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: 10548871
• 负责人: MANISHA DESAI
• 金额: $ 33.45万
• 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/10548871
• 关键词: Accelerometer; Address; Adopted; Algorithms; Attention; Behavior; Behavioral; Blood Pressure; Body mass index; Categories; Clinical Trials; Communities; Computer software; Data; Devices; Dimensions; Environment; Exclusion; Fasting; 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; Recommendation; Research; Risk Factors; Sampling; Security; Series; Signal Transduction; Sleep; Time; Validation; Visualization; cardiovascular health; cardiovascular risk factor; data resource; diabetes risk; digital; digital monitoring; 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 Current studies rely on outdated methods for identifying non-wear and addressing missing data, potentially yielding biased and inefficient estimates of relationships between behavioral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 我们提出了新的统计和机器学习方法来处理和分析加速度计数据 用于研究身体活动、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身体活动、久坐 行为和睡眠是至关重要的。加速度计已被广泛采用为标准 对自由生活的人类运动的客观测量。最近的进展产生了一些工具， 收集大量的数据，这些数据已经远远超过了研究界的能力， 解读它们。目前的研究依赖于过时的方法来识别非磨损和解决缺失的数据， 潜在地产生对行为活动之间的关系的有偏见的和低效的估计。 模式和结果。重要的是，用于区分非磨损期和 代表久坐不动的行为或睡眠没有得到验证，在自由生活的背景下使用黄金标准。 使用利用多元和时间的统计有效方法处理非磨损期 数据的系列性质尚待开发。因此，需要新的方法来解决目前的差距。 我们建议开发和验证一个集成分类器来区分非磨损时间。我们会适应， 验证利用数据的多变量和时间序列性质来处理的多种插补方法 在分析中使用整个身体活动曲线的非磨损时间。具体来说，我们将评估 当应用自适应时，用于处理非磨损缺失数据的多重插补方法 聚类算法，以确定不同的模式的睡眠和活动，以便将它们与结果在一个 广义线性混合效应模型我们将创建开源的用户友好的软件， 被研究界采纳和加强。我们的方法集成了三种新的数据资源， 开发我们的方法-两个具有真实活动和非磨损的知识，第三个是从独特的 在一个真实的- 世界设定它提供了一个机会来开发和说明使用可穿戴设备生成的数据的方法 设备在自然环境中，包括丢失的数据。这是第一个纳入缺失数据的研究 加速度计广义线性混合效应模型框架下的学习算法 问题研究这些方法对于现实世界中的观察和临床试验研究都至关重要， 其中不直接观察磨损和非磨损时间。由此产生的见解和工具也将高度 适用于其他类型的密集采样串行数据的处理和分析，例如 从移动的数字设备产生。