Tailoring Mobile Health Technology to Reduce Obesity and Improve Cardiovascular Health in Resource-Limited Neighborhood Environments

Overview

Background: Heart disease is a leading cause of death. People can reduce their heart disease risk by exercising more. Mobile health technology may make people more successful at increasing their exercise. This includes things like physical activity monitors and smartphone apps. Objective: To find out if mobile health technology can increase physical activity. Eligibility: African American women ages 21-75 who: * Are overweight or obese * Live in certain areas near Washington, DC * Have a smartphone that can use the study app Design: At visit 1, participants will * Answer survey questions. These may be about medical history, physical activity, and weight. They may also cover body image, health perception, and spirituality. * Have body size measured and get blood tests * Get a device to wear on the wrist. It will record physical activity and hours of sleep. * Learn how to download and use the study mobile app For 2 weeks, researchers will collect data about participants physical activity. Then participants will have a study visit with additional blood tests. All participants will get messages from the app that encourage exercise. Some participants will get data from the app about exercise near their home or work. Some participants may get face-to-face coaching. Participants may get wireless devices. These measure body weight, blood pressure, and blood glucose. Participants can measure these at home and upload the data to the app for the study. Participants will have visits after 3 and 6 months. They will repeat the visit 1 tests.

Targeted, effective behavioral interventions are critically needed to ameliorate the disproportionate prevalence of poor cardiometabolic health for African-American women. We propose a sequential, multiple-assignment, randomized trial targeting physical activity (PA) among at-risk African-American women in resource-limited, Washington, D.C. communities using mobile health (mHealth) technology. We hypothesize that by beginning a community-based, adaptive PA intervention with remote coaching tailored to neighborhood environment PA resources, we will see greater increases in PA levels as compared to standard remote coaching. In Aim 1, we will determine if beginning an adaptive intervention with remote coaching tailored to neighborhood environment resources and delivered using mHealth technology (wearables and mobile applications) will lead to a greater PA increase (as measured by steps per day) as compared to standard remote coaching. In Aim 2, we will examine which of four embedded adaptive interventions produce the largest PA increase over the six-month study period. In Aim 3, we will evaluate the feasibility of remote capture of cardiometabolic measures, including blood pressure, weight, and glucose, using mHealth technology. We will also examine intervention effects on cardiometabolic health (adiposity, blood pressure, fasting lipids/glucose, self-reported PA, dietary intake, cigarette smoking). In Aim 4a, we will characterize effects of increasing PA on integrated serologic cytokine/chemokine and lipid inflammatory intermediates to identify potential novel inflammatory pathways linked to cardiometabolic risk phenotypes most responsive to the multi-level, community-based PA intervention. In Aim 4b, we examine the feasibility of measuring potential psychosocial and behavioral mediators of the relationship between PA change and CV health. In Aim 5, we will conduct iterative testing of the mobile health technology used in the protocol with a user-centered design approach. In Aim 6a and 6b, we will assess for changes in cardiac structure and function as well as body composition using MRI before and after the intervention. We will also determine the feasibility of measuring behavioral and psychosocial mediating factors of the relationship between PA change and cardiometabolic health in this intervention, including chronic psychological/environmental stress and sedentary behavior/sleep. In addition, because of the COVID-19 pandemic in 2020, we will measure exposure to COVID-19 and psychosocial stress caused by the pandemic as potential confounders of immunologic outcomes and psychosocial stressors in this study. Finally, we will explore the relationships between PA, social determinants of health, and biological markers in this intervention cohort and compare them to other populations using available cohort data. This project provides fundamental knowledge towards the development of tailored, effective behavioral interventions incorporating mHealth technology to promote health among populations most impacted by health disparities.