Personal informatics and negative emotions during commuter driving: Effects of data visualization on cardiovascular reactivity & mood

Abstract

Mobile technology and wearable sensors can provide objective measures of psychological stress in everyday life.Data from sensors can be visualized and viewed by the user to increase self-awareness and promote adaptive coping strategies. A capacity to effectively self-regulate negative emotion can mitigate the biological process of inflammation, which has implications for long-term health. Two studies were undertaken utilizing a mobile lifelogging platform to collect cardiovascular data over a week of real-life commuter driving. The first was designed to establish a link between cardiovascular markers of inflammation and the experience of anger during commuter driving in the real world. Results indicated that an ensemble classification model provided an accuracy rate of 73.12% for the binary classification of episodes of high vs. low anger based upon a combination of features derived from driving (e.g. vehicle speed) and cardiovascular psychophysiology (heart rate, heart rate variability, pulse transit time). During the second study, participants interacted with an interactive, geolocated visualisation of vehicle parameters, photographs and cardiovascular psychophysiology collected over two days of commuter driving (pre-test). Data were subsequently collected over two days of driving following their interaction with the dynamic, data visualization (post-test). A comparison of pre-and post-test data revealed that heart rate significantly reduced during episodes of journey impedance after interaction with the data visualization. There was also evidence that heart rate variability increased during the post-test phase, suggesting greater vagal activation and adaptive coping. Subjective mood data were collected before and after each journey, but no statistically significant differences were observed between pre-and post-test periods. The implications of both studies for ambulatory monitoring, user interaction and the capacity of personal informatics to enhance long-term health are discussed.

Flow theory in the information systems life cycle: The state of the art and future research agenda

“…Marsden and Pingry (2018) reported that interviews, field‐experiments, quasi‐experiments, controlled laboratory experiments, empirically observed and third party fee‐for‐service data are alternative ways to gather numerical data in IS research. Moreover, with the recent advances in technology, wearable sensors, electromyographic signals and eye movement data can be used to infer human behavioural intentions (Fairclough & Dobbins, 2020; Park et al, 2016). As such, these tools could be used a complement to conventional research methods.…”

“…This is one area that might need consideration while designing data collection tools for flow-based studies. Marsden and Pingry (2018) (Fairclough & Dobbins, 2020;Park et al, 2016). As such, these tools could be used a complement to conventional research methods.…”

Research on the Influence of Visual Factors on Emotion Regulation Interaction

“…Users can view the data from sensors and objectively measure the psychological pressure in daily life to improve self-awareness and promote emotion regulation or behavior change. A notable experiment showed that interactive data visualization could reduce the user’s heart rate during traffic congestion ( Fairclough and Dobbins, 2020 ). With the proposal of the meta-universe, virtual reality has attracted more attention.…”

Are Psychophysiological Wearables Suitable for Comparing Pedagogical Teaching Approaches?

“…These can monitor various parameters of the observed students (and teachers) during the process of teaching and learning in a complex physical and sociological environment such as a classroom. Autonomous wearable measurement devices that are worn on the user’s body to monitor the user’s physical, (psycho)physiological, mental, emotional, and behavioral activity are currently being used with increasing regularity [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ] but are not widely used in empirical research in educational science. They are usually used to measure students’ physical activity, and few are used to measure students’ psychophysiology [ 35 , 52 , 53 ].…”