Depression predicts chronic pain interference in racially-diverse, low-income patients [preprint]
| dc.contributor.author | Nephew, Benjamin C. | |
| dc.contributor.author | Incollingo Rodriguez, Angela C. | |
| dc.contributor.author | Melican, Veronica | |
| dc.contributor.author | Polcari, Justin J. | |
| dc.contributor.author | Nippert, Kathryn E. | |
| dc.contributor.author | Rashkovskii, Mikhail | |
| dc.contributor.author | Linnell, Lilly-Beth | |
| dc.contributor.author | Hu, Ruofan | |
| dc.contributor.author | Ruiz, Carolina | |
| dc.contributor.author | King, Jean A. | |
| dc.contributor.author | Gardiner, Paula | |
| dc.date | 2022-08-11T08:08:28.000 | |
| dc.date.accessioned | 2022-08-23T15:55:57Z | |
| dc.date.available | 2022-08-23T15:55:57Z | |
| dc.date.issued | 2021-07-06 | |
| dc.date.submitted | 2021-08-18 | |
| dc.identifier.citation | <p>medRxiv 2021.06.17.21259108; doi: https://doi.org/10.1101/2021.06.17.21259108. <a href="https://doi.org/10.1101/2021.06.17.21259108" target="_blank" title="view preprint on medRxiv">Link to preprint on medRxiv.</a></p> | |
| dc.identifier.doi | 10.1101/2021.06.17.21259108 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14038/29866 | |
| dc.description | <p>This article is a preprint. Preprints are preliminary reports of work that have not been certified by peer review.</p> | |
| dc.description.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. | |
| dc.language.iso | en_US | |
| dc.relation | <p>Now published in Pain Medicine, doi: https://doi.org/10.1093/pm/pnab342.</p> | |
| dc.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. | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Pain Medicine | |
| dc.subject | depression | |
| dc.subject | chronic pain | |
| dc.subject | machine learning | |
| dc.subject | regression analyses | |
| dc.subject | racial diversity | |
| dc.subject | low income | |
| dc.subject | Artificial Intelligence and Robotics | |
| dc.subject | Epidemiology | |
| dc.subject | Health Psychology | |
| dc.subject | Integrative Medicine | |
| dc.subject | Movement and Mind-Body Therapies | |
| dc.subject | Pain Management | |
| dc.subject | Pathological Conditions, Signs and Symptoms | |
| dc.subject | Psychiatry and Psychology | |
| dc.subject | Race and Ethnicity | |
| dc.title | Depression predicts chronic pain interference in racially-diverse, low-income patients [preprint] | |
| dc.type | Preprint | |
| dc.source.journaltitle | medRxiv | |
| dc.identifier.legacyfulltext | https://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=3092&context=faculty_pubs&unstamped=1 | |
| dc.identifier.legacycoverpage | https://escholarship.umassmed.edu/faculty_pubs/2073 | |
| dc.identifier.contextkey | 24381678 | |
| refterms.dateFOA | 2022-08-23T15:55:57Z | |
| html.description.abstract | <p><p id="x-x-x-x-x-x-x-p-5"><strong>Background</strong> 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. <p id="x-x-x-x-x-x-x-p-6"><strong>Objective and Design</strong> 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. <p id="x-x-x-x-x-x-x-p-7"><strong>Methods</strong> 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. <p id="x-x-x-x-x-x-x-p-8"><strong>Results</strong> 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. <p id="x-x-x-x-x-x-x-p-9"><strong>Conclusions</strong> 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.</p> | |
| dc.identifier.submissionpath | faculty_pubs/2073 | |
| dc.contributor.department | Center for Integrated Primary Care | |
| dc.contributor.department | Department of Family Medicine and Community Health |

