Four weeks of sprint interval training improves 5-km run performance
- Denham, Joshua, Feros, Simon, O'Brien, Brendan
- 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.
- 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
- Full Text:
- Reviewed:
- 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
- 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
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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).
- 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).
Identifying high risk loading conditions for in-season injury in elite Australian football players
- Stares, Jordan, Dawson, Brian, Peeling, Peter, Heasman, Jarryd, Rogalski, Brent, Drew, Michael, Colby, Marcus, Dupont, Gregory, Lester, Leanne
- Authors: Stares, Jordan , Dawson, Brian , Peeling, Peter , Heasman, Jarryd , Rogalski, Brent , Drew, Michael , Colby, Marcus , Dupont, Gregory , Lester, Leanne
- Date: 2018
- Type: Text , Journal article
- Relation: Journal of Science and Medicine in Sport Vol. 21, no. 1 (2018), p. 46-51
- Full Text: false
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- Description: Objectives To examine different timeframes for calculating acute to chronic workload ratio (ACWR) and whether this variable is associated with intrinsic injury risk in elite Australian football players. Design Prospective cohort study. Methods Internal (session rating of perceived exertion: sRPE) and external (GPS distance and sprint distance) workload and injury data were collected from 70 players from one AFL club over 4 seasons. Various acute (1–2 weeks) and chronic (3–8 weeks) timeframes were used to calculate ACWRs: these and chronic load categories were then analysed to determine the injury risk in the subsequent month. Poisson regression with robust errors within a generalised estimating equation were utilised to determine incidence rate ratios (IRR). Results Altering acute and/or chronic timeframes did not improve the ability to detect high injury risk conditions above the commonly used 1:4 week ACWR. Twenty-seven ACWR/chronic load combinations were found to be “high risk conditions” (IRR > 1, p < 0.05) for injury within 7 days. Most (93%) of these conditions occurred when chronic load was low or very low and ACWR was either low (<0.6) or high (>1.5). Once a high injury risk condition was entered, the elevated risk persisted for up to 28 days. Conclusions Injury risk was greatest when chronic load was low and ACWR was either low or high. This heightened risk remained for up to 4 weeks. There was no improvement in the ability to identify high injury risk situations by altering acute or chronic time periods from 1:4 weeks.
How much is enough in rehabilitation? High running workloads following lower limb muscle injury delay return to play but protect against subsequent injury
- Stares, Jordan, Dawson, Brian, Peeling, Peter, Drew, Michael, Heasman, Jarryd, Rogalski, Brent, Colby, Marcus
- 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.
- 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
- Full Text:
- Reviewed:
- 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.
Rating of perceived exertion is a stable and appropriate measure of workload in judo
- Bromley, Sally, Drew, Michael, McIntosh, Andrew, Talpey, Scott
- Authors: Bromley, Sally , Drew, Michael , McIntosh, Andrew , Talpey, Scott
- Date: 2018
- Type: Text , Journal article
- Relation: Journal of Science and Medicine in Sport Vol. 21, no. 10 (2018), p. 1008-1012
- Full Text: false
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- Description: Objectives: Heart rate (HR), blood lactate concentration [La] and/or rating of perceived exertion (RPE) have been utilised to monitor judo training load in technical and randori (competition training) sessions, but are yet to be investigated in mixed sessions containing both elements. Therefore the purpose of this study was to: (1) determine the stability of these variables, and (2) to assess the efficacy of RPE as a load variable for mixed judo sessions. Design: Cross-sectional study. Methods: Twenty-nine athletes attended two mixed training sessions at an international training camp. Bout and session characteristics, including RPE, physical and mental effort, heart rate (HR) and post-session [La] were recorded. A two-way random-effects intra-class correlation assessed variable stability. Multilevel mixed-effects ordered logistic regression investigated relationships between RPE and other variables for bouts and sessions. Results: Average and minimum HR across sessions correlated highly (ICC = 0.95 and 0.94, respectively). Good correlations existed between [La], session-RPE and mental effort, and fair correlation of max HR and physical effort. No relationships existed between [La]/HR and session-RPE. A unit increase in bout-RPE resulted in a 2.09 unit increase in physical, or a 1.36 unit increase in mental, effort holding all other bout variables constant. Gender and competitive level did not influence statistical models. Conclusions: Results provide further evidence that RPE can be used across a range of competitive levels and genders to monitor workload of mixed sessions and individual randori in judo. Physical effort may play a larger role than mental effort when athletes reflect on exertion during training. © 2018
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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