- Hulme, Adam, Salmon, Paul, Nielsen, Rasmus, Read, Gemma, Finch, Caroline
- Authors: Hulme, Adam , Salmon, Paul , Nielsen, Rasmus , Read, Gemma , Finch, Caroline
- Date: 2017
- Type: Text , Journal article
- Relation: Theoretical Issues in Ergonomics Science Vol. 18, no. 4 (2017), p. 338-359
- Full Text: false
- Reviewed:
- Description: The popularity of running as a form of exercise continues to increase dramatically worldwide. Alongside this participation growth is the burden of running-related injury (RRI). Over the past four decades, traditional scientific research applications have primarily attempted to isolate discrete risk factors for RRI using observational study designs as commonly used in public health epidemiology. Unfortunately, only very few randomised controlled trials have evaluated the efficacy associated with a well-specified RRI prevention intervention. Even though the knowledge about risk factors as generated in observational studies is valuable for better understanding why RRI develops, it nonetheless means that there remains a major knowledge gap about how best to prevent it, especially in a way that fully addresses all causal factors. Alongside the continuing use of traditional scientific approaches, a particular systems ergonomics methodology should also be considered in light of its potential to visualise the complete distance running system. This article adapts the Systems Theoretic Accident Mapping and Processes (STAMP) model to the RRI research prevention context. The direct application of STAMP might offer new knowledge about how to prevent RRI, such as exposing questions around the feasibility of adopting novel injury prevention interventions that do not directly target runners themselves. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
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).
Risk and protective factors for middle- and long-distance running-related injury
- Hulme, Adam, Nielsen, Rasmus, Timpka, Toomas, Verhagen, Evert, Finch, Caroline
- Authors: Hulme, Adam , Nielsen, Rasmus , Timpka, Toomas , Verhagen, Evert , Finch, Caroline
- Date: 2016
- Type: Text , Journal article
- Relation: Sports Medicine Vol. 47, no. 5 (2016), p. 869-886
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
- Full Text:
- Reviewed:
- Description: BACKGROUND: Despite a rapidly growing body of research, a systematic evidence compilation of the risk and protective factors for middle- and long-distance running-related injury (RRI) was lacking. OBJECTIVES: Our objective was to compile the evidence about modifiable and non-modifiable training-related and behavioral risk and protective factors for middle- and long-distance RRI. METHODS: We searched five databases (PubMed, CINAHL, MEDLINE, SPORTDiscus, and PsycINFO) for the dates 1 January 1970 to 31 December 2015, inclusive, for original peer-reviewed articles. The eligible designs were cross-sectional, case-control, longitudinal observational studies, and randomized controlled trials involving runners competing at distances from >/=800 m to =42.2 km. Outcomes were any specific and/or general RRI, and exposures included training-related and behavioral factors. We extracted authors and date, study design, injury type(s), descriptors and comparators for each exposure, and results and measures of association from the selected studies. Methodological quality was independently appraised using two separate checklists: a modified checklist for observational study designs and the Physiotherapy Evidence Database (PEDro) scale for randomized controlled trials. RESULTS: Among 73 articles eligible for inclusion, 19 (26.0%) and 30 (41.0%) were of high or satisfactory methodological quality, respectively. As a non-modifiable exposure, a history of previous injury was found to be associated with an increased risk of both general and specific RRI. In terms of modifiable exposures, irregular and/or absent menstruation was found to be associated with an increased risk of stress fracture development, whereas the use of oral contraceptives was found to be associated with a decreased risk. High clinical, methodological, and statistical heterogeneity meant it was not feasible to estimate a pooled effect size across similar studies. CONCLUSIONS: A history of previous injury was associated with an increased risk of both general and specific RRI. The use of oral contraceptives was found to be associated with a decreased risk of skeletal stress fracture. Conversely, irregular and/or absent menstruation was associated with an increased risk. The varied effect directions and/or a number of statistically insignificant results associated with the majority of factors hindered our ability to draw any definitive conclusions about their relationship to RRI risk.
- Authors: Hulme, Adam , Nielsen, Rasmus , Timpka, Toomas , Verhagen, Evert , Finch, Caroline
- Date: 2016
- Type: Text , Journal article
- Relation: Sports Medicine Vol. 47, no. 5 (2016), p. 869-886
- Relation: http://purl.org/au-research/grants/nhmrc/1058737
- Full Text:
- Reviewed:
- Description: BACKGROUND: Despite a rapidly growing body of research, a systematic evidence compilation of the risk and protective factors for middle- and long-distance running-related injury (RRI) was lacking. OBJECTIVES: Our objective was to compile the evidence about modifiable and non-modifiable training-related and behavioral risk and protective factors for middle- and long-distance RRI. METHODS: We searched five databases (PubMed, CINAHL, MEDLINE, SPORTDiscus, and PsycINFO) for the dates 1 January 1970 to 31 December 2015, inclusive, for original peer-reviewed articles. The eligible designs were cross-sectional, case-control, longitudinal observational studies, and randomized controlled trials involving runners competing at distances from >/=800 m to =42.2 km. Outcomes were any specific and/or general RRI, and exposures included training-related and behavioral factors. We extracted authors and date, study design, injury type(s), descriptors and comparators for each exposure, and results and measures of association from the selected studies. Methodological quality was independently appraised using two separate checklists: a modified checklist for observational study designs and the Physiotherapy Evidence Database (PEDro) scale for randomized controlled trials. RESULTS: Among 73 articles eligible for inclusion, 19 (26.0%) and 30 (41.0%) were of high or satisfactory methodological quality, respectively. As a non-modifiable exposure, a history of previous injury was found to be associated with an increased risk of both general and specific RRI. In terms of modifiable exposures, irregular and/or absent menstruation was found to be associated with an increased risk of stress fracture development, whereas the use of oral contraceptives was found to be associated with a decreased risk. High clinical, methodological, and statistical heterogeneity meant it was not feasible to estimate a pooled effect size across similar studies. CONCLUSIONS: A history of previous injury was associated with an increased risk of both general and specific RRI. The use of oral contraceptives was found to be associated with a decreased risk of skeletal stress fracture. Conversely, irregular and/or absent menstruation was associated with an increased risk. The varied effect directions and/or a number of statistically insignificant results associated with the majority of factors hindered our ability to draw any definitive conclusions about their relationship to RRI risk.
A framework for the etiology of running-related injuries
- Bertelsen, Michael, Hulme, Adam, Petersen, Jesper, Brund, Rene, Sørensen, Henrik, Finch, Caroline, Parner, Erik, Nielsen, Rasmus
- Authors: Bertelsen, Michael , Hulme, Adam , Petersen, Jesper , Brund, Rene , Sørensen, Henrik , Finch, Caroline , Parner, Erik , Nielsen, Rasmus
- Date: 2017
- Type: Text , Journal article , Review
- Relation: Scandinavian Journal of Medicine and Science in Sports Vol. 27, no. 11 (2017), p. 1170-1180
- Full Text: false
- Reviewed:
- Description: The etiology of running-related injury is important to consider as the effectiveness of a given running-related injury prevention intervention is dependent on whether etiologic factors are readily modifiable and consistent with a biologically plausible causal mechanism. Therefore, the purpose of the present article was to present an evidence-informed conceptual framework outlining the multifactorial nature of running-related injury etiology. In the framework, four mutually exclusive parts are presented: (a) Structure-specific capacity when entering a running session; (b) structure-specific cumulative load per running session; (c) reduction in the structure-specific capacity during a running session; and (d) exceeding the structure-specific capacity. The framework can then be used to inform the design of future running-related injury prevention studies, including the formation of research questions and hypotheses, as well as the monitoring of participation-related and non-participation-related exposures. In addition, future research applications should focus on addressing how changes in one or more exposures influence the risk of running-related injury. This necessitates the investigation of how different factors affect the structure-specific load and/or the load capacity, and the dose-response relationship between running participation and injury risk. Ultimately, this direction allows researchers to move beyond traditional risk factor identification to produce research findings that are not only reliably reported in terms of the observed cause-effect association, but also translatable in practice. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
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.
- Hulme, Adam, Salmon, Paul, Nielsen, Rasmus, Read, Gemma, Finch, Caroline
- Authors: Hulme, Adam , Salmon, Paul , Nielsen, Rasmus , Read, Gemma , Finch, Caroline
- Date: 2017
- Type: Text , Journal article
- Relation: Applied Ergonomics Vol. 65, no. (2017), p. 345-354
- Full Text: false
- Reviewed:
- Description: Introduction There is a need for an ecological and complex systems approach for better understanding the development and prevention of running-related injury (RRI). In a previous article, we proposed a prototype model of the Australian recreational distance running system which was based on the Systems Theoretic Accident Mapping and Processes (STAMP) method. That model included the influence of political, organisational, managerial, and sociocultural determinants alongside individual-level factors in relation to RRI development. The purpose of this study was to validate that prototype model by drawing on the expertise of both systems thinking and distance running experts. Materials and methods This study used a modified Delphi technique involving a series of online surveys (December 2016- March 2017). The initial survey was divided into four sections containing a total of seven questions pertaining to different features associated with the prototype model. Consensus in opinion about the validity of the prototype model was reached when the number of experts who agreed or disagreed with survey statement was ≥75% of the total number of respondents. Results A total of two Delphi rounds was needed to validate the prototype model. Out of a total of 51 experts who were initially contacted, 50.9% (n = 26) completed the first round of the Delphi, and 92.3% (n = 24) of those in the first round participated in the second. Most of the 24 full participants considered themselves to be a running expert (66.7%), and approximately a third indicated their expertise as a systems thinker (33.3%). After the second round, 91.7% of the experts agreed that the prototype model was a valid description of the Australian distance running system. Conclusion This is the first study to formally examine the development and prevention of RRI from an ecological and complex systems perspective. The validated model of the Australian distance running system facilitates theoretical advancement in terms of identifying practical system-wide opportunities for the implementation of sustainable RRI prevention interventions. This ‘big picture’ perspective represents the first step required when thinking about the range of contributory causal factors that affect other system elements, as well as runners' behaviours in relation to RRI risk. © 2017 Elsevier Ltd
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