Feasibility, Acceptability, and Preliminary Efficacy of an App-Based Meditation Intervention to Decrease Firefighter Psychological Distress and Burnout: A One-Group Pilot Study.

BACKGROUND: Firefighters are often exposed to occupational stressors that can result in psychological distress (ie, anxiety and depression) and burnout. These occupational stressors have only intensified with the onset of the COVID-19 pandemic and will likely persist in the postpandemic world. OBJECTIVE: To address occupational stressors confronting firefighters, we pilot tested a novel, cost-effective, smartphone app-based meditation intervention created by Healthy Minds Innovations that focused on mindfulness (awareness) training along with practices designed to cultivate positive relationships (connection), insight into the nature of the self (insight), and a sense of purpose in the context of challenge (purpose) with a sample of professional firefighters from a large metropolitan area in southwestern United States. METHODS: A total of 35 participants were recruited from a closed online group listserv and completed the self-guided 10-unit meditation app over the course of 10 days, at 1 unit per day. We assessed anxiety symptoms, depression symptoms, burnout, and negative affect as well as saliva diurnal cortisol rhythm, an objective indicator of stress-related biology, before and after use of the meditation app. RESULTS: This study demonstrated the meditation app was both feasible and acceptable for use by the majority of firefighters. We also found significant reductions in firefighters' anxiety (P=.01), burnout (P=.05), and negative affect (P=.04), as well as changes in cortisol diurnal rhythm, such as waking cortisol (P=.02), from before to after use of the meditation app. CONCLUSIONS: Our study findings call for future research to demonstrate the efficacy of this meditation app to reduce psychological distress and burnout in firefighters.

Discrimination is associated with C-reactive protein among young sexual minority men.

This report examines associations between everyday discrimination, microaggressions, and CRP to gain insight on potential mechanisms that may underlie increased CVD risk among sexual minority male young adults. The sample consisted of 60 participants taken from the P18 cohort between the ages of 24 and 28 years. Multinomial logistic regression models were used to examine the association between perceived everyday discrimination and LGBQ microaggressions with C-reactive protein cardiovascular risk categories of low-, average-, and high-risk, as defined by the American Heart Association and Centers for Disease Control. Adjustments were made for BMI. Individuals who experienced more everyday discrimination had a higher risk of being classified in the high-risk CRP group compared to the low-risk CRP group (RRR = 3.35, p = 0.02). Interpersonal LGBQ microaggressions were not associated with CRP risk category. Everyday discrimination, but not specific microaggressions based on sexual orientation, were associated with elevated levels of CRP among young sexual minority men (YSMM). Thus, to implement culturally and age-appropriate interventions, further researcher is needed to critically examine the specific types of discrimination and the resultant impact on YSMM's health.

A Mindfulness-Based Intervention to Alleviate Stress From Discrimination Among Young Sexual and Gender Minorities of Color: Protocol for a Pilot Optimization Trial.

BACKGROUND: Young sexual and gender minorities (SGMs) of color may face unique experiences of discrimination based on their intersectional positions (eg, discrimination based on both racial or ethnic identity and sexual identity). Emerging evidence suggests that mindfulness practices may reduce stress from discrimination and improve overall well-being among young SGM. Moreover, the omnipresence of smartphone access among racial or ethnic and sexual minority communities provides a method through which to administer mindfulness-based interventions among young SGMs of color. OBJECTIVE: This paper outlines the protocol of the Optimizing a Daily Mindfulness Intervention to Reduce Stress from Discrimination among Young Sexual and Gender Minorities of Color (REDUCE) study, a pilot optimization trial of a smartphone-based mindfulness intervention that was developed in conjunction with the Healthy Minds Program (HMP) with the aim of reducing stress from discrimination among young SGMs. METHODS: In total, 80 young (ages 18-29 years) SGMs of color will be enrolled in the study. The HMP is a self-guided meditation practice, and participants will be randomized to either a control condition or an intervention that uses a neuroscience-based approach to mindfulness. We will use the multiphase optimization strategy to assess which combination of mindfulness interventions is the most effective at reducing stress from discrimination among young SGMs of color. A combination of mindfulness-based meditation intervention components will be examined, comprising mindfulness-based practices of awareness, connection, and purpose. Awareness refers to the practice of self-awareness, which reduces the mind's ability to be distracted and instead be present in the moment. Connection refers to the practice of connection with oneself and others and emphasizes on empathy and compassion with oneself and others. Purpose encourages goal-making in accordance with one's values and management of behavior in accordance with these goals. In addition, we will assess the feasibility and acceptability of the HMP application among young SGMs of color. RESULTS: The REDUCE study was approved by the Institutional Review Board of New York University, and recruitment and enrollment began in the winter of 2021. We expect to complete enrollment by the summer of 2022. The results will be disseminated via social media, journal articles, abstracts, or presentations, as well as to participants, who will be given the opportunity to provide feedback to the researchers. CONCLUSIONS: This optimization trial is designed to test the efficacy, feasibility, and acceptability of implementing an application-based, mindfulness-based intervention to reduce stress from discrimination and improve well-being among young SGMs of color. Evidence from this study will assist in the creation of a sustainable, culturally relevant mobile app-based mindfulness intervention to reduce stress from discrimination among young SGMs of color. TRIAL REGISTRATION: Clinicaltrials.gov NCT05131360; https://clinicaltrials.gov/ct2/show/NCT05131360. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35593.

Social Determinants in Machine Learning Cardiovascular Disease Prediction Models: A Systematic Review.

INTRODUCTION: Cardiovascular disease is the leading cause of death worldwide, and cardiovascular disease burden is increasing in low-resource settings and for lower socioeconomic groups. Machine learning algorithms are being developed rapidly and incorporated into clinical practice for cardiovascular disease prediction and treatment decisions. Significant opportunities for reducing death and disability from cardiovascular disease worldwide lie with accounting for the social determinants of cardiovascular outcomes. This study reviews how social determinants of health are being included in machine learning algorithms to inform best practices for the development of algorithms that account for social determinants. METHODS: A systematic review using 5 databases was conducted in 2020. English language articles from any location published from inception to April 10, 2020, which reported on the use of machine learning for cardiovascular disease prediction that incorporated social determinants of health, were included. RESULTS: Most studies that compared machine learning algorithms and regression showed increased performance of machine learning, and most studies that compared performance with or without social determinants of health showed increased performance with them. The most frequently included social determinants of health variables were gender, race/ethnicity, marital status, occupation, and income. Studies were largely from North America, Europe, and China, limiting the diversity of the included populations and variance in social determinants of health. DISCUSSION: Given their flexibility, machine learning approaches may provide an opportunity to incorporate the complex nature of social determinants of health. The limited variety of sources and data in the reviewed studies emphasize that there is an opportunity to include more social determinants of health variables, especially environmental ones, that are known to impact cardiovascular disease risk and that recording such data in electronic databases will enable their use.