A mobile sensing system to monitor symptoms during chemotherapy

癌症治疗可能会导致各种管理不善的症状和毒性， 损害生活质量和功能，并导致过早停止延长生命 看病本研究的主要目的为：（1）发展一种新型的移动的传感器 系统，以被动监测化疗期间的症状负担和（2）评估 利用该系统推荐患者-提供者的可行性和可接受性 通信为了实现目标1，我们将招募200名肿瘤患者， 化疗方案，并将持续收集智能手机和可穿戴传感器数据， 以及90天内每天通过智能手机报告的患者症状。使用机器学习 方法，我们将回顾性分析前100名参与者的数据，并制定一个 可推广和简约的人口水平模型，以确定严重的症状天。为 第二个100名参与者，我们将实时运行这些计算模型， 前瞻性评估我们的分类相对于患者报告的症状的准确性。 对于目标2，我们将招募50名患者参加一项前瞻性单臂试验， 基于传感和机器学习的推断，以推荐与提供者的联系， 将评估患者增加、流失和依从性以及来自两名患者的信息 和他们的供应商关于系统的可接受性和感知的有用性。科学 该提议的前提是，移动的感测行为中的细微波动， 实时计算建模可以实现更早的检测， 癌症治疗期间的严重症状管理。拟议项目建立在我们的 事先研究并完成必要的开发和可行性工作，以支持大型 比较移动的传感系统与标准治疗以确定对 症状负担、生活质量、卫生保健利用和生存率。

Cancer treatments can cause a variety of poorly-managed symptoms and toxicities that can impair quality of life and functioning and lead to early discontinuation of life-prolonging medical treatment. The goals of the proposed study are (1) to develop a novel mobile sensing system to passively monitor symptom burden during chemotherapy and (2) to evaluate the feasibility and acceptability of using this system to recommend patient-provider communication. To accomplish Aim 1, we will enroll 200 oncology patients starting a new chemotherapy regimen and will collect smartphone and wearable sensor data continuously as well as daily patient-reported symptoms via smartphones for 90 days. Using machine learning methods, we will retrospectively analyze data from the first 100 participants and develop a generalizable and parsimonious population-level model to identify severe symptom days. For the second 100 participants, we will run these computational models in real-time and prospectively evaluate the accuracy of our classifications relative to patient-reported symptoms. For Aim 2, we will enroll 50 patients in a prospective single-arm trial of a system that uses inferences based on sensing and machine learning to recommend contact with providers and will evaluate patient accrual, attrition, and compliance as well as information from both patients and their providers about acceptability and perceived usefulness of the system. The scientific premise of this proposal is that mobile sensing of subtle fluctuations in behavior coupled with real-time computational modeling could enable earlier detection and ultimately better management of severe symptoms during cancer treatment. The proposed project builds on our prior research and completes the necessary development and feasibility work to support a large multisite trial comparing the mobile sensing system to standard of care to determine effects on symptom burden, quality of life, health care utilization, and survival.