Athletic InjuriesMLGTNCT07683091
Machine Learning-Guided Training for Elite Athletes (MLGT)
A study testing Adaptive Machine Learning Workload Optimization in people with Athletic Injuries, run by Debre Berhan University at 2 sites in Ethiopia.
Trial summary
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 Read more about this study
- Who is it for?
- Ages 18 to 35 yearsHealthy volunteers can take part
- Conditions the study is for
- Athletic Injuries
- What is being tested?
- Adaptive Machine Learning Workload Optimization
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 120 participants
- Where
- 2 sitesEthiopia
- Study dates
- 1 Jan 2023to 30 Sep 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 35 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- 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.
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- 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.
Who cannot join
None listed.
Trial locations and contacts
2 sites in 1 country.
Ethiopia2 sites
Dr. Arefayne
Debre Berhan, Shewa, 445
M Dessye
Debre Berhan, Shewa, 445
About this study
Official title: 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
Read the detailed description
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.
- Conditions
- Athletic Injuries
- Being tested
- Adaptive Machine Learning Workload Optimization
- Keywords
- Machine Learning, Injury Prevention, Athletic Performance, Elite Athletes, Sports Biomechanics, Training Load Optimization
- Medical subject headings
- Athletic Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Adaptive Machine Learning Workload OptimizationBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| Control Cohort | Active comparator | 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. Receives: Behavioral: Adaptive Machine Learning Workload Optimization |
| Algorithmic Cohort | Experimental | 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. Receives: Behavioral: Adaptive Machine Learning Workload Optimization |
What is being measured
The primary outcome is the main question the study is designed to answer.
Changes in Sprint Performance Time
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.
Measured: 12 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jul 2026
- Study start1 Jan 2023
- Primary completion30 Sep 2023
- Study completion30 Sep 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Athletic InjuriesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- A parallel-group randomized controlled trial design was utilized to compare two distinct high-performance training tracks over a 9-month competitive season. Elite athletes were randomly allocated into either the experimental arm (undergoing dynamic, machine learning-driven training load and biomechanical optimization) or the active control arm (undergoing traditional, structured high-performance athletic preparation). Both groups trained concurrently under monitored conditions to isolate the effects of the technology-driven protocol on injury incidence and performance markers.
- Masking
- None (open label)
- Enrolment
- 120 participants (actual)
Sponsor and investigators
- Lead sponsor
- Debre Berhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Dr. Arefayne Mesfen Dessye, Debre Berhan University
- Investigators
- Dr. Arefayne M Dessye, PhD, Principal investigator, Debre Berhan Univeristy
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07683091
- Sponsor's protocol ID
- DBU-SS-2023-008
- Other IDs
- IRB#DBU-SS-2023-008
- Acronym
- MLGT
- Status last verified
- Jun 2026
- First submitted
- 27 Jun 2026
- Last updated
- 6 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data (IPD) will not be shared publicly to maintain the confidentiality of the elite athletes involved and to protect proprietary training protocols. Aggregated study results and statistical analyses will be available through academic publication.
Study at a glance
From the registry record, last updated by the sponsor on 6 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Machine Learning-Guided Training Approach to Reduce Injuries and Enhance Performance in Elite Athletes: A Prospective Cohort Evaluation
- ClinicalTrials.gov ID
- NCT07683091
- Acronym / sponsor ID
- MLGT / DBU-SS-2023-008
- Conditions
- Athletic Injuries
- Keywords
- Machine Learning, Injury Prevention, Athletic Performance, Elite Athletes, Sports Biomechanics, Training Load Optimization
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 120 participants (actual)
- Lead sponsor
- Debre Berhan UniversityUniversity, hospital or other
- Study start
- 1 Jan 2023 (actual)
- Primary completion
- 30 Sep 2023 (actual)
- Study completion
- 30 Sep 2023 (actual)
- First posted
- 6 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Detailed description
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.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- A parallel-group randomized controlled trial design was utilized to compare two distinct high-performance training tracks over a 9-month competitive season. Elite athletes were randomly allocated into either the experimental arm (undergoing dynamic, machine learning-driven training load and biomechanical optimization) or the active control arm (undergoing traditional, structured high-performance athletic preparation). Both groups trained concurrently under monitored conditions to isolate the effects of the technology-driven protocol on injury incidence and performance markers.
| Arm | Type | Receives |
|---|---|---|
| Control Cohort | Active comparator | Behavioral: Adaptive Machine Learning Workload OptimizationElite 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. |
| Algorithmic Cohort | Experimental | Behavioral: Adaptive Machine Learning Workload OptimizationElite 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. |
Who may qualify
- Ages
- 18 to 35 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (9 listed)
- 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.
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Adaptive Machine Learning Workload OptimizationA 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. | Behavioural | Algorithmic Cohort, Control Cohort |
Primary outcome
Changes in Sprint Performance Time
Time frame: 12 weeks
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jul 2026.
- Individual participant dataSharing planned: No. Individual participant data (IPD) will not be shared publicly to maintain the confidentiality of the elite athletes involved and to protect proprietary training protocols. Aggregated study results and statistical analyses will be available through academic publication.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Dr. Arefayne M Dessye, PhD principal investigator
Debre Berhan Univeristy
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Adaptive Machine Learning Workload Optimization in people with Athletic Injuries, run by Debre Berhan University at 2 sites in Ethiopia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 6 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Ethiopia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Changes in Sprint Performance Time (time frame: 12 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Jul 2026.