Department of Family Medicine and Community Health; Center for Integrated Primary Care
Artificial Intelligence and Robotics | Epidemiology | Health Psychology | Integrative Medicine | Movement and Mind-Body Therapies | Pain Management | Pathological Conditions, Signs and Symptoms | Psychiatry and Psychology | Race and Ethnicity
Background Chronic pain is one of the most common reasons adults seek medical care in the US, with estimates of prevalence 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 socially diverse, low income patients suffering from chronic pain and depression.
Methods This 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 racially diverse, low income populations, demographics often neglected in studies of chronic pain.
Pain Medicine, depression, chronic pain, machine learning, regression analyses, racial diversity, low income
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The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
DOI of Published Version
medRxiv 2021.06.17.21259108; doi: https://doi.org/10.1101/2021.06.17.21259108. Link to preprint on medRxiv.
Now published in Pain Medicine, doi: https://doi.org/10.1093/pm/pnab342.
Nephew BC, Incollingo Rodriguez AC, Melican V, Polcari JJ, Nippert KE, Rashkovskii M, Linnell L, Hu R, Ruiz C, King JA, Gardiner P. (2021). Depression predicts chronic pain interference in racially-diverse, low-income patients [preprint]. UMass Chan Medical School Faculty Publications. https://doi.org/10.1101/2021.06.17.21259108. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/2073
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Artificial Intelligence and Robotics Commons, Epidemiology Commons, Health Psychology Commons, Integrative Medicine Commons, Movement and Mind-Body Therapies Commons, Pain Management Commons, Pathological Conditions, Signs and Symptoms Commons, Psychiatry and Psychology Commons, Race and Ethnicity Commons