Depression predicts chronic pain interference in racially-diverse, low-income patients [preprint]
Authors
Nephew, Benjamin C.Incollingo Rodriguez, Angela C.
Melican, Veronica
Polcari, Justin J.
Nippert, Kathryn E.
Rashkovskii, Mikhail
Linnell, Lilly-Beth
Hu, Ruofan
Ruiz, Carolina
King, Jean A.
Gardiner, Paula
UMass Chan Affiliations
Center for Integrated Primary CareDepartment of Family Medicine and Community Health
Document Type
PreprintPublication Date
2021-07-06Keywords
Pain Medicinedepression
chronic pain
machine learning
regression analyses
racial diversity
low income
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
Metadata
Show full item recordAbstract
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.Source
medRxiv 2021.06.17.21259108; doi: https://doi.org/10.1101/2021.06.17.21259108. Link to preprint on medRxiv.
DOI
10.1101/2021.06.17.21259108Permanent Link to this Item
http://hdl.handle.net/20.500.14038/29866Notes
This article is a preprint. Preprints are preliminary reports of work that have not been certified by peer review.
Related Resources
Now published in Pain Medicine, doi: https://doi.org/10.1093/pm/pnab342.
Rights
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.Distribution License
http://creativecommons.org/licenses/by-nc-nd/4.0/ae974a485f413a2113503eed53cd6c53
10.1101/2021.06.17.21259108
Scopus Count
Except where otherwise noted, this item's license is described as 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.