This paper explores the notion that the availability and analysis of large data sets have the capacity to improve practice and change the nature of science in the sport and exercise setting. The increasing use of data and information technology in sport is giving rise to this change. Web sites hold large data repositories, and the development of wearable technology, mobile phone applications, and related instruments for monitoring physical activity, training, and competition provide large data sets of extensive and detailed measurements. Innovative approaches conceived to more fully exploit these large data sets could provide a basis for more objective evaluation of coaching strategies and new approaches to how science is conducted. An emerging discipline, sports analytics, could help overcome some of the challenges involved in obtaining knowledge and wisdom from these large data sets. Examples of where large data sets have been analyzed, to evaluate the career development of elite cyclists and to characterize and optimize the training load of well-trained runners, are discussed. Careful verification of large data sets is time consuming and imperative before useful conclusions can be drawn. Consequently, it is recommended that prospective studies be preferred over retrospective analyses of data. It is concluded that rigorous analysis of large data sets could enhance our knowledge in the sport and exercise sciences, inform competitive strategies, and allow innovative new research and findings.
Louis Passfield and James G. Hopker
Richard Ebreo, Louis Passfield and James Hopker
Purpose: To evaluate the reliability of calculating gross efficiency (GE) conventionally and using a back extrapolation (BE) method during high-intensity exercise (HIE). Methods: A total of 12 trained participants completed 2 HIE bouts (P1 = 4 min at 80% maximal aerobic power [MAP]; P2 = 4 min at 100%MAP). GE was calculated conventionally in the last 3 minutes of submaximal (50%MAP) cycling bouts performed before and after HIE (Pre50%MAP and Post50%MAP). To calculate GE using BE (BGE), a linear regression of GE submaximal values post-HIE were back extrapolated to the end of the HIE bout. Results: BGE was significantly correlated with Post50%MAP GE in P1 (r = .63; P = .01) and in P2 (r = .85; P = .002). Reliability data for P1 and P2 BGE demonstrate a mean coefficient of variation of 7.8% and 9.8% with limits of agreement of 4.3% and 4.5% in relative GE units, respectively. P2 BGE was significantly lower than P2 Post50%MAP GE (18.1% [1.6%] vs 20.3% [1.7%]; P = .01). Using a declining GE from the BE method, there was a 44% greater anaerobic contribution compared with assuming a constant GE during 4-minute HIE at 100%MAP. Conclusion: HIE acutely reduced BGE at 100%MAP. A greater anaerobic contribution to exercise as well as excess postexercise oxygen consumption at 100%MAP may contribute to this decline in efficiency. The BE method may be a reliable and valid tool in both estimating GE during HIE and calculating aerobic and anaerobic contributions.
James S. Hogg, James G. Hopker and Alexis R. Mauger
The novel self-paced maximal-oxygen-uptake (VO2max) test (SPV) may be a more suitable alternative to traditional maximal tests for elite athletes due to the ability to self-regulate pace. This study aimed to examine whether the SPV can be administered on a motorized treadmill.
Fourteen highly trained male distance runners performed a standard graded exercise test (GXT), an incline-based SPV (SPVincline), and a speed-based SPV (SPVspeed). The GXT included a plateau-verification stage. Both SPV protocols included 5 × 2-min stages (and a plateau-verification stage) and allowed for self-pacing based on fixed increments of rating of perceived exertion: 11, 13, 15, 17, and 20. The participants varied their speed and incline on the treadmill by moving between different marked zones in which the tester would then adjust the intensity.
There was no significant difference (P = .319, ES = 0.21) in the VO2max achieved in the SPVspeed (67.6 ± 3.6 mL · kg−1 · min−1, 95%CI = 65.6–69.7 mL · kg−1 · min−1) compared with that achieved in the GXT (68.6 ± 6.0 mL · kg−1 · min−1, 95%CI = 65.1–72.1 mL · kg−1 · min−1). Participants achieved a significantly higher VO2max in the SPVincline (70.6 ± 4.3 mL · kg−1 · min−1, 95%CI = 68.1–73.0 mL · kg−1 · min−1) than in either the GXT (P = .027, ES = 0.39) or SPVspeed (P = .001, ES = 0.76).
The SPVspeed protocol produces VO2max values similar to those obtained in the GXT and may represent a more appropriate and athlete-friendly test that is more oriented toward the variable speed found in competitive sport.
Andy Galbraith, James Hopker, Marco Cardinale, Brian Cunniffe and Louis Passfield
To examine the training and concomitant changes in laboratory- and field-test performance of highly trained endurance runners.
Fourteen highly trained male endurance runners (mean ± SD maximal oxygen uptake [VO2max] 69.8 ± 6.3 mL · kg−1 · min−1) completed this 1-y training study commencing in April. During the study the runners undertook 5 laboratory tests of VO2max, lactate threshold (LT), and running economy and 9 field tests to determine critical speed (CS) and the modeled maximum distance performed above CS (D′). The data for different periods of the year were compared using repeated-measures ANOVA. The influence of training on laboratory- and field-test changes was analyzed by multiple regression.
Total training distance varied during the year and was lower in May–July (333 ± 206 km, P = .01) and July–August (339 ± 206 km, P = .02) than in the subsequent January–February period (474 ± 188 km). VO2max increased from the April baseline (4.7 ± 0.4 L/min) in October and January periods (5.0 ± 0.4 L/min, P ≤ .01). Other laboratory measures did not change. Runners’ CS was lowest in August (4.90 ± 0.32 m/s) and highest in February (4.99 ± 0.30 m/s, P = .02). Total training distance and the percentage of training time spent above LT velocity explained 33% of the variation in CS.
Highly trained endurance runners achieve small but significant changes in VO2max and CS in a year. Increases in training distance and time above LT velocity were related to increases in CS.
Arthur H. Bossi, Wouter P. Timmerman and James G. Hopker
Purpose: There are several published equations to calculate energy expenditure (EE) from gas exchanges. The authors assessed whether using different EE equations would affect gross efficiency (GE) estimates and their reliability. Methods: Eleven male and 3 female cyclists (age 33  y; height: 178  cm; body mass: 76.0 [15.1] kg; maximal oxygen uptake: 51.4 [5.1] mL·kg−1·min−1; peak power output: 4.69 [0.45] W·kg−1) completed 5 visits to the laboratory on separate occasions. In the first visit, participants completed a maximal ramp test to characterize their physiological profile. In visits 2 to 5, participants performed 4 identical submaximal exercise trials to assess GE and its reliability. Each trial included three 7-minute bouts at 60%, 70%, and 80% of the gas exchange threshold. EE was calculated with 4 equations by Péronnet and Massicotte, Lusk, Brouwer, and Garby and Astrup. Results: All 4 EE equations produced GE estimates that differed from each other (all P < .001). Reliability parameters were only affected when the typical error was expressed in absolute GE units, suggesting a negligible effect—related to the magnitude of GE produced by each EE equation. The mean coefficient of variation for GE across different exercise intensities and calculation methods was 4.2%. Conclusions: Although changing the EE equation does not affect GE reliability, exercise scientists and coaches should be aware that different EE equations produce different GE estimates. Researchers are advised to share their raw data to allow for GE recalculation, enabling comparison between previous and future studies.
Marco Arkesteijn, Simon Jobson, James Hopker and Louis Passfield
Previous research has shown that cycling in a standing position reduces cycling economy compared with seated cycling. It is unknown whether the cycling intensity moderates the reduction in cycling economy while standing.
The aim was to determine whether the negative effect of standing on cycling economy would be decreased at a higher intensity.
Ten cyclists cycled in 8 different conditions. Each condition was either at an intensity of 50% or 70% of maximal aerobic power at a gradient of 4% or 8% and in the seated or standing cycling position. Cycling economy and muscle activation level of 8 leg muscles were recorded.
There was an interaction between cycling intensity and position for cycling economy (P = .03), the overall activation of the leg muscles (P = .02), and the activation of the lower leg muscles (P = .05). The interaction showed decreased cycling economy when standing compared with seated cycling, but the difference was reduced at higher intensity. The overall activation of the leg muscles and the lower leg muscles, respectively, increased and decreased, but the differences between standing and seated cycling were reduced at higher intensity.
Cycling economy was lower during standing cycling than seated cycling, but the difference in economy diminishes when cycling intensity increases. Activation of the lower leg muscles did not explain the lower cycling economy while standing. The increased overall activation, therefore, suggests that increased activation of the upper leg muscles explains part of the lower cycling economy while standing.
Andy Galbraith, James Hopker, Stephen Lelliott, Louise Diddams and Louis Passfield
To compare critical speed (CS) measured from a single-visit field test of the distance–time relationship with the “traditional” treadmill time-to-exhaustion multivisit protocol.
Ten male distance runners completed treadmill and field tests to calculate CS and the maximum distance performed above CS (D′). The field test involved 3 runs on a single visit to an outdoor athletics track over 3600, 2400, and 1200 m. Two field-test protocols were evaluated using either a 30-min recovery or a 60-min recovery between runs. The treadmill test involved runs to exhaustion at 100%, 105%, and 110% of velocity at VO2max, with 24 h recovery between runs.
There was no difference in CS measured with the treadmill and 30-min- and 60-minrecovery field tests (P < .05). CS from the treadmill test was highly correlated with CS from the 30- and 60-min-recovery field tests (r = .89, r = .82; P < .05). However there was a difference and no correlation in D′ between the treadmill test and the 30 and 60-min-recovery field tests (r = .13; r = .33, P > .05). A typical error of the estimate of 0.14 m/s (95% confidence limits 0.09–0.26 m/s) was seen for CS and 88 m (95% confidence limits 60–169 m) for D′. A coefficient of variation of 0.4% (95% confidence limits: 0.3–0.8%) was found for repeat tests of CS and 13% (95% confidence limits 10–27%) for D′.
The single-visit method provides a useful alternative for assessing CS in the field.
Arthur H. Bossi, Ciaran O’Grady, Richard Ebreo, Louis Passfield and James G. Hopker
Purpose : To describe pacing strategy and competitive behavior in elite-level cyclo-cross races. Methods: Data from 329 men and women competing in 5 editions (2012–2016) of Union Cycliste Internationale Cyclo-Cross World Championships were compiled. Individual mean racing speeds from each lap were normalized to the mean speeds of the whole race. Lap and overall rankings were also explored. Pacing strategy was compared between sexes and between top- and bottom-placed cyclists. Results: A significant main effect of laps was found in 8 out of 10 races (4 positive, 3 variable, 2 even, and 1 negative pacing strategies), and an interaction effect of ranking-based groups was found in 2 (2016, male and female races). Kendall tau-b correlations revealed an increasingly positive relationship between intermediate and overall rankings throughout the races. The number of overtakes during races decreased from start to finish, as suggested by significant Friedman tests. In the first lap, normalized cycling speeds were different in 3 out of 5 editions—men were faster in 1 and slower in 2 editions. In the last lap, however, normalized cycling speeds of men were lower than those of women in 4 editions. Conclusions : Elite cyclo-cross competitors adopt slightly distinct pacing strategies in each race, but positive pacing strategies are highly probable in most events, with more changes in rankings during the first laps. Sporadically, top- and bottom-placed groups might adopt different pacing strategies during either men’s or women’s races. Men and women seem to distribute their efforts differently, but this effect is of small magnitude.
Nicki Winfield Almquist, Gertjan Ettema, James Hopker, Øyvind Sandbakk and Bent R. Rønnestad
Background: Cycling competitions are often of long duration and include repeated high-intensity efforts. Purpose: To investigate the effect of repeated maximal sprints during 4 hours of low-intensity cycling on gross efficiency (GE), electromyography patterns, and pedaling technique compared with work-matched low-intensity cycling in elite cyclists. Methods: Twelve elite, male cyclists performed 4 hours of cycling at 50% of maximal oxygen uptake either with 3 sets of 3 × 30-second maximal sprints (E&S) during the first 3 hours or a work-matched cycling without sprints (E) in a randomized order. Oxygen uptake, electromyography, and pedaling technique were recorded throughout the exercises. Results: GE was reduced from start to the end of exercise in both conditions (E&S: 19.0 [0.2] vs 18.1 [0.2], E: 19.1% [0.2%] vs 18.1% [0.2%], both P = .001), with no difference in change between conditions (condition × time interaction, P = .8). Integrated electromyography increased from start to end of exercise in m. vastus lateralis and m. vastus medialis (m. vastus medialis: 9.9 [2.4], m. vastus lateralis: 8.5 [4.0] mV, main effect of time: P < .001 and P = .03, respectively) and E&S increased less than E in m. vastus medialis (mean difference −3.3 [1.5] mV, main effect of condition: P = .03, interaction, P = .06). The mechanical effectiveness only decreased in E&S (E&S: −2.2 [0.7], effect size = 0.24 vs E: −1.3 [0.8] percentage points: P = .04 and P = .8, respectively). The mean power output during each set of 3 × 30-second sprints in E&S did not differ (P = .6). Conclusions: GE decreases as a function of time during 4 hours of low-intensity cycling. However, the inclusion of maximal repeated sprinting does not affect the GE changes, and the ability to sprint is maintained throughout the entire session.
Alfred Nimmerichter, Bernhard Prinz, Kevin Haselsberger, Nina Novak, Dieter Simon and James G. Hopker
While a number of studies have investigated gross efficiency (GE) in laboratory conditions, few studies have analyzed it in field conditions. Therefore, the aim of this study was to analyze the effect of gradient and cadence on GE in field conditions.
Thirteen trained cyclists (mean ± SD age 23.3 ± 4.1 y, stature 177.0 ± 5.5 cm, body mass 69.0 ± 7.2 kg, maximal oxygen uptake [V̇O2max] 68.4 ± 5.1 mL ∙ min–1 ∙ kg–1) completed an incremental graded exercise test to determine ventilatory threshold (VT) and 4 field trials of 6 min duration at 90% of VT on flat (1.1%) and uphill terrain (5.1%) with 2 different cadences (60 and 90 rpm). V̇O2 was measured with a portable gas analyzer and power output was controlled with a mobile power crank that was mounted on a 26-in mountain bike.
GE was significantly affected by cadence (20.6% ± 1.7% vs 18.1% ± 1.3% at 60 and 90 rpm, respectively; P < .001) and terrain (20.0% ± 1.5% vs 18.7% ± 1.7% at flat and uphill cycling, respectively; P = .029). The end-exercise V̇O2 was 2536 ± 352 and 2594 ± 329 mL/min for flat and uphill cycling, respectively (P = .489). There was a significant difference in end-exercise V̇O2 between 60 (2352 ± 193 mL/min) and 90 rpm (2778 ± 431 mL/min) (P < .001).
These findings support previous laboratory-based studies demonstrating reductions in GE with increasing cadence and gradient that might be attributed to changes in muscle-activity pattern.