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
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Condições
Intervenção / Tratamento
Descrição detalhada
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.
Tipo de estudo
Inscrição (Real)
Estágio
- Não aplicável
Contactos e Locais
Locais de estudo
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Shewa
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Debre Berhan, Shewa, Etiópia, 445
- Dr. Arefayne
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Debre Berhan, Shewa, Etiópia, 445
- M Dessye
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Critérios de participação
Critérios de elegibilidade
Idades elegíveis para estudo
- Adulto
Aceita Voluntários Saudáveis
Descrição
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.
Plano de estudo
Como o estudo é projetado?
Detalhes do projeto
- Finalidade Principal: Prevenção
- Alocação: Randomizado
- Modelo Intervencional: Atribuição Paralela
- Mascaramento: Nenhum (rótulo aberto)
Armas e Intervenções
Grupo de Participantes / Braço |
Intervenção / Tratamento |
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Comparador Ativo: 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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Experimental: 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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O que o estudo está medindo?
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
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Changes in Sprint Performance Time
Prazo: 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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Colaboradores e Investigadores
Patrocinador
Investigadores
- Investigador principal: Dr. Arefayne M Dessye, PhD, Debre Berhan Univeristy
Datas de registro do estudo
Datas Principais do Estudo
Início do estudo (Real)
Conclusão Primária (Real)
Conclusão do estudo (Real)
Datas de inscrição no estudo
Enviado pela primeira vez
Enviado pela primeira vez que atendeu aos critérios de CQ
Primeira postagem (Real)
Atualizações de registro de estudo
Última Atualização Postada (Real)
Última atualização enviada que atendeu aos critérios de controle de qualidade
Última verificação
Mais Informações
Termos relacionados a este estudo
Palavras-chave
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Outros números de identificação do estudo
- DBU-SS-2023-008
- IRB#DBU-SS-2023-008 (Identificador de registro: ClinicalTrials.gov)
Plano para dados de participantes individuais (IPD)
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Descrição do plano IPD
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