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.
Keywords: Bland-Altman plots; heart rate variability; psychophysiological data; root mean square error; skin conductance response; stationary equipment; wearable device.
© 2020 Society for Psychophysiological Research.