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      Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling

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          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.

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          Most cited references 53

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          A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health.

          A major focus of recent work on the spatial patterning of health has been the study of how features of residential environments or neighborhoods may affect health. Place effects on health emerge from complex interdependent processes in which individuals interact with each other and their environment and in which both individuals and environments adapt and change over time. Traditional epidemiologic study designs and statistical regression approaches are unable to examine these dynamic processes. These limitations have constrained the types of questions asked, the answers received, and the hypotheses and theoretical explanations that are developed. Agent-based models and other systems-dynamics models may help to address some of these challenges. Agent-based models are computer representations of systems consisting of heterogeneous microentities that can interact and change/adapt over time in response to other agents and features of the environment. Using these models, one can observe how macroscale dynamics emerge from microscale interactions and adaptations. A number of challenges and limitations exist for agent-based modeling. Nevertheless, use of these dynamic models may complement traditional epidemiologic analyses and yield additional insights into the processes involved and the interventions that may be most useful.
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            Clinical outcomes and cost-effectiveness of strategies for managing people at high risk for diabetes.

            Lifestyle modification can forestall diabetes in high-risk people, but the long-term cost-effectiveness is uncertain. To estimate the effects of the lifestyle modification program used in the Diabetes Prevention Program (DPP) on health and economic outcomes. Cost-effectiveness analysis using the Archimedes model. Published basic and epidemiologic studies, clinical trials, and Kaiser Permanente administrative data. Adults at high risk for diabetes (body mass index >24 kg/m2, fasting plasma glucose level of 5.2725 to 6.9375 mmol/L [95 to 125 mg/dL], 2-hour glucose tolerance test result of 7.77 to 11.0445 mmol/L [140 to 199 mg/dL]). 5 to 30 years. Patient, health plan, and societal. No prevention, DPP's lifestyle modification program, lifestyle modification begun after a person develops diabetes, and metformin. Diagnosis and complications of diabetes. Compared with no prevention program, the DPP lifestyle program would reduce a high-risk person's 30-year chances of getting diabetes from about 72% to 61%, the chances of a serious complication from about 38% to 30%, and the chances of dying of a complication of diabetes from about 13.5% to 11.2%. Metformin would deliver about one third the long-term health benefits achievable by immediate lifestyle modification. Compared with not implementing any prevention program, the expected 30-year cost/quality-adjusted life-year (QALY) of the DPP lifestyle intervention from the health plan's perspective would be about 143,000 dollars. From a societal perspective, the cost/QALY of the lifestyle intervention compared with doing nothing would be about 62,600 dollars. Either using metformin or delaying the lifestyle intervention until after a person develops diabetes would be more cost-effective, costing about 35,400 dollars or 24,500 dollars per QALY gained, respectively, compared with no program. Compared with delaying the lifestyle program until after diabetes is diagnosed, the marginal cost-effectiveness of beginning the DPP lifestyle program immediately would be about 201,800 dollars. Variability and uncertainty deriving from the structure of the model were tested by comparing the model's results with the results of real clinical trials of diabetes and its complications. The most critical element of uncertainty is the effectiveness of the lifestyle program, as expressed by the 95% CI of the DPP study. The most important potentially controllable factor is the cost of the lifestyle program. Compared with no program, lifestyle modification for high-risk people can be made cost-saving over 30 years if the annual cost of the intervention can be reduced to about 100 dollars. Results depend on the accuracy of the model. Lifestyle modification is likely to have important effects on the morbidity and mortality of diabetes and should be recommended to all high-risk people. The program used in the DPP study may be too expensive for health plans or a national program to implement. Less expensive methods are needed to achieve the degree of weight loss seen in the DPP.
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              Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players

              Aim The purpose of this study was to investigate the relationship between physical workload and injury risk in elite youth football players. Methods The workload data and injury incidence of 32 players were monitored throughout 2 seasons. Multiple regression was used to compare cumulative (1, 2, 3 and 4-weekly) loads and acute:chronic (A:C) workload ratios (acute workload divided by chronic workload) between injured and non-injured players for specific GPS and accelerometer-derived variables:total distance (TD), high-speed distance (HSD), accelerations (ACC) and total load. Workloads were classified into discrete ranges by z-scores and the relative risk was determined. Results A very high number of ACC (≥9254) over 3 weeks was associated with the highest significant overall (relative risk (RR)=3.84) and non-contact injury risk (RR=5.11). Non-contact injury risk was significantly increased when a high acute HSD was combined with low chronic HSD (RR=2.55), but not with high chronic HSD (RR=0.47). Contact injury risk was greatest when A:C TD and ACC ratios were very high (1.76 and 1.77, respectively) (RR=4.98). Conclusions In general, higher accumulated and acute workloads were associated with a greater injury risk. However, progressive increases in chronic workload may develop the players' physical tolerance to higher acute loads and resilience to injury risk.
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                Author and article information

                Journal
                Br J Sports Med
                Br J Sports Med
                bjsports
                bjsm
                British Journal of Sports Medicine
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                0306-3674
                1473-0480
                May 2019
                18 June 2018
                : 53
                : 9
                : 560-569
                Affiliations
                [1 ] departmentFaculty of Arts, Business and Law , Centre for Human Factors and Sociotechnical Systems, University of the Sunshine Coast , Maroochydore DC, Queensland, Australia
                [2 ] departmentFaculty of Architecture, Building and Planning, Melbourne School of Design , Transport, Health and Urban Design (THUD) Research Hub, University of Melbourne , Melbourne, Victoria, Australia
                [3 ] departmentDepartment of Public Health, Section for Sports Science , RunSafe Research Group, Aarhus University , Aarhus, Denmark
                Author notes
                [Correspondence to ] Dr Adam Hulme, Faculty of Arts, Business and Law, Centre for Human Factors and Sociotechnical Systems, University of the Sunshine Coast, Maroochydore DC, QLD 4556, Australia; ahulme@ 123456usc.edu.au
                Article
                bjsports-2017-098871
                10.1136/bjsports-2017-098871
                6579554
                29915127
                © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2019. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                Product
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000923, Australian Research Council;
                Categories
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                Sports medicine

                distance running, agent-based modelling, complex systems, sports injury

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