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. American Academy of Pain Medicine.

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.

BACKGROUND: High added sugar consumption has been associated with the development of metabolic syndrome (MetS). The American Heart Association (AHA) diet is designed to prevent and treat MetS; however, it remains unclear whether the AHA diet is effective on decreasing added sugar consumption. The aim of our study was to evaluate the effect of the AHA dietary counselling on added sugar consumption among participants with MetS. METHODS: The AHA dietary counselling was conducted among 119 participants with MetS from June 2009 to January 2014 (ClinicalTrials.gov: NCT00911885). Unannounced 24-hour recalls were collected at baseline, 3, 6 and 12 months. Added sugar consumption patterns over time were examined using linear mixed models. RESULTS: After 1-year dietary counselling, intake of added sugars decreased by 23.8 g/day (95% CI 15.1, 32.4 g/day); intake of nonalcoholic beverages dropped from the leading contributor of added sugar intake to number 7 (from 11.9 to 4.4%); the Alternative Healthy Eating Index (AHEI) score increased by 5.4 (95% CI 2.9, 8.0); however, added sugar intake for 48% participants still exceeded the recommendation. Added sugar intake per meal among different meal type was similar (24.2-25.8%) at baseline. After the 1-year dietary counselling, breakfast became the major resource of added sugar intake (33.3%); the proportion of added sugar intake from snacks decreased from 25.8% (CI 23.1, 28.5%) to 20.9% (CI 19.6, 22.3%). CONCLUSION: Although the consumption of added sugars in participants with MetS decreased after the 1-year AHA dietary counselling, added sugar intake from majority of participants still exceeds recommended limits. Actions of successful public health strategies that focus on reducing added sugar intake are needed.

Obesity is one of the top health issues around the globe. Rapid adoption of smartphones presents an opportunity for delivering technology-based interventions that are designed to tackle behaviors that contribute to weight gain. Research shows that the vast majority of weight loss apps in the market place do not go beyond deploying tracking based strategies that are burdensome to the users. In this study, we present a new mobile app and an intervention system called SlipBuddy that puts less burden on users and implements stimulus control strategy to help users lose weight. We describe the SlipBuddy system in detail and present the results of the first phase of a pilot study. Our findings indicate that a mobile app that simply helps users identify and track overeating episodes can potentially result in weight loss.