101 Measurement of Tapping During the Interstimulus Interval as a Validation Metric for the 3-Minute Psychomotor Vigilance Test

Abstract Introduction The Psychomotor Vigilance Test is a well-validated measure of sustained attention used to assess daytime alertness in sleep research studies.1 It is commonly used in a variety of research settings due to its high sensitivity to sleep loss and absence of learning effects,2 making it an ideal tool to assess objective alertness. As some types of sleep research transition out of controlled laboratory environments, tools like the PVT require modification to maximize their reliability. The validation of the 3-minute version (PVT-B) against the 10-minute PVT is an example of this modification.3 However, considerable work is needed to improve trust in the utility of the PVT-B in and outside of traditional laboratory settings. Methods We carefully analyzed data from a mobile-based version of the PVT-B, noting responses that occurred during the interstimulus interval which were termed “wrong taps.” Wrong taps indicated that participants were not performing the task as instructed. In some cases, wrong taps occurred across multiple trials of the same PVT block, indicative of participants repeatedly tapping the screen throughout the task to minimize response times. A comprehensive examination of wrong taps was carried out in order to identify instances where this pattern emerged. Results A total of 1,338,538 PVT-B trials from 7,028 participants were examined to determine the number of wrong taps present across all trials. While 91.7% of PVT-B trials were free of wrong taps, 8.3% of PVT-B trials contained 1 or more wrong taps and 5.2% contained 2 or more wrong taps. It appears that a maximum of one wrong tap per trial is acceptable and trials containing 2 or more should be excluded to maximize PVT data quality. Conclusion Utilizing a metric like wrong taps can help identify individuals taking the PVT-B who are tapping the screen multiple times prior to stimulus display. Closely examining this metric can help to ensure the validity of PVT-B administrations. Two possible uses of the metric could be to provide feedback during training trials and to remove trials where this strategy was employed. Support (if any) This analysis was supported by the VA San Diego Healthcare System Research Service.

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Response speed measurements on the psychomotor vigilance test: how precise is precise enough?

SLEEP ◽

10.1093/sleep/zsaa121 ◽

2020 ◽

Author(s):

Mathias Basner ◽

Tyler M Moore ◽

Jad Nasrini ◽

Ruben C Gur ◽

David F Dinges

Keyword(s):

Standard Deviation ◽

Performance Metrics ◽

Response Speed ◽

Total N ◽

Psychomotor Vigilance Test ◽

Pvt Data ◽

Average System ◽

Vigilance Test ◽

Attentional Lapses ◽

Psychomotor Vigilance

Abstract Study Objectives The psychomotor vigilance test (PVT) is frequently used to measure behavioral alertness in sleep research on various software and hardware platforms. In contrast to many other cognitive tests, PVT response time (RT) shifts of a few milliseconds can be meaningful. It is, therefore, important to use calibrated systems, but calibration standards are currently missing. This study investigated the influence of system latency bias and its variability on two frequently used PVT performance metrics, attentional lapses (RTs ≥500 ms) and response speed, in sleep-deprived and alert participants. Methods PVT data from one acute total (N = 31 participants) and one chronic partial (N = 43 participants) sleep deprivation protocol were the basis for simulations in which response bias (±15 ms) and its variability (0–50 ms) were systematically varied and transgressions of predefined thresholds (i.e. ±1 for lapses, ±0.1/s for response speed) recorded. Results Both increasing bias and its variability caused deviations from true scores that were higher for the number of lapses in sleep-deprived participants and for response speed in alert participants. Threshold transgressions were typically rare (i.e. <5%) if system latency bias was less than ±5 ms and its standard deviation was ≤10 ms. Conclusions A bias of ±5 ms with a standard deviation of ≤10 ms could be considered maximally allowable margins for calibrating PVT systems for timing accuracy. Future studies should report the average system latency and its standard deviation in addition to adhering to published standards for administering and analyzing the PVT.

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Using interstimulus interval to maximise sensitivity of the Psychomotor Vigilance Test to fatigue

Accident Analysis & Prevention ◽

10.1016/j.aap.2015.10.013 ◽

2017 ◽

Vol 99 ◽

pp. 406-410 ◽

Cited By ~ 9

Author(s):

Raymond W. Matthews ◽

Sally A. Ferguson ◽

Charli Sargent ◽

Xuan Zhou ◽

Anastasi Kosmadopoulos ◽

...

Keyword(s):

Interstimulus Interval ◽

Psychomotor Vigilance Test ◽

Vigilance Test ◽

Psychomotor Vigilance

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The Effects of an Energy Drink on Psychomotor Vigilance in Trained Individuals

Journal of Functional Morphology and Kinesiology ◽

10.3390/jfmk4030047 ◽

2019 ◽

Vol 4 (3) ◽

pp. 47 ◽

Cited By ~ 2

Author(s):

Antonio ◽

Kenyon ◽

Horn ◽

Jiannine ◽

Carson ◽

...

Keyword(s):

Reaction Time ◽

Fixed Time ◽

Energy Drink ◽

Time Frame ◽

Caffeine Intake ◽

Test Reaction ◽

Double Blind ◽

Psychomotor Vigilance Test ◽

Vigilance Test ◽

Psychomotor Vigilance

The psychomotor vigilance test (PVT) measures one’s behavioral alertness. It is a visual test that involves measuring the speed at which a person reacts to visual stimuli over a fixed time frame (e.g., 5 min). The purpose of this study was to assess the effects of an energy drink on psychomotor vigilance as well as a simple measure of muscular endurance (i.e., push-ups). A total of 20 exercise-trained men (n = 11) and women (n = 9) (mean SD: age 32 7 years; height 169 10 cm; weight; 74.5 14.5 kg; percent body fat 20.3 6.2%; years of training 14 9; daily caffeine intake 463 510 mg) volunteered for this randomized, double-blind, placebo-controlled, crossover trial. In a randomized counterbalanced order, they consumed either the energy drink (ED) (product: BANG®, Weston Florida) or a similar tasting placebo drink (PL). In the second visit after a 1-week washout period, they consumed the alternate drink. A full 30 minutes post-consumption, they performed the following tests in this order: a 5-minute psychomotor vigilance test, three sets of push-ups, followed once more by a 5-minute psychomotor vigilance test. Reaction time was recorded. For the psychomotor vigilance test, lapses, false starts and efficiency score are also assessed. There were no differences between groups for the number of push-ups that were performed or the number of false starts during the psychomotor vigilance test. However, the ED treatment resulted in a significantly lower (i.e., faster) psychomotor vigilance mean reaction time compared to the PL (p = 0.0220) (ED 473.8 42.0 milliseconds, PL 482.4 54.0 milliseconds). There was a trend for the ED to lower the number of lapses (i.e., reaction time > 500 milliseconds) (p = 0.0608). The acute consumption of a commercially available ED produced a significant improvement in psychomotor vigilance in exercise-trained men and women.

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0144 Relationship Between Sleep EEG K-Complex Slow Wave Coupling and Next-Day Thalamic Activity During Psychomotor Vigilance Test in Obstructive Sleep Apnea

SLEEP ◽

10.1093/sleep/zsaa056.142 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A57-A57

Author(s):

A A Parekh ◽

K Kam ◽

A Mullins ◽

A Fakhoury ◽

B Castillo ◽

...

Keyword(s):

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Slow Wave ◽

Excessive Daytime Sleepiness ◽

Daytime Sleepiness ◽

Wave Coupling ◽

Psychomotor Vigilance Test ◽

Obstructive Sleep ◽

Vigilance Test ◽

Psychomotor Vigilance

Abstract Introduction There is large inter-individual variability in the relationship between obstructive sleep apnea (OSA) severity and lapses in vigilance as measured using psychomotor vigilance test (PVT). We have previously shown that overnight sleep EEG K-complex slow wave coupling (∆SWAK) exhibits a dose-responsive relationship with next-day lapses in vigilance in OSA on and off treatment. We hypothesized that a variable thalamic dysfunction in OSA explains difference in lapses in vigilance and alterations in ∆SWAK across individuals. Methods Five newly diagnosed severe OSA subjects (mean apnea-hypopnea index [AHI4%=57.1±22.8/hr.]) with excessive daytime sleepiness (Epworth Sleepiness Scale=11±3.4) underwent nocturnal polysomnography followed by PVT testing within a 3T SKYRA MRI scanner. The PVT task inside the scanner (PVT-fMRI) was adapted to match the gold standard PVT-192 device. Each fMRI scanning session consisted of 2 10-min PVT runs interleaved with 2 control conditions wherein the subject pressed the response button at random intervals absent of a visual stimulus. fMRI data was analyzed in 2-step procedure (individual time-series followed by group analysis) using Analysis of Functional Neuroimages (AFNI) software package. To estimate thalamic activity during PVT-fMRI, parameter estimates of the %change in blood-oxygen-level-dependent (BOLD) signal using the contrast PVT-Control were used as the primary metric. The region of interest was limited to the bilateral thalamus using the Eickhoff-Zilles macro labels from the MNI N27 template. Results In a preliminary test, PVT performance for the subjects inside the scanner was not significantly different from that outside the scanner (PVTLapsesfMRI=7.3±2.1 vs. PVTLapsesPVT192=6.4±3.6 mean±std; PVTLapses=reaction time > 500 ms.). Within subjects, a trend toward lower thalamic recruitment was observed during PVT-fMRI (-0.17±0.2%; p=0.1). Further, lower thalamic activity during PVT-fMRI also showed a trend to lower overnight ∆SWAK (mean -1.2±1.4) values (r = 0.61, p = 0.17). Conclusion In severe OSA subjects with excessive daytime sleepiness, we observed a trend to reduced thalamic activity during daytime PVT. Overnight EEG K-complex slow wave coupling showed a similar trend with next-day thalamic activity during PVT, however the small sample size may have limited our ability to detect this association with statistical significance. Support AASM Foundation 199-FP-18; NIH K24HL109156

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