Doctors’ alertness, contentedness and calmness before and after night shifts: a latent profile analysis

Abstract

BackgroundWhile night shifts are crucial for patient care, they threaten doctors’ well-being and performance. Knowledge of how the impact of night shifts differs for doctors is needed to attenuate the adverse effects of night shifts. This study aimed to obtain more precise insight into doctors’ feelings surrounding night shift by: identifying profiles based on doctors’ alertness, contentedness and calmness scores before and after night shifts (research question (RQ) 1); assessing how doctors’ pre- and post-shift profiles change (RQ2); and determining associations of doctors’ demographics and shift circumstances with alertness, contentedness and calmness change (RQ3).MethodsLatent Profile Analysis using doctors’ pre- and post-shift self-rated alertness, contentedness and calmness scores was employed to identify pre- and post-shift profiles (RQ1). A cross-tabulation revealed pre- and post-shift profile changes (RQ2). Multiple regressions determined associations of demographics (i.e. age, sex, specialty) and night shift circumstances (i.e. hours worked pre-call, hours awake pre-call, shift duration, number of consecutive shifts, total hours of sleep) with alertness, contentedness and calmness change (RQ3).ResultsIn total, 211 doctors participated with a mean age of 39.8 ± 10 years; 47.4% was male. The participants included consultants (46.4%) and trainees (53.6%) of the specialties surgery (64.5%) and obstetrics/gynaecology (35.5%). Three pre-shift (Indifferent, Ready, Engaged) and four post-shift profiles (Lethargic, Tired but satisfied, Excited, Mindful) were found. Most doctors changed from Ready to Tired but satisfied, with alertness reducing most. Age, specialty, sleep, shift duration and the number of consecutive shifts associated with alertness, contentedness and calmness changes.ConclusionsThe results provided nuanced insight into doctors’ feelings before and after night shifts. Future research may assess whether specific subgroups benefit from tailored interventions.