Four weeks of sprint interval training improves 5-km run performance
- Authors: Denham, Joshua , Feros, Simon , O'Brien, Brendan
- Date: 2015
- Type: Text , Journal article
- Relation: Journal of Strength and Conditioning Research Vol. 29, no. 8 (2015), p. 2137-2141
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- Description: Sprint interval training (SIT) rapidly improves cardiorespiratory fitness but demands less training time and volume than traditional endurance training. Although the health and fitness benefits caused by SIT have received considerable research focus, the effect of short-term SIT on 5-km run performance is unknown. Thirty healthy untrained participants (aged 18-25 years) were allocated to a control (n = 10) or a SIT (n = 20) group. Sprint interval training involved 3-8 sprints at maximal intensity, 3 times a week for 4 weeks. Sprints were progressed to 8 by the 12th session. All participants completed a 5-km time trial on a public running track and an incremental treadmill test in an exercise physiology laboratory to determine 5-km run performance and maximum oxygen uptake, respectively, before and after the 4-week intervention. Relative to the controls, sprint interval-trained participants improved 5-km run performance by 4.5% (p < 0.001), and this was accompanied by improvements in absolute and relative maximum oxygen uptake (4.9%, p 0.04 and 4.5%, p = 0.045, respectively). Therefore, short-term SIT significantly improves 5-km run performance in untrained young men. We believe that SIT is a time-efficient means of improving cardiorespiratory fitness and 5-km endurance performance. © 2015 National Strength and Conditioning Association.
Time-to-event analysis for sports injury research part 1 : Time-varying exposures
- 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
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- 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).
How much is enough in rehabilitation? High running workloads following lower limb muscle injury delay return to play but protect against subsequent injury
- Authors: Stares, Jordan , Dawson, Brian , Peeling, Peter , Drew, Michael , Heasman, Jarryd , Rogalski, Brent , Colby, Marcus
- Date: 2018
- Type: Text , Journal article
- Relation: Journal of Science and Medicine in Sport Vol. 21, no. 10 (2018), p. 1019-1024
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- Description: Objectives: Examine the influence of rehabilitation training loads on return to play (RTP) time and subsequent injury in elite Australian footballers. Design: Prospective cohort study. Methods: Internal (sessional rating of perceived exertion: sRPE) and external (distance, sprint distance) workload and lower limb non-contact muscle injury data was collected from 58 players over 5 seasons. Rehabilitation periods were analysed for running workloads and time spent in 3 rehabilitation stages (1: off-legs training, 2: non-football running, 3: group football training) was calculated. Multi-level survival analyses with random effects accounting for player and season were performed. Hazard ratios (HR) and 95% confidence intervals (CI) for each variable were produced for RTP time and time to subsequent injury. Results: Of 85 lower limb muscle injuries, 70 were rehabilitated to RTP, with 30 cases of subsequent injury recorded (recurrence rate = 11.8%, new site injury rate = 31.4%). Completion of high rehabilitation workloads delayed RTP (distance: >49,775 m [reference: 34,613–49,775 m]: HR 0.12, 95%CI 0.04–0.36, sRPE: >1266 AU [reference: 852–1266 AU]: HR 0.09, 95%CI 0.03–0.32). Return to running within 4 days increased subsequent injury risk (3–4 days [reference: 5–6 days]: HR 25.88, 95%CI 2.06–324.4). Attaining moderate-high sprint distance (427–710 m) was protective against subsequent injury (154–426 m: [reference: 427–710 m]: HR 37.41, 95%CI 2.70–518.64). Conclusions: Training load monitoring can inform player rehabilitation programs. Higher rehabilitation training loads delayed RTP; however, moderate-high sprint running loads can protect against subsequent injury. Shared-decision making regarding RTP should include accumulated training loads and consider the trade-off between expedited RTP and lower subsequent injury risk.
Time-to-event analysis for sports injury research part 2 : Time-varying outcomes
- 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
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- 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.