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

Digital Health Reviews (1724) Machine Learning (2076) Anxiety and Stress Disorders (1330) Wearable Devices and Sensors (1133) Mobile Health (mhealth) (3464) Artificial Intelligence (2129) AI Applications in Biomedical Engineering (15)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52622, first published .
The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis

The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis

The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis

Authors of this article:

Alaa Abd-alrazaq1 Author Orcid Image ;   Mohannad Alajlani2 Author Orcid Image ;   Reham Ahmad2 Author Orcid Image ;   Rawan AlSaad1 Author Orcid Image ;   Sarah Aziz1 Author Orcid Image ;   Arfan Ahmed1 Author Orcid Image ;   Mohammed Alsahli3 Author Orcid Image ;   Rafat Damseh4 Author Orcid Image ;   Javaid Sheikh1 Author Orcid Image
Article Authors Cited by (22) Tweetations (3) Metrics

Journals

  1. Chen J, Yuan D, Dong R, Cai J, Ai Z, Zhou S. Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis. Frontiers in Psychology 2024;15 View
  2. Benouis M, Andre E, Can Y. Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy–Added Federated Learning Settings: Quantitative Study. JMIR Mental Health 2024;11:e60003 View
  3. Yıldız E. AI–Augmented Psychosocial Interventions: A Bibliometric Review and Implications for Nursing. Journal of Psychosocial Nursing and Mental Health Services 2025;63(6):11 View
  4. LeBlanc V, Mastoras G, Hicks C, MacGregor P, O'Rielly C, Petrosoniak A, Tavares W. The stressed heart: Validity evidence supporting mobile heart rate variability applications to detect psychological stress in healthcare learners. Medical Education 2025;59(7):729 View
  5. Adams M, Bowness J, Nelson A, Hurley R, Narouze S. A roadmap for artificial intelligence in pain medicine: current status, opportunities, and requirements. Current Opinion in Anaesthesiology 2025;38(5):680 View
  6. Lialiou P, Maglogiannis I. Students’ Burnout Symptoms Detection Using Smartwatch Wearable Devices: A Systematic Literature Review. AI Sensors 2025;1(1):2 View
  7. Marquez-Carpintero L, Gomez-Donoso F, Cazorla M. Engineering young faculty's acceptance of real-time behavior measurement software. Frontiers in Computer Science 2025;7 View
  8. Shen S, Qi W, Zeng J, Li S, Liu X, Zhu X, Dong C, Wang B, Shi Y, Yao J, Wang B, Lou X, Gu S, Li P, Wang J, Jiang G, Cao S. Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review. Journal of Medical Internet Research 2025;27:e77066 View
  9. Mishra D, Sahu P. The Potential of Wearable Sensor Technologies in Enhancing Personalized Health Monitoring and Management. Biomedical Materials & Devices 2025 View
  10. Lalwani S, Ferdowsi S. Predictive Modelling of Exam Outcomes Using Stress-Aware Learning from Wearable Biosignals. Sensors 2025;25(18):5628 View
  11. Van Oost C, Masci F, Malisse A, Schyvens A, Peters B, Dirix H, Ross V, Wets G, Neven A, Verbraecken J, Aerts J. Accuracy of Heart Rate Measurement Under Transient States: A Validation Study of Wearables for Real-Life Monitoring. Sensors 2025;25(20):6319 View
  12. Rana R, Higgins N, Stedman T, March S, Gucciardi D, Barua P, Joshi R. Passive AI Detection of Stress and Burnout Among Frontline Workers. Nursing Reports 2025;15(11):373 View
  13. Zhang H, Wang S, Huang Y, Xiu L, Wang Y. Inverted-U association between daily steps and WHO-5 in university students: non-linear modeling and robustness checks. Frontiers in Behavioral Neuroscience 2025;19 View
  14. Ghonge M, Shukla V, Pradeep N, Solanki R, Ghonge M, Shukla V, Garg L, Liu H, Tran T. Recent Advances in Multimodal Deep Learning for Stress Prediction: Toward Cycle-Aware and Gender-Sensitive Health Analytics. EPJ Web of Conferences 2025;341:01059 View
  15. Oleh U, Obermaisser R. Recognition of Anxiety-Related Activities using 1DCNNBiLSTM on Sensor Data from a Commercial Wearable Device. Procedia Computer Science 2025;272:351 View

Books/Policy Documents

  1. Soni A, Shaikh Z, Taqi M, Bijja V, Ratnakaram S. Wearable Devices and Smart Technology for Educational Teaching Assistance. View
  2. Ghosh R, Singh G, Sharma N. Harnessing AI and Machine Learning for Precision Wellness. View
  3. Guerriero M, di Padova M, Moscatelli F, Bellantonio S, Dipace A, Basta A. Higher Education Learning Methodologies and Technologies Online. View
  4. Efthymiou E, Katsarou D, Sofologi M, Megari K, Papadopoulou S, Mantsos E, Argyriadis A, Argyriadi A. AI in Learning, Educational Leadership, and Special Education. View
  5. Morales J, Ruiz E, Espinoza A, García A, Mass J, Mejía F. Artificial Intelligence – COMIA 2025. View
  6. Fulmer R, Li N, Liu S. Emancipatory Education Without Boundaries in the Age of Neoliberalism, Artificial Intelligence and Digital Learning Platforms. View

Conference Proceedings

  1. Özküçük G, Burunkaya M. 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA). Investigating Effects of the Window Size on Stress Detection Through Machine Learning View