App-Assisted Day Reconstruction to Reduce Treatment Burden and Logistic Toxicity in Cancer Patients

项目总结 每年，美国有170多万新的癌症患者接受密集的多模式治疗， 这在管理治疗和日常生活优先事项方面带来了许多后勤挑战。在当前 癌症护理系统，“后勤毒性”--实施癌症的后勤负担所造成的毒性影响 与治疗相关的任务--与患者福祉有关--在很大程度上没有得到衡量和解决。当前 衡量物流毒性的方法会产生供研究人员使用的回溯性评估。他们确实是这样 不提供及时的信息，使患者能够向护理提供者、雇主、家庭、 还有朋友。它们也没有赋予提供者探索越来越多的患者可用的治疗选择的权力- 以癌症护理为中心。该提案旨在将一种新的方法-APP辅助的日重建-应用于 开发第一个数字健康工具，使患者能够远程监控物流毒性，这是 为解决这一问题制定有效的护理干预措施是必要的第一步。 我们的产品在概念和技术上都是创新的。从概念上讲，我们应用了白天重建 方法-最初由幸福感研究人员创建的一种方法，用于收集更准确的日常生活数据 经验-收集与癌症治疗任务相关的活动参与度和幸福感信息。 在技术上，我们利用我们现有的专利技术和新的机器学习技术来实现 客观移动传感与患者主观输入的新整合。移动传感和机器 学习将构成APP为与后勤毒性相关的日常重建提供的“辅助”， 显著减少了召回错误和对手动输入的需要。该“协助”还将促使患者 提供移动传感或机器无法检测到的主观幸福感评级等信息 学习，产生比现有方法更准确和全面的物流毒性测量。 该项目有三个具体目标，包括(1)基于癌症输入的初步系统设计 患者和癌症护理利益相关者，(2)原型开发和初始测试，以及(3)应用程序的现场测试 在接受癌症治疗的60名不同的患者中。在目标3中，患者将对应用程序的质量进行评分 使用移动应用程序评级量表(MARS)以及他们对三个关键应用程序功能的满意度：1)应用程序的 能够捕获与外出治疗相关的活动和行程，2)易于输入的界面 以家庭为基础的治疗相关活动和幸福感评级；3)后勤毒性的有用性 摘要报告。该项目的结果将是一个最终的原型应用程序，70%的患者表示 火星的总体得分为4.0或更高，对这三个功能感到满意。在第二阶段，团队将测试 应用程序的功效-单独使用或与护理协调、远程医疗和基于家庭的应用程序结合使用 治疗--在一项随机对照试验中减少逻辑毒性和改善治疗结果。

PROJECT SUMMARY Each year, more than 1.7 million new cancer patients in the U.S. undergo intense, multimodal treatments that that create numerous logistical challenges in managing treatment and everyday life priorities. In the current cancer care system, “logistic toxicity”—the toxic effects imposed by the logistical burden of carrying out cancer treatment-related tasks on patient well-being—has been largely unmeasured and unaddressed. Current methods for measuring logistic toxicity generate retrospective assessments intended for researchers. They do not offer timely information that empower patients to solicit assistance from care providers, employers, family, and friends. Nor do they empower providers to explore the increasingly available treatment options for patient- centered cancer care. This proposal aims to apply a new method—app-assisted day reconstruction—to develop the first digital health tool to enable remote patient monitoring of logistic toxicity, which is the necessary first step towards developing effective care interventions for addressing it. Our product is both conceptually and technically innovative. Conceptually, we apply the day reconstruction method—a method initially created by well-being researchers for collecting more accurate data on daily life experiences—to collect activity engagement and well-being information related to cancer treatment tasks. Technically, we leverage our existing patented technology and new machine learning techniques to enable novel integration of objective mobile sensing with subjective patient input. Mobile sensing and machine learning will constitute the “assist” that the app provides for day reconstruction in relation to logistic toxicity, significantly reducing recall errors and the need for manual input. The “assist” will also prompt patients to provide information such as subjective well-being ratings that are not detectable by mobile sensing or machine learning, generating more accurate and comprehensive measures of logistic toxicity than existing methods. The project has three specific aims, including (1) an initial system design based upon input from cancer patients and cancer care stakeholders, (2) prototype development and initial tests, and (3) field tests of the app among 60 diverse patients undergoing treatment for cancer. In Aim 3, patients will rate the quality of the app using the Mobile App Rating Scale (MARS) and their satisfaction with the three key app features: 1) the app’s ability to capture out-of-home treatment-related activities and trips, 2) the ease of the interface for inputting home-based treatment-related activities and well-being ratings, and 3) the usefulness of the logistic toxicity summary report. The outcome of this project will be a final prototype app with 70% of patients indicating an overall MARS score of 4.0 or more and satisfaction with the three features. In Phase II, the team will test the efficacy of the app—both separately and in conjunction with care coordination, telemedicine, and home-based treatments—in reducing logistic toxicity and improving treatment outcomes in a randomized controlled trial.