Sleep during naturally occurring respiratory infections: A pilot study
2019
Abstract There is strong experimental support that infections increase the drive for sleep in animals, and it is widely believed that more sleep is part of an adaptive immune response. While respiratory infections (RI) are very prevalent in humans, there is a striking lack of systematic knowledge on how it affects sleep. We recruited 100 people, among whom 28 became sick with an RI during the study period (fulfilling criteria for influenza-like illness, ILI, or acute respiratory infection, ARI). We measured sick participants’ sleep at home, both objectively (actigraphy) and subjectively (diary ratings), for one week as well as four weeks later when healthy. During the week with RI, people spent objectively longer time in bed and had a longer total sleep time compared to the healthy week. During the infection, participants also had more awakenings, but no significant differences in sleep latency or sleep efficiency. While sick, people also reported increased difficulties falling asleep, worse sleep quality, more restless sleep and more shallow sleep, while they did not report sleep to be less sufficient. Most problems occurred at the beginning of the sickness week, when symptoms were strong, and showed signs of recovery thereafter (as indicated by interactions between condition and day/night of data collection for all the 10 sleep outcomes). The degree of symptoms of RI was related to a worse sleep quality and more restless sleep, but not to any of the objective sleep outcomes or the other subjective sleep variables. Having a higher body temperature was not significantly related to any of the sleep variables. This study suggests that having a respiratory infection is associated with spending more time in bed and sleeping longer, but also with more disturbed sleep, both objectively and subjectively. This novel study should be seen as being of pilot character. There is a need for larger studies which classify pathogen type and include baseline predictors, or that manipulate sleep, in order to understand whether the sleep alterations seen during infections are adaptive and whether sleep interventions could be used to improve recovery from respiratory infections.
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