Evaluation of a Psychomotor Vigilance Task for Touch Screen Devices

Arsintescu, Lucia; Mulligan, Jeffrey B.; Flynn‐Evans, Erin E.

doi:10.1177/0018720816688394

Cited by 29 publications

(23 citation statements)

References 21 publications

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“…it subtracts) the mobile device and software latency, which we characterized using the same RTBox apparatus (Li, et al, ) used to develop the PC‐PVT and PC‐PVT 2.0 software (Khitrov et al, ; Reifman, et al, ) The hardware–software RT latency for the 2B‐Alert App was 58 and 68 ms for the iPhone 6s and iPad Air 2, respectively, running iOS 10.3.2, and 79 ms for the Samsung Note 4 running Android 6.0.1. We also assessed whether RT latencies depend on the orientation of the smartphone (portrait vs. landscape) and the finger used for responding (index vs. thumb; Arsintescu, Mulligan, & Flynn‐Evans, ), but found no significant statistical differences among the configurations. Nonetheless, we recommend that PVTs in the app be performed consistently, always using the same configuration.…”

Section: Methodsmentioning

confidence: 98%

“…We also assessed whether RT latencies depend on the orientation of the smartphone (portrait vs. landscape) and the finger used for responding (index vs. thumb; Arsintescu, Mulligan, & Flynn-Evans, 2017), but found no significant statistical differences among the configurations. Nonetheless, we recommend that PVTs in the app be performed consistently, always using the same configuration.…”

Section: Pvt Settings and Latency Characterizationmentioning

confidence: 97%

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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: Methodsmentioning

confidence: 98%

Section: Pvt Settings and Latency Characterizationmentioning

confidence: 97%

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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“…NOTE: This is especially important for the PVT task where the accuracy of the internal stopwatch is influenced by connectivity functions, thus influencing the reaction time 38 . 3.…”

Section: Instruct Participants To Keep the Touchscreen Device In Airpmentioning

confidence: 99%

“…In addition, the PVT shows little to no learning effects and is simple to use, making it a practical test for deploying in field environments where study participants may not be observed during testing 36 . The ubiquity of touch-screen devices allows for easy deployment of the PVT, but researchers should be cautious when implementing the PVT, because there are numerous aspects of touch-screen devices that can introduce error into the collection of PVT data 37,38 . For example, different hardware and software combinations have different system latencies, and other applications running in the background can introduce unknown error into the recorded reaction times.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, it is important to collect PVT data using a validated PVT, with consistent hardware and software, with WiFi, and with all other applications turned off. In addition, given that it is not practical to observe study participants during tests in operational environments, it is critical that participants are trained to complete each PVT with the device in the same orientation, using the same finger 38,39 .…”

Section: Introductionmentioning

confidence: 99%

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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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Differences in brain activity between fast and slow responses on psychomotor vigilance task: an fNIRS study

Nogueira

Silvestrin

Barreto

et al. 2022

Brain Imaging and Behavior

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Attention is a basic human function underlying every other cognitive process. It is demonstrated in the functional Magnetic Resonance Imaging literature that frontoparietal networks are involved with attentive performance while default mode networks are involved with inattentive performance. Yet, it is still not clear whether similar results would be found with functional Near-Infrared Spectroscopy. The goal of our study was to investigate differences in hemodynamic activity measured by functional Near-Infrared Spectroscopy between fast and slow responses on a simple sustained attention task both before and after stimulus onset. Thirty healthy adults took part in the study. Our results have shown differences between fast and slow responses only on channels over medial frontal cortex and inferior parietal cortex (p < 0,05). These differences were observed both before and after stimulus presentation. It is discussed that functional Near-Infrared Spectroscopy is a good tool to investigate the frontoparietal network and its relationship with performance on attention tasks; it could be used to further investigate other approaches on attention, such as the dual network model of cognitive control and brain states views based on complex systems analysis; and nally, it could be used to investigate attention on naturalistic settings. Consent to participateAll participants signed a freely given, informed consent to participate term. This term was previously approved by the aforementioned committee. Consent to publishThis work does not include materials from participants that require consent to publish. Author's contributionAuthor contributions included conception and study design (MGN, CEB, JRS, AFB), data collection or acquisition (MGN), statistical analysis (MGN, MS, CSFB), interpretation of results (MGN, CEB, JRS, RCM, AFB

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Evaluation of a Psychomotor Vigilance Task for Touch Screen Devices

Cited by 29 publications

References 21 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

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

Differences in brain activity between fast and slow responses on psychomotor vigilance task: an fNIRS study

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