Methodologies for Perioperative Stress Assessment in Surgeons: A Systematic Review

Surgeons are frequently exposed to psychological stress across the perioperative trajectory, which may affect surgeons' well-being and patient safety. Recent advances in wearable sensing, multimodal data analysis, and machine learning have expanded the methodological approaches available for perioperative stress assessment. However, a comprehensive synthesis of these methodological developments remains limited.

This systematic review included empirical studies published between January 2015 and June 2025 that investigated methodologies for surgeons' perioperative stress assessment. A total of 83 studies were included after screening records retrieved from PubMed, PsycArticles, CINAHL, Web of Science, and IEEE Xplore. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A thematic analysis was performed to identify methodological patterns across the included studies, and methodological quality was appraised using the Mixed Methods Appraisal Tool.

Self-report measures were the most frequently used approach, followed by physiological monitoring and behavioral observation. Across the included studies, increasing adoption of wearable sensing, multimodal data analysis, and machine learning was identified. The review also found substantial methodological variation in physiological data acquisition and processing, multimodal data integration, and machine learning implementation.

This review provides a comprehensive overview of the methodological landscape for surgeons' perioperative stress assessment and identifies key methodological trends and research gaps across current approaches. The findings provide a reference for future methodological research on perioperative stress assessment in surgeons.