict the pain outcomes of average pain, pain severity, and pain interference. RESULTS: Our analyses identified and characterized a predictive relationship between depression and chronic pain interference. This prediction was mediated by high perceived stress, low pain self-efficacy, and poor sleep quality, potential targets for attenuating the adverse effects of depression on functional outcomes. CONCLUSIONS: In the context of the associated clinical trial and similar interventions, these insights may inform future treatment optimization, targeting, and application efforts in racialized, income disadvantaged populations, demographics often neglected in studies of chronic pain. TRIAL REGISTRATION: NCT from clinicaltrials.gov: 02262377. American Academy of Pain Medicine.

Objectives: Nonadherence to medications has been documented, but the combined effect of social risk factors on medication nonadherence has not been investigated. Methods: We conducted a cross-sectional analysis of data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a population-based prospective cohort. The sample (N = 1506) included subjects who at Year 20 (2005-06) were taking prescription medications and completed a 4-item Medication Adherence Scale. Social risk factors were education of high school or less, annual household income < $25,000, high financial strain, high chronic stress, low social support, and high social strain. Results: In a fully adjusted logistic regression model, income < $25,000 (OR = 2.37 [95% CI 1.12-4.98], p < .05) and high chronic stress (OR = 2.07 [95% CI 1.09-3.94], p < .05) were significantly associated with medication nonadherence. Individuals with > /=3 social risk factors had > 3 times higher odds of nonadherence than counterparts with no social risk factors (OR = 3.26 [95% CI 1.72-6.19], p < .001). Conclusion: Low income and chronic stress are associated with medication nonadherence, and the odds of nonadherence increase with the accumulation of social risk factors. Findings may be used to develop risk prediction tools to identify individuals who can benefit from adherence-promoting interventions.