Novel machine learning and missing data methods for improving estimates of physical activity, sedentary behavior and sleep using accelerometer data

新颖的机器学习和缺失数据方法，可使用加速度计数据改进对身体活动、久坐行为和睡眠的估计

• 批准号: 10400835
• 负责人: MANISHA DESAI
• 金额: $ 33.15万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-03 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10400835
• 关键词: Accelerometer; Address; Adopted; Algorithms; Attention; Behavior; Behavioral; Blood Pressure; Body mass index; Clinical Trials; Communities; Computer software; Data; Devices; Environment; Fasting; Gaussian model; Gold; Health; Hour; Human; Hyperlipidemia; Hypertension; Inflammation; Insulin Resistance; Knowledge; Link; Lipids; Machine Learning; Measurement; Measures; Methods; Modeling; Monitor; Movement; Nature; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Participant; Pattern; Personal Satisfaction; Persons; Physical activity; Public Health; Research; Risk Factors; Sampling; Security; Series; Signal Transduction; Sleep; Time; Validation; Visualization; base; cardiovascular health; cardiovascular risk factor; data resource; diabetes risk; digital; health data; improved; insight; instrument; interest; learning algorithm; machine learning method; markov model; monitoring device; novel; obesity in children; open source; prevent; public health relevance; sedentary; sedentary lifestyle; simulation; sleep behavior; sleep pattern; standard measure; statistical and machine learning; tool; user friendly software; waist circumference; wearable device

PROJECT SUMMARY We propose novel statistical and machine learning methods for processing and analyzing accelerometer data for studying physical activity, sedentary behavior, and sleep and their effects on outcomes such as cardiovascular health. Methods to accurately estimate and characterize physical activity, sedentary behavior and sleep are crucially needed. Accelerometers have been widely adopted as the standard objective measure of movement in free-living humans. Recent advances have spawned instruments that collect enormous amounts of data that has far outpaced the research community’s ability to meaningfully interpret them. Current studies rely on outdated methods for identifying non-wear and addressing missing data, potentially yielding biased and inefficient estimates of relationships between behavioral activity patterns and outcomes. Importantly, methods for distinguishing between non-wear periods and those that represent sedentary behavior or sleep have not been validated using a gold standard in free-living contexts. The handling of non-wear periods using a statistically valid approach that exploits the multivariate and time- series nature of the data has yet to be developed. Thus, new methods are needed to address current gaps. We propose developing and validating an ensemble classifier to distinguish non-wear time. We will adapt and validate multiple imputation methods that exploit the multivariate and time-series nature of the data to handle non-wear time in analyses that make use of entire profiles of physical activity. Specifically, we will evaluate methods for incorporating multiple imputation for handling missing data from non-wear when applying adaptive clustering algorithms to identify distinct patterns of sleep and activity in order to relate them to outcomes in a generalized linear mixed effects model framework. We will create open-source user-friendly software that can be adopted and enhanced by the research community. Our approach integrates three novel data resources to develop our methods – two with knowledge of true activity and non-wear, and a third generated from a unique four-year longitudinal time series for both accelerometry and cardiovascular risk factor measures in a real- world setting. It offers an opportunity to develop and illustrate methods using data generated from wearable devices in a natural environment that includes missing data. This is the first study to incorporate missing data methods into learning algorithms under a generalized linear mixed effects model framework for accelerometer studies. Such methods will be critical for both observational and clinical trial research in real-world settings, where wear and non-wear time are not directly observed. The resulting insights and tools will also be highly applicable to the processing and analysis of other types of intensively sampled serial data, such as those generated from mobile digital devices.

项目摘要 我们提出了用于处理和分析加速计数据的新型统计和机器学习方法 用于研究体育锻炼，久坐行为和睡眠及其对结果的影响 心血管健康。准确估计和表征体育锻炼的方法久坐 行为和睡眠是完全需要的。加速度计已被广泛采用作为标准 自由生活人类运动的客观测量。最近的进步已经催生了 收集大量数据，这些数据已经超过了研究界的有意义的能力 解释它们。当前的研究依靠过时的方法来识别非服装并解决丢失的数据， 可能产生行为活动之间关系的偏见和效率低下的估计 模式和结果。重要的是，区分非服装时期的方法和 在自由生活环境中，尚未使用黄金标准来代表久坐的行为或睡眠。 使用具有统计有效的方法来处理非磨损时期，该方法利用了多元和时间 - 数据的系列性质尚未开发。这是需要新的方法来解决当前差距。 我们建议开发和验证整体分类器以区分非衣服时间。我们将适应 验证多种利用数据的多元和时间序列性质以处理的插补方法 分析的非磨损时间，这些分析利用了整个体育锻炼的特征。具体来说，我们将评估 应用自适应时，用于编码多个插入的方法来处理非服装的丢失数据 聚类算法以识别不同的睡眠和活动模式，以将其与结果相关联 广义线性混合效应模型框架。我们将创建开源用户友好的软件 被研究界采用和增强。我们的方法将三个新的数据资源集成到 开发我们的方法 - 两个具有真实活动和非磨损的知识，三分之一是由独特的 在实施中，加速度计和心血管危险因素的四年纵向时间序列 世界环境。它提供了使用可穿戴的数据开发和说明方法的机会 自然环境中的设备包括丢失数据。这是第一项纳入丢失数据的研究 在加速度计的广义线性混合效应模型框架下学习算法的方法 研究。这种方法对于现实世界中的观察和临床试验研究至关重要， 磨损和非磨损时间未直接观察到。由此产生的见解和工具也将是高度的 适用于处理和分析其他类型的深入采样序列数据，例如 由移动数字设备生成。