Modelling human locomotion: applications to cycling

Making History in 1 h: How Sex, Aging, Technology, and Elevation Affect the Cycling Hour Record

PURPOSE

The purpose of this article was to analyze more than a century of cycling hour records (CHR) to examine the effects of sex, age, and altitude on cycling performance. Our hypotheses were that men's performance (distance) would exceed those of women by more than 10% but would decline at similar rates with aging and that altitude would have a small benefit, which might reach a maximum.

METHODS

Data were cultivated from the Facebook World Hour Record Discussion Group's crowd-sourced database of more than 600-known-hour records and verified through extensive online research and/or personal communication. Regression and statistical modeling were produced using STATA v15.0. R2 values were used to ascertain model quality, with four distinct models being produced for comparisons. Alpha was set at 0.05 significance for all tests.

RESULTS

R2 values ranged from 65% to 74.9%. Women's distances were 10.8% shorter ( P < 0.001) than those of men, but the difference was narrower than either the historical elite women's difference of 14.2% or the 2022 record difference of 13.3%. Age-related decline modeling indicates performance declines significantly past age 40 yr at a rate of 1.08% per year. Altitude had a significant ( P < 0.001) marginal improvement up to 1000 m before declining. The marginal benefits of altitude were small, but this is consistent with the finding benefits reach a maximum at a moderate altitude with "benefits" becoming ambiguous starting at ~1000 m. Technological advancement was estimated to be a small but significant ( P < 0.001) improvement of ~0.18% per year.

CONCLUSIONS

Across decades of CHR data in well-trained endurance cyclists, men are only ~11% faster, and this difference remains stable until at least age 80 yr. CHR attempts greater than 500 m likely offer at best a small advantage. Despite small year-on-year improvements, the CHR has likely improved more than 10 km because of technological advancements.

Assessment of bike handling during cycling individual time trials with a novel analytical technique adapted from motorcycle racing

A methodology to study bike handling of cyclists during individual time trials (ITT) is presented. Lateral and longitudinal accelerations were estimated from GPS data of professional cyclists (n=53) racing in two ITT of different length and technical content. Acceleration points were plotted on a plot (g-g diagram) and they were enclosed in an ellipse. A correlation analysis was conducted between the area of the ellipse and the final ITT ranking. It was hypothesized that a larger area was associated to a better performance. An analytical model for the bike-cyclist system dynamics was used to conduct a parametric analysis on the influence of riding position on the shape of the g-g diagram. A moderate (n=27, r=-0.40, p=0.038) and a very large (n=26, r=-0.83, p<0.0001) association were found between the area of the enclosing ellipse and the final ranking in the two ITT. Interestingly, this association was larger in the shorter race with higher technical content. The analytical model suggested that maximal decelerations are highly influenced by the cycling position, road slope and speed. This investigation, for the first time, explores a novel methodology that can provide insights into bike handling, a large unexplored area of cycling performance.

Maximal muscular power: lessons from sprint cycling

Maximal muscular power production is of fundamental importance to human functional capacity and feats of performance. Here, we present a synthesis of literature pertaining to physiological systems that limit maximal muscular power during cyclic actions characteristic of locomotor behaviours, and how they adapt to training. Maximal, cyclic muscular power is known to be the main determinant of sprint cycling performance, and therefore we present this synthesis in the context of sprint cycling. Cyclical power is interactively constrained by force-velocity properties (i.e. maximum force and maximum shortening velocity), activation-relaxation kinetics and muscle coordination across the continuum of cycle frequencies, with the relative influence of each factor being frequency dependent. Muscle cross-sectional area and fibre composition appear to be the most prominent properties influencing maximal muscular power and the power-frequency relationship. Due to the role of muscle fibre composition in determining maximum shortening velocity and activation-relaxation kinetics, it remains unclear how improvable these properties are with training. Increases in maximal muscular power may therefore arise primarily from improvements in maximum force production and neuromuscular coordination via appropriate training. Because maximal efforts may need to be sustained for ~15-60 s within sprint cycling competition, the ability to attenuate fatigue-related power loss is also critical to performance. Within this context, the fatigued state is characterised by impairments in force-velocity properties and activation-relaxation kinetics. A suppression and leftward shift of the power-frequency relationship is subsequently observed. It is not clear if rates of power loss can be improved with training, even in the presence adaptations associated with fatigue-resistance. Increasing maximum power may be most efficacious for improving sustained power during brief maximal efforts, although the inclusion of sprint interval training likely remains beneficial. Therefore, evidence from sprint cycling indicates that brief maximal muscular power production under cyclical conditions can be readily improved via appropriate training, with direct implications for sprint cycling as well as other athletic and health-related pursuits.

Using Field Based Data to Model Sprint Track Cycling Performance

Cycling performance models are used to study rider and sport characteristics to better understand performance determinants and optimise competition outcomes. Performance requirements cover the demands of competition a cyclist may encounter, whilst rider attributes are physical, technical and psychological characteristics contributing to performance. Several current models of endurance-cycling enhance understanding of performance in road cycling and track endurance, relying on a supply and demand perspective. However, they have yet to be developed for sprint-cycling, with current athlete preparation, instead relying on measures of peak-power, speed and strength to assess performance and guide training. Peak-power models do not adequately explain the demands of actual competition in events over 15-60 s, let alone, in World-Championship sprint cycling events comprising several rounds to medal finals. Whilst there are no descriptive studies of track-sprint cycling events, we present data from physiological interventions using track cycling and repeated sprint exercise research in multiple sports, to elucidate the demands of performance requiring several maximal sprints over a competition. This review will show physiological and power meter data, illustrating the role of all energy pathways in sprint performance. This understanding highlights the need to focus on the capacity required for a given race and over an event, and therefore the recovery needed for each subsequent race, within and between races, and how optimal pacing can be used to enhance performance. We propose a shift in sprint-cyclist preparation away from training just for peak power, to a more comprehensive model of the actual event demands.

Physical characteristics and resistance parameters of typical urban cyclists

This study investigates the rolling and drag resistance parameters and bicycle and cargo masses of typical urban cyclists. These factors are important for modelling of cyclist speed, power and energy expenditure, with applications including exercise performance, health and safety assessments and transportation network analysis. However, representative values for diverse urban travellers have not been established. Resistance parameters were measured utilizing a field coast-down test for 557 intercepted cyclists in Vancouver, Canada. Masses were also measured, along with other bicycle attributes such as tire pressure and size. The average (standard deviation) of coefficient of rolling resistance, effective frontal area, bicycle plus cargo mass, and bicycle-only mass were 0.0077 (0.0036), 0.559 (0.170) m², 18.3 (4.1) kg, and 13.7 (3.3) kg, respectively. The range of measured values is wider and higher than suggested in existing literature, which focusses on sport cyclists. Significant correlations are identified between resistance parameters and rider and bicycle attributes, indicating higher resistance parameters for less sport-oriented cyclists. The findings of this study are important for appropriately characterising the full range of urban cyclists, including commuters and casual riders.

Performance differences when using 26- and 29-inch-wheel bikes in Swiss National Team cross-country mountain bikers

The purpose of this study was to analyse the effect of bike type - the 26-inch-wheel bike (26" bike) and the 29-inch-wheel bike (29" bike) - on performance in elite mountain bikers. Ten Swiss National Team athletes (seven males, three females) completed six trials with individual start on a simulated cross-country course with 35 min of active recovery between trials (three trials on a 26" bike and three trials on a 29" bike, alternate order, randomised start-bike). The course consisted of two separate sections expected to favour either the 29" bike (section A) or the 26" bike (section B). For each trial performance, power output, cadence and heart rate were recorded and athletes' experiences were documented. Mean overall performance (time: 304 ± 27 s vs. 311 ± 29 s; P < 0.01) and performance in sections A (P < 0.001) and B (P < 0.05) were better when using the 29" bike. No significant differences were observed for power output, cadence or heart rate. Athletes rated the 29" bike as better for performance in general, passing obstacles and traction. The 29" bike supports superior performance for elite mountain bikers, even on sections supposed to favour the 26" bike.

Impact of Altitude on Power Output during Cycling Stage Racing

PURPOSE

The purpose of this study was to quantify the effects of moderate-high altitude on power output, cadence, speed and heart rate during a multi-day cycling tour.

METHODS

Power output, heart rate, speed and cadence were collected from elite male road cyclists during maximal efforts of 5, 15, 30, 60, 240 and 600 s. The efforts were completed in a laboratory power-profile assessment, and spontaneously during a cycling race simulation near sea-level and an international cycling race at moderate-high altitude. Matched data from the laboratory power-profile and the highest maximal mean power output (MMP) and corresponding speed and heart rate recorded during the cycling race simulation and cycling race at moderate-high altitude were compared using paired t-tests. Additionally, all MMP and corresponding speeds and heart rates were binned per 1000 m (<1000 m, 1000-2000, 2000-3000 and >3000 m) according to the average altitude of each ride. Mixed linear modelling was used to compare cycling performance data from each altitude bin.

RESULTS

Power output was similar between the laboratory power-profile and the race simulation, however MMPs for 5-600 s and 15, 60, 240 and 600 s were lower (p ≤ 0.005) during the race at altitude compared with the laboratory power-profile and race simulation, respectively. Furthermore, peak power output and all MMPs were lower (≥ 11.7%, p ≤ 0.001) while racing >3000 m compared with rides completed near sea-level. However, speed associated with MMP 60 and 240 s was greater (p < 0.001) during racing at moderate-high altitude compared with the race simulation near sea-level.

CONCLUSION

A reduction in oxygen availability as altitude increases leads to attenuation of cycling power output during competition. Decrement in cycling power output at altitude does not seem to affect speed which tended to be greater at higher altitudes.

Accuracy of Indirect Estimation of Power Output From Uphill Performance in Cycling

Purpose:

To use measurement by cycling power meters (Pmes) to evaluate the accuracy of commonly used models for estimating uphill cycling power (Pest). Experiments were designed to explore the influence of wind speed and steepness of climb on accuracy of Pest. The authors hypothesized that the random error in Pest would be largely influenced by the windy conditions, the bias would be diminished in steeper climbs, and windy conditions would induce larger bias in Pest.

Methods:

Sixteen well-trained cyclists performed 15 uphill-cycling trials (range: length 1.3–6.3 km, slope 4.4–10.7%) in a random order. Trials included different riding position in a group (lead or follow) and different wind speeds. Pmes was quantified using a power meter, and Pest was calculated with a methodology used by journalists reporting on the Tour de France.

Results:

Overall, the difference between Pmes and Pest was –0.95% (95%CI: –10.4%, +8.5%) for all trials and 0.24% (–6.1%, +6.6%) in conditions without wind (>2 m/s). The relationship between percent slope and the error between Pest and Pmes were considered trivial.

Conclusions:

Aerodynamic drag (affected by wind velocity and orientation, frontal area, drafting, and speed) is the most confounding factor. The mean estimated values are close to the power-output values measured by power meters, but the random error is between ±6% and ±10%. Moreover, at the power outputs (>400 W) produced by professional riders, this error is likely to be higher. This observation calls into question the validity of releasing individual values without reporting the range of random errors.

The age of peak performance in Ironman triathlon: a cross-sectional and longitudinal data analysis

BACKGROUND

The aims of the present study were, firstly, to investigate in a cross-sectional analysis the age of peak Ironman performance within one calendar year in all qualifiers for Ironman Hawaii and Ironman Hawaii; secondly, to determine in a longitudinal analysis on a qualifier for Ironman Hawaii whether the age of peak Ironman performance and Ironman performance itself change across years; and thirdly, to determine the gender difference in performance.

METHODS

In a cross-sectional analysis, the age of the top ten finishers for all qualifier races for Ironman Hawaii and Ironman Hawaii was determined in 2010. For a longitudinal analysis, the age and the performance of the annual top ten female and male finishers in a qualifier for Ironman Hawaii was determined in Ironman Switzerland between 1995 and 2010.

RESULTS

In 19 of the 20 analyzed triathlons held in 2010, there was no difference in the age of peak Ironman performance between women and men (p > 0.05). The only difference in the age of peak Ironman performance between genders was in 'Ironman Canada' where men were older than women (p = 0.023). For all 20 races, the age of peak Ironman performance was 32.2 ± 1.5 years for men and 33.0 ± 1.6 years for women (p > 0.05). In Ironman Switzerland, there was no difference in the age of peak Ironman performance between genders for top ten women and men from 1995 to 2010 (F = 0.06, p = 0.8). The mean age of top ten women and men was 31.4 ± 1.7 and 31.5 ± 1.7 years (Cohen's d = 0.06), respectively. The gender difference in performance in the three disciplines and for overall race time decreased significantly across years. Men and women improved overall race times by approximately 1.2 and 4.2 min/year, respectively.

CONCLUSIONS

Women and men peak at a similar age of 32-33 years in an Ironman triathlon with no gender difference. In a qualifier for Ironman Hawaii, the age of peak Ironman performance remained unchanged across years. In contrast, gender differences in performance in Ironman Switzerland decreased during the studied period, suggesting that elite female Ironman triathletes might still narrow the gender gap in the future.

How to assess performance in cycling: the multivariate nature of influencing factors and related indicators

Finding an optimum for the cycling performance is not a trivial matter, since the literature shows the presence of many controversial aspects. In order to quantify different levels of performance, several indexes have been defined and used in many studies, reflecting variations in physiological and biomechanical factors. In particular, indexes such as Gross Efficiency (GE), Net Efficiency (NE) and Delta Efficiency (DE) have been referred to changes in metabolic efficiency (Eff_Met), while the Indexes of Effectiveness (IE), defined over the complete crank revolution or over part of it, have been referred to variations in mechanical effectiveness (Eff_Mech). All these indicators quantify the variations of different factors [i.e., muscle fibers type distribution, pedaling cadence, setup of the bicycle frame, muscular fatigue (MFat), environmental variables, ergogenic aids, psychological traits (Psych_Tr)], which, moreover, show high mutual correlation. In the attempt of assessing cycling performance, most studies in the literature keep all these factors separated. This may bring to misleading results, leaving unanswered the question of how to improve cycling performance. This work provides an overview on the studies involving indexes and factors usually related to performance monitoring and assessment in cycling. In particular, in order to clarify all those aspects, the mutual interactions among these factors are highlighted, in view of a global performance assessment. Moreover, a proposal is presented advocating for a model-based approach that considers all factors mentioned in the survey, including the mutual interaction effects, for the definition of an objective function E representing the overall effectiveness of a training program in terms of both Eff_Met and Eff_Mech.

Validity and reproducibility of the ErgomoPro power meter compared with the SRM and Powertap power meters

PURPOSE

The ErgomoPro (EP) is a power meter that measures power output (PO) during outdoor and indoor cycling via 2 optoelectronic sensors located in the bottom bracket axis. The aim of this study was to determine the validity and the reproducibility of the EP compared with the SRM crank set and Powertap hub (PT).

METHODS

The validity of the EP was tested in the laboratory during 8 submaximal incremental tests (PO: 100 to 400 W), eight 30-min submaximal constant-power tests (PO = 180 W), and 8 sprint tests (PO > 750 W) and in the field during 8 training sessions (time: 181 +/- 73 min; PO: approximately 140 to 160 W). The reproducibility was assessed by calculating the coefficient of PO variation (CV) during the submaximal incremental and constant tests.

RESULTS

The EP provided a significantly higher PO than the SRM and PT during the submaximal incremental test: The mean PO differences were +6.3% +/- 2.5% and +11.1% +/- 2.1% respectively. The difference was greater during field training sessions (+12.0% +/- 5.7% and +16.5% +/- 5.9%) but lower during sprint tests (+1.6% +/- 2.5% and +3.2% +/- 2.7%). The reproducibility of the EP is lower than those of the SRM and PT (CV = 4.1% +/- 1.8%, 1.9% +/- 0.4%, and 2.1% +/- 0.8%, respectively).

CONCLUSIONS

The EP power meter appears less valid and reliable than the SRM and PT systems.

Effect of a novel pedal design on maximal power output and mechanical efficiency in well-trained cyclists

In this study, we evaluated the effects of a novel pedal design, characterized by a downward and forward shift of the cleat fixing platform relative to the pedal axle, on maximal power output and mechanical efficiency in 22 well-trained cyclists. Maximal power output was measured during a series of short (5-s) intermittent sprints on an isokinetic cycle ergometer at cadences from 40 to 120 rev min(-1). Mechanical efficiency was evaluated during a submaximal incremental exercise test on a bicycle ergometer using continuous VO(2) and VCO(2) measurement. Similar tests with conventional pedals and the novel pedals, which were mounted on the individual racing bike of the participant, were randomized. Maximal power was greater with novel pedals than with conventional pedals (between 6.0%, s(x) = 1.5 at 40 rev min(-1) and 1.8%, s(x) = 0.7 at 120 rev min(-1); P = 0.01). Torque production between crank angles of 60 degrees and 150 degrees was higher with novel pedals than with conventional pedals (P = 0.004). The novel pedal design did not affect whole-body VO(2) or VCO(2). Mechanical efficiency was greater with novel pedals than with conventional pedals (27.2%, s(x) = 0.9 and 25.1%, s(x) = 0.9% respectively; P = 0.047; effect size = 0.9). In conclusion, the novel pedals can increase maximal power output and mechanical efficiency in well-trained cyclists.

Upper body power as a determinant of classical cross-country ski performance

The purpose of this study was to evaluate the relationship between short (< or =60 s) and long duration (4-12 min) measures of upper body power (UBP) and mass start classical cross-country ski performance. Several experienced skiers (eight men, five women) completed three separate tests of UBP on a double poling ergometer: two tests of highest average power output for 10 s (UBP(10)) and 60 s (UBP(60)), and an incremental test to exhaustion to measure peak oxygen uptake (VO(2PEAK)) and peak power output (UBP(PEAK)). Lastly, subjects competed in a 10-km classical cross-country ski race from which race speed (RS) was computed. RS correlated highly with UBP(10) (r = 0.93; P < 0.05), UBP(60) (r = 0.92; P < 0.05), and UBP(PEAK) (r = 0.94; P < 0.05); the correlation was lower but still significant for VO(2PEAK) (r = 0.88; P < 0.05). These findings suggest that both short and long duration measures of UBP are important determinants of mass start classical ski race performance.

Aerodynamic Characteristics as Determinants of the Drafting Effect in Cycling

PURPOSE

To determine whether cyclists' individual aerodynamic characteristics influence the magnitude of the drafting effect in cycling.

METHODS

Thirteen competitive male cyclists performed two field protocols (individual and drafting). Hub-based power meters were used to measure power output and velocity, from which drag area (Ad) was calculated. The three subjects obtaining maximum (MAX), intermediate (INT), and minimum (MIN) values for Ad during the individual protocol acted as leaders during the drafting protocol. Measures of Ad were then made while subjects drafted each of the three leaders. The drafting effect was specifically quantified as the decrement on measured drag coefficient (Cd) and power output.

RESULTS

The mean drafting effect increased with leader Ad (DeltaCd: MIN = 35.55%, INT = 41.31%, MAX = 50.47%; Delta power: MIN = 111.1 W, INT = 124.05 W, MAX = 159.23 W; P < 0.0001). Regressions between leader Ad and drafting effect for individual drafters indicated substantial interdrafter variability (slope ranged from 29.4 to 190.5%.m) but little intradrafter variability (mean r = 0.9689), suggesting an interaction between leader and drafter. Correlating leader:drafter ratios for Ad, Ap, and body mass to the drafting effect supported this interaction (r = 0.69-0.78, P < 0.01), but only when data for all three groups were pooled.

CONCLUSIONS

Alteration of leader Ad elicits a linear increase in the drafting effect. However, the Ad of the leader does not explain all of the interdrafter variability in the drafting effect, which is specific to the drafting subject but is minimally explained by their aerodynamic characteristics. This interdrafter variability may be attributable to the drafter's skill in obtaining maximum benefit from drafting.

Validation of a field test to determine the maximal aerobic power in triathletes and endurance cyclists

OBJECTIVE

To validate a field test to assess the maximal and submaximal exercise aerobic adaptation under specific conditions, for endurance modality cyclists and triathletes.

METHODS

30 male and 4 female endurance modality cyclists and triathletes, with heterogeneous performance levels, performed three incremental tests: one in the laboratory and two in the field. Assessment of the validity of the field protocol was carried out by the Student's t test, intraclass correlation coefficient (ICC) and coefficient of variation (CV) of the maximal variables (maximal aerobic speed (MAS), maximal aerobic power (MAP), maximal heart rate (HR(max)), maximal blood lactate concentration ([La(-)](max)) and maximal oxygen uptake (VO(2max))) and submaximal variables (heart rate, HR) measured in each one of the tests. The errors in measurement were calculated. The repeatability of the field tests was assessed by means of the test-retest of the two field tests, and the validity by means of the test-retest of the laboratory test with respect to the mean of the two field tests.

RESULTS

No significant differences were found between the two field tests for any of the variables studied, but differences did exist for some variables between the laboratory tests with respect to the field tests (MAP, [La(-)](max), humidity (H), barometric pressure (Pb) and some characteristics of the protocols). The ICC of all the variables was high and the CV for the MAP was small. Furthermore, the measurement errors were small and therefore, assumable.

CONCLUSIONS

The incremental protocol of the proposed field test turned out to be valid to assess the maximal and submaximal aerobic adaptation.

The critical power and related whole-body bioenergetic models

This paper takes a performance-based approach to review the broad expanse of literature relating to whole-body models of human bioenergetics. It begins with an examination of the critical power model and its assumptions. Although remarkably robust, this model has a number of shortcomings. Attention to these has led to the development of more realistic and more detailed derivatives of the critical power model. The mathematical solutions to and associated behaviour of these models when subjected to imposed "exercise" can be applied as a means of gaining a deeper understanding of the bioenergetics of human exercise performance.

Accuracy of SRM and Power Tap Power Monitoring Systems for Bicycling

PURPOSE

: Although manufacturers of bicycle power monitoring devices SRM and Power Tap (PT) claim accuracy to within 2.5%, there are limited scientific data available in support. The purpose of this investigation was to assess the accuracy of SRM and PT under different conditions.

METHODS

: First, 19 SRM were calibrated, raced for 11 months, and retested using a dynamic CALRIG (50-1000 W at 100 rpm). Second, using the same procedure, five PT were repeat tested on alternate days. Third, the most accurate SRM and PT were tested for the influence of cadence (60, 80, 100, 120 rpm), temperature (8 and 21 degrees C) and time (1 h at ~300 W) on accuracy. Finally, the same SRM and PT were downloaded and compared after random cadence and gear surges using the CALRIG and on a training ride.

RESULTS

: The mean error scores for SRM and PT factory calibration over a range of 50 - 1000 W were 2.3 +/- 4.9% and -2.5 +/- 0.5%, respectively. A second set of trials provided stable results for 15 calibrated SRM after 11 months (-0.8 +/- 1.7%), and follow-up testing of all PT units confirmed these findings (-2.7 +/- 0.1%). Accuracy for SRM and PT was not largely influenced by time and cadence; however, power output readings were noticeably influenced by temperature (5.2% for SRM and 8.4% for PT). During field trials, SRM average and max power were 4.8% and 7.3% lower, respectively, compared with PT.

CONCLUSIONS

: When operated according to manufacturers instructions, both SRM and PT offer the coach, athlete, and sport scientist the ability to accurately monitor power output in the lab and the field. Calibration procedures matching performance tests (duration, power, cadence, and temperature) are, however, advised as the error associated with each unit may vary.

Improving cycling performance: how should we spend our time and money

Cycling performance is dependent on physiological factors which influence mechanical power production and mechanical and environmental factors that affect power demand. The purpose of this review was to summarize these factors and to rank them in order of importance. We used a model by Martin et al. to express all performance changes as changes in 40 km time trial performance. We modelled the performance of riders with different ability ranging from novice to elite cyclists. Training is a first and most obvious way to improve power production and was predicted to have the potential to improve 40 km time trial performance by 1 to 10% (1 to 7 minutes). The model also predicts that altitude training per se can cause a further improvement of 23 to 34 seconds. Carbohydrate-electrolyte drinks may decrease 40 km time by 32 to 42 seconds. Relatively low doses of caffeine may improve 40 km time trial performance by 55 to 84 seconds. Another way of improving time trial performance is by reducing the power demand of riding at a certain velocity. Riding with hands on the brake hoods would improve aerodynamics and increase performance time by approximately 5 to 7 minutes and riding with hands on the handlebar drops would increase performance time by 2 to 3 minutes compared with a baseline position (elbows on time trail handle bars). Conversely, riding with a carefully optimised position could decrease performance time by 2 to 2.5 minutes. An aerodynamic frame saved the modelled riders 1:17 to 1:44 min:sec. Furthermore, compared with a conventional wheel set, an aerodynamic wheel set may improve time trial performance time by 60 to 82 seconds. From the analysis in this article it becomes clear that novice cyclists can benefit more from the suggested alterations in position, equipment, nutrition and training compared with elite cyclists. Training seems to be the most important factor, but sometimes large improvements can be made by relatively small changes in body position. More expensive options of performance improvement include altitude training and modifications of equipment (light and aerodynamic bicycle and wheels). Depending on the availability of time and financial resources cyclists have to make decisions about how to achieve their performance improvements. The data presented here may provide a guideline to help make such decisions.