Community Engagement to Optimize the Use of Web-Based and Wearable Technology in a Cardiovascular Health and Needs Assessment Study: A Mixed Methods Approach.

BACKGROUND: Resource-limited communities in Washington, D.C. have high rates of obesity-related cardiovascular disease in addition to inadequate physical activity (PA) facilities and limited Internet access. Engaging community members in the design and implementation of studies to address these health disparities is essential to the success of community-based PA interventions. OBJECTIVE: The objective of the study was to use qualitative and quantitative methods to evaluate the feasibility and acceptability of PA-monitoring wristbands and Web-based technology by predominantly African American, church-based populations in resource-limited Washington, D.C. neighborhoods. METHODS: To address cardiovascular health in at-risk populations in Washington, D.C., we joined community leaders to establish a community advisory board, the D.C. Cardiovascular Health and Obesity Collaborative (D.C. CHOC). As their first initiative, the Washington, D.C. Cardiovascular Health and Needs Assessment intends to evaluate cardiovascular health, social determinants of health, and PA-monitoring technologies. At the recommendation of D.C. CHOC members, we conducted a focus group and piloted the proposed PA-monitoring system with community members representing churches that would be targeted by the Cardiovascular Health and Needs Assessment. Participants (n=8) agreed to wear a PA-monitoring wristband for two weeks and to log cardiovascular health factors on a secure Internet account. Wristbands collected accelerometer-based data that participants uploaded to a wireless hub at their church. Participants agreed to return after two weeks to participate in a moderated focus group to share experiences using this technology. Feasibility was measured by Internet account usage, wristband utilization, and objective PA data. Acceptability was evaluated through thematic analysis of verbatim focus group transcripts. RESULTS: Study participants (5 males, 3 females) were African American and age 28-70 years. Participant wristbands recorded data on 10.1±1.6 days. Two participants logged cardiovascular health factors on the website. Focus group transcripts revealed that participants felt positively about incorporating the device into their church-based populations, given improvements were made to device training, hub accessibility, and device feedback. CONCLUSIONS: PA-monitoring wristbands for objectively measuring PA appear to be a feasible and acceptable technology in Washington, D.C., resource-limited communities. User preferences include immediate device feedback, hands-on device training, explicit instructions, improved central hub accessibility, and designation of a church member as a trained point-of-contact. When implementing technology-based interventions in resource-limited communities, engaging the targeted community may aid in early identification of issues, suggestions, and preferences. TRIAL REGISTRATION: ClinicalTrials.gov NCT01927783; https://clinicaltrials.gov/ct2/show/NCT01927783 (Archived by WebCite at http://www.webcitation.org/6f8wL117u).

Health Insurance Status as a Barrier to Ideal Cardiovascular Health for U.S. Adults: Data from the National Health and Nutrition Examination Survey (NHANES).

BACKGROUND: Little is known about the association between cardiovascular (CV) health and health insurance status. We hypothesized that U.S. adults without health insurance coverage would have a lower likelihood of ideal cardiovascular health. METHODS AND RESULTS: Using National Health and Nutrition Examination Survey (NHANES) data from 2007-2010, we examined the relationship between health insurance status and ideal CV health in U.S. adults aged ≥19 years and <65 (N = 3304). Ideal CV health was defined by the American Heart Association (AHA) as the absence of clinically manifested CV disease and the simultaneous presence of 6-7 "ideal" CV health factors and behaviors. Logistic regression modeling was used to determine the relationship between health insurance status and the odds of ideal CV health. Of the U.S. adult population, 5.4% attained ideal CV health, and 23.5% were without health insurance coverage. Those without health insurance coverage were more likely to be young (p<0.0001), male (p<0.0001), non-white (p<0.0001), with less than a high school degree (p<0.0001), have a poverty-to-income ratio less than 1 (p<0.0001) and unemployed (p<0.0001) compared to those with coverage. Lack of health insurance coverage was associated with a lower likelihood of ideal CV health; however, this relationship was attenuated by socioeconomic status. CONCLUSIONS: U.S. adults without health insurance coverage are less likely to have ideal CV health. Population-based strategies and interventions directed at the community-level may be one way to improve overall CV health and reach this at-risk group.

Change in Neighborhood Socioeconomic Status and Weight Gain: Dallas Heart Study.

INTRODUCTION: Despite a proposed connection between neighborhood environment and obesity, few longitudinal studies have examined the relationship between change in neighborhood socioeconomic deprivation, as defined by moving between neighborhoods, and change in body weight. The purpose of this study is to examine the longitudinal relationship between moving to more socioeconomically deprived neighborhoods and weight gain as a cardiovascular risk factor. METHODS: Weight (kilograms) was measured in the Dallas Heart Study (DHS), a multiethnic cohort aged 18-65 years, at baseline (2000-2002) and 7-year follow-up (2007-2009, N=1,835). Data were analyzed in 2013-2014. Geocoded addresses were linked to Dallas County, TX, census block groups. A block group-level neighborhood deprivation index (NDI) was created. Multilevel difference-in-difference models with random effects and a Heckman correction factor (HCF) determined weight change relative to NDI change. RESULTS: Forty-nine percent of the DHS population moved (263 to higher NDI, 586 to lower NDI, 47 within same NDI), with blacks more likely to move than whites or Hispanics (p<0.01), but similar baseline BMI and waist circumference were observed in movers versus non-movers (p>0.05). Adjusting for HCF, sex, race, and time-varying covariates, those who moved to areas of higher NDI gained more weight compared to those remaining in the same or moving to a lower NDI (0.64 kg per 1-unit NDI increase, 95% CI=0.09, 1.19). Impact of NDI change on weight gain increased with time (p=0.03). CONCLUSIONS: Moving to more-socioeconomically deprived neighborhoods was associated with weight gain among DHS participants.