AI-Driven Wearable HRV Monitoring for Early Detection of Nurse Fatigue and Its Impact on Clinical Performance and Patient Safety: A Systematic Literature Review - Rusana Rusana; Novita Anggraenni; Iwan Purnawan; Yuli Widyastuti; Risti Linta Chumaira; Deni Irawan; Ady Irawan; Nova Maulana (GLOBAL HEALTH SCIENCE GROUP, 2026)
Nurse fatigue is a major occupational health concern that negatively affects clinical performance, recovery, and patient safety. Prolonged working hours, night shifts, and job-related stress disrupt autonomic balance and increase the likelihood of clinical errors. Heart Rate Variability (HRV) has been widely recognised as a sensitive physiological biomarker of fatigue and occupational stress. Integrating Artificial Intelligence (AI) with HRV-enabled wearable technology offers a promising approach for real-time and objective fatigue monitoring in nursing populations. This systematic literature review aimed to synthesise evidence on AI-enhanced HRV wearable technology for early detection and management of nurse fatigue. The review followed PRISMA guidelines and was registered in PROSPERO (CRD420251251457). Searches were conducted across six electronic databases (Scopus, ProQuest, ScienceDirect, SAGE, Wiley, and SpringerLink) using predefined Boolean keywords, including “wearable device”, “wearable technology”, “smartwatch”, “heart rate variability”, “artificial intelligence”, “machine learning”, “nurse*”, “nursing practice”, “fatigue”, and “clinical decision support”*. Articles published in English between 2015 and 2025 were included. From 1,689 records identified, 11 studies met the inclusion criteria after screening and methodological quality appraisal using Joanna Briggs Institute tools. AI-integrated wearable systems demonstrated high diagnostic performance, with fatigue detection accuracy around 80% for machine learning models and exceeding 99% for multimodal biosensing systems. Physiological biomarkers—including HRV, cortisol, electrodermal activity, and skin temperature—consistently reflected objective fatigue, particularly during extended working hours. Intervention studies showed that AI-supported HRV biofeedback and cognitive behavioural approaches improved autonomic regulation and nurse wellbeing. AI-enabled HRV wearable technology represents a feasible and promising strategy for early fatigue detection and wellbeing optimisation among nurses, with potential benefits for clinical performance and patient safety. However, practical implementation and long-term integration into healthcare systems remain key considerations for future research.