From monocausality to systems thinking: a complementary and alternative conceptual approach for better understanding the development and prevention of sports injury
- Hulme, Adam, Finch, Caroline
- Authors: Hulme, Adam , Finch, Caroline
- Date: 2015
- Type: Text , Journal article , Review
- Relation: Injury Epidemiology Vol. 2, no. 1 (2015), p. 1-12
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
- Full Text:
- Reviewed:
- Description: The science of sports injury control, including both its cause and prevention, has largely been informed by a biomedical and mechanistic model of health. Traditional scientific practice in sports injury research has routinely involved collapsing the broader socioecological landscape down in order to analyse individual-level determinants of injury - whether biomechanical and/or behavioural. This approach has made key gains for sports injury prevention research and should be further encouraged and allowed to evolve naturally. However, the public health, Applied Human Factors and Ergonomics, and injury epidemiological literature more broadly, has accepted the value of a socioecological paradigm for better understanding disease and injury processes, and sports injury research will fall further behind unless it does the same. A complementary and alternative conceptual approach towards injury control known as systems thinking that builds on socioecological science, both methodologically and analytically, is readily available and fast developing in other research areas. This review outlines the historical progression of causal concepts in the field of epidemiology over the course of the modern scientific era. From here, causal concepts in injury epidemiology, and models of aetiology as found in the context of sports injury research are presented. The paper finishes by proposing a new research agenda that considers the potential for a systems thinking approach to further enhance sports injury aetiological understanding. A complementary systems paradigm, however, will require that sports injury epidemiologists bring their knowledge and skillsets forwards in an attempt to use, adapt, and even refine existing systems-based approaches. Alongside the natural development of conventional scientific methodologies and analyses in sports injury research, progressing forwards to a systems paradigm is now required. © 2015, Hulme and Finch.
- Authors: Hulme, Adam , Finch, Caroline
- Date: 2015
- Type: Text , Journal article , Review
- Relation: Injury Epidemiology Vol. 2, no. 1 (2015), p. 1-12
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
- Full Text:
- Reviewed:
- Description: The science of sports injury control, including both its cause and prevention, has largely been informed by a biomedical and mechanistic model of health. Traditional scientific practice in sports injury research has routinely involved collapsing the broader socioecological landscape down in order to analyse individual-level determinants of injury - whether biomechanical and/or behavioural. This approach has made key gains for sports injury prevention research and should be further encouraged and allowed to evolve naturally. However, the public health, Applied Human Factors and Ergonomics, and injury epidemiological literature more broadly, has accepted the value of a socioecological paradigm for better understanding disease and injury processes, and sports injury research will fall further behind unless it does the same. A complementary and alternative conceptual approach towards injury control known as systems thinking that builds on socioecological science, both methodologically and analytically, is readily available and fast developing in other research areas. This review outlines the historical progression of causal concepts in the field of epidemiology over the course of the modern scientific era. From here, causal concepts in injury epidemiology, and models of aetiology as found in the context of sports injury research are presented. The paper finishes by proposing a new research agenda that considers the potential for a systems thinking approach to further enhance sports injury aetiological understanding. A complementary systems paradigm, however, will require that sports injury epidemiologists bring their knowledge and skillsets forwards in an attempt to use, adapt, and even refine existing systems-based approaches. Alongside the natural development of conventional scientific methodologies and analyses in sports injury research, progressing forwards to a systems paradigm is now required. © 2015, Hulme and Finch.
The epistemic basis of distance running injury research : A historical perspective
- Hulme, Adam, Finch, Caroline
- Authors: Hulme, Adam , Finch, Caroline
- Date: 2016
- Type: Text , Journal article , Editorial
- Relation: Journal of Sport and Health Science Vol. 5, no. 2 (2016), p. 172-175
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
- Full Text:
- Reviewed:
- Authors: Hulme, Adam , Finch, Caroline
- Date: 2016
- Type: Text , Journal article , Editorial
- Relation: Journal of Sport and Health Science Vol. 5, no. 2 (2016), p. 172-175
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
- Full Text:
- Reviewed:
Theoretical perspectives on using epidemiology and systems thinking to better understand the aetiology and prevention of distance running-related injury
- Authors: Hulme, Adam
- Date: 2017
- Type: Text , Thesis , PhD
- Full Text:
- Description: On a global scale, the sporting activity of distance running has increased in popularity. This is likely attributable to a growing societal concern for the documented rise in several lifestyle-related chronic diseases. As a form of exercise, running provides significant beneficial effects on a range of biomedical health indices, and is the preferred physical activity of choice for many people given its high accessibility and relatively low financial cost. Notwithstanding the many health-related benefits associated with running, the risk of sustaining a distance running-related injury (RRI) can be high. Therefore, from an injury prevention perspective, understanding why runners sustain RRI is of primary scientific importance. Over the last fifty years, the science behind RRI causation and prevention has attracted considerable interest amongst sports injury prevention researchers. During that time, there has been a concerted scholarly effort to understand the aetiology of RRI from an epidemiological and clinical research-based standpoint. Traditional scientific approaches have attempted to identify the effect of discrete trainingrelated, behavioural, and/or biomechanical exposures on the risk of developing either general or specific RRI. Despite what is now a considerable body of work, several descriptive and systematic reviews have found a history of previous injury to be the only definitive risk factor for subsequent RRI development. Alongside the continuing application of traditional scientific approaches, this PhD thesis promotes the use of a complementary ‘systems thinking’ theoretical perspective for better understanding the development and prevention of RRI. There are several contained chapters, the first of which is a systematic review of the RRI aetiological literature. After examining in closer detail the causal mechanism underpinning RRI development, a series of papers urge injury prevention scientists to consider drawing on alternative philosophical perspectives when planning and designing research. In building on the preceding arguments, the final chapters involve the construction of a systems ergonomics control structure model of the Australian distance running system, including the way RRI is managed and controlled.
- Description: Doctor of Philosophy
- Authors: Hulme, Adam
- Date: 2017
- Type: Text , Thesis , PhD
- Full Text:
- Description: On a global scale, the sporting activity of distance running has increased in popularity. This is likely attributable to a growing societal concern for the documented rise in several lifestyle-related chronic diseases. As a form of exercise, running provides significant beneficial effects on a range of biomedical health indices, and is the preferred physical activity of choice for many people given its high accessibility and relatively low financial cost. Notwithstanding the many health-related benefits associated with running, the risk of sustaining a distance running-related injury (RRI) can be high. Therefore, from an injury prevention perspective, understanding why runners sustain RRI is of primary scientific importance. Over the last fifty years, the science behind RRI causation and prevention has attracted considerable interest amongst sports injury prevention researchers. During that time, there has been a concerted scholarly effort to understand the aetiology of RRI from an epidemiological and clinical research-based standpoint. Traditional scientific approaches have attempted to identify the effect of discrete trainingrelated, behavioural, and/or biomechanical exposures on the risk of developing either general or specific RRI. Despite what is now a considerable body of work, several descriptive and systematic reviews have found a history of previous injury to be the only definitive risk factor for subsequent RRI development. Alongside the continuing application of traditional scientific approaches, this PhD thesis promotes the use of a complementary ‘systems thinking’ theoretical perspective for better understanding the development and prevention of RRI. There are several contained chapters, the first of which is a systematic review of the RRI aetiological literature. After examining in closer detail the causal mechanism underpinning RRI development, a series of papers urge injury prevention scientists to consider drawing on alternative philosophical perspectives when planning and designing research. In building on the preceding arguments, the final chapters involve the construction of a systems ergonomics control structure model of the Australian distance running system, including the way RRI is managed and controlled.
- Description: Doctor of Philosophy
Time-to-event analysis for sports injury research part 1 : Time-varying exposures
- Nielsen, Rasmus, Bertelsen, Michael, Ramskov, Daniel, Møller, Merete, Hulme, Adam, Theisen, Daniel, Finch, Caroline, Fortington, Lauren, Mansournia, Mohammad, Parner, Erik
- Authors: Nielsen, Rasmus , Bertelsen, Michael , Ramskov, Daniel , Møller, Merete , Hulme, Adam , Theisen, Daniel , Finch, Caroline , Fortington, Lauren , Mansournia, Mohammad , Parner, Erik
- Date: 2019
- Type: Text , Journal article , Review
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 61-68
- Full Text:
- Reviewed:
- Description: Background: 'How much change in training load is too much before injury is sustained, among different athletes?' is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology. Aim: To discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes. Content: Time-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills. Conclusion: To increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data. © 2019 Author(s).
- Authors: Nielsen, Rasmus , Bertelsen, Michael , Ramskov, Daniel , Møller, Merete , Hulme, Adam , Theisen, Daniel , Finch, Caroline , Fortington, Lauren , Mansournia, Mohammad , Parner, Erik
- Date: 2019
- Type: Text , Journal article , Review
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 61-68
- Full Text:
- Reviewed:
- Description: Background: 'How much change in training load is too much before injury is sustained, among different athletes?' is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology. Aim: To discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes. Content: Time-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills. Conclusion: To increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data. © 2019 Author(s).
Time-to-event analysis for sports injury research part 2 : Time-varying outcomes
- Nielsen, Rasmus, Bertelsen, Michael, Ramskov, Daniel, Møller, Merete, Hulme, Adam, Theisen, Daniel, Finch, Caroline, Fortington, Lauren, Mansournia, Mohammad, Parner, Erik
- Authors: Nielsen, Rasmus , Bertelsen, Michael , Ramskov, Daniel , Møller, Merete , Hulme, Adam , Theisen, Daniel , Finch, Caroline , Fortington, Lauren , Mansournia, Mohammad , Parner, Erik
- Date: 2019
- Type: Text , Journal article , Review
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 70-78
- Full Text:
- Reviewed:
- Description: Background: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. Content: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. Conclusion: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.
- Authors: Nielsen, Rasmus , Bertelsen, Michael , Ramskov, Daniel , Møller, Merete , Hulme, Adam , Theisen, Daniel , Finch, Caroline , Fortington, Lauren , Mansournia, Mohammad , Parner, Erik
- Date: 2019
- Type: Text , Journal article , Review
- Relation: British Journal of Sports Medicine Vol. 53, no. 1 (2019), p. 70-78
- Full Text:
- Reviewed:
- Description: Background: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. Content: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. Conclusion: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.
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