This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (3472) Ecological Momentary Assessment (EMA) (405) New Methods (1136) mHealth for Wellness, Behavior Change and Prevention (3320) Wearable Devices and Sensors (1137) Anxiety and Stress Disorders (1330) mHealth for Data Collection and Research (1198)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39995, first published .
Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study

Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study

Detecting Prolonged Stress in Real Life Using Wearable Biosensors and Ecological Momentary Assessments: Naturalistic Experimental Study

Authors of this article:

Rayyan Tutunji1 Author Orcid Image ;   Nikos Kogias1 Author Orcid Image ;   Bob Kapteijns1 Author Orcid Image ;   Martin Krentz1 Author Orcid Image ;   Florian Krause1 Author Orcid Image ;   Eliana Vassena1, 2 Author Orcid Image ;   Erno J Hermans1 Author Orcid Image
Article Authors Cited by (16) Tweetations (3) Metrics

Journals

  1. Bahameish M, Stockman T, Requena Carrión J. Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features. Sensors 2024;24(10):3210 View
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  3. Krause F, Linden D, Hermans E. Getting stress-related disorders under control: the untapped potential of neurofeedback. Trends in Neurosciences 2024;47(10):766 View
  4. Weale V, Love J, Clays E, Oakman J. The feasibility and acceptability of using EMA and physiological data to measure day-to-day occupational stress, musculoskeletal pain and mental health. BMC Research Notes 2024;17(1) View
  5. Grigorenko E. The extraordinary “ordinary magic” of resilience. Development and Psychopathology 2024;36(5):2481 View
  6. Li S, Fan C, Kargarandehkordi A, Sun Y, Slade C, Jaiswal A, Benzo R, Phillips K, Washington P. Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing. AI 2024;5(4):2725 View
  7. Kargarandehkordi A, Li S, Lin K, Phillips K, Benzo R, Washington P. Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review. Biosensors 2025;15(4):202 View
  8. Lialiou P, Maglogiannis I. Students’ Burnout Symptoms Detection Using Smartwatch Wearable Devices: A Systematic Literature Review. AI Sensors 2025;1(1):2 View
  9. Ometov A, Mezina A, Lin H, Arponen O, Burget R, Nurmi J. Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectives. Journal of Healthcare Informatics Research 2025;9(3):247 View
  10. Yangöz Ş, Turan Kavradım S, Özer Z. Wearable Technologies in Nursing Research. CIN: Computers, Informatics, Nursing 2025 View
  11. Raposa M, Brooks A, Miclette M, Holley D. Predicting Anxiety Severity with Digital Ecological Momentary Assessment of Stress, Sleep, and Mood. Journal of Technology in Behavioral Science 2025 View
  12. Liu Y, Palacio M, Bikki T, Toledo C, Ouyang Y, Li Z, Wang Z, Toledo F, Zeng H, Herrero M. Machine Learning, Physiological Signals, and Emotional Stress/Anxiety: Pitfalls and Challenges. Applied Sciences 2025;15(21):11777 View
  13. Bögemann S, Krause F, van Kraaij A, Marciniak M, van Leeuwen J, Weermeijer J, Mituniewicz J, Puhlmann L, Zerban M, Reppmann Z, Kobylińska D, Yuen K, Kleim B, Walter H, Myin-Germeys I, Kalisch R, Veer I, Roelofs K, Hermans E. Triggering just-in-time adaptive interventions based on real-time detection of daily-life stress: Methodological development and longitudinal multicenter evaluation. Behavior Research Methods 2025;58(1) View

Books/Policy Documents

  1. Kittelmann L, Reindl-Spanner P, Prommegger B, Gensichen J, Krcmar H. Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. View

Conference Proceedings

  1. Lin H, Ometov A, Arponen O, Nikunen K, Nurmi J. Proceedings of the 2024 International Conference on Information Technology for Social Good. Stress DeTech-tion: Revolutionizing Wellbeing in Future Networks View
  2. Maurya S, Bhutani A, Uchoi E. 2025 Global Conference in Emerging Technology (GINOTECH). Deep Learning-Based Multimodal Fusion for Heart Rate and Stress Estimation using remote PPG View