In a session on the neurobiology of sleep loss in humans, Andrea Spaeth, Daniel Aeschbach, PhD, and Clare Anderson, PhD, presented findings about the effects of sleep deprivation on various biological measures in humans.
Published Online: June 04, 2013
Andrea Spaeth from the University of Pennsylvania began the session with a discussion of sleep in the context of energy balance. She explained that approximately 28% of the population sleeps fewer than 6 hours per night, and that this reduction in sleep results in an increase in weight. Mechanisms for this effect include an increase in energy intake, reduction in energy use, or both. In a study examining the effect of sleep reduction on calorie intake, participants experienced 2 days of normal sleep, with a bedtime around 10 PM. These initial days represent normal sleep patterns of a typical weekend, as opposed to sleep deprivation during the work week. In the subsequent 5 days of the study, subjects experienced sleep deprivation (they were kept awake until 4 AM), mirroring the weekend/workweek pattern experienced by many workers. The results demonstrated an increase in caloric intake with more time spent awake; subjects consumed extra calories (about 650 calories) during the late-night hours, between 10 PM and 4 AM. The morning intake of calories was decreased by about 100 calories, leaving a net increase in the calorie intake.
Sleep restriction during the work week with compensation on the weekend may also be associated with increased caloric intake. Investigators split subjects into 2 groups: in one group, subjects had a single recovery night between episodes of sleep restriction, and in the other group, subjects had 3 nights of recovery between episodes of sleep restriction. Investigators hypothesized that additional recovery time would result in reduced caloric intake during episodes of sleep restriction. Subjects were similar in age, body mass index, and daily caloric intake. Subjects had free access to food during the study, and food and caloric intake data were monitored by investigators directly.
The profile of caloric intake across protocol days demonstrated no overall increase in caloric intake; however, caloric intake was lower during recovery days. Compared with baseline intake, caloric intake increased with sleep restriction, and intake during the recovery days went down with recovery from sleep restriction. Caloric intake was shown to increase during delayed bedtime days, with a P
value of .06, which approaches statistical significance.
Late-night food intake between 10 PM and 4 AM was about 650 calories. Whether subjects experienced 3 recovery days or 1 recovery day, no significant difference in calories consumed during sleep restriction was observed. In other words, 3 nights of recovery sleep did not provide an additional protective effect against late-night caloric intake.
Next, Daniel Aeschbach, PhD, of Brigham and Women’s Hospital and Harvard Medical School, presented on the synergistic effects of acute and chronic sleep loss on circadian rhythms and alertness. In real-life situations, chronic sleep loss may be important. However, Aeschbach explained that chronic sleep loss may not be a distinct factor in an interaction with the effects of acute sleep loss.
In prior studies, performing psychomotor vigilance tests (PVTs) in chronic sleep loss amplified the effects of acute sleep loss on hormone secretion during the circadian phase. The investigators asked whether this finding was specific to PVT or whether other measures of inattention might also be affected. The investigators also sought a connection between increased secretion of circadian hormones during sleep loss and markers of inattention. Dr Aeschbach discussed the protocol of the study, which quantified the effects of progressively increased sleep loss over 43 days. Before the trial began, subjects underwent complete recovery of prior sleep loss. The circadian cycle was evaluated with 43 hours of sleep, with 33 hours of waking time, and with 10 hours of sleep time. This sleep pattern induced a chronic sleep loss in test subjects.
On electrooculogram (EOG) and electroencephalogram (EEG) recordings, Aeschbach measured slow eye movements, which are a measure of complete attentional failure. This measure allowed continuous monitoring of attentional failure. Whereas slow eye movement requires a complete failure of attention, PVT, by contrast, is more sensitive and detects partial loss of attention.
Dr Aeschbach observed that over 15 to 20 hours, few attentional failures occurred; however, toward the end of 33 hours of wakefulness, attentional failures began to occur with increasing frequency. It was with maximum melatonin levels that patients experienced the greatest magnitude of attentional failure.
Looking at circadian phases, subjects with high levels of sleep loss experienced higher peak levels of hormones associated with regulating the circadian rhythm than non–sleep deprived individuals. Higher peak levels of hormone secretion were directly related to attentional failure increases. Dr Aeschbach explained that, as time went by, the number of attentional failures also increased as sleep deficiency accumulated during the study. With a chronic sleep deficit, the frequency of attentional failures increased the secretion of hormones associated with the circadian rhythm.
Summarizing the results, Dr Aeschbach showed that attentional failures increased as time went by and as a chronic sleep deficit accumulated. Using a 3-dimensional plot that summarized the data, he showed how hormone secretion increased in amplitude as sleep debt accrued. Chronic sleep deficit increased both the rate of attentional failure occurrence and the effects of the circadian phase at night. These findings, as Dr Aeschbach explained, have huge implications for individuals working nighttime shifts.
The third speaker, Clare Anderson, PhD, from Brigham and Women’s Hospital and Harvard Medical School, presented on the quality of ocular attentional failure and psychomotor vigilance tests (PVTs) in measuring sleep-related impairment. Anderson explained that the cause of PVT lapse is unknown; however, distraction, microsleeps, eye blinks, and perceptual blindness have been proposed as potential causes of these lapses in attention.
Understanding the relationship between PVT and other markers of inattention may improve understanding of how inattention is measured in sleep research. In a study, Anderson investigated the changes in attention processes as measured by the PVT in relation to other markers of attention loss such as diverted gaze and behavioral microsleeps. Eleven individuals without psychiatric difficulties or sleep pathologies entered a constant routine of 8 hours of sleep and 16 hours of wakefulness. Over the course of the trial, roughly 14 PVTs occurred per participant. The subjects were also monitored for eye tracking by infrared scan, with an image of the eyes recorded every 8 milliseconds. Anderson also defined different types of PVT lapses, which included those that indicate distraction, microsleep, simple blinking, or perceptual lapses.
After 16 hours awake, the number of lapses increased. The individuals’ eyes remained on target for the first 16 hours, but experienced a sharp drop-off after those 16 hours had elapsed. A diverted gaze may be seen in the early stages of drowsiness, but in later stages, eye closures occur.
The numbers of lapses were averaged by type. Of these, more microsleep lapses occurred when compared with visiomotor lapses and perceptual lapses. With regard to duration, the behavior microsleeps demonstrated much longer durations than other types of lapses.
Anderson summarized the data, explaining that a change in attention may be caused by a diverted gaze, and that behavioral microsleeps were the most frequent occurrences in distraction. Implications of this research include the identification of early markers of drowsiness, especially in individuals who are driving. Systems could be designed to analyze the signs of distraction, which serve as markers of drowsiness. Drowsiness monitoring systems might also reduce the incidence of car accidents in the future.