Real Time Data Collection with Adaptive Sampling and Innovative Technologies

利用自适应采样和创新技术进行实时数据收集

• 批准号: 8676139
• 负责人: LORA Emilie BURKE
• 金额: $ 11.22万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-15 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8676139
• 关键词: Accounting; Adherence; Adult; Affect; Area; Attention; Behavior; Behavior monitoring; Behavioral; Behavioral Sciences; Body Weight; Body Weight decreased; Calculi; Cardiovascular system; Cellular Phone; Chronic Disease; Complex; Computer software; Data; Data Collection; Data Quality; Data Sources; Development; Devices; Diabetes Mellitus; Diet; Eating; Elements; Environment; Environmental Health; Epidemiology; Equilibrium; Event; Exercise; Feeling; Food; Frequencies; Future; Health behavior; Individual; Investigation; Kidney Diseases; Length; Link; Location; Malignant Neoplasms; Measurement; Measures; Methodology; Methods; Modeling; Modification; Monitor; Moods; Natural Sciences; Pain; Paper; Participant; Patient Self-Report; Patients; Pattern; Physical activity; Probability Samples; Process; Recording of previous events; Relapse; Reporting; Research; Research Design; Research Personnel; Risk; Sampling; Schedule; Science; Self Efficacy; Self Management; Sleep; Social Interaction; Social Sciences; Statistical Models; Stress; Study Subject; Study models; Surveys; Survival Analysis; System; Technology; Telephone; Testing; Time; Time Study; Tooth structure; Weight; Work; actigraphy; base; behavior change; behavioral/social science; data exchange; design; diaries; experience; hazard; improved; innovative technologies; insight; instrument; longitudinal design; model design; programs; public health relevance; research study; response; smoking relapse

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.

描述（由申请人提供）：生态瞬时评估（EMA）是一种数据收集方法，用于评估个人在自然环境中实时发生的体验。但是，由于现有的技术和它给参与者带来的负担，它的有用性受到限制。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，我们提出的方法将减轻与EMA相关的局限性，并提供一种量化EMA中传统上不测量的变量的方法，例如，睡眠持续时间和质量、身体活动、每日体重和位置，在一项有意的、有监督的成年人减肥研究中。我们将通过实时数据传输来验证我们的假设，将智能手机、体重秤、日记、活动记录仪和加速度计的信息联系起来。修改后的ema抽样将是反应适应性的——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。我们在这里提出进一步发展的模型的价值将适用于广泛的条件，在这些条件下，自我强加的行为改变过程得以维持或逆转，包括坚持自我管理慢性疾病（如糖尿病、肾病、癌症）。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，而且广泛适用于生物医学的生存分析，环境卫生的空间流行病学以及社会和行为科学的事件历史数据。