Data quality challenges for person-generated health and wellness data
Data quality challenges for person-generated health and wellness data
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DOI：
10.1147/jrd.2017.2762218
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发表时间：
2018-01-01
影响因子：
1.3
通讯作者：
Chen, C-H
中科院分区：
文献类型：
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作者：
Codella, J.;Partovian, C.;Chen, C-H
由可穿戴设备和智能手机应用程序生成的个人健康数据（PGHD）正在迅速增长。人们越来越多地努力采用先进的分析方法，从这些数据中产生见解，以帮助人们改变生活方式，改善健康状况。PGHD-如步数，运动日志，营养日记和睡眠记录-通常是不完整的，不准确的，并且收集的时间太短。用户对可穿戴和移动的技术的参与不足，以及缺乏传感器验证、数据收集的标准化、数据处理假设的透明度以及对消费级传感器相关数据的可访问性，也会对数据质量产生负面影响。关于PGHD数据质量的文献稀少且零散，几乎没有为数据分析师提供如何评估和优先考虑数据质量问题的指导。在本文中，我们总结了我们作为数据分析师与PGHD合作的经验，概述了使用PGHD进行洞察力生成的一些挑战，并讨论了解决这些挑战的一些既定方法。我们回顾了有关PGHD数据质量的文献，确定了PGHD生态系统中的主要利益相关者，并应用了一个既定的数据质量框架，为每个利益相关者提出了最相关的数据质量挑战。
Person-generated health data (PGHD) generated by wearable devices and smartphone applications are growing rapidly. There is increasing effort to employ advanced analytical methods to generate insights from these data in order to help people change their lifestyle and improve their health. PGHD-such as step counts, exercise logs, nutritional diaries, and sleep records-are often incomplete, inaccurate, and collected over too short a duration. Insufficient user engagement with wearable and mobile technologies, as well as lack of sensor validation, standardization of data collection, transparency of data processing assumptions, and accessibility to relevant data from consumer-grade sensors, also negatively affects data quality. The literature on data quality for PGHD is sparse and fragmented, providing little guidance to data analysts on how to assess and prioritize data quality concerns. In this paper, we summarize our experiences as data analysts working with PGHD, outline some of the challenges in using PGHD for insight generation, and discuss some established methods for addressing these challenges. We review the literature on PGHD data quality, identify the major stakeholders in the PGHD ecosystem, and apply an established data quality framework to present the most relevant data quality challenges for each stakeholder.