A mobile sensing system to monitor symptoms during chemotherapy

癌症治疗可能导致各种管理不善的症状和毒性，这些症状和毒性 损害生活质量和功能，并导致提早停止延寿 医疗救治。提出的研究目标是(1)开发一种新型的移动传感 被动监测化疗期间症状负担的系统和(2)评估 利用该系统推荐供方的可行性和可接受性 沟通。为了实现目标1，我们将招收200名肿瘤科患者开始新的 化疗方案，并将持续收集智能手机和可穿戴传感器的数据 以及90天内每天通过智能手机报告的症状。使用机器学习 方法，我们将对前100名参与者的数据进行回顾分析，并制定一项 识别严重症状天数的概括性和简约性人口水平模型。为 第二个100名参与者，我们将实时运行这些计算模型 根据患者报告的症状，前瞻性评估我们分类的准确性。 对于目标2，我们将招募50名患者参加一项前瞻性的单臂试验，该系统使用 基于感知和机器学习的推论，以推荐与提供商和 将评估患者的累积、自然减员和依从性，以及来自两个患者的信息 以及他们的提供者对系统的可接受性和感知的有用性的看法。科学的 这一提议的前提是，移动感知行为的细微波动与 实时计算建模可以实现更早的检测，并最终更好地 癌症治疗中严重症状的处理。拟议的项目建立在我们的 前期研究并完成必要的开发和可行性工作，以支持大型 将移动传感系统与护理标准进行比较以确定影响的多点试验 症状负担、生活质量、卫生保健利用和生存。

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.