System Dynamics Model for Acute Non-contact Lower Extremity Injuries Prediction

NCT ID: NCT05425303

Last Updated: 2025-03-28

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 99 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-07-22
• Study Completion Date: 2025-07-31

Brief 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 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.

Conditions

Sport Injury; Hamstring Injury; Risk Factors; Interaction

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

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.

Exposure to risk for injury

Intervention Type: DIAGNOSTIC_TEST

Athletes will be examined in the preseason stage and will be monitored during the competitive season.

Interventions

Exposure to risk for injury

Athletes will be examined in the preseason stage and will be monitored during the competitive season.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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.

• Minimum Eligible Age: 17 Years
• Maximum Eligible Age: 40 Years
• Eligible Sex: MALE
• Accepts Healthy Volunteers: Yes

Sponsors

University of Patras

OTHER

Sponsor Role: lead

Responsible Party

Nikolaos I. Liveris

Assistant researcher

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Sofia A Xergia, PhD

Role: STUDY_DIRECTOR

University of Patras

Elias Tsepis, PhD

Role: STUDY_DIRECTOR

University of Patras

George Papageorgiou, PhD

Role: STUDY_DIRECTOR

European University Cyprus

Konstantinos Fousekis, PhD

Role: STUDY_DIRECTOR

University of Patras

Charis Tsarbou, MSc

Role: PRINCIPAL_INVESTIGATOR

University of Patras

Nikolaos I Liveris, MSc

Role: PRINCIPAL_INVESTIGATOR

University of Patras

Locations

University of Patras

Pátrai, Rio, Greece

Countries

Greece

References

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.

Reference Type: BACKGROUND

PMID: 21335353 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 26746908 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 25749009 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 32299793 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 24121082 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 27445362 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 30448782 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 29670568 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 21119022 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 28544083 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 10453772 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 18396410 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 16505073 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 20969789 (View on PubMed)

Darabi N, Hosseinichimeh N. System dynamics modeling in health and medicine: a systematic literature review. Syst Dyn Rev. 2020;36(1):29-73.

Reference Type: BACKGROUND

Hovmand P, Rouwette E, Andersen D, et al. Scriptapedia: A Handbook of Scripts for Developing Structured Group Model Building Sessions. Soc Sci Med. 2011

Reference Type: BACKGROUND

Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: Irwin/McGraw-Hill; 2000.

Reference Type: BACKGROUND

Other Identifiers

12126

Identifier Type: -

Identifier Source: org_study_id