Predicting Attrition from a Lifestyle Medicine Intervention

世界贸易中心（WTC）相关的疾病对WTC的健康和福祉造成重大损失 响应者。这些使人衰弱的状况可以通过生活方式医学来改善甚至逆转 针对营养和饮食、体育活动、睡眠和压力管理的计划。尽管它们的功效， 生活方式医学项目受到高流失率的限制。辅助干预措施， 如果开发出准确的预测算法来识别谁最有可能放弃，就可以部署保留 出去先前的研究发现了一系列人口统计学和行为风险因素。然而， 足够强大的单独准确地预测未来的辍学，他们还没有被组合成一个 验证预测算法。此外，最近的技术进步使得有可能确定新的 使用客观的、被动感知的数据（自然语言、睡眠和 运动）。然而，这些被动感知的数据用于预测减员的效用尚未得到严格的评估。 证明了为了解决这些差距，目前项目的目标是确定心理和行为 根据自我报告、体检和被动感知评估的因素可预测生活方式中的磨损 医学计划。我们将利用斯托尼布鲁克世贸中心卫生项目的现有基础设施 大学评估800世贸中心响应者，因为他们在一个既定的3个月的生活方式医学计划注册。 在项目开始时，我们将（a）评估确定的风险因素，（B）记录首次治疗访视，以获得 自然语言预测器，以及（c）给参与者一个FitBit，让他们戴上一周来测量身体活动 和睡眠模式。我们的目标是（1）确定个人的预测磨损的生活方式 医学程序和（2）结合联合收割机预测开发算法来预测磨损（使用机器 学习方法）。这项研究将提高我们识别有磨损风险的患者的能力， 许多潜在预测因素的辍学风险概况，揭示了可以通过以下方法针对的流失途径： 未来研究中的支持性干预（例如，动机访谈，及时适应性干预 基于无源感测数据）。拟议项目采取第一步，以解决高 在WTC反应者和其他患者人群中的生活方式医学项目的损耗。它将铺平道路 支持性干预措施的随机临床试验的方法，患者由算法确定为可能 退学。

World Trade Center (WTC) related disorders inflict a major toll on the health and well-being of WTC responders. These debilitating conditions can be ameliorated or even reversed with lifestyle medicine programs targeting nutrition and diet, physical activity, sleep, and stress management. Despite their efficacy, lifestyle medicine programs are constrained by high attrition rates. Adjunctive interventions to increase retention can be deployed if accurate forecasting algorithms are developed to identify who is most likely to drop out. Previous studies found a host of demographic and behavioral risk factors for attrition. However, none are sufficiently strong individually to accurately predict future dropout, and they have not yet been combined into a validated predictive algorithm. Furthermore, recent technological advances made it possible to identify new powerful predictors of dropout using objective, passively sensed data (natural language, sleep, and movement). However, the utility of these passively sensed data for predicting attrition has not been rigorously proven. To address these gaps, the goal of the current project is to identify psychological and behavioral factors assessed from self-report, medical exams, and passive sensing that predict attrition in a lifestyle medicine program. We will use existing infrastructure within the WTC Health Program at Stony Brook University to assess 800 WTC responders as they enroll in an established 3-month lifestyle medicine program. At program intake, we will (a) assess established risk factors, (b) record the first treatment visit to obtain natural language predictors, and (c) give participants a FitBit to wear for 1 week to measure physical activity and sleep patterns via passive sensing. Our aims are to (1) identify individual predictors of attrition in a lifestyle medicine program and (2) combine predictors to develop an algorithm to forecast attrition (using machine learning methods). This study will improve our ability to identify patients at risk of attrition and also characterize profile of dropout risk across many potential predictors, revealing pathways to attrition that can be targeted by supportive interventions in future studies (e.g., motivational interviewing, just-in-time adaptive interventions based on passive sensing data). The proposed project takes the first step to address the problem of high attrition in lifestyle medicine programs among WTC responders and other patient populations. It will pave the way for randomized clinical trials of supportive interventions with patients identified by the algorithm as likely to drop out.