Overview

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.

Eligibility

Inclusion Criteria:

• Healthy (no neurological, cardiovascular or musculoskeletal disorders of any kind)
• Male or female
• No prior knowledge of the concept of MF
• No medication
• Non-smoker
• 18-35 years of age
• Experienced runners: (≥15km/week and/or ≥2u/week during the last 6 months)

Exclusion Criteria:

• Injuries in the past 6 months, affecting running performance
• Suffering from a chronic health condition (could be neurological, cardiovascular, internal or musculoskeletal)
• Participating in any concomitant care or research trials
• History of suffering from any mental/psychiatric disorders
• Use of medication
• Use of caffeine and heavy efforts 24 hours prior each trial
• Suffering from colour vision deficiencies
• Not eating a standardized meal, the morning of each trial and the evening before each trial