Taxi ROADmAP (Realizing Optimization Around Diet And Physical activity)

项目摘要 出租车ROADmAP（实现饮食和身体活动的优化）是一种有效的- 使用多阶段优化策略（MOST）实现混合1型设计， 解决超重和肥胖危机，在一个日益增长的，有风险的，多语言的低社会经济 社会经济地位（SES），难以接触，主要是移民和少数民族的基本工人人口： 出租车和出租车（FHV）司机（Lyft，Uber等）。社会变革管理计划是一个创新框架， 高效的随机实验，以评估个体治疗的效果 组件，以引导实现目标结果的优化治疗包的组装 以最低的资源消耗和参与者负担。混合试验， 有效性和实施研究，可以导致更快的翻译吸收， 有效执行。美国有超过75万名有执照的出租车和FHV司机， 在纽约市有超过185，000人。他们有更高的超重/肥胖率 体重指数（BMI）高于纽约人（77%对56%）， 腰围、久坐不动的行为、不良的饮食习惯和医疗保健服务利用不足。 肥胖是一种共同的心血管疾病和癌症风险，其后果影响少数人 和低社会经济地位人口不成比例。可改变的因素，如身体活动不足， 复合驾驶员的肥胖风险。虽然有很多证据表明有效的多成分生活方式 干预措施侧重于非少数群体的体重减轻，如糖尿病预防 程序（DPP）和展望未来，这些程序的设计是不可翻译的， 社区环境，被认为过于昂贵和繁琐，无法广泛传播。 关于如何为少数群体优化这些方法的数据很少（他们受益于 比非西班牙裔白人更少的减肥计划），以减少参与者的负担和成本 但仍能有效减肥更少的文献涉及执行潜力 和这种干预的战略。ROADmAP建立在我们独特的前期工作基础上， 使用混合1型设计和MOST，以解决这些差距。ROADmAP将测试4 基于证据和理论（社会认知理论[SCT]）的行为改变干预 由Memorial Sloan Kettering Immigrant Health and Cancer开发和试点 Disparities Center，包括DPP和Look AHEAD功能。我们将使用MOST 确定4个组成部分中哪一个对减肥最重要，最具成本效益 在工作场所健康博览会（HF）和虚拟招聘的纽约市司机中。目标是 应用MOST为出租车/FHV设计可扩展的生活方式干预的优化版本 驱动因素，然后进行混合方法多利益相关方过程评价， 促进广泛干预的实施。

Project Summary Taxi ROADmAP (Realizing Optimization Around Diet And Physical activity) is an effectiveness- implementation hybrid type 1 design using the Multiphase Optimization Strategy (MOST) to address the overweight and obesity crisis in a growing, at-risk, multilingual low socioeconomic status (SES), hard-to-reach, predominantly immigrant and minority essential worker population: taxi and for-hire vehicle (FHV) drivers (Lyft, Uber, etc.). MOST, an innovative framework, involves highly efficient randomized experimentation to assess the effects of individual treatment components to guide assembly of an optimized treatment package that achieves target outcomes with the lowest resource consumption and participant burden. Hybrid trials, which blend effectiveness and implementation studies, can lead to more rapid translational uptake and more effective implementation. There are over 750,000 licensed taxi and FHV drivers in in the U.S. and over 185,000 in New York City (NYC). They have higher rates of overweight/obese range body mass index (BMI) than New Yorkers in general (77% vs 56%) and have high rates of elevated waist circumference, sedentary behavior, poor diets, and health care services underutilization. Obesity, a shared cardiovascular disease and cancer risk, and its consequences affect minority and low SES populations disproportionately. Modifiable factors, such as physical inactivity, compound drivers’ obesity risk. While there is much evidence on effective multicomponent lifestyle interventions focused on weight loss in non-minority populations, such as the Diabetes Prevention Program (DPP) and Look AHEAD, these programs were not designed to be translatable to community settings and are considered too expensive and burdensome to be widely disseminated. There is a paucity of data on how to optimize such approaches for minorities (who have benefitted less from weight loss programs than non-Hispanic whites), to reduce participant burden and costs but still lead to meaningful weight loss. Even less literature addresses implementation potential and strategies for such interventions. ROADmAP builds on our unique preliminary work and uses a hybrid type 1 design and MOST, to address these gaps. ROADmAP will test 4 evidence- and theory-based (Social Cognitive Theory [SCT]) behavior change intervention components, developed and piloted by the Memorial Sloan Kettering Immigrant Health and Cancer Disparities Center, and which include DPP and Look AHEAD features. We will use MOST to identify which of the 4 components contribute most significantly and cost-effectively to weight loss among NYC drivers recruited at workplace health fairs (HFs) and virtually. Objectives are to apply MOST to design an optimized version of a scalable, lifestyle intervention for taxi/FHV drivers, and then to conduct a mixed methods multistakeholder process evaluation to facilitate widespread intervention implementation.