Fatigue has always been an inevitable part of working life, but our understanding of its causes and effects, and how it can be managed to reduce the risk of accidents and adverse long-term effects on people’s health, has evolved considerably in recent years. What used to be seen as merely a temporary personal discomfort and inconvenience—something to be pushed through with determination or a strong coffee—is now recognised as a measurable form of impairment that significantly affects safety, performance and personal wellbeing. Around the world, and across industries, the cost of fatigue is striking: reduced productivity, preventable accidents, long-term health consequences and, in extreme cases, loss of life [1]. Yet despite this, many organisations still rely on outdated assumptions about what tiredness means and how to manage it.

Research and modern science now offer a more realistic picture. Fatigue is not only the result of the odd late night or a particularly demanding and stressful week; it is a physiological and mental state that can undermine judgment, slow reaction time, and increase the likelihood of errors. In safety-critical environments such as aviation, rail, road transport, healthcare and manufacturing, the implications are even more stark. Understanding fatigue and managing it effectively in any working environment now plays an essential role in safeguarding workers and, in some cases, protecting the general public.

Although we often treat tiredness as a subjective feeling, fatigue involves measurable changes in how the body functions. These changes are tangible and measurable and they dictate how we think, behave, feel, and react to the people and activities around us. Reaction times slow. Concentration lapses. Memory becomes less reliable. Decision-making grows erratic. Emotional control diminishes.

Fatigue impairment can be measured. Research indicates that being awake for 17 hours hampers performance to the same extent as having a blood alcohol level of 0.05%. After 24 hours without sleep, this rises to 0.10%—legally intoxicated in most jurisdictions. Unlike alcohol, however, we often lack the objective tools to detect it until it is too late [2].

One of the most concerning effects is the potential for microsleeps—brief, involuntary episodes which may last only a few seconds [3]. These moments of “switching off” may even go completely unnoticed by the person experiencing them. For someone responsible for driving a train, coordinating an air-traffic sector, administering medication, or operating heavy equipment, the risk hardly needs elaboration.

Fatigue is also cumulative. A single night of poor sleep may not cause major impairment in performance, but several in a row build a “sleep debt”, reducing cognitive capacity over periods of days and weeks. The effects can also continue long after a shift ends. Chronic sleep deprivation has been linked with long-term health issues, including cardiovascular disease, depression and weakened immune function [4].

In summary, fatigue can be considered both a short-term performance threat and a long-term health concern. Seeing it as nothing more than “feeling a bit tired” understates how the body responds to sustained lack of sleep.

For decades, many industries have relied on prescriptive Hours-of-Work (HoW) limits—fixing the length of shifts, defining mandatory rest periods, and, where possible, restricting night-duty periods. While these rules remain an important basis for protecting workers, on their own, they are not enough.

The first problem is that not all hours worked are equal. A work shift of eight hours beginning at 2 am is likely to affect the body very differently from working eight hours at 9 am. Our circadian rhythms (the body's internal clock, which expects us to be awake during the day and asleep at night) strongly influence our ability to feel rested, be alert and to perform well.

The second problem is that rest does not guarantee good quality, restorative sleep. For example, a ten-hour break may only permit six hours of actual sleep if interrupted by noise, family responsibilities, commuting or stress. Therefore, from a strictly human physiology perspective, a shift rota that “complies” with a company’s HoW regulations may still leave workers dangerously fatigued.

Individual differences also matter. Beyond the natural tendency of most people to prefer being awake during the hours of daylight, some people naturally function better in the morning, while others perform better later in the day. The combination of how much proper sleep a person can manage, and whether they work at a time of day which suits their natural rhythms, will both influence how quickly fatigue accumulates and how much recovery time they subsequently need to feel rested and alert.

Finally, strict rule-based systems can create a “tick-box mentality”. If a shift pattern complies with limits based solely on the number of hours worked and the rest periods in between, companies may assume it is automatically safe, even when the real-world effects of shift timing may indicate otherwise. Compliance can therefore become a substitute for more sophisticated risk management, which considers sleep quality, work patterns, and operational pressures.

Advances in technology and science have transformed the way fatigue is understood. Rather than relying solely on self-assessment—something notoriously unreliable—organisations can now use more objective tools to detect the onset of fatigue in real time and even predict it before it becomes a problem..

Wearable devices and sensors can track a range of physiological signals: heart-rate variability, skin temperature, movement patterns, blink rates and more. These metrics provide a real-time picture of a person’s alertness, enabling early intervention when tiredness reaches unsafe levels [5].

However, building on detection, predictive systems that use biomathematical models to forecast fatigue before it even occurs are now emerging. These models incorporate two well-established scientific principles: sleep pressure (the longer you are awake, the greater your need for sleep) and circadian rhythm. When combined with sleep history, shift patterns and time-of-day data, these models can estimate fatigue levels hours or even days ahead.

Ever-evolving artificial intelligence and machine learning are now being used to enhance these systems further. By analysing patterns across large populations of shift workers, they refine predictions and adapt to individual sleep profiles. The result is a more personalised and accurate assessment of when someone is likely to be fatigued, even if they feel alert.

This ability to anticipate the onset of fatigue represents a significant advance in risk management. Instead of responding after mistakes happen, organisations can intervene before fatigue becomes dangerous, adjusting duties, reallocating tasks among team members or providing additional rest where needed.

While different sectors face different operational realities, the underlying challenge is the same: people will not perform safely or effectively when fatigued.

Aviation is widely seen as the most mature sector in this area. International standards, decades of scientific research and a strong reporting culture have helped shape a sophisticated approach to FRMS [x]. Airlines use predictive modelling to design safer rosters, particularly for long-haul and night operations, and regulators now treat fatigue management as a fundamental safety requirement [5].

Rail and road transport are starting to use similar methods. Long hours, changing schedules, and repetitive work make drivers especially at risk for fatigue. Many companies now check schedules with biomathematical models before using them, to spot risky combinations like early starts, long shifts, and night work.

Healthcare, especially medical residency programs, has its own challenges. Long shifts, sudden busy periods, and emotional stress all add to fatigue. Lately, there is more interest in using fatigue models to plan residency schedules, focusing on when work happens instead of just cutting total hours [6].

Manufacturing, construction, and utilities are starting to use predictive tools to identify high-risk periods and adjust work assignments before mistakes occur. These industries may be at different stages, but they all agree that fatigue is a known risk that needs active management [7].

In many industries, fatigue monitoring tools are increasingly being integrated directly into daily operations. Wearable devices measure sleep using actigraphy, which tracks movement patterns that correlate with sleep stages. Supervisors receive hour-by-hour predictions of expected alertness across a shift, allowing them to plan workloads and allocate tasks to match risk.

More sophisticated platforms identify “fatigue hotspots”, forecasting periods of high risk up to two weeks ahead. This insight enables operations teams to optimise rosters, adjust staffing levels and make informed decisions about when complex or safety-critical work should be undertaken.

Any monitoring system that collects biometric data is likely to raise understandable concerns among workers. People want reassurance that their personal information will be used to protect their safety rather than to evaluate performance, track their movements, or even result in disciplinary measures.

Building trust in the use of these tools is therefore essential. Clear governance policies, developed with input from employees and unions, help define how data will be used and, crucially, who will have access to it. Transparency about purpose—improving worker safety and wellbeing, not tracking productivity—will make adoption more acceptable.

Wearables also shift responsibility in a meaningful way. Instead of expecting individuals to recognise and report their own fatigue, the employer receives objective information that can prompt timely intervention. However, for workers to accept this technology, they must perceive it as genuinely supportive—a tool that helps them by sharing the burden of fatigue detection, rather than simply adding another layer of monitoring. This reduces the pressure on workers to self-disclose tiredness in environments where doing so may be difficult or discouraged.

Even the most advanced technology will not be fully effective if company culture does not encourage honest reporting. In many workplaces, employees underreport fatigue because they feel nothing will change, fear losing overtime, or worry that admitting tiredness will be seen as weakness.

Building a "just culture" is essential [8]. Workers must be confident that reporting fatigue will lead to constructive action rather than blame or damage to their career prospects. Supervisors need training to handle fatigue conversations sensitively and appropriately. When people feel safe to speak up, organisations gain better insight into the real conditions under which work is performed.

Fatigue risk management continues to evolve. Predictive models are becoming more precise, wearable devices are improving in accuracy and comfort, and integration with scheduling and operational systems is gradually becoming routine. The underlying challenge, however, has not changed: turning scientific knowledge into everyday practice.

The most effective programmes share three features: reliable data, a culture that encourages openness, and clear organisational commitment. They treat fatigue as a predictable risk rather than an inevitable by-product of demanding work. This approach supports workers by improving rest opportunities and reducing avoidable strain, while also giving organisations a firmer basis for planning, staffing and operational decision-making.

Fatigue itself is not new. What has changed is the ability to measure, anticipate and address it. For companies willing to use these tools, the gains extend well beyond individual wellbeing. Improvements in safety, consistency, productivity and long-term operational resilience tend to reinforce one another, creating benefits for both workers and the organisation as a whole.