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Choosing the right sleep tracker for the operational environment

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.


https://ibr.iad1.qualtrics.com/jfe/form/SV_8jqI4x7dAwM5WEC


Disclaimers: The information provided by the Institutes for Behavior Resources, Inc. through the CST match-making quiz is for general informational purposes only. All information included in the quiz results is provided in good faith, however we make no representation or warranty of any kind, express or implied, regarding the accuracy, adequacy, validity, reliability, availability, or completeness of any information on the site. Under no circumstances shall we have any liability to you for any loss or damage of any kind incurred as a result of the use of the quiz or reliance on any information provided by the quiz results. Your use of the quiz and your reliance on any information on the site is solely at your own risk.


The CST match-making quiz is hosted through Qualtrics, LLC. No personal information will be requested or collected through the completion of this quiz. Any data you provide may be used by IBR, Inc. for internal purposes. By taking this quiz, you agree to the collection and use of information in accordance with the terms of service and privacy statement as outlined by Qualtrics, LLC.


The quiz may contain links to other websites or content belonging to or originating from third parties. Such external links are not monitored by us. We do not warrant, endorse, guarantee, or assume responsibility for the accuracy or reliability of any information offered by third-party websites linked through this quiz. We will not be a party to or in any way responsible for monitoring any transition between you and third-party providers of products or services.


Footnotes

1. Alkozei, A., et al., Chronic sleep restriction affects the association between implicit bias and explicit social decision making. Sleep Health, 2018. 4(5): p. 456-462.

2. Baglioni, C., et al., Sleep and mental disorders: A meta-analysis of polysomnographic research. Psychol Bull, 2016. 142(9): p. 969-990.

3. Banks, S. and D.F. Dinges, Behavioral and physiological consequences of sleep restriction. J Clin Sleep Med, 2007. 3(5): p. 519-28.

4. Boardman, J.M., et al., The impact of sleep loss on performance monitoring and error-monitoring: A systematic review and meta-analysis. Sleep Med Rev, 2021. 58: p. 101490.

5. Cappuccio, F.P. and M.A. Miller, Sleep and Cardio-Metabolic Disease. Curr Cardiol Rep, 2017. 19(11): p. 110.

6. Covassin, N. and P. Singh, Sleep Duration and Cardiovascular Disease Risk: Epidemiologic and Experimental Evidence. Sleep Med Clin, 2016. 11(1): p. 81-9.

7. Grandner, M.A., Sleep, Health, and Society. Sleep Med Clin, 2017. 12(1): p. 1-22.

8. Hsieh, S., C.Y. Tsai, and L.L. Tsai, Error correction maintains post-error adjustments after one night of total sleep deprivation. J Sleep Res, 2009. 18(2): p. 159-66.

9. Luyster, F.S., et al., Sleep: a health imperative. Sleep, 2012. 35(6): p. 727-34.

10. Palmer, C.A. and C.A. Alfano, Sleep and emotion regulation: An organizing, integrative review. Sleep Med Rev, 2017. 31: p. 6-16.

11. Scullin, M.K., et al., Experimental sleep loss, racial bias, and the decision criterion to shoot in the Police Officer's Dilemma task. Sci Rep, 2020. 10(1): p. 20581.

12. St-Onge, M.P., et al., Sleep Duration and Quality: Impact on Lifestyle Behaviors and Cardiometabolic Health: A Scientific Statement From the American Heart Association. Circulation, 2016. 134(18): p. e367-e386.

13. Uehli, K., et al., Sleep problems and work injuries: a systematic review and meta-analysis. Sleep Med Rev, 2014. 18(1): p. 61-73.

14. Dawson, D. and K. McCulloch, Managing fatigue: it's about sleep. Sleep Med Rev, 2005. 9(5): p. 365-80.

15. Devine, J.K., et al., Practice parameters for the use of actigraphy in the military operational context: the Walter Reed Army Institute of Research Operational Research Kit-Actigraphy (WORK-A). Mil Med Res, 2020. 7(1): p. 31.

16. Lujan, M.R., I. Perez-Pozuelo, and M.A. Grandner, Past, Present, and Future of Multisensory Wearable Technology to Monitor Sleep and Circadian Rhythms. Frontiers in Digital Health, 2021: p. 104.

17. I. ARC. Wearable Sleep Trackers Market Forecast (2021-2026). 2021. https://www.industryarc.com/Report/19669/wearable-sleep-trackers-market.html

18. Arizton. Sleep Market - Global Outlook & Forecast 2021-2026. 2021. https://www.researchandmarkets.com/reports/5380212/sleep-market-global-outlook-and-forecast-2021

19. P. Reports. Global Smart Sleep Tracking Devices Sales Market Report 2021. 2021. http://www.precisionreports.co/global-smart-sleep-tracking-device-sales-market-17567525

20. G.M. Insights. Sleep Tech Devices Market Size By Product. 2021. https://www.gminsights.com/industry-analysis/sleep-tech-devices-market

21. Chinoy, E.D., et al., Performance of seven consumer sleep-tracking devices compared with polysomnography. Sleep, 2021. 44(5).

22. Depner, C.M., et al., Wearable technologies for developing sleep and circadian biomarkers: a summary of workshop discussions. Sleep, 2020. 43(2).

23. .Researcher Preferences for Consumer Sleep Technology: A Survey of Experts. 2021.