122 Different Duration Psychomotor Vigilance Tests Show Robust Stable Relationships Across Sleep Loss That Deteriorate in Recovery

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A50-A50
Author(s):  
Caroline Antler ◽  
Erika Yamazaki ◽  
Courtney Casale ◽  
Tess Brieva ◽  
Namni Goel
Keyword(s):  
Sleep Deprivation ◽  
Repeated Measures ◽  
Response Speed ◽  
Sleep Loss ◽  
Sleep Restriction ◽  
Psychomotor Vigilance Test ◽  
Time In Bed ◽  
Psychomotor Vigilance ◽  
The Relationship ◽  
Time Point

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

119 Behavioral Attention and Sleepiness Display Robust Stable Relationships Across Sleep Loss but not Across Recovery

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A48-A49
Author(s):  
Namni Goel ◽  
Courtney Casale ◽  
Tess Brieva ◽  
Caroline Antler ◽  
Erika Yamazaki
Keyword(s):  
Sleep Deprivation ◽  
Repeated Measures ◽  
Time Course ◽  
Response Speed ◽  
Sleep Loss ◽  
Psychomotor Vigilance Test ◽  
Stable Relationship ◽  
Time In Bed ◽  
Subjective Sleepiness ◽  
Time Point

Abstract Introduction The 10-minute and 3-minute versions of the Psychomotor Vigilance Test (PVT10 and PVT3) and the Karolinska Sleepiness Scale (KSS) are commonly used to assess objective behavioral attention deficits and subjective sleepiness in response to sleep loss, respectively. However, the precise time course of relationships between behavioral attention and subjective sleepiness across sleep loss and recovery remains unknown but is critical for determining whether objective and subjective measures track each other. Repeated measures correlation (rmcorr) examined within-individual association between these measures 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 two baseline nights (10h-12h time-in-bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the PVT10, PVT3, and KSS, was administered every 2h during wakefulness. Rmcorr compared PVT10 [lapses (reaction time [RT] >500ms) and 1/RT (response speed)], PVT3 (lapses [RT>355ms], 1/RT), and KSS scores by examining correlations by day (e.g., Baseline day 2) and time point (e.g., 1000h-2000h). Rmcorr ranges: r=0.1:small; r=0.3:moderate; r=0.5:large. Results Generally, the correlations between the PVT10 and KSS and the PVT3 and KSS showed a similar pattern for lapses and 1/RT. PVT lapses and KSS scores showed small or non-significant correlations during baseline and recovery, whereas SR and TSD showed moderate correlations. PVT 1/RT and KSS scores showed moderate correlations during baseline, moderate to large correlations during SR and TSD, but small correlations during recovery. PVT10 and PVT3 1/RT showed stronger correlations with KSS scores than lapses. Additionally, all relationships showed moderate to large correlations by time point across the study. Conclusion Overall, the relationship between behavioral attention and sleepiness was stronger across sleep loss (SR or TSD) relative to fully rested states while it was consistently relatively strong at specific times of day throughout the study. In contrast to published literature, there is a remarkably stable relationship between an individual’s objective behavioral attention performance and perceptions of sleepiness during sleep loss, which is not evident during recovery or at baseline. Support (if any) ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

127 Cognitive Throughput, Behavioral Attention, and Sleepiness Show Robust Relationships During Sleep Loss but Not During Recovery

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A52-A52
Author(s):  
Tess Brieva ◽  
Caroline Antler ◽  
Erika Yamazaki ◽  
Courtney Casale ◽  
Namni Goel
Keyword(s):  
Sleep Deprivation ◽  
Repeated Measures ◽  
Time Course ◽  
Response Speed ◽  
Sleep Loss ◽  
Entire Study ◽  
Psychomotor Vigilance Test ◽  
Time In Bed ◽  
Symbol Substitution ◽  
Time Point

Abstract Introduction The Digit Symbol Substitution Task (DSST) is a frequently used measure to determine cognitive throughput responses to sleep loss. However, the specific time course of relationships between cognitive throughput and behavioral attention [using the 10-minute Psychomotor Vigilance Test (PVT10)] and subjective sleepiness [using the Karolinska Sleepiness Scale (KSS)] across sleep loss and recovery remains unknown yet is critical for assessing whether tasks involving learning and those without learning track each other. Repeated measures correlation (rmcorr) examined within-individual associations between measures of these tests 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 two baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the DSST, the KSS, and the PVT10, was administered every 2h during wakefulness. Rmcorr analyses compared DSST [number correct], KSS score, and PVT10 performance [lapses (reaction time [RT] >500ms) and 1/RT (response speed)] by examining correlations by day (e.g., Baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr ranges were as follows: r=0.1: small; r=0.3: moderate; r=0.5: large. Results During SR and TSD, correlations were significant, ranging from moderate to large, with the strongest correlation occurring during TSD. By contrast, baseline and recovery correlations were not significant or were small for DSST relative to PVT10 lapses, PVT10 response speed, or KSS scores. Additionally, all three pairs showed moderate to large correlations by time point across the entire study. Conclusion The various test measure relationships were consistently strong at specific times of day throughout the study. In addition, the associations between cognitive throughput and behavioral attention and sleepiness were strongest during sleep loss, particularly during TSD, suggesting that these measures are most acutely attuned to neurobehavioral changes resulting from sleep loss. The lack of a significant relationship at baseline and at recovery may be due to the learning effect reported for the DSST that is not present for the PVT10 or KSS. Support (if any) ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

scholarly journals Residual, differential neurobehavioral deficits linger after multiple recovery nights following chronic sleep restriction or acute total sleep deprivation

SLEEP ◽  
2020 ◽  
Author(s):  
Erika M Yamazaki ◽  
Caroline A Antler ◽  
Charlotte R Lasek ◽  
Namni Goel
Keyword(s):  
Sleep Deprivation ◽  
Response Speed ◽  
Sleep Loss ◽  
Sleep Restriction ◽  
Total Sleep Deprivation ◽  
Psychomotor Vigilance Test ◽  
Recovery Sleep ◽  
Time In Bed ◽  
Neurobehavioral Deficits ◽  
Chronic Sleep

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.

121 Behavioral Attention Relationships Vary Between Demographic Groups Across Sleep Loss and Recovery

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A49-A50
Author(s):  
Caroline Antler ◽  
Erika Yamazaki ◽  
Tess Brieva ◽  
Courtney Casale ◽  
Namni Goel
Keyword(s):  
Sleep Deprivation ◽  
Repeated Measures ◽  
Age Groups ◽  
Response Speed ◽  
Sleep Loss ◽  
Demographic Groups ◽  
Time In Bed ◽  
Median Split ◽  
The Relationship ◽  
High Bmi

Abstract Introduction The Psychomotor Vigilance Test (PVT) is a behavioral attention measure widely used to describe sleep loss deficits. Although there are reported differences in PVT performance for various demographic groups, no study has examined the relationship between measures on the 10-minute PVT (PVT10) and the 3-minute PVT (PVT3) within sex, age, and body mass index (BMI) groups throughout a highly controlled sleep deprivation study. Methods Forty-one healthy adults (mean±SD ages, 33.9±8.9y) participated in a 13-night experiment [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. Repeated measures correlation (rmcorr) compared PVT10 and PVT3 lapses (reaction time [RT] >355ms [PVT3] and >500ms [PVT10]) and response speed (1/RT) by examining correlations by day (e.g., baseline day 2) and time point (e.g., 1000h-2000h) within sex groups (18 females), within age groups defined by a median split (median=32, range=21-49y), and within BMI groups defined by a median split (median=25, range=17-31). Results PVT10 and PVT3 1/RT was significantly correlated at all study days and time points excluding at baseline for the younger group and at R2 for the higher BMI group. PVT10 and PVT3 lapses showed overall lower correlations across the study relative to 1/RT. Lapses were not significantly correlated at baseline for any group, for males across recovery (R1-R4), for the high BMI group at R2-R4, for the older group at R2-R3, or for the younger group at SR5 or R3. Conclusion Differentiating participants based on age, sex, or BMI revealed important variation in the relationship between PVT10 and PVT3 measures across the study. Surprisingly, lapses were not significantly correlated at baseline for any demographic group or across recovery for males or the high BMI or older group. Thus, PVT10 and PVT3 lapses may be less comparable in certain populations when well-rested. These findings add to a growing literature suggesting demographic factors may be important factors to consider when evaluating the effects of sleep loss. Support (if any) ONR Award N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243; NIHR01DK117488

116 Behavioral Attention Raw Scores Best Differentiate Cognitive Resilience and Vulnerability to Sleep Loss

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A47-A48
Author(s):  
Erika Yamazaki ◽  
Tess Brieva ◽  
Courtney Casale ◽  
Caroline Antler ◽  
Namni Goel
Keyword(s):  
Correlation Coefficients ◽  
Response Speed ◽  
Sleep Loss ◽  
Vulnerable Groups ◽  
Average Performance ◽  
Psychomotor Vigilance Test ◽  
Time In Bed ◽  
Attention Performance ◽  
Optimal Approach ◽  
Psychomotor Vigilance

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

115 Age and Sex Differences in Behavioral Attention Across Baseline, Sleep Loss, and Recovery

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A47-A47
Author(s):  
Erika Yamazaki ◽  
Courtney Casale ◽  
Tess Brieva ◽  
Caroline Antler ◽  
Namni Goel
Keyword(s):  
Individual Differences ◽  
Repeated Measures ◽  
Response Speed ◽  
Sleep Loss ◽  
Group Differences ◽  
Significant Time ◽  
Psychomotor Vigilance Test ◽  
Time In Bed ◽  
Performance Differences ◽  
Age And Sex

Abstract Introduction There are established individual differences in performance resulting from sleep loss. However, differences in behavioral attention performance between demographic subgroups remain unclear, especially during recovery after sleep loss. Thus, we examined demographic subgroup performance differences during baseline, sleep loss (sleep restriction [SR] and total sleep deprivation [TSD]), and recovery (R). Methods Forty-one healthy adults 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 10-minute Psychomotor Vigilance Test (PVT), measuring behavioral attention, was administered every 2h during wakefulness. PVT lapses (reaction time [RT]>500ms) and 1/RT (response speed) were measured. PVT performance differences were investigated by sex (18 females) and by median split on age (range: 21-49y; median: 32y). Repeated measures ANOVAs on each study day examined PVT performance with demographic groups as the between-subject factor. Results SR1-2 and R1-2 showed significant between-subject effects by age: the older group had faster mean 1/RT than the younger group. SR2 showed a significant time*age group interaction: the older group had faster 1/RT from 0800h-1400h. B2, SR1, and R1 showed significant between-subject effects by sex: males had faster mean 1/RT than females. SR3 showed a significant time*sex interaction: males had faster 1/RT at 0800h and 1200h. PVT lapses (log transformed) analyses by age and by sex revealed significant between-subject effects at SR1 and R1. The direction of effects for lapses paralleled those for 1/RT: the younger group and females had more lapses than the older group and males, respectively. No other study days showed significant between-subjects or interaction effects. Conclusion For both age and sex, significant between-subject effects and/or interactions were revealed only in the beginning half of SR or recovery and not during TSD. These findings suggest that group differences exist when the effects of sleep loss are mild (i.e., SR1-3) or when the post-effects of sleep loss have diminished (i.e., R3-4); however, when the effects of sleep loss become more severe (i.e., SR4-5 or after a night of TSD), the well-established individual differences in response to sleep loss may overwhelm group differences. Support (if any) ONR Award No.N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

118 Relationships Between Perceptions of Subjective States Differ by Sleep Loss and During Recovery in Healthy Adults

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A48-A48
Author(s):  
Courtney Casale ◽  
Tess Brieva ◽  
Erika Yamazaki ◽  
Caroline Antler ◽  
Namni Goel
Keyword(s):  
Sleep Deprivation ◽  
Repeated Measures ◽  
Time Course ◽  
Sleep Loss ◽  
Time Of Day ◽  
Healthy Adults ◽  
Time In Bed ◽  
Positive Correlations ◽  
Subjective States ◽  
Time Point

Abstract Introduction The Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue and Vigor subscales (POMS-F and POMS-V) are commonly used to assess subjective sleepiness, fatigue and vigor in response to sleep loss. However, the detailed time course of relationships between these measures across sleep loss and recovery remains unknown yet is critical for assessing varying changes in perception of subjective states. Repeated measures correlation (rmcorr) examined within-individual association between the measures 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 two baseline nights (10h-12h time-in-bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the KSS, POMS-F, and POMS-V, was administered every 2h during wakefulness. Rmcorr compared KSS, POMS-F, and POMS-V scores by examining correlations by study day (e.g., Baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr cutoffs were as follows: r=0.1:small, 0.3:moderate, 0.5:large. Results KSS and POMS-F maintained positive correlations throughout the study, whereas POMS-F and POMS-V and KSS and POMS-V were inversely correlated. All correlations were significant except those for POMS-F and POMS-V across recovery day 1 and KSS and POMS-F across recovery day 4. All measure pairs showed moderate to large correlations across baseline and SR1-5, but only small to moderate correlations across recovery. KSS and POMS-F and KSS and POMS-V showed moderate to large correlations across TSD; however, POMS-F and POMS-V only showed a small correlation. All three pairs showed consistent moderate (POMS-F and POMS-V) or large (KSS and POMS-F, KSS and POMS-V [moderate at 2000h]) correlations when analyzed by time point across the study. Conclusion Overall, the strength of relationships between KSS, POMS-F, and POMS-V scores varied as a function of type of sleep loss (SR or TSD) and by fully rested states, but not by time of day. This demonstrates the importance of determining perceptions of sleepiness, fatigue, and vigor in relation to each other, especially during recovery for all three constructs. Support (if any) ONR Award No. N00014-11-1-0361; NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

scholarly journals 0312 Performance on a Cognitive Interference Task in Children After One-Night of Sleep Restriction

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A117-A118
Author(s):  
G de Queiroz Campos ◽  
D P Dickstein ◽  
M A Carskadon ◽  
J M Saletin
Keyword(s):  
Repeated Measures ◽  
Sleep Loss ◽  
Sleep Restriction ◽  
Adhd Symptoms ◽  
Cognitive Interference ◽  
Interference Task ◽  
Attentional Processes ◽  
Short Sleep ◽  
Time In Bed ◽  
Fmri Session

Abstract Introduction Short sleep contributes to attention failure in conditions such as ADHD. Whether sleep loss affects attentional processes as a task varies in cognitive interference is unclear. We used a multi-source interference task (MSIT) in a sleep restriction paradigm in children with a range of ADHD symptoms to examine how short sleep disrupts attention in these youth. Methods Thirteen children (7F, 11.7±1.28 years) with a range of ADHD symptom severity completed a repeated-measures experiment on two consecutive nights in the laboratory: baseline (BSLN; 9.5h time-in-bed) and sleep restriction (SR; 4h time-in-bed). Each morning they took part in an fMRI session including the MSIT, in which participants respond to a series of 3-digit numbers by indicating which digit is different on no-interference (e.g., 003; correct=3) or interference (e.g., 311, correct=3) trials. Performance measures were inverse reaction time (1/RT) and accuracy. A two-way within-subject ANOVA assessed performance across interference and sleep conditions respectively. Results 1/RT showed main-effects of sleep loss (BSLN vs. SR; F(1,148)=4.01;p<0.05;η 2=0.026) and trial type (no-interference vs. interference; F(1,148)=24.7;p<0.001;η 2=0.143). Responses were slower for interference (BSLN RT: 799.3ms, SR RT: 895.8ms) than no-interference (BSLN RT: 653.2ms, SR RT: 697.4ms) trials. No interaction between interference and sleep loss was found (F(1,148)=0.11;p>0.05;η 2=0.001). Likewise, accuracy was lower (F(1,148) = 31.1, p<.001;η 2=0.174) in interference trials (73.5%) than in no-interference trials (92.2%), however with no effect of sleep loss, nor an interaction of interference and sleep loss (all p’s > .05). Conclusion These data provide evidence that partial sleep loss disrupts attention processes in children, yet these differences do not appear to depend on cognitive interference in our sample. Future analyses will examine whether ADHD symptoms distinguish individual differences, as well as analyze fMRI data to probe neural processes underlying attention control. Support K01MH09854 (to JMS); Brown University UTRA (to GDQC).

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
Keyword(s):  
Group Performance ◽  
Sleep Loss ◽  
Vulnerable Groups ◽  
Sleep Restriction ◽  
Psychomotor Vigilance Test ◽  
Performance Change ◽  
Vigilance Test ◽  
Selection For ◽  
Psychomotor Vigilance ◽  
Metric Selection

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.

Self-Reported Sleep Need, Subjective Resilience, and Cognitive Performance Following Sleep Loss and Recovery Sleep

2020 ◽  
pp. 003329411989989
Author(s):  
Janna Mantua ◽  
Allison J. Brager ◽  
Sara E. Alger ◽  
Folarin Adewle ◽  
Lillian Skeiky ◽  
...  
Keyword(s):  
Sleep Deprivation ◽  
Cognitive Performance ◽  
Test Performance ◽  
Cost Effective ◽  
Sleep Loss ◽  
Subjective Sleep ◽  
Psychomotor Vigilance Test ◽  
Recovery Sleep ◽  
Vigilance Test ◽  
Psychomotor Vigilance

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.

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