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FAST Talk Recap: Predicting Fatigue Beyond the Three-Process Model

Education is an important part of fatigue risk management. Everyone knows how to sleep, but getting proper rest and maintaining alertness under certain working conditions is just not intuitive. Humans are not built to sleep during the day or adjust rapidly to travel across time zones. We get burnt out if the workload is too high and bored if the workload is too low. Shift workers, transportation professionals, military, and first responders cannot rely on their instincts to guide proper recovery following fatiguing work events because their instincts are receiving confusing inputs.

Environmental cues like light exposure, social activity, temperature, and noise may be at odds with signals from the body’s internal clock if you’re trying to sleep during the day or in a different time zone. This circadian misalignment disrupts sleep, meaning that you may not be able to fully recharge even if you have the opportunity to sleep as long as you’d like. Add to this long working hours mixed with short rest opportunities, and it is easy to see how quickly fatigue may accumulate. To add insult to injury, someone who is repeatedly exposed to fatiguing conditions becomes accustomed to it and can no longer accurately evaluate their own ability to perform. Workers under these circumstances cannot rely on their intrinsic sleep management skills, which is why education about fatigue is so important.


Scientists in laboratories all over the world study the causes of operational fatigue and test its countermeasures. Researchers regularly give talks about their studies at academic conferences or webinars but rarely have the opportunity to speak directly with the populations they are trying to help—i.e., workers. The science team at the Institutes for Behavior Resources (IBR) works directly with safety-sensitive industries worldwide through the licensing of SAFTE-FAST software, development of policy documents and education materials, and consulting services. IBR scientists also attend many of the conferences, forums, and talks given by other fatigue researchers and pass this information on to our industry partners.


One of our initiatives in 2024 has been to launch a webinar series that brings scientists from other institutions directly into contact with industry specialists who may be using SAFTE-FAST as part of their fatigue risk management system (FRMS). We recently hosted our first talk in the series, called FAST Talk, on June 11, 2024. Director of the Sleep and Performance Research Center at Washington State University Spokane, Dr. Hans P.A. Van Dongen, PhD, did us the honor of being the first FAST Talk speaker. Dr. Van Dongen is a long-time friend and collaborator with IBR President and Chief Scientist Dr. Steven Hursh. Moreover, his laboratory at Washington State University is developing and testing their own biomathematical model of fatigue.


Dr. Van Dongen’s FAST talk was entitled, “Predicting Fatigue Beyond the Three-Process Model”. You can watch the on-demand recording of the webinar here. Dr. Van Dongen discussed the Washington State University biomathematical model of fatigue and performance [1] and the addition of a new process—the allostatic process—to their model [2]. In brief, the allostatic process modulates the urge to sleep in order to maintain homeostasis—your body’s internal state of equilibrium. Homeostasis is your body’s ability to maintain balance between all your bodily functions; allostasis can be thought of as how your body defines equilibrium. The allostatic process gradually adjusts based on prior sleep history. That is to say, if you have been sleeping poorly over a long period of time, a few nights of really good sleep are not going to erase the damage done to your performance capabilities.


How does this compare to the impact of chronic poor sleep as modeled in SAFTE-FAST? The allostatic process is not included in the original SAFTE model patent from 2003 [3], but the first peer-reviewed publications describing the SAFTE model does account for allostatic modulation of the sleep homeostat [4,5]. In the early 2000s, several laboratory sleep restriction studies indicated that the homeostatic sleep process underwent a relatively permanent change resulting from chronic restriction of sleep [6,7].  These observations suggested that sleep regulation involves a gradual change that results in slow recovery after prolonged sleep restriction. So, a simple gradual downregulation of the sleep reservoir capacity, or set point, during chronic sleep restriction was added to the SAFTE model to account for this “allosteric” change. The SAFTE model was revised in 2003 and was the first commercially available model to account for this “fourth” process. In a 2004 comparison of biomathematical models [8], the SAFTE model with this fourth process included was compared to other models available at the time. The author summarized that “This [change] constitutes a substantial improvement with respect to the earlier predictions from this and all the other models.” [8]


The 2004 comparison reports remain the largest scale peer-reviewed comparison across models. The paper summarizes the key features of seven biomathematical models that were reviewed as part of the Fatigue and Performance Modeling Workshop held in Seattle, WA, on June 13–14, 2002. The Workshop was jointly sponsored by the National Aeronautics and Space Administration, U.S. Department of Defense, U.S. Army Medical Research and Materiel Command, Office of Naval Research, Air Force Office of Scientific Research, and U.S. Department of Transportation. The goals, capabilities, inputs, and outputs of each biomathematical model were assessed and juxtaposed to provide a framework for comparing features of seven models: 1) the Two Process Model; 2) the Sleep/Wake Predictor Model; 3) the System for Aircrew Fatigue Evaluation (SAFE) Model; 4) the Interactive Neurobehavioral Model; 5) the Fatigue Audit InterDyne (FAID) Model; 6) Circadian Alertness Simulator (CAS) Model; and, of course 7) the SAFTE model.


You may be wondering why there are multiple biomathematical models of fatigue, how they differ, and which one is “best”. The 2004 report concluded that despite differences in the number and type of input and output variables, goals, and capabilities, the seven biomathematical models had a fundamental similarity in how they modeled performance as a function of the sleep-wake cycle [8]. Biomathematical models of fatigue describe the biological phenomenon of the sleep-wake cycle and its impact on cognitive performance. These models are influenced by an underlying theory about the human sleep-wake cycle called the two-process model [9]; reassuringly, predictions of alertness agree fairly well across models when using objective sleep measurements like actigraphy [8]. I like to compare biomathematical models of fatigue to love songs. There are countless love songs in existence. Some love songs are better than others and some are more appropriate for certain events than others. (Dolly Parton’s “Jolene” is a poor choice to play for newlyweds’ first dance, for example). The fact that there are so many songs about love serves to strengthen, rather than undermine, our belief that love exists. The same is true about biomathematics.


Where models differ is in their application to specific purposes or industries. Math can be adjusted easily to adapt to a new scenario. For example, even in SAFTE-FAST, we adjust parameters to tailor the system to each specific organization’s needs. Additionally, we are constantly making improvements to the model based on findings from laboratory and field science as well as input from real-world SAFTE-FAST users. Some of our recent improvements include the dynamic sleep rhythm amplitude (read the blog post here) and the addition of the workload calculator (read the white paper here). SAFTE-FAST always aims to improve not just the model, but also resources to combat fatigue in other areas, such as our upcoming SleepTank app and the ongoing FAST talk series. The next FAST Talk will be hosted on August 12, 2024. The speaker will be Dr. Tracy Jill Doty from the Walter Reed Army Institute of Research Sleep Research Center, and the topic will be “Cutting Edge Solutions for Fatigue Mitigation”. We hope to see you there.


References

1. McCauley P, Kalachev LV, Smith AD, Belenky G, Dinges DF, Van Dongen HP. A new mathematical model for the homeostatic effects of sleep loss on neurobehavioral performance. Journal of theoretical biology. 2009; 256 (2): 227-239
2. McCauley ME, McCauley P, Kalachev LV, Banks S, Dinges DF, Van Dongen HP. The dynamics of neurobehavioral impairment and recovery sleep: improved biomathematical modeling for fatigue risk management in operational settings. Frontiers in Environmental Health. 2024; 3: 1362755
3. Hursh SR. System and method for evaluating task effectiveness based on sleep pattern. In: Google Patents; 2003
4. Johnson ML, Belenky G, Redmond DP, et al. Modulating the homeostatic process to predict performance during chronic sleep restriction. Aviat Space Environ Med. 2004; 75 (3 Suppl): A141-146. Available from: https://www.ncbi.nlm.nih.gov/pubmed/15018276
5. Hursh SR, Redmond DP, Johnson ML, et al. Fatigue models for applied research in warfighting. Aviat Space Environ Med. 2004; 75 (3 Suppl): A44-53; discussion A54-60. Available from: https://www.ncbi.nlm.nih.gov/pubmed/15018265
6. Belenky G, Wesensten NJ, Thorne DR, et al. Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: a sleep dose-response study. J Sleep Res. 2003; 12 (1): 1-12. Available from: https://www.ncbi.nlm.nih.gov/pubmed/12603781
7. Van Dongen HP, Maislin G, Mullington JM, Dinges DF. The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep. 2003; 26 (2): 117-126. Available from: https://www.ncbi.nlm.nih.gov/pubmed/12683469
8. Mallis MM, Mejdal S, Nguyen TT, Dinges DF. Summary of the key features of seven biomathematical models of human fatigue and performance. Aviat Space Environ Med. 2004; 75 (3 Suppl): A4-14. Available from: https://www.ncbi.nlm.nih.gov/pubmed/15018262
9. Borbély AA. A two process model of sleep regulation. Hum neurobiol. 1982; 1 (3): 195-204

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