Examining relationships between perceptions and objective assessments of neighborhood environment and sedentary time: Data from the Washington, D.C. Cardiovascular Health and Needs Assessment.

Sedentary time (ST) and neighborhood environment (NE) are predictors of cardiovascular (CV) health. However, little is known about ST's relationship with NE. We examined associations of perceived and objective NE with ST in the predominantly African American faith-based population of the Washington, D.C. CV Health and Needs Assessment. After using community-based research principles, participants reported NE perceptions, including sidewalks, recreational areas, and crime presence. Factor analysis was conducted to explore pertinent constructs; factor sums were created and combined as Total Perception Score (TPS) (higher score = more favorable perception). Objective NE was assessed using Google Maps and the Active Neighborhood Checklist (ANC). ST was self-reported. Linear regression determined relationships between TPS and ST, and ANC scores and ST, for 1) overall population, 2) lower median-income D.C. areas, and 3) higher median-income DC and Maryland areas. For the sample (N = 98.9% African-American, 78% female), lower median-income areas had significantly lower mean TPS and ANC scores than higher median-income areas (p < 0.001). Three factors (neighborhood violence, physical/social environment, and social cohesion) were associated with overall NE perception. Among those in lower median-income areas, there was a negative association between TPS and ST that remained after covariate adjustment; this was not observed in higher median-income areas. There was no association between ANC scores and ST. Poorer NE perception is associated with greater ST for those in lower income areas, while objective environment is not related to ST. Multi-level interventions are needed to improve NE perceptions in lower-median income areas, reduce ST, and improve CV health.

Simulating the Impact of Crime on African American Women's Physical Activity and Obesity.

OBJECTIVE: The objective of this study was to quantify the impact of crime on physical activity location accessibility, leisure-time physical activity (LTPA), and obesity among African American women. METHODS: An agent-based model was developed in 2016 to represent resource-limited Washington, DC, communities and their populations to simulate the impact of crime on LTPA and obesity among African American women under different circumstances. RESULTS: Data analysis conducted between 2016 and 2017 found that in the baseline scenario, African American women had a 25% probability of exercising. Reducing crime so more physical activity locations were accessible (increasing from 10% to 50%) decreased the annual rise in obesity prevalence by 2.69%. Increasing the probability of African American women to exercise to 37.5% further increased the impact of reducing crime on obesity (2.91% annual decrease in obesity prevalence). CONCLUSIONS: These simulations showed that crime may serve as a barrier to LTPA. Reducing crime and increasing propensity to exercise through multilevel interventions (i.e., economic development initiatives to increase time available for physical activity and subsidized health care) may promote greater than linear declines in obesity prevalence. Crime prevention strategies alone can help prevent obesity, but combining such efforts with other ways to encourage physical activity can yield even greater benefits.

Adherence with physical activity monitoring wearable devices in a community-based population: observations from the Washington, D.C., Cardiovascular Health and Needs Assessment.

Wearable mobile health (mHealth) technologies offer approaches for targeting physical activity (PA) in resource-limited, community-based interventions. We sought to explore user characteristics of PA tracking, wearable technology among a community-based population within a health and needs assessment. In 2014-2015, we conducted the Washington, D.C., Cardiovascular Health and Needs Assessment in predominantly African-American churches among communities with higher obesity rates and lower household incomes. Participants received a mHealth PA monitor and wirelessly uploaded PA data weekly to church data collection hubs. Participants (n = 99) were 59 ± 12 years, 79% female, and 99% African-American, with a mean body mass index of 33 ± 7 kg/m2. Eighty-one percent of participants uploaded PA data to the hub and were termed "PA device users." Though PA device users were more likely to report lower household incomes, no differences existed between device users and non-users for device ownership or technology fluency. Findings suggest that mHealth systems with a wearable device and data collection hub may feasibly target PA in resource-limited communities.