Computational methods to model complex systems in sports injury research: agent-based modelling (ABM) and systems dynamics (SD) modelling

Hulme, Adam; McLean, Scott; Salmon, Paul M.; Thompson, Jason; Lane, Ben R.; Nielsen, Rasmus Oestergaard

doi:10.1136/bjsports-2018-100098

Cited by 19 publications

(32 citation statements)

References 15 publications

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“…The emergence of an injury is a complex phenomenon ( Bittencourt et al, 2016 ) involving a large number of parameters (performance, workload, physiology, sleep, fatigue & recovery, psychology, lifestyle, etc.). From a holistic perspective, computational methods may help in modeling the complex systems of sport injury risk ( Hulme et al, 2019 ). Relationships between workload and injuries are recursive and individualistic, with variables interacting in a non-linear way and with differing levels of impact.…”

Section: Discussionmentioning

confidence: 99%

“…Consequently, small variations can generate large effects as shown in complex systems. Abrupt changes without adequate adaptation to maintain equilibrium can result in a tipping point or system failure ( Hulme et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Irrespective of methodology, using a single workload ratio can be considered an overly simplistic method for determining the optimal training load or overall injury risk. As such, concerns have been expressed regarding injury prediction when using these ratios ( Hulme et al, 2019 ; Wang et al, 2020 ). Based only on these ratios, the sweet spot method proposed is similar to an isolated variable approach, which hinders monitoring processes and consequently good practice in the field of sport science ( Haugen, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Does an Optimal Relationship Between Injury Risk and Workload Represented by the “Sweet Spot” Really Exist? An Example From Elite French Soccer Players and Pentathletes

et al. 2020

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Objective To examine the relationships between the occurrence and severity of injuries using three workload ratios (ACWR, EWMA, REDI) in elite female soccer players and international male and female pentathletes. Materials and Methods Female soccer players in the U16 to U18 national French teams ( n = 24) and international athletes ( n = 12, 4 women and 8 men) in the French modern pentathlon team were monitored throughout an entire season. The Acute Chronic Workload Ratio (ACWR), the Exponentially Weighted Moving Averages (EWMA), and the Robust Exponential Decreasing Index (REDI) were calculated for internal load by the ROE method in soccer and external load in pentathlon. The occurrence and severity of injuries (determined according to time-loss) were quantified in the sweet spot zone [0.8; 1.3] and compared to the other zones of load variation: [0; 0.8], [1.3; 1.5], [1.5; +8], using the three ratios. Results Over the study period, a total of sixty-six injuries (2.75 per athlete) were reported in the soccer players and twelve in pentathletes (1 per athlete). The cumulative severity of all injuries was 788 days lost in soccer and 36 in pentathlon: respectively, 11.9 days lost per injury in soccer player and 3.0 per pentathlete. The mean values across the three methods in soccer showed a higher number of injuries detected in the [0; 0.8] workload ratio zone: 22.3 ± 6.4. They were 17.3 ± 3.5 in the sweet spot ([0.8–1.3] zone) and 17.6 ± 5.5 in the [1.5; +8] zone. In comparison to the [1.5; +8] zone, soccer players reported a higher number of days lost to injuries in the presumed sweet spot and in the [0–0.8] zone: 204.7 ± 28.7 and 275.0 ± 120.7 days, respectively. In pentathletes, ten of the twelve injuries (83.3%) occurred in the presumed sweet spot. REDI was the only method capable of tracking workloads over all-time series. Conclusion In the present cohort of elite soccer players and pentathletes, acute chronic workload calculations showed an association with injury occurrence and severity but did not provide evidence supporting existence of a sweet spot diminishing injury risk.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Does an Optimal Relationship Between Injury Risk and Workload Represented by the “Sweet Spot” Really Exist? An Example From Elite French Soccer Players and Pentathletes

et al. 2020

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“…Sports systems have been shown to possess many of the accepted characteristics of complex systems (Cilliers, 1999 ), including multiple components, non-linear interactions, emergent properties, dynamism, re-currant feedback loops, path dependence, and ignorance of components. Building on this, complex systems analyses have recently been applied in sport to injury prevention (Bittencourt et al, 2016 ; Hulme et al, 2018a ), performance and performance analysis (Duarte et al, 2012 ; McLean et al, 2017 , 2019 ), and sports science generally (Soltanzadeh and Mooney, 2016 ; Mooney et al, 2017 ). As a result, systems approaches in sport research have created new knowledge, however, further work is required to translate these into practice.…”

Section: Introductionmentioning

confidence: 99%

“…Notational analysis is a pertinent example of how sports scientists have fixated on decomposing sports team and player performances into isolated components such as examining passing, tackling, and running as isolated events (O'Donoghue et al, 2008 ; Rampinini et al, 2009 ). This common approach in sport science overlooks the interdependent relationships of the system components which are the defining elements of system behavior (Hulme and Finch, 2015 ; McLean et al, 2017 , 2019 ; Hulme et al, 2018a ). The result of this independent sub-discipline analysis is a detailed understanding of each isolated component, but a limited understanding of the interactions of systems components.…”

Section: Introductionmentioning

confidence: 99%

Beyond the Tip of the Iceberg: Using Systems Archetypes to Understand Common and Recurring Issues in Sports Coaching

McLean

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Hulme

et al. 2019

Front. Sports Act. Living

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Background: Systems thinking, a fundamental approach for understanding complexity, is beginning to gain traction in sports science. Systems archetypes (SAs) describe common recurring patterns of system behaviors and have been used extensively in other domains to explain the system wide influences on behavior. SAs look at the deeper levels of systemic structure by identifying what creates system behaviors, which supports the development of interventions to identify and resolve problem sources. Methods: Four commonly used SAs were used to explain the dynamics underpinning recurring issues for coaching in football: (1) Fixes that fail, (2) Shifting the burden, (3) Drifting goals, and (4) Success to the successful. The SAs models were built, refined and validated by seven subject matter experts (SMEs) including experienced football researchers, systems thinking experts, an international football coach, a skill acquisition specialist, and an experienced exercise scientist. Results: The findings show that the SAs fit well in the football coaching context, providing further evidence that a complex system thinking approach is required when considering football performance and its optimization. The developed SAs identify the factors that play a role in recurring issues in football coaching and highlight the systemic structures that contribute to the issues. The developed SAs identify the appropriate leverage points in the system where sustainable change can be made to improve coaching practice and subsequent performance of players. Discussion: A common theme emerging across the analyses was that systemic problems often arise in football when quick fixes are attempted. Whereas, improvements to system behavior usually require a delay after the implementation of the appropriate corrective action. The SAs developed in the current study also provide practical templates of common problems in football that can be used to prompt discussions around how to avoid ineffective interventions and instead make sustainable improvements across multiple aspects of football performance.

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Cost–benefit analysis of construction waste source reduction: a system dynamics approach

Pan²,

Zuo

et al. 2022

Environ Sci Pollut Res

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Computational methods to model complex systems in sports injury research: agent-based modelling (ABM) and systems dynamics (SD) modelling

Cited by 19 publications

References 15 publications

Does an Optimal Relationship Between Injury Risk and Workload Represented by the “Sweet Spot” Really Exist? An Example From Elite French Soccer Players and Pentathletes

Does an Optimal Relationship Between Injury Risk and Workload Represented by the “Sweet Spot” Really Exist? An Example From Elite French Soccer Players and Pentathletes

Beyond the Tip of the Iceberg: Using Systems Archetypes to Understand Common and Recurring Issues in Sports Coaching

Cost–benefit analysis of construction waste source reduction: a system dynamics approach

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