Individualising speed and acceleration thresholds for training load monitoring Posted on 12th October 2020 (18th August 2021) by Ben Bradley Blog Post written by Dr. Will Abbott Determining associations between training load and injury occurrence in team sports has been a popular topic of research in recent years. A fine balance exists between applying the optimum training stimulus to promote adaptation, and exceeding the optimum stimulus, which is associated with a higher incidence of injury (Bowen et al., 2017). As a result, monitoring and understanding training load is vital to optimise physical performance, and reduce injury risk. Distance travelled performing high-speed locomotion tasks (e.g. high-speed running, very high-speed running, and sprinting) has received significant attention when investigating training load. Despite only 1-4% of locomotion distance travelled during Football being classed as ‘sprinting’, these moments typically occur during the most significant moments of competition (Barnes et al., 2014). See the YouTube video example below. Any excuse to show a magnificent goal. In an attempt to quantify and monitor athlete locomotion, activities have typically been banded using speed thresholds. Examples are walking, jogging, running, high-speed running, very high-speed running, and sprinting (Akenhead et al., 2013; Carling, 2013). Often, these thresholds are applied systematically, or ‘globally’ to all athletes, irrespective of varying ages and maturation. Although global speed thresholds allow practitioners to compare the absolute workload completed by athletes, each individual’s internal response varies for the same external demands, resulting in differing degrees of adaptation. Individual variations are even more pronounced when monitoring athletes of different ages and maturation levels, particularly important when working with youth athletes in academy environments. Due to inherent physiological, biomechanical, and metabolic differences during exercise, speed thresholds based upon post-pubescent athletes may not be suitable for pre-pubescent athletes (Cummins et al., 2013). For example, post-pubescent athletes are demonstrated to have increased muscle mass when compared to pre-pubescent athletes, resulting in greater levels of maximal strength, power output, and sprinting performance (Meylan et al., 2010). Given the limitations associated with applying global speed thresholds systematically to a group of athletes, research has since individualised speed thresholds using various physical performance markers (Abt & Lovell, 2009; Hunter et al., 2015; Mendez-Villanueva et al., 2013). For example, using gas ventilatory thresholds (Abt & Lovell, 2009; Hunter et al., 2015; Lovell & Abt, 2013), or maximum sprint speed (Gabbett, 2015). There is certainly scientific underpinning to these methods, however issues often emerge when attempting to apply them to practice. For applied practitioners, identification of gas ventilatory thresholds can be difficult, with access to facilities, equipment, and staff required to accurately administer the tests. As a result, this often proves expensive, time consuming, and infeasible to many applied practitioners. Issues have also been highlighted when individualising locomotion using only one performance marker (e.g. maximum sprint speed), with suggestions that doing so reduces threshold accuracy. A proposed method of increasing the specificity and accuracy of speed thresholds is using the athlete’s functional limits of endurance and sprint locomotor capacities. These can be calculated using field-based tests, in the form of maximum aerobic speed and maximum sprint speed protocols. Maximum aerobic speed is strongly correlated with vV̇O2max, whilst maximum sprint speed allows for the estimation of an individual’s anaerobic speed reserve, and transition to sprinting. This method provides an applied alternative to laboratory testing, and by incorporating multiple performance markers, allows for increased accuracy when representing the relative locomotive training load elicited upon athletes. In practice there are numerous barriers for practitioners testing to determine individualised speed thresholds. Time is the biggest limiting factor, especially when working within a youth team sport environment. Large groups of athletes, limited time dedicated to testing, and typically a single member of sport science staff in place, all contribute towards time pressures. Within the applied environment I currently operate in, we were interested in determining the discrepancies between individualised and global speed thresholds, and consequently designed an investigation to examine this (Abbott et al., 2018). The thought process was to determine whether the differences between the thresholds were significant, and therefore whether testing for and applying individualised speed thresholds was an effective use of time. Nineteen, male, professional Footballers completed maximum sprint and maximum aerobic speed protocols (see Abbott et al., 2018 for more detail on protocols), and were divided into groups dependent upon maximum aerobic speed performance (high – HIMAS, medium – MEMAS, and low – LOMAS). Locomotion data was collected using GPS units over the course of a six-week training and competition period. Each individual’s data was analysed using both global and individualised speed thresholds to determine distances travelled performing high-speed running (HSR), very high-speed running (VHSR), and sprinting (SPR). Please see Abbott et al (2018) for more details on the methodology. Results demonstrated that for LOMAS athletes, individualised speed thresholds produced significantly higher HSR, VHSR and SPR distances compared to global thresholds (mean differences 390m, 310m, and 88m respectively). This is demonstrated in Figure 1. Figure 1. Mean (± SD) Distance travelled HSR, VHSR, and SPR when utilising global or individualised speed thresholds, in LOMAS athletes. N.B. asterisk represents significant difference of p < 0.01, d represents effect size. In MEMAS athletes, no significant differences were found between global and individualised speed thresholds for HSR and VHSR. For SPR however, individualised speed thresholds produced significantly higher distances when compared to the global speed thresholds (mean difference 76m). This is demonstrated in Figure 2. Figure 2. Mean (± SD) Distance travelled HSR, VHSR, and SPR when utilising global or individualised speed thresholds, in MEMAS athletes. N.B. asterisk represents significance difference of p < 0.01, d represents effect size. In HIMAS athletes, individualised speed thresholds produced significantly lower HSR and VHSR distances compared to global (mean differences 549m and 341m respectively). There were no differences in SPR distances between global and individualised speed thresholds. This is demonstrated in Figure 3. Figure 3. Mean (± SD) Distance travelled HSR, VHSR, and SPR when utilising global or individualised speed thresholds, in HIMAS athletes. N.B. asterisk represents significance difference of p < 0.01, d represents effect size. In summary, global speed thresholds were adequate when quantifying high-speed locomotion for MEMAS athletes. However, they significantly underestimated high-speed locomotion for LOMAS athletes, and significantly overestimated high-speed locomotion for HIMAS athletes. The variation in physical capacities of athletes within the same squad can be vast. This is a result of differences in training age, maturation, playing position, amongst other factors. Therefore, as the current results suggest, application of global speed thresholds to a squad of athletes of different physical capacities, may result in large inaccuracies in training load for individuals. Recent research has focused upon determining associations between training load and injury occurrence. Before determining associations between training load and injury, it is vital to ensure the training load data utilised is a valid representation of load elicited upon the athlete. Previous research suggests individualised speed thresholds produce more accurate representations of the training load elicited, due to individual’s physical performance capacities being accounted for (Hunter et al., 2015). Not acknowledging an athlete’s physical capacities within speed thresholds may result in inaccurate representations of locomotive training load. An inaccurate representation of training load may increase the difficulty associated with prescribing optimal training loads, increasing the probability of inappropriate training load prescription and injury risk. As the thresholds used are consistent for all athletes, global speed thresholds allow practitioners to compare performance, vital for benchmarking athletes. Global speed thresholds also allow assessment as to an individual’s ability to tolerate locomotive training load, important for determining an athlete’s ability to achieve the physical demands of competition. Whether a practitioner chooses to apply global or individualised speed thresholds should be as a result of the outcomes they wish to achieve. If the aim is to benchmark athlete’s performance against others, global speed thresholds are most appropriate. If the aim is to accurately quantify the intensity of high-speed locomotion, individualised speed are most appropriate. Research has demonstrated that individualised speed thresholds can be calculated using field-based maximum aerobic speed, and maximum sprint speed tests. This provides practitioners operating with large squads in applied settings an efficient and cost-effective method to individualise the monitoring of high-speed locomotion. However, if practitioners wish to implement individualised speed thresholds, there is an important consideration. Physical performance has been demonstrated to fluctuate over the course of a season, and individualised speed thresholds should be reassessed regularly to reflect this. If physical performance testing and consequential update of individualised thresholds is not performed on a regular basis, an inaccurate representation of training load may be provided. Currently there are no recommendations for the frequency of re-testing. However, the consensus is the more frequently testing is conducted, the higher the accuracy of individualised speed thresholds and high-speed locomotion data. References Abbott, W., Brickley, G., & Smeeton, N.J. (2018). An individual approach to monitoring locomotive training load in English Premier League academy soccer players. International Journal of Sports Science & Coaching, 13(3), 421-428.Abt, G., & Lovell, R. (2009). The use of individualized speed and intensity thresholds for determining the distance run at high-intensity in professional soccer. Journal of Sports Science, 27(9), 893-898.Akenhead, R., Hayes, P. R., Thompson, K. G., & French, D. (2013). Diminuations of acceleration and deceleration output during professional football match play. Journal of Science and Medicine in Sport, 16(6), 556-561.Bowen, L., Gross, A. S., Gimpel, M., & Li, F. (2017). Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. British Journal of Sports Medicine, 51(5), 452-459.Barnes, C., Archer, D. T., Hogg, B., Bush, M., & Bradley, P. S. (2014). The evolution of physical and technical performance parameters in the English Premier League. International Journal of Sports Medicine, 35(13), 1-6.Carling C. (2013). Interpreting physical performance in professional soccer match-play: Should we be more pragmatic in our approach? Journal of Sports Medicine, 43(8), 655-663.Cummins, C., Orr, R., O’Connor, H. & West, C. (2013). Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review. Journal of Sports Medicine, 43(10), 1025-1043.Gabbett, T. J. (2015). The use of relative speed zones increases the high-speed running performed in team sport match-play. Journal of Strength and Conditioning Research, 29(12), 3353-3359.Hunter, F., Bray, J., Towlson, C., Smith, M., Barrett, S., Madden, J., Abt, G., & Lovell, R. (2015). Individualisation of time-motion analysis: a method comparison and case report series. International Journal of Sports Medicine, 36(1), 41-48.Lovell, R., & Abt, G. (2013) Individualization of time-motion analysis: a case-cohort example. International Journal of Sports Physiology and Performance, 8(4), 456-458.Mendez-Villanueva, A., Buchheit, M., Simpson, B., & Bourdon, P. C. (2013). Match play intensity distribution in youth soccer. International Journal of Sports Medicine, 34(2), 101-110.Meylan, C., Cronin, J., Oliver, J., & Hughes, M. (2010). Talent identification in soccer: the role of maturity status on physical, physiological and technical characteristics. International Journal of Sports Science & Coaching, 5(4), 571-592.