A selection from Stockholm University publication database

• Performance prediction by sleepiness-related subjective symptoms during 26-hour sleep deprivation

  2008. Kosuke Kaida (et al.). Sleep and Biological Rhythms 6, 234-241

  Article

  Sleepiness is a major cause of lower productivity and higher risk of accidents in various work situation. Developing sleepiness monitoring techniques is important to important to improve work efficiency and to reduce accident risk, so that people can take a rest/break in appropriate timing before an accident or a mistake occurs. The aim of the present study are (1) to explain subjective sleepiness using sleep-related symptoms, and (2) to examine which symptoms are useful to predict performance errors. Participants were healthy paid volunteers (six males, six females; mean ± SD, 31.5 ± 10.74 years). Participants took part in 26-h sleep deprivation. During sleep deprivation, they carried out several performance tasks every 3 h and an hourly rating of questionnaires to evaluate subjective symptoms including two types of Karolinska Sleepiness Scale (KSS). The present study confirmed that performance errors can be predicted by subjective symptoms. While mental fatigue was correlated to KSS scores linearly, eye-related subjective symptoms showed quadratic correlation to KSS. By taking into consideration this noteworthy relationskap between subjective symptoms and sleepiness, more accurate introspection of sleepiness and performance errors prediction (detection) may be possible.

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• Predictions from the Three-Process Model of Alertness

  2004. Torbjörn Åkerstedt, Simon Folkard, Christian Portin. Aviation, Space, and Environmental Medicine 75 (3, Suppl.), A75-A83

  Article

  This paper summarizes a computer model for predicting alertness/performance in daily life. The model uses the timing of work hours (and/or sleep hours) as input and uses both a circadian and a homeostatic component (amount of prior wake and amount of prior sleep) that are summed to yield predicted subjective alertness (on a scale between 1 and 21). The model also includes prediction of psychomotor performance on various tasks, an identification of levels at which the risk of performance/alertness impairment start, as well as a prediction of sleep latency and time of awakening from sleep. The model is currently being used to evaluate work/rest schedules for navy, airline and railway applications. It is also used for teaching sleep/wake regulation and for generating research hypotheses.

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