High Risk, Low Rest: A New Framework for Monitoring Sleep Vulnerability in Emergency Medicine

Laura Schmidt^1,2*Marion Trousselard^3,4,5Clément Perez^6,7Eve Reynaud¹Bérénice Valero⁸Sophie Schlatter^10,9Karim Tazarourte^11,6,9Marion Douplat^6,7,9Stéphanie Mazza¹

• ¹Forgetting Team, U1028 UMR5292, INSERM, CNRS, Centre de Recherche en Neurosciences de Lyon, Bron, France
• ²Laboratory Research on Healthcare Performance (RESHAPE), INSERM U1290, Universite Claude Bernard Lyon 1 - Domaine de Rockefeller, Lyon, France
• ³Institut de Recherche Biomedicale des Armees, Brétigny-sur-Orge, France
• ⁴Inserm, INSPIIRE, F-54000, Universite de Lorraine, Nancy, France
• ⁵École de Psychologues Praticiens, Institut Catholique de Paris, Paris, France
• ⁶Emergency Department, Hospices Civils de Lyon, Lyon, France
• ⁷Hopital Lyon Sud, Pierre-Bénite, France
• ⁸Human Science Department, Centre Leon Berard, Lyon, France
• ⁹Laboratory “Research on Healthcare Performance” (RESHAPE), INSERM U1290, Universite Claude Bernard Lyon 1 - Domaine de Rockefeller, Lyon, France
• ¹⁰Healthcare Simulation Center (CLESS), SIMULYON,, Hospices Civils de Lyon, Lyon, France
• ¹¹Hopital Edouard Herriot, Lyon, France

Background: Shift work in emergency care settings disrupts circadian rhythms and sleep, increasing health risks and performance. A key aspect of addressing these challenges lies in predicting the burden of shift work to develop safer schedules. This study introduces the Shift Load Index (SLI) as an advanced and sensitive metric for quantifying recovery constraints and examined its association with objective sleep outcomes in emergency healthcare professionals. Methods: A two-phase observational field study was conducted with 72 nurses and physicians from two French emergency departments. In the theoretical validation phase, 140 work shifts were analyzed using the SLI and compared to validated FAID Quantum fatigue scores. In the behavioral validation phase, weekly actigraphy data from 35 EHPs were collected to assess time in bed, total sleep time, and Sleep Regularity Index (SRI). We employed generalized linear mixed-effects models to assess the association of SLI with sleep outcomes. Results: SLI scores significantly predicted FAID Quantum scores (all p < 0.001). Emergency healthcare professionals obtained on average 6h09 min of sleep for 8h09min in bed, with irregular sleep patterns (mean SRI = 52%). Higher SLI scores were associated with reduced time in bed (β = -33.19, p < 0.001), shorter sleep (β = -18.30, p < 0.001), and lower SRI (β = -1.06, p < 0.001). SRI and total sleep time, as independent factors, together explained 48% of SLI variance (including random effects, 18% by fixed effects only). Discussion: Higher shift load is associated with both reduced sleep quantity and regularity. The SLI provides a useful tool to assess recovery burden, with potential applications in optimizing shift schedules and informing fatigue risk management strategies for emergency healthcare professionals.