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
Christopher R.J. Fennell and James G. Hopker
Purpose: There has been paucity in research investigating the individualization of recovery interval duration during cycling-based high-intensity interval training (HIIT). The main aim of the study was to investigate whether individualizing the duration of the recovery interval based upon the resolution of muscle oxygen consumption would improve the performance during work intervals and the acute physiological response of the HIIT session, when compared with a standardized (2:1 work recovery ratio) approach. Methods: A total of 16 well-trained cyclists (maximal oxygen consumption: 60  mL·kg−1·min−1) completed 6 laboratory visits: (Visit 1) incremental exercise test, (Visit 2) determination of the individualized (IND) recovery duration, using the individuals’ muscle oxygen consumption recovery duration to baseline from a 4- and 8-minute work interval, (Visits 3–6) participants completed a 6 × 4- and a 3 × 8-minute HIIT session twice, using the IND and standardized recovery intervals. Results: Recovery duration had no effect on the percentage of the work intervals spent at >90% and >95% of maximal oxygen consumption, maximal minute power output, and maximal heart rate, during the 6 × 4- and 3 × 8-minute HIIT sessions. Recovery duration had no effect on mean work interval power output, heart rate, oxygen consumption, blood lactate, and rating of perceived exertion. There were no differences in reported session RPE between recovery durations for the 6 × 4- and 3 × 8-minute HIIT sessions. Conclusion: Individualizing HIIT recovery duration based upon the resolution of muscle oxygen consumption to baseline levels does not improve the performance of the work intervals or the acute physiological response of the HIIT session, when compared with standardized recovery duration.
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.
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.
Ciaran O’Grady, Louis Passfield, and James G. Hopker
Purpose: Rating of perceived exertion (RPE) as a training-intensity prescription has been extensively used by athletes and coaches. However, individual variability in the physiological response to exercise prescribed using RPE has not been investigated. Methods: Twenty well-trained competitive cyclists (male = 18, female = 2, maximum oxygen consumption = 55.07 [11.06] mL·kg−1·min−1) completed 3 exercise trials each consisting of 9 randomized self-paced exercise bouts of either 1, 4, or 8 minutes at RPEs of 9, 13, and 17. Within-athlete variability (WAV) and between-athletes variability (BAV) in power and physiological responses were calculated using the coefficient of variation. Total variability was calculated as the ratio of WAV to BAV. Results: Increased RPEs were associated with higher power, heart rate, work, volume of expired oxygen (VO2), volume of expired carbon dioxide (VCO2), minute ventilation (V E), deoxyhemoglobin (ΔHHb) (P < .001), and lower tissue saturation index (ΔTSI%) and ΔO2Hb (oxyhaemoglobin; P < .001). At an RPE of 9, shorter durations resulted in lower VO2 (P < .05) and decreased ΔTSI%, and the ΔHHb increased as the duration increased (P < .05). At an RPE of 13, shorter durations resulted in lower VO2, V E, and percentage of maximum oxygen consumption (P < .001), as well as higher power, heart rate, ΔHHb (P < .001), and ΔTSI% (P < .05). At an RPE of 17, power (P < .001) and ΔTSI% (P < .05) increased as duration decreased. As intensity and duration increased, WAV and BAV in power, work, heart rate, VO2, VCO2, and VE decreased, and WAV and BAV in near-infrared spectroscopy increased. Conclusions: Self-paced intensity prescriptions of high effort and long duration result in the greatest consistency on both a within- and between-athletes basis.
Arthur H. Bossi, Cristian Mesquida, Louis Passfield, Bent R. Rønnestad, and James G. Hopker
Purpose: Maximal oxygen uptake (
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.
Hannah F. Sangan, James G. Hopker, Glen Davison, and Shaun J. McLaren
Purpose: To assess the reliability and construct validity of a self-paced, submaximal run test (SRTRPE) for monitoring aerobic fitness. The SRTRPE monitors running velocity (v), heart rate (HRex), and blood lactate concentration (B[La]), during three 3-minute stages prescribed by ratings of perceived exertion (RPEs) of 10, 13, and 17. Methods: Forty (14 female) trained endurance runners completed a treadmill graded exercise test for the determination of maximal oxygen consumption (VO2max), v at VO2max (vVO2max), and v at 2 mmol·L−1 (vLT1) and 4 mmol·L−1 (vLT2) B[La]. Within 7 days, participants completed the SRTRPE. Convergent validity between the SRTRPE and graded exercise test parameters was assessed through linear regression. Eleven participants completed a further 2 trials of the SRTRPE within a 72-hour period to quantify test–retest reliability. Results: There were large correlations between v at all stages of the SRTRPE and VO2max (r range = .57–.63), vVO2max (.50–.66), and vLT2 (.51–.62), with vRPE 17 displaying the strongest associations (r > .60). Intraclass correlation coefficients (ICC3,1) were moderate to high for parameters v (range = .76–.84), HRex (.72–.92), and %HRmax (.64–.89) at all stages of the SRTRPE. The corresponding coefficients of variation were 2.5% to 5.6%. All parameters monitored at intensity RPE 17 displayed the greatest reliability. Conclusions: The SRTRPE was shown to be a valid and reliable test for monitoring parameters associated with aerobic fitness, displaying the potential of this submaximal, time-efficient test to monitor responses to endurance training.
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.
Arthur H. Bossi, Guilherme G. Matta, Guillaume Y. Millet, Pedro Lima, Leonardo C. Pertence, Jorge P. de Lima, and James G. Hopker
To describe pacing strategy in a 24-h running race and its interaction with sex, age group, athletes’ performance group, and race edition.
Data from 398 male and 103 female participants of 5 editions were obtained based on a minimum 19.2-h effective-running cutoff. Mean running speed from each hour was normalized to the 24-h mean speed for analyses.
Mean overall performance was 135.6 ± 33.0 km with a mean effective-running time of 22.4 ± 1.3 h. Overall data showed a reverse J-shaped pacing strategy, with a significant reduction in speed from the second-to-last to the last hour. Two-way mixed ANOVAs showed significant interactions between racing time and both athlete performance group (F = 7.01, P < .001, ηp 2 = .04) and race edition (F = 3.01, P < .001, ηp 2 = .02) but not between racing time and either sex (F = 1.57, P = .058, ηp 2 < .01) or age group (F = 1.25, P = .053, ηp 2 = .01). Pearson product–moment correlations showed an inverse moderate association between performance and normalized mean running speed in the first 2 h (r = –.58, P < .001) but not in the last 2 h (r = .03, P = .480).
While the general behavior represents a rough reverse J-shaped pattern, the fastest runners start at lower relative intensities and display a more even pacing strategy than slower runners. The “herd behavior” seems to interfere with pacing strategy across editions, but not sex or age group of runners.