Sport InjurySDLENCT05425303
System Dynamics Model for Acute Non-contact Lower Extremity Injuries Prediction
An observational study of Sport Injury and Hamstring Injury, run by University of Patras at 1 site in Greece.
Trial summary
Despite the extensive research on prevention and prediction strategies, hamstrings strains injury (HSI) persists at a high rate in team sports and specifically in football. An initial injury increases the risk for re-injury and affects performance, whereas the financial cost for athletes and teams is crucial due to… Read more about this study
- Who is it for?
- Ages 17 to 40 years, men onlyHealthy volunteers can take part
- Conditions the study is for
- Sport Injury, Hamstring Injury, Risk Factors
- What is being tested?
- Exposure to risk for injury
- How many people
- 99 participants
- Where
- 1 siteGreece
- Study dates
- 22 Jul 2022to Jul 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 17 to 40 years
- Men only
- Healthy volunteers accepted
- Child, Adult
Study population: Male professional football and handball players fully participate in the team's activities.
Who may be able to join
- Male professional football and handball teams.
- Healthy team sports athletes fully participating in the team's activities.
- The athlete should be free of injury at the time of measurements or fully rehabilitated from a previous injury.
Who cannot join
- Injured athlete at the time of pre-season measurements.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Development of a System Dynamics Model for the Prediction of Acute Non-contact Lower Extremity Injuries in Team Sports
Despite the extensive research on prevention and prediction strategies, hamstrings strains injury (HSI) persists at a high rate in team sports and specifically in football. An initial injury increases the risk for re-injury and affects performance, whereas the financial cost for athletes and teams is crucial due to the time needed for appropriate rehabilitation. For that reason, it is critical to formulate better strategies in order to predict and prevent HSI. This study aims to develop a system dynamics (SD) model to evaluate HSI risk. First, a literature review will be carried out on the current approaches and identification of intrinsic and extrinsic risk factors of hamstrings strain injuries. Second, co-creation workshops based on the method of Group Modeling Building (GMB) will be applied to develop the SD for the HSI model. This co-creation process will involve stakeholders such as sports physiotherapists, doctors, and sports scientists. After creating the SD for HSI model, a one-year prospective cohort study will be performed to validate the model with real data and evaluate the ability of the model to predict HSIs. Sports teams will be invited to take part in the validation of the model. Multiple biomechanical parameters and other personal characteristics will be collected. Then, athletes will be monitored for the occurrence of injury and their exposure to injury risk during training and games. The factors' non-linear interaction will be assessed with the statistical method of structural equation modeling and factor analysis. In this way, the factors' interactions extracted for the qualitative phase of the study (group modeling building process) will be quantitatively evaluated. Validating the model with real data will provide a computer simulation platform to test plausible strategies for preventing hamstrings strain injuries prior to implementation and optimize intervention programs.
Read the detailed description
Introduction
Appropriate hamstring muscle function is essential for the execution of most athletic activities. Muscle injuries, especially the hamstrings muscles, are among the injuries with the higher incidence in team sports. Specifically, muscle injuries constitute approximately one-third of all time-loss injuries in European football clubs, whereas injuries in the hamstrings muscle represent 12% of all injuries. Moreover, the financial impact of the one-month rehabilitation of a player with an HSI in a European team is equal to 500.000 euros. Recently, a systematic review examined 179 HSI's related risk factors and concluded that there is a need to explore these factors' complex and nonlinear interrelationship. Τhe utilization of complex systems computational methods in the sports injury field provides a valid insight into injury etiology and, consequently, a more effective injury prediction.
SD modeling and its application to health-related research has been rapidly increasing over the last few years. Examples of successful SD applications include the topics of obesity and diabetes, cancer, cardiovascular, and other chronic diseases in order to capture and better understand the complex etiology and the recovery of the concussion.
To sum up, the SD modeling method has proven to be an effective approach to deal with health system problems. However, to the investigators' knowledge, no study has been carried out using SD modeling in order to investigate the complex and dynamic nature of interaction among the factors that contribute to HSI.
Aim of the study
This study aims first to develop a System Dynamics for Lower Extremity (SDLE) model for evaluating the risk of hamstrings injuries. Further, the model will be calibrated and validated with real data to quantify the factors' interaction and test the ability of the model to predict HSIs. The final aim is to test plausible prevention strategies and propose appropriate policies.
Methodology
General description of methodological procedure of the SDLE project The proposed project's methodological phases and the proposed study's timetable are presented. Following a clear problem statement, a review of HSI risk factors is to be carried out. Risk factors will be used as variables for developing the SDLE model. This will be facilitated by initially employing the causal loop modeling technique in a series of co-creation workshops with the main stakeholders. Here, the methodology of Group Modeling Building (GMB) will be employed. The aim is to get valuable input from stakeholders such as sports physiotherapists, doctors, coaches, and sports scientists. The output of the co-creation workshops will be a causal loop model depicting the main interrelationships among the HSI risk factors. The creation of the CLD will serve as a communication tool to share the various mental models that exist among stakeholders. Following the development of the CLD in agreement with the stakeholders, we will proceed to the stock and flow model, whereby we quantify the variables identified in the CLD and distinguish between stocks and flows. Then, the formulated SDLE model will be calibrated using real data (risk factors, injuries, exposure rate) from team sports athletes. The final step will be running the simulation model through sensitivity analysis. We will carry out experiments by testing plausible interventions prior to implementation to reduce the risks and tackle the problem of hamstrings injuries. The main phases of the proposed project are described in more detail as follows.
SDLE model validation with real data
Teams' sports athletes will be invited to participate in this study. The athlete should be free of injury for at least six months or fully rehabilitated from a previous injury to participate in the study. In this phase, a one-year prospective cohort study will be conducted. This phase includes pre-season measurements, injuries and exposure rate data collection during the season, and follow-up measurements in the middle of the season. The recorded data will be inserted into the formulated SDLE model to calibrate and validate the model. Firstly, demographic details and medical history will be collected. Then, specific biomechanical measurements will be assessed.
Data processing and statistical analysis
The structural equation model approach (SEM) will be used to quantify the interrelationships among collecting variables. Structural equation modeling is a set of statistical techniques used to measure the complex relationships among variables to test the validity of theory using real data.
Sensitivity analysis and scenario planning
After the SDLE model has been calibrated and validated, different scenarios will be assessed. Different interventions will be applied in order to evaluate the impact of these interventions by means of the SDLE simulation model. As a result, effective policies for tackling the problem of acute noncontact LE injuries will be proposed.
- Conditions
- Sport Injury, Hamstring Injury, Risk Factors, Interaction
- Being tested
- Exposure to risk for injury
- Keywords
- Hamstrings strain, Complex Systems, System dynamics, Risk factors interaction, Predictive modeling, Nonlinear
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Exposure to risk for injuryDiagnostic test
Athletes will be examined in the preseason stage and will be monitored during the competitive season.
| Study group | Role | What they receive |
|---|---|---|
| Healthy athletes | Healthy professorial team sports athletes will be monitored for the occurrence of an acute lower extremity injury during a competitive season. After the end of the season, the cohort will be divided into a healthy athlete group, athletes injured in the hamstring muscles, and athletes with other types of lower extremity injuries. Receives: Diagnostic Test: Exposure to risk for injury |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of Acute Hamstring Strain Injuries of the participants
An acute hamstring strain injury, resulting from a specific event and producing at least 24 hours of time loss for training or game.
Measured: up to 32 weeks
Demographic, previous injuries, and sports characteristics
Through questionnaire will be collected data such as age, sports, playing position, number of years in sports, level of competition, training volume of the previous season, Previous injuries, and details about previous injuries such as severity, time loss,
Measured: Pre season examination
BMI
Weight (kg) and Height (cm) data will be collected to report BMI in kg/m^2
Measured: Pre season examination
Anthropocentric variables
Leg length will be measured (cm) using a measuring tape from the anterior superior iliac spine (ASIS) to the medial malleolus. The tibia length will be measured (cm) using a measuring tape to measure the distance between the lateral knee joint line to the distal aspect of the lateral malleolus minus.
Measured: Pre season examination
Lower extremity Flexibility and Range of motion measurement
The test that will be used to assess lower extremity flexibility and range of motion includes the passive knee extension test (Hamstrings flexibility), Modified Thomas test (Hip extension), Hip internal/external rotation in sitting position, and Weight bearing lung test (Ankle dorsiflexion). A goniometer and the bubble inclinometer will be used. All measurements will be expressed in degrees
Measured: Pre season examination
Participants' core muscle endurance
For the examination of core muscle, endurance will be used the Prone Bridging Test for the assessment of the Abdominal muscle endurance, the Side Bridging Test for the assessment of lateral abdominal endurance, and the Biering-Sorensen test for the assessment of Back muscle endurance. The total time that the participant can hold the proper position for each specific test will be recorded.
Measured: Pre season examination
Single leg hamstring bridge (SLHB)
The hamstring endurance will be measured using the Single leg hamstring bridge (SLHB) test. The total number of repetition for each leg will be measured.
Measured: Pre season examination
Lower extremity strength measurements
Abductor, hamstrings, and quadriceps strength examination using a handheld dynamometer (mictofet 2, Hoggan scientific)
Measured: Pre season examination
Single-leg triple hop for distance test
We will record asymmetries in distance between the dominant and non-dominant leg.
Measured: Pre season examination
Secondary outcomes (1)
Number of Acute lower extremity injuries of the participants
Acute lower extremity injuries resulting from a specific event and producing at least 24 hours of time loss for training or game.
Measured: up to 32 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Jun 2022
- Study start22 Jul 2022
- Primary completion30 Jul 2023
- Study completionJul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (17)Papers the sponsor has linked to this study.
- Other trials for Sport InjuryStudies 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 99 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Patras
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Nikolaos I. Liveris, University of Patras
- Investigators
- Sofia A Xergia, PhD, Study director, University of PatrasElias Tsepis, PhD, Study director, University of PatrasGeorge Papageorgiou, PhD, Study director, European University CyprusKonstantinos Fousekis, PhD, Study director, University of PatrasCharis Tsarbou, MSc, Principal investigator, University of PatrasNikolaos I Liveris, MSc, Principal investigator, University of Patras
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05425303
- Sponsor's protocol ID
- 12126
- Acronym
- SDLE
- Status last verified
- Dec 2025
- First submitted
- 10 Jun 2022
- Last updated
- 7 May 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Ekstrand J, Hagglund M, Walden M. Epidemiology of muscle injuries in professional football (soccer). Am J Sports Med. 2011 Jun;39(6):1226-32. doi: 10.1177/0363546510395879. Epub 2011 Feb 18.
PubMed 21335353Ekstrand J, Walden M, Hagglund M. Hamstring injuries have increased by 4% annually in men's professional football, since 2001: a 13-year longitudinal analysis of the UEFA Elite Club injury study. Br J Sports Med. 2016 Jun;50(12):731-7. doi: 10.1136/bjsports-2015-095359. Epub 2016 Jan 8.
PubMed 26746908Finch CF, Kemp JL, Clapperton AJ. The incidence and burden of hospital-treated sports-related injury in people aged 15+ years in Victoria, Australia, 2004-2010: a future epidemic of osteoarthritis? Osteoarthritis Cartilage. 2015 Jul;23(7):1138-43. doi: 10.1016/j.joca.2015.02.165. Epub 2015 Mar 5.
PubMed 25749009Green B, Bourne MN, van Dyk N, Pizzari T. Recalibrating the risk of hamstring strain injury (HSI): A 2020 systematic review and meta-analysis of risk factors for index and recurrent hamstring strain injury in sport. Br J Sports Med. 2020 Sep;54(18):1081-1088. doi: 10.1136/bjsports-2019-100983. Epub 2020 Apr 16.
PubMed 32299793Opar DA, Serpell BG. Is there a potential relationship between prior hamstring strain injury and increased risk for future anterior cruciate ligament injury? Arch Phys Med Rehabil. 2014 Feb;95(2):401-5. doi: 10.1016/j.apmr.2013.07.028. Epub 2013 Oct 9.
PubMed 24121082Bittencourt NFN, Meeuwisse WH, Mendonca LD, Nettel-Aguirre A, Ocarino JM, Fonseca ST. Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition-narrative review and new concept. Br J Sports Med. 2016 Nov;50(21):1309-1314. doi: 10.1136/bjsports-2015-095850. Epub 2016 Jul 21.
PubMed 27445362Hulme A, Mclean S, Salmon PM, Thompson J, Lane BR, Nielsen RO. Computational methods to model complex systems in sports injury research: agent-based modelling (ABM) and systems dynamics (SD) modelling. Br J Sports Med. 2019 Dec;53(24):1507-1510. doi: 10.1136/bjsports-2018-100098. Epub 2018 Nov 17. No abstract available.
PubMed 30448782Kenzie ES, Parks EL, Bigler ED, Wright DW, Lim MM, Chesnutt JC, Hawryluk GWJ, Gordon W, Wakeland W. The Dynamics of Concussion: Mapping Pathophysiology, Persistence, and Recovery With Causal-Loop Diagramming. Front Neurol. 2018 Apr 4;9:203. doi: 10.3389/fneur.2018.00203. eCollection 2018.
PubMed 29670568Fousekis K, Tsepis E, Poulmedis P, Athanasopoulos S, Vagenas G. Intrinsic risk factors of non-contact quadriceps and hamstring strains in soccer: a prospective study of 100 professional players. Br J Sports Med. 2011 Jul;45(9):709-14. doi: 10.1136/bjsm.2010.077560. Epub 2010 Nov 30.
PubMed 21119022De Blaiser C, De Ridder R, Willems T, Danneels L, Vanden Bossche L, Palmans T, Roosen P. Evaluating abdominal core muscle fatigue: Assessment of the validity and reliability of the prone bridging test. Scand J Med Sci Sports. 2018 Feb;28(2):391-399. doi: 10.1111/sms.12919. Epub 2017 Jun 28.
PubMed 28544083McGill SM, Childs A, Liebenson C. Endurance times for low back stabilization exercises: clinical targets for testing and training from a normal database. Arch Phys Med Rehabil. 1999 Aug;80(8):941-4. doi: 10.1016/s0003-9993(99)90087-4.
PubMed 10453772Coorevits P, Danneels L, Cambier D, Ramon H, Vanderstraeten G. Assessment of the validity of the Biering-Sorensen test for measuring back muscle fatigue based on EMG median frequency characteristics of back and hip muscles. J Electromyogr Kinesiol. 2008 Dec;18(6):997-1005. doi: 10.1016/j.jelekin.2007.10.012. Epub 2008 Apr 8.
PubMed 18396410Fuller CW, Ekstrand J, Junge A, Andersen TE, Bahr R, Dvorak J, Hagglund M, McCrory P, Meeuwisse WH. Consensus statement on injury definitions and data collection procedures in studies of football (soccer) injuries. Br J Sports Med. 2006 Mar;40(3):193-201. doi: 10.1136/bjsm.2005.025270.
PubMed 16505073Beran TN, Violato C. Structural equation modeling in medical research: a primer. BMC Res Notes. 2010 Oct 22;3:267. doi: 10.1186/1756-0500-3-267.
PubMed 20969789Darabi N, Hosseinichimeh N. System dynamics modeling in health and medicine: a systematic literature review. Syst Dyn Rev. 2020;36(1):29-73.
Study at a glance
From the registry record, last updated by the sponsor on 7 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a System Dynamics Model for the Prediction of Acute Non-contact Lower Extremity Injuries in Team Sports
- ClinicalTrials.gov ID
- NCT05425303
- Acronym / sponsor ID
- SDLE / 12126
- Conditions
- Sport Injury, Hamstring Injury, Risk Factors, Interaction
- Keywords
- Hamstrings strain, Complex Systems, System dynamics, Risk factors interaction, Predictive modeling, Nonlinear
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 99 participants (actual)
- Lead sponsor
- University of PatrasUniversity, hospital or other
- Study start
- 22 Jul 2022 (actual)
- Primary completion
- 30 Jul 2023 (actual)
- Study completion
- Jul 2027 (estimated)
- First posted
- 21 Jun 2022 (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.
Despite the extensive research on prevention and prediction strategies, hamstrings strains injury (HSI) persists at a high rate in team sports and specifically in football. An initial injury increases the risk for re-injury and affects performance, whereas the financial cost for athletes and teams is crucial due to the time needed for appropriate rehabilitation. For that reason, it is critical to formulate better strategies in order to predict and prevent HSI. This study aims to develop a system dynamics (SD) model to evaluate HSI risk. First, a literature review will be carried out on the current approaches and identification of intrinsic and extrinsic risk factors of hamstrings strain injuries. Second, co-creation workshops based on the method of Group Modeling Building (GMB) will be applied to develop the SD for the HSI model. This co-creation process will involve stakeholders such as sports physiotherapists, doctors, and sports scientists. After creating the SD for HSI model, a one-year prospective cohort study will be performed to validate the model with real data and evaluate the ability of the model to predict HSIs. Sports teams will be invited to take part in the validation of the model. Multiple biomechanical parameters and other personal characteristics will be collected. Then, athletes will be monitored for the occurrence of injury and their exposure to injury risk during training and games. The factors' non-linear interaction will be assessed with the statistical method of structural equation modeling and factor analysis. In this way, the factors' interactions extracted for the qualitative phase of the study (group modeling building process) will be quantitatively evaluated. Validating the model with real data will provide a computer simulation platform to test plausible strategies for preventing hamstrings strain injuries prior to implementation and optimize intervention programs.
Detailed description
Introduction
Appropriate hamstring muscle function is essential for the execution of most athletic activities. Muscle injuries, especially the hamstrings muscles, are among the injuries with the higher incidence in team sports. Specifically, muscle injuries constitute approximately one-third of all time-loss injuries in European football clubs, whereas injuries in the hamstrings muscle represent 12% of all injuries. Moreover, the financial impact of the one-month rehabilitation of a player with an HSI in a European team is equal to 500.000 euros. Recently, a systematic review examined 179 HSI's related risk factors and concluded that there is a need to explore these factors' complex and nonlinear interrelationship. Τhe utilization of complex systems computational methods in the sports injury field provides a valid insight into injury etiology and, consequently, a more effective injury prediction.
SD modeling and its application to health-related research has been rapidly increasing over the last few years. Examples of successful SD applications include the topics of obesity and diabetes, cancer, cardiovascular, and other chronic diseases in order to capture and better understand the complex etiology and the recovery of the concussion.
To sum up, the SD modeling method has proven to be an effective approach to deal with health system problems. However, to the investigators' knowledge, no study has been carried out using SD modeling in order to investigate the complex and dynamic nature of interaction among the factors that contribute to HSI.
Aim of the study
This study aims first to develop a System Dynamics for Lower Extremity (SDLE) model for evaluating the risk of hamstrings injuries. Further, the model will be calibrated and validated with real data to quantify the factors' interaction and test the ability of the model to predict HSIs. The final aim is to test plausible prevention strategies and propose appropriate policies.
Methodology
General description of methodological procedure of the SDLE project The proposed project's methodological phases and the proposed study's timetable are presented. Following a clear problem statement, a review of HSI risk factors is to be carried out. Risk factors will be used as variables for developing the SDLE model. This will be facilitated by initially employing the causal loop modeling technique in a series of co-creation workshops with the main stakeholders. Here, the methodology of Group Modeling Building (GMB) will be employed. The aim is to get valuable input from stakeholders such as sports physiotherapists, doctors, coaches, and sports scientists. The output of the co-creation workshops will be a causal loop model depicting the main interrelationships among the HSI risk factors. The creation of the CLD will serve as a communication tool to share the various mental models that exist among stakeholders. Following the development of the CLD in agreement with the stakeholders, we will proceed to the stock and flow model, whereby we quantify the variables identified in the CLD and distinguish between stocks and flows. Then, the formulated SDLE model will be calibrated using real data (risk factors, injuries, exposure rate) from team sports athletes. The final step will be running the simulation model through sensitivity analysis. We will carry out experiments by testing plausible interventions prior to implementation to reduce the risks and tackle the problem of hamstrings injuries. The main phases of the proposed project are described in more detail as follows.
SDLE model validation with real data
Teams' sports athletes will be invited to participate in this study. The athlete should be free of injury for at least six months or fully rehabilitated from a previous injury to participate in the study. In this phase, a one-year prospective cohort study will be conducted. This phase includes pre-season measurements, injuries and exposure rate data collection during the season, and follow-up measurements in the middle of the season. The recorded data will be inserted into the formulated SDLE model to calibrate and validate the model. Firstly, demographic details and medical history will be collected. Then, specific biomechanical measurements will be assessed.
Data processing and statistical analysis
The structural equation model approach (SEM) will be used to quantify the interrelationships among collecting variables. Structural equation modeling is a set of statistical techniques used to measure the complex relationships among variables to test the validity of theory using real data.
Sensitivity analysis and scenario planning
After the SDLE model has been calibrated and validated, different scenarios will be assessed. Different interventions will be applied in order to evaluate the impact of these interventions by means of the SDLE simulation model. As a result, effective policies for tackling the problem of acute noncontact LE injuries will be proposed.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Healthy athletes | – | Diagnostic Test: Exposure to risk for injuryHealthy professorial team sports athletes will be monitored for the occurrence of an acute lower extremity injury during a competitive season. After the end of the season, the cohort will be divided into a healthy athlete group, athletes injured in the hamstring muscles, and athletes with other types of lower extremity injuries. |
Who may qualify
- Ages
- 17 to 40 years
- Sex
- Male only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- Male professional football and handball players fully participate in the team's activities.
Key inclusion criteria (3 listed)
- Male professional football and handball teams.
- Healthy team sports athletes fully participating in the team's activities.
- The athlete should be free of injury at the time of measurements or fully rehabilitated from a previous injury.
Key exclusion criteria (1 listed)
- Injured athlete at the time of pre-season measurements.
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 |
|---|---|---|
| Exposure to risk for injuryAthletes will be examined in the preseason stage and will be monitored during the competitive season. | Diagnostic test | Healthy athletes |
Primary outcome
Number of Acute Hamstring Strain Injuries of the participants
Time frame: up to 32 weeks
An acute hamstring strain injury, resulting from a specific event and producing at least 24 hours of time loss for training or game.
Demographic, previous injuries, and sports characteristics
Time frame: Pre season examination
Through questionnaire will be collected data such as age, sports, playing position, number of years in sports, level of competition, training volume of the previous season, Previous injuries, and details about previous injuries such as severity, time loss,
BMI
Time frame: Pre season examination
Weight (kg) and Height (cm) data will be collected to report BMI in kg/m^2
Anthropocentric variables
Time frame: Pre season examination
Leg length will be measured (cm) using a measuring tape from the anterior superior iliac spine (ASIS) to the medial malleolus. The tibia length will be measured (cm) using a measuring tape to measure the distance between the lateral knee joint line to the distal aspect of the lateral malleolus minus.
Lower extremity Flexibility and Range of motion measurement
Time frame: Pre season examination
The test that will be used to assess lower extremity flexibility and range of motion includes the passive knee extension test (Hamstrings flexibility), Modified Thomas test (Hip extension), Hip internal/external rotation in sitting position, and Weight bearing lung test (Ankle dorsiflexion). A goniometer and the bubble inclinometer will be used. All measurements will be expressed in degrees
Participants' core muscle endurance
Time frame: Pre season examination
For the examination of core muscle, endurance will be used the Prone Bridging Test for the assessment of the Abdominal muscle endurance, the Side Bridging Test for the assessment of lateral abdominal endurance, and the Biering-Sorensen test for the assessment of Back muscle endurance. The total time that the participant can hold the proper position for each specific test will be recorded.
Single leg hamstring bridge (SLHB)
Time frame: Pre season examination
The hamstring endurance will be measured using the Single leg hamstring bridge (SLHB) test. The total number of repetition for each leg will be measured.
Lower extremity strength measurements
Time frame: Pre season examination
Abductor, hamstrings, and quadriceps strength examination using a handheld dynamometer (mictofet 2, Hoggan scientific)
Single-leg triple hop for distance test
Time frame: Pre season examination
We will record asymmetries in distance between the dominant and non-dominant leg.
1 secondary outcome
Number of Acute lower extremity injuries of the participants
Time frame: up to 32 weeks
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 May 2026.
- Background: PubMed 21335353Ekstrand J, Hagglund M, Walden M. Epidemiology of muscle injuries in professional football (soccer). Am J Sports Med. 2011 Jun;39(6):1226-32. doi: 10.1177/0363546510395879. Epub 2011 Feb 18.
- Background: PubMed 26746908Ekstrand J, Walden M, Hagglund M. Hamstring injuries have increased by 4% annually in men's professional football, since 2001: a 13-year longitudinal analysis of the UEFA Elite Club injury study. Br J Sports Med. 2016 Jun;50(12):731-7. doi: 10.1136/bjsports-2015-095359. Epub 2016 Jan 8.
- Background: PubMed 25749009Finch CF, Kemp JL, Clapperton AJ. The incidence and burden of hospital-treated sports-related injury in people aged 15+ years in Victoria, Australia, 2004-2010: a future epidemic of osteoarthritis? Osteoarthritis Cartilage. 2015 Jul;23(7):1138-43. doi: 10.1016/j.joca.2015.02.165. Epub 2015 Mar 5.
- Background: PubMed 32299793Green B, Bourne MN, van Dyk N, Pizzari T. Recalibrating the risk of hamstring strain injury (HSI): A 2020 systematic review and meta-analysis of risk factors for index and recurrent hamstring strain injury in sport. Br J Sports Med. 2020 Sep;54(18):1081-1088. doi: 10.1136/bjsports-2019-100983. Epub 2020 Apr 16.
- Background: PubMed 24121082Opar DA, Serpell BG. Is there a potential relationship between prior hamstring strain injury and increased risk for future anterior cruciate ligament injury? Arch Phys Med Rehabil. 2014 Feb;95(2):401-5. doi: 10.1016/j.apmr.2013.07.028. Epub 2013 Oct 9.
- Background: PubMed 27445362Bittencourt NFN, Meeuwisse WH, Mendonca LD, Nettel-Aguirre A, Ocarino JM, Fonseca ST. Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition-narrative review and new concept. Br J Sports Med. 2016 Nov;50(21):1309-1314. doi: 10.1136/bjsports-2015-095850. Epub 2016 Jul 21.
- Background: PubMed 30448782Hulme A, Mclean S, Salmon PM, Thompson J, Lane BR, Nielsen RO. Computational methods to model complex systems in sports injury research: agent-based modelling (ABM) and systems dynamics (SD) modelling. Br J Sports Med. 2019 Dec;53(24):1507-1510. doi: 10.1136/bjsports-2018-100098. Epub 2018 Nov 17. No abstract available.
- Background: PubMed 29670568Kenzie ES, Parks EL, Bigler ED, Wright DW, Lim MM, Chesnutt JC, Hawryluk GWJ, Gordon W, Wakeland W. The Dynamics of Concussion: Mapping Pathophysiology, Persistence, and Recovery With Causal-Loop Diagramming. Front Neurol. 2018 Apr 4;9:203. doi: 10.3389/fneur.2018.00203. eCollection 2018.
- Background: PubMed 21119022Fousekis K, Tsepis E, Poulmedis P, Athanasopoulos S, Vagenas G. Intrinsic risk factors of non-contact quadriceps and hamstring strains in soccer: a prospective study of 100 professional players. Br J Sports Med. 2011 Jul;45(9):709-14. doi: 10.1136/bjsm.2010.077560. Epub 2010 Nov 30.
- Background: PubMed 28544083De Blaiser C, De Ridder R, Willems T, Danneels L, Vanden Bossche L, Palmans T, Roosen P. Evaluating abdominal core muscle fatigue: Assessment of the validity and reliability of the prone bridging test. Scand J Med Sci Sports. 2018 Feb;28(2):391-399. doi: 10.1111/sms.12919. Epub 2017 Jun 28.
- Background: PubMed 10453772McGill SM, Childs A, Liebenson C. Endurance times for low back stabilization exercises: clinical targets for testing and training from a normal database. Arch Phys Med Rehabil. 1999 Aug;80(8):941-4. doi: 10.1016/s0003-9993(99)90087-4.
- Background: PubMed 18396410Coorevits P, Danneels L, Cambier D, Ramon H, Vanderstraeten G. Assessment of the validity of the Biering-Sorensen test for measuring back muscle fatigue based on EMG median frequency characteristics of back and hip muscles. J Electromyogr Kinesiol. 2008 Dec;18(6):997-1005. doi: 10.1016/j.jelekin.2007.10.012. Epub 2008 Apr 8.
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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.
Sofia A Xergia, PhD study director
University of Patras
Elias Tsepis, PhD study director
University of Patras
George Papageorgiou, PhD study director
European University Cyprus
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Sport Injury and Hamstring Injury, run by University of Patras at 1 site in Greece.
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 3 inclusion and 1 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: under way, not looking for participants, as of the sponsor's last update on 7 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Greece. 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?
This study is still under way. Results are not available in the source record yet.
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?
Number of Acute Hamstring Strain Injuries of the participants (time frame: up to 32 weeks). The study lists 9 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05425303, which also lists 17 linked publications.
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 7 May 2026; AzTrials last refreshed its copy on 31 Oct 2026.