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A Just-In-Time Adaptive Mobile Application Intervention To Reduce Sodium Intake And Blood Pressure In Hypertensive Patients

A Just-In-Time Adaptive Mobile Application Intervention To Reduce Sodium Intake And Blood Pressure In Hypertensive Patients
实时自适应移动应用干预可减少高血压患者的钠摄入量和血压
批准号:
10512817
负责人:
Michael Dorsch
金额:
$75.74万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-09-05 至 2026-08-31

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中文摘要
翻译
项目摘要 高膳食钠摄入与高血压(HTN)和心血管事件有关。相反地, 较低的钠摄入量已被证明可以降低血压(BP)和预防动脉粥样硬化 心血管疾病(ASCVD)。美国心脏协会的指南提倡每天少摄入钠 超过2,300毫克,但美国人的平均钠摄入量估计为每天3,400毫克。近年来, 预加工食品和餐馆食品的消费量大幅增加,超过75%的钠 美国人日常饮食中的维生素D都来自这些来源。 尽管人们对高钠饮食摄入的危害有着广泛的了解,但临床医生面临的一个持续挑战是, 正在让患者参与基于生活方式的治疗,以扭转这些趋势,并促进持续的自我- 长期坚持低钠饮食。尽管数字健康越来越有希望 平台可能有助于克服这些问题,但很少有实证研究证明其有效性, 时间依靠健康行为理论中强有力的概念框架,我们最近在早期 基于移动的应用程序的即时适应性干预(JITAI)减少了膳食钠摄入量 (R21HS 024567)。这个JITAI使用地理围栏技术提供量身定制的上下文推送通知, 家庭、杂货店和餐馆,在移动的中链接到钠基营养信息 应用程序.虽然我们的初步结果令人兴奋,但知识上的关键差距仍有待检验 1)基于移动的应用程序的JITAI专注于降低钠摄入量是否会导致 BP,2)哪些饮食成分介导了干预对BP的影响,以及3)如何优化参与者 参与,以维持饮食和血压的影响,随着时间的推移,并防止习惯。 我们研究计划的长期目标是开发成功的基于移动的应用的策略, 由自适应技术驱动,支持患者在常见心血管疾病中的自我管理。 我们的总体目标是评估饮食钠吉泰降低血压的效果,建立 改变饮食,调节血压的改善,并优化吉泰的长期患者参与。我们 中心假设是,针对膳食钠的JITAI将随着时间的推移降低血压,并且这些作用将 通过优化JITAI更好地维持。因此,我们将执行两步顺序, 在400例HTN患者中进行前瞻性随机对照试验,以达到我们的总体目标。第一步,我们 将调查吉泰对BP的影响,并进行微随机试验,以优化吉泰, 患者参与。在第二步中,我们将研究一个优化的吉泰对BP的持续影响。
英文摘要
PROJECT SUMMARY High dietary sodium intake is linked to incident hypertension (HTN) and cardiovascular events. Conversely, lower sodium intake has been shown to reduce both blood pressure (BP) and prevent atherosclerotic cardiovascular diseases (ASCVD). American Heart Association guidelines advocate daily sodium intake of less than 2,300 mg, but the estimated average sodium intake for Americans is 3,400 mg per day. In recent years, consumption of pre-processed and restaurant foods has substantially increased, and more than 75% of sodium in the average U.S. diet now comes from these sources. Despite widespread knowledge of the harms of high dietary sodium intake, an ongoing challenge for clinicians is engaging patients in lifestyle-based treatments to reverse these trends as well as promote ongoing self- management and long-term adherence to low sodium diets. Although there is growing hope that digital health platforms may help to overcome these issues, few empirical studies have demonstrated their efficacy over time. Relying on a strong conceptual framework in health behavior theory, we recently demonstrated in early work that a mobile application-based just-in-time adaptive intervention (JITAI) reduces dietary sodium intake (R21HS024567). This JITAI uses geofencing technology to provide tailored and contextual push notifications at home, grocery stores, and restaurants with links to sodium-based nutrition information within the mobile application. While our preliminary results were exciting, the critical gaps in knowledge that remain to be tested are 1) whether a mobile application-based JITAI focused on lowering sodium intake will lead to a decrease in BP, 2) which dietary components mediate the intervention's effect on BP, and 3) how to optimize participant engagement to sustain both dietary and BP effects over time and prevent habituation. The long-term goal of our research program is to develop successful mobile application-based strategies, driven by adaptive technologies, that support patient self-management in common cardiovascular conditions. Our overall objectives are to evaluate the efficacy of a dietary sodium JITAI on reducing BP, establish the changes in diet that mediate improvement in BP, and optimize the JITAI for long-term patient engagement. Our central hypothesis is that JITAIs targeting dietary sodium will reduce BP over time and that these effects will be better sustained through optimization of JITAIs. Accordingly, we will perform a two-step sequential, prospective, randomized controlled trial in 400 HTN patients to meet our overall objectives. In the first step, we will investigate the effect of the JITAI on BP and perform a micro-randomized trial to optimize the JITAI for patient engagement. In the second step, we will study the sustained effects of an optimized JITAI on BP.
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