Concordance of multiple methods to define resiliency and vulnerability to sleep loss depends on Psychomotor Vigilance Test metric

SLEEP ◽  
2021 ◽  
Author(s):  
Erika M Yamazaki ◽  
Courtney E Casale ◽  
Tess E Brieva ◽  
Caroline A Antler ◽  
Namni Goel

Abstract Study Objectives Sleep restriction (SR) and total sleep deprivation (TSD) reveal well-established individual differences in Psychomotor Vigilance Test (PVT) performance. While prior studies have used different methods to categorize such resiliency/vulnerability, none have systematically investigated whether these methods categorize individuals similarly. Methods 41 adults participated in a 13-day laboratory study consisting of 2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The PVT was administered every 2h during wakefulness. Three approaches (Raw Score [average SR performance], Change from Baseline [average SR minus average baseline performance], and Variance [intraindividual variance of SR performance]), and within each approach, six thresholds (±1 standard deviation and the best/worst performing 12.5%, 20%, 25%, 33%, and 50%) classified Resilient/Vulnerable groups. Kendall’s tau-b correlations examined the concordance of group categorizations of approaches within and between PVT lapses and 1/reaction time (RT). Bias-corrected and accelerated bootstrapped t-tests compared group performance. Results Correlations comparing the approaches ranged from moderate to perfect for lapses and zero to moderate for 1/RT. Defined by all approaches, the Resilient groups had significantly fewer lapses on nearly all study days. Defined by the Raw Score approach only, the Resilient groups had significantly faster 1/RT on all study days. Between-measures comparisons revealed significant correlations between the Raw Score approach for 1/RT and all approaches for lapses. Conclusion The three approaches defining vigilant attention resiliency/vulnerability to sleep loss resulted in groups comprised of similar individuals for PVT lapses but not for 1/RT. Thus, both method and metric selection for defining vigilant attention resiliency/vulnerability to sleep loss is critical.

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A47-A48
Author(s):  
Erika Yamazaki ◽  
Tess Brieva ◽  
Courtney Casale ◽  
Caroline Antler ◽  
Namni Goel

Abstract Introduction There are substantial, stable individual differences in cognitive performance resulting from sleep restriction (SR) and total sleep deprivation (TSD). The best method for defining cognitive resilience and vulnerability to sleep loss remains an unanswered, yet important question. To investigate this, we compared multiple approaches and cutoff thresholds to define resilience and vulnerability using the 10-minute Psychomotor Vigilance Test (PVT). Methods Forty-one healthy adults (ages 21-49; mean±SD, 33.9±8.9y; 18 females) participated in a 13-night experiment [2 baseline nights (10h-12h time-in-bed, TIB), 5 SR nights (4h TIB), 4 recovery nights (12h TIB), and 36h TSD]. The PVT was administered every 2h during wakefulness. PVT lapses (reaction time [RT]>500 ms) and 1/RT (response speed) were measured. Resilient and vulnerable groups were defined by three approaches: average performance during SR1-5, average performance change from baseline to SR1-5, and variance in performance during SR1-5. Within each approach, resilient/vulnerable groups were defined by +/- 1 standard deviation and by the top and bottom 12.5%, 20%, 25%, 33%, 50%. Bias-corrected and accelerated bootstrapped t-tests compared PVT performance between the resilient and vulnerable groups during baseline and SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group. Results T-tests revealed that the resilient and vulnerable PVT lapses groups, defined by all three approaches, had significantly different mean PVT lapses at all cutoffs. Resilient and vulnerable PVT 1/RT groups, defined by raw scores and by change from baseline, had significantly different mean PVT 1/RT at all cutoffs. However, resilient/vulnerable PVT 1/RT groups defined by variance only differed at the 33% and 50% cutoffs. Notably, raw scores at baseline significantly differed between resilient/vulnerable groups for both PVT measures. Variance vs. raw scores and variance vs. change from baseline had the lowest correlation coefficients for both PVT measures. Conclusion Defining resilient and vulnerable groups by raw scores during SR1-5 produced the clearest differentiation between resilient and vulnerable groups at every cutoff threshold for PVT lapses and response speed. As such, we propose that using PVT raw score is the optimal approach to define resilient and vulnerable groups for behavioral attention performance during sleep loss. Support (if any) ONR Award No.N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488


SLEEP ◽  
2011 ◽  
Vol 34 (5) ◽  
pp. 581-591 ◽  
Author(s):  
Mathias Basner ◽  
David F. Dinges

2015 ◽  
Vol 24 (6) ◽  
pp. 702-713 ◽  
Author(s):  
Mathias Basner ◽  
Sarah Mcguire ◽  
Namni Goel ◽  
Hengyi Rao ◽  
David F. Dinges

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A50-A50
Author(s):  
Caroline Antler ◽  
Erika Yamazaki ◽  
Courtney Casale ◽  
Tess Brieva ◽  
Namni Goel

Abstract Introduction The Psychomotor Vigilance Test (PVT), a behavioral attention measure widely used to capture sleep loss deficits, is available in 10-minute (PVT10) and 3-minute (PVT3) versions. The PVT3 is a briefer and presumably comparable assessment to the more commonly used PVT10 yet the relationship between the measures from the two versions across specific time points and in recovery after sleep loss has not been investigated. Repeated measures correlation (rmcorr) evaluated within-individual associations between measures on the PVT10 and PVT3 throughout a highly controlled sleep deprivation study. Methods Forty-one healthy adults (ages 21-49; mean±SD, 33.9±8.9y; 18 females) participated in a 13-night experiment consisting of 2 baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR1-5) nights (4h TIB), 4 recovery nights (R1-R4; 12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the PVT10 and PVT3 was completed every 2h during wakefulness. Rmcorr compared PVT10 and PVT3 lapses (reaction time [RT] >355ms [PVT3] or >500ms [PVT10]) and response speed (1/RT) by examining correlations by day (e.g., baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr ranges were as follows: 0.1-0.3, small; 0.3-0.5, moderate; 0.5-0.7, large; 0.7-0.9, very large. Results All time point correlations (1000h-2000h) were significant (moderate to large for lapses; large to very large for 1/RT). Lapses demonstrated large correlations during R1, moderate correlations during SR1-SR5 and TSD, and small correlations during R2 and R4, and showed no significant correlations during baseline or R3. 1/RT correlations were large for SR1-SR4 and TSD, moderate for SR5 and R1-R4, and small for baseline. Conclusion The various PVT relationships were consistently strong at specific times of day throughout the study. In addition, higher correlations observed for 1/RT relative to lapses and during SR and TSD relative to baseline and recovery suggest that the PVT10 and PVT3 are most similar and best follow performance when most individuals are experiencing behavioral attention deficits during sleep loss. Both measures track SR and TSD performance well, with 1/RT presenting as more comparable between the PVT10 and PVT3. Support (if any) ONR Award N00014-11-1-0361; NIH UL1TR000003; NASA NNX14AN49G and 80NSSC20K0243; NIHR01DK117488


2020 ◽  
pp. 003329411989989
Author(s):  
Janna Mantua ◽  
Allison J. Brager ◽  
Sara E. Alger ◽  
Folarin Adewle ◽  
Lillian Skeiky ◽  
...  

Objective Individuals vary in response to sleep loss: some individuals are “vulnerable” and demonstrate cognitive decrements following insufficient sleep, while others are “resistant” and maintain baseline cognitive capability. Physiological markers (e.g., genetic polymorphisms) have been identified that can predict relative vulnerability. However, a quick, cost-effective, and feasible subjective predictor tool has not been developed. The objective of the present study was to determine whether two factors—“subjective sleep need” and “subjective resilience”—predict cognitive performance following sleep deprivation. Methods Twenty-seven healthy, sleep-satiated young adults participated. These individuals were screened for sleep disorders, comorbidities, and erratic sleep schedules. Prior to 40 hours of in-laboratory total sleep deprivation, participants were questioned on their subjective sleep need and completed a validated resilience scale. During and after sleep deprivation, participants completed a 5-minute psychomotor vigilance test every 2 hours. Results Both subjective resilience and subjective sleep need individually failed to predict performance during sleep loss. However, these two measures interacted to predict performance. Individuals with low resilience and low sleep need had poorer cognitive performance during sleep loss. However, in individuals with medium or high resilience, psychomotor vigilance test performance was not predicted by subjective sleep need. Higher resilience may be protective against sleep loss-related neurobehavioral impairments in the context of subjective sleep need. Conclusions Following sleep loss (and recovery sleep), trait resilient individuals may outperform those with lower resiliency on real-world tasks that require continuous attention. Future studies should determine whether the present findings generalize to other, operationally relevant tasks and additional cognitive domains.


SLEEP ◽  
2021 ◽  
Author(s):  
Olga Galli ◽  
Christopher W Jones ◽  
Olivia Larson ◽  
Mathias Basner ◽  
David F Dinges

Abstract Interindividual differences in the neurobehavioral response to sleep loss are largely unexplained and phenotypic in nature. Numerous factors have been examined as predictors of differential response to sleep loss, but none have yielded a comprehensive view of the phenomenon. The present study examines the impact of baseline factors, habitual sleep–wake patterns, and homeostatic response to sleep loss on accrued deficits in psychomotor vigilance during chronic partial sleep restriction (SR), in a total of 306 healthy adults that participated in one of three independent laboratory studies. Findings indicate no significant impact of personality, academic intelligence, subjective reports of chronotype, sleepiness and fatigue, performance on working memory, and demographic factors such as sex, ethnicity, and body mass index, on neurobehavioral vulnerability to the negative effects of sleep loss. Only superior baseline performance on the psychomotor vigilance test and ability to sustain wakefulness on the maintenance of wakefulness test were associated with relative resilience to decrements in vigilant attention during SR. Interindividual differences in vulnerability to the effects of sleep loss were not accounted for by prior sleep history, habitual sleep patterns outside of the laboratory, baseline sleep architecture, or homeostatic sleep response during chronic partial SR. A recent theoretical model proposed that sleep–wake modulation may be influenced by competing internal and external demands which may promote wakefulness despite homeostatic and circadian signals for sleep under the right circumstances. Further research is warranted to examine the possibility of interindividual differences in the ability to prioritize external demands for wakefulness in the face of mounting pressure to sleep.


SLEEP ◽  
2020 ◽  
Author(s):  
Erika M Yamazaki ◽  
Caroline A Antler ◽  
Charlotte R Lasek ◽  
Namni Goel

Abstract Study Objectives The amount of recovery sleep needed to fully restore well-established neurobehavioral deficits from sleep loss remains unknown, as does whether the recovery pattern differs across measures after total sleep deprivation (TSD) and chronic sleep restriction (SR). Methods In total, 83 adults received two baseline nights (10–12-hour time in bed [TIB]) followed by five 4-hour TIB SR nights or 36-hour TSD and four recovery nights (R1–R4; 12-hour TIB). Neurobehavioral tests were completed every 2 hours during wakefulness and a Maintenance of Wakefulness Test measured physiological sleepiness. Polysomnography was collected on B2, R1, and R4 nights. Results TSD and SR produced significant deficits in cognitive performance, increases in self-reported sleepiness and fatigue, decreases in vigor, and increases in physiological sleepiness. Neurobehavioral recovery from SR occurred after R1 and was maintained for all measures except Psychomotor Vigilance Test (PVT) lapses and response speed, which failed to completely recover. Neurobehavioral recovery from TSD occurred after R1 and was maintained for all cognitive and self-reported measures, except for vigor. After TSD and SR, R1 recovery sleep was longer and of higher efficiency and better quality than R4 recovery sleep. Conclusions PVT impairments from SR failed to reverse completely; by contrast, vigor did not recover after TSD; all other deficits were reversed after sleep loss. These results suggest that TSD and SR induce sustained, differential biological, physiological, and/or neural changes, which remarkably are not reversed with chronic, long-duration recovery sleep. Our findings have critical implications for the population at large and for military and health professionals.


2019 ◽  
Vol 4 (3) ◽  
pp. 47 ◽  
Author(s):  
Antonio ◽  
Kenyon ◽  
Horn ◽  
Jiannine ◽  
Carson ◽  
...  

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.


SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A57-A57
Author(s):  
A A Parekh ◽  
K Kam ◽  
A Mullins ◽  
A Fakhoury ◽  
B Castillo ◽  
...  

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


2012 ◽  
Vol 21 (6) ◽  
pp. 659-674 ◽  
Author(s):  
SRINIVASAN RAJARAMAN ◽  
SRIDHAR RAMAKRISHNAN ◽  
DAVID THORSLEY ◽  
NANCY J. WESENSTEN ◽  
THOMAS J. BALKIN ◽  
...  

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