Predicting Attrition from a Lifestyle Medicine Intervention

世界贸易中心(WTC)相关疾病对WTC的健康和福祉造成重大损害 响应者。这些衰弱的情况可以通过生活方式药物来改善甚至逆转。 针对营养和饮食、体力活动、睡眠和压力管理的计划。尽管它们很有效， 生活方式医学计划受到高流失率的限制。辅助性干预增加 如果开发了准确的预测算法来确定谁最有可能放弃，就可以部署留任 出去。之前的研究发现了一系列人口统计和行为风险因素导致人员流失。然而，没有一个是 它们各自都足够强大，足以准确预测未来的辍学情况，而且它们还没有组合成 经过验证的预测算法。此外，最近的技术进步使识别新的 使用客观、被动感知的数据(自然语言、睡眠和 移动)。然而，这些被动感知的数据在预测磨损方面的效用还不是很严格 已经证明了。为了解决这些差距，当前项目的目标是确定心理和行为 从自我报告、体检和被动感觉中评估的预测生活方式损耗的因素 医学项目。我们将使用石溪WTC健康计划中的现有基础设施 大学评估800名WTC应答者，因为他们注册了一个为期3个月的生活方式医学计划。 在计划接收时，我们将(A)评估已确定的风险因素，(B)记录首次治疗访问以获得 自然语言预报器，以及(C)给参与者一个FitBit来测量体力活动 以及通过被动感知的睡眠模式。我们的目标是(1)确定生活方式中的自然减退的个体预测因素 医学程序和(2)结合预报器来开发预测磨损的算法(使用机器 学习方法)。这项研究将提高我们识别有自然减员风险的患者的能力，并 许多潜在预测因素的辍学风险概况，揭示了可以针对的流失路径 未来研究中的支持性干预措施(例如，激励性访谈、适时适应性干预 基于被动传感数据)。拟议的项目迈出了解决高房价问题的第一步 生活方式医学项目中WTC响应者和其他患者群体的自然减员。它将铺平 支持性干预措施的随机临床试验方法，算法确定的患者很可能 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.