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Mental fatigue (MF) negatively affects both cognitive and physical performance, increasing the risk of errors in high-stakes environments such as sports and surgery. Traditional methods to assess MF rely on subjective self-report scales, which are prone to bias, or on complex brain measurements (e.g. EEG) that are impractical outside laboratory settings. This study aims to develop a real-time, objective monitoring method for MF using wearable physiological sensors. The study will recruit healthy, trained runners (18-35 years old) who will complete both an MF-inducing cognitive task (Stroop test) and a control condition (watching a documentary) in a randomized, counterbalanced, crossover design. Heart rate variability, respiration rate, and pupil metrics will be continuously recorded using wearable devices. Machine learning models will be used to predict MF-level as well as the effect of MF on physical performance (5-km time trial on a treadmill) using the physiological data as input.
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30 participants in 2 patient groups
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Emilie Schampheleer, Msc
Data sourced from clinicaltrials.gov
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