Honn Kimberly A, VAN Dongen Hans P A, Dawson Drew
Sleep and Performance Research Center and Elson S. Floyd College of Medicine, Washington State University, USA.
Appleton Institute, CQUniversity, Australia.
Ind Health. 2019 Apr 1;57(2):264-280. doi: 10.2486/indhealth.SW-8. Epub 2019 Jan 31.
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.
传统上，为限制与疲劳相关的风险而进行的工作时间安排采用了一种规定性方法，即设定最长轮班时长，以防止在轮班期间过度积累疲劳（以及相关的风险增加），并设定最短休息时长，以便在轮班期间和/或轮班之间有足够的时间休息和恢复。当从与疲劳相关的风险角度来看，规定性规则集能够将安全工作时间归类为允许的，而将不安全工作时间归类为不允许时，它们可能会取得成功。然而，规定性规则集忽略了驱动疲劳的生物因素的重要方面（如昼夜节律和稳态过程之间的相互作用），而这些因素是工作时长与疲劳相关风险之间关系的关键调节因素。因此，在全天候运营中，当人们必须在正常白天时间之外工作时，合规与安全之间的关系往往会瓦解，从而这些规则集变得不那么有效。为了解决这个问题，基于风险管理的方法已被设计出来，以规范与管理疲劳相关风险相关的程序。这些基于风险管理的方法适用于夜间运营和各种其他非标准工作时间表，并且可以根据特定工作或行业进行调整。尽管这些疲劳风险管理方法的目的是控制疲劳风险，但疲劳风险无法直接测量。因此，目标不是直接管理疲劳风险本身，而是制定程序，以便在潜在的与疲劳相关的事件发生之前、期间和之后应对疲劳。示例包括用于工作排班的疲劳预测数学模型、工作场所的主动疲劳监测以及事件发生后的反应性跟进。由于不同行业存在不同风险和不同需求，不存在一种适用于所有情况的管理疲劳相关风险的方法。然而，将规定性规则集和基于风险管理的方法相结合的混合策略可以创造出必要的灵活性，以便根据不同工作环境的特定需求降低与疲劳相关的风险，同时保持适当的监管监督。
