Shift Hours' Impact on Fatigue and Tracking of Eye Dynamics

Sleep Deprivation, Sleepiness, Eye-Tracking Technology, Radiologists, Tomography, Electroencephalography

Night-Shift, Radiology, performance, Eye-tracking, Fatigue, Eye strain

The goal of this clinical trial is to learn how night shift-induced sleep debt affects oculomotor patterns, attentional state, and diagnostic performance in emergency radiologists. The main questions it aims to answer are: * Does sleep debt from a night shift alter oculomotor parameters, as eyes movements (speed and amplitude), fixation duration, pupil size? * Does a night shift impact radiologists' diagnostic accuracy, attentional state, and perceived fatigue? Researchers will compare radiologists after a night shift (sleep-deprived) with the same radiologists after a night of rest (control) to see if fatigue-related changes affect both visual exploration strategies and diagnostic performance. Participants will: * Perform a guided saccade task assessed by eye tracking (primary endpoint), * Read thoracic CT scans (with and without pulmonary embolism cases) to assess diagnostic performance and visual exploration patterns, * Undergo EEG recording to measure attentional state, * Complete self-report questionnaires on sleepiness and fatigue.

Rationale Physician burnout is a significant problem, particularly for emergency physicians and radiologists working in emergency imaging. Over the past 15 years, the workload of 'emergency radiologists' has increased significantly, impacting their job satisfaction and perceived health. Night shifts and reduced recovery time disrupt the sleep-wake cycle, leading to decreased cognitive performance and increased stress. Fatigue at work is closely linked to a higher risk of medical errors. Radiologists face high demands on attention, pressure to report quickly, and the need to interpret complex images, all of which contribute to errors. Eye tracking has been used to analyze radiologists' visual search patterns, However, few studies have examined the impact of work patterns, particularly night shifts, on these behaviors. It is important to study the effect of night shift fatigue on radiologists' eye movement patterns and cognitive abilities, and to relate these findings to their diagnostic performance. Research Hypothesis The investigators hypothesize that eye movement patterns and attention during medical image reading will be influenced by sleep debt and ocular fatigue induced by night shifts. The investigators therefore aim to compare radiologists' eye movement patterns during medical image reading, as measured by oculometry, and their attentional state, as measured by EEG, according to their sleep debt within the IMADIS Group. STUDY DESIGN Study type Longitudinal (non-randomized controlled trial design), single-center, multi-site study (IMADIS Group) Study procedure Inclusion Before any research-related act or intervention, the investigator will inform the participant and obtain their free, informed, and written consent to take part in the study. Each participant will perform 2 measures. Radiologists will undergo each measurement in an order determined by their work schedule within the IMADIS Group (after on-call night or after night of rest). Measure After the participant's consent is obtained, data will be collected using the case report form, including demographic data and sleep-related information. This visit will also serve to schedule all other study visits with the participant. Actimeters (Geneactive) will be handed over to participants with instructions for use (continuous wear during the day and night preceding each measurement). The actimeter is worn day and night before the oculometry measurement to record their sleep-wake rhythms. A chronotype questionnaire will also be administered. Morningness-Eveningness Questionnaire (MEQ, ordinal 16-86) Eye -tracking and EEG measurements Upon arrival at the IMADIS Group on-call center (between 08:00 and 10:00 after a night of rest) or before leaving the center after an on-call night (between 08:00 and 10:00), participants will complete brief questionnaires (Mental Fatigue VAS score, Karolinska Sleepiness Scale (KSS, ordinal 0-9), Insomnia Severity Index (ISI, ordinal 0-28), and Epworth Sleepiness Scale (ESS, ordinal 0-14).) while being fitted with the EEG headset. This will be followed by an eye tracker calibration phase. The eye-tracking study scenario will then be launched on the Tobii Pro Lab platform. It will include a guided saccade task of approximately 5 minutes, followed by an observation phase comprising 10 videos simulating navigation through multiple slices of a thoracic CT scan. The videos will differ between measurement phases but equivalent. During the experimentation, recording of the brain activity modulations and attentional state (Theta, Alpha, Beta, Gamma waves) will be performed with EPOC X EEG headset (EMOTIV). Statistical aspects Required sample size For this study, the investigators will use a significant change in the slope of the relationship between peak saccadic velocity and saccadic amplitude. Based on the results of prvious studies and considering two comparison groups with an effect size of 0.8, the required sample size is 8 participants to achieve a statistical power of at least 80% with a 5% type I error risk. The calculations were performed using the power.anova.test function from the stat package (R software®, version 4.0.5). The investigators chose to add 32 participants to account for potential missing or poor-quality data, leading to a total of 40 participants. This estimate is supported by the literature.. Statistical methods Univariate descriptive analyses Descriptive analyses will be performed on quantitative and ordinal variables. Data will be summarised for the entire sample and separately by group, reporting frequencies and percentages for qualitative variables, and means, medians, standard deviations, and quartiles for quantitative variables. The description will include sociodemographic characteristics (age, sex, years of experience, physical fitness), as well as the analysis of quantitative variables (slope of the relationship between peak velocity and saccadic amplitude, fixation duration in milliseconds, EEG frequency analysis \[Theta, Alpha, Beta, Gamma waves\], and sleep quantity). Ordinal variables will also be described: Mental Fatigue VAS score, Karolinska Sleepiness Scale (KSS, ordinal 0-9), Morningness-Eveningness Questionnaire (MEQ, ordinal 16-86), Insomnia Severity Index (ISI, ordinal 0-28), and Epworth Sleepiness Scale (ESS, ordinal 0-14). Analyses for primary and secondary objectives Primary objective To address the primary objective (assessment of eye movement patterns during a guided saccade task), the investigators will perform a linear regression adjusted for age, years of experience, physical fitness, pre-study fatigue levels, and sleep duration on the day preceding measurement. The independent variable will be the measurement condition (Night shift vs. Rested night), and the dependent variable will be the slope of the relationship between peak velocity and saccadic amplitude during chest X-ray reading. Secondary objectives Oculomotor behaviour - To evaluate the impact of sleep debt on radiologists' oculomotor behaviour during chest medical image interpretation, the investigators will perform a linear regression adjusted for the same covariates. The dependent variables will be fixation durations (ms) on regions of interest and pupil diameters. Detection performance - To assess the effect of sleep debt on the number of detected pulmonary embolisms (% detection), the investigators will perform a similar linear regression with % detection as the dependent variable. EEG-based attentional level - To evaluate the effect of sleep debt on attentional level during observation, measured by portable EEG, the investigators will conduct a regression with EEG frequency bands (Theta, Alpha, Beta, Gamma) as dependent variables. Subjective perception of sleep and sleepiness - To evaluate the impact of sleep debt on self-reported sleep and sleepiness, the investigators will conduct a regression using subjective measures: Karolinska Sleepiness Scale (KSS), Epworth Sleepiness Scale (ESS), and Mental Fatigue VAS. Handling of Missing Data Measurement errors In the event of actimeter malfunction or removal for a short period (\< 1 hour), data will be considered missing at random. Consequently, the data already collected will be averaged over the total period. Missing questionnaire data If an item is left unanswered, the calculation of the MEQ, Mental Fatigue VAS, ISI, ESS, or KSS score will be invalidated. If the data remains missing, the participant will not be included in the regression models involving that variable. Software used Tobii Pro Lab: eye-tracking Actiware software: actimetry data collection EMOTIV Pro Desktop (local data storage): EEG data collection Matlab (local data storage): actimetry and EEG data analysis R (local data storage): statistical analysis Expected outcomes The findings will contribute to: * A better understanding of the relationships between sleep, oculomotor patterns, attention, and diagnostic performance in radiology. * Increased awareness of the importance of sleep quality in maintaining optimal analytical performance. * Evidence to inform organizational changes aimed at minimizing the impact of fatigue, such as adapted shift scheduling or targeted fatigue countermeasures. This study bridges cognitive neuroscience, medical imaging, and occupational health, with the goal of translating research findings into practical measures to support radiologists' performance and patient safety, particularly in the demanding context of emergency imaging

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