Raw scores on subjective sleepiness, fatigue, and vigor metrics consistently define resilience and vulnerability to sleep loss

SLEEP ◽  
2021 ◽  
Author(s):  
Courtney E Casale ◽  
Erika M Yamazaki ◽  
Tess E Brieva ◽  
Caroline A Antler ◽  
Namni Goel
Keyword(s):  
Sleep Loss ◽  
Mood States ◽  
Vulnerable Groups ◽  
Sleep Restriction ◽  
Profile Of Mood States ◽  
Baseline Score ◽  
Score Change ◽  
Subjective Responses ◽  
Subjective Sleepiness ◽  
Karolinska Sleepiness Scale

Abstract Study Objectives Although trait-like individual differences in subjective responses to sleep restriction (SR) and total sleep deprivation (TSD) exist, reliable characterizations remain elusive. We comprehensively compared multiple methods for defining resilience and vulnerability by subjective metrics. Methods 41 adults participated in a 13-day experiment:2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue (POMS-F) and Vigor (POMS-V) were administered every 2h. Three approaches (Raw Score [average SR score], Change from Baseline [average SR minus average baseline score], and Variance [intraindividual SR score variance]), and six thresholds (±1 standard deviation, and the highest/lowest scoring 12.5%, 20%, 25%, 33%, 50%) categorized Resilient/Vulnerable groups. Kendall’s tau-b correlations compared the group categorization’s concordance within and between KSS, POMS-F, and POMS-V scores. Bias-corrected and accelerated bootstrapped t-tests compared group scores. Results There were significant correlations between all approaches at all thresholds for POMS-F, between Raw Score and Change from Baseline approaches for KSS, and between Raw Score and Variance approaches for POMS-V. All Resilient groups defined by the Raw Score approach had significantly better scores throughout the study, notably including during baseline and recovery, whereas the two other approaches differed by measure, threshold, or day. Between-measure correlations varied in strength by measure, approach, or threshold. Conclusion Only the Raw Score approach consistently distinguished Resilient/Vulnerable groups at baseline, during sleep loss, and during recovery‒‒we recommend this approach as an effective method for subjective resilience/vulnerability categorization. All approaches created comparable categorizations for fatigue, some were comparable for sleepiness, and none were comparable for vigor. Fatigue and vigor captured resilience/vulnerability similarly to sleepiness but not each other.

117 Comparison of Various Methods to Differentiate Resilience and Vulnerability to Sleep Loss Using Self-Rated Measures

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A48-A48
Author(s):  
Courtney Casale ◽  
Erika Yamazaki ◽  
Tess Brieva ◽  
Caroline Antler ◽  
Namni Goel
Keyword(s):  
Sleep Loss ◽  
Vulnerable Groups ◽  
Sleep Restriction ◽  
Average Score ◽  
Profile Of Mood States ◽  
Differential Impact ◽  
Subjective Perceptions ◽  
Time In Bed ◽  
Average Change ◽  
Open Question

Abstract Introduction There are robust, trait-like individual differences in subjective perceptions in response to sleep restriction (SR) and total sleep deprivation (TSD). How to best define neurobehavioral resilience and vulnerability to sleep loss remains an open question. We compared multiple approaches and cutoff thresholds for defining resilience and vulnerability using scores on the Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue and Vigor (POMS-F and POMS-V) subscales. Methods Forty-one adults (33.9±8.9y;18 females) participated in a 13-night experiment (two baseline nights [10h-12h time in bed, TIB], 5 SR nights [4h TIB], 4 recovery nights [12h TIB], and 36h TSD). The KSS, POMS-F, and POMS-V were administered every 2h during wakefulness. Resilience and vulnerability were defined by the following: average score during SR1-5, average change from baseline to SR1-5, and variance during SR1-5. Resilient and vulnerable groups were defined by the following cutoffs: the top and bottom 12.5%, 20%, 25%, 33%, 50%, and +/-1 standard deviation. Bias-corrected and accelerated bootstrapped t-tests compared the scores of resilient and vulnerable groups during baseline and across SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group (tau=0.4:moderate,0.7:strong). Results Resilient and vulnerable groups for POMS-F, as defined by all three approaches, significantly differed in their scores at all cutoffs during SR. However, only raw score and change from baseline approaches defined significantly different resilient and vulnerable groups during SR for KSS, and only raw score and variance approaches defined significantly different groups during SR for POMS-V. Notably, raw scores at baseline significantly differed between resilient and vulnerable groups for all measures. Correlations revealed moderate to strong associations between all three approaches at all cutoffs for POMS-F, between raw score and change from baseline approaches for KSS, and between raw score and variance approaches for POMS-V. Conclusion Defining resilience and vulnerability on self-rated measures by change from baseline was comparable to using raw score for KSS and POMS-F, whereas defining these groups by variance was comparable for POMS-F and POMS-V. Differences across methods may be due to the differential impact of SR on these various distinct subjective states. Support (if any) ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

scholarly journals Consecutive Nights of Moderate Sleep Loss Does Not Affect Mood in Healthy Young Males

2021 ◽  
Vol 3 (3) ◽  
pp. 442-448
Author(s):  
Christiana Harous ◽  
Gregory D. Roach ◽  
Thomas G. Kontou ◽  
Ashley J. Montero ◽  
Nicole Stuart ◽  
...  
Keyword(s):  
Sleep Loss ◽  
Mood States ◽  
Mood Disturbance ◽  
Sleep Restriction ◽  
Profile Of Mood States ◽  
Young Males ◽  
Time In Bed ◽  
Total Mood Disturbance ◽  
Linear Effects ◽  
Healthy Males

Sleep loss causes mood disturbance in non-clinical populations under severe conditions, i.e., two days/nights of sleep deprivation or a week of sleep restriction with 4–5 h in bed each night. However, the effects of more-common types of sleep loss on mood disturbance are not yet known. Therefore, the aim of this study was to examine mood disturbance in healthy adults over a week with nightly time in bed controlled at 5, 6, 7, 8 or 9 h. Participants (n = 115) spent nine nights in the laboratory and were given either 5, 6, 7, 8 or 9 h in bed over seven consecutive nights. Mood was assessed daily using the Profile of Mood States (POMS-2). Mixed-linear effects models examined the effect of time in bed on total mood disturbance and subscales of anger-hostility, confusion-bewilderment, depression-dejection, fatigue-inertia, tension-anxiety, vigour-activity and friendliness. There was no effect of time in bed on total mood disturbance (F(4, 110.42) = 1.31, p = 0.271) or any of the subscales except fatigue-inertia. Fatigue-inertia was higher in the 5 h compared with the 9 h time in bed condition (p = 0.012, d = 0.75). Consecutive nights of moderate sleep loss (i.e., 5–7 h) does not affect mood but does increase fatigue in healthy males.

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.

Profile of Mood States 2nd Edition™

2012 ◽  
Author(s):  
Juvia P. Heuchert ◽  
Douglas M. McNair
Keyword(s):  
Mood States ◽  
Profile Of Mood States

Profile of Mood States--Brief Scales

1989 ◽  
Author(s):  
Edward Guadagnoli ◽  
Vincent Mor
Keyword(s):  
Mood States ◽  
Profile Of Mood States

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.

137 Recovery Dynamics in a Biomathematical Model of Fatigue

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A56-A56
Author(s):  
Mark McCauley ◽  
Peter McCauley ◽  
Hans Van Dongen
Keyword(s):  
Sleep Deprivation ◽  
Goodness Of Fit ◽  
Sleep Loss ◽  
Sleep Restriction ◽  
Commercial Aviation ◽  
Circadian Misalignment ◽  
Total Sleep Deprivation ◽  
Homeostatic Process ◽  
Recovery Sleep

Abstract Introduction In commercial aviation and other operational settings where biomathematical models of fatigue are used for fatigue risk management, accurate prediction of recovery during rest periods following duty periods with sleep loss and/or circadian misalignment is critical. The recuperative potential of recovery sleep is influenced by a variety of factors, including long-term, allostatic effects of prior sleep/wake history. For example, recovery tends to be slower after sustained sleep restriction versus acute total sleep deprivation. Capturing such dynamics has proven to be challenging. Methods Here we focus on the dynamic biomathematical model of McCauley et al. (2013). In addition to a circadian process, this model features differential equations for sleep/wake regulation including a short-term sleep homeostatic process capturing change in the order of hours/days and a long-term allostatic process capturing change in the order of days/weeks. The allostatic process modulates the dynamics of the homeostatic process by shifting its equilibrium setpoint, which addresses recently observed phenomena such as reduced vulnerability to sleep loss after banking sleep. It also differentiates the build-up and recovery rates of fatigue under conditions of chronic sleep restriction versus acute total sleep deprivation; nonetheless, it does not accurately predict the disproportionately rapid recovery seen after total sleep deprivation. To improve the model, we hypothesized that the homeostatic process may also modulate the allostatic process, with the magnitude of this effect scaling as a function of time awake. Results To test our hypothesis, we added a parameter to the model to capture modulation by the homeostatic process of the allostatic process build-up during wakefulness and dissipation during sleep. Parameter estimation using previously published laboratory datasets of fatigue showed this parameter as significantly different from zero (p<0.05) and yielding a 10%–20% improvement in goodness-of-fit for recovery without adversely affecting goodness-of-fit for pre-recovery days. Conclusion Inclusion of a modulation effect of the allostatic process by the homeostatic process improved prediction accuracy in a variety of sleep loss and circadian misalignment scenarios. In addition to operational relevance for duty/rest scheduling, this finding has implications for understanding mechanisms underlying the homeostatic and allostatic processes of sleep/wake regulation. Support (if any) Federal Express Corporation

Profile of Mood States: Racial Differences in a Delinquent Population

1978 ◽  
Vol 43 (2) ◽  
pp. 348-350 ◽  
Author(s):  
Thomas J. Fagan ◽  
Frank T. Lira
Keyword(s):  
Young Adult ◽  
Racial Differences ◽  
Affective Responses ◽  
Mood States ◽  
Mood Disturbance ◽  
Profile Of Mood States ◽  
Total Mood Disturbance ◽  
Recreational Programs ◽  
Racial Balance

When affective responses of 40 white and 40 black young adult delinquents were compared, white delinquents scored significantly higher on four of the six factors, Confusion, Tension, Depression, and Fatigue. Also, white subjects obtained significantly higher total mood disturbance scores. Results are discussed in terms of the racial balance of the institution, number of previous legal contacts, pre-confinement affiliations with other inmates, and failure or inability comfortably to pursue clinical and recreational programs aimed at reducing tension associated with incarceration.

Profile of Mood States: Australian Normative Data

1990 ◽  
Vol 66 (2) ◽  
pp. 509-510 ◽  
Author(s):  
Kathleen Moore ◽  
Robb Stanley ◽  
Graham Burrows
Keyword(s):  
Normative Data ◽  
Negative Mood ◽  
Mood States ◽  
Profile Of Mood States ◽  
Negative Mood States

The Profile of Mood States was administered to 90 Australian women, 30 depressed, 30 anxious, and 30 nonpsychiatric controls. Both clinical groups scored higher than the McNair, Lorr, and Droppleman (1971) normative samples on the negative mood states and scored lower on Vigor. The means for these groups are presented and compared with the 1971 normative data of McNair, Lorr, and Droppleman.

Sign in / Sign up

Export Citation Format

Share Document