Louis Passfield

Comparing cycling world hour records, 1967-1996: modeling with empirical data.

PURPOSE The world hour record in cycling has increased dramatically in recent years. The present study was designed to compare the performances of former/current record holders, after adjusting for differences in aerodynamic equipment and altitude. Additionally, we sought to determine the ideal elevation for future hour record attempts. METHODS The first step was constructing a mathematical model to predict power requirements of track cycling. The model was based on empirical data from wind-tunnel tests, the relationship of body size to frontal surface area, and field power measurements using a crank dynamometer (SRM). The model agreed reasonably well with actual measurements of power output on elite cyclists. Subsequently, the effects of altitude on maximal aerobic power were estimated from published research studies of elite athletes. This information was combined with the power requirement equation to predict what each cyclists power output would have been at sea level. This allowed us to estimate the distance that each rider could have covered using state-of-the-art equipment at sea level. According to these calculations, when racing under equivalent conditions, Rominger would be first, Boardman second, Merckx third, and Indurain fourth. In addition, about 60% of the increase in hour record distances since Brackes record (1967) have come from advances in technology and 40% from physiological improvements. RESULTS AND CONCLUSIONS To break the current world hour record, field measurements and the model indicate that a cyclist would have to deliver over 440 W for 1 h at sea level, or correspondingly less at altitude. The optimal elevation for future hour record attempts is predicted to be about 2500 m for acclimatized riders and 2000 m for unacclimatized riders.

Changes in cycling efficiency and performance after endurance exercise

PURPOSE This study was designed to examine the effects of moderate-intensity endurance exercise on cycling performance, gross efficiency, and 30-s sprint power output. METHODS Two separate experiments were conducted. After a controlled warm-up, subjects completed as much work as possible in a 5-min performance test (EXP1) or a maximal 30-s sprint test (EXP2). These initial exercise bouts were followed by approximately 60 min of cycling at approximately 60% VO2peak or an equivalent period of rest (control) before repeating the warm-up exercise and either the 5-min performance or 30-s sprint test. Expired gas for calculation of cycling gross efficiency was collected over the last minute of each warm-up period. RESULTS Average 5-min performance power output was significantly reduced (12 W) after exercise in EXP1, and in EXP2 both peak and mean power output were significantly lower (26 and 35 W, respectively). Gross efficiency decreased significantly with exercise in both EXP1 and EXP2. Moreover, the change in gross efficiency was correlated with the change in 5-min performance (r = 0.91, P < 0.01), but not with the change in mean or peak 30-s sprint power output. CONCLUSIONS After sustained moderate-intensity cycling significant reductions in 5-min performance, gross efficiency and sprint power output were observed in endurance trained cyclists. The reduction in 5-min performance was related to the exercise induced decrease in gross efficiency.

Changes in cycling efficiency during a competitive season

PURPOSE To monitor training-related changes in gross efficiency (GE) over the course of a competitive cycling season. METHODS Fourteen trained cyclists (mean +/- SD: 34 + 8 yr, 74.3 +/- 7.4 kg, Wmax = 406 +/- 43 W, V O2max = 59.5 +/- 3.8 mL x kg x min) with at least 3 yr competitive experience completed five laboratory tests during a competitive cycling season. The tests measured lactate threshold (LT), onset of blood lactate accumulation (OBLA), maximal oxygen uptake (V O2max), maximal minute power (Wmax), and GE. The data were analyzed using repeated-measures ANOVA and Pearsons product-moment correlation coefficient. RESULTS GE changed significantly over the course of the competitive cycling season (P < 0.05), increasing over the precompetition phase of the season (19.6% vs 20.6%; P < 0.05). GE was maintained during the main competitive phase of the season (20.6% vs 20.3%; P > 0.05) and then decreased during the postcompetitive phase to 19.4% (P < 0.05). The precompetition changes in GE were related to the total time spent training and the time spent above OBLA intensity (r = 0.84 and 0.80, respectively). Riders who spent the most time training between LT and OBLA intensities (r = 0.87; P < 0.05) were better able to maintain GE. A significant inverse relationship was also identified between the changes in GE and the percentage change in training below LT over the competitive phase of the season. CONCLUSION GE changes over the course of a competitive cycling season and is related to the volume and intensity of training conducted.

Validity and reliability of the Wattbike cycle ergometer.

The purpose of this study was to assess the validity and reliability of the Wattbike cycle ergometer against the SRM Powermeter using a dynamic calibration rig (CALRIG) and trained and untrained human participants. Using the CALRIG power outputs of 50-1 250  W were assessed at cadences of 70 and 90  rev x min(-1). Validity and reliability data were also obtained from 3 repeated trials in both trained and untrained populations. 4 work rates were used during each trial ranging from 50-300  W. CALRIG data demonstrated significant differences (P<0.05) between SRM and Wattbike across the work rates at both cadences. Significant differences existed in recorded power outputs from the SRM and Wattbike during steady state trials (power outputs 50-300  W) in both human populations (156±72  W vs. 153±64  W for SRM and Wattbike respectively; P<0.05). The reliability (CV) of the Wattbike in the untrained population was 6.7% (95%CI 4.8-13.2%) compared to 2.2% with the SRM (95%CI 1.5-4.1%). In the trained population the Wattbike CV was 2.6% (95%CI 1.8-5.1%) compared to 1.1% with the SRM (95%CI 0.7-2.0%). These results suggest that when compared to the SRM, the Wattbike has acceptable accuracy. Reliability data suggest coaches and cyclists may need to use some caution when using the Wattbike at low power outputs in a test-retest setting.

The Analysis and Utilization of Cycling Training Data

Most mathematical models of athletic training require the quantification of training intensity and quantity or ‘dose’. We aim to summarize both the methods available for such quantification, particularly in relation to cycle sport, and the mathematical techniques that may be used to model the relationship between training and performance.Endurance athletes have used training volume (kilometres per week and/or hours per week) as an index of training dose with some success. However, such methods usually fail to accommodate the potentially important influence of training intensity. The scientific literature has provided some support for alternative methods such as the session rating of perceived exertion, which provides a subjective quantification of the intensity of exercise; and the heart rate-derived training impulse (TRIMP) method, which quantifies the training stimulus as a composite of external loading and physiological response, multiplying the training load (stress) by the training intensity (strain). Other methods described in the scientific literature include ‘ordinal categorization’ and a heart rate-based excess post-exercise oxygen consumption method.In cycle sport, mobile cycle ergometers (e.g. SRM™ and PowerTap™) are now widely available. These devices allow the continuous measurement of the cyclists’ work rate (power output) when riding their own bicycles during training and competition. However, the inherent variability in power output when cycling poses several challenges in attempting to evaluate the exact nature of a session. Such variability means that average power output is incommensurate with the cyclist’s physiological strain. A useful alternative may be the use of an exponentially weighted averaging process to represent the data as a ‘normalized power’.Several research groups have applied systems theory to analyse the responses to physical training. Impulse-response models aim to relate training loads to performance, taking into account the dynamic and temporal characteristics of training and, therefore, the effects of load sequences over time. Despite the successes of this approach it has some significant limitations, e.g. an excessive number of performance tests to determine model parameters. Non-linear artificial neural networks may provide a more accurate description of the complex non-linear biological adaptation process. However, such models may also be constrained by the large number of datasets required to ‘train’ the model.A number of alternative mathematical approaches such as the Performance- Potential-Metamodel (PerPot), mixed linear modelling, cluster analysis and chaos theory display conceptual richness. However, much further research is required before such approaches can be considered as viable alternatives to traditional impulse-response models. Some of these methods may not provide useful information about the relationship between training and performance. However, they may help describe the complex physiological training response phenomenon.

The effect of training volume and intensity on competitive cyclists' efficiency.

The impact of different intensity training on cycling efficiency in competitive cyclists is unknown. Twenty-nine endurance-trained competitive male cyclists completed 3 laboratory visits during a 12-week training period. At each visit, their cycling efficiency and maximal oxygen uptake were determined. After the first visit, cyclists were randomly split into 2 groups (A and B). Over the first 6 weeks, between tests 1 and 2, group A was prescribed specific high-intensity training sessions, whereas group B was restricted in the amount of intensive work undertaken. After test 2 and for the second 6-week period, group B was allowed to conduct high-intensity training. Gross efficiency (GE) increased in group A (+1.6 +/- 1.4%; p < 0.05) following the high-intensity training, whereas no significant change was seen in group B (+0.1 +/- 0.7%; p > 0.05). Group B cyclists increased their GE between tests 2 and 3 (+1.4 +/- 0.8%; p < 0.05) but no changes in GE were observed in group A over this period (+0.4 +/- 0.4%; p > 0.05). Delta efficiency (DE) did not change significantly in either group across the study period. This study demonstrates that GE is increased following high-intensity training in competitive male cyclists after 12 weeks.

Influence of body position when considering the ecological validity of laboratory time-trial cycling performance

Abstract The aims of this study were to compare the physiological demands of laboratory- and road-based time-trial cycling and to examine the importance of body position during laboratory cycling. Nine male competitive but non-elite cyclists completed two 40.23-km time-trials on an air-braked ergometer (Kingcycle) in the laboratory and one 40.23-km time-trial (RD) on a local road course. One laboratory time-trial was conducted in an aerodynamic position (AP), while the second was conducted in an upright position (UP). Mean performance speed was significantly higher during laboratory trials (UP and AP) compared with the RD trial (P < 0.001). Although there was no difference in power output between the RD and UP trials (P > 0.05), power output was significantly lower during the AP trial than during both the RD (P = 0.013) and UP trials (P = 0.003). Similar correlations were found between AP power output and RD power output (r = 0.85, P = 0.003) and between UP power output and RD power output (r = 0.87, P = 0.003). Despite a significantly lower power output in the laboratory AP condition, these results suggest that body position does not affect the ecological validity of laboratory-based time-trial cycling.

Perfectionism and Burnout in Junior Athletes: A Three-Month Longitudinal Study.

Perfectionism in sports has been shown to be associated with burnout in athletes. Whether perfectionism predicts longitudinal changes in athlete burnout, however, is still unclear. Using a two-wave cross-lagged panel design, the current study examined perfectionistic strivings, perfectionistic concerns, and athlete burnout in 101 junior athletes (mean age 17.7 years) over 3 months of active training. When structural equation modeling was employed to test a series of competing models, the best-fitting model showed opposite patterns for perfectionistic strivings and perfectionistic concerns. Whereas perfectionistic concerns predicted increases in athlete burnout over the 3 months, perfectionistic strivings predicted decreases. The present findings suggest that perfectionistic concerns are a risk factor for junior athletes contributing to the development of athlete burnout whereas perfectionistic strivings appear to be a protective factor.

Reliability of Cycling Gross Efficiency Using the Douglas Bag Method

Book review of Eric Schaefers edited collection Sex Scene: Media and the Sexual Revolution published in the journal Film Studies as part of the special issue Sex and the Cinema.PURPOSE The aim of this study was to establish the reliability of gross efficiency (GE) measurement (the ratio of mechanical power input to metabolic power output, expressed as a percentage) using the Douglas bag method. METHODS The experiment was conducted in two parts. Part 1 examined the potential for errors in the Douglas bag method arising from gas concentration analysis, bag residual volume, and bag leakage or gas diffusion rates. Part 2 of this study examined the within-subject day-to-day variability of GE in 10 trained male cyclists using the Douglas bag method. Participants completed three measurements of GE on separate days at work rates of 150, 180, 210, 240, 270, and 300 W. RESULTS The results demonstrate that the reliability of gas sampling is high with a coefficient of variation (CV) <0.5% for both O2 and CO2. The bag residual volume CV was ∼15%, which amounts to +0.4 L. This could cause the largest error, but this can be minimized by collecting large gas sample volumes. For part 2, a mean CV of 1.5% with limits of agreement of +0.6% in GE units, around a mean GE of 20.0%, was found. CONCLUSIONS The Douglas bag method of measuring expired gases and GE was found to have very high reliability and could be considered the gold-standard approach for evaluating changes in GE. Collecting larger expired gas samples minimizes potential sources of error.

The influence of training status, age, and muscle fiber type on cycling efficiency and endurance performance

The purpose of this study was to assess the influence of age, training status, and muscle fiber-type distribution on cycling efficiency. Forty men were recruited into one of four groups: young and old trained cyclists, and young and old untrained individuals. All participants completed an incremental ramp test to measure their peak O2 uptake, maximal heart rate, and maximal minute power output; a submaximal test of cycling gross efficiency (GE) at a series of absolute and relative work rates; and, in trained participants only, a 1-h cycling time trial. Finally, all participants underwent a muscle biopsy of their right vastus lateralis muscle. At relative work rates, a general linear model found significant main effects of age and training status on GE (P < 0.01). The percentage of type I muscle fibers was higher in the trained groups (P < 0.01), with no difference between age groups. There was no relationship between fiber type and cycling efficiency at any work rate or cadence combination. Stepwise multiple regression indicated that muscle fiber type did not influence cycling performance (P > 0.05). Power output in the 1-h performance trial was predicted by average O2 uptake and GE, with standardized β-coefficients of 0.94 and 0.34, respectively, although some mathematical coupling is evident. These data demonstrate that muscle fiber type does not affect cycling efficiency and was not influenced by the aging process. Cycling efficiency and the percentage of type I muscle fibers were influenced by training status, but only GE at 120 revolutions/min was seen to predict cycling performance.