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Stress Detection Using Experience Sampling: A Systematic Mapping Study

Author

Listed:
  • Gulin Dogan

    (Department of Computer Engineering, Istanbul Kultur University, Istanbul 34158, Turkey)

  • Fatma Patlar Akbulut

    (Department of Computer Engineering, Istanbul Kultur University, Istanbul 34158, Turkey)

  • Cagatay Catal

    (Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar)

  • Alok Mishra

    (Informatics and Digitalization Group, Faculty of Logistics, Molde University College-Specialized University in Logistics, 6410 Molde, Norway
    Software Engineering Department, Atilim University, Ankara 06830, Turkey)

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.

Suggested Citation

  • Gulin Dogan & Fatma Patlar Akbulut & Cagatay Catal & Alok Mishra, 2022. "Stress Detection Using Experience Sampling: A Systematic Mapping Study," IJERPH, MDPI, vol. 19(9), pages 1-39, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5693-:d:810409
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    References listed on IDEAS

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    1. Fatma Patlar Akbulut, 2022. "Hybrid deep convolutional model-based emotion recognition using multiple physiological signals," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 25(15), pages 1678-1690, October.
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    Cited by:

    1. Nusrat, Anam & He, Yong & Luqman, Adeel & Mehrotra, Ankit & Shankar, Amit, 2023. "Unraveling the psychological and behavioral consequences of using enterprise social media (ESM) in mitigating the cyberslacking," Technological Forecasting and Social Change, Elsevier, vol. 196(C).

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