This narrative review examines the theoretical foundations of mental workload, evaluates biometric monitoring methods, addresses ethical and privacy issues, and highlights future directions for longitudinal research. We focus on the integration of wearable sensors, multimodal data, artificial intelligence (AI), and machine learning (ML) frameworks to enhance adaptive task scheduling and safety in cognitively demanding professions. Continuous, real-time monitoring through wearable devices and multidimensional data analysis show promise for identifying and managing cognitive overload before it degrades performance. Despite this potential, significant challenges exist, including data protection, sensor reliability, calibration consistency, information processing, network capabilities, and variability in individuals' responses. Physiological and behavioral measures as well as subjective and performance indicators offer valuable insights into the early signs of cognitive strain, suggesting that biometric monitoring could help organizations detect performance decline sooner. Evidence shows that these technologies are feasible in professions that require high precision, rapid decision-making, and sustained attention. However, only sparse longitudinal comparisons exist regarding the effectiveness of different biometric tools in real-world operational contexts, particularly with respect to data security and standardization. Integrating physiological and behavioral data with subjective assessments analyzed through AI and ML may enable early warning signs for overload in both individuals and teams working in high-stress, time-critical settings. Such approaches could inform work-recovery cycles, reduce error rates, and sustain cognitive performance. Further empirical research is necessary to confirm sensor accuracy in applied environments and to validate AI and ML predictions before large-scale deployment in sectors such as air traffic control, public safety, healthcare, and industrial operations.
O’Hara et al. (Fri,) studied this question.