Using wearable sensors to monitor mental fatigue and its effect on running performance
Monitoring the Effect of Mental Fatigue on Physical Performance Using Wearable Sensors and Physiological Parameters
This project will test whether wearable sensors can detect mental fatigue in healthy trained runners (18–35) and predict how that fatigue affects a 5-km treadmill time trial.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 30 (estimated) |
| Ages | 18 Years to 35 Years |
| Sex | All |
| Sponsor | Vrije Universiteit Brussel Academic / other |
| Locations | 1 site (Brussels) |
| Trial ID | NCT07323810 on ClinicalTrials.gov |
What this trial studies
Healthy, experienced runners complete two sessions in a randomized, counterbalanced crossover: a mental-fatigue induction using a Stroop cognitive task and a low-demand control (watching a documentary). During and after each session, heart rate variability, respiration rate, and pupil metrics are recorded continuously using wearable devices while participants perform a 5-km treadmill time trial. Machine learning models will use those physiological signals to predict mental-fatigue level and its impact on physical performance. The approach aims to produce a real-time, objective monitor of mental fatigue that can be used outside laboratory settings.
Who should consider this trial
Good fit: Ideal participants are healthy, non-smoking runners aged 18–35 who run regularly (≥15 km/week or ≥2 sessions/week for the past 6 months), take no medication, and have no neurological, cardiovascular, musculoskeletal, or psychiatric conditions.
Not a fit: People who are older, sedentary, on medication, smokers, have chronic health or psychiatric conditions, colour-vision deficiencies, or who are not experienced runners are unlikely to benefit from these specific results.
Why it matters
Potential benefit: If successful, wearable-based monitoring could let coaches, clinicians, and operators spot mental fatigue in real time and reduce performance drops or error risk by guiding rest or task adjustments.
How similar studies have performed: Previous small studies have shown wearable signals like HRV and pupil metrics can reflect fatigue, but combining these signals with machine learning to predict real-world performance is still relatively novel and not widely validated.
Eligibility criteria
Show full inclusion / exclusion criteria
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
Where this trial is running
Brussels
- Brussels Labo voor Inspanning en Topsport — Brussels, Belgium (Recruiting)
Study contacts
- Study coordinator: Emilie Schampheleer, Msc
- Email: emilie.schampheleer@vub.be
- Phone: 26292222
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.