Next Article in Journal
The Impact of Sleep on Haematological Parameters in Firefighters
Previous Article in Journal
SleepSync: Early Testing of a Personalised Sleep–Wake Management Smartphone Application for Improving Sleep and Cognitive Fitness in Defence Shift Workers
 
 
Journals
Clocks & Sleep
Volume 6
Issue 2
10.3390/clockssleep6020020
clockssleep-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Brief Report

Time of Day and Sleep Deprivation Effects on Risky Decision Making

by
Noelia Ruiz-Herrera
1,2,
Mia Friedman
1,
Melissa A. St. Hilaire
1,2,3,
Arturo Arrona-Palacios
1,2,
Charles A. Czeisler
1,2 and
Jeanne F. Duffy
1,2,*
1
Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
2
Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
3
Department of Computer and Data Sciences, School of Engineering and Computational Sciences, Merrimack College, North Andover, MA 01845, USA
*
Author to whom correspondence should be addressed.
Clocks & Sleep 2024, 6(2), 281-290; https://doi.org/10.3390/clockssleep6020020
Submission received: 24 April 2024 / Revised: 11 June 2024 / Accepted: 13 June 2024 / Published: 20 June 2024
(This article belongs to the Section Human Basic Research & Neuroimaging)
Browse Figures
Versions Notes

Abstract

:
Previous research has revealed that daily variations in human neurobehavioral functions are driven in part by the endogenous circadian system. The objective of this study was to explore whether there exists a circadian influence on performance regarding a risky decision-making task and to determine whether the performance changes with sleep deprivation (SD). Thirteen participants underwent a 39 h constant routine (CR) protocol, during which they remained awake in constant conditions and performed the BART (balloon analogue risk task) every two hours. The mean pumps (gains) (p < 0.001) and balloons popped (losses) (p = 0.003) exhibited variation during the CR. The reaction time (RT) also showed significant variation across the CR (p < 0.001), with slower mean RTs in the morning hours following SD. A greater risk propensity was observed around midday before SD and a lower risk propensity after 29.5 h of being awake. The sensitivity to punishment varied during the CR, but did not follow a predictable trend. Further research using real monetary incentives and neurophysiological measures is warranted to elucidate these findings.

1. Introduction

Most neurobehavioral functions (including basic cognitive processes such as executive functions) show both sleep–wake homeostatic (related to the duration of time awake and effects of chronic sleep loss) and circadian (time of day) variations.
The effects of sleep loss on neurobehavioral functions are related to changes in brain networks, synapses, and even intrinsic neuronal membrane properties [1,2]. These effects can result in negative impacts on many aspects of cognitive performance [3,4,5,6,7], risks to safety due to inattention, and long-term risks to both medical and psychological health [8,9].
Although there is much evidence to support that sleep loss [5,10,11] and circadian disruption [12,13,14] impair alertness and vigilance, the effects of sleep loss and disrupted circadian rhythmicity on higher-order cognitive processing are less clear [15,16].
Previous studies concerning executive function, including supervisory control, problem solving, divergent thinking capacity, verbal creativity, flexibility, inhibition, cognitive set shifting, and risky decision making [17,18,19,20,21], have been carried out to explore whether the time of day or sleep loss impact performance. While some of those studies have reported significant impacts of the time of day on the performance regarding decision-making tasks [22,23], others have not [24,25], suggesting the need for additional studies to clarify whether the time of day impacts executive functions, and furthermore whether there is a circadian rhythm in performance on specific tasks of executive function.
In addition to studies of how executive function varies with the time of day, there have been studies on the impact of sleep deprivation (SD) on decision making, and these have shown inconsistencies. In a protocol of seven nights of sleep restriction followed by one night of acute SD (40 h awake), the participants took more financial risks during chronic sleep restriction but not following acute SD [26]. In another study where the Iowa Gambling Task was used, there was an increased risk in responses as the game progressed across 49.5 h awake [27]. The authors suggested that the change of strategy to more risky decisions after sleep loss was analogous to those seen in patients with lesions to the ventromedial prefrontal lobes. Similarly, in another study that kept participants awake for 24 h, the authors observed that choices involving a higher relative risk elicited greater activation in the right nucleus accumbens (an elevated expectation of higher reward) once the riskier choice was made. Concurrently, activation for losses in the insular and orbitofrontal cortices was reduced, denoting a diminished response to losses [28]. This same research group reported that 24 h of being awake evoked a strategy shift during risky decision making such that participants moved from defending against losses to seeking increased gains [29]. However, another study that used a multi-attribute decision-making task found no differences between sleep-deprived (33 h) and non-sleep-deprived subjects [30].
In summary, there appears to be no consensus on the effects of the time of day and sleep loss on decision-making tasks. However, study methodologies have differed markedly, and, to date, no data have been reported on risky decision making under controlled behavioral conditions across an acute sleep-loss protocol.
Therefore, the aim of this work was to explore whether there is a circadian rhythm in risky decision making, and, through the use of the constant routine (CR) protocol, to further explore how acute sleep loss impacts risky decision making in a sample of healthy young adults.

2. Results

Fifteen healthy adults (nine women, six men) between 21 and 31 years old (M = 26.00 ± 4.05 years) began a five-day inpatient circadian rhythm study. One male participant was disempaneled on day two due to COVID exposure, and one female participant was disempaneled on day four due to becoming ill. No data from either of those participants is included in the analysis presented.
Among the thirteen participants who completed the study (eight women, five men between 21 and 31 years old, M = 26.46 ± 4.16 years), there were occasional missing test sessions. One participant did not complete one session of the balloon analogue risk task (BART) during the baseline and another participant missed six of seven sessions of the psychomotor vigilance task (PVT) and five of seven sessions of BART during the baseline. One participant missed the second test session of the CR for both the BART and the PVT due to schedule delays. PVT data were not collected from one participant in session 10 of the CR and for one participant in session 1 of the CR.
Mixed model analysis with TIME AWAKE as a fixed factor and PARTICIPANT as a random factor showed that there was no significant effect of time awake on any BART or PVT variable across the seven baseline sessions (all p’s > 0.5). Mixed model analysis with the factors CONDITION (Baseline vs. CR), TIME AWAKE, and their interaction was used to compare the seven baseline day test sessions with the CR sessions. This found no significant effect of the condition (baseline day vs. CR) and no interaction (all p’s > 0.5), suggesting no learning effects on any of the BART or PVT variables.
In the PVT task, the mean RT [F(18, 468.33) = 21.80] and lapses [F(18, 230.02) = 14.89] showed significant variation across the 39 h of the CR (all p’s < 0.001), with the worst performance happening during the late night to morning hours (Figure 1). For the RT, the test sessions after 11.5 h of being awake and from 19.5 h of being awake onwards were significantly slower than other sessions (p’s < 0.05). The number of lapses of attention significantly increased in all sessions from 19.5 h awake onwards (all p’s < 0.05).
In the BART task, the number of mean pumps [F(18, 233) = 2.58, p < 0.001] and balloons popped [F(18, 233) = 2.29, p = 0.003] varied significantly across the CR. With respect to mean pumps, differences were found at 5.5 and 29.5 h awake (p’s < 0.05; Figure 2A and Figure S1). For the balloons popped, differences were found at multiple timepoints [3.5, 7.5, 9.5, 11.5, 13.5, 21.5, 25.5, 29.5, 35.5 h awake (all p’s < 0.05: Figure 2B and Figure S2)].
The RT performance on the BART task also showed significant variations across the CR [Mean RT F(18, 346.06) = 3.20, p < 0.001], with significantly slower mean RTs in the morning hours (23.5, 25.5, 27.5, and 29.5 h awake; all p’s < 0.05, Figure 2C and Figure S3). The mean pumping reward [F(18, 232.98) = 1.60, p = 0.06] and total amount collected [F(18, 232.98) = 1.59, p = 0.06] showed a similar trend (Figure S4).

Additional Analysis

When comparing the BART tests carried out in the morning vs. the afternoon only, no significant differences were observed in the number of mean pumps, balloons popped, or total amount collected between the morning and afternoon hours (1.5 h awake vs. 7.5 h awake or 1.5 h awake vs. 9.5 h awake; all p’s > 0.05; see a summary of the results in Table S1).

3. Discussion

Results on the PVT revealed that under CR conditions, vigilance—as assessed using RTs and lapses of attention—remains relatively stable throughout the initial ~16 h, which corresponds to the habitual waking day. Vigilance then deteriorates during the habitual nighttime hours, reaching its worst in the morning just after the usual waketime. While vigilance subsequently improves over the late morning and into the afternoon, overall, it is worse after missing a night of sleep than during the same hours at the start of the CR. These results are in agreement with those previously reported, indicating an interaction between a circadian rhythm that promotes increased vigilance during the biological day and decreased vigilance during the biological night and a sleep–wake homeostatic process that degrades vigilance the longer one is awake [10,31,32,33].
In terms of the RT, the performance on the BART and the PVT followed similar trends, becoming significantly slower during the usual nighttime and slowing down even more after ~19.5 h awake, and then improving somewhat later in the day. However, the RT on the BART task was more stable across the CR than the RT was on the PVT. Why the RT on the decision-making task appears to be less sensitive to sleep loss than on the PVT is not clear. This could be because decision making involves a more complex process that goes beyond simply reacting to a stimulus. It requires deeper cognitive evaluations, such as assessing risks and benefits, considering relevant information, and weighing alternative options, any of which could interact with and potentially compensate for the attentional impairment.
Based on the reinforcement sensitivity theory, risk behavior is modulated by sensitivity to reward and punishment [34]. In the context of the present study, sensitivity to reward would be translated to the number of pumps (gains) and sensitivity to punishment to balloons popped (losses). Variations in performance across the CR relating to a greater propensity for risk (reflected in a high number of pumps) were only observed around midday (before any sleep loss), and performance variations indicating a lower risk propensity were observed after 29.5 h awake. These results are inconsistent with previous studies that reported a greater risk propensity after sleep loss [27,28]. Nevertheless, the methodological differences between these investigations are substantial, including differences in the sleep-loss protocols, the types of decision-making tasks, and the methods of assessing performance, rendering a comparison of the findings challenging.
In terms of risk propensity being understood as sensitivity to punishment, the number of balloons popped varied throughout the CR, but did not seem to follow a predictable trend. This could mean that it may not matter what time of day it is (no circadian variation in sensitivity to punishment/loss) or that there is little to no impact of sleep loss on sensitivity to punishment or loss. Alternatively, it may be that with sleep loss, the individual loses interest or has a sense of futility towards punishment or loss. Some studies have reported that when the amount of real incentive is increased, decision making and neural activity may change [35,36,37]. In this case, our results might be explained by the fact that the participants did not experience any real punishment or loss based on their task performance, and if instead they could have realized actual monetary rewards, then their task performance might have differed.
Finally, when we attempted to replicate the previously reported diurnal variations in risky decision making [23] by comparing non-sleep-deprived test sessions (morning vs. afternoon) on the BART, we did not find any significant differences between the morning and afternoon test performances. However, our sample (N = 13) was smaller than that of the previous study (where sample size was N = 28), and our post hoc power analysis indicated that it was insufficient to detect morning vs. afternoon differences in performance. However, our sample size was sufficient to find circadian effects in the reaction time performances across the CR for both PVT and BART outcomes.
It is also possible that we could not replicate the prior findings because our participants could not realize any actual financial reward related to their performance, whereas in the prior studies, the participants did receive real financial gains associated with their performance on the task.
There are some limitations in the current study. First, the sample was relatively small, which may have impacted our ability to detect small differences in performance. Because the study was not originally designed to detect diurnal variations in decision making, no a priori sample size calculations were conducted for this specific purpose.
Also, previous studies have shown that age is an important factor to consider when studying risky decision making [27], so the age range in our study, which was 21–31 years old, may be a limitation. As noted above, the reward condition on the BART was hypothetical, and prior research has demonstrated that real vs. hypothetical financial incentives may change how individuals behave. Therefore, our findings might have differed if the participants had a financial incentive when performing the task. Finally, we assessed risky decision making using only behavioral tasks. In the future, it may be beneficial to use multiple types of decision-making tasks or to combine behavioral tasks with neurophysiologic techniques, such as fMRI and EEG, to better probe the components of task performance in order to understand how they change regarding the circadian phase and sleep loss.

4. Materials and Methods

4.1. Participant Recruitment and Eligibility Criteria

Participants were recruited from the community using online notices and advertisements. They had to be free of any acute or chronic medical and psychological condition and to be taking no medication (excluding hormonal birth control). Eligibility was determined using their medical history, a physical examination, electrocardiogram, screening blood tests (complete blood count and comprehensive metabolic panel), urinalysis, psychological questionnaires (Minnesota Multiphasic Personality Inventory-2 [38], Beck Depression Inventory-II [39], Symptom Checklist-90 [40], and the State-Trait Anxiety Inventory [41]), and an interview with a clinical psychologist [42].
Participants had to report no sleep disorders or chronic sleep complaints (assessed with the Pittsburg Sleep Quality Index [43]), had to have a habitual sleep duration between 7 and 9 h per night, have no history of regular night work or rotating shift work, no recent travel across more than two time zones, and no self-reported daytime sleepiness on the Epworth Sleepiness Scale [44].

4.2. Study Protocol

Participants were asked to keep a regular sleep schedule of 9 h time-in-bed at home for at least two weeks prior to the day of admission to their 5-day study. Participants were studied individually in the Intensive Physiological Monitoring Unit of the Brigham and Women’s Hospital Center for Clinical Investigation and remained in their study room for the duration of their study. After admission in the afternoon of Day 1, each participant was oriented to the battery of neurobehavioral tests (~25 min). They took 4 practice test batteries on Day 1 to become familiar with the requirements of each test. After a 9 h scheduled sleep episode at their habitual time, the participant woke to a Baseline Day. During the Baseline Day, they took the test battery every 2 h for a total of 7 times. After a second 9 h scheduled sleep episode, upon awakening on Day 3 the participant began a ~39 h CR. This consisted of continuous wakefulness in a semi-recumbent posture in bed with a dim light (<15 lux) and with the nutritional intake being divided into identical hourly snacks [45]. Test batteries were given every two hours throughout the CR, starting 1.6 h after waking, for a total of 19 CR testing times. After the CR, the participant was scheduled for a 10 h recovery sleep, and they were discharged in the early afternoon on Day 5.

4.3. Test Battery

4.3.1. Psychomotor Vigilance Task (PVT)

The PVT assesses the visual reaction time (RT) and was used as an objective measure of sustained vigilance [46]. In it, the participant was instructed to focus their gaze on a fixation point on a computer monitor and to respond as quickly as possible with a button press when a stimulus appeared on the screen. The inter-stimulus interval varied between 2 and 10 s, and the task was scheduled for 10 min, resulting in approximately 70–100 trials per test session. This task has been demonstrated to be sensitive to the circadian phase [47,48,49] and to both acute and chronic sleep loss [30,48], while not showing any long-lasting training effects.

4.3.2. Balloon Analogue Risk Task (BART)

The BART is a validated assessment of risk-taking behavior [50]. In it, the participant is shown a balloon on the computer screen and instructed to either inflate the balloon or collect money. Each time the participant inflates the balloon, the monetary compensation increases, but there is also a higher possibility that the balloon will pop. The potential reward is lost if the balloon pops. The time of the task varied from 2–6 min per session, depending on how long the participant took to respond to a total of 30 balloons that were presented in each session. Each balloon had a different probability of popping that was arranged through constructing an array of N numbers. The number 1 was designated as indicating a balloon explosion. On each pump of the balloon, a number was selected without replacement from the array. The balloon exploded if the number 1 was selected. The maximal hypothetical reward was one dollar for each successful pump.

4.3.3. Data Analysis

For the PVT, the mean RT and lapses of attention were used in the analyses. RTs > 500 milliseconds (ms) were considered lapses. Any RT < 100 ms was considered a false start and was excluded from subsequent analyses [46,51,52].
For the BART, the indexes used to assess performance were as follows: mean pump reward (the amount added to the reward after a balloon inflation that did not pop the balloon), total amount collected, and the mean RT (the time the participant took to decide to pump or collect each balloon). The mean pumps (mean number of pumps per balloon) and the balloons popped (number of balloons that popped) were considered measures of risk preference [50].
The RTs in both tasks did not follow a normal distribution (Kolmogorov–Smirnov p’s > 0.05), and we therefore applied a reciprocal transformation of the RTs on each task to better approximate a normal distribution.
Statistical analyses were performed using a mixed model regression analysis on raw data, incorporating TIME AWAKE as a fixed factor and PARTICIPANT as a random factor in the model. Due to the increased family-wise error of the 19 comparisons, a Bonferroni correction was applied.
To determine if there were learning or practice effects on the tasks, a variable CONDITION (baseline vs. CR) was included in the model as a fixed factor and the interaction CONDITION*TIME AWAKE was explored.
The package SPSS 28.0 software for Windows (IBM, Armonk, NY, USA) was used for all above analyses.
Finally, to compare our data with those previously reported [23] in which a significant time of day effect was observed for the average number of pumps and the total reward amount but not the balloons popped, we used paired Student’s t-tests on the average number of pumps, balloons popped, and total amount collected during two daytime tests. For methodological similarity, we compared a morning test (taken after ~1.5 h awake) to an afternoon test (tests at both 7.5 and 9.5 h awake) during the CR. For those comparisons, post hoc calculations on our observed data using G*Power 3.1.9.7 (Heinrich Heine University, Düsseldorf, Germany) indicated that a sample size of at least n = 40 would have been required to have 80% power at α = 0.05 to detect an effect size of −0.404 (Cohen’s d, as reported in [23]) between 1.5 and 7.5 h awake in mean pumps, and a sample size of n = 23 would have been required to have 80% power to detect an effect size of −0.543 between 1.5 and 7.5 h awake in the total reward amount. Similarly, a sample size of n = 40 and n = 23 would have been required to have 80% power at α = 0.05 to detect differences between 1.5 and 9.5 h awake in mean pumps and the total reward amount, respectively.

5. Conclusions

In the present study, the RT in a decision-making task was impaired during the morning hours after a night of sleep loss. Risky decision making related to seeking gains peaked during midday and was significantly inhibited by sleep loss after 29.5 h awake. The decision-making process related to loss avoidance followed an unstable pattern and did not vary significantly across the day or with increased sleep loss.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/clockssleep6020020/s1, Figure S1. Mean Pumps (n) of the Balloon Analogue Risk Task (BART) across 39 h awake for each participant, Figure S2. Balloons popped (n) of the Balloon Analogue Risk Task (BART) across 39 h awake for each participant, Figure S3. Reaction time (ms) of the Balloon Analogue Risk Task (BART) across 39 h awake for each participant, Figure S4. Total Collected (A) and Mean Pumping Reward (B) (+ standard error, shown only in the positive direction) on the Balloon Analogue Risk Task (BART) across 39 h awake, Table S1. Comparison of day vs. evening data on Number of Pumps and Total Gains with results of Li et al. (2020) [23].

Author Contributions

Conceptualization, J.F.D. and N.R.-H.; methodology, J.F.D., N.R.-H. and M.A.S.H.; formal analysis, N.R.-H.; investigation, A.A.-P., N.R.-H. and M.F.; resources, J.F.D. and C.A.C.; data curation, N.R.-H., M.F. and J.F.D.; writing—original draft preparation, N.R.-H. and J.F.D.; writing—review and editing, J.F.D., M.A.S.H., A.A.-P., M.F. and C.A.C.; supervision, J.F.D.; funding acquisition, C.A.C. and J.F.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the US National Institutes of Health (NIH) grant R01 HL148704 (to Emmanuel Mignot, C.A.C. and J.F.D.). The studies were carried out at the Brigham and Women’s Hospital Center for Clinical Investigation, with support from Harvard Catalyst, The Harvard Clinical, and Translational Science Center (National Center for Advancing Translational Sciences, NIH Award UL1 TR002541), and financial contributions from Brigham and Women’s Hospital, Harvard University, and its affiliated academic healthcare centers. N.R.-H. was supported in part by a research travel grant from the International University of La Rioja, Spain; A.A.-P. was supported by a fellowship from the Sara Elizabeth O’Brien Trust, Bank of America, N.A. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard Catalyst, Harvard University and its affiliated academic healthcare centers, or the NIH.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Mass General Brigham Institutional Review Board (protocol 2019-P-001247), original approval date 2 August 2019.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

The authors will make de-identified data from the current study available upon written request. Execution of a Materials Transfer Agreement is required if the data will be used in research supported by a for-profit company, per Mass General Brigham Institutional Review Board policy. The specific data to be shared will be PVT and BART related data with the associated wake duration information from the 7 baseline and 19 CR sessions.

Acknowledgments

The authors wish to thank the study participants; the staff at the BWH Center for Clinical Investigation and the Division of Sleep and Circadian Disorders technical staff for their assistance in carrying out the study; the BWH Chronobiology Core staff for monitoring the participants during the CRs; A.S. Murphy for assistance with participant screening and study execution; J.S. Williams for providing medical support for the study; E. Pardilla-Delgado for serving as the Project Leader for several of the studies; and E. Mignot for overall support.

Conflicts of Interest

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Drs. Ruiz-Herrera, Arrona-Palacios, and Duffy as well as Ms. Friedman declare no conflicts of interest. Dr. St. Hilaire currently serves as a subject matter expert for easyJet plc, Luton UK, but this work is unrelated to the current manuscript. Dr. Czeisler serves as the incumbent of an endowed professorship provided to Harvard Medical School by Cephalon, Inc., and reports institutional support for a Quality Improvement Initiative from Delta Airlines and Puget Sound Pilots; education support to Harvard Medical School Division of Sleep Medicine and support to Brigham and Women’s Hospital from: Jazz Pharmaceuticals PLC, Inc., Philips Respironics, Inc., Optum, and ResMed, Inc.; research support to Brigham and Women’s Hospital from Axome Therapeutics, Inc., Dayzz Ltd., Peter Brown and Margaret Hamburg, Regeneron Pharmaceuticals, Sanofi SA, Casey Feldman Foundation, Summus, Inc., Takeda Pharmaceutical Co., LTD., Abbaszadeh Foundation, CDC Foundation; educational funding to the Sleep and Health Education Program of the Harvard Medical School Division of Sleep Medicine from ResMed, Inc., Teva Pharmaceuticals Industries, Ltd., and Vanda Pharmaceuticals; personal royalty payments on sales of the Actiwatch-2 and Actiwatch- Spectrum devices from Philips Respironics, Inc.; personal consulting fees from Axsome, Inc., Bryte Foundation, With Deep, Inc., and Vanda Pharmaceuticals, the Institute of Digital Media and Child Development, the Klarman Family Foundation, and the UK Biotechnology and Biological Sciences Research Council; honoraria from the Associated Professional Sleep Societies, the Massachusetts Medical Society, the National Council for Mental Wellbeing, and the National Sleep Foundation; lecture fees from Teva Pharma Australia PTY Ltd. and Emory University. Dr. Czeisler has received personal fees for serving as an expert witness on a number of civil matters, criminal matters, and arbitration cases, including those involving the following commercial and government entities: Amtrak; Bombardier, Inc.; C&J Energy Services; Dallas Police Association; Delta Airlines/Comair; Enterprise Rent-A-Car; FedEx; Greyhound Lines, Inc./Motor Coach Industries/FirstGroup America; PAR Electrical Contractors, Inc.; Puget Sound Pilots; and the San Francisco Sheriff’s Department; Schlumberger Technology Corp.; Union Pacific Railroad; United Parcel Service; Vanda Pharmaceuticals. Dr. Czeisler has received travel support from the Stanley Ho Medical Development Foundation, Merck Sharpe and Dohme; equity interest in Vanda Pharmaceuticals, With Deep, Inc., and Signos, Inc.; and institutional educational gifts to Brigham and Women’s Hospital from Johnson & Johnson, Mary Ann and Stanley Snider via Combined Jewish Philanthropies, Alexandra Drane, DR Capital, Harmony Biosciences, LLC, San Francisco Bar Pilots, Whoop, Inc., Harmony Biosciences LLC, Eisai Co., LTD., Idorsia Pharmaceuticals LTD., Sleep Number Corp., Apnimed, Inc., Avadel Pharmaceuticals, Bryte Foundation, f.lux Software, LLC, and the Stuart F. and Diana L. Quan Charitable Fund. Dr Czeisler’s interests, which are all outside the scope of this work, were reviewed and are managed by the Brigham and Women’s Hospital and Mass General Brigham in accordance with their conflict of interest policies.

References

  1. Frank, M.G. Circadian regulation of synaptic plasticity. Biology 2016, 5, 31. [Google Scholar] [CrossRef] [PubMed]
  2. Frank, M.G. Clocking in: A circadian model of synaptic plasticity. Curr. Opin. Physiol. 2020, 15, 96–103. [Google Scholar] [CrossRef]
  3. Alhola, P.; Polo-Kantola, P. Sleep deprivation: Impact on cognitive performance. Neuropsychiatr. Dis. Treat. 2007, 3, 553–567. [Google Scholar] [PubMed]
  4. Choshen-Hillel, S.; Ishqer, A.; Mahameed, F.; Reiter, J.; Gozal, D.; Gileles-Hillel, A.; Berger, I. Acute and chronic sleep deprivation in residents: Cognition and stress biomarkers. Med. Educ. 2021, 55, 174–184. [Google Scholar] [CrossRef] [PubMed]
  5. Groeger, J.A.; Viola, A.U.; Lo, J.C.; von Schantz, M.; Archer, S.N.; Dijk, D.J. Early morning executive functioning during sleep deprivation is compromised by a PERIOD3 polymorphism. Sleep 2008, 31, 1159–1167. [Google Scholar] [PubMed]
  6. Tononi, G.; Cirelli, C. Sleep and the price of plasticity: From synaptic and cellular homeostasis to memory consolidation and integration. Neuron 2014, 81, 12–34. [Google Scholar] [CrossRef] [PubMed]
  7. Hudson, A.N.; Van Dongen, H.; Honn, K.A. Sleep deprivation, vigilant attention, and brain function: A review. Neuropsychopharmacology 2020, 45, 21–30. [Google Scholar] [CrossRef]
  8. Buysse, D.J. Sleep health: Can we define it? Does it matter? Sleep 2014, 37, 9–17. [Google Scholar] [CrossRef]
  9. Liew, S.C.; Aung, T. Sleep deprivation and its association with diseases—A review. Sleep Med. 2021, 77, 192–204. [Google Scholar] [CrossRef]
  10. Dijk, D.J.; Duffy, J.F.; Czeisler, C.A. Circadian and sleep/wake dependent aspects of subjective alertness and cognitive performance. J. Sleep Res. 1992, 1, 112–117. [Google Scholar] [CrossRef]
  11. Stojanoski, B.; Benoit, A.; Van Den Berg, N.; Ray, L.B.; Owen, A.M.; Shahidi Zandi, A.; Quddus, A.; Comeau, F.J.E.; Fogel, S.M. Sustained vigilance is negatively affected by mild and acute sleep loss reflected by reduced capacity for decision making, motor preparation, and execution. Sleep 2019, 42, zsy200. [Google Scholar] [CrossRef] [PubMed]
  12. Chellappa, S.L.; Morris, C.J.; Scheer, F.A. Daily circadian misalignment impairs human cognitive performance task-dependently. Sci. Rep. 2018, 8, 3041. [Google Scholar] [CrossRef] [PubMed]
  13. Blatter, K.; Cajochen, C. Circadian rhythms in cognitive performance: Methodological constraints, protocols, theoretical underpinnings. Physiol. Behav. 2007, 90, 196–208. [Google Scholar] [CrossRef] [PubMed]
  14. Schmidt, C.; Collette, F.; Cajochen, C.; Peigneux, P. A time to think: Circadian rhythms in human cognition. Cogn. Neuropsychol. 2007, 24, 755–789. [Google Scholar] [CrossRef] [PubMed]
  15. Harrison, Y.; Horne, J.A. The impact of sleep deprivation on decision making: A review. Exp. Psychol. Appl. 2000, 6, 236. [Google Scholar] [CrossRef] [PubMed]
  16. Jones, K.; Harrison, Y. Frontal lobe function, sleep loss and fragmented sleep. Sleep Med. Rev. 2001, 5, 463–475. [Google Scholar] [CrossRef] [PubMed]
  17. Harrison, Y.; Horne, J. Sleep loss impairs short and novel language tasks having a prefrontal focus. J. Sleep Res. 1998, 7, 95–100. [Google Scholar] [CrossRef] [PubMed]
  18. McMahon, W.R.; Ftouni, S.; Diep, C.; Collet, J.; Lockley, S.W.; Rajaratnam, S.M.W.; Maruff, P.; Drummond, S.P.A.; Anderson, C. The impact of the wake maintenance zone on attentional capacity, physiological drowsiness, and subjective task demands during sleep deprivation. J. Sleep Res. 2021, 30, e13312. [Google Scholar] [CrossRef] [PubMed]
  19. May, C.P.; Hasher, L. Synchrony effects in inhibitory control over thought and action. J. Exp. Psychol. Hum. Percept. Perform. 1998, 24, 363. [Google Scholar] [CrossRef]
  20. Wimmer, R.; Hoffman, R.P.; Bonato, R.A.; Moffitt, A.R. The effects of sleep deprivation on divergent thinking and attention processes. J. Sleep Res. 1992, 1, 223–230. [Google Scholar] [CrossRef]
  21. Correa, A.; Alguacil, S.; Ciria, L.F.; Jiménez, A.; Ruz, M. Circadian rhythms and decision-making: A review and new evidence from electroencephalography. Chronobiol. Int. 2020, 37, 520–541. [Google Scholar] [CrossRef]
  22. Byrne, J.E.; Murray, G. Diurnal rhythms in psychological reward functioning in healthy young men: ‘Wanting’, liking, and learning. Chronobiol. Int. 2017, 34, 287–295. [Google Scholar] [CrossRef] [PubMed]
  23. Li, M.; Mai, Z.; Yang, J.; Zhang, B.; Ma, N. Ideal time of day for risky decision making: Evidence from the Balloon Analogue Risk Task. Nat. Sci. Sleep. 2020, 12, 477–486. [Google Scholar] [CrossRef]
  24. Correa, A.; Ruiz-Herrera, N.; Ruz, M.; Tonetti, L.; Martoni, M.; Fabbri, M.; Natale, V. Economic decision-making in morning/evening-type people as a function of time of day. Chronobiol. Int. 2017, 34, 139–147. [Google Scholar] [CrossRef]
  25. Ingram, K.K.; Ay, A.; Kwon, S.B.; Woods, K.; Escobar, S.; Gordon, M.; Smith, I.H.; Bearden, N.; Filipowicz, A.; Jain, K. Molecular insights into chronotype and time-of-day effects on decision-making. Sci. Rep. 2016, 6, 29392. [Google Scholar] [CrossRef] [PubMed]
  26. Maric, A.; Montvai, E.; Werth, E.; Storz, M.; Leemann, J.; Weissengruber, S.; Ruff, C.C.; Huber, R.; Poryazova, R.; Baumann, C.R. Insufficient sleep: Enhanced risk-seeking relates to low local sleep intensity. Ann. Neurol. 2017, 82, 409–418. [Google Scholar] [CrossRef]
  27. Killgore, W.D.; Balkin, T.J.; Wesensten, N.J. Impaired decision making following 49 h of sleep deprivation. J. Sleep Res. 2006, 15, 7–13. [Google Scholar] [CrossRef] [PubMed]
  28. Venkatraman, V.; Chuah, Y.L.; Huettel, S.A.; Chee, M.W. Sleep deprivation elevates expectation of gains and attenuates response to losses following risky decisions. Sleep 2007, 30, 603–609. [Google Scholar] [CrossRef] [PubMed]
  29. Venkatraman, V.; Huettel, S.A.; Chuah, L.Y.; Payne, J.W.; Chee, M.W. Sleep deprivation biases the neural mechanisms underlying economic preferences. J. Neurosci. 2011, 31, 3712–3718. [Google Scholar] [CrossRef] [PubMed]
  30. Linde, L.; Edland, A.; Bergström, M. Auditory attention and multiattribute decision-making during a 33h sleep-deprivation period: Mean performance and between-subject dispersions. Ergonomics 1999, 42, 696–713. [Google Scholar] [CrossRef]
  31. Dorrian, J.; Rogers, N.L.; Dinges, D.F. Psychomotor vigilance performance: Neurocognitive assay sensitive to sleep loss. In Sleep Deprivation; CRC Press: Boca Raton, FL, USA, 2004; pp. 39–70. [Google Scholar]
  32. Graw, P.; Kräuchi, K.; Knoblauch, V.; Wirz-Justice, A.; Cajochen, C. Circadian and wake-dependent modulation of fastest and slowest reaction times during the psychomotor vigilance task. Physiol. Behav. 2004, 80, 695–701. [Google Scholar] [CrossRef]
  33. Van Dongen, H.P.; Maislin, G.; Mullington, J.M.; Dinges, D.F. The cumulative cost of additional wakefulness: Dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep 2003, 26, 117–126. [Google Scholar] [CrossRef]
  34. Corr, P.J. (Ed.) Reinforcement Sensitivity Theory (RST): Introduction. In The Reinforcement Sensitivity Theory of Personality; Cambridge University Press: New York, NY, USA, 2008; pp. 1–43. [Google Scholar]
  35. Fantino, E.; Gaitan, S.; Kennelly, A.; Stolarz-Fantino, S. How reinforcer type affects choice in economic games. Behav. Process. 2007, 75, 107–114. [Google Scholar] [CrossRef]
  36. Xu, S.; Pan, Y.; Wang, Y.; Spaeth, A.M.; Qu, Z.; Rao, H. Real and hypothetical monetary rewards modulate risk taking in the brain. Sci. Rep. 2016, 6, 29520. [Google Scholar] [CrossRef]
  37. Xu, S.; Pan, Y.; Qu, Z.; Fang, Z.; Yang, Z.; Yang, F.; Wang, F.; Rao, H. Differential effects of real versus hypothetical monetary reward magnitude on risk-taking behavior and brain activity. Sci. Rep. 2018, 8, 3712. [Google Scholar] [CrossRef] [PubMed]
  38. Butcher, J.N.; Dahlstrom, W.G.; Graham, J.R.; Tellegen, A.; Kaemmer, B. The Minnesota Multiphasic Personality Inventory-2 (MMPI-2): Manual for Administration and Scoring; University of Minnesota Press: Minneapolis, MN, USA, 1989. [Google Scholar]
  39. Beck, A.T.; Steer, R.A.; Brown, G.K. Manual for the Beck Depression Inventory-II; Psychological Corporation: San Antonio, TX, USA, 1996. [Google Scholar]
  40. Derogatis, L.R.; Lipman, R.S.; Rickels, K.; Uhlenhuth, E.H.; Covi, L. The Hopkins Symptom Checklist (HSCL): A self-report symptom inventory. Behav. Sci. 1974, 19, 1–15. [Google Scholar] [CrossRef]
  41. Spielberger, C.D. Manual for the State-Trait Anxiety Inventory; Consulting Psychologists Press: Palo Alto, CA, USA, 1983. [Google Scholar]
  42. Amira, S.A.; Bressler, B.L.; Lee, J.H.; Czeisler, C.A.; Duffy, J.F. Psychological screening for exceptional environments: Laboratory circadian rhythm and sleep research. Clocks Sleep 2020, 2, 13. [Google Scholar] [CrossRef] [PubMed]
  43. Buysse, D.J.; Reynolds, C.F., III; Monk, T.H.; Berman, S.R.; Kupfer, D.J. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res. 1989, 28, 193–213. [Google Scholar] [CrossRef]
  44. Johns, M.W. A new method for measuring daytime sleepiness: The Epworth sleepiness scale. Sleep 1991, 14, 540–545. [Google Scholar] [CrossRef] [PubMed]
  45. Duffy, J.F.; Dijk, D.J. Getting through to circadian oscillators: Why use constant routines? J. Biol. Rhythm. 2002, 17, 4–13. [Google Scholar] [CrossRef]
  46. Dinges, D.F.; Powell, J.W. Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations. Beh. Res. Meth. Instr. Comp. 1985, 17, 652–655. [Google Scholar] [CrossRef]
  47. Silva, E.J.; Wang, W.; Ronda, J.M.; Wyatt, J.K.; Duffy, J.F. Circadian and wake-dependent influences on subjective sleepiness, cognitive throughput, and reaction time performance in older and young adults. Sleep 2010, 33, 481–490. [Google Scholar] [CrossRef]
  48. Duffy, J.F.; Willson, H.J.; Wang, W.; Czeisler, C.A. Healthy older adults better tolerate sleep deprivation than young adults. J. Am. Geriatr. Soc. 2009, 57, 1245–1251. [Google Scholar] [CrossRef]
  49. Wyatt, J.K.; Ritz-De Cecco, A.; Czeisler, C.A.; Dijk, D.J. Circadian temperature and melatonin rhythms, sleep, and neurobehavioral function in humans living on a 20-h day. Am. J. Physiol. Regul. Integr. Comp. Physiol. 1999, 277, 1152–1163. [Google Scholar] [CrossRef]
  50. Lejuez, C.W.; Aklin, W.M.; Zvolensky, M.J.; Pedulla, C.M. Evaluation of the Balloon Analogue Risk Task (BART) as a predictor of adolescent real-world risk-taking behaviours. J. Adolesc. 2002, 26, 475–479. [Google Scholar] [CrossRef]
  51. Yun, C.H.; Kim, H.; Lee, S.K.; Suh, S.; Lee, S.H.; Park, S.H.; Thomas, R.J.; Au, R.; Shin, C. Daytime sleepiness associated with poor sustained attention in middle and late adulthood. Sleep Med. 2015, 16, 143–151. [Google Scholar] [CrossRef]
  52. Dinges, D.F.; Pack, F.; Williams, K.; Gillen, K.A.; Powell, J.W.; Ott, G.E.; Aptowicz, C.; Pack, A.I. Cumulative sleepiness, mood disturbance, and psychomotor vigilance performance decrements during a week of sleep restricted to 4–5 hours per night. Sleep 1997, 20, 267–277. [Google Scholar]
Clockssleep 06 00020 g001
Figure 1. Mean (+standard error) reaction time (A) and lapses (B) on the psychomotor vigilance task (PVT) across the CR. The dashed red box indicates the timing of the habitual sleep episode. Asterisks indicate significant pairwise Bonferroni-corrected comparisons [* p< 0.05: * p < 0.001]. ms = milliseconds; h = hours; lapses = RT > 500 ms.
Figure 1. Mean (+standard error) reaction time (A) and lapses (B) on the psychomotor vigilance task (PVT) across the CR. The dashed red box indicates the timing of the habitual sleep episode. Asterisks indicate significant pairwise Bonferroni-corrected comparisons [* p< 0.05: * p < 0.001]. ms = milliseconds; h = hours; lapses = RT > 500 ms.
Clockssleep 06 00020 g001
Clockssleep 06 00020 g002
Figure 2. Mean (+standard error) pumps (A), balloons popped (B), and reaction time (C) on the balloon analogue risk task (BART) across the CR. The dashed red box indicates the timing of the habitual sleep episode. Asterisks indicate significant pairwise Bonferroni-corrected comparisons. [* p < 0.05: * p < 0.001]. N = number; ms = milliseconds; h = hours.
Figure 2. Mean (+standard error) pumps (A), balloons popped (B), and reaction time (C) on the balloon analogue risk task (BART) across the CR. The dashed red box indicates the timing of the habitual sleep episode. Asterisks indicate significant pairwise Bonferroni-corrected comparisons. [* p < 0.05: * p < 0.001]. N = number; ms = milliseconds; h = hours.
Clockssleep 06 00020 g002
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ruiz-Herrera, N.; Friedman, M.; St. Hilaire, M.A.; Arrona-Palacios, A.; Czeisler, C.A.; Duffy, J.F. Time of Day and Sleep Deprivation Effects on Risky Decision Making. Clocks & Sleep 2024, 6, 281-290. https://doi.org/10.3390/clockssleep6020020

AMA Style

Ruiz-Herrera N, Friedman M, St. Hilaire MA, Arrona-Palacios A, Czeisler CA, Duffy JF. Time of Day and Sleep Deprivation Effects on Risky Decision Making. Clocks & Sleep. 2024; 6(2):281-290. https://doi.org/10.3390/clockssleep6020020

Chicago/Turabian Style

Ruiz-Herrera, Noelia, Mia Friedman, Melissa A. St. Hilaire, Arturo Arrona-Palacios, Charles A. Czeisler, and Jeanne F. Duffy. 2024. "Time of Day and Sleep Deprivation Effects on Risky Decision Making" Clocks & Sleep 6, no. 2: 281-290. https://doi.org/10.3390/clockssleep6020020

Article Metrics

Back to TopTop