Traditionally, working time arrangements to limit fatigue-related risk have taken a prescriptive approach, which sets maximum shift durations in order to prevent excessive buildup of fatigue (and the associated increased risk) within shifts and sets minimum break durations to allow adequate time for rest and recovery within and/or between shifts. Prescriptive rule sets can be successful when, from a fatigue-related risk standpoint, they classify safe work hours as permitted and unsafe work hours as not permitted. However, prescriptive rule sets ignore important aspects of the biological factors (such as the interaction between circadian and homeostatic processes) that drive fatigue, which are critical modulators of the relationship between work hours and fatigue-related risk. As such, in around-the-clock operations when people must work outside of normal daytime hours, the relationship between regulatory compliance and safety tends to break down, and thus these rule sets become less effective. To address this issue, risk management-based approaches have been designed to regulate the procedures associated with managing fatigue-related risk. These risk management-based approaches are suitable for nighttime operations and a variety of other non-standard work schedules, and can be tailored to the particular job or industry. Although the purpose of these fatigue risk management approaches is to curb fatigue risk, fatigue risk cannot be measured directly. Thus, the goal is not on regulating fatigue risk per se, but rather to put in place procedures that serve to address fatigue before, during, and after potential fatigue-related incidents. Examples include predictive mathematical modeling of fatigue for work scheduling, proactive fatigue monitoring in the workplace, and reactive post-incident follow-up. With different risks and different needs across industries, there is no “one size fits all” approach to managing fatigue-related risk. However, hybrid strategies combining prescriptive rule sets and risk management-based approaches can create the flexibility necessary to reduce fatigue-related risk based on the specific needs of different work environments while maintaining appropriate regulatory oversight.

A cognitive throughput task known as the Digit Symbol Substitution Test (DSST) (or Symbol Digit Modalities Test) has been used as an assay of general cognitive slowing during sleep deprivation. Here, the effects of total sleep deprivation (TSD) on specific cognitive processes involved in DSST performance, including visual search, spatial memory, paired-associate learning, and motor response, were investigated through targeted task manipulations. A total of 12 DSST variants, designed to manipulate the use of specific cognitive processes, were implemented in two laboratory-based TSD studies with N = 59 and N = 26 subjects, respectively. In each study, the Psychomotor Vigilance Test (PVT) was administered alongside the DSST variants. TSD reduced cognitive throughput on all DSST variants, with response time distributions exhibiting rightward skewing. All DSST variants showed practice effects, which were however minimized by inclusion of a pause between trials. Importantly, TSD-induced impairment on the DSST variants was not uniform, with a principal component analysis revealing three factors. Diffusion model decomposition of cognitive processes revealed that inter-individual differences during TSD on a two-alternative forced choice DSST variant were different from those on the PVT. While reduced cognitive throughput has been interpreted to reflect general cognitive slowing, such TSD-induced impairment appears to reflect cognitive instability, like on the PVT, rather than general slowing. Further, comparisons between task variants revealed not one, but three distinct underlying processes impacted by sleep deprivation. Moreover, the practice effect on the task was found to be independent of the TSD effect and minimized by a task pacing manipulation.