Fatigue Risk Management Preferences for Consumer Sleep Technologies and Data Sharing in Aviation.

INTRODUCTION: Employees from any type of aviation services industry were asked to give their opinions about the usefulness of consumer sleep technologies (CSTs) during operations and their willingness to share data from CSTs with their organizations for fatigue risk management purposes under a variety of circumstances.METHODS: Respondents provided information about position in aviation and use of CST devices. Respondents ranked sleep issues and feedback metrics by perceived level of importance to operational performance. Respondents rated their likelihood to share data with their organization under a series of hypothetical situations.RESULTS: Between January-July 2023, 149 (N = 149) aviation professionals responded. Pilots comprised 72% (N = 108) of respondents; 84% (N = 125) of all respondents worked short- or medium-haul operations. "Nighttime operations" and "inconsistent sleep routines" ranked as the most important issues affecting sleep. "Sleep quality history" and "projected alertness levels" ranked as most important feedback metrics for personal management of fatigue. Respondents were split between CST users (N = 64) and nonusers (N = 68). CST users did not indicate a strong preference for a specific device brand. The most-reported reason for not using a CST was due to not owning one or no perceived need. Respondents indicated greater likelihood of data sharing under conditions where the device was provided to them by their organization.DISCUSSION: These results suggest that aviation professionals are more concerned about schedule-related disturbances to sleep than they are about endogenous sleep problems. Organizations may be able to increase compliance to data collection for fatigue risk management by providing employees with company-owned CSTs of any brand.Devine JK, Choynowski J, Hursh SR. Fatigue risk management preferences for consumer sleep technologies and data sharing in aviation. Aerosp Med Hum Perform. 2024; 95(5):265-272.

Using behavioral economics to optimize safer undergraduate late-night transportation.

Many universities sponsor student-oriented transit services that could reduce alcohol-induced risks but only if services adequately anticipate and adapt to student needs. Human choice data offer an optimal foundation for planning and executing late-night transit services. In this simulated choice experiment, respondents opted to either (a) wait an escalating delay for a free university-sponsored "safe" option, (b) pay an escalating fee for an on-demand rideshare service, or (c) pick a free, immediately available "unsafe" option (e.g., ride with an alcohol-impaired driver). Behavioral-economic nonlinear models of averaged-choice data describe preference across arrangements. Best-fit metrics indicate adequate sensitivity to contextual factors (i.e., wait time, preceding late-night activity). At short delays, students preferred the free transit option. As delays extend beyond 30 min, most students preferred competing alternatives. These data depict a policy-relevant delay threshold to better safeguard undergraduate student safety.

Consumer preferences for sleep-tracking wearables: The role of scientific evaluation and endorsement.

OBJECTIVES: Accuracy and relevance to health outcomes are important to researchers and clinicians who use consumer sleep technologies, but economic demand motivates consumer sleep technology design. This report quantifies the value of scientific relevance to the general consumer in a dollar amount to convey the importance of device accuracy in terms that consumer sleep technology manufacturers can appreciate. METHODS: Survey data were collected from 368 participants on Amazon mTurk. Participants ranked sleep metrics, evaluation methods, and scientific endorsement by perceived level of importance. Participants indicated their likelihood of purchasing a hypothetical consumer sleep technology that had either (1) not been evaluated or endorsed; (2) had been evaluated but not endorsed, and; (3) had been evaluated and endorsed by a sleep science authority. Demand curves determined the relative value of each consumer sleep technology. RESULTS: Devices that were evaluated and endorsed had the most value, followed by those only evaluated, and then those with no evaluation. The unit price at which there was 50% probability of purchase increased by $30 or $48 for evaluation or endorsement, respectively, relative to a nonvalidated device. Respondents indicated the most valuable sleep metric was sleep duration, the most important evaluation method was against laboratory/hospital standards for sleep, and that the highest value of endorsement came from a medical institution. CONCLUSIONS: Consumer demand is greatest for a device that has been evaluated by an independent laboratory and is endorsed by a medical institution. Consumer sleep technology manufacturers may be able to increase sales by partnering with sleep science authorities to produce a scientifically superior device.

Time Cost and Demand: Implications for Public Policy.

The success of policy involves not only good design but a good understanding of how the public will respond behaviorally to the benefits or detriments of that policy. Behavioral science has greatly contributed to how we understand the impact of monetary costs on behavior and has therefore contributed to policy design. Consumption taxes are a direct result of this; for example, cigarette taxes that aim to reduce cigarette consumption. In addition to monetary costs, time may also be conceptualized as a constraint on consumption. Time costs may therefore have policy implications, for example, long waiting times could deter people from accessing certain benefits. Recent data show that behavioral economic demand curve methods used to understand monetary cost may also be used to understand time costs. In this article we discuss how the impact of time cost can be conceptualized as a constraint on demand for public benefits utilization and public health when there are delays to receiving the benefits. Policy examples in which time costs may be relevant and demand curve methods may be useful are discussed in the areas of government benefits, public health, and transportation design.

A general model of demand and discounting.

OBJECTIVE: A simulation using a two-commodity demand model, with time as the constraint, replicated the primary findings from delay discounting experiments and introduces explicit terms for time elasticity and cross-price substitution into the delay discounting paradigm. METHOD: A two-commodity temporal demand equation based on Hursh and Silberberg (2008) and Hursh (2014) was used to emulate delay discounting experiments. The own-price and cross-price demand curves intersected and plotting those indifference points emulated the usual hyperbolic discount function for substitutes. Simulations examined delay discounting in relation to (a) time elasticity of demand, (b) substitution between the delayed and immediate alternatives, and (c) amplitude of demand for the delayed alternative. RESULTS: The simulated discount functions with substitutes were hyperbolic. The discount rate was a direct function of increasing time elasticity and substitutability of delayed alternative demand, shifting the function toward an exponential model. Amplitude of demand for the delayed alternative was inversely proportional to discount rate and supported a hyperboloid model with a power function of time (Killeen, 2015; Rachlin, 2006). The emulation of cross-commodity discounting involving drugs points to amplitude and persistence of time-dependent demand and cross-commodity substitution as primary factors. CONCLUSIONS: This report describes the first general model of time-dependent demand and delay discounting. The model implicates cross-commodity substitution as a potential factor in delay discounting. In the context of substance use disorder, the model underscores the importance of defining the properties of multicommodity demand (time elasticity, substitution, and amplitude) specific to the commodities and context. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Latent factor structure of behavioral economic heroin and cocaine demand curves.

Research has shown that behavioral economic demand curve indices can be characterized by a two-factor latent structure and that these factors can predict dimensions of substance use. No study to date has examined the latent factor structure of heroin and cocaine demand curves. The objective of this study was to use exploratory factor analysis to examine the underlying factor structure of the facets of heroin and cocaine reinforcement derived from heroin and cocaine demand curves. Participants were 143 patients from two samples that met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5; American Psychiatric Association [APA], 2013) criteria for opioid dependance and were undergoing medication-assisted treatment (methadone or buprenorphine). Heroin and cocaine demand curves were generated via hypothetical purchase tasks (HPT) that assessed consumption at 9 or 17 levels of prices from $0 to $500. Five facets of demand were generated from the tasks (Q0, 1/α, Pmax, Omax, and break point). Principal components analysis was used to examine the latent structure among the variables. The results revealed a two-factor solution for both heroin and cocaine demand. These factors were interpreted as persistence, consisting of 1/α, Pmax, Omax, and break point, and amplitude, consisting of Q0 and Omax, and in one case, 1/α. Heroin factors had some predictive power for future substance use, but cocaine factors did not. These findings suggest that heroin and cocaine demand indices can be reduced to two factors indicating sensitivity and volume of consumption, and that these factors may be able to predict substance use for heroin. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Behavioral economic methods predict future COVID-19 vaccination.

Increasing vaccine utilization is critical for numerous diseases, including COVID-19, necessitating novel methods to forecast uptake. Behavioral economic methods have been developed as rapid, scalable means of identifying mechanisms of health behavior engagement. However, most research using these procedures is cross-sectional and evaluates prediction of behaviors with already well-established repertories. Evaluation of the validity of hypothetical tasks that measure behaviors not yet experienced is important for the use of these procedures in behavioral health. We use vaccination during the COVID-19 pandemic to test whether responses regarding a novel, hypothetical behavior (COVID-19 vaccination) are predictive of later real-world response. Participants (N = 333) completed a behavioral economic hypothetical purchase task to evaluate willingness to receive a hypothetical COVID-19 vaccine based on efficacy. This was completed in August 2020, before clinical trial data on COVID-19 vaccines. Participants completed follow-up assessments approximately 1 year later when the COVID-19 vaccines were widely available in June 2021 and November 2021 with vaccination status measured. Prediction of vaccination was made based on data collected in August 2020. Vaccine demand was a significant predictor of vaccination after controlling for other significant predictors including political orientation, delay discounting, history of flu vaccination, and a single-item intent to vaccinate. These findings show predictive validity of a behavioral economic procedure explicitly designed to measure a behavior for which a participant has limited-to-no direct prior experience or exposure. Positive correspondence supports the validity of these hypothetical arrangements for predicting vaccination utilization and advances behavioral economic methods.

A goal of behavioral science is to develop methods that can predict future behavior to inform preventive health efforts and identify ways people engage in positive health behaviors. Behavioral economic methods apply easy to use and rapid assessment tools to evaluate these mechanisms of health behavior engagement. Here, we show how similar methods can be applied to novel behaviors yet experienced like intentions to vaccinate against COVID-19. We find that responses on a behavioral economic task designed to measure vaccination likelihood closely corresponded to the likelihood of being vaccinated 1 year later. This prediction was above and beyond common predictors of vaccination including demographics like political orientation and age. These findings provide support for these novel methods in the context of the COVID-19 pandemic, specifically, and behavioral health, broadly.

Applied behavioral economics and public health policies: Historical precedence and translational promise.

Behavioral economics is an approach to understanding behavior though integrating behavioral psychology and microeconomic principles. Advances in behavioral economics have resulted in quick-to-administer tasks to assess discounting (i.e., decrements in the subjective value of a commodity due to delayed or probabilistic receipt) and demand (i.e., effort exerted to defend baseline consumption of a commodity amidst increasing constraints)-these tasks are built upon decades of foundational work from the experimental analysis of behavior and exhibit adequate psychometric properties. We propose that the behavioral economic approach is particularly well suited, then, for experimentally evaluating potential public policy decisions, particularly during urgent times or crises. Using examples from our collaborations (e.g., cannabis legalization, happy hour alcohol pricing, severe weather alerts, COVID-19 vaccine marketing), we demonstrate how behavioral economic approaches have rendered novel insights to guide policy development and garnered widespread attention outside of academia. We conclude with implications on multidisciplinary work and other areas in need of behavioral economic investigations.

Technical, Regulatory, Economic, and Trust Issues Preventing Successful Integration of Sensors into the Mainstream Consumer Wearables Market.

Sensors that track physiological biomarkers of health must be successfully incorporated into a fieldable, wearable device if they are to revolutionize the management of remote patient care and preventative medicine. This perspective article discusses logistical considerations that may impede the process of adapting a body-worn laboratory sensor into a commercial-integrated health monitoring system with a focus on examples from sleep tracking technology.

Behavioral economic methods to inform infectious disease response: Prevention, testing, and vaccination in the COVID-19 pandemic.

The role of human behavior to thwart transmission of infectious diseases like COVID-19 is evident. Psychological and behavioral science are key areas to understand decision-making processes underlying engagement in preventive health behaviors. Here we adapt well validated methods from behavioral economic discounting and demand frameworks to evaluate variables (e.g., delay, cost, probability) known to impact health behavior engagement. We examine the contribution of these mechanisms within a broader response class of behaviors reflecting adherence to public health recommendations made during the COVID-19 pandemic. Four crowdsourced samples (total N = 1,366) completed individual experiments probing a response class including social (physical) distancing, facemask wearing, COVID-19 testing, and COVID-19 vaccination. We also measure the extent to which choice architecture manipulations (e.g., framing, opt-in/opt-out) may promote (or discourage) behavior engagement. We find that people are more likely to socially distance when specified activities are framed as high risk, that facemask use during social interaction decreases systematically with greater social relationship, that describing delay until testing (rather than delay until results) increases testing likelihood, and that framing vaccine safety in a positive valence improves vaccine acceptance. These findings collectively emphasize the flexibility of methods from diverse areas of behavioral science for informing public health crisis management.

Predictive Biomathematical Modeling Compared to Objective Sleep During COVID-19 Humanitarian Flights.

BACKGROUND: Biomathematical modeling software like the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model and Fatigue Avoidance Scheduling Tool (FAST) help carriers predict fatigue risk for planned rosters. The ability of a biomathematical model to accurately estimate fatigue risk during unprecedented operations, such as COVID-19 humanitarian ultra-long-range flights, is unknown. Azul Cargo Express organized and conducted five separate humanitarian missions to China between May and July 2020. Prior to conducting the missions, a sleep-prediction algorithm (AutoSleep) within SAFTE-FAST was used to predict in-flight sleep duration and pilot effectiveness. Here we compare AutoSleep predictions against pilots' sleep diary and a sleep-tracking actigraphy device (Zulu watch, Institutes for Behavior Resources) from Azul's COVID-19 humanitarian missions.METHODS: Pilots wore Zulu watches throughout the mission period and reported sleep duration for their in-flight rest periods using a sleep diary. Agreement between AutoSleep, diary, and Zulu watch measures was compared using intraclass correlation coefficients (ICC). Goodness-of-fit between predicted effectiveness distribution between scenarios was evaluated using the R² statistic.RESULTS: A total of 20 (N = 20) pilots flying across 5 humanitarian missions provided sleep diary and actigraphy data. ICC and R² values were >0.90, indicating excellent agreement between sleep measures and predicted effectiveness distribution, respectively.DISCUSSION: Biomathematical predictions of in-flight sleep during unprecedented humanitarian missions were in agreement with actual sleep patterns during flights. These findings indicate that biomathematical models may retain accuracy even under extreme circumstances. Pilots may overestimate the amount of sleep that they receive during extreme flight-duty periods, which could constitute a fatigue risk.Devine JK, Garcia CR, Simoes AS, Guelere MR, de Godoy B, Silva DS, Pacheco PC, Choynowski J, Hursh SR. Predictive biomathematical modeling compared to objective sleep during COVID-19 humanitarian flights. Aerosp Med Hum Perform. 2022; 93(1):4-12.

Pilot Sleep Behavior across Time during Ultra-Long-Range Flights.

Fatigue risk to the pilot has been a deterrent for conducting direct flights longer than 12 h under normal conditions, but such flights were a necessity during the COVID-19 pandemic. Twenty (N = 20) pilots flying across five humanitarian missions between Brazil and China wore a sleep-tracking device (the Zulu watch), which has been validated for the estimation of sleep timing (sleep onset and offset), duration, efficiency, and sleep score (wake, interrupted, light, or deep Sleep) throughout the mission period. Pilots also reported sleep timing, duration, and subjective quality of their in-flight rest periods using a sleep diary. To our knowledge, this is the first report of commercial pilot sleep behavior during ultra-long-range operations under COVID-19 pandemic conditions. Moreover, these analyses provide an estimate of sleep score during in-flight sleep, which has not been reported previously in the literature.

Addressing fatigue in medical residents with biomathematical fatigue modeling.

Fatigue in resident physicians has been identified as a factor that contributes to burnout and a decline in overall wellbeing. Fatigue risk exists because of poor sleep habits and demanding work schedules that have only increased due to the COVID-19 pandemic. At this time, it is important not to lose sight of how fatigue can impact residents and how fatigue risk can be mitigated. While fatigue mitigation is currently addressed by duty hour restrictions and education about fatigue, Fatigue Risk Management Systems (FRMSs) offer a more comprehensive strategy for addressing these issues. An important component of FRMS in other shiftwork industries, such as aviation and trucking, is the use of biomathematical models to prospectively identify fatigue risk in work schedules. Such an approach incorporates decades of knowledge of sleep and circadian rhythm research into shift schedules, taking into account not just duty hour restrictions but the temporal placement of work schedules. Recent research has shown that biomathematical models of fatigue can be adapted to a resident physician population and can help address fatigue risk. Such models do not require subject matter experts and can be applied in graduate medical education program shift scheduling. It is important for graduate medical education program providers to consider these alternative methods of fatigue mitigation. These tools can help reduce fatigue risk and may improve wellness as they allow for a more precise fatigue management strategy without reducing overall work hours.

Biomathematical Modeling Predicts Fatigue Risk in General Surgery Residents.

OBJECTIVE: To assess resident fatigue risk using objective and predicted sleep data in a biomathematical model of fatigue. DESIGN: 8-weeks of sleep data and shift schedules from 2019 for 24 surgical residents were assessed with a biomathematical model to predict performance ("effectiveness"). SETTING: Greater Washington, DC area hospitals RESULTS: As shift lengths increased, effectiveness scores decreased and the time spent below criterion increased. Additionally, 11.13% of time on shift was below the effectiveness criterion and 42.7% of shifts carried excess sleep debt. Sleep prediction was similar to actual sleep, and both predicted similar performance (p ≤ 0.001). CONCLUSIONS: Surgical resident sleep and shift patterns may create fatigue risk. Biomathematical modeling can aid the prediction of resident sleep patterns and performance. This approach provides an important tool to help educators in creating work-schedules that minimize fatigue risk.

Integrating Operant and Cognitive Behavioral Economics to Inform Infectious Disease Response: Prevention, Testing, and Vaccination in the COVID-19 Pandemic.

The role of human behavior to thwart transmission of infectious diseases like COVID-19 is evident. Yet, many areas of psychological and behavioral science are limited in the ability to mobilize to address exponential spread or provide easily translatable findings for policymakers. Here we describe how integrating methods from operant and cognitive approaches to behavioral economics can provide robust policy relevant data. Adapting well validated methods from behavioral economic discounting and demand frameworks, we evaluate in four crowdsourced samples (total N = 1,366) behavioral mechanisms underlying engagement in preventive health behaviors. We find that people are more likely to social distance when specified activities are framed as high risk, that describing delay until testing (rather than delay until results) increases testing likelihood, and that framing vaccine safety in a positive valence improves vaccine acceptance. These findings collectively emphasize the flexibility of methods from diverse areas of behavioral science for informing public health crisis management.

A zero-bounded model of operant demand.

Contemporary approaches for evaluating the demand for reinforcers use either the Exponential or the Exponentiated model of operant demand, both derived from the framework of Hursh and Silberberg (2008). This report summarizes the strengths and complications of this framework and proposes a novel implementation. This novel implementation incorporates earlier strengths and resolves existing shortcomings that are due to the use of a logarithmic scale for consumption. The Inverse Hyperbolic Sine (IHS) transformation is reviewed and evaluated as a replacement for the logarithmic scale in models of operant demand. Modeling consumption in the "log10 -like" IHS scale reflects relative changes in consumption (as with a log scale) and accommodates a true zero bound (i.e., zero consumption values). The presence of a zero bound obviates the need for a separate span parameter (i.e., k) and the span of the model may be more simply defined by maximum demand at zero price (i.e., Q0 ). Further, this reformulated model serves to decouple the exponential rate constant (i.e., α) from variations in span, thus normalizing the rate constant to the span of consumption in IHS units and permitting comparisons when spans vary. This model, called the Zero-bounded Exponential (ZBE), is evaluated using simulated and real-world data. The direct reinstatement ZBE model showed strong correspondence with empirical indicators of demand and with a normalization of α (ZBEn) across empirical data that varied in reinforcing efficacy (dose, time to onset of peak effects). Future directions in demand curve analysis are discussed with recommendations for additional replication and exploration of scales beyond the logarithm when accommodating zero consumption data.

Purchase task sensitivity to drug and nondrug reinforcers in opioid-agonist treatment patients.

The behavioral economics of substance abuse has been increasingly recognized as a method of determining the value of abused substances for individuals who use those substances. It has been hypothesized that such analyses could serve as a clinical tool and that demand functions can be targeted predictors for the level of intervention necessary. This study evaluated the sensitivity of a demand task in 2 patient groups in a medication assisted treatment program (methadone maintenance), those who had used opioids in the last 2 months and those who had not used opioids in at least 18 months. Demand for 7 drugs and a control was assessed using hypothetical purchase tasks. Participants maintaining long-term abstinence had significantly higher α (sensitivity to price) and lower Q0 (intensity of demand) for heroin than participants who had recently used opioids. Further research is necessary to illustrate if treatment is responsible for this reduction in demand. If so, demand analyses may provide clinical utility as an aid for treatment planning or as a target for treatment.

Behavioral economic demand in opioid treatment: Predictive validity of hypothetical purchase tasks for heroin, cocaine, and benzodiazepines.

BACKGROUND: Behavioral economics provides a framework in which to understand choice and motivation in the field of substance use disorders. Hypothetical purchase tasks (HPT), which indicate the amount or probability of purchasing substances at different prices, have been suggested as a clinical tool that can help predict future substance use and identify targets for intervention. METHODS: Hypothetical demand for heroin, cocaine, and benzodiazepines was assessed at baseline and after six-months in 52 opioid-agonist treatment patients. The results were analyzed using a novel exponential demand equation (normalized zero-bounded exponential model [ZBEn]) that uses a log-like transform that accommodates zero consumption values. RESULTS: Demand for these drugs was well described by the ZBEn model. After six months, demand intensity for heroin was decreased and demand metrics for cocaine and benzodiazepines increased. Multiple demand curve indices at baseline predicted the percentage of drug-positive urinalysis results at follow-up, even after controlling for covariates. Additionally, participants were divided into High and Low baseline demand groups for each drug based on demand indices. Participants with High demand at baseline for 8 out of 9 groups had significantly more drug-positive urine samples in the subsequent 6-month period. CONCLUSIONS: This report provides evidence that demand assessment is predictive of future substance use and could help guide treatment planning at intake. These results also demonstrated that the ZBEn model provides good fits to consumption data and allows for sensitive statistical analyses.

Fatigue in surgical residents an analysis of duty-hours and the effect of hypothetical naps on predicted performance.

PURPOSE: Sleep loss and fatigue, common in resident physicians, are related to increased medical errors and decreased physician wellbeing. Biomathematical modeling of fatigue can illuminate the relationship between surgical resident fatigue and work scheduling. METHODS: General surgery resident schedules were analyzed using the Sleep, Activity, Fatigue and Task Effectiveness model to predict resident performance during work hours. Hypothetical naps were built into the model to assess their effect on predicted performance and fatigue risk. RESULTS: 12 months of duty-hours logged by 89 residents, ranging from post-graduate year (PGY) 1-5, were analyzed. Residents had moderate levels of fatigue risk over 12 month schedules, with at least an 8-h sleep debt during 24.36% of shifts. Performance scores decreased as shift lengths increased. The addition of hypothetical naps increased predicted performance and reduced shift time with fatigue risk. CONCLUSIONS: Biomathematical modeling of resident schedules and predicts a concerning level of fatigue and decreased effectiveness. Naps may improve performance without decreasing scheduled hours.

Trends in Strategic Napping in Surgical Residents by Gender, Postgraduate Year, Work Schedule, and Clinical Rotation.

OBJECTIVE: To identify surgical resident and clinical rotation attributes which predict on-shift napping through objectively measured sleep patterns and work schedules over a 2-month period. DESIGN: In a cross-sectional study, participants provided schedules, completed the Epworth Sleepiness Scale (ESS), and wore sleep-tracking devices (Zulu watch) continuously for 8 weeks. Multiple linear regression predicted percent days with on-shift napping from resident and rotation characteristics. SETTING: Greater Washington, DC area hospitals. PARTICIPANTS: Twenty-two (n = 22) surgical residents rotating in at least 1 of 5 different clinical rotation categories. RESULTS: Residents slept 6 hours within a 24-hour period (370 ± 129 minutes) with normal sleep efficiency (sleep efficiency (SE): 87.13% ± 7.55%). Resident ESS scores indicated excessive daytime sleepiness (11.64 ± 4.03). Ninety-five percent (n = 21) of residents napped on-shift. Residents napped on-shift approximately 32% of their working days and were most likely to nap when working between 23:00 and 05:00 hours. Earlier shift start times predicted less on-shift napping (B = -0.08, SE = 0.04, ß = -2.40, t = -2.09, p = 0.05) while working more night shifts (B = 1.55, SE = 0.44, ß = 4.12, t = 3.52, p = 0.003) and shifts over 24 hours (B = 1.45, SE = 0.55, ß = 1.96, t = 2.63, p = 0.01) predicted more frequent on-shift napping. CONCLUSIONS: Residents are taking advantage of opportunities to nap on-shift. Working at night seems to drive on-shift napping. However, residents still exhibit insufficient sleep and daytime sleepiness which could reduce competency and represent a safety risk to themselves and/or patients. These findings will help inform intervention strategies which are tailored to surgical residents using a biomathematical model of fatigue.