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

Obesity

Community-Based Participatory Research, Obesity, Cardiovascular Disease Risk, Social Determinants of Health

Background Obesity and physical inactivity contribute substantially to cardiovascular disease risk, diabetes, hypertension, and dyslipidemia. African American women experience disproportionately high rates of obesity and have among the lowest levels of physical activity and greatest sedentary time in the United States. Resource-limited neighborhoods may further constrain physical activity through limited access to safe parks, recreation facilities, walking routes, and other health-promoting resources. This protocol builds on prior community-based work in Washington, D.C., which demonstrated the feasibility of wearable activity monitoring among predominantly African American women in faith-based, lower-resource communities. It uses community-based participatory research principles and mobile health technology to deliver physical-activity coaching tailored either to individual factors or to available neighborhood resources. Objectives Primary objective To determine whether initiating an adaptive physical-activity intervention with remote, mobile-app coaching tailored to neighborhood physical-activity resources "tailored-to-place" coaching-produces a greater increase in steps per day over approximately three months than standard remote coaching. Secondary objectives The study will also: Compare four embedded adaptive intervention strategies over approximately six months. Assess the feasibility of remotely measuring weight, blood pressure, and blood glucose using connected devices. Evaluate effects on cardiometabolic measures, including BMI, blood pressure, lipids, glucose, dietary intake, self-reported physical activity, and smoking. Examine inflammatory, lipid, immunometabolic, psychosocial, behavioral, and social-determinants-of-health pathways related to physical-activity change. Evaluate changes in cardiac structure and function and body composition using optional cardiovascular and body-composition MRI. Assess relationships among sleep, ambulatory blood pressure, menopause-related factors, and immunometabolic markers in a postmenopausal sleep sub-study. Conduct user-centered testing of the mobile application and wearable/mobile health technologies. Explore COVID-19 exposure and pandemic-related psychosocial stress as potential confounders of immunologic and psychosocial outcomes. Eligibility: Inclusion Criteria Participants must: Be African American women aged 21-75 years. Have overweight or obesity, defined as BMI \>=25 kg/m\^2 Live in Washington, D.C., Wards 5, 7, or 8, or in neighboring areas of Prince George's County, Maryland. Have access to a smartphone compatible with the study application. Be able to provide informed consent independently. Speak and read English at approximately an eighth-grade level. Participants enter a two-week run-in period. Those averaging 7,500 steps per day or fewer and who complete the run-in are eligible for randomization into the intervention. Main study exclusion criteria Potential participants are excluded for conditions that could make increased physical activity unsafe or interfere with study assessments, including: Medical conditions or physical limitations that prohibit safe participation in physical activity. Recent myocardial infarction, documented obstructive coronary artery disease, recent coronary stenting, or significant decompensated structural heart disease. Pregnancy; pregnancy status is assessed as applicable during study visits. Other conditions determined by the investigator to make participation unsafe. Sleep sub-study eligibility The optional sleep sub-study is limited to postmenopausal women who are enrolled in or have completed the main study. Additional exclusions include night-shift work, conditions that interfere with overnight polysomnography, recent initiation of sleep-modifying medications, inability to tolerate ambulatory blood-pressure monitoring, and certain arm, vascular, skin, bleeding, or neurologic conditions. MRI participation is optional and requires separate screening for MRI-incompatible implanted devices or metal and, when applicable, eligibility for gadolinium contrast. Study design A six-month adaptive randomized trial will enroll overweight or obese African American women. After a two-week run-in, participants averaging \<=7,500 steps/day will receive either standard mobile-app coaching or coaching tailored to neighborhood physical-activity resources. At three months, nonresponders will be reassigned to intensified coaching. Steps will be measured with a wearable device, with assessments at baseline, three months, and six months. Optional monitoring and MRI/sleep sub-studies will assess cardiometabolic outcomes....

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. In Aim 7a and 7b, the intersection of common biological signatures of menopause, sleep disruption, and blood pressure as a marker of cardiometabolic risk in the setting of adverse social determinants of health will be investigated in the study population. 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. Also, since PA has the potential to improve sleep, vascular function, autonomic regulation, and inflammatory status, incorporating the sleep study and ambulatory blood pressure monitoring (ABPM) further provides more insights on how the behavioral changes from the Step-it-Up intervention translate into clinically meaningful physiological improvements in this population. 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.

United States