Trying to mitigate fatigue without accounting for sleep is like trying to maintain a goal weight without counting calories—it’s possible, but you’re missing a huge part of the picture. Not only is sleep directly related to subjective and objective sleepiness, but sleep is related to a number of physical and mental health outcomes which could influence on-the-job performance (1-13). Sleep is so heavily related to areas of fatigue risk that it needs to be ruled out before other causes of poor performance can reasonably be considered (14).
The SAFTE-FAST Auto-Sleep function allows users to model fatigue risk without using objective sleep data, but SAFTE-FAST can also evaluate fatigue risk from previously-collected objective sleep data. SAFTE-FAST uses sleep timing information (i.e., bedtime and wake time or bedtime and sleep duration) from objective data to model effectiveness. This means that any objective sleep measurement that provides a measure of sleep duration, bedtime, and/or waketime can be used as a SAFTE-FAST input. Back in the day, that meant either collecting sleep data with a) a written sleep diary, or b) a research-grade actigraph- a wrist-worn accelerometer that has been used to estimate sleep-wake patterns by researchers since the 1970s. Actigraphs bin accelerometry data into activity counts; sleep is determined based on patterns of inactivity post-hoc by an algorithm or a researcher (15, 16). Actigraphy and sleep diary are still reliable methods for collecting sleep data in the real world today.
However, we are entering a new era of fieldable sleep-tracking technology. Sleep tracking has become popular on the consumer market in the past decade, and the global sleep tracking device market is estimated to be worth between $20-$50 billion before the end of this decade (17-20). Many consumer sleep technology (CST) devices resemble the classic actigraph, but CSTs are undergoing an evolutionary explosion at the moment. For an excellent review of the history and future of the sensors and science behind CSTs, I recommend “Past, Present, and Future of Multisensory Wearable Technology to Monitor Sleep and Circadian Rhythms”, published by Lujan, Perez-Pozuelo, and Grandner in the August edition of Frontiers in Digital Health (https://www.frontiersin.org/articles/10.3389/fdgth.2021.721919/full) (16). Our friends at Clockwork Research, Ltd. also recently put out a guide on how to use sleep trackers that is industry-relevant and very readable (http://www.clockworkresearch.com/wp-content/uploads/2021/07/How-to-use-personal-sleep-tracking-devices.pdf). In brief, CSTs all measure sleep duration, bedtime, and wake time. Most CSTs also measure objective sleep quality, like how many times a user awakens during the night, and some estimate sleep stages as light, deep, or REM sleep. Many wearables now measure and report biometric data like heart rate or breathing rate in combination with sleep tracking and activity monitoring.
When it comes to sleep trackers, there’s a lot of history, a lot of information to consider, and a lot of choices at the moment. Judging from how many people avoid me at parties, I’m guessing that most folks don’t enjoy discussing this topic as much as I do. You may just want to know which device works best for your organization. Data from almost any device can be used to predict fatigue in SAFTE-FAST as long as the times and dates of sleep episodes are provided and the data can be reformatted into a .csv file. Beyond modeling, things to consider in the operational context include 1) finding a scientifically-valid device that workers will actually use; 2) identifying what data you need to collect and how to analyze it; 3) accessing data from multiple devices while still respecting workers’ privacy; and of course, 4) cost.
An absolute prerequisite when picking a CST is validation against laboratory measures (21, 22), and knowing what other researchers want is a good place to start when selecting a sleep tracker to collect data for operational purposes. IBR recently conducted a survey of real-world sleep research experts from academia and industry to determine which features of a CST were most desirable for research purposes. The consensus among this sample of 46 experts was that they wanted a wrist-worn device that could reliably estimate sleep as short as 20 minutes, used a combination of motor activity and biometric data for sleep-wake determination, and could reliably differentiate between real sleep versus other periods of inactivity (23). The full results from this survey are under review and hopefully will be published soon. The science team at IBR also has plans to evaluate how much scientific endorsement of a CST means to the individual consumer. But how does any of this help you personally, reader, find your ideal device?
Your needs may not be the same as the average consumer or the academic sleep scientist. You may not, for example, want to monitor your operators 24 hours a day for weeks on end. Your goal may be to provide operators with a tool to improve their own sleep hygiene without requiring access to their data, or you may want to evaluate the quality of work-rest facilities rather than individual sleepers. With so many different kinds of devices and applications available, it’s hard to know where to start! So, I put together a quick quiz to tease out which kind of device would work best under given circumstances. Taking the quiz will direct you to a general description of your best-fit CST and, with permission from the makers, a specific example of a CST device that is currently on the market and fits your criteria. I invite you to take this match-making quiz to see what kind of CST may work best for your organization or yourself.
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