DATA ANALYTICS FOR HEALTH-RELEVANT EVENTS DETECTION BASED UPON LONGITUDINAL FITBIT HEART RATE DATA
DATA ANALYTICS FOR HEALTH-RELEVANT EVENTS DETECTION BASED UPON LONGITUDINAL FITBIT HEART RATE DATA
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DOI：
10.1093/geroni/igad104.2810
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发表时间：
2023-12-21
影响因子：
7
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可穿戴设备的日益普及使得能够低成本地长期收集健康相关数据，例如心率、运动和睡眠信号。目前，这些数据用于监测短期变化，对其与健康的相关性解释有限。这些数据为监测日常和长期活动模式提供了一种尚未开发的资源。从这些数据中确定的变化和趋势可以为许多随时间变化的慢性病的管理提供见解和指导。在这项研究中，我们对多年来从Fitbit设备收集的纵向心率数据进行了基于机器学习的分析。我们建立了一个多分辨率的管道时间序列分析，使用无模型聚类方法的启发，统计共形预测框架。通过这种方法，我们能够检测健康相关事件，它们的有趣模式（例如，日常生活、季节差异和异常），以及与健康状况的急性和慢性变化的相关性。我们提出的结果，经验教训和见解，以及如何解决缺乏标签的挑战。该研究证实了长期心率数据对健康监测和监督的价值，作为医疗保健提供者广泛但间歇性检查的补充。
The increasing prevalence of wearable devices enables low-cost, long-term collection of health relevant data such as heart rate, exercise, and sleep signals. Currently these data are used to monitor short term changes with limited interpretation of their relevance to health. These data provide an untapped resource to monitor daily and long-term activity patterns. Changes and trends identified from such data can provide insights and guidance to the management of many chronic conditions that change over time. In this study we conducted a machine learning based analysis of longitudinal heart rate data collected over multiple years from Fitbit devices. We built a multi-resolutional pipeline for time series analysis, using model-free clustering methods inspired by statistical conformal prediction framework. With this method, we were able to detect health relevant events, their interesting patterns (e.g., daily routines, seasonal differences, and anomalies), and correlations to acute and chronic changes in health conditions. We present the results, lessons, and insights learned, and how to address the challenge of lack of labels. The study confirms the value of long-term heart rate data for health monitoring and surveillance, as complementary to extensive yet intermittent examinations by health care providers.