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，用于评估个人在真实的时间和自然环境中的经历。然而，由于现有技术和它给参与者带来的负担，它的用处有限。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自适应评估系统来改进EMA，该系统使用最先进的科学硬件和软件来限制受试者负担，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时间表来提高数据质量，同时减轻研究参与者的负担。我们假设，我们提出的方法将减轻与EMA相关的限制，并提供一种方法来量化EMA中传统上不测量的变量，例如，持续时间和睡眠质量，身体活动，每日体重和位置，在一项有意识的，有监督的成年人减肥研究中。我们将通过使用数据的实时传输来测试我们的假设，这些数据将来自智能手机、体重秤、日常日记、活动记录仪和加速计的信息联系起来。改良EMA抽样将是反应自适应的-增加随机评估的频率，以应对风险增加的指标，如3天的情绪低落或高水平的压力。我们建议在这里进一步开发的模型的价值将适用于广泛的条件，其中自我强加的行为改变的过程被维持或逆转，包括坚持慢性疾病的自我管理（例如，糖尿病、肾病、癌症）。此外，EMA的抽样设计和模型拟合，我们建议开发的框架预计将广泛适用于不仅EMA，但也生存分析在生物医学，空间流行病学在环境健康，并在社会和行为科学的事件历史数据。

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.