Measurement of Heart Rate and Heart Rate Variability in NeuroIS Research: Review of Empirical Results

Interruption science as a research field: Towards a taxonomy of interruptions as a foundation for the field

“…This allows for a better understanding of the development, adoption, and impact of ICTs by examining the underlying human behavior and users’ cognitive and affective processes in human-computer interaction to determine how and why certain effects occur when using digital technologies ( Riedl et al, 2010a ; Dimoka et al, 2012 ; Riedl and Léger, 2016 ; Riedl et al, 2017 ). As an example, heart rate and heart rate variability are relevant measures for several IS research domains in the area of human-computer interaction, which are used to objectively measure a person’s ability to respond to environmental demands ( Stangl and Riedl, 2022b ). Such measures could be considered in a potential early warning system as physiological indicators to measure autonomic nervous system activity to measure stress-related disturbances (i.e., interruptions) during task performance ( Stangl and Riedl, 2022a , 2022c ).…”

“…As an example, heart rate and heart rate variability are relevant measures for several IS research domains in the area of human-computer interaction, which are used to objectively measure a person’s ability to respond to environmental demands ( Stangl and Riedl, 2022b ). Such measures could be considered in a potential early warning system as physiological indicators to measure autonomic nervous system activity to measure stress-related disturbances (i.e., interruptions) during task performance ( Stangl and Riedl, 2022a , 2022c ). Indeed, empirical research showed that a combination of biometric data together with computer interaction data can predict with high accuracy the interruptibility of software developers at a given moment to avoid inappropriate moments for interruptions ( Züger and Fritz, 2015 ; Züger et al, 2018 ).…”

A primer on sampling rates of ambulatory assessments.

“…The most common locations for these wearable devices include the wrist, head, torso, chest, ear, and arm; and they are equipped to measure an array of biophysiological variables. For instance, they can provide data on heart rate (Fuller et al, 2020), heart rate variability (HRV; Stangl & Riedl, 2022), body temperature, respiration rate (De Fazio et al, 2021), oxygen saturation in the blood (Chan et al, 2022), and electrodermal activity (Sagl et al, 2019). Also, modern mobile neuroimaging techniques, such as functional near-infrared spectroscopy (Sun et al, 2018), portable magnetic encephalogram caps (Boto et al, 2018), and mobile deep brain recording (Topalovic et al, 2020), allow to measure neural responses in daily life (Reichert et al, 2021).…”

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

“…Utilizing AI algorithms, these devices can detect patterns or anomalies in health data indicative of emerging problems. For instance, wearables can analyze heart rate variability [133], other cardiac markers [134], and sleep patterns [135] to predict the risk of heart conditions and sleep disorders, facilitating early preventive measures. For example, a novel deep learning framework based on a hybrid CNN-LSTM model forecasts sleep apnea occurrence from single-lead ECG with an accuracy of up to 94.95% when validated on 70 sleep recordings [135].…”