Machine Learning-Guided Training for Elite Athletes (MLGT) (MLGT)
A Machine Learning-Guided Training Approach to Reduce Injuries and Enhance Performance in Elite Athletes: A Prospective Cohort Evaluation
Plaintext The purpose of this study is to evaluate whether a personalized training protocol driven by machine learning can successfully reduce time-loss sports injuries and enhance athletic performance in elite athletes.
During a 9-month competitive sports season, a group of elite athletes was divided into two training
Studieoversigt
Status
Betingelser
Intervention / Behandling
Detaljeret beskrivelse
This study evaluated the efficacy of an adaptive, machine learning-driven training protocol compared to traditional athletic preparation over a full 9-month competitive sports season. The primary objective was to determine if a dynamic, technology-led approach to training load management could minimize time-loss injuries while concurrently optimizing athletic performance markers.
Participants were elite athletes randomly allocated into two parallel groups:
- The Experimental Group, which underwent training regimens dynamically adjusted using a machine learning algorithm that analyzed individual biomechanical data and historical workload parameters to optimize training volume and intensity.
- The Control Group, which followed standard, predetermined high-performance athletic training protocols typical for competitive season preparation.
Throughout the 9-month intervention period, daily tracking was maintained by technical and coaching staff. Data collection focused on the incidence, severity, and duration of all time-loss sports injuries. Concurrently, sport-specific performance parameters were periodically assessed to evaluate physical conditioning and competitive readiness. Statistical analyses were subsequently conducted to compare cumulative injury rates, total days lost to injury, and net performance adaptations between the two cohorts.
Undersøgelsestype
Tilmelding (Faktiske)
Fase
- Ikke anvendelig
Kontakter og lokationer
Studiesteder
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Shewa
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Debre Berhan, Shewa, Etiopien, 445
- Dr. Arefayne
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Debre Berhan, Shewa, Etiopien, 445
- M Dessye
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Deltagelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
- Voksen
Tager imod sunde frivillige
Beskrivelse
Inclusion Criteria:
- Must be a competitive, elite-level or sub-elite track and field athlete specializing in short-to-mid distance running events.
- Aged between 18 and 35 years old.
- Actively participating in structured athletic training programs for at least 2 years prior to enrollment.
- Free from any acute musculoskeletal injuries or medical conditions that prevent full participation in high-intensity training protocols.
- Capable and willing to provide written informed consent to participate in the study.
Exclusion Criteria: 1. Current or recent (within the past 3 months) major lower-limb injury or surgery that restricts maximal sprint or aerobic performance.
2. Concurrent use of performance-enhancing drugs or medications that influence metabolic or cardiovascular responses.
3. Inability to maintain consistent participation in the designated training protocols due to scheduling conflicts or travel.
4. Any underlying cardiovascular, respiratory, or systemic condition that creates a health risk during exhaustive exercise testing.
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
- Primært formål: Forebyggelse
- Tildeling: Randomiseret
- Interventionel model: Parallel tildeling
- Maskning: Ingen (Åben etiket)
Våben og indgreb
Deltagergruppe / Arm |
Intervention / Behandling |
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Aktiv komparator: Control Cohort
Elite adolescent sprinters who followed standard, predetermined high-performance athletic training protocols typical for competitive season preparation.
This group received structured training volume and intensity matching standard athletic coaching guidelines, without any machine learning interventions or adaptive workload adjustments.
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A personalized, data-driven training intervention where athletic workloads are dynamically adjusted based on predictive modeling.
The protocol continuously tracks individual physiological markers, biomechanical data, and workload history to optimize training volume and intensity.
This adaptive approach aims to maximize performance gains while minimizing the risk of overtraining and injury during the competitive season.
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Eksperimentel: Algorithmic Cohort
Elite adolescent sprinters who received a personalized training protocol dynamically optimized by a machine learning algorithm.
The framework evaluated individual biomechanical variables, morning heart rate variability (HRV), sleep quality, and physiological fatigue metrics to adjust training volume and intensity.
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A personalized, data-driven training intervention where athletic workloads are dynamically adjusted based on predictive modeling.
The protocol continuously tracks individual physiological markers, biomechanical data, and workload history to optimize training volume and intensity.
This adaptive approach aims to maximize performance gains while minimizing the risk of overtraining and injury during the competitive season.
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Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
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Changes in Sprint Performance Time
Tidsramme: 12 weeks
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Sprint performance will be assessed using electronic timing gates to record running times over a specific distance from a stationary start.
Lower times indicate improved sprint performance.
Measurements will be taken at baseline and at the conclusion of the training intervention period to evaluate the impact of the workload protocols.
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12 weeks
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Samarbejdspartnere og efterforskere
Sponsor
Efterforskere
- Ledende efterforsker: Dr. Arefayne M Dessye, PhD, Debre Berhan Univeristy
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Primær færdiggørelse (Faktiske)
Studieafslutning (Faktiske)
Datoer for studieregistrering
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Nøgleord
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
- DBU-SS-2023-008
- IRB#DBU-SS-2023-008 (Registry Identifier: ClinicalTrials.gov)
Plan for individuelle deltagerdata (IPD)
Planlægger du at dele individuelle deltagerdata (IPD)?
IPD-planbeskrivelse
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Kliniske forsøg med Adaptive Machine Learning Workload Optimization
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Copenhagen Academy for Medical Education and SimulationRigshospitalet, DenmarkAfsluttetPsykologisk stress | Uddannelse, Medicin | Tilstandsangst | E-læring | Akut Medicinsk AfdelingDanmark