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
The growing prevalence of depression and suicidal ideation among college students further exacerbated by the Coronavirus pandemic is alarming, highlighting the need for universal mental illness screening technology. With traditional screening questionnaires too burdensome to achieve universal screening in this population, data collected through mobile applications has the potential to rapidly identify at-risk students. While prior research has mostly focused on collecting passive smartphone modalities from students, smartphone sensors are also capable of capturing active modalities. The general public has demonstrated more willingness to share active than passive modalities through an app, yet no such dataset of active mobile modalities for mental illness screening exists for students. Knowing which active modalities hold strong screening capabilities for student populations is critical for developing targeted mental illness screening technology. Thus, we deployed a mobile application to over 300 students during the COVID-19 pandemic to collect the Student Suicidal Ideation and Depression Detection (StudentSADD) dataset. We report on a rich variety of machine learning models including cutting-edge multimodal pretrained deep learning classifiers on active text and voice replies to screen for depression and suicidal ideation. This unique StudentSADD dataset is a valuable resource for the community for developing mobile mental illness screening tools.
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% and relatively higher rates in diverse populations. 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. The objective of the present study was to assess the effects of race on the primary pain outcomes and evaluate potential relationships between race and additional patient characteristics in data from a randomized clinical trial of IMGV in socially diverse, marginalized patients suffering from chronic pain and depression. It was hypothesized that there would be racial differences in the effects of IMGV on pain outcomes. Our analyses identified significant racial differences in the response to IMGV. Black subjects had increased pain severity throughout the duration of the 21-week study but were less likely to respond to the pain intervention compared to White subjects. These results may be related to differential comorbidity rates, catastrophizing, and digital health literacy among these participant groups. To improve patient outcomes in similar studies, interactions between pain outcomes and these factors require further investigation to affect levels and trajectory of pain severity and enhance the response to complimentary interventions.
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.
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