Regardless of what interpretative criteria are used for assessing lung scans in PE, the frequency of silent PE is 40% to 50% in patients with DVT. A baseline lung scan may easily detect PE in these patients but is not useful for predicting early thromboembolic recurrences that may occur during therapy.

Background Community-based participatory research is an effective tool for improving health outcomes in minority communities. Few community-based participatory research studies have evaluated methods of optimizing smartphone apps for health technology-enabled interventions in African Americans. Objective This study aimed to utilize focus groups (FGs) for gathering qualitative data to inform the development of an app that promotes physical activity (PA) among African American women in Washington, DC. Methods We recruited a convenience sample of African American women (N=16, age range 51-74 years) from regions of Washington, DC metropolitan area with the highest burden of cardiovascular disease. Participants used an app created by the research team, which provided motivational messages through app push notifications and educational content to promote PA. Subsequently, participants engaged in semistructured FG interviews led by moderators who asked open-ended questions about participants’ experiences of using the app. FGs were audiorecorded and transcribed verbatim, with subsequent behavioral theory-driven thematic analysis. Key themes based on the Health Belief Model and emerging themes were identified from the transcripts. Three independent reviewers iteratively coded the transcripts until consensus was reached. Then, the final codebook was approved by a qualitative research expert. Results In this study, 10 main themes emerged. Participants emphasized the need to improve the app by optimizing automation, increasing relatability (eg, photos that reflect target demographic), increasing educational material (eg, health information), and connecting with community resources (eg, cooking classes and exercise groups). Conclusions Involving target users in the development of a culturally sensitive PA app is an essential step for creating an app that has a higher likelihood of acceptance and use in a technology-enabled intervention. This may decrease health disparities in cardiovascular diseases by more effectively increasing PA in a minority population.

IntroductionA mixed-method, co-design approach to studying the adoption of mobile health (mHealth) technology among African-American (AA) women has not been fully explored. Qualitative data may contextualise existing knowledge surrounding perceptions of mHealth among AA women as part of formative work for designing a physical activity application (app).MethodsA convenience sample of 16 AA women completed an informatics survey prior to participating in focus groups exploring their use of mobile technology and health apps. Survey responses provided frequency data, while iterative transcript analysis of focus groups identified themes.ResultsThe majority of participants (mean age=62.1 years, SD=6.6) felt comfortable using a tablet/smartphone (75.0%). Most (68.8%) reported using health-related apps, primarily focused on physical activity and nutrition. Focus groups revealed four overarching concepts, including (1) user attachment, (2) technology adoption, (3) potential facilitators and (4) potential barriers. Important features which may serve as facilitators or barriers to future adoption of a mobile app for an mHealth intervention include individual app tailoring and software concerns, respectively.DiscussionThematic analysis revealed high user attachment to smartphones and described participants’ process for adopting new mHealth technology.ConclusionEarly engagement of target end users as a part of a broader co-design and community-based participatory research process for developing mHealth technologies may be useful for sustained adoption of these tools in future mHealth behavioural interventions.

Little is known about the spatial clustering of neighborhood deprivation across the United States (US). Using data from the 2010 US Census Bureau, we created a neighborhood deprivation index (NDI: higher NDI indicates higher deprivation/ lower neighborhood socioeconomic status) for each county within the US County level scores were loaded into ArcGIS 10.5.1 where they were mapped and analyzed using Moran's I and Anselin Local Moran's I. Ultimately, NDI varies spatially across the US. The highest NDI scores were found in the Southeastern and Southwestern US states, and inland regions of Southern California. This information is critical for public health initiative development as planners may need to tailor the scale of their efforts based on the higher NDI neighborhoods of the county or geographic region with potentially greater chronic disease burden.