Abstract

Abstract Introduction Although sleep is fundamental for human well-being, factors that contribute to an individual’s experience and report of sleep quality remain poorly understood. Utilizing that sleepiness is known to impact vigilance performance, this study sets out to explore how self-reported sleep quality changes with behavioral performance and how this variation is affected by seasonal changes. Methods This work is an interim analysis of self-reported sleep quality and behavioral performance data collected in the Ultra Long-term Sleep (ULTS) study (ClinicalTrials.gov Identifier: NCT04513743). In the study 20 healthy participants (average 33±13 years of age) were enrolled for 365 continuous days to observe the seasonal variation in sleep and cognitive performance. The outcome from the daily psychomotor vigilance task (PVT) and sleep questionnaire is analyzed and reported. The sleep questionnaire is a composite of questions from the Sleep Satisfaction Tool, the Karolinska Sleep Diary, questions regarding feelings of pain and outside disturbances, easiness of waking up, and the Karolinska Sleepiness Scale (KSS). The PVT was designed for self-administered high-frequency testing and has a short 3-minute test period. Monthly changes were examined from June to November. This period was chosen since all subjects were active. Results The first eight participants have now completed the study. Repeated measures correlations between KSS and mean PVT reaction time showed moderate but highly robust associations between the two measures over time (r=0.2; 95% CI 0.18-0.23). From the data collected thus far from the entire population, fastest PVT mean reaction times were found on Saturdays and slowest on Tuesdays. Similarly, KSS had best scores in weekends. There was an overall increase in mean PVT reaction time during the investigated period from June to November. This was also observed in KSS. Conclusion Our findings show a moderate correlation between the mean PVT reaction time and Karolinska Sleepiness Scale with up to 365 datapoints per subject with weekly and seasonal trends observed. Support (If Any) This research is supported by Innovation Fund Denmark.

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