University of Massachusetts Medical School Faculty Publications

UMMS Affiliation

Department of Family Medicine and Community Health; Center for Integrated Primary Care

Publication Date

2021-07-06

Document Type

Article Preprint

Disciplines

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

Abstract

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.

Keywords

Pain Medicine, depression, chronic pain, machine learning, regression analyses, racial diversity, low income

Rights and Permissions

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

10.1101/2021.06.17.21259108

Source

medRxiv 2021.06.17.21259108; doi: https://doi.org/10.1101/2021.06.17.21259108. Link to preprint on medRxiv.

Comments

This article is a preprint. Preprints are preliminary reports of work that have not been certified by peer review.

Journal/Book/Conference Title

medRxiv

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Share

COinS