Nelson Benjamin W, Low Carissa A, Jacobson Nicholas, Areán Patricia, Torous John, Allen Nicholas B
Department of Psychology, University of Oregon, Eugene, OR USA.
Department of Psychiatry and Behavioral Sciences and Department of Rehabilitation Medicine, University of Washington, Seattle, WA USA.
NPJ Digit Med. 2020 Jun 26;3:90. doi: 10.1038/s41746-020-0297-4. eCollection 2020.
Researchers have increasingly begun to use consumer wearables or wrist-worn smartwatches and fitness monitors for measurement of cardiovascular psychophysiological processes related to mental and physical health outcomes. These devices have strong appeal because they allow for continuous, scalable, unobtrusive, and ecologically valid data collection of cardiac activity in "big data" studies. However, replicability and reproducibility may be hampered moving forward due to the lack of standardization of data collection and processing procedures, and inconsistent reporting of technological factors (e.g., device type, firmware versions, and sampling rate), biobehavioral variables (e.g., body mass index, wrist dominance and circumference), and participant demographic characteristics, such as skin tone, that may influence heart rate measurement. These limitations introduce unnecessary noise into measurement, which can cloud interpretation and generalizability of findings. This paper provides a brief overview of research using commercial wearable devices to measure heart rate, reviews literature on device accuracy, and outlines the challenges that non-standardized reporting pose for the field. We also discuss study design, technological, biobehavioral, and demographic factors that can impact the accuracy of the passive sensing of heart rate measurements, and provide guidelines and corresponding checklist handouts for future study data collection and design, data cleaning and processing, analysis, and reporting that may help ameliorate some of these barriers and inconsistencies in the literature.
研究人员越来越多地开始使用消费级可穿戴设备、腕戴式智能手表和健身监测器来测量与身心健康结果相关的心血管心理生理过程。这些设备具有很大的吸引力，因为在“大数据”研究中，它们能够对心脏活动进行连续、可扩展、不引人注意且符合生态效度的数据收集。然而，由于数据收集和处理程序缺乏标准化，以及在技术因素（如设备类型、固件版本和采样率）、生物行为变量（如体重指数、手腕优势和周长）和参与者人口统计学特征（如肤色）等可能影响心率测量的因素方面报告不一致，未来的可重复性和再现性可能会受到阻碍。这些限制给测量引入了不必要的噪声，可能会模糊研究结果的解释和普遍性。本文简要概述了使用商业可穿戴设备测量心率的研究，回顾了关于设备准确性的文献，并概述了非标准化报告给该领域带来的挑战。我们还讨论了可能影响心率测量被动感知准确性的研究设计、技术、生物行为和人口统计学因素，并为未来的研究数据收集与设计、数据清理与处理、分析和报告提供指导方针及相应的清单手册，这可能有助于改善文献中的一些障碍和不一致之处。
