Optimization and Evaluation of a Tailored Behavioral eHealth/mHealth Weight Loss Intervention for Cardiac Rehabilitation Patients Using the Multiphase Optimization Strategy

项目总结/摘要 心血管疾病（CVD）是美国的主要死亡原因。心脏康复（CR） 一个以证据为基础的，具有成本效益的，广泛提供的多学科计划，结合监督 运动与心理教育的健康行为改变，旨在改善整体健康和减少 心血管疾病的风险。尽管CR充分注意增加身体 活动（PA）和改善饮食，它几乎不产生体重减轻（WL）。WL可显著改善CR 患者的健康状况，大多数患者因肥胖而出现CR。黄金标准的亲身行为减肥 干预措施（BWL）产生临床上显著的WL，改善健康和疾病风险/严重程度，但这些 这些计划是如此繁重和昂贵，以至于它们不可能在现有的CR计划中进行。的 初级导师的全自动3个月的在线计划，Rx减肥（RxWL），临床产生 医生转诊患者（N=154）的显著平均值±SD WL为初始体重的5.8± 4.4%，5.8 ± 5.2% 在工作场所（N=75）和社区环境（N=230）中分别为4.2± 5.3%;这一比例在6个月时保持不变。博士 候选人Goldstein的目标是进行形成性工作，以定制RxWL用于CR。然后，她将使用 多阶段优化策略（MOST）快速筛选4种创新的电子健康/移动健康干预措施 当与核心RxWL结合时，具有改善该特殊人群WL潜力的组分 程序.在n=20的初步测试之后，对160名患者（每个患者 随机接受0-4种组分）将用于测试4种新的干预组分：（a） 定制的交互式干预，以使用Fitbit促进结构化PA;（B）咬合计数装置，以促进 热量限制;（c）基于网络的虚拟现实（VR）干预BWL技能实施;以及（d） 虚拟会议，以提高WL自我效能。到6个月随访时，这种设计将允许候选人 确定哪些组件最大化WL以及是否存在有利的组件组合。博士 戈尔茨坦将测试哪些成分是（或不是）有效的，为什么或如何发挥其作用，这是 关键是了解他们的行动机制（或不行动）。每个组成部分都必须满足 最终处理包中包含的2% WL优化标准。这项研究将导致一个全面的- 针对CR优化的自动化BWL治疗包，将在随机 在K23资助期间开发的R 01中进行的对照试验（RCT）。考虑到候选人的成功， 她在肯特州立大学获得博士学位，并完成了NHLBI资助的心血管行为学 医学T32在米里亚姆医院和布朗大学，候选人无疑将利用 资源在她的模范研究环境发展成为一个成功的，独立的科学家。通过 一个强大的培训计划和创新的研究项目，戈尔茨坦博士将成为唯一的独立的 科学家使用电子健康/移动健康技术来促进心血管疾病（特别是CR）患者的WL。

PROJECT SUMMARY/ABSTRACT Cardiovascular disease (CVD) is the leading cause of death in the United States. Cardiac rehabilitation (CR) is an evidence-based, cost-effective, and widely-available multidisciplinary program that combines supervised exercise with psychoeducation on health behavior change aimed at improving overall health and reducing cardiovascular risk in individuals with established CVD. Despite CR's ample attention to increasing physical activity (PA) and improving diet it produces almost no weight loss (WL). WL would significantly improve the CR patients' health, the majority of who present to CR with obesity. Gold standard in-person behavioral weight loss interventions (BWLs) produce clinically significant WL that improves health and disease risk/severity, but these programs are so burdensome and costly that they are impossible to conduct within existing CR programs. The primary mentor's fully automated 3-month online program, Rx Weight Loss (RxWL), produced clinically significant mean±SD WL of 5.8±4.4 % of initial body weight in physician-referred patients (N=154), 5.8 ±5.2 % in worksites (N=75), and 4.2±5.3 % in community settings (N=230); this was maintained at 6-months. Dr. Goldstein, the candidate, aims to conduct formative work to tailor RxWL for use in CR. She will then use the Multiphase Optimization Strategy (MOST) to rapidly screen 4 innovative eHealth/mHealth intervention components with the potential to improve WL in this special population when combined with the core RxWL program. Following pilot testing with n=20, a fully-powered 24 full factorial experiment with 160 patients (each randomized to receive 0-4 of the components) will be used to test the 4 novel intervention components: (a) a tailored interactive intervention to promote structured PA using a Fitbit; (b) a bite counting device to promote caloric restriction; (c) a Web-based virtual reality (VR) intervention for BWL skills implementation; and (d) virtual meetings to increase WL self-efficacy. By the 6-month follow-up, this design will allow the candidate to determine which components maximize WL and whether there are favorable component combinations. Dr. Goldstein will test which components are (or are not) effective and why or how they exert their effects, which is critical for understanding their mechanism of action (or inaction). Each component will be required to meet a 2% WL optimization criterion for inclusion in the final treatment package. This research will result in a fully- automated BWL treatment package optimized for CR, which will be submitted for testing in a randomized controlled trial (RCT) in an R01 developed during the K23 grant period. Given the candidate's success while earning her PhD from Kent State University and completing an NHLBI-funded Cardiovascular Behavioral Medicine T32 at The Miriam Hospital and Brown University, the candidate will undoubtedly leverage the resources in her exemplary research environment to develop into a successful, independent scientist. Through a robust training plan and innovative research project, Dr. Goldstein will become one of the only independent scientists to use eHealth/mHealth technology to facilitate WL in CVD, specifically CR, patients.