Date of Award

2017

Degree Type

Thesis

Degree Name

Doctor of Philosophy

School

School of Medical and Health Sciences

First Advisor

Associate Professor Chris Abbiss

Second Advisor

Dr Paolo Menaspa

Third Advisor

Vincent Villerius

Fourth Advisor

Dr Jeremiah Peiffer

Field of Research Code

110602

Abstract

Athletes regularly monitor exercise workload in an attempt to improve and maintain exercise performance. Within road cycling, workload is commonly measured using power output. Yet, it is plausible that power output during road cycling is influenced by several factors such as topography, road gradient or rider specialities. If these factors do influence power output they may influence quantification of workload demands. As such, the purpose of this thesis was to improve our understanding of external workload in professional road cycling and describe the factors which influence power output during performance analysis. Specifically, this thesis examined the power output within single stage (1 day, Study One) and multi-stage races (4-21 days, Study Two, Three and Four). The within seasonal changes in power output of professional cyclists were also examined (Study Five).

Study One calculated the frequency distribution of maximal power output (POpeak) values during road cycling events over different topography categories and analysed the power output 600 s prior to POpeak using a new time series analysis called changepoint. Changepoint estimated the four largest statistical changes in power output to find distinct segments. Seven professional male road cyclists (mean ± SD: age 29.5 ± 2.8 y, mass 69.7 ± 5.5 kg, height 182 ± 5 cm) participated in Study One and were all members of a single professional cycling team. It was found that a greater frequency of POpeak values (54%) occurred during flat stages in the final 80 to 100% of race time compared with the previous 0 to 80% race time. Using changepoint, power output was lower (P <0.05) in segment four compared with POpeak in all topography categories (flat: 235 vs. 823 W, semi-mountainous: 157 vs. 886 W and mountainous: 171 vs. 656 W). These results demonstrate that POpeak values occur at differing time points depending on the topography category and that changepoint demonstrated its ability to analyse power output data. Study Two calculated the maximal mean power (MMP) of professional cyclists from grand tour events. The MMP was examined across various topographies and rider specialities. Study Two also examined the percentage of race time spent in different power output bands between topographies, road gradients and rider specialities. Thirteen male professional cyclists (mean ± SD: age 25 ± 3 y, mass 69 ± 7.5 kg, height 178 ± 0.5 cm) participated in Study Two. MMP for durations longer than 1200 s were greater in semi-mountainous and mountainous stages, when compared with flat stages (1200 s: 5.1 ± 0.2, 5.2 ± 0.3, 4.5 ± 0.3 W·kg-1 respectively; P <0.05). Sprinters and climbers spent greater percentage of race time at a power output greater than 7.5 W·kg-1, when compared with general classification riders and domestiques (11.3, 11.4, 7.1 and 5.3%, respectively; P <0.05). A greater proportion of race time was spent at a power output above 3.7 W·kg-1 when cycling at a road gradient greater than 5% (P <0.05), compared with road gradients 0 to 5% and less than 0%. In conclusion, caution should be taken when comparing MMP between different races of varying topography or rider specialities.

It was found in Study Two that MMP differs between flat and mountainous stages. Given that critical power (CP) can be estimated from MMP values during competition it is plausible that such differences will influence CP estimation. It is also plausible that difference in MMP between flat and mountainous stages is because cyclists are able to produce greater power output uphill rather than on flat gradients. As such, Study Three examined the use of MMP in the estimation of CP when calculated from stages of differing topographies. Also, Study Three compared estimated CP from a flat (mean gradient 0.4%) and uphill (mean gradient 6.2%) field-based test. Data from thirteen professional male road cyclists (age 29 ± 4 y, height 171 ± 0.9 cm, mass 67 ± 8.2 kg) were analysed. No differences (P >0.05) were observed in estimated CP between topography categories. However, a large effects size (d = 0.8) was observed in CP between flat stages and both semi-mountainous and mountainous stages. Estimated CP was 11.6% lower in flat field-based test, compared with the uphill field-based test (5.0 vs. 5.6 W·kg-1). Study Three demonstrates a large difference between estimated CP from alternative topography categories and from two different gradient specific field-based tests. With an 11.6% difference in CP observed in Study Three between 0 and 6.2% road gradients, Study Four investigated the magnitude of change in 1 and 5 min MMP from grand tour mountain stages. Road gradients of -5% to +5% were compared chronologically from lowest to highest. Seven professional male road cyclists (age 30 ± 4 y, height 169 ± 8 cm, body mass 69 ± 9 kg) from two professional cycling teams were analysed. In total 50 mountainous stages were analysed in Study Four from grand tours between 2011 and 2016. Power output from road gradient -1% was lower (P <0.001) in both 1 and 5 MMP compared with 0% (2.4 to 3.3 and 2.2 to 3.1 W·kg-1, respectively). Power output from road gradient 1% was lower in both 1 and 5 MMP compared with 2% (3.6 to 4.2 and 3.4 to 4.1 W·kg-1; (P <0.05)). These results highlight the need to consider road gradient when using power output for cycling performance analysis.

Study Five described the within-season external workloads of professional male road cyclists for optimal training prescription. Four professional male cyclists (mean ± SD: age 24 ± 2 y, body mass 77.6 ± 1.5 kg, height 184 ± 4.3 cm) from the same professional cycling team were monitored for 12 months. Within three seasonal phases (phase one: Oct-Jan, phase two: Feb-May and, phase three: June-Sept), the volume and exercise intensity during training and racing was measured. Total distance (3859 ± 959 vs 10911 ± 620 km) and time (240.5 ± 37.5 vs 337.5 ± 26 h) was lower (P <0.01) in phase one compared with phase two, respectively. Total distance decreased (P <0.01) from phase two compared with phase three (10911 ± 620 vs 8411 ± 1399 km, respectively). Mean absolute (236 ± 12.1 vs. 197 ± 3 W) and relative (3.1 ± 0 vs. 2.5 ± 0 W·kg-1) power output was higher (P <0.05) during racing compared with training, respectively. These results highlight the importance in acknowledging the difference in volume and intensity changes during a season.

In conclusion, this thesis demonstrates that cycling power output is affected by multiple factors including topography, road gradient and a rider’s speciality. Caution should be taken when interpreting cycling performance analysis using power output measures such as MMP and CP.

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