To Be or Not to Be in Flow at Work: Physiological Classification of Flow Using Machine Learning

The focal role of flow in promoting desirable outcomes in companies, such as increased employees' well-being and performance, led scholars to study flow in the context of work. However, current measurement approaches which assess flow via self-report scales after task execution are limited due to obtrusiveness and a lack of real-time support. Hence, new measurement approaches must be created to overcome these limitations. In this article, we use cardiac features (heart rate variability; HRV) and a Random Forest classifier to distinguish high and low flow. Our results from a large-scale lab experiment with 158 participants and a field study with nine participants reveal, that with HRV features alone, flow-classifiers can be built with an accuracy of 68.5 percent (lab) and 70.6 percent (field). Our research contributes to the challenge of developing a less obtrusive, real-time measurement method of flow based on physiological features and to investigate flow from a physiological perspective. Our findings may serve as foundation for future work aiming to build physio-adaptive systems which can improve employee's performance. For instance, these systems could ensure that no notifications are forwarded to employees when they are 'sensing' flow.

Automated summary

This article presents the research results of using heart rate variability (HRV) and a Random Forest classifier to distinguish high and low flow, contributing to the investigation of flow from a physiological perspective. The findings demonstrate that flow-classifiers based on HRV features alone can achieve accuracies of 68.5% (lab) and 70.6% (field). These findings lay the foundation for future development of physio-adaptive systems to enhance employee performance.