Correlating Multimodal Physical Sensor Information with Biological Analysis in Ultra Endurance Cycling

The sporting domain has traditionally been used as a testing ground for new technologies which subsequently make their way into the public domain. This includes sensors. In this article a range of physical and biological sensors deployed in a 64 hour ultra-endurance non-stop cycling race are described. A novel algorithm to estimate the energy expenditure while cycling and resting during the event are outlined. Initial analysis in this noisy domain of “sensors in the field” are very encouraging and represent a first with respect to cycling

Cyclist Performance Classification System based on Submaximal Fitness Test

Performances among cyclist always measured by time traveled from start to finish line and then the winner in cycling event also decided by time or who crossed the finish line first. On the other hand, cyclist performance can be measured through cardiorespiratory and physical fitness, and this performance can be enhanced by proper training to increase fitness and skill without burden. A wireless sensor network (WSN) system developed by combined various sensing element to capture physiological and bicycle’s kinetics feedback. Physiological data such as heart rate variability (HRV) and kinetic data such as paddling power and cadence used as input in Astrand-Ryhming and PWC150 submaximal test to classify the performance group among cyclist. Developed HRV system using Photoplethysmography (PPG) provides the significant output with R2 value was 0.967. A group of 15 cyclists from three different backgrounds was used as a subject in this study. Maximal oxygen intake (VO2max) produced by AstrandRyhming test correlated with estimated paddling power produced by PWC150 test with P<0.01 and the R2 value was 0.8656. Discriminant analysis was 88.3% successfully classified cyclist into 3 group and group of trained and untrained cyclist clearly separated

Power Profile Index: An Adjustable Metric for Load Monitoring in Road Cycling

Workload is calculated from exercise volume and intensity. In endurance sports, intensity has been measured using heart rate or RPE, giving rise to load indexes such as sRPE or TRIMP. In cycling, the advent of power meters led to new indexes, such as TSS. All these indexes have limitations, especially for high intensity exercise. Therefore, a new index for cycling is proposed, the Power Profile Index (PPi), which includes a weighting factor obtained from the relative exercise intensity and stage type. Using power data from 67 WorldTour cyclists and fatigue records in different stage types from 102 road cyclists, weighting factors for intensity and stage type were determined. Subsequently, the PPi was computed and compared to current indexes using data from a WorldTour team during the 2018 Tour de France. The proposed index showed a strong correlation with perceived fatigue as a function of stage type (R2 = 0.9996), as well as no differences in the load quantification in different types of stage profiles (p = 0.292), something that does not occur with other indexes such as TSS, RPE, or eTRIMP (p < 0.001). Therefore, PPi is a new index capable of quantifying the high intensity efforts that produce greater fatigue, as well as considering the stage type

Stationary roller versus velodrome for maximal cycling test: a comparison

The present study aimed to compare the acute cardio-respiratory responses of elite cyclists to a maximal progressive exercise carried out in two different conditions: in a laboratory (using a braked roller) and in an uncovered velodrome. In both testing conditions, ten elite male cyclists (age, 22.3 ± 3.9 years) performed a maximal discontinuous progressive test of 6 minutes per level with 150 W of initial load and increasing 50 W at each level until exhaustion. The heart rate and the ventilation parameters were measured breath-by-breath using a portable metabolic cart gas analysis system with telemetry data transmission. In the first 4 levels of effort, no significant differences were found between the two test conditions regarding VO2, (p=0.193), heart rate (p=0.973) and pedaling cadence (p=0.116). Comparing the maximum values achieved by each athlete in both exercise conditions, significant differences were found for heart rate (p=0.008) and pedaling cadence (p=0.005) but not for VO2max and peak power. Each variable showed a strong correlation between both assessments (VO2, r=0.984, p=0,000; heart rate, r=0.944, p=0.005; pedaling cadence, r=0.900, p=0.014). The amount of variability explained by the linear regression model for both cardio-respiratory parameters also showed a good fit value close to one (VO2max, r2=0.968; heart rate, r2=0.892). Our results suggest that identical cycling protocols conducted in different testing conditions with the same bike leads to equivalent performance but significantly different pedaling cadence and heart rate responses

The performance analysis of power output in professional male road cyclists

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 \u3c0.05) 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 \u3c0.05). 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 \u3c0.05). 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 \u3c0.05), 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 \u3e0.05) 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 \u3c0.001) 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 \u3c0.05)). 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 \u3c0.01) in phase one compared with phase two, respectively. Total distance decreased (P \u3c0.01) 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 \u3c0.05) 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

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 1000m (3000m) 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 \u3e3000 m compared with rides completed near sea-level. However, speed associated with MMP 60 and 240 s was greater (p \u3c 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 altitude

Physiological and metabolic responses to constant and variable load cycling performance

The experiments described in this thesis comprise a series of related, yet independent investigations examining the physiological and metabolic responses of well-trained amateur cyclists under conditions designed to mimic actual competitive situations, during individual and mass start races. In Section A the physiological responses to constant load and steady state exercise are determined. In Section B, the metabolic factors associated with constant and variable load cycling performance are examined

A monetary reward alters pacing but not performance in competitive cyclists

Money has frequently been used as an extrinsic motivator since it is assumed that humans are willing to invest more effort for financial reward. However, the influence of a monetary reward on pacing and performance in trained athletes is not well-understood. Therefore, the aim of this study was to analyse the influence of a monetary reward in well-trained cyclists on their pacing and performance during short and long cycling time trials (TT). Twentythree cyclists (6 ♀, 17 ♂) completed 4 self-paced time trials (TTs, 2 short: 4 km and 6 min; 2 long: 20 km and 30 min); in a randomized order. Participants were separated into parallel, non-randomized “rewarded” and “non-rewarded” groups. Cyclists in the rewarded group received a monetary reward based on highest mean power output across all TTs. Cyclists in the non-rewarded group did not receive a monetary reward. Overall performance was not significantly different between groups in short or long TTs (p \u3e 0.48). Power output showed moderatly lower effect sizes at comencement of the short TTs (Pmeandiff = 36.6 W; d \u3e 0.44) and the 20 km TT (Pmeandiff = 22.6 W; d = 0.44) in the rewarded group. No difference was observed in pacing during the 30 min TT (p = 0.95). An external reward seems to have influenced pacing at the commencement of time trials. Participants in the non-rewarded group adopted a typical parabolic shaped pattern, whereas participants in the rewarded group started trials more conservatively. Results raise the possibility that using money as an extrinsic reward may interfere with regulatory processes required for effective pacing