Study Objectives: Night work plays an irreplaceable role in healthcare systems. However, sleep loss due to night shifts can negatively affect healthcare workers′ cognition. Identifying individual variables predicting night shift tolerance may help to mitigate the negative consequences of sleep loss on workers′ and patients′ safety. This study aims to explore, in a healthcare workers′ sample, which variables predict the possible impairment in sustained attention and increase in risk-taking behavior after a night shift.
Methods: 22 healthcare workers (avg. age 39.4 ± 11.5 y, 63% females) participated in the experiment. Participants wore a wrist actigraph during a night shift and the preceding 6 days. At the beginning (t0) and at the end (t1) of the night shift, they underwent an assessment of sustained attention (Psychomotor Vigilance Task, PVT) and risk-taking behavior (Balloon Analogue Risk Task, BART). Questionnaires on risk propensity (Domain-Specific Risk Taking, DOSPERT) and on demographics were also administered. Linear regression models were estimated to disentangle the effect of age, risk propensity and actigraphically-defined sleep metrics on cognitive variables.
Results: Wake after sleep onset (WASO) significantly predicted t1-t0 PVT metric ″major lapses″ ("β" =-0.11, p=0.0007) while age and DOSPERT significantly predicted t1-t0 BART score ("β" =-0.26, p=0.023 and "β" =-0.097, p=0.043, respectively).
Conclusions: Sleep parameters predict the impairment in sustained attention, while the combination of age and risk propensity predicts an increase in risk-taking behavior. The implementation of measures to prevent cognitive decline during night shifts should be designed according to the type of tasks workers must accomplish.