- Harris, Rachel, Trease, Larissa, Wilkie, Kellie, Drew, Michael
- Authors: Harris, Rachel , Trease, Larissa , Wilkie, Kellie , Drew, Michael
- Date: 2020
- Type: Text , Journal article
- Relation: British Journal of Sports Medicine Vol. 54, no. 16 (2020), p. 991-996
- Full Text: false
- Reviewed:
- Description: Aim To describe the demographics, frequency, location, imaging modality and clinician-identified factors of rib stress injury in a cohort of elite rowers over the Rio Olympiad (2012-2016). Methods Analysis of prospectively recorded medical records for the Australian Rowing Team in 2013-2015 and the combined Australian Rowing Team and Olympic Shadow Squad in 2016, examining all rib stress injuries. Results 19 rib stress injuries (12 reactions and 7 fractures) were identified among a cohort of 151 athletes and included 12 female and 7 male cases, 11 open weight, 8 lightweight, 12 scull and 7 sweep cases. The most common locations of injury identified by imaging, were the mid-axillary line and rib 6. Period prevalence varied from 4 to 15.4 and incidence ranged from 0.27 to 0.13 per 1000 athlete days. There were no significant differences in prevalence by sex, sweep versus scull or weight class. There was a statistically significant increase in incidence in the pre-Olympic year (2015, p<0.001). MRI was the most commonly used modality for diagnosis. Stress fracture resulted in median 69 (IQR 56-157) and bone stress reaction resulted in 57 (IQR 45-78) days lost to full on water training. Conclusions In our 4-year report of rib stress injury in elite rowing athletes, period prevalence was consistent with previous reports and time lost (median
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.
- Trease, Larissa, Wilkie, Kellie, Lovell, Greg, Drew, Michael, Hooper, Ivan
- Authors: Trease, Larissa , Wilkie, Kellie , Lovell, Greg , Drew, Michael , Hooper, Ivan
- Date: 2020
- Type: Text , Journal article
- Relation: British Journal of Sports Medicine Vol. 54, no. 21 (2020), p. 1288-1293
- Full Text: false
- Reviewed:
- Description: Aim To report the epidemiology of injury and illness in elite rowers over eight seasons (two Olympiads). Methods All athletes selected to the Australian Rowing Team between 2009 and 2016 were monitored prospectively under surveillance for injury and illness. The incidence and burden of injury and illness were calculated per 1000 athlete days (ADs). The body area, mechanism and type of all injuries were recorded and followed until the resumption of full training. We used interrupted time series analyses to examine the association between fixed and dynamic ergometer testing on rowers' injury rates. Time lost from illness was also recorded. Results All 153 rowers selected over eight seasons were observed for 48 611 AD. 270 injuries occurred with an incidence of 4.1-6.4 injuries per 1000 AD. Training days lost totalled 4522 (9.2% AD). The most frequent area injured was the lumbar region (84 cases, 1.7% AD) but the greatest burden was from chest wall injuries (64 cases, 2.6% AD.) Overuse injuries (n=224, 83%) were more frequent than acute injuries (n=42, 15%). The most common activity at the time of injury was on-water rowing training (n=191, 68). Female rowers were at 1.4 times the relative risk of chest wall injuries than male rowers; they had half the relative risk of lumbar injuries of male rowers. The implementation of a dynamic ergometers testing policy (Concept II on sliders) was positively associated with a lower incidence and burden of low back injury compared with fixed ergometers (Concept II). Illness accounted for the greatest number of case presentations (128, 32.2% cases, 1.2% AD). Conclusions Chest wall and lumbar injuries caused training time loss. Policy decisions regarding ergometer testing modality were associated with lumbar injury rates. As in many sports, illness burden has been under-recognised in elite Australian rowers. ©
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