Department of Psychology, University of Cyprus, Nicosia, Cyprus.
Department of Computer Science, University of Cyprus, Nicosia, Cyprus.
Psychophysiology. 2020 May;57(5):e13551. doi: 10.1111/psyp.13551. Epub 2020 Feb 19.
Wearable devices capable of capturing psychophysiological signals are popular. However, such devices have, yet, to be established in experimental and clinical research. This study, therefore, compared psychophysiological data (skin conductance level (SCL), heart rate (HR), and heart rate variability (HRV)) captured with a wearable device (Microsoft band 2) to those of a stationary device (Biopac MP150), in an experimental pain induction paradigm. Additionally, the present study aimed to compare two analytical techniques of HRV psychophysiological data: traditional (i.e., peaks are detected and manually checked) versus automated analysis using Python programs. Forty-three university students (86% female; Mage = 21.37 years) participated in the cold pressor pain induction task. Results showed that the majority of the correlations between the two devices for the mean HR were significant and strong (rs > .80) both during baseline and experimental phases. For the time-domain measure of mean RR (function of autonomic influences) of HRV, the correlations between the two devices at baseline were almost perfect (rs = .99), whereas at the experimental phase were significantly strong (rs > .74). However, no significant correlations were found for mean SCL (p> .05). Additionally, automated analysis led to similar features for HRV stationary data as the traditional analysis. Implications for data collection include the establishment of a methodology to compare stationary to mobile devices and a new, more cost efficient way of collecting psychophysiological data. Implications for data analysis include analyzing the data faster, with less effort and allowing for large amounts of data to be recorded.
可穿戴设备能够捕获心理生理信号，因此很受欢迎。然而，这类设备在实验和临床研究中尚未得到充分验证。因此，本研究在实验性疼痛诱发范式中，比较了可穿戴设备（Microsoft band 2）和固定式设备（Biopac MP150）所捕获的心理生理数据（皮肤电导水平（SCL）、心率（HR）和心率变异性（HRV））。此外，本研究旨在比较 HRV 心理生理数据的两种分析技术：传统分析（即，检测并手动检查峰值）与使用 Python 程序的自动分析。43 名大学生（86%为女性；平均年龄 21.37 岁）参与了冷加压疼痛诱发任务。结果表明，在基线和实验阶段，两种设备的大多数平均 HR 相关系数都是显著且较强的（rs>.80）。对于 HRV 的时域测量平均 RR（自主神经影响的函数），两种设备在基线时的相关性几乎是完美的（rs=.99），而在实验阶段的相关性显著较强（rs>.74）。然而，对于平均 SCL（p>.05），没有发现显著相关性。此外，自动分析得出的 HRV 固定数据特征与传统分析相似。数据收集的意义包括建立一种将固定式设备与移动式设备进行比较的方法，以及一种新的、更具成本效益的收集心理生理数据的方法。数据分析的意义包括更快、更省力地分析数据，并允许记录大量数据。
