An integrative review of cognitive workload assessment for safety management

Accurate measurement of cognitive workload is crucial for ensuring system performance and reliability. Currently, cognitive workload has been measured by questionnaire, task performance and physiological parameters. With the rapid development of wearable devices and data mining, more and more researchers applied these technologies to achieve an objective and real-time assessment of cognitive workload, so a review with the updated research on its assessment is in great need. To depict a comprehensive view of cognitive workload assessment in safety management, and offer advice on future assessment for nurse managers. This study was conducted following the integrative review method of Whittemore and Knafl. A systematic search of cognitive workload assessment in safety management was conducted across PubMed, Web of Science, Scopus and EBSCO from the inception to January 1st 2025. Data from each article were extracted and summarized in accordance with research question. 146 articles were included, most of them came from high-risk industries, only 4 were conducted by nurse researchers. NASA-TLX is the most widely used questionnaire. EEG, EOG and ECG were the top 3 preferred signals for classification. Multi-signals can facilitate better classification performance. Among all the classification method, SVM, KNN and composite classifier were more preferred. Measuring cognitive workload by physiological parameters through machine learning can facilitate objective and real-time assessment, but feature artifacts and classification efficiency are two major concerns. Most subjects in included studies were male. Given the uniqueness of nursing work, though this review can offer enlightenment on future cognitive workload assessment, more researches should be done to establish adaptable cognitive workload assessment method.