Stress Detection Using Experience Sampling: A Systematic Mapping Study

Abstract

Stress has been designated the “Health Epidemic of the 21st Century” by the World Health Organization and negatively affects the quality of individuals’ lives by detracting most body systems. In today’s world, different methods are used to track and measure various types of stress. Among these techniques, experience sampling is a unique method for studying everyday stress, which can affect employees’ performance and even their health by threatening them emotionally and physically. The main advantage of experience sampling is that evaluating instantaneous experiences causes less memory bias than traditional retroactive measures. Further, it allows the exploration of temporal relationships in subjective experiences. The objective of this paper is to structure, analyze, and characterize the state of the art of available literature in the field of surveillance of work stress via the experience sampling method. We used the formal research methodology of systematic mapping to conduct a breadth-first review. We found 358 papers between 2010 and 2021 that are classified with respect to focus, research type, and contribution type. The resulting research landscape summarizes the opportunities and challenges of utilizing the experience sampling method on stress detection for practitioners and academics.

Investigating the effects of stress on achievement: BIOSTRESS dataset

“…Non-adjustable parameters consist of sampling rates of 64 Hz for BVP, 4 Hz for EDA, 32 Hz for ACC, and 4 Hz for temperature signals. EDA and BVP signals are also considered to be associated with stress-related physiological signals [6] , [7] , [8] , [9] .…”

Neurophysiological and biosignal data for investigating occupational mental fatigue: MEFAR dataset

“…Analyzing and recognizing this particular sort of fatigue, which is an emotion that diminishes situational awareness, the fundamental aspect of human and occupational safety [7] , might help minimize any negative consequences. Currently, the focus of research on emotion recognition often revolves around analyzing physiological information [8 , 9] utilizing artificial intelligence [10] . These experiments demonstrated a significant success rate and found that signals originating from locations directly influenced by the parasympathetic nervous system were more favored.…”

Digitalising mental health care: Practical recommendations from the European Psychiatric Association

“…Nonetheless, first evidence suggests that EMAs could increase precision in the characterisation of at-risk populations [ 50 ], in detecting stressful experiences at the individual level [ 51 ] and in managing major psychiatric disorders, such as depression [ 44 ] or schizophrenic psychoses [ 52 ]. An especially promising approach could be to utilise digital phenotypes for machine-learning-based prediction models in order to define group and individual prognostic trajectories better, to take into account a wider range of influencing factors, to optimise treatments, and to more reliably predict clinical outcomes [ 53 ].…”