A Smart Ring for Cuffless Blood Pressure to Reduce Health Disparities in People of Color

项目摘要/摘要 高血压是最普遍的心血管危险因素，并不成比例地导致不良反应。 在非裔美国人中的结果。患有高血压的非裔美国人也更有可能不知道 血压升高，进一步加剧心血管疾病的并发症。因此，检测 对非裔美国人进行准确的血压检测是缓解高血压的关键一步 结果。该项目的目标是创建一种不显眼的无袖带血压监测仪，用于 有色人种社区隐性高血压的测量和识别。对象的当前状态 结合袖带的技术很麻烦，而且不允许被动/频繁测量。这个 无袖带解决方案的趋势主要集中在不起作用的光体积描记(PPG)上 对肤色较深的人和体重指数较高的参与者也是如此。该项目利用生物阻抗作为 以手指佩戴的设备形式的传感设备，如智能戒指。我们的设备提供强大的和几乎 连续的生物阻抗读数，对应于血容量和血流动力学的变化，这将是 使用机器学习和人工智能算法转换为血压。生物阻抗不具有 PPG的局限性；它对不同的肤色不敏感，具有深度渗透能力，因此 较高的BMI(以及皮肤下较厚的脂肪层)不会影响测量结果。的主要关注点 我们的调查是为了深入了解隐蔽性高血压，目的是捕捉血压 经常在白天和晚上。我们建议的技术可以帮助识别特定的短期 动态包括血压的变化，并允许有效地监测对药物的反应 对于亚特兰大的非裔美国人，佐治亚州的非裔美国人以及德克萨斯州大学站的非裔美国人和拉丁裔社区。这 该项目包含两个目标。Aim 1打算验证已经开发的智能环技术 在德克萨斯州A&M，对非裔美国人和拉丁裔参与者(N=60)进行了实验室设置中不同BMI的研究，没有 已知的肥胖以外的既往疾病，存在各种血压动作(冷加压， 握力和运动)和三种姿势(仰卧、站立和坐着)。目标2扩展了对 我们的技术用于有和没有高血压病史的非裔美国人患者(N=144) 佐治亚州亚特兰大，混合实验室和流动环境，以独特的重点探索所报告的现象 目的是改善隐性高血压的状况，并提高有色人种社区的技术接受度和社会相关性。 经过近十年的工业界和学术界的技术发展，提取血压指数 使用PPG，据我们所知，这项努力第一次解决了PPG对Darker的有限有效性 肤色和那些有更高BMI的人。文献表明，更高的患病率 非裔美国人社区和肥胖者中的高血压，这进一步呼吁BMI 独立的技术开发和验证。

PROJECT SUMMARY/ABSTRACT Hypertension is the most prevalent cardiovascular risk factor and disproportionately responsible for adverse outcomes in African Americans. African Americans with hypertension are also more likely to be unaware of elevated blood pressures, which further exacerbates complications of cardiovascular disease. Thus, detection of accurate blood pressure in African Americans is a critical step towards alleviating hypertension disparate outcomes. The objective of this project is to create an unobtrusive cuffless blood pressure monitor for measurement and identification for masked hypertension in communities of color. Current state of the technology that incorporates cuffs is cumbersome and does not allow for passive/frequent measurements. The trends for cuffless solutions are primarily focused on photoplethysmography (PPG) which does not operate well on darker skin tones and on participants with higher BMIs. This project leverages bioimpedance as a sensing modality in the form of a finger-worn device, like smart rings. Our device provides robust and nearly continuous bioimpedance readings, corresponding to blood volume and hemodynamic changes, that will be translated to blood pressure using machine learning and AI algorithms. Bioimpedance does not have the limitations of PPG; it is insensitive to varying skin tones and has deep penetration capabilities and therefore a higher BMI (and a thicker layer of fat under the skin) does not impact the measurements. The principal focus of our investigation is to gain insights into masked hypertension with the objective of capturing blood pressure frequently during the day and at nighttime. Our proposed technology can help identify certain short-term dynamics inclusive of variations of blood pressure and allows effective monitoring of response to medication for African American in Atlanta, GA and African American and Latinx communities in College Station, TX. This project incorporates two aims. Aim 1 intends to validate the smart ring technology that has been developed at Texas A&M on African American and Latinx participants (N=60) with varying BMI in lab settings, with no known pre-existing condition beyond obesity, in presence of various blood pressure maneuvers (cold pressor, handgrip and exercise) and for three postures (supine, standing and sitting). Aim 2 expands the validation of our technology in African American patients (N=144) with and without a history of hypertension in Atlanta, GA in mixed lab and ambulatory settings with a unique focus to explore the reported phenomenon of masked hypertension and to advance technology acceptance and social relevance in communities of color. After nearly ten years of industrial and academic technology development to extract blood pressure indices using PPG, to our knowledge, this effort for the first time addresses the limited effectiveness of PPG on darker skin tones and those who have a higher BMI. The literature demonstrates the higher prevalence of hypertension in African American communities and among those who have obesity, which further calls for BMI independent technology development and validation.