Cardiovascular Health and Needs Assessment in Washington D.C.

我们使用定性和定量方法来评估资源有限的华盛顿特区社区中主要是非裔美国人、教会人口使用pa监测腕带和网络技术的可行性和可接受性。在社区咨询委员会的建议下，我们组织了一个焦点小组，并与代表心血管健康和需求评估所针对的教会的社区成员一起试验拟议的pa监测系统。我们论证了在线账户使用、手环使用和客观PA数据测量的可行性。焦点小组记录显示，考虑到设备培训、中心可访问性和设备反馈方面的改进，参与者（N=8）对将设备纳入他们的教会人口持积极态度。在资源有限的社区实施基于技术的干预措施时，让目标社区参与可能有助于及早发现问题、建议和偏好。

We used 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, DC, neighborhoods. At the recommendation of the community advisory board, we conducted a focus group and piloted the proposed PA-monitoring system with community members representing churches that would be targeted by the Cardiovascular (CV) Health and Needs Assessment. We demonstrated the feasibility of online account usage, wristband utilization and measurement of objective PA data. Focus group transcripts revealed that participants (N=8) felt positively about incorporating the device into their church-based populations given improvements were made to device training, hub accessibility, and device feedback. When implementing technology-based interventions in resource-limited communities, engaging the targeted community may aid in early identification of issues, suggestions and preferences. We also explored user characteristics of PA-tracking, wearable technology among the health and needs assessment population. Washington, DC CV Health and Needs Assessment participants received a mobile health (mHealth) PA monitor and wirelessly uploaded PA data weekly to church data collection hubs. Participants (n=99) were 59+/-12 years, 79% female, 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. Community-based behavioral interventions targeting cardiovascular health in resource-limited communities should consider incorporation of wearable mHealth technology. Efforts to reduce barriers to using mHealth technology in resource-limited settings may aid in decreasing cardiovascular health disparities in at-risk populations. We also examined the acceptability and feasibility of digital food records among health and needs assessment participants using a mixed methods approach. Participants (N=17) received a mobile device (iPod Touch) to photo-document their dietary intake for a 3-day digital food record using a mobile app, FitNinja (Vibrent Health). The acceptability of the digital food record was explored through the thematic analysis of verbatim transcripts from a moderated focus group (n=8). Feasibility was evaluated by the percentage of participants complying with instructions (ie, capturing both before and after meal photos for at least 2 meals/day for 3 days). Overall, the participants accepted the digital food record by demonstrating satisfaction with the tool and intent to continue the use (eg, participants recorded an average of 5.2, SD 7, consecutive days). Furthermore, of the 17 participants, 15 photodocumented at least 1 meal during the study period and 3 fully complied with the digital food record instructions. This study demonstrated digital food records as an acceptable tool in CBPR and identified contributors and barriers to the feasibility of digital food records for future research. We have also begun to develop tools for assessing the relationship between the neighborhood built environment and health behaviors or outcomes for the target populations in the Washington DC CV Health and Needs Assessment. We evaluated a scoring method for virtual neighborhood audits utilizing the Active Neighborhood Checklist (the Checklist), a neighborhood audit measure, and assessed street segment representativeness in lower-income neighborhoods. Eighty-two home neighborhoods in the Washington, DC CV Health and Needs Assessment were audited using Google Street View imagery and the Checklist (five sections with 89 total questions). Twelve street segments per home address were assessed for (1) Land-Use Type; (2) Public Transportation Availability; (3) Street Characteristics; (4) Environment Quality and (5) Sidewalks/Walking/Biking features. Checklist items were scored 0-2 points/question. A combinations algorithm was developed to assess street segments representativeness. Spearman correlations were calculated between built environment quality scores and Walk Score, a validated neighborhood walkability measure. Street segment quality scores ranged 10-47 (Mean = 29.4+/-6.9) and overall neighborhood quality scores, 172-475 (Mean = 352.3 +/- 63.6). Walk scores ranged 0-91 (Mean = 46.7 +/- 26.3). Street segment combinations correlation coefficients ranged 0.75-1.0. Significant positive correlations were found between overall neighborhood quality scores, four of the five Checklist subsection scores, and Walk Scores (r = 0.62, p < 0.001). This scoring method adequately captures neighborhood features in lower-income, residential areas and may aid in delineating impact of specific built environment features on health behaviors and outcomes. We also examined associations of perceived and objective neighborhood environment (NE) with sedentary time (ST) in the Washington, DC CV Health and Needs Assessment. 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=99% African-American, 79% 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. Finally, we used data from the DC CV Health and Needs Assessment to quantify the impact of crime on physical activity location accessibility, leisure-time physical activity (LTPA) and obesity among African-American women. We developed an agent-based model representing 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. Our simulations show that 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.