Tailoring Mobile Health Technology to Reduce Obesity and ImproveCardiovascular Health in Resource-Limited Neighborhood Environments: A Multi-Level, Community-Based Physical Activity Intervention

华盛顿心血管（CV）健康和需求评估中的女性数据提供了对mHealth用户参与制定体力活动（PA）干预队列的初步见解。 在评估的女性中（99%的非裔美国人平均年龄=59（12）岁），90%的体重指数（BMI）被归类为超重或肥胖，30%的人患有I级肥胖，19%的人患有II级肥胖，17%的人患有III级肥胖。在各体重类别中，PA降低（p <0.05），自我报告的久坐时间增加（p <0.05）。 虽然舒张压和空腹血糖在女性体重类别中显著增加，但血压，胆固醇和葡萄糖控制相对良好，平均值与美国心脏协会CV健康临界点的理想或中间水平一致。 在参与研究的女性中，PA监测系统的依从性在30天的研究期间保持在60%以上，在研究期间肥胖女性的依从性相似。因此，移动健康技术与CBPR战略的部署可以帮助目标PA改善资源有限社区的非裔美国妇女的心血管健康。 作为干预的第一步，我们收集了定性数据，以告知华盛顿的非裔美国妇女中促进PA的移动的应用程序的开发。我们从心血管疾病负担最高的华盛顿大都市区招募了一组非裔美国妇女（N=16，年龄范围51-74岁）。参与者使用研究团队创建的应用程序，该应用程序通过应用程序推送通知和教育内容提供激励信息，以促进PA。随后，参与者参与了由主持人领导的半结构化焦点小组访谈，主持人询问了关于参与者使用该应用程序的体验的开放式问题。焦点小组进行了录音和逐字转录，随后进行了行为理论驱动的主题分析。从成绩单中确定了基于健康信念模型和新兴主题的关键主题。三名独立的评审员反复编码转录本，直到达成共识。然后，最终的码本由定性研究专家批准。在这项研究中，出现了10个主要主题。参与者强调需要通过优化自动化、增加相关性（例如，反映目标人口统计的照片）、增加教育材料（例如，健康信息）以及与社区资源（例如，烹饪课程和锻炼团体）的连接来改进应用程序。让目标用户参与开发具有文化敏感性的PA应用程序是创建一个在技术支持的干预中更有可能被接受和使用的应用程序的重要步骤。这可能通过更有效地增加少数人群中的PA来减少心血管疾病的健康差异。

Data on women in the Washington, DC Cardiovascular (CV) Health and Needs Assessment provided initial insights into mHealth user engagement for the development of a physical activity (PA) intervention cohort. Among women in the assessment (99% African American mean age=59 (12) years), 90% had a body mass index (BMI) categorized as overweight or obese, with 30% having Class-I obesity, 19% having Class-II obesity, and 17% having Class-III obesity. Across weight classes, PA decreased (p0.05) and self-reported sedentary time increased (p0.05). Although diastolic blood pressure and fasting blood glucose significantly increased across weight categories among women, blood pressure, cholesterol, and glucose were relatively well-controlled with mean values consistent with ideal or intermediate levels of the American Heart Associations CV health cut-points. PA-monitoring system compliance remained above 60% for the 30-day study period among women participating in the study, with similar compliance among women with obesity over the study period. Therefore, deployment of mHealth technology with CBPR strategies can help target PA for improving cardiovascular health among African American women in resource-limited communities. As a first step for the intervention, we gathered qualitative data to inform the development of a mobile app that promotes PA among African American women in Washington, DC. We recruited a convenience sample of African American women (N=16, age range 51-74 years) from regions of the 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 semi-structured focus group interviews led by moderators who asked open-ended questions about participants' experiences of using the app. Focus groups were audio-recorded 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. In this study, 10 main themes emerged. Participants emphasized the need to improve the app by optimizing automation, increasing relatability (eg, photos that reflect the target demographic), increasing educational material (eg, health information), and connecting with community resources (eg, cooking classes and exercise groups). 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.