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，可以评估个人在实时和自然环境中发生的经验。但是，它的实用性受到了可用技术及其对参与者的负担的限制。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和测试的自适应评估系统，并使用科学硬件和软件限制主题负担，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来测量瞬时协变量的采样时间表来提高数据质量，同时减少研究参与者的负担。我们假设我们提出的方法将减轻与EMA相关的限制，并提供一种量化不传统上在EMA中测量的变量的方法，例如，在成人有意，有监督的体重减轻的研究中，睡眠，体育锻炼，日常体重和位置的持续时间和质量，身体活动，日常体重和位置。我们将通过使用数据实时传输来链接来自智能手机，重量尺度，日记，行动仪和加速度计的信息来检验我们的假设。修改的 - EMA采样将是反应自适应 - 响应增加风险的指标，例如3天的情绪低落或高水平的压力，从而增加随机评估的频率。我们建议在这里进一步开发的模型的价值将适用于多种疾病，在这些条件下，自我强制行为改变或逆转的过程，包括遵守慢性疾病的自我管理（例如糖尿病，肾脏疾病，癌症）。此外，我们建议开发的EMA采样设计和模型拟合的框架预计将不仅适用于EMA，而且适用于生物医学，环境健康中的空间流行病学以及社会和行为科学的事件历史数据。