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Sleepiness and fatigue are important risk factors in the transport sector and bio-mathematical sleepiness, sleep and fatigue modeling is increasingly becoming a valuable tool for assessing safety of work schedules and rosters in Fatigue Risk Management Systems (FRMS). The present study sought to validate the inner workings of one such model, Three...
Citations
... While expected, this finding reiterates the need for tailored support for defence personnel to improve sleep and increase operational readiness. Previous research has shown that this individual variability, particularly for sustained attention can be traced to key group of factors: environmental and behavioural influences, such as shift schedules, tasks, daily sleep, behavioural and lifestyle choices; and individual's endogenous physiological parameters, such as chronotype and circadian timing, sensitivity, and response to light [31][32][33][34][35]. While this study accounted for variability in environmental and behavioural influences, through calendar integration, allowing participants to enter their shifts and other commitments, as well as habitual and daily sleep, it did not account for variability in their circadian timing. ...
Shift work, long work hours, and operational tasks contribute to sleep and circadian disruption in defence personnel, with profound impacts on cognition. To address this, a digital technology, the SleepSync app, was designed for use in defence. A pre-post design study was undertaken to examine whether four weeks app use improved sleep and cognitive fitness (high performance neurocognition) in a cohort of shift workers from the Royal Australian Air Force. In total, 13 of approximately 20 shift-working personnel from one base volunteered for the study. Sleep outcomes were assessed using the Insomnia Severity Index (ISI), the Patient-Reported Outcomes Measurement Information System (PROMIS), Sleep Disturbance and Sleep-Related Impairment Scales, the Glasgow Sleep Effort Scale, the Sleep Hygiene Index, and mental health was assessed using the Depression, Anxiety, and Stress Scale-21. Sustained attention was measured using the 3-min Psychomotor Vigilance Task (PVT) and controlled response using the NBack. Results showed significant improvements in insomnia (ISI scores 10.31 at baseline and 7.50 after app use), sleep-related impairments (SRI T-scores 53.03 at baseline to 46.75 post-app use), and healthy sleep practices (SHI scores 21.61 at baseline to 18.83 post-app use; all p < 0.001). Trends for improvement were recorded for depression. NBack incorrect responses reduced significantly (9.36 at baseline; reduced by −3.87 at last week of app use, p < 0.001), but no other objective measures improved. These findings suggest that SleepSync may improve sleep and positively enhance cognitive fitness but warrants further investigation in large samples. Randomised control trials with other cohorts of defence personnel are needed to confirm the utility of this intervention in defence settings.
... from 8 a.m. on day one to 8 a.m. on day two without constraints. Interval[b, d] represents the sleep stage, interval[b, c] represents deep sleep stage, interval[c, d] represents normal sleep stage, and interval [d, a] represents wakefulness stage. If a person is unable to fall asleep at point b due to a job, the alertness will continue to decrease.Michael et al. (2014) linearly transformed the level of alertness expressed by Eq. (1) into a measurement standard for sleepiness, known as the Karolinska Sleepiness Scale (KSS), to complete the quantitative measurement of the fatigue level. ...
Safety is a top concern for the civil aviation industry, and the risk of safety accidents will increase due to pilot fatigue. To ensure the safety of civil aviation, this paper proposes a method to solve the crew scheduling problem considering pilot fatigue. In order to reflect individual differences and fatigue levels of pilots, an improved three-stage alertness calculation model is first proposed based on subjective and objective perspectives to represent pilots’ alertness levels and fatigue working duration quantitatively. Then, for the crew scheduling problem considering pilot fatigue, a mixed integer programming model is constructed to simultaneously achieve the optimization objectives of reducing the overall scheduling cost and crew fatigue working duration. Next, since the actual crew scheduling problem is large-scale, a solution algorithm based on a column generation framework is developed to improve the quality and efficiency of solving the large-scale crew scheduling problem. Furthermore, in the case study, we collected actual data from an airline company to validate the effectiveness of our proposed method. Finally, through multiple experimental comparisons and analyses, to balance the two optimization objectives mentioned above, it is more reasonable to handle pilot fatigue working duration with soft constraints. Sensitivity analysis reveals the variation rules of the crew cost and fatigue, providing some valuable managerial insights for the crew scheduling problem considering pilot fatigue.
... These models incorporate dynamic homeostatic and physiological processes that predict alertness based on physiology and sleep-wake constraints. A variety of these models have been designed to predict sleep based on work-rest schedules of shift workers (Ingre et al., 2014;McCauley et al., 2009;Riedy, Fekedulegn, et al., 2020;Riedy, Roach, & Dawson, 2020). Biomathematical models are used as an adjunct in safety management across industries, providing a global risk assessment for when an average employee will be most sleepy or to predict performance (Dawson et al., 2017;Ramakrishnan et al., 2015). ...
... Biomathematical models are used as an adjunct in safety management across industries, providing a global risk assessment for when an average employee will be most sleepy or to predict performance (Dawson et al., 2017;Ramakrishnan et al., 2015). Biomathematical models have been tested for the accuracy of their predictions of sleep and alertness in shift work, as well as prediction of circadian phase (Åkerstedt et al., 2007;Dawson et al., 2017;Flynn-Evans et al., 2020;Ingre et al., 2014;Knock et al., 2021;Riedy, Fekedulegn, et al., 2020). ...
Sleep disturbances and circadian disruption play a central role in adverse health, safety, and performance outcomes in shift workers. While biomathematical models of sleep and alertness can be used to personalise interventions for shift workers, their practical implementation of is undertested. This study tested the feasibility of implementing two biomathematical models—the Phillips–Robinson Model and the Model for Arousal Dynamics—in 28 shift‐working nurses, 14 in each group. The study examined the overlap and adherence between model recommendations and sleep behaviours, and changes in sleep following the implementation of recommendations. For both groups combined, the mean (SD) percentage overlap between when a model recommended an individual to sleep and when sleep was obtained was 73.62% (10.24%). Adherence between model recommendations and sleep onset and offset times was significantly higher with the Model of Arousal Dynamics compared to the Phillips–Robinson Model. For the Phillips–Robinson model, 27% of sleep onset and 35% of sleep offset times were within ± 30 min of model recommendations. For the Model of Arousal Dynamics, 49% of sleep onset, and 35% of sleep offset times were within ± 30 min of model recommendations. Compared to pre‐study, significant improvements were observed post‐study for sleep disturbance (Phillips–Robinson Model), and insomnia severity and sleep‐related impairments (Model of Arousal Dynamics). Participants reported that using a digital, automated format for the delivery of sleep recommendations would enable greater uptake. These findings provide a positive proof‐of‐concept for using biomathematical models to recommend sleep in operational contexts.
... The Three Process Model of Alertness [79] was chosen and implemented on the server. This model is the result of extensive previous research and has been used in the study of sleep schedules in shift work [29] and road-crash prediction [80]. It can be used to estimate current, past, and future alertness based on a user's circadian rhythm and the timings of their past sleep. ...
... In Awari, the user can interact directly with the three process model of alertness. This model is foremost not intended to be used by end users but instead provides rigorous structure in the recommending and testing schedules in aviation [29], in the maritime industry [67], and in automated long haul trucking [4]. Although people can fnd and read the theory and work out the model being used, as the community did for the artifcial pancreas machines [9], the outputs of such models alone do not provide agency over, and understanding of, one's own bodily processes. ...
... Biomathematical models (BMM) such as BAM (Ingre et al., 2014) build on the traditional methods of measuring fatigue and imply algorithm-based calculated off-duty sleep times. Problems associated with this and other issues with BMM are described in the next paragraphs. ...
Abstract
The aim of this dissertation was to examine two so far separately considered complex constructs, fatigue and mental health, concerning a target group that has to cope with high stress, extraordinary workload, high risks and responsibility: professional pilots. The complexity of the psychophysiological construct fatigue should be highlighted. Potential correlations and interactions of stress with fatigue, sleep problems, mental health and well-being should be investigated. It seemed necessary to consider pilot fatigue not only in the context of sleep medicine, but also in context with the Theory of Allostasis, clinical, work psychology and burnout research. Studies one and two inves-tigated, if our comprehensive dataset of 406 pilots would support the Theory of Allo-stasis. Complex analyses confirmed that acute and chronic work-related and psychoso-cial stress were significantly associated with more psychophysiological wear and tear processes like high fatigue, sleep problems, impaired well-being and more symptoms of depression, anxiety, and CMD. The third study was a Qualitative Content Analysis of pilots’ experiences, which perfectly confirmed the quantitative results of all five studies and the Theory of Allostasis. Studies 4, 5 and 6 compared groups of pilots. Australian pilots were slightly more affected than EASA-based pilots. Short-haul pilots of low-cost-carriers were most affected, reporting excessive fatigue, the most sleep problems, the most symptoms of depression, anxiety and CMD, and the most impaired well-being. These first six exploratory studies have not received any funding but have identi-fied important new research topics. These complex, new results should be the basis of future research regarding pilots’ fatigue, health and flight safety in general.
... The first is survey-based and the second uses a sinusoidal function that includes a parameter implying individual nurses' chronotype (propensity to sleep at different times), based on work presented in [17] to approximate fatigue at the end of a week. [9] proposes the TDSPFM, a Truck Driver Scheduling Model where fatigue is modeled using the non-linear fatigue model proposed in the model of [33], which is itself based on the three process model of [3]. The non-linear TDSPFM is solved using the evolutionary algorithm of the built-in Excel 2013 solver. ...
We use a real Nurse Rostering Problem and a validated model of human sleep to formulate the Nurse Rostering Problem with Fatigue. The fatigue modelling includes individual biologies, thus enabling personalised schedules for every nurse. We create an approximation of the sleep model in the form of a look-up table, enabling its incorporation into nurse rostering. The problem is solved using an algorithm that combines Mixed-Integer Programming and Constraint Programming with a Large Neighbourhood Search. A post-processing algorithm deals with errors, to produce feasible rosters minimising global fatigue. The results demonstrate the realism of protecting nurses from highly fatiguing schedules and ensuring the alertness of staff. We further demonstrate how minimally increased staffing levels enable lower fatigue, and find evidence to suggest biological complementarity among staff can be used to reduce fatigue. We also demonstrate how tailoring shifts to nurses’ biology reduces the overall fatigue of the team, which means managers must grapple with the issue of fairness in rostering.
... All participants received written material covering the content of each session, as well as online access to an adapted version of a biomathematical model (ArturNurse). ArturNurse evaluated fatigue risk levels based on their work schedules 25 and provided suggestions of strategies from the programme on how to optimise sleep in relation to different shifts. See online supplemental file 1 for more detail about the intervention. ...
Objectives
To examine if a proactive recovery intervention for newly graduated registered nurses (RNs) could prevent the development of sleep problems, burn-out, fatigue or somatic symptoms.
Methods
The study was a randomised control trial with parallel design. Newly graduated RNs with less than 12 months’ work experience were eligible to participate. 461 RNs from 8 hospitals in Sweden were invited, of which 207 signed up. These were randomised to either intervention or control groups. After adjustments, 99 RNs were included in the intervention group (mean age 27.5 years, 84.7% women) and 108 in the control group (mean age 27.0 years, 90.7% women). 82 RNs in the intervention group attended a group-administered recovery programme, involving three group sessions with 2 weeks between each session, focusing on proactive strategies for sleep and recovery in relation to work stress and shift work. Effects on sleep, burn-out, fatigue and somatic symptoms were measured by questionnaires at baseline, postintervention and at 6 months follow-up.
Results
Preventive effect was seen on somatic symptoms for the intervention group. Also, the intervention group showed less burn-out and fatigue symptoms at postintervention. However, these latter effects did not persist at follow-up. Participants used many of the strategies from the programme.
Conclusions
A proactive, group-administered recovery programme could be helpful in strengthening recovery and preventing negative health consequences for newly graduated RNs.
Trial registration number
NCT04246736 .
... In this regard, countries worldwide are making efforts to establish effective barriers to mitigate the fatigue risk either via prescriptive flight and duty time limitations or via the implementation of fatigue risk management systems [6]. A recent experiment carried out by the European Aviation Safety Agency (EASA) [7] investigated the effectiveness of some prescriptive rules and scenarios in Europe using two well-known biomathematical models: the Sleep, Activity, Fatigue, and Task Effectiveness Fatigue Avoidance Scheduling Tool (SAFTE-FAST) [8] and the Boeing Alertness Model (BAM) [9]. One of their findings revealed that despite of being necessary, the prescriptive rules are not fully sufficient to mitigate the fatigue risks, specially during disruptive and/or night shifts. ...
... Moreover, the analysis of speech parameters and its correlations with fatigue and sleepiness under operational circumstances is also a good practical example of research being successfully applied to aviation accident investigations [14]. Several model based approaches have been used to predict fatigue and/or sleepiness outcomes due to sleep loss and/or circadian disruptions [8,9,[15][16][17]. More recently, Cochrane et al. also emphasized the importance of considering non-linear relationships between fatigue and risk [18]. ...
This work evaluates the potential root causes of fatigue using a biomathematical model and a robust sample of aircrew rosters from the Brazilian regular aviation. The fatigue outcomes derive from the software Sleep, Activity, Fatigue, and Task Effectiveness Fatigue Avoidance Scheduling Tool (SAFTE-FAST). The average minimum SAFTE-FAST effectiveness during critical phases of flight decreases cubically with the number of shifts that elapse totally or partially between mid-night and 6 a.m. within a 30-day period ($N_{NS}$). As a consequence, the relative fatigue risk increases by 23.3% (95% CI, 20.4-26.2%) when increasing $N_{NS}$ from 1 to 13. The average maximum equivalent wakefulness in critical phases also increases cubically with the number of night shifts and exceeds 24 hours for rosters with $N_{NS}$ above 10. The average fatigue hazard area in critical phases of flight varies quadratically with the number of departures and landings within 2 and 6 a.m. ($N_{Wocl}$). These findings demonstrate that both $N_{NS}$ and $ N_{Wocl}$ should be considered as key performance indicators and be kept as low as reasonably practical when building aircrew rosters. The effectiveness scores at 30 minute time intervals allowed a model estimate for the relative fatigue risk as a function of the time of the day, whose averaged values show reasonable qualitative agreement with previous measurements of pilot errors. Tailored analyses of the SAFTE-FAST inputs for afternoon naps before night shifts, commuting from home to station and vice-versa, and bedtime before early-start shifts show relevant group effects ($p < 0.001$) comparing the groups with and without afternoon naps, with one or two hours of commuting and with or without the advanced bedtime feature of the SAFTE-FAST software, evidencing the need of a better and more accurate understanding of these parameters when modelling fatigue risk factors.
... As detailed later, the negative cognitive, metabolic, and health outcomes of sleep curtailment are numerous, and probably play a role in the adverse effects associated with shift work. medical residents (Basner et al., 2017), police officers (Boivin et al., 2012a;Boudreau et al., 2013a), miners (Ferguson et al., , 2011, marine pilots (Boudreau et al., 2018), professional truck drivers (Anund et al., 2018), train drivers (Jay et al., 2006), and airline pilots (Ingre et al., 2014;Sallinen et al., 2017;Aljurf et al., 2018;Sallinen et al., 2020). Extended wakefulness, lack of adequate recovery sleep between shifts, and being awake during the circadian trough of alertness, at night or in the early morning, lead to excessive sleepiness in shift workers (Mullins et al., 2014). ...
The various non-standard schedules required of shift workers force abrupt changes in the timing of sleep and light-dark exposure. These changes result in disturbances of the endogenous circadian system and its misalignment with the environment. Simulated night-shift experiments and field-based studies with shift workers both indicate that the circadian system is resistant to adaptation from a day- to a night-oriented schedule, as determined by a lack of substantial phase shifts over multiple days in centrally controlled rhythms, such as those of melatonin and cortisol. There is evidence that disruption of the circadian system caused by night-shift work results not only in a misalignment between the circadian system and the external light-dark cycle, but also in a state of internal desynchronization between various levels of the circadian system. This is the case between rhythms controlled by the central circadian pacemaker and clock genes expression in tissues such as peripheral blood mononuclear cells, hair follicle cells, and oral mucosa cells. The disruptive effects of atypical work schedules extend beyond the expression profile of canonical circadian clock genes and affects other transcripts of the human genome. In general, after several days of living at night, most rhythmic transcripts in the human genome remain adjusted to a day-oriented schedule, with dampened group amplitudes. In contrast to circadian clock genes and rhythmic transcripts, metabolomics studies revealed that most metabolites shift by several hours when working nights, thus leading to their misalignment with the circadian system. Altogether, these circadian and sleep-wake disturbances emphasize the all-encompassing impact of night-shift work, and can contribute to the increased risk of various medical conditions. Here, we review the latest scientific evidence regarding the effects of atypical work schedules on the circadian system, sleep and alertness of shift-working populations, and discuss their potential clinical impacts.
... Secondly, the question used in EpiHealth has been previously related to sleep timing and estimated circadian phase. 47 Although exposure misclassification may not be completely ruled-out, it would likely be unrelated to the outcome, resulting in effect estimates biased toward the null. ...
Study objectives
Individuals with evening chronotype have a higher risk of cardiovascular and metabolic disorders, although the underlying mechanisms are not well understood. In a population-based cohort, we aimed to investigate the association between chronotype and 242 circulating proteins from three panels of established or candidate biomarkers of cardiometabolic processes.
Methods
In 2,471 participants (49.7% men, mean age 61.2±8.4 SD years) from the EpiHealth cohort, circulating proteins were analyzed with a multiplex proximity extension technique. Participants self-reported their chronotype on a five-level scale from extreme morning to extreme evening chronotype. With the intermediate chronotype set as the reference, each protein was added as the dependent variable in a series of linear regression models adjusted for confounders. Next, the chronotype coefficients were jointly tested and the resulting p-values adjusted for multiple testing using false discovery rate (5%). For the associations identified, we then analyzed the marginal effect of each chronotype category.
Results
We identified 17 proteins associated with chronotype. Evening chronotype was positively associated with proteins previously linked to insulin resistance and cardiovascular risk, namely retinoic acid receptor protein 2, fatty acid-binding protein adipocyte, tissue-type plasminogen activator, and plasminogen activator inhibitor 1 (PAI-1). Additionally, PAI-1 was inversely associated with the extreme morning chronotype.
Conclusions
In this population-based study, proteins previously related with cardiometabolic risk were elevated in the evening chronotypes. These results may guide future research in the relation between chronotype and cardiometabolic disorders.
