Real Time Data Collection with Adaptive Sampling and Innovative Technologies

描述(由申请人提供)：生态瞬时评估(EMA)是一种数据收集方法，用于评估个体在自然环境中实时发生的经历。然而，由于现有的技术和它给参与者带来的负担，它的实用性受到了限制。我们建议通过进一步开发和测试由我们的跨学科团队设计的适应性评估系统来改进EMA，该系统使用最先进的硬件和软件来限制受试者的负担，同时促进实时数据收集。我们对EMA的修改旨在通过优化随机提示测量瞬时协变量的采样计划来提高数据质量，同时减轻研究参与者的负担。我们假设，我们提出的方法将缓解与EMA相关的限制，并提供一种方法来量化在EMA中传统上没有测量的变量，例如睡眠时间和质量、体力活动、日常体重和位置，在一项关于成人故意、监督减肥的研究中。我们将通过使用实时数据传输来链接来自智能手机、体重秤、日常日记、活动记录仪和加速计的信息来验证我们的假设。修改后的EMA抽样将是响应自适应的--增加随机评估的频率，以响应风险增加的指标，如3天的低情绪或高水平的压力。我们建议在这里进一步开发的模型的价值将适用于维持或逆转自我强加的行为改变过程的广泛条件，包括坚持慢性病(例如糖尿病、肾脏疾病、癌症)的自我管理。此外，我们建议开发的EMA抽样设计和模型拟合框架预计不仅广泛适用于EMA，而且还适用于生物医学中的生存分析、环境卫生中的空间流行病学以及社会科学和行为科学中的事件历史数据。

DESCRIPTION (provided by applicant): Ecological momentary assessment (EMA) is a data collection method that assesses individual's experiences as they occur in real time and in the natural environment. Its usefulness has been limited, however, by the available technology and the burden it places on the participant. We propose to improve EMA by further developing and testing an adaptive assessment system designed by our interdisciplinary team using state-of-the-science hardware and software that limits subject burden while facilitating real-time data collection. Our modifications to EMA are designed to increase data quality by optimizing the sampling schedule for random prompts measuring momentary covariates, while reducing the burden on study participants. We hypothesize that our proposed method will mitigate limitations associated with EMA and provide a means to quantify variables not traditionally measured in EMA, e.g., duration and quality of sleep, physical activity, daily weight and location, in a study of intentional, supervised weight loss in adults. We will test our hypothesis by using real-time transmission of data to link information from smart-phones, weight scales, daily diaries, actigraphs, and accelerometers. Modified-EMA sampling will be response adaptive - increasing the frequency of random assessments in response to indicators of increased risk, such as low mood for 3 days or high levels of stress. The value of the models we propose to further develop here will be applicable to a wide range of conditions in which the process of self-imposed behavior change is maintained or reversed, including adherence to self-management of chronic diseases (e.g., diabetes, kidney disease, cancer). Additionally, the framework for EMA sampling design and model fitting that we propose to develop is anticipated to be broadly applicable not only to EMA, but also to survival analysis in biomedicine, spatial epidemiology in environmental health, and to event history data in the social and behavioral sciences.