mystery of yawning
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La parakinésie brachiale oscitante
Yawning: its cycle, its role
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Fetal yawning assessed by 3D and 4D sonography
Le bâillement foetal
Le bâillement, du réflexe à la pathologie
Le bâillement : de l'éthologie à la médecine clinique
Le bâillement : phylogenèse, éthologie, nosogénie
 Le bâillement : un comportement universel
La parakinésie brachiale oscitante
Yawning: its cycle, its role
Warum gähnen wir ?
 
Fetal yawning assessed by 3D and 4D sonography
Le bâillement foetal
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Sleep Med.
2007;9(1):71-79.
Awareness of sleepiness and ability to predict sleep onset: can drivers avoid falling asleep at the wheel?
Kaplan KA, Itoi A, Dement WC
Department of Psychology, University of California, Berkeley, USA.

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ERGOLOGY
Abstract
 
Regarding the causes of sleep-related accidents, this study assesses whether individuals can anticipate sleep onset accurately and how individuals acknowledge and use physiological and cognitive cues to make judgments related to sleep onset.
 
A group of 41 partially sleep-deprived subjects predicted the likelihood of sleep in 30 consecutive two-minute intervals and noted physiological and cognitive signs of sleepiness, including involuntary eye closure, head-nodding, wandering thoughts, yawns, and instances of sleep, collectively referred to as "sleep complaints". Continuous polysomnographic recording compared these predictions to actual instances of sleep.
 
Subjects varied in their ability to predict sleep onset. For all subjects, the mean prediction of the likelihood of sleep prior to sleep was significantly higher than the mean prediction of the likelihood of sleep prior to intervals in which no sleep occurred (78% vs. 42%; p<.05). However, subjects tended to predict much lower likelihoods of sleep onset before their first sleep event (55%) than before later sleep events. On average, the rate at which subjects reported miscellaneous sleep complaints (such as head-nodding, eye closure, and wandering thoughts) was higher prior to sleep than prior to intervals in which sleep did not occur.
Subjects who acknowledged a limited number of physiological and cognitive indicators of sleepiness tended to be poor predictors. Subjects whose physiological and cognitive signs of sleepiness failed to provide a strong indication of whether or not sleep onset would occur also tended to be poor predictors. Inability to judge sleep onset and, hence, susceptibility to sleep-related accidents, may be attributable to both a scarcity of meaningful warning signs and a failure to acknowledge the importance of physiological and cognitive indicators.
 
One of the most practical questions sleep research can hope to answer is how and to what extent sleep-related traffic accidents might be avoided. Researchers estimate that sleep plays a role in 1&endash;4% of all automobile accidents in theUnited States, although as many as 19%of accidents involving mortality or serious injury may be attributed to acute sleepiness. Many aspects of this problem have been investigated in previous studies, and a link between sleepiness and accidents is well established. While previous research has focused on performance degradation as a cause of sleep-related accidents, many more recent studies have investigated the relationship between awareness of sleepiness and vehicular accidents in both simulated [9,10] and prospective designs. Although research has investigated awareness of sleepiness in general, no study to our knowledge has identified the physiological and cognitive cues that constitute this awareness of sleepiness or the way in which these cues relate to accurately predicting sleep onset. This study aims to identify such physiological warning signs and investigate their relationship to the prediction of sleep onset.
 
It is easy to imagine a scenario in which a prudent driver with adequate information about the consequences of his or her actions can make a decision to avoid an accident. What is it, then, that makes the sleepy driver continue to drive in the face of such risk? Several explanations are possible. The driver may fail to make the connection between his or her physiological state and his or her dangerously high risk of falling asleep. Previous research into an alertness indicator, a device that might detect and advise drivers of decreases in alertness, is based on the assumption that lack of attention to physiological and cognitive cues indicating an elevated sleep tendency is the cause of sleep-related accidents. An alternative explanation is that the driver may fail to make the connection between a microsleep and the corresponding high risk of having an accident. Both driving while fatigued and dozing while driving are relatively commonplace occurrences. According to the National Sleep Foundation's 2005 Sleep in America poll, 60% of drivers admitted to driving while fatigued in the past year, and 37% reported falling asleep at the wheel. Because instances of driving drowsy and falling asleep at the wheel far exceed the number of sleep-related accidents, however, sleepy drivers may not always consider the risk of having an accident significant. Finally, the driver may have adequate information about sleepiness and the consequences of his or her actions but remain highly motivated to continue driving such that, measuring the risks of an accident against the benefits of arriving at the destination, he or she decides to continue driving despite the risk. Some researchers emphasize this point in criticism of an alertness indicator, arguing that drivers may continue to drive despite awareness of sleepiness . Thus, awareness of sleepiness, awareness of the consequences of sleepiness, and motivation to continue in spite of sleepiness are all factors that can contribute to the safety of the sleepy driver. This study concentrates on the first factor: to what extent are drivers aware of their sleepiness, and what are the mechanisms behind this awareness? Studies suggest that it is nearly impossible to fall asleep without any ''warning'' whatsoeve. Stated differently, certain physiological and cognitive cues invariably precede sleep, including straining and closure of the eyes, excessive yawning, wandering thoughts, head-nodding, and struggling to fight sleep. However, the question remains open as to whether these and other physiological and psychological cues provide sufficient information for an individual to determine accurately when sleep will occur.
 
In order to address these issues, we partially deprived college student volunteers of one night's sleep and had them complete a one-hour computerized exercise the following morning. This exercise involved making repeated judgments about the likelihood of sleep onset and the frequency of sleep complaints to understand the relationship between attention to physiological and cognitive signals and awareness of sleep onset.
 
Ability of subjects to predict sleep onset In general, subjects displayed some ability to discern future sleep from no-sleep with their predictions. However, subjects were frequently surprised by their first sleep event; predictions prior to the first sleep event were much worse than predictions prior to subsequent sleep events. There was a wide variation among individual subjects' ability to predict sleep onset, with a notable number of subjects exhibiting a limited ability to predict sleep.
 
As described earlier, all subjects were asked to predict the likelihood of falling asleep in the next two minutes on a scale from 0% to 100%; if subjects were perfect predictors of sleep, they would always predict 100% prior to sleep and 0% prior to no-sleep. For all subjects, the mean prediction of sleep likelihood prior to sleep was 78% ± 22%, significantly different from the mean prediction of sleep likelihood prior to no-sleep (42% ± 25%; p < .05). The mean prediction prior to subjects' first sleep (55% ± 22%) was not significantly different from mean predictions prior to episodes of no-sleep (p > .05). Higher predictions generally corresponded to a higher frequency of sleep occurrence.
 
The relationship between mean predictions and sleep was also examined on a subject-by-subject basis. We evaluated the degree of difference between subjects' predictions prior to periods in which sleep did and did not occur. This difference can be taken as a measure of subjects' ability to discriminate sleep from no-sleep, with larger differences representing better discrimination. For example, a subject who always predicts a 100% likelihood of sleep before sleeping and a 0% likelihood before not sleeping is considered maximally able to discern sleep likelihood. Table 2 summarizes the results, listing subjects by the difference between their mean predictions prior to sleep and prior to no-sleep. For 19 subjects, the gap between their mean prediction prior to sleep and prior to no-sleep was less than 30%, indicating a relatively poor degree of accuracy in predictions.
 
Relationship between sleep complaints and sleep onset In general, the rates at which subjects reported feeling signs of sleepiness were significantly higher before sleep than before no-sleep (p < .05). A notable exception occurred in the frequency of yawns: complaints of yawning were significantly more frequent prior to intervals in which sleep did not occur (p < .05), indicating that yawning is a less accurate indicator of imminent sleep. Subject-by-subject analyses revealed that subjects differ widely in the relationship between sleep complaints and predictions of sleep onset. In particular, some subjects showed very little difference in complaint rates between sleeps and no-sleeps.
 
Subjects clicked on computer icons during the course of the study to record physiological and cognitive indicators of sleepiness, including sensations of the eyes, nods of the head, episodes of sleep, yawns, wandering thoughts, and any other indicators of sleepiness they felt (''other'' complaints). For each subject, each interval, and each complaint type, a complaint rate was calculated, measuring the number of complaints recorded per minute the subject was awake. A total complaint rate for each subject was calculated by summing the individual complaint rates for each subject per interval, thereby gathering a rate of all complaints experienced per subject in any given interval. To illustrate how complaint rates for various physiological and cognitive indicators relate to sleep onset, we compared complaint rates prior to intervals in which sleep occurred to those prior to intervals in which no sleep occurred. We found that the mean total complaint rate was significantly higher prior to sleep (3.50 complaints/min) than prior to no-sleep (1.82 complaints/min; p < .05). The rates at which subjects selected the ''sleep,'' ''head,'' ''eyes,'' and ''thought'' icons were significantly higher before sleep than before no-sleep (p < .05). There was no significant difference, however, in the mean rate at which subjects selected the ''other'' icon between sleep and no-sleep intervals, and, as noted above, yawning was not a good predictor of sleep likelihood. Table 3 summarizes the means and standard deviations of complaint rates prior to sleep (S) and no-sleep (NS).
 
Fig. 4 illustrates the relationship between total complaint rate and subsequent sleep across all subjects and intervals, with the upward trend in the data indicating that higher complaint rates corresponded to a higher frequency of sleep occurrence 4. Discussion
 
The results of this study establish that people do have a limited ability to predict the onset of sleep. The wide variation in predictions both before episodes of sleep and before episodes of no-sleep establishes that subjects certainly do fall asleep at times when they think sleep is highly unlikely and fail to fall asleep at times when they think sleep is highly likely. The mean prediction before subjects' first sleep was low, suggesting that subjects in general judged sleep to be fairly unlikely on first occurrence. Mean predictions prior to future episodes of sleep, however, were significantly higher than mean predictions prior to future episodes of no-sleep. Stated differently, subjects learned how to more accurately predict sleep onset over the course of the study. This improvement over time suggests that individuals may be able to increase their awareness of imminent sleep onset, or ''learn'' to be better predictors, through targeted education and self-awareness focused on the internal cues that precede sleep.
 
Since there is a significant chance that a sleepy driver will not be alive to experience his or her second, third, or fourth sleep attack, predictions made prior to the first sleep event are of particular importance in the current study. There was a wide variation in subjects' ability to predict their first sleep onset. Predictions prior to the first sleep event ranged from as high as 93.3% to as low as 4.9% on the prediction scale. The possibility that subjects can predict a very low likelihood of sleep and subsequently fall asleep easily translates into a potential to make misjudgments on the road during a prolonged state of sleepiness. Indeed, subjects may have even greater difficulty accurately predicting the likelihood of sleep in real-world situations such as driving. The current protocol required subjects to focus and report on feelings of sleepiness and sleep onset in a distraction- free environment. Drivers, by contrast, face greater distraction on the road and less impetus to pay attention to physiological cues; these, in turn, could hinder drivers' ability to predict first sleep onset. There are many reasons why the ability to predict sleep onset may be limited. First, the information people receive by way of physiological and cognitive signs of sleepiness is imperfect. There is some positive relationship between the sleep complaints measured in the study (eyes, head, thoughts, and prior sleep) and imminent sleep onset (see Table 3), but this relationship is imperfect in the sense that there are times, particularly during extended periods in which an individual is struggling to stay awake, when sleep can occur suddenly and without clear preemptive warning signs. In addition, individual variation exists in the strength of the relationship between indicators of sleepiness and imminent sleep onset. Fig. 6 indicates that subjects whose sleep complaints were not strongly correlated with sleep onset tended to predict sleep onset rather poorly. Poor predictions can thus be viewed as caused by poor quality of information (i.e., physiological and cognitive signs) available upon which to base predictions. Finally, the information available to sleepy individuals may be misleading or misrepresentative of imminent sleep onset. As illustrated in Table 3, yawning was more frequent prior to no-sleep intervals than to intervals in which sleep occurred, and the experience of miscellaneous sleep complaints did not differ between sleep and no-sleep episodes. Thus, physiological and cognitive cues may not always accurately predict sleep onset, even when acknowledged by the sleepy individual.
 
Subjects' ability to predict sleep onset may also be limited because they may not adequately consider the physiological and cognitive indicators of sleepiness that they notice and record. Stated differently, although individuals have information that could provide a good indication that sleep will occur, they may ignore this information in judging the likelihood of sleep onset. The results of this study show that subjects who paid little heed to physiological and cognitive indicators in making their predictions tended to be poor predictors. Inattention to the warning signs of sleep onset is a particularly difficult problem to address when conflated with the aforementioned problem of inadequate or misleading warning signs. However, it is our contention that both problems need to be solved, and that the best solution for the first is education. People need to understand that their ability to judge sleep onset is, in fact, limited, and that they may not experience a drastic change in their physiological state immediately prior to falling to asleep. Some researchers have suggested that the validity of an alertness indicator is questionable because it fails to address this problem of ignoring warning signs of sleepiness. An alertness indicator is a worthy approach given the low quality of information normally available for sleep-related decision-making, but it is only worthwhile to the extent that people can understand their inability to judge sleep onset accurately without it. Closely related to the problem of limited information is the problem of overconfidence regarding the ability to anticipate sleep onset. Though this study does not specifically investigate the phenomenon of ability estimation, some attempts have been made to fill in the gaps. After the study, a questionnaire was administered to a separate group of subjects participating in a different experiment. The protocol of our current experiment was explained to this separate group, and participants were asked if they thought they could accurately judge sleep onset in this context. Most subjects indicated that they would be able to predict above 90% before every sleep and below 10% before every no-sleep. In our study, no subjects were able to predict sleep onset with anywhere near this degree of accuracy. The fact that people expect to discern sleep onset very accurately seems to make them more willing to test the limits of their ability to stay awake, even in risky situations such as driving. As suggested above, a partial solution to this problem is education. People should understand that they often do not have adequate information vis-a`-vis signs of sleepiness, or that they often disregard signs of sleepiness, and may not always be able to avoid sleep-related accidents if driving while tired. Even if people do have the ability to predict the likelihood of sleep onset in a two-minute period with great accuracy, it is still unclear whether they would take appropriate action to avoid sleep-related accidents. An individual might continue to drive until sleep is imminent, expecting a clear psychological warning sign moments before this sleep onset occurs. However, at least one study found that drivers do not appear to have any last-second warning but that ''fatigue. . . develops insidiously, and even those who know they are tired may actually lose consciousness suddenly and without trying to decelerate'' [20]. We are able to control our sleep impulse to some extent, but in some situations it can easily be mistaken as an ability to control sleep in all environments; in monotonous tasks, however, our study suggests that the chance of falling asleep increases with time, and our ability to sustain wakefulness diminishes over this period.
 
The probability scale methodology in this study provides a consistent short-term subjective measure of anticipated sleep occurrence. Although two-minute prediction intervals were chosen as the upper limit of sleep onset in the current paradigm, future research might evaluate predictive success over longer intervals (e.g., five minutes) to evaluate various risks associated with longer periods of sleepy driving. Future research should examine the extent to which people can improve their abilities to predict sleep onset through practice and/or training.