Use of Subjective and Physiological Indicators of Sleepiness to Predict Performance during a Vigilance Task

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  • KAIDA Kosuke
    National Institute of Occupational Safety and Health Department of Public Health Sciences, Karolinska Institutet National Institute for Psychosocial Medicine (IPM)
  • ÅKERSTEDT Torbjörn
    Department of Public Health Sciences, Karolinska Institutet National Institute for Psychosocial Medicine (IPM)
  • KECKLUND Göran
    Department of Public Health Sciences, Karolinska Institutet National Institute for Psychosocial Medicine (IPM)
  • NILSSON Jens P.
    National Institute for Psychosocial Medicine (IPM)
  • AXELSSON John
    Department of Public Health Sciences, Karolinska Institutet National Institute for Psychosocial Medicine (IPM)

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Sleepiness is a major risk factor for serious injury and death in accidents. Although it is important to develop countermeasures to sleepiness to reduce risks, it is equally important to determine the most effective timing for these countermeasures. To determine optimum timing for necessary countermeasures, we must be able to predict performance errors. This study examined the predictability of subjective and physiological indicators of sleepiness during a vigilance task. Thirteen healthy male volunteers (mean age, 26.9 yr; SD = 5.98 yr; range 22-43 yr) participated in the study. Participants used the Karolinska sleepiness scale (KSS) to rate their subjective sleepiness every 4 min during a 40-min Mackworth clock test. Electrophysiological and performance data were divided into 10 epochs (i.e., 1 epoch lasted for 4 min). To estimate predictability, the data from the sleepiness indicators used for the correlation analysis were preceded by one epoch to the performance data. Results showed that sleepiness indicators (KSS score and electroencephalographic [EEG] alpha activity) and standard deviation of heart rate (SDNN) were significantly correlated with succeeding performance on the vigilance test. These findings suggest that the KSS score, EEG alpha activity, and SDNN could be used to predict performance errors.<br>

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