Towards a Complex Systems Approach in Sports Injury Research: Simulating Running-related Injury Development with Agent-based Modelling

Overview

Journal Br J Sports Med

Publisher BMJ Publishing Group

Specialty Orthopedics

Date 2018 Jun 20

PMID 29915127

Citations 18

Authors

Adam Hulme

Jason Thompson

Rasmus Oestergaard Nielsen

Gemma J M Read

Paul M Salmon

Affiliations

Soon will be listed here.

Abstract

Objectives: There have been recent calls for the application of the complex systems approach in sports injury research. However, beyond theoretical description and static models of complexity, little progress has been made towards formalising this approach in way that is practical to sports injury scientists and clinicians. Therefore, our objective was to use a computational modelling method and develop a dynamic simulation in sports injury research.

Methods: Agent-based modelling (ABM) was used to model the occurrence of sports injury in a synthetic athlete population. The ABM was developed based on sports injury causal frameworks and was applied in the context of distance running-related injury (RRI). Using the acute:chronic workload ratio (ACWR), we simulated the dynamic relationship between changes in weekly running distance and RRI through the manipulation of various 'athlete management tools'.

Results: The findings confirmed that building weekly running distances over time, even within the reported ACWR 'sweet spot', will eventually result in RRI as athletes reach and surpass their individual physical workload limits. Introducing training-related error into the simulation and the modelling of a 'hard ceiling' dynamic resulted in a higher RRI incidence proportion across the population at higher absolute workloads.

Conclusions: The presented simulation offers a practical starting point to further apply more sophisticated computational models that can account for the complex nature of sports injury aetiology. Alongside traditional forms of scientific inquiry, the use of ABM and other simulation-based techniques could be considered as a complementary and alternative methodological approach in sports injury research.

Citing Articles

Critical Fluctuations as an Early Warning Signal of Sports Injuries? A Proof of Concept Using Football Monitoring Data.

Neumann N, Brauers J, van Yperen N, van der Linde M, Lemmink K, Brink M Sports Med Open. 2024; 10(1):129.

PMID: 39680265 PMC: 11649608. DOI: 10.1186/s40798-024-00787-5.

ACL Injury Etiology in Its Context: A Systems Thinking, Group Model Building Approach.

Tsarbou C, Liveris N, Xergia S, Papageorgiou G, Kvist J, Tsepis E J Clin Med. 2024; 13(16).

PMID: 39201070 PMC: 11355078. DOI: 10.3390/jcm13164928.

Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives.

Reis F, Alaiti R, Vallio C, Hespanhol L Braz J Phys Ther. 2024; 28(3):101083.

PMID: 38838418 PMC: 11215955. DOI: 10.1016/j.bjpt.2024.101083.

Mathematical simulation of damage detection for fighting athletes and equipment based on conjugated polymer development.

Lu Y, Zhao Y, Wu J, Chen X, Zhang Q Front Chem. 2024; 11:1286290.

PMID: 38260045 PMC: 10800464. DOI: 10.3389/fchem.2023.1286290.

Towards the Development of a System Dynamics Model for the Prediction of Lower Extremity Injuries.

Liveris N, Papageorgiou G, Tsepis E, Fousekis K, Tsarbou C, Xergia S Int J Exerc Sci. 2023; 16(3):1052-1065.

PMID: 37649464 PMC: 10464767. DOI: 10.70252/OJBI8280.

References

Mi Q, Riviere B, Clermont G, Steed D, Vodovotz Y . Agent-based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factor-beta1. Wound Repair Regen. 2007; 15(5):671-82. DOI: 10.1111/j.1524-475X.2007.00271.x. View

Carey D, Blanch P, Ong K, Crossley K, Crow J, Morris M . Training loads and injury risk in Australian football-differing acute: chronic workload ratios influence match injury risk. Br J Sports Med. 2016; 51(16):1215-1220. PMC: 5537557. DOI: 10.1136/bjsports-2016-096309. View

Bonabeau E . Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci U S A. 2002; 99 Suppl 3:7280-7. PMC: 128598. DOI: 10.1073/pnas.082080899. View

Mooney M, Charlton P, Soltanzadeh S, Drew M . Who 'owns' the injury or illness? Who 'owns' performance? Applying systems thinking to integrate health and performance in elite sport. Br J Sports Med. 2017; 51(14):1054-1055. DOI: 10.1136/bjsports-2016-096649. View

Hulme A, Finch C . The epistemic basis of distance running injury research: A historical perspective. J Sport Health Sci. 2018; 5(2):172-175. PMC: 6188616. DOI: 10.1016/j.jshs.2016.01.023. View

Schwellnus M, Soligard T, Alonso J, Bahr R, Clarsen B, Dijkstra H . How much is too much? (Part 2) International Olympic Committee consensus statement on load in sport and risk of illness. Br J Sports Med. 2016; 50(17):1043-52. PMC: 5013087. DOI: 10.1136/bjsports-2016-096572. View

Marshall B, Galea S . Formalizing the role of agent-based modeling in causal inference and epidemiology. Am J Epidemiol. 2014; 181(2):92-9. PMC: 4351348. DOI: 10.1093/aje/kwu274. View

Nianogo R, Arah O . Agent-based modeling of noncommunicable diseases: a systematic review. Am J Public Health. 2015; 105(3):e20-31. PMC: 4330820. DOI: 10.2105/AJPH.2014.302426. View

Bittencourt N, Meeuwisse W, Mendonca L, Nettel-Aguirre A, Ocarino J, Fonseca S . 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; 50(21):1309-1314. DOI: 10.1136/bjsports-2015-095850. View

10.

Eddy D, Schlessinger L, Kahn R . Clinical outcomes and cost-effectiveness of strategies for managing people at high risk for diabetes. Ann Intern Med. 2005; 143(4):251-64. DOI: 10.7326/0003-4819-143-4-200508160-00006. View

11.

Nielsen R, Malisoux L, Moller M, Theisen D, Parner E . Shedding Light on the Etiology of Sports Injuries: A Look Behind the Scenes of Time-to-Event Analyses. J Orthop Sports Phys Ther. 2016; 46(4):300-11. DOI: 10.2519/jospt.2016.6510. View

12.

Luke D, Stamatakis K . Systems science methods in public health: dynamics, networks, and agents. Annu Rev Public Health. 2012; 33:357-76. PMC: 3644212. DOI: 10.1146/annurev-publhealth-031210-101222. View

13.

Moore D, Dray A, Green R, Hudson S, Jenkinson R, Siokou C . Extending drug ethno-epidemiology using agent-based modelling. Addiction. 2009; 104(12):1991-7. DOI: 10.1111/j.1360-0443.2009.02709.x. View

14.

Caro J, Briggs A, Siebert U, M Kuntz K . Modeling good research practices--overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force--1. Value Health. 2012; 15(6):796-803. DOI: 10.1016/j.jval.2012.06.012. View

15.

Venkatramanan S, Lewis B, Chen J, Higdon D, Vullikanti A, Marathe M . Using data-driven agent-based models for forecasting emerging infectious diseases. Epidemics. 2017; 22:43-49. PMC: 5568513. DOI: 10.1016/j.epidem.2017.02.010. View

16.

Hulme A, Finch C . From monocausality to systems thinking: a complementary and alternative conceptual approach for better understanding the development and prevention of sports injury. Inj Epidemiol. 2015; 2(1):31. PMC: 4673096. DOI: 10.1186/s40621-015-0064-1. View

17.

Bowen L, Gross A, Gimpel M, Li F . Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. Br J Sports Med. 2016; 51(5):452-459. PMC: 5460663. DOI: 10.1136/bjsports-2015-095820. View

18.

Gabbett T . The training-injury prevention paradox: should athletes be training smarter and harder?. Br J Sports Med. 2016; 50(5):273-80. PMC: 4789704. DOI: 10.1136/bjsports-2015-095788. View

19.

Nielsen R, Bertelsen M, Moller M, Hulme A, Windt J, Verhagen E . Training load and structure-specific load: applications for sport injury causality and data analyses. Br J Sports Med. 2017; 52(16):1016-1017. DOI: 10.1136/bjsports-2017-097838. View

20.

Day T, Ravi N, Xian H, Brugh A . An Agent-Based Modeling Template for a Cohort of Veterans with Diabetic Retinopathy. PLoS One. 2013; 8(6):e66812. PMC: 3689690. DOI: 10.1371/journal.pone.0066812. View