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

项目总结 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 用于研究体力活动、久坐行为和睡眠及其对结果的影响，例如 心血管健康。准确估计和表征久坐的体力活动的方法 行为和睡眠是至关重要的。加速度计已被广泛采用为标准 对自由生活的人类活动的客观测量。最近的进步催生了一些工具， 收集的海量数据远远超过了研究界有意义地 解读它们。目前的研究依赖于过时的方法来识别非磨损和寻址丢失的数据， 潜在地产生对行为活动之间关系的有偏见和低效的估计 模式和结果。重要的是，区分非磨损时期和非磨损时期的方法 代表久坐的行为或睡眠尚未在自由生活环境中使用黄金标准进行验证。 使用统计上有效的方法来处理非磨损周期，该方法利用了多变量和时间- 数据的系列性还有待开发。因此，需要新的方法来弥补目前的差距。 我们建议开发和验证一个集成分类器来区分不磨损时间。我们将适应和 验证利用要处理的数据的多变量和时间序列性质的多种补偿方法 利用整个体力活动轮廓的分析中的无磨损时间。具体地说，我们将评估 在应用自适应时合并多个补偿以处理来自非磨损的缺失数据的方法 聚类算法，以识别不同的睡眠和活动模式，以便将它们与 广义线性混合效应模型框架。我们将创建开源的用户友好的软件，可以 被研究界采纳和加强。我们的方法集成了三个新的数据资源来 开发我们的方法-两个具有真实活动和非磨损的知识，第三个由独特的 加速度量学和心血管危险因素测量的四年纵向时间序列 世界背景。它提供了使用可穿戴设备生成的数据来开发和说明方法的机会 包括丢失数据的自然环境中的设备。这是第一项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 学习。这些方法对于现实世界环境中的观察性和临床试验研究都是至关重要的， 其中磨损和非磨损时间不能直接观察到。由此产生的洞察力和工具也将高度 适用于其他类型密集采样的串行数据的处理和分析，如 由移动数字设备生成。

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 Current studies rely on outdated methods for identifying non-wear and addressing missing data, potentially yielding biased and inefficient estimates of relationships between behavioral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.