Sleep and Alertness in a Duty-Hour Flexibility Trial in Internal Medicine.

BACKGROUND: A purpose of duty-hour regulations is to reduce sleep deprivation in medical trainees, but their effects on sleep, sleepiness, and alertness are largely unknown. METHODS: We randomly assigned 63 internal-medicine residency programs in the United States to follow either standard 2011 duty-hour policies or flexible policies that maintained an 80-hour workweek without limits on shift length or mandatory time off between shifts. Sleep duration and morning sleepiness and alertness were compared between the two groups by means of a noninferiority design, with outcome measures including sleep duration measured with actigraphy, the Karolinska Sleepiness Scale (with scores ranging from 1 [extremely alert] to 9 [extremely sleepy, fighting sleep]), and a brief computerized Psychomotor Vigilance Test (PVT-B), with long response times (lapses) indicating reduced alertness. RESULTS: Data were obtained over a period of 14 days for 205 interns at six flexible programs and 193 interns at six standard programs. The average sleep time per 24 hours was 6.85 hours (95% confidence interval [CI], 6.61 to 7.10) among those in flexible programs and 7.03 hours (95% CI, 6.78 to 7.27) among those in standard programs. Sleep duration in flexible programs was noninferior to that in standard programs (between-group difference, -0.17 hours per 24 hours; one-sided lower limit of the 95% confidence interval, -0.45 hours; noninferiority margin, -0.5 hours; P = 0.02 for noninferiority), as was the score on the Karolinska Sleepiness Scale (between-group difference, 0.12 points; one-sided upper limit of the 95% confidence interval, 0.31 points; noninferiority margin, 1 point; P<0.001). Noninferiority was not established for alertness according to the PVT-B (between-group difference, -0.3 lapses; one-sided upper limit of the 95% confidence interval, 1.6 lapses; noninferiority margin, 1 lapse; P = 0.10). CONCLUSIONS: This noninferiority trial showed no more chronic sleep loss or sleepiness across trial days among interns in flexible programs than among those in standard programs. Noninferiority of the flexible group for alertness was not established. (Funded by the National Heart, Lung, and Blood Institute and American Council for Graduate Medical Education; ClinicalTrials.gov number, NCT02274818.).

Sleep and Alertness Among Interns in Intensive Care Compared to General Medicine Rotations: A Secondary Analysis of the iCOMPARE Trial.

BACKGROUND: Medical interns are at risk for sleep deprivation from long and often rotating work schedules. However, the effects of specific rotations on sleep are less clear. OBJECTIVE: To examine differences in sleep duration and alertness among internal medicine interns during inpatient intensive care unit (ICU) compared to general medicine (GM) rotations. METHODS: This secondary analysis compared interns during a GM or ICU rotation from a randomized trial (2015-2016) of 12 internal medicine residency programs assigned to different work hour limit policies (standard 16-hour shifts or no shift-length limits). The primary outcome was sleep duration/24-hour using continuous wrist actigraphy over a 13-day period. Secondary outcomes assessed each morning during the concomitant actigraphy period were sleepiness (Karolinska Sleepiness Scale [KSS]), alertness (number of Brief Psychomotor Vigilance Test [PVT-B] lapses), and self-report of excessive sleepiness over past 24 hours. Linear mixed-effect models with random program intercept determined associations between each outcome by rotation, controlling for age, sex, and work hour policy followed. RESULTS: Of 398 interns, 386 were included (n = 261 GM, n = 125 ICU). Average sleep duration was 7.00±0.08h and 6.84±0.10h, and number of PVT lapses were 5.5±0.5 and 5.7±0.7 for GM and ICU, respectively (all P > .05). KSS was 4.8±0.1 for both rotations. Compared to GM, ICU interns reported more days of excessive sleepiness from 12am-6am (2.6 vs 1.7, P < .001) and 6am-12pm (2.6 vs 1.9, P = .013) and had higher percent of days with sleep duration < 6 hours (27.6% vs 23.4%, P < .001). GM interns reported more days with no excessive sleepiness (5.3 vs 3.7, P < .001). CONCLUSIONS: Despite ICU interns reporting more excessive sleepiness in morning hours and more days of insufficient sleep (<6 hours), overall sleep duration and alertness did not significantly differ between rotations.

Optimization of biomathematical model predictions for cognitive performance impairment in individuals: accounting for unknown traits and uncertain states in homeostatic and circadian processes.

Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy. These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became available, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became increasingly more accurate and had progressively smaller 95% confidence intervals, as the model parameters converged efficiently to those that best characterized each individual. Even when more challenging simulations were run (mimicking a change in the initial homeostatic state; simulating the data to be sparse), the predictions were still considerably more accurate than would have been achieved by the two-process model alone. Although the work described here is still limited to periods of consolidated wakefulness with stable circadian rhythms, the results obtained thus far indicate that the Bayesian forecasting procedure can successfully overcome some of the major outstanding challenges for biomathematical prediction of cognitive performance in operational settings.

Efficient driver drowsiness detection at moderate levels of drowsiness.

Previous research on driver drowsiness detection has focused primarily on lane deviation metrics and high levels of fatigue. The present research sought to develop a method for detecting driver drowsiness at more moderate levels of fatigue, well before accident risk is imminent. Eighty-seven different driver drowsiness detection metrics proposed in the literature were evaluated in two simulated shift work studies with high-fidelity simulator driving in a controlled laboratory environment. Twenty-nine participants were subjected to a night shift condition, which resulted in moderate levels of fatigue; 12 participants were in a day shift condition, which served as control. Ten simulated work days in the study design each included four 30-min driving sessions, during which participants drove a standardized scenario of rural highways. Ten straight and uneventful road segments in each driving session were designated to extract the 87 different driving metrics being evaluated. The dimensionality of the overall data set across all participants, all driving sessions and all road segments was reduced with principal component analysis, which revealed that there were two dominant dimensions: measures of steering wheel variability and measures of lateral lane position variability. The latter correlated most with an independent measure of fatigue, namely performance on a psychomotor vigilance test administered prior to each drive. We replicated our findings across eight curved road segments used for validation in each driving session. Furthermore, we showed that lateral lane position variability could be derived from measured changes in steering wheel angle through a transfer function, reflecting how steering wheel movements change vehicle heading in accordance with the forces acting on the vehicle and the road. This is important given that traditional video-based lane tracking technology is prone to data loss when lane markers are missing, when weather conditions are bad, or in darkness. Our research findings indicated that steering wheel variability provides a basis for developing a cost-effective and easy-to-install alternative technology for in-vehicle driver drowsiness detection at moderate levels of fatigue.