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Developing Tools for Fatigue Risk Management in First Responders

I want two kinds of people to be alert on their job:

  1. anyone who’s driving me

  2. anyone who’s working to save my life.

First responders sit at the top of that list since they often drive themselves to the location where a life needs to be saved.

A first responder is loosely defined as a person with specialized training who is among the first to arrive and provide assistance at the scene of an emergency, such as law enforcement officers, emergency medical technicians (EMTs), or firefighters. You may think that because of the high-risk work environment, fatigue risk management systems (FRMS) for first responders are tightly controlled and their working hours restricted to ensure maximum effectiveness on the job. A FRMS is a set of policies, procedures, and best-jet lag practices developed by operational personnel in order to systematically manage (you guessed it) risks like accidents or errors made because of operator fatigue. Commercial aviation is often upheld as the gold standard for FRMS practices. Fatigue risk in commercial aviation is highly-regulated and includes restrictions to flight duty hours, time off between duty periods, jet-lag, and even recently, the 48-hour period following injection of the COVID-19 vaccine (Gabbai et al. 2021).

But fatigue risk is harder to manage for first responders. Emergencies don’t happen on a schedule, making it difficult to construct a work schedule that avoids fatigue during critical work events. First responders are likely to work night shifts, rotating shifts, extended shift hours, and may have limited time to recover between shifts. They are likely to experience fatigue due to sleep loss as well as circadian misalignment, a phenomenon known as shift work disorder (Akerstedt et al. 2009, Barger et al. 2009). Some first responders work in a part-time or volunteer capacity. Reduced work hours may sound like a reduced opportunity for fatigue, but a volunteer or part-time worker is likely to have a second job. The more hours someone is working, the less opportunity they have to get the sleep that they need. Fatigue does not care if those hours are split across two or three different paychecks. Fatigue can also be compounded by the physical, mental, and emotional stress of the job of responding to emergency situations.

Perhaps you’re fatigued and stressed just hearing about how difficult it is to manage fatigue in first responder operations. So, I will switch gears and start talking about some of the tools IBR has been working on to help manage fatigue in first responders. Last winter, we conducted a beta test of our SleepTank™ app in a group of Norwegian Helicopter Emergency Medical Services (HEMS) crews working 24-hour shifts over 7 consecutive days while living on-base to provide around the clock coverage for on-demand medical service. The logistics of conducting this study were worse than the data analysis, in my opinion. Since the crews responded to emergencies in both Norway and Denmark, we needed ethical approval to collect data in both those countries as well as the USA. Also, because the development of the SleepTank™ app has been supported by the Medical Technology Enterprise Consortium (MTEC), we needed to get approval from the Department of Defense Human Research Protection Office as well. There was not very good guidance on the Scandinavia ethics committee websites about the approval process in English, so I spent a lot of time translating Danish and Norwegian terms on Google. Did you know that the Norwegian word for “documentation of consent” is “samtykkeerklæringer”?

Eventually, we got approval from all the committees we needed. But then, we had to figure out how to ship Fitbit Versa 2 sleep trackers from Baltimore to our participants at their home addresses in Scandinavia. We discovered that import taxes are not the same in Norway and Denmark. Once all the participants had received their research Fitbit devices, they were informed to wear the device during one-week-long duty period without access to the SleepTank™ app, and then use the SleepTank™ app during their next scheduled workweek. The participants did not work every week; they could have up to 21 days off between shifts. We had a color-coded spreadsheet marked with participation dates that would make a soccer mom proud. I sent the HEMS participants more email reminders than a spammer trying to reach you about your car’s extended warranty. We did manage to collect their sleep data from Fitbit, a series of questionnaires about sleep, fatigue, and app usability, and even a 10-minute Psychomotor Vigilance Task (PVT) taken on their mobile device daily during both study weeks. All the data was extracted remotely, but it was still nice that we only lost a single Fitbit during the course of the study. In the end, HEMS crew members had better sleep quality and faster reaction times and fewer lapses on the PVT during the week when they had access to the SleepTank™ app. The app is currently undergoing improvements and development, but I hope to be blogging about it more in the future.

Another project has been the launch of a free online web tool to estimate fatigue risk during emergency medical services (EMS) schedules with support from the National Association of State EMS Officials (NASEMSO). The tool is not the same as SAFTE-FAST software. Work-sleep rules and effectiveness estimates were developed in SAFTE-FAST using objective data from EMS providers collected in collaboration with the University of Pittsburgh Department of Emergency Medicine EMS Shift Work Project (Patterson et al. 2021). Since there are currently no official cutoffs for fatigue in EMS, risk levels were adapted from U.S. aviation and rail regulations (Szabo 2011, Huerta 2012). The webtool uses the output from the SAFTE-FAST analysis, but only allows users to evaluate fatigue risk for a limited range of hypothetical work schedules. It cannot predict fatigue risk for rotating schedules and users cannot import objective sleep data like they could with the SFC web or console software. The webtool represents a step forward towards understanding fatigue risk in EMS with the goal of mitigation and is freely available for anyone who wishes to predict fatigue risk in EMS work schedules at I do suggest trying the tool out even if you work a 9-5-never-respond-to-an-emergency-ever job; it’s fun to see how the risk levels change as you adjust the settings.

A third prospect I’m excited about is calibrating SAFTE-FAST’s Auto-Sleep module to predict sleep in first responder populations like firefighters or police officers. Auto-Sleep is SAFTE-FAST’s sleep prediction algorithm; Auto-Sleep estimates the timing and duration of sleep events based on work schedule and time-of-day data (Roma et al. 2012). Auto-Sleep contains a set of decision rules with adjustable variables, such as ideal bedtime, commute time, and whether workers are permitted to nap during duty hours. Through a process we call harmonization, a spectrum of Auto-Sleep settings can be systematically contrasted against objective sleep data in order to calibrate the algorithm for sleep prediction in the target population. The best fit Auto-Sleep settings can then be used for future analysis of work schedules without needing to collect objective sleep data. Biomathematical modeling has been suggested as a helpful FRMS tool for EMS, surgical residents, police officers, firefighters, and nurses (Barger et al. 2009, James et al. 2018, Riedy 2019, James et al. 2020, Jeklin et al. 2021, Schwartz et al. 2021), and developing an accurate sleep predictor specifically for first responder populations would be a big step forward. We are preparing to run the harmonizer with data collected from firefighters in collaboration with a researcher from the Southeastern Oklahoma State University Department of Occupational Safety and Health, so hopefully, I will be blogging about this endeavor more in the future as well. I just hope that I don’t have to look up the Norwegian word for “fire extinguisher”!

I did anyway. It’s “brannslukker”.


The EMS Sleep Health Study: A Randomized Controlled Trial.

Akerstedt, T. and K. P. Wright, Jr. (2009). "Sleep Loss and Fatigue in Shift Work and Shift Work Disorder." Sleep Med Clin 4(2): 257-271.

Barger, L. K., S. W. Lockley, S. M. Rajaratnam and C. P. Landrigan (2009). "Neurobehavioral, health, and safety consequences associated with shift work in safety-sensitive professions." Curr Neurol Neurosci Rep 9(2): 155-164.

Gabbai, D., A. Ekshtein, O. Tehori, O. Ben-Ari and S. Shapira (2021). "COVID-19 Vaccine and Fitness to Fly." Aerospace Medicine and Human Performance 92(9): 698-701.

Huerta, M. P. (2012). 14 CFR Parts 117, 119, and 121 Flightcrew Member Duty and Rest Requirements. D. o. T. Federal Aviation Administration (FAA). 14 CFR Parts 117, 119, and 121.

James, F. O., L. B. Waggoner, P. M. Weiss, P. D. Patterson, J. S. Higgins, E. S. Lang and H. P. A. Van Dongen (2018). "Does Implementation of Biomathematical Models Mitigate Fatigue and Fatigue-related Risks in Emergency Medical Services Operations? A Systematic Review." Prehosp Emerg Care 22(sup1): 69-80.

James, L., S. M. James, M. Wilson, N. Brown, E. J. Dotson, C. Dan Edwards and P. Butterfield (2020). "Sleep health and predicted cognitive effectiveness of nurses working 12-hour shifts: an observational study." Int J Nurs Stud 112: 103667.

Jeklin, A. T., H. W. Davies, S. S. Bredin, A. S. Perrotta, B. A. Hives, L. Meanwell and D. E. Warburton (2021). "Using a biomathematical model to assess fatigue risk and scheduling characteristics in Canadian wildland firefighters." International Journal of Wildland Fire 30(6): 467-473.

Patterson, P. D., K. A. Mountz, M. G. Agostinelli, M. D. Weaver, Y. C. Yu, B. M. Herbert, M. A. Markosyan, D. R. Hopkins, A. C. Alameida, J. A. Maloney Iii, S. E. Martin, B. N. Brassil, C. Martin-Gill, F. X. Guyette, C. W. Callaway and D. J. Buysse (2021). "Ambulatory blood pressure monitoring among emergency medical services night shift workers." Occup Environ Med 78(1): 29-35.

Riedy, S. M. (2019). Ecological and Internal Validity of Predicting Police Officers' Sleep and Fatigue from Work-Rest Schedules, Washington State University.

Roma, P. G., S. R. Hursh, A. M. Mead and T. E. Nesthus (2012). Flight attendant work/rest patterns, alertness, and performance assessment: Field validation of biomathematical fatigue modeling, Federal Aviation Administration Oklahoma City Ok Civil Aerospace Medical Inst.

Schwartz, L. P., J. K. Devine, S. R. Hursh, J. E. Davis, M. Smith, L. Boyle and S. C. Fitzgibbons (2021). "Addressing fatigue in medical residents with biomathematical fatigue modeling." J Occup Health 63(1): e12267.

Szabo, J. C. (2011). 49 CFR Part 228 Hours of Service of Railroad Employees; Substantive Regulations for Train Employees Providing Commuter and Intercity Rail Passenger Transportation; Conforming Amendments to Recordkeeping Requirements. D. o. T. D. Federal Railroad Administration (FRA).


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