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

应用程序辅助日间重建可减少癌症患者的治疗负担和后勤毒性

• 批准号: 10483109
• 负责人: Yingling Fan
• 金额: $ 39.99万
• 依托单位: DAYNAMICA, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10483109
• 关键词: Address; Advocate; Affect; American; Android; COVID-19; Cancer Burden; Cancer Center; Cancer Patient; Caring; Cellular Phone; Clinic; Clinical; Coin; Collaborations; Combined Modality Therapy; Communities; Data; Detection; Development; Diagnosis; Electronic Health Record; Esthetics; Family; Friends; Goals; Health; Home; Hospital Administrators; Inpatients; Intervention; Interview; Legal patent; Life; Life Experience; Life Style; Logistics; Machine Learning; Malignant Neoplasms; Manuals; Measurement; Measures; Methods; Minnesota; Monitor; Nurses; Oncologist; Outcome; Participant; Patient Care; Patient Self-Report; Patients; Personal Satisfaction; Phase; Provider; Randomized Controlled Trials; Research Personnel; Resources; Self Management; Summary Reports; Surveys; System; Techniques; Technology; Telemedicine; Testing; Time; Toxic effect; Treatment outcome; Treatment-Related Cancer; Universities; base; cancer care; cancer health disparity; cancer therapy; care coordination; care delivery; care providers; care systems; design; digital health; disadvantaged population; efficacy testing; field study; follow-up; improved; innovation; member; mobile application; mobile sensing; novel; optimal treatments; patient oriented; prototype; reconstruction; recruit; remote patient monitoring; satisfaction; social health determinants; socioeconomic disadvantage; spatiotemporal; tool; treatment planning; trend; usability

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.

项目摘要 每年，美国有超过170万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造成了许多后勤挑战。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效应 治疗相关任务对患者健康的影响--在很大程度上没有得到测量和解决。电流 用于测量逻辑毒性的方法产生旨在用于研究者的回顾性评估。他们 没有及时提供信息，使患者能够向护理提供者、雇主、家人寻求帮助， 和朋友它们也没有授权提供者为患者探索越来越多的治疗选择- 以癌症为中心的治疗该提案旨在应用一种新的方法-应用程序辅助的白天重建-以 开发第一个数字健康工具，使远程病人监测后勤毒性，这是 为解决这一问题制定有效的护理干预措施的必要的第一步。 我们的产品在概念和技术上都是创新的。从概念上讲，我们将白天重建 方法-最初由幸福研究人员创建的方法，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和健康信息。 从技术上讲，我们利用现有的专利技术和新的机器学习技术， 客观移动的感测与主观患者输入的新颖集成。移动的传感和机器 学习将构成APP提供的与逻辑毒性有关的白天重建的“辅助”， 显着减少召回错误和手动输入的需要。“协助”还将提示患者 提供诸如不能被移动的传感或机器检测到的主观幸福感评级的信息 学习，产生比现有方法更准确和全面的逻辑毒性测量。 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）原型开发和初步测试，以及（3）应用程序的现场测试 在60名接受癌症治疗的不同患者中。在目标3中，患者将对应用程序的质量进行评分 使用移动的应用程序评级量表（MARS）和他们对三个关键应用程序功能的满意度：1）应用程序的 能够捕获与家庭外治疗相关的活动和旅行，2）易于输入的界面 基于家庭的治疗相关活动和健康评级，以及3）逻辑毒性的有用性 摘要报告。该项目的成果将是一个最终的原型应用程序，70%的患者表示 总体MARS评分为4.0或更高，并且对三个特征感到满意。在第二阶段，该小组将测试 应用程序的有效性-无论是单独使用还是与护理协调、远程医疗和基于家庭的 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。