3-minute smartphone-based and tablet-based psychomotor vigilance tests for the assessment of reduced alertness due to sleep deprivation

Grant, Devon A.; Honn, Kimberly; Layton, Matthew E.; Riedy, Samantha M.; Dongen, Hans P A Van

doi:10.3758/s13428-016-0763-8

Cited by 79 publications

(65 citation statements)

References 28 publications

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“…Compared with the PC-PVT, the 2B-Alert PVT yielded faster RTs and more false starts, presumably due to devicedependent factors (Honn, Riedy, & Grant, 2015). Grant and colleagues observed similar results, with 3-min PVTs on a smartphone app yielding faster RTs, fewer lapses and more false starts than on a tablet (Grant et al, 2017). Except for false starts, effect sizes of 2B-Alert PVT-derived statistics were generally slightly lower or similar to those derived from PC-PVT, a trend that was comparable to that observed in Brunet et al (2017).…”

Section: Discussionmentioning

confidence: 85%

2B‐Alert App: A mobile application for real‐time individualized prediction of alertness

Reifman

Ramakrishnan

et al. 2018

Journal of Sleep Research

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Knowing how an individual responds to sleep deprivation is a requirement for developing personalized fatigue management strategies. Here we describe and validate the 2B-Alert App, the first mobile application that progressively learns an individual's trait-like response to sleep deprivation in real time, to generate increasingly more accurate individualized predictions of alertness. We incorporated a Bayesian learning algorithm within the validated Unified Model of Performance to automatically and gradually adapt the model parameters to an individual after each psychomotor vigilance test. We implemented the resulting model and the psychomotor vigilance test as a smartphone application (2B-Alert App), and prospectively validated its performance in a 62-hr total sleep deprivation study in which 21 participants used the app to perform psychomotor vigilance tests every 3 hr and obtain real-time individualized predictions after each test. The temporal profiles of reaction times on the appconducted psychomotor vigilance tests were well correlated with and as sensitive as those obtained with a previously characterized psychomotor vigilance test device.The app progressively learned each individual's trait-like response to sleep deprivation throughout the study, yielding increasingly more accurate predictions of alertness for the last 24 hr of total sleep deprivation as the number of psychomotor vigilance tests increased. After only 12 psychomotor vigilance tests, the accuracy of the model predictions was comparable to the peak accuracy obtained using all psychomotor vigilance tests. With the ability to make real-time individualized predictions of the effects of sleep deprivation on future alertness, the 2B-Alert App can be used to tailor personalized fatigue management strategies, facilitating self-management of alertness and safety in operational and non-operational settings. K E Y W O R D Salertness, caffeine, individualized predictions, psychomotor vigilance test, sleep, smartphoneThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Discussionmentioning

confidence: 85%

2B‐Alert App: A mobile application for real‐time individualized prediction of alertness

Reifman

Ramakrishnan

et al. 2018

Journal of Sleep Research

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“…In addition, the participants were not observed by researchers when conducting the test. A previous study examined the validity of a 3-minute smartphone-based PVT test against a 5-minute laptop-based PVT test 22) . Further investigations are necessary to determine how the PVT performance test is appropriately conducted in real settings without an observer.…”

Section: Discussionmentioning

confidence: 99%

Day-to-day variations in daily rest periods between working days and recovery from fatigue among information technology workers: One-month observational study using a fatigue app

Kubo

Izawa

Tsuchiya

et al. 2018

Journal of Occupational Health

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Background: The European Union's Working Time Directive stipulates recovery periods as "Eleven consecutive hour daily rest periods (DRPs) between working days." In Japan, adopting this regulation has been discussed. Objective: To examine the association between weekday DRPs and recovery from fatigue by conducting a one-month observational study using a fatigue monitoring application (fatigue app) on a smart tablet. Methods: Fifty-five daytime employees working at an information technology company participated in this study. Visual analogue scales (VAS) regarding fatigue, psychological detachment from work, a psychomotor vigilance task (PVT), and sleep-wake logs were repeatedly measured using a fatigue app that we developed for this study. Sleep was measured by a wrist actigraph. Results: The average and SD of weekday DRPs was 13.1 ± 2.3 h. However, 54.5% of employees experienced less than the minimum 11-hour DRP at least once a month. Sleep duration was significantly changed with weekday DRPs, though no significant difference was found in sleep efficiency. Linear mixed effects models showed that sleep duration increased by approximately 15 minutes for each hour of weekday DRPs (β = 0.24, 95% CI; 0.17, 0.32, p <.001). VAS-measured fatigue and psychological detachment from work significantly improved as DRPs increased (β = −3.4, 95% CI; −4.7, −2.1, p <.001; β = 2.6, 95% CI; 1.2, 4.0, p <.001, respectively). However, PVT performance did not significantly change with weekday DRPs. Conclusions: Ensuring weekday DRPs plays an essential role in protecting the opportunities to sleep, with better recovery from fatigue and stress among information technology workers.

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“…The PVT has been validated under conditions of acute and chronic sleep loss and circadian misalignment 4,5,30 . The duration of the task can be varied based on the design of the study 31,32 ; although, the traditional 10 min duration is preferred in laboratory studies 33,34 . while a 5 min duration PVT is typically more feasible in field studies where operational demands can interfere with the administration of the test 35 .…”

Section: Introductionmentioning

confidence: 99%

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Arsintescu

Kato²,

Hilditch

et al. 2019

JoVE

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Sleep loss and circadian misalignment contribute to a meaningful proportion of operational accidents and incidents. Countermeasures and work scheduling designs aimed at mitigating fatigue are typically evaluated in controlled laboratory environments, but the effectiveness of translating such strategies to operational environments can be challenging to assess. This manuscript summarizes an approach for collecting sleep, circadian, fatigue, and performance data in a complex operational environment. We studied 44 airline pilots over 34 days while they flew a fixed schedule, which included a baseline data collection with 5 days of mid-morning flights, four early flights, four high-workload mid-day flights, and four late flights that landed after midnight. Each work block was separated by 3-4 days of rest. To assess sleep, participants wore a wristworn research-validated activity monitor continuously and completed daily sleep diaries. To assess the circadian phase, pilots were asked to collect all urine produced in four or eight hourly bins during the 24 h after each duty block for the assessment of 6-sulfatoxymelatonin (aMT6s), which is a biomarker of the circadian rhythm. To assess subjective fatigue and objective performance, participants were provided with a touchscreen device used to complete the Samn-Perelli Fatigue Scale and Psychomotor Vigilance Task (PVT) during and after each flight, and at waketime, mid-day, and bedtime. Using these methods, it was found that sleep duration was reduced during early starts and late finishes relative to baseline. Circadian phase shifted according to duty schedule, but there was a wide range in the aMT6s peak between individuals on each schedule. PVT performance was worse on the early, high-workload, and late schedules relative to baseline. Overall, the combination of these methods was practical and effective for assessing the influence of sleep loss and circadian phase on fatigue and performance in a complex operational environment. Video LinkThe video component of this article can be found at https://www.jove.com/video/59851/ Given the differences between laboratory and real-world environments, implementation of strategies and countermeasures developed in the laboratory do not always translate into operations as expected. Individual differences, a broad range of operational work schedules, irregular and unpredictable operations, organizational practices and culture, and labor agreements are some of the factors that can complicate the application of science into practical operational use. As a result, it is important to evaluate the impact of such interventions using consistent and reliable methods to assess sleep, circadian rhythms, fatigue or alertness, and performance. The level of monitoring and data collecting must be kept proportional to the anticipated levels of fatigue and associated risks to safety within an operation 8 . Furthermore, in any safety-sensitive setting, maintaining safe operations is paramount to the investigatory protocol.The gold standard method for a...

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3-minute smartphone-based and tablet-based psychomotor vigilance tests for the assessment of reduced alertness due to sleep deprivation

Cited by 79 publications

References 28 publications

2B‐Alert App: A mobile application for real‐time individualized prediction of alertness

2B‐Alert App: A mobile application for real‐time individualized prediction of alertness

Day-to-day variations in daily rest periods between working days and recovery from fatigue among information technology workers: One-month observational study using a fatigue app

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

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