Biomathematical Models Of Fatigue Research Articles

Biomathematical modeling of fatigue due to sleep inertia

Biomathematical models of fatigue capture the physiology of sleep/wake regulation and circadian rhythmicity to predict changes in neurobehavioral functioning over time. We used a biomathematical model of fatigue linked to the adenosinergic neuromodulator/receptor system in the brain as a framework to predict sleep inertia, that is, the transient neurobehavioral impairment experienced immediately after awakening. Based on evidence of an adenosinergic basis for sleep inertia, we expanded the biomathematical model with novel differential equations to predict the propensity for sleep inertia during sleep and its manifestation after awakening. Using datasets from large laboratory studies of sleep loss and circadian misalignment, we calibrated the model by fitting just two new parameters and then validated the model’s predictions against independent data. The expanded model was found to predict the magnitude and time course of sleep inertia with generally high accuracy. Analysis of the model’s dynamics revealed a bifurcation in the predicted manifestation of sleep inertia in sustained sleep restriction paradigms, which reflects the observed escalation of the magnitude of sleep inertia in scenarios with sleep restriction to less than ∼ 4 h per day. Another emergent property of the model involves a rapid increase in the predicted propensity for sleep inertia in the early part of sleep followed by a gradual decline in the later part of the sleep period, which matches what would be expected based on the adenosinergic regulation of non-rapid eye movement (NREM) sleep and its known influence on sleep inertia. These dynamic behaviors provide confidence in the validity of our approach and underscore the predictive potential of the model. The expanded model provides a useful tool for predicting sleep inertia and managing impairment in 24/7 settings where people may need to perform critical tasks immediately after awakening, such as on-demand operations in safety and security, emergency response, and health care.

The dynamics of neurobehavioral impairment and recovery sleep: improved biomathematical modeling for fatigue risk management in operational settings

Adequate sleep is essential for maintaining health, safety, and neurobehavioral functioning in 24/7 operational settings. Loss of sleep causes fatigue, which results in neurobehavioral impairment. Neurobehavioral impairment builds up disproportionately under conditions of total sleep deprivation compared to sustained sleep restriction; and recuperation due to recovery sleep is disproportionately fast after total sleep deprivation compared to sustained sleep restriction. These sleep schedule-dependent dynamics have been captured in a previously published, biomathematical model of fatigue, which includes positive feedback regulation of a relatively fast (hours to days) sleep/wake homeostatic process by a slower (days to weeks) allostatic process—a feature that suggests adenosinergic mechanisms are involved. However, the previously published model underestimates the rate of recuperation due to recovery sleep after acute total sleep deprivation as well as after consecutive days of sleep restriction. The objective of the present research is to modify the model to improve the accuracy of its predictions for recuperation due to recovery sleep. This can be accomplished by including in the model an additional, reciprocal feedback mechanism, presumed to be predominantly adenosinergic in nature, which provides feedback from the faster homeostatic process back onto the slower allostatic process. Adding a single new model parameter and refitting three existing model parameters significantly improves the predictions for recuperation due to recovery sleep after both acute total sleep deprivation and sustained sleep restriction. This model modification also improves the predictions of the build-up of neurobehavioral impairment across days of sustained sleep restriction, without adversely affecting the accuracy of the model in other scenarios including circadian misalignment and sleep inertia. The modified model preserves the previously developed capability to predict the differential dynamics of fatigue for objective performance impairment and subjective sleepiness. With the improved predictions for recuperation due to recovery sleep, the expanded model can be used to provide quantitative estimates for potentially impactful work scheduling decisions, such as the duration of time off needed before workers would be safe to return to the work floor. This enhances the usefulness of the model as a tool for predicting and managing neurobehavioral functioning and safety in 24/7 operational settings.

Assessing Flight Crew Fatigue under Extra Augmented Crew Schedule Using a Multimodality Approach

During the COVID-19 pandemic, the question of how to reduce the risk of viral infection for international airline pilots without increasing the risk of fatigue was a novel and urgent theoretical and practical problem, which had never been encountered in the world civil aviation industry. A new scheduling method implemented by the Civil Aviation Administration of China (CAAC) is the extra augmented crew (EAC) schedule, which avoids crew layover in another country on international flights by extending the maximum duty time and adding two additional crew members to such long-haul flights. In this study, a multi-day flight crew fatigue assessment was conducted to evaluate the impact of EAC flight. We recruited 71 pilots as participants, and their fatigue during EAC flights was measured using a multimodality approach integrating a subjective fatigue report, a psychomotor vigilance task, sleep monitoring, and biomathematical model predictions. The results showed that the subjective fatigue level increased during duty time compared to off-duty time, but still with acceptable levels of under 7, as measured by the Karolinska Sleepiness Scale; objective secondary task performance, as measured by the classic psychomotor vigilance task, showed no differences; pilots were able to get around 6 h of sleep, although they slept less during duty time compared to off-duty time. Model fitting using the FAID biomathematical model of fatigue confirmed that the EAC scheduling was compliant with the FAID tolerance level 91.3% of the time. The results suggest that the EAC flight created some moderate level of increased fatigue but no severe fatigue to cross-continent long-haul flight crews. This research can inform current and future scheduling and fatigue risk control during the pandemic or for future time-sensitive periods.

Potential Effects of Permanent Daylight Savings Time on Daylight Exposure and Risk during Commute Times across United States Cities in 2023–2024 Using a Biomathematical Model of Fatigue

Background: Permanent Daylight Savings Time (DST) may improve road safety by providing more daylight in the evening but could merely shift risk to morning commutes or increase risk due to fatigue and circadian misalignment. Methods: To identify how potential daylight exposure and fatigue risk could differ between permanent DST versus permanent Standard Time (ST) or current time arrangements (CTA), generic work and school schedules in five United States cities were modeled in SAFTE-FAST biomathematical modeling software. Commute data were categorized by morning (0700–0900) and evening (1600–1800) rush hours. Results: Percent darkness was greater under DST compared with ST for the total waking day (t = 2.59, p = 0.03) and sleep periods (t = 2.46, p = 0.045). Waketimes occurred before sunrise 63 ± 41% percent of the time under DST compared with CTA (42 ± 37%) or ST (33 ± 38%; F(2,74) = 76.37; p < 0.001). Percent darkness was greater during morning (16 ± 31%) and lower during evening rush hour (0 ± 0%) in DST compared with either CTA (morning: 7 ± 23%; evening: 7 ± 14%) or ST (morning: 7 ± 23%; evening: 7 ± 15%). Discussion: Morning rush hour overlaps with students’ commutes and shift workers’ reverse commutes, which may increase traffic congestion and risk compared with evening rush hour. Switching to permanent DST may be more disruptive than either switching to ST or keeping CTA without noticeable benefit to fatigue or potential daylight exposure.

Individualisation method of biomathematical model of fatigue for predicting individual performance in mild and irregular sleep deprivation

Individual biomathematical models of fatigue (BMMF) are promising tools for detecting fatigue and possible incidents. Existing individual BMMFs have been validated in laboratory experiments in which subjects experience total sleep deprivation (TSD) and regular chronic sleep deprivation (CSD). However, some shift populations experience mild sleep deprivation (MSD) or irregular sleep deprivation (ISD) in real life. We employed the adaptive momentum estimation algorithm to adjust the classical SAFTE model for an individual. Model individualisation can be performed in real-time when new performance data are collected. The individual SAFTE model was compared with existing BMMFs in TSD, CSD, MSD, and ISD. The validation results show that the individual SAFTE model has advantages in MSD and ISD datasets collected from officers and truck drivers in real life. This study expands previous research results on the real-time individualisation of BMMFs and exposes individual BMMFs to various sleep-deprivation conditions in the field. Practitioner summary: This study proposes an individual biomathematical models of fatigue to predict human performance in mild and irregular sleep deprivation. The validation results in both laboratory and field show the proposed model has advantages over existing models when predicting officers’ and truck drivers’ performance in real life.

0202 Predicting Sleep Inertia in a Biomathematical Model of Fatigue and Performance: A Novel Approach

Abstract Introduction Biomathematical models of fatigue typically include sleep inertia as an additive process during wakefulness. However, there is predictive information to be gained from tracking the propensity for sleep inertia through sleep periods. We propose a novel approach involving a neurobiological sleep inertia process with relatively fast dynamics (in the order of several minutes) interacting with the much slower dynamics of the established processes of sleep/wake regulation. This sleep inertia process is captured by the addition of two ordinary differential equations (ODEs) in the model framework of McCauley and colleagues (2009, 2013, 2021) – one for wakefulness to track impairment from sleep inertia, and one for sleep to track the propensity for sleep inertia upon awakening. A single time constant is introduced to control the dynamic behavior of these ODEs to capture the dynamics of sleep inertia. Methods 398 healthy young adults (ages 21–49 years) each participated in one of eight multi-day laboratory studies of total sleep deprivation, sustained sleep restriction, or simulated shift work. At 2–4 hour intervals while awake, participants performed a Psychomotor Vigilance Test (PVT), for which number of lapses (RT>500ms) was assessed, and rated their sleepiness on the Karolinska Sleepiness Scale (KSS). Sleep periods were recorded polysomnographically. Data were divided into a calibration set (five studies) used to estimate a single new model parameter capturing sleep inertia, and a validation set (three studies) used to independently verify model validity. Results Based on the calibration data set, the sleep inertia time constant estimate was 0.71h±0.01. Based on the validation data set, goodness-of-fit root-mean-square-error was 2.28 for PVT and 0.733 for KSS, indicating high predictive accuracy. A dynamic buildup and then decline of predicted propensity for sleep inertia during sleep emerged, peaking 2–3h into the sleep period. Conclusion The model expansion with a one-parameter sleep inertia process captured the transient effect of sleep inertia accurately across a range of sleep deprivation, sleep restriction, and simulated shift work scenarios. The emerging dynamic of sleep inertia propensity during sleep is consistent with findings on the magnitude of sleep inertia as a function of sleep duration and stage of awakening. Support (If Any) WSU HPC

Predicting the Effects of Permanent Daylight Savings Time on Light Exposure and Risk Using a Biomathematical Model of Fatigue

Predicting the Effects of Permanent Daylight Savings Time on Light Exposure and Risk Using a Biomathematical Model of Fatigue

Biomathematical Modeling Predicts Fatigue Risk in General Surgery Residents

To assess resident fatigue risk using objective and predicted sleep data in a biomathematical model of fatigue. 8-weeks of sleep data and shift schedules from 2019 for 24 surgical residents were assessed with a biomathematical model to predict performance ("effectiveness"). Greater Washington, DC area hospitals RESULTS: As shift lengths increased, effectiveness scores decreased and the time spent below criterion increased. Additionally, 11.13% of time on shift was below the effectiveness criterion and 42.7% of shifts carried excess sleep debt. Sleep prediction was similar to actual sleep, and both predicted similar performance (p ≤ 0.001). Surgical resident sleep and shift patterns may create fatigue risk. Biomathematical modeling can aid the prediction of resident sleep patterns and performance. This approach provides an important tool to help educators in creating work-schedules that minimize fatigue risk.

137 Recovery Dynamics in a Biomathematical Model of Fatigue

Abstract Introduction In commercial aviation and other operational settings where biomathematical models of fatigue are used for fatigue risk management, accurate prediction of recovery during rest periods following duty periods with sleep loss and/or circadian misalignment is critical. The recuperative potential of recovery sleep is influenced by a variety of factors, including long-term, allostatic effects of prior sleep/wake history. For example, recovery tends to be slower after sustained sleep restriction versus acute total sleep deprivation. Capturing such dynamics has proven to be challenging. Methods Here we focus on the dynamic biomathematical model of McCauley et al. (2013). In addition to a circadian process, this model features differential equations for sleep/wake regulation including a short-term sleep homeostatic process capturing change in the order of hours/days and a long-term allostatic process capturing change in the order of days/weeks. The allostatic process modulates the dynamics of the homeostatic process by shifting its equilibrium setpoint, which addresses recently observed phenomena such as reduced vulnerability to sleep loss after banking sleep. It also differentiates the build-up and recovery rates of fatigue under conditions of chronic sleep restriction versus acute total sleep deprivation; nonetheless, it does not accurately predict the disproportionately rapid recovery seen after total sleep deprivation. To improve the model, we hypothesized that the homeostatic process may also modulate the allostatic process, with the magnitude of this effect scaling as a function of time awake. Results To test our hypothesis, we added a parameter to the model to capture modulation by the homeostatic process of the allostatic process build-up during wakefulness and dissipation during sleep. Parameter estimation using previously published laboratory datasets of fatigue showed this parameter as significantly different from zero (p<0.05) and yielding a 10%–20% improvement in goodness-of-fit for recovery without adversely affecting goodness-of-fit for pre-recovery days. Conclusion Inclusion of a modulation effect of the allostatic process by the homeostatic process improved prediction accuracy in a variety of sleep loss and circadian misalignment scenarios. In addition to operational relevance for duty/rest scheduling, this finding has implications for understanding mechanisms underlying the homeostatic and allostatic processes of sleep/wake regulation. Support (if any) Federal Express Corporation

Fatigue risk management based on self-reported fatigue: Expanding a biomathematical model of fatigue-related performance deficits to also predict subjective sleepiness

Biomathematical models of fatigue can be used to predict neurobehavioral deficits during sleep/wake or work/rest schedules. Current models make predictions for objective performance deficits and/or subjective sleepiness, but known differences in the temporal dynamics of objective versus subjective outcomes have not been addressed. We expanded a biomathematical model of fatigue previously developed to predict objective performance deficits as measured on the Psychomotor Vigilance Test (PVT) to also predict subjective sleepiness as self-reported on the Karolinska Sleepiness Scale (KSS). Four model parameters were re-estimated to capture the distinct dynamics of the KSS and account for the scale difference between KSS and PVT. Two separate ensembles of datasets – drawn from laboratory studies of sleep deprivation, sleep restriction, simulated night work, napping, and recovery sleep – were used for calibration and subsequent validation of the model for subjective sleepiness. The expanded model was found to exhibit high prediction accuracy for subjective sleepiness, while retaining high prediction accuracy for objective performance deficits. Application of the validated model to an example scenario based on cargo aviation operations revealed divergence between predictions for objective and subjective outcomes, with subjective sleepiness substantially underestimating accumulating objective impairment, which has important real-world implications. In safety-sensitive operations such as commercial aviation, where self-ratings of sleepiness are used as part of fatigue risk management, the systematic differences in the temporal dynamics of objective versus subjective measures of functional impairment point to a potentially significant risk evaluation sensitivity gap. The expanded biomathematical model of fatigue presented here provides a useful quantitative tool to bridge this previously unrecognized gap.

Addressing fatigue in medical residents with biomathematical fatigue modeling.

Fatigue in resident physicians has been identified as a factor that contributes to burnout and a decline in overall wellbeing. Fatigue risk exists because of poor sleep habits and demanding work schedules that have only increased due to the COVID‐19 pandemic. At this time, it is important not to lose sight of how fatigue can impact residents and how fatigue risk can be mitigated. While fatigue mitigation is currently addressed by duty hour restrictions and education about fatigue, Fatigue Risk Management Systems (FRMSs) offer a more comprehensive strategy for addressing these issues. An important component of FRMS in other shiftwork industries, such as aviation and trucking, is the use of biomathematical models to prospectively identify fatigue risk in work schedules. Such an approach incorporates decades of knowledge of sleep and circadian rhythm research into shift schedules, taking into account not just duty hour restrictions but the temporal placement of work schedules. Recent research has shown that biomathematical models of fatigue can be adapted to a resident physician population and can help address fatigue risk. Such models do not require subject matter experts and can be applied in graduate medical education program shift scheduling. It is important for graduate medical education program providers to consider these alternative methods of fatigue mitigation. These tools can help reduce fatigue risk and may improve wellness as they allow for a more precise fatigue management strategy without reducing overall work hours.

Using a biomathematical model to assess fatigue risk and scheduling characteristics in Canadian wildland firefighters

This study examined the shift parameters that contribute to sleep loss and on-duty fatigue in British Columbia Wildfire Service (BCWS) firefighters using sleep–wake data, work–rest data and alertness and fatigue predictions from a biomathematical model (BMM) of fatigue. A total of 40 firefighters (age: 30.4 ± 11.6 years; 13 F, 26 M) volunteered over a 14-day consecutive fireline deployment, followed by a 3-day rest period, at two separate fires in British Columbia (during the 2015 fire season). Sleep–wake data were obtained using a wrist-worn accelerometer and self-reported sleep logs. Shift start and end times were provided by the BCWS at the completion of the study. Sleep and shift data were manually entered into a validated BMM (Circadian Alertness Simulator) to generate fatigue scores and shift work patterns. Shift duration was the major contributor to fatigue, as 46% (n = 274) of shifts were ≥14 h in length and the average shift length was 13.0 ± 0.62 h. However, none of the firefighters had a high-risk fatigue score (>60). The findings from this study indicated that using a BMM of fatigue can provide important insights into shift-work parameters that contribute to workplace fatigue and sleep loss in wildland firefighters.

Trends in Strategic Napping in Surgical Residents by Gender, Postgraduate Year, Work Schedule, and Clinical Rotation

To identify surgical resident and clinical rotation attributes which predict on-shift napping through objectively measured sleep patterns and work schedules over a 2-month period. In a cross-sectional study, participants provided schedules, completed the Epworth Sleepiness Scale (ESS), and wore sleep-tracking devices (Zulu watch) continuously for 8 weeks. Multiple linear regression predicted percent days with on-shift napping from resident and rotation characteristics. Greater Washington, DC area hospitals. Twenty-two (n = 22) surgical residents rotating in at least 1 of 5 different clinical rotation categories. Residents slept 6 hours within a 24-hour period (370 ± 129 minutes) with normal sleep efficiency (sleep efficiency (SE): 87.13% ± 7.55%). Resident ESS scores indicated excessive daytime sleepiness (11.64 ± 4.03). Ninety-five percent (n = 21) of residents napped on-shift. Residents napped on-shift approximately 32% of their working days and were most likely to nap when working between 23:00 and 05:00 hours. Earlier shift start times predicted less on-shift napping (B = -0.08, SE = 0.04, β = -2.40, t = -2.09, p = 0.05) while working more night shifts (B = 1.55, SE = 0.44, β = 4.12, t = 3.52, p = 0.003) and shifts over 24 hours (B = 1.45, SE = 0.55, β = 1.96, t = 2.63, p = 0.01) predicted more frequent on-shift napping. Residents are taking advantage of opportunities to nap on-shift. Working at night seems to drive on-shift napping. However, residents still exhibit insufficient sleep and daytime sleepiness which could reduce competency and represent a safety risk to themselves and/or patients. These findings will help inform intervention strategies which are tailored to surgical residents using a biomathematical model of fatigue.

Fatigue and short-term unplanned absences among police officers

PurposeThe purpose of this paper is to assess whether shift work, sleep loss and fatigue are related to short-term unplanned absences in policing.Design/methodology/approachN = 367 police officers from the Buffalo Police Department were studied. Day-by-day work and sick leave data were obtained from the payroll. Absenteeism was defined as taking a single sick day on a regularly scheduled workday. Biomathematical models of fatigue (BMMF) predicted officers' sleep–wake behaviors and on-duty fatigue and sleepiness. Prior sleep, fatigue and sleepiness were tested as predictors of absenteeism during the next shift.FindingsA total of 513,666 shifts and 4,868 cases of absenteeism were studied. The odds of absenteeism increased as on-duty fatigue and sleepiness increased and prior sleep decreased. This was particularly evident for swing shift officers and night shift officers who were predicted by BMMF to obtain less sleep and have greater fatigue and sleepiness than day shift officers. The odds of absenteeism were higher for female officers than male officers; this finding was not due to a differential response to sleep loss, fatigue or sleepiness.Practical implicationsAbsenteeism may represent a self-management strategy for fatigue or compensatory behavior to reduced sleep opportunity. Long and irregular work hours that reduce sleep opportunity may be administratively controllable culprits of absenteeism.Originality/valuePolice fatigue has consequences for police officers, departments and communities. BMMF provide a potential tool for predicting and mitigating police fatigue. BMMF were used to investigate the effects of sleep and fatigue on absenteeism.

Sleep-wake behaviors exhibited by shift workers in normal operations and predicted by a biomathematical model of fatigue.

To compare rail workers' actual sleep-wake behaviors in normal operations to those predicted by a biomathematical model of fatigue (BMMF). To determine whether there are group-level residual sources of error in sleep predictions that could be modeled to improve group-level sleep predictions. The sleep-wake behaviors of 354 rail workers were examined during 1,722 breaks that were 8-24 h in duration. Sleep-wake patterns were continuously monitored using wrist-actigraphy and predicted from the work-rest schedule using a BMMF. Rail workers' actual and predicted sleep-wake behaviors were defined as split-sleep (i.e. ≥2 sleep periods in a break) and consolidated-sleep (i.e. one sleep period in a break) behaviors. Sleepiness was predicted from the actual and predicted sleep-wake data. Consolidated-sleep behaviors were observed during 1,441 breaks and correctly predicted during 1,359 breaks. Split-sleep behaviors were observed during 280 breaks and correctly predicted during 182 breaks. Predicting the wrong type of sleep-wake behavior resulted in a misestimation of hours of sleep during a break. Relative to sleepiness predictions derived from actual sleep-wake data, predicting the wrong type of sleep-wake behavior resulted in a misestimation of sleepiness predictions during the subsequent shift. All workers with the same work-rest schedule have the same predicted sleep-wake behaviors; however, these workers do not all exhibit the same sleep-wake behaviors in real-world operations. Future models could account for this group-level residual variance with a new approach to modeling sleep, whereby sub-group(s) may be predicted to exhibit one of a number of sleep-wake behaviors.

U.S. police rosters: Fatigue and public complaints.

Recent experimental research indicates a substantial impact of shift work related fatigue on police officers' encounters with the public. In recent years, biomathematical models of fatigue have provided a new way to identify potential relationships between working time arrangements and job performance. This study focused on public complaints against police officers and determined whether the odds of a public complaint were associated with work schedules and/or a biomathematical model's predictions of fatigue and sleepiness. N = 144 police officers from two municipal police departments in the United States reported shift start times, shift hours, court hours, and public complaints each duty day during study participation. A biomathematical model of fatigue (FAID Quantum) predicted sleep duration and sleep timing and inferred fatigue and sleepiness for 15 744 shifts. Fatigue, sleepiness, 24 hr sleep estimates, and work schedule were tested as predictors of public complaints. Greater fatigue, greater sleepiness, and less sleep in the 24 hr prior to a shift increased the odds of a public complaint (F ≥ 9.14, p < 0.01). Working back-to-back night shifts increased the odds of a public complaint (OR = 4.27, p < 0.01), particularly when off-duty court hours were worked between the night shifts (OR = 4.73, p < 0.01). On-duty fatigue and sleepiness, sleep obtained prior to a shift, and working night shifts were strongly associated with public complaints. Off-duty court appearances reduced sleep between night shifts and further increased the odds of a public complaint. The results suggest that off-duty court hours should be limited between night shifts and duty schedules should be considered when scheduling court appearances.

0041 PREDICTING TRAIT-LIKE VARIABILITY IN THE COEFFICIENTS OF THE TWO PROCESS MODEL OF PERFORMANCE

Biomathematical models of fatigue are often used to predict alertness degradation as a function of prior sleep-wake history. There are large inter-individual differences in the magnitude of an individual’s degradation in response to sleep loss which, if unaccounted for, can lead to significant inaccuracies in model performance. It has been shown that these differences are stable within the individual across variations in sleep history. Current methods of individualization require periodic performance measurement feedback from the user. Here we have developed a method for estimating these stable, trait-like inter-individual differences based solely upon a priori, ambulatory data. Seven days of biometric and motion sensor data were collected from 16 subjects under restricted, ambulatory conditions prior to controlled, in-lab collection of performance data across a 28-hour sleep deprivation period. A parameterized two process model of fatigue was utilized to simulate performance degradation under sleep loss. The model coefficients were optimized such that they accounted for inter-individual differences in performance degradation. A proprietary, machine-learning-based algorithm was trained, using 12 of the ambulatory datasets, to predict the optimized coefficients for each individual. The effectiveness of the algorithm was tested on the remaining four datasets. The maximum percent error in the test set of each of the estimated coefficients was 3.12, 48.55, 33.29, and 18.44, respectively. Conversely, the percent error of a group-aggregate coefficient compared across the optimized coefficients of all subjects was found to be 7.84 ± 7.56, 209.72 ± 193.78, 163.22 ± 162.04, and 124.33 ± 107.04, respectively. The coefficient estimation algorithm developed is novel, in that it is capable of approximating an individual’s trait-like vulnerability in the coefficients of a parameterized two process model of fatigue using only a priori, ambulatory data and has shown significant potential in improving model performance. CurAegis Technologies, Inc.

Computational cognitive modeling of the temporal dynamics of fatigue from sleep loss.

Computational models have become common tools in psychology. They provide quantitative instantiations of theories that seek to explain the functioning of the human mind. In this paper, we focus on identifying deep theoretical similarities between two very different models. Both models are concerned with how fatigue from sleep loss impacts cognitive processing. The first is based on the diffusion model and posits that fatigue decreases the drift rate of the diffusion process. The second is based on the Adaptive Control of Thought - Rational (ACT-R) cognitive architecture and posits that fatigue decreases the utility of candidate actions leading to microlapses in cognitive processing. A biomathematical model of fatigue is used to control drift rate in the first account and utility in the second. We investigated the predicted response time distributions of these two integrated computational cognitive models for performance on a psychomotor vigilance test under conditions of total sleep deprivation, simulated shift work, and sustained sleep restriction. The models generated equivalent predictions of response time distributions with excellent goodness-of-fit to the human data. More importantly, although the accounts involve different modeling approaches and levels of abstraction, they represent the effects of fatigue in a functionally equivalent way: in both, fatigue decreases the signal-to-noise ratio in decision processes and decreases response inhibition. This convergence suggests that sleep loss impairs psychomotor vigilance performance through degradation of the quality of cognitive processing, which provides a foundation for systematic investigation of the effects of sleep loss on other aspects of cognition. Our findings illustrate the value of treating different modeling formalisms as vehicles for discovery.

Managing fatigue: It really is about sleep

Biomathematical models of fatigue can assist organisations to estimate the fatigue consequences of a roster before operations commence. These estimates do not account for the diversity of sleep behaviours exhibited by employees. The purpose of this study was to develop sleep transfer functions describing the likely distributions of sleep around fatigue level estimates produced by a commercial biomathematical model of fatigue. Participants included 347 (18 females, 329 males) train drivers working commercial railway operations in Australia. They provided detailed information about their sleep behaviours using sleep diaries and wrist activity monitors. On average, drivers slept for 7.7 (±1.7)h in the 24h before work and 15.1 (±2.5)h in the 48h before work. The amount of sleep obtained by drivers before shifts differed only marginally across morning, afternoon and night shifts. Shifts were also classified into one of seven ranked categories using estimated fatigue level scores. Higher fatigue score categories were associated with significant reductions in the amount of sleep obtained before shifts, but there was substantial within-category variation. The study findings demonstrate that biomathematical models of fatigue have utility for designing round-the-clock rosters that provide sufficient sleep opportunities for the average employee. Robust variability in the amount of sleep obtained by drivers indicate that models are relatively poor tools for ensuring that all employees obtain sufficient sleep. These findings demonstrate the importance of developing approaches for managing the sleep behaviour of individual employees.

Dynamic Circadian Modulation in a Biomathematical Model for the Effects of Sleep and Sleep Loss on Waking Neurobehavioral Performance

Recent experimental observations and theoretical advances have indicated that the homeostatic equilibrium for sleep/wake regulation--and thereby sensitivity to neurobehavioral impairment from sleep loss--is modulated by prior sleep/wake history. This phenomenon was predicted by a biomathematical model developed to explain changes in neurobehavioral performance across days in laboratory studies of total sleep deprivation and sustained sleep restriction. The present paper focuses on the dynamics of neurobehavioral performance within days in this biomathematical model of fatigue. Without increasing the number of model parameters, the model was updated by incorporating time-dependence in the amplitude of the circadian modulation of performance. The updated model was calibrated using a large dataset from three laboratory experiments on psychomotor vigilance test (PVT) performance, under conditions of sleep loss and circadian misalignment; and validated using another large dataset from three different laboratory experiments. The time-dependence of circadian amplitude resulted in improved goodness-of-fit in night shift schedules, nap sleep scenarios, and recovery from prior sleep loss. The updated model predicts that the homeostatic equilibrium for sleep/wake regulation--and thus sensitivity to sleep loss--depends not only on the duration but also on the circadian timing of prior sleep. This novel theoretical insight has important implications for predicting operator alertness during work schedules involving circadian misalignment such as night shift work.