- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05425303
System Dynamics Model for Acute Non-contact Lower Extremity Injuries Prediction (SDLE)
Development of a System Dynamics Model for the Prediction of Acute Non-contact Lower Extremity Injuries in Team Sports
Study Overview
Status
Conditions
Intervention / Treatment
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 Type
Enrollment (Actual)
Contacts and Locations
Study Locations
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Rio
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Patra, Rio, Greece, 26504
- University of Patras
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- 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.
Exclusion Criteria:
- Injured athlete at the time of pre-season measurements.
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Prospective
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
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.
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Athletes will be examined in the preseason stage and will be monitored during the competitive season.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Number of Acute Hamstring Strain Injuries of the participants
Time Frame: up to 32 weeks
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An acute hamstring strain injury, resulting from a specific event and producing at least 24 hours of time loss for training or game.
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up to 32 weeks
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Demographic, previous injuries, and sports characteristics
Time Frame: Pre season examination
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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,
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Pre season examination
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BMI
Time Frame: Pre season examination
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Weight (kg) and Height (cm) data will be collected to report BMI in kg/m^2
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Pre season examination
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Anthropocentric variables
Time Frame: Pre season examination
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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.
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Pre season examination
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Lower extremity Flexibility and Range of motion measurement
Time Frame: Pre season examination
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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
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Pre season examination
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Participants' core muscle endurance
Time Frame: Pre season examination
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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.
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Pre season examination
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Single leg hamstring bridge (SLHB)
Time Frame: Pre season examination
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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.
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Pre season examination
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Lower extremity strength measurements
Time Frame: Pre season examination
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Abductor, hamstrings, and quadriceps strength examination using a handheld dynamometer (mictofet 2, Hoggan scientific)
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Pre season examination
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Single-leg triple hop for distance test
Time Frame: Pre season examination
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We will record asymmetries in distance between the dominant and non-dominant leg.
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Pre season examination
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Number of Acute lower extremity injuries of the participants
Time Frame: up to 32 weeks
|
Acute lower extremity injuries resulting from a specific event and producing at least 24 hours of time loss for training or game.
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up to 32 weeks
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Collaborators and Investigators
Sponsor
Investigators
- Study Director: Sofia A Xergia, PhD, University of Patras
- Study Director: Elias Tsepis, PhD, University of Patras
- Study Director: George Papageorgiou, PhD, European University Cyprus
- Study Director: Konstantinos Fousekis, PhD, University of Patras
- Principal Investigator: Charis Tsarbou, MSc, University of Patras
- Principal Investigator: Nikolaos I Liveris, MSc, University of Patras
Publications and helpful links
General Publications
- De 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.
- McGill 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.
- Fousekis 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.
- 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.
- Ekstrand 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.
- Finch 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.
- Green 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.
- Opar 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.
- Bittencourt 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.
- Hulme 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.
- Kenzie 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.
- Coorevits 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.
- Fuller 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.
- Beran 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.
- Darabi N, Hosseinichimeh N. System dynamics modeling in health and medicine: a systematic literature review. Syst Dyn Rev. 2020;36(1):29-73.
- Hovmand P, Rouwette E, Andersen D, et al. Scriptapedia: A Handbook of Scripts for Developing Structured Group Model Building Sessions. Soc Sci Med. 2011
- Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: Irwin/McGraw-Hill; 2000.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- 12126
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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