The analysis and utilization of cycling training data

Competitive demands during international sprint-distance triathlon races according to the course type: The influence of cycling on subsequent running performance

BACKGROUND

Despite the great contribution of the cycling segment to the Sprint-Distance Triathlon (SDT) races, very few studies have reported the power output of elite triathletes during races. The aim of this study was to analyse the competitive demands of elite triathletes during the cycling segment of SDT races and their influence on the subsequent running segment performance, considering the different types of race courses.

METHODS

Power variables during the cycling segment as well as the running performance metrics during 82 SDT races organised by World Triathlon (68 Continental Cups and Championships, 12 World Cups, and 2 World Triathlon Series) were analysed in 10 male and 7 female U23 participants.

RESULTS

The number of power peaks above 800 W and 1000 W for males was significantly greater (p < 0.05) in the technical courses (23 ± 13 and 5 ± 6 peaks, respectively) compared to the rolling courses (10 ± 6 and 2 ± 2 peaks, respectively). Similarly, females presented more (p < 0.05) power peaks above 500 W in the technical courses (24 ± 9 peaks) than in the rolling courses (14 ± 7 peaks). Additionally, the percentage of race time in severe power bands increased from rolling to technical courses in both sexes (males 21 ± 1% to 24 ± 2% and females 12 ± 1% to 15 ± 1%, both p < 0.05). Males spent a greater percentage of race time in the moderate (< 2 W·kg⁻¹) and severe (> 6 W·kg⁻¹) power bands, but a lower percentage in the heavy (2-6 W·kg⁻¹) band compared to females (p < 0.05). Time spent in the heavy (200-400 W) and severe (> 400 W) power bands showed a strong correlation with running rankings for males on both rolling (r = 0.62) and technical (r = 0.55) courses, as well as for females on rolling courses (r = 0.52).

CONCLUSIONS

An increased number of corners in SDT cycling courses requires more focused training on repeated power peaks and spending more time in the > 6 W·kg⁻¹ power bands to minimize performance losses in the subsequent running segment.

Impact of Menstrual Cycles or Combined Oral Contraception on Training Loads Assessed Using Latent Effort States in Female Elite Cyclists

To quantify the impact of the regular or irregular menstrual cycle (MC) or combined oral contraception (OC) on the time spent at the maximum effort exertion per training, assessed using latent effort states through a Hidden Markov chain Model (HMM). 6303 training sessions with heart rate (HR) and power output (PO) recorded every second were used to train HMM in order to determine latent effort states of 12 elite French cyclists followed up over 30 months. A total of 101 MC/OCs full cycles were analyzed. A calendar method was used to estimate regular MC phases (menstruation, estimated follicular phase, estimated luteal phase). Irregular MC was divided into menstruations/no menstruations and OC into break/active pill taking. Four latent effort states were identified: high, medium+, medium-, and low. Focused on high intensity-oriented training sessions, the proportion of time spent in high intensity effort state was significantly lower during menstruation (34.5%) compared to estimated follicular (65.2%, p = 0.0009) and luteal (55.4% p = 0.024) phases for regular MC, and during pills' break (43.7%) compared to active pill taking phase (62.6% p = 0.031) for OC cycles. During the high intensity-oriented training sessions, the proportion of time spent in high effort state is almost twice higher in mid-regular cycles whereas is lower during menstruation or pill's break in elite cyclists. These findings rely on repeated assessment of training loads in a real-world context, analyzed using novel machine learning techniques that objectively quantify both internal and external training loads in elite female cyclists.

Determination of Ventilatory Thresholds Using Near-Infrared Spectroscopy in Recreational Endurance and CrossFit Athletes

To define training zones, ventilatory thresholds (VTs) are commonly established by cardiopulmonary gas-exchange analysis during incremental exercise tests. Portable near-infrared spectroscopy (NIRS) devices have emerged as a potential tool for detecting these thresholds by monitoring muscle oxygenation. This study evaluated the accuracy of NIRS measurements to determine VTs or critical power (CP) based on muscle oxygen saturation and assesses the device's consistency across 2 constant-load tests. Data from 2 cross-sectional studies involving trained recreational endurance athletes (26 from study 1) and CrossFit athletes (59 from study 2) were examined. Incremental ramp tests on a cycle ergometer were performed and followed by either a constant-load test (study 1) or a CP test (study 2). When comparing power output or heart rate between NIRS-derived breakpoints and VTs, weak to moderate agreement was found. Mean differences in power output and heart rate ranged from 16.8 to 22.4 W and 3.8 to 6.0 beats·min-1 at the first threshold and 27.4 to 31.2 W and 7.1 to 7.8 beats·min-1 at the second threshold. Comparing with CP, mean differences ranged from -0.4 to 0.4 W and -0.6 to 0.9 beats·min-1. Test-retest reliability showed moderate agreement, with a mean bias of 1.2 percentage points between constant-load tests. Thus, NIRS may not be accurate for determining VTs or CP during exercise due to limited agreement in power output or hear rate, notable variability on individual level, and moderate reproducibility.

Differences in Workloads of Maximal Tasks in Active-Duty Firefighters

The purpose of this study was to compare the workload of a maximal treadmill test (TREAD) and a fire suppression task (BURN) in firefighters and to examine their relationships to fitness as measured by body mass index (BMI), percent body fat (BF%), and peak aerobic capacity (VO2PEAK). The amount of time spent in the heart rate (HR) intensity ranges of 50-59% HRMAX (ZONE1), 60-69% HRMAX (ZONE2), 70-79% HRMAX (ZONE3), 80-89% HRMAX (ZONE4), and ≥90% HRMAX (ZONE5) quantified the workload as the Edward's Training Impulse for TREAD (ETRIMPTREAD) and BURN (ETRIMPBURN). The ETRIMPTREAD was significantly less than ETRIMPBURN. For TREAD, ZONE5 > ZONE2 and ZONE3. For BURN, ZONE4 > ZONE1, ZONE2, and ZONE5 > ZONE1, ZONE2, and ZONE3. A lower BF% and greater VO2PEAK were related to a greater ETRIMPTREAD and unrelated to ETRIMPBURN. For BURN only, a lower BF% and greater VO2PEAK were related to less time in ZONE5. BMI was unrelated to all workload measures. Laboratory-based maximal exercise testing does not adequately reflect the workload of simulated fire suppression and therefore may not be indicative of firefighter readiness to meet job demands. Less-fit firefighters rely on higher cardiovascular intensities to complete the same workload, and practitioners should consider this when selecting strategies to reduce job-associated cardiovascular risk.

Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors

Objectives

To enhance the daily training quality of athletes without inducing significant physiological fatigue, aiming to achieve a balance between training efficiency and load.

Design methods

Firstly, we developed an activity classification training model using the random forest algorithm and introduced the "effective training rate" (the ratio of effective activity time to total time) as a metric for assessing athlete training efficiency. Secondly, a method for rating athlete training load was established, involving qualitative and quantitative analyses of physiological fatigue through subjective fatigue scores and heart rate data. Lastly, an optimization system for training efficiency and load balance, utilizing multiple inertial sensors, was created. Athlete states were categorized into nine types based on the training load and efficiency ratings, with corresponding management recommendations provided.

Results

Overall, this study, combining a sports activity recognition model with a physiological fatigue assessment model, has developed a training efficiency and load balance optimization system with excellent performance. The results indicate that the prediction accuracy of the sports activity recognition model is as high as 94.70%. Additionally, the physiological fatigue assessment model, utilizing average relative heart rate and average RPE score as evaluation metrics, demonstrates a good overall fit, validating the feasibility of this model.

Conclusions

This study, based on relative heart rate and wearable devices to monitor athlete physiological fatigue, has developed a balanced optimization system for training efficiency and load. It provides a reference for athletes' physical health and fatigue levels, offering corresponding management recommendations for coaches and relevant professionals.

Power Profile during Cycling in World Triathlon Series and Olympic Games

This study aimed to analyze the power profile (PP) during the cycling segment of international-level triathletes in the World Triathlon Series (WTS) and Olympics and to evaluate the influence of circuit type, race distance (Sprint or Olympic distance) and race dynamics on the development of the cycling leg and the final race position. Four male triathletes participated in the study. Twenty races were analyzed using geolocation technology and power-meter data to analyze PP, race dynamics, and course characteristics. Before the races, incremental tests of volitional exhaustion with gas analysis were performed to determine power intensity zones. Nonparametric Mann-Whitney U tests and correlation analyses were conducted to identify differences and relationships between various variables. A correlation between the time spent above maximal aerobic power (MAP) and dangerous curves per kilometer (r = 0.46; p < 0.05) and bike split result (BSR) (r = -0.50; p < 0.05) was observed. Also, moderate correlation was found between BSR and the final race position (r = 0.46; p < 0.01). No differences were found between sprint and Olympic distance races in any variable. Power output variability, influenced by technical circuit segments, remains the main characteristic in international short-distance races. The results of the present study suggest that the triathletes who are better adapted to intermittent high intensity efforts perform better cycling legs at international high-level races.

Impact of menstrual cycle or combined oral contraception on elite female cyclists' training responses through a clustering analysis of training sessions

Objectives

(i) To classify training sessions of elite female cyclists according to an intensity index based on a longitudinal follow-up using multiparametric data collected in situ (ii) to measure the effect of estimated menstrual cycle (MC) phases and oral contraceptive pills (OC) phases on the athletes' training responses on each type of training identified.

Method

Thirteen elite French cyclists were followed up over 30 months and 5,190 training sessions were collected and 81 MC/OCs full cycles analyzed. Power sensors and position devices captured training data in situ, which was summarized into 14 external load variables. Principal Component Analysis and K-means clustering were used to identify cycling sessions according to an intensity load index. The clusters were then verified and categorized through the analysis of heart rate and rate of perceived effort. A calendar method was used to estimate 3 phases of the MC: menstruation, mid-cycle phase (MP) and late-cycle phase (LP). Two phases were defined among monophasic OC users: pills' taking/withdrawal.

Results

Four main types of training effort were identified: Intensive, Long, Medium and Light. In the MC group (n = 7; 52 cycles), the intensity index is 8% higher during the mid-cycle (vs. menstrual phase, p = 0.032) in the Intensive effort sessions. No differences were observed in Long, Medium or Light effort, nor between the phases of pills' taking/withdrawal among OC users.

Conclusion

The clustering analyses developed allows a training classification and a robust method to investigate the influence of the MC/OC in situ. A better training response during the mid-cycle when the sessions are the most intense suggest an impact of the MC when the athletes approach their maximal capacity.

A model for calculating the mechanical demands of overground running

An energy-based approach to quantifying the mechanical demands of overground, constant velocity and/or intermittent running patterns is presented. Total mechanical work done (Wtotal) is determined from the sum of the four sub components: work done to accelerate the centre of mass horizontally (Whor), vertically (Wvert), to overcome air resistance (Wair) and to swing the limbs (Wlimbs). These components are determined from established relationships between running velocity and running kinematics; and the application of work-energy theorem. The model was applied to constant velocity running (2-9 m/s), a hard acceleration event and a hard deceleration event. The estimated Wtotal and each sub component were presented as mechanical demand (work per unit distance) and power (work per unit time), for each running pattern. The analyses demonstrate the model is able to produce estimates that: 1) are principally determined by the absolute running velocity and/or acceleration; and 2) can be attributed to different mechanical demands given the nature of the running bout. Notably, the proposed model is responsive to varied running patterns, producing data that are consistent with established human locomotion theory; demonstrating sound construct validity. Notwithstanding several assumptions, the model may be applied to quantify overground running demands on flat surfaces.

An exploration of context and learning in endurance sports coaching

Introduction

This study explored contextual factors which influence coach learning of an international cohort of endurance sports coaches.

Methods

Following ethical approval, 839 coaches, 612 coached athletes and 8,352 non-coached athletes participated in the research. A critical realist research philosophy was adopted, in which self-completion surveys were developed in consultation with coaches and industry end-users.

Results and Discussion

The context was dominated by remote coaching practices and digital technology which shaped how coaches learn and thus, what it meant to be a coach. Unmediated learning sources were biophysically biased and largely delivered through marketised platforms designed to sell products. The study findings have broader implications within sport and education, in which it is suggested that remote coaching and learning platforms may sometimes create a sense of psycho-emotional detachment in which capacity for learning can be limited.

The relationship between training characteristics and durability in professional cyclists across a competitive season

RESULTS

Absolute 5MMP_fatigue, 12MMP_fatigue and relative 12MMP_fatigue were significantly lower in late-season compared with early- and mid-season (p < 0.05). The difference in absolute 12MMP_fresh and 12MMP_fatigue was significantly greater in late than in early- and mid-season.A significant relationship was found between training time below the first ventilatory threshold (Time < VT1) and improvements in absolute and relative 2MMP_fatigue (r = 0.43 p = 0.018 and r = 0.376 p = 0.04 respectively); and between a shift towards a polarised training intensity distribution and improvements in absolute and relative 12MMP_fatigue (r = 0.414p = 0.023 for both) between subsequent periods.

CONCLUSION

There is greater variability in the fatigue power profile across a competitive season than the fresh power profile.

Power profiling and the power-duration relationship in cycling: a narrative review

Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P(t) = W'/t + CP (W'-work capacity above CP; t-time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist's power profile.

Caveats and Recommendations to Assess the Validity and Reliability of Cycling Power Meters: A Systematic Scoping Review

A large number of power meters have become commercially available during the last decades to provide power output (PO) measurement. Some of these power meters were evaluated for validity in the literature. This study aimed to perform a review of the available literature on the validity of cycling power meters. PubMed, SPORTDiscus, and Google Scholar have been explored with PRISMA methodology. A total of 74 studies have been extracted for the reviewing process. Validity is a general quality of the measurement determined by the assessment of different metrological properties: Accuracy, sensitivity, repeatability, reproducibility, and robustness. Accuracy was most often studied from the metrological property (74 studies). Reproducibility was the second most studied (40 studies) property. Finally, repeatability, sensitivity, and robustness were considerably less studied with only 7, 5, and 5 studies, respectively. The SRM power meter is the most used as a gold standard in the studies. Moreover, the number of participants was very different among them, from 0 (when using a calibration rig) to 56 participants. The PO tested was up to 1700 W, whereas the pedalling cadence ranged between 40 and 180 rpm, including submaximal and maximal exercises. Other exercise conditions were tested, such as torque, position, temperature, and vibrations. This review provides some caveats and recommendations when testing the validity of a cycling power meter, including all of the metrological properties (accuracy, sensitivity, repeatability, reproducibility, and robustness) and some exercise conditions (PO range, sprint, pedalling cadence, torque, position, participant, temperature, vibration, and field test).

Understanding the Relationship between Intrinsic Cardiorespiratory Fitness and Serum and Skeletal Muscle Metabolomics Profile

Intrinsic cardiorespiratory fitness (iCRF) indicates the CRF level in the sedentary state. However, even among sedentary individuals, a wide interindividual variability is observed in the iCRF levels, whose associated molecular characteristics are little understood. This study aimed to investigate whether serum and skeletal muscle metabolomics profiles are associated with iCRF, measured by maximal power output (MPO). Seventy sedentary young adults were submitted to venous blood sampling, a biopsy of the vastus lateralis muscle and iCRF assessment. Blood serum and muscle tissue samples were analyzed by proton nuclear magnetic resonance (¹H NMR) spectroscopy. Metabolites related to iCRF were those supported by three levels of evidence: (1) correlation with iCRF, (2) significant difference between individuals with low and high iCRF, and (3) metabolite contribution to significant pathways associated with iCRF. From 43 serum and 70 skeletal muscle analyzed metabolites, iCRF was positively associated with levels of betaine, threonine, proline, ornithine, and glutamine in serum and lactate, fumarate, NADP+, and formate in skeletal muscle. Serum betaine and ornithine and skeletal muscle lactate metabolites explained 31.2 and 16.8%, respectively, of the iCRF variability in addition to body mass. The results suggest that iCRF in young adults is positively associated with serum and skeletal muscle metabolic levels, indicative of the amino acid and carbohydrate metabolism.

Validity of the Favero Assioma Duo Power Pedal System for Measuring Power Output and Cadence

Cycling power meters enable monitoring external loads and performance changes. We aimed to determine the concurrent validity of the novel Favero Assioma Duo (FAD) pedal power meter compared with the crank-based SRM system (considered as gold standard). Thirty-three well-trained male cyclists were assessed at different power output (PO) levels (100-500 W and all-out 15-s sprints), pedaling cadences (75-100 rpm) and cycling positions (seating and standing) to compare the FAD device vs. SRM. No significant differences were found between devices for cadence nor for PO during all-out efforts (p > 0.05), although significant but small differences were found for efforts at lower PO values (p < 0.05 for 100-500 W, mean bias 3-8 W). A strong agreement was observed between both devices for mean cadence (ICC > 0.87) and PO values (ICC > 0.81) recorded in essentially all conditions and for peak cadence (ICC > 0.98) and peak PO (ICC > 0.99) during all-out efforts. The coefficient of variation for PO values was consistently lower than 3%. In conclusion, the FAD pedal-based power meter can be considered an overall valid system to record PO and cadence during cycling, although it might present a small bias compared with power meters placed on other locations such as SRM.

Evaluation of strategy and tactics in cycling: a systematic review of evaluation methods and possible performance implications

INTRODUCTION

Cycling performance is affected by many factors and is the expression of a multitude of variables. Different studies aiming to describe variables determining cycling performance are focused mainly on metabolic efficiency optimization and mechanical efficiency optimization. Strategy and tactics analysis in cycling represent a key additional performance variable, however, the knowledge of methods to assess these parameters and the possible performance implications is low. The main purposes of the study were to systematically review the state of the art related to strategy and tactics analysis in cycling and describe and analyze the possible implications and possible evaluation methods of tactics and strategy in cycling.

EVIDENCE ACQUISITION

MEDLINE®/PubMed and Scopus databases were searched with additional integration from external sources, between March and April 2020. To meet the inclusion criteria, studies published from 2000 to 2020 that evaluated the impact of strategies and/or tactics on cycling performance or aimed to study and develop strategy and/or tactic models to improve cycling performance were selected.

EVIDENCE SYNTHESIS

Starting from the 12972 identified records, totally 22 studies met the inclusion criteria and were included in the current systematic review. Studies emerged from the selection focused mainly on time trials strategies analysis (54.55%), track cycling strategy analysis (22.73%) and other cycling disciplines strategy evaluation (road cycling, mountain bike, cyclocross; 22.73%). According to the studies' objectives, four main topics of investigation emerged from the research: evaluation of the impact of different starting strategies on time-trial performance; evaluation of different pacing strategies on performance; evaluation of aerodynamics and drag coefficients according to racing strategy in team pursuit; application of video analysis or strategy/tactics effect on performance.

CONCLUSIONS

Strategy and tactics analysis in cycling represent a key additional performance variable to add to the traditionally more studied and analyzed parameters. However, few studies deeply analyzed these variables. Future works may focus on these aspects to investigate strategy and tactics insights and application of evaluation methods in cycling.

Is There a Relationship Between Workload and Occurrence of Back Pain and Back Injuries in Athletes?

The back is subjected to a great deal of strain in many sports. Up to 20% of all sports injuries involve an injury to the lower back or neck. Repetitive or high impact loads (e.g., running, gymnastics, skiing) and weight loading (e.g., weightlifting) affect the lower back. Rotation of the torso (e.g., golf, tennis) causes damage to both, the lumbar and thoracic spine. The cervical spine is most commonly injured in contact sports (e.g., boxing, football). One of the factors that increases the odds of injuries in athletes is excessive and rapid increases in training loads. In spite of currently emerging evidence on this issue, little is known about the balance between physiological loading on the spine and athletic performance, versus overloading and back pain and/or injury in athletes. This scoping review aims (i) to map the literature that addresses the association between the training load and the occurrence of back pain and/or injury, especially between the Acute:Chronic Workload Ratio (ACWR) and back problems in athletes of individual and team sports, and (ii) to identify gaps in existing literature and propose future research on this topic. A literature search of six electronic databases (i.e., MEDLINE, PubMed, Web of Science, SCOPUS, SportDiscus, and CINAHL) was conducted. A total of 48 research articles met the inclusion criteria. Findings identified that fatigue of the trunk muscles induced by excessive loading of the spine is one of the sources of back problems in athletes. In particular, high training volume and repetitive motions are responsible for the high prevalence rates. The most influential are biomechanical and physiological variations underlying the spine, though stress-related psychological factors should also be considered. However, limited evidence exists on the relationship between the ACWR and back pain or non-contact back injuries in athletes from individual and team sports. This may be due to insufficiently specified the acute and chronic time window that varies according to sport-specific schedule of competition and training. More research is therefore warranted to elucidate whether ACWR, among other factors, is able to identify workloads that could increase the risk of back problems in athletes.

Power Assessment in Road Cycling: A Narrative Review

Nowadays, the evaluation of physiological characteristics and training load quantification in road cycling is frequently performed through power meter data analyses, but the scientific evidence behind this tool is scarce and often contradictory. The aim of this paper is to review the literature related to power profiling, functional threshold testing, and performance assessment based on power meter data. A literature search was conducted following preferred reporting items for review statement (PRISMA) on the topic of {“cyclist” OR “cycling” AND “functional threshold” OR “power meter”}. The reviewed evidence provided important insights regarding power meter-based training: (a) functional threshold testing is closely related to laboratory markers of steady state; (b) the 20-min protocol represents the most researched option for functional threshold testing, although shorter durations may be used if verified on an individual basis; (c) power profiling obtained through the recovery of recorded power outputs allows the categorization and assessment of the cyclist’s fitness level; and (d) power meters represent an alternative to laboratory tests for the assessment of the relationship between power output and cadence. This review elucidates the increasing amount of studies related to power profiling, functional threshold testing, and performance assessment based on power meter data, highlighting the opportunity for the expanding knowledge that power meters have brought in the road cycling field.

Stress in Academic and Athletic Performance in Collegiate Athletes: A Narrative Review of Sources and Monitoring Strategies

College students are required to manage a variety of stressors related to academic, social, and financial commitments. In addition to the burdens facing most college students, collegiate athletes must devote a substantial amount of time to improving their sporting abilities. The strength and conditioning professional sees the athlete on nearly a daily basis and is able to recognize the changes in performance and behavior an athlete may exhibit as a result of these stressors. As such, the strength and conditioning professional may serve an integral role in the monitoring of these stressors and may be able to alter training programs to improve both performance and wellness. The purpose of this paper is to discuss stressors experienced by collegiate athletes, developing an early detection system through monitoring techniques that identify the detrimental effects of stress, and discuss appropriate stress management strategies for this population.

Estimating an individual's oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study

Measurement of oxygen uptake during exercise ([Formula: see text]) is currently non-accessible to most individuals without expensive and invasive equipment. The goal of this pilot study was to estimate cycling [Formula: see text] from easy-to-obtain inputs, such as heart rate, mechanical power output, cadence and respiratory frequency. To this end, a recurrent neural network was trained from laboratory cycling data to predict [Formula: see text] values. Data were collected on 7 amateur cyclists during a graded exercise test, two arbitrary protocols (Prot-1 and -2) and an "all-out" Wingate test. In Trial-1, a neural network was trained with data from a graded exercise test, Prot-1 and Wingate, before being tested against Prot-2. In Trial-2, a neural network was trained using data from the graded exercise test, Prot-1 and 2, before being tested against the Wingate test. Two analytical models (Models 1 and 2) were used to compare the predictive performance of the neural network. Predictive performance of the neural network was high during both Trial-1 (MAE = 229(35) mlO2min-1, r = 0.94) and Trial-2 (MAE = 304(150) mlO2min-1, r = 0.89). As expected, the predictive ability of Models 1 and 2 deteriorated from Trial-1 to Trial-2. Results suggest that recurrent neural networks have the potential to predict the individual [Formula: see text] response from easy-to-obtain inputs across a wide range of cycling intensities.

Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation

This study aims to model training adaptation using Artificial Neural Network (ANN) geometric optimisation. Over 26 weeks, 38 swimmers recorded their training and recovery data on a web platform. Based on these data, ANN geometric optimisation was used to model and graphically separate adaptation from maladaptation (to training). Geometric Activity Performance Index (GAPI), defined as the ratio of the adaptation to the maladaptation area, was introduced. The techniques of jittering and ensemble modelling were used to reduce overfitting of the model. Correlation (Spearman rank) and independence (Blomqvist β) tests were run between GAPI and performance measures to check the relevance of the collected parameters. Thirteen out of 38 swimmers met the prerequisites for the analysis and were included in the modelling. The GAPI based on external load (distance) and internal load (session-Rating of Perceived Exertion) showed the strongest correlation with performance measures. ANN geometric optimisation seems to be a promising technique to model training adaptation and GAPI could be an interesting numerical surrogate to track during a season.

Player Load and Metabolic Power Dynamics as Load Quantifiers in Soccer

There has recently been an increase in quantification and objective analysis of soccer performance due to improvements in technology using load indexes such as Player Load (PL) and Metabolic Power (MP). The objectives of this study were: (1) to describe the performance of PL and MP in competition according to the specific role, match-to- match variation, periods of play, game location and match status according to game periods, and (2) to analyze the relationship between both indexes. Twenty-one national-level soccer players were distributed in the following specific positional roles: external defenders (ED) (n = 4), central defenders (CD) (n = 4), midfielders (M) (n = 5), external midfielders (EM) (n = 4) and attackers (A) (n = 4). A total of 12 matches played by a Spanish Third Division team during the 2016/2017 season were analyzed. WIMU PROTM inertial devices (RealTrack System, Almeria, Spain) were used for recording the data. The main results were: (1) a performance reduction in both variables over the course of match time, (2) significant differences in both variables based on the specific position, (3) differences in physical demands during the season matches, (4) winning during a game period and the condition of being the visitor team provoked higher demands, and (5) a high correlation between both variables in soccer. In conclusion, different contextual variables influence the external load demands; both indexes are related so they could be used for external load quantification, and it is necessary to analyze physical demands of the competition for a specific and individualized load design in training sessions.

Relationships Between Training Load Indicators and Training Outcomes in Professional Soccer

BACKGROUND

In professional senior soccer, training load monitoring is used to ensure an optimal workload to maximize physical fitness and prevent injury or illness. However, to date, different training load indicators are used without a clear link to training outcomes.

OBJECTIVE

The aim of this systematic review was to identify the state of knowledge with respect to the relationship between training load indicators and training outcomes in terms of physical fitness, injury, and illness.

METHODS

A systematic search was conducted in four electronic databases (CINAHL, PubMed, SPORTDiscus, and Web of Science). Training load was defined as the amount of stress over a minimum of two training sessions or matches, quantified in either external (e.g., duration, distance covered) or internal load (e.g., heart rate [HR]), to obtain a training outcome over time.

RESULTS

A total of 6492 records were retrieved, of which 3304 were duplicates. After screening the titles, abstracts and full texts, we identified 12 full-text articles that matched our inclusion criteria. One of these articles was identified through additional sources. All of these articles used correlations to examine the relationship between load indicators and training outcomes. For pre-season, training time spent at high intensity (i.e., >90 % of maximal HR) was linked to positive changes in aerobic fitness. Exposure time in terms of accumulated training, match or combined training, and match time showed both positive and negative relationships with changes in fitness over a season. Muscular perceived exertion may indicate negative changes in physical fitness. Additionally, it appeared that training at high intensity may involve a higher injury risk. Detailed external load indicators, using electronic performance and tracking systems, are relatively unexamined. In addition, most research focused on the relationship between training load indicators and changes in physical fitness, but less on injury and illness.

CONCLUSION

HR indicators showed relationships with positive changes in physical fitness during pre-season. In addition, exposure time appeared to be related to positive and negative changes in physical fitness. Despite the availability of more detailed training load indicators nowadays, the evidence about the usefulness in relation to training outcomes is rare. Future research should implement continuous monitoring of training load, combined with the individual characteristics, to further examine their relationship with physical fitness, injury, and illness.

Quantification of brake data acquired with a brake power meter during simulated cross-country mountain bike racing

There is currently a dearth of information describing cycling performance outside of propulsive and physiological variables. The aim of the present study was to utilise a brake power meter to quantify braking during a multi-lap cross-country mountain bike time trial and to determine how braking affects performance. A significant negative association was determined between lap time and brake power (800.8 ± 216.4 W, mean ± SD; r = -0.446; p < 0.05), while the time spent braking (28.0 ± 6.4 s) was positively associated with lap time (314.3 ± 37.9 s; r = 0.477; p < 0.05). Despite propulsive power decreasing after the first lap (p < 0.05), lap time remained unchanged (p > 0.05) which was attributed to decreased brake work (p < 0.05) and brake time (p < 0.05) in both the front and rear brakes by the final lap. A multiple regression model incorporating braking and propulsion was able to explain more of the variance in lap time (r² = 0.935) than propulsion alone (r² = 0.826). The present study highlights that riders' braking contributes to mountain bike performance. As riders repeat a cross-country mountain bike track, they are able to change braking, which in turn can counterbalance a reduction in power output. Further research is required to understand braking better.

Considerations on the Assessment and Use of Cycling Performance Metrics and their Integration in the Athlete's Biological Passport

Over the past few decades the possibility to capture real-time data from road cyclists has drastically improved. Given the increasing pressure for improved transparency and openness, there has been an increase in publication of cyclists' physiological and performance data. Recently, it has been suggested that the use of such performance biometrics may be used to strengthen the sensitivity and applicability of the Athlete Biological Passport (ABP) and aid in the fight against doping. This is an interesting concept which has merit, although there are several important factors that need to be considered. These factors include accuracy of the data collected and validity (and reliability) of the subsequent performance modeling. In order to guarantee high quality standards, the implementation of well-structured Quality-Systems within sporting organizations should be considered, and external certifications may be required. Various modeling techniques have been developed, many of which are based on fundamental intensity/time relationships. These models have increased our understanding of performance but are currently limited in their application, for example due to the largely unaccounted effects of environmental factors such as, heat and altitude. In conclusion, in order to use power data as a performance biometric to be integrated in the biological passport, a number of actions must be taken to ensure accuracy of the data and better understand road cycling performance in the field. This article aims to outline considerations in the quantification of cycling performance, also presenting an alternative method (i.e., monitoring race results) to allow for determination of unusual performance improvements.

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

Validity of a device designed to measure braking power in bicycle disc brakes

Real-world cycling performance depends not only on exercise capacities, but also on efficiently traversing the bicycle through the terrain. The aim of this study was to determine if it was possible to quantify the braking done by a cyclist in the field. One cyclist performed 408 braking trials (348 on a flat road; 60 on a flat dirt path) over 5 days on a bicycle fitted with brake torque and angular velocity sensors to measure brake power. Based on Newtonian physics, the sum of brake work, aerodynamic drag and rolling resistance was compared with the change in kinetic energy in each braking event. Strong linear relationships between the total energy removed from the bicycle-rider system through braking and the change in kinetic energy were observed on the tar-sealed road (r² = 0.989; p < 0.0001) and the dirt path (r² = 0.952; p < 0.0001). T-tests revealed no difference between the total energy removed and the change in kinetic energy on the road (p = 0.715) or dirt (p = 0.128). This study highlights that brake torque and angular velocity sensors are valid for calculating brake power on the disc brakes of a bicycle in field conditions. Such a device may be useful for investigating cyclists' ability to traverse through various terrains.

How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury

Athletes participating in elite sports are exposed to high training loads and increasingly saturated competition calendars. Emerging evidence indicates that poor load management is a major risk factor for injury. The International Olympic Committee convened an expert group to review the scientific evidence for the relationship of load (defined broadly to include rapid changes in training and competition load, competition calendar congestion, psychological load and travel) and health outcomes in sport. We summarise the results linking load to risk of injury in athletes, and provide athletes, coaches and support staff with practical guidelines to manage load in sport. This consensus statement includes guidelines for (1) prescription of training and competition load, as well as for (2) monitoring of training, competition and psychological load, athlete well-being and injury. In the process, we identified research priorities.

Knowledge is power: Issues of measuring training and performance in cycling

Mobile power meters provide a valid means of measuring cyclists' power output in the field. These field measurements can be performed with very good accuracy and reliability making the power meter a useful tool for monitoring and evaluating training and race demands. This review presents power meter data from a Grand Tour cyclist's training and racing and explores the inherent complications created by its stochastic nature. Simple summary methods cannot reflect a session's variable distribution of power output or indicate its likely metabolic stress. Binning power output data, into training zones for example, provides information on the detail but not the length of efforts within a session. An alternative approach is to track changes in cyclists' modelled training and racing performances. Both critical power and record power profiles have been used for monitoring training-induced changes in this manner. Due to the inadequacy of current methods, the review highlights the need for new methods to be established which quantify the effects of training loads and models their implications for performance.

Effects of four-month handbike training under free-living conditions on physical fitness and health in wheelchair users

PURPOSE

Recognizing the encouraging effect of challenging events, the HandbikeBattle (HBB) was created to promote exercise among wheelchair users. The purpose of this study was to reveal the effects on physical fitness and health outcomes of four-month handbike training under free-living conditions in preparation for the event.

METHODS

In this prospective cohort study, 59 relatively inexperienced handyclists participated in the HBB of 2013 or 2014. Incremental exercise tests were conducted, respiratory function was tested and anthropometrics were measured before and after the preparation period. Main outcome measures were peak power output (POpeak), peak oxygen uptake (VO2peak) and waist circumference, of which the changes were tested using repeated measures ANOVA. To detect possible determinants of changes in physical fitness, a linear regression analysis was conducted with personal characteristics, executed training volume and upper-extremity complaints during the training period as independent variables.

RESULTS

POpeak, VO2peak and waist circumference improved significantly with 17%, 7% and 4.1%, respectively. None of the included variables were significant determinants for the changes in POpeak found as a result of the training.

CONCLUSION

A challenging event such as the HBB provokes training regimes among participants of sufficient load to realize substantial improvements in physical fitness and health outcomes. Implications for Rehabilitation Due to the often impaired muscle function in the lower-limbs and an inactive lifestyle, wheelchair users generally show considerably lower levels of fitness compared to able-bodied individuals. This prospective cohort study showed that four months of handbike training under free-living conditions in preparation for this event resulted in substantial improvements in physical fitness and health outcomes in wheelchair users. The creation of a challenging event such as the HandbikeBattle as part of a follow-up rehabilitation practice can therefore be a useful tool to help wheelchair users initiate or keep training to improve their physical fitness and health.

Evaluation of tactical training in team handball by means of artificial neural networks

While tactical performance in competition has been analysed extensively, the assessment of training processes of tactical behaviour has rather been neglected in the literature. Therefore, the purpose of this study is to provide a methodology to assess the acquisition and implementation of offensive tactical behaviour in team handball. The use of game analysis software combined with an artificial neural network (ANN) software enabled identifying tactical target patterns from high level junior players based on their positions during offensive actions. These patterns were then trained by an amateur junior handball team (n = 14, 17 (0.5) years)). Following 6 weeks of tactical training an exhibition game was performed where the players were advised to use the target patterns as often as possible. Subsequently, the position data of the game was analysed with an ANN. The test revealed that 58% of the played patterns could be related to the trained target patterns. The similarity between executed patterns and target patterns was assessed by calculating the mean distance between key positions of the players in the game and the target pattern which was 0.49 (0.20) m. In summary, the presented method appears to be a valid instrument to assess tactical training.

Effects of power variation on cycle performance during simulated hilly time-trials

It has previously been shown that cyclists are unable to maintain a constant power output during cycle time-trials on hilly courses. The purpose of the present study is therefore to quantify these effects of power variation using a mathematical model of cycling performance. A hypothetical cyclist (body mass: 70 kg, bicycle mass: 10 kg) was studied using a mathematical model of cycling, which included the effects of acceleration. Performance was modelled over three hypothetical 40-km courses, comprising repeated 2.5-km sections of uphill and downhill with gradients of 1%, 3%, and 6%, respectively. Amplitude (5-15%) and distance (0.31-20.00 km) of variation were modelled over a range of mean power outputs (200-600 W) and compared to sustaining a constant power. Power variation was typically detrimental to performance; these effects were augmented as the amplitude of variation and severity of gradient increased. Varying power every 1.25 km was most detrimental to performance; at a mean power of 200 W, performance was impaired by 43.90 s (±15% variation, 6% gradient). However at the steepest gradients, the effect of power variation was relatively independent of the distance of variation. In contrast, varying power in parallel with changes in gradient improved performance by 188.89 s (±15% variation, 6% gradient) at 200 W. The present data demonstrate that during hilly time-trials, power variation that does not occur in parallel with changes in gradient is detrimental to performance, especially at steeper gradients. These adverse effects are substantially larger than those previously observed during flat, windless time-trials.

Determining optimal cadence for an individual road cyclist from field data

The cadence that maximises power output developed at the crank by an individual cyclist is conventionally determined using a laboratory test. The purpose of this study was two-fold: (i) to show that such a cadence, which we call the optimal cadence, can be determined using power output, heart-rate, and cadence measured in the field and (ii) to describe methodology to do so. For an individual cyclist's sessions, power output is related to cadence and the elicited heart-rate using a non-linear regression model. Optimal cadences are found for two riders (83 and 70 revolutions per minute, respectively); these cadences are similar to the riders’ preferred cadences (82–92 rpm and 65–75 rpm). Power output reduces by approximately 6% for cadences 20 rpm above or below optimum. Our methodology can be used by a rider to determine an optimal cadence without laboratory testing intervention: the rider will need to collect power output, heart-rate, and cadence measurements from training and racing sessions over an extended period (>6 months); ride at a range of cadences within those sessions; and calculate his/her optimal cadence using the methodology described or a software tool that implements it.

Comparison of power output during ergometer and track cycling in adolescent cyclists

The aim of this study is to establish the level of agreement between test performance of young elite cyclists in a laboratory and a track field-based trial. Fourteen adolescent cyclists (age: 14.8 ± 1.1 years; (Equation is included in full-text article.): 63.5 ± 5.6 ml·min(-1)·kg(-1)) performed 3 tests of 10 seconds, 1 minute, and 3 minutes on an air-braked ergometer (Wattbike) and on a 250-m track using their own bikes mounted with mobile power meters (SRM). The agreement between the maximum and mean power output (Pmax and Pmean) measured on the Wattbike and SRM was assessed with the 95% limits of agreement (LoA). Power output was strongly correlated between Wattbike and SRM for all tests (r = 0.94-0.96; p < 0.001). However, power output was significantly higher on the Wattbike compared with track cycling during all tests. The bias and 95% LoA were 76 ± 78 W (8.8 ± 9.5%; p = 0.003, d = 0.38) for Pmax10s and 82 ± 55 W (10.9 ± 7.9%; p < 0.001, d = 0.46) for Pmean10s. During the 1- and 3-minute test, the bias and 95% LoA were 72 ± 30 W (17.9 ± 7.1%; p < 0.001, d = 0.84) and 28 ± 20 W (9.6 ± 6.1%; p < 0.001, d = 0.51), respectively. Laboratory tests, as assessed using a stationary ergometer, resulted in maximal and mean power output scores that were consistently higher than a track field-based test using a mobile ergometer. These results might be attributed to the technical ability of the riders and their experience to optimize gearing and cadence to maximize performance. Prediction of field-based testing on the track from laboratory tests should be used with caution.

Monitoring training load to understand fatigue in athletes

Many athletes, coaches, and support staff are taking an increasingly scientific approach to both designing and monitoring training programs. Appropriate load monitoring can aid in determining whether an athlete is adapting to a training program and in minimizing the risk of developing non-functional overreaching, illness, and/or injury. In order to gain an understanding of the training load and its effect on the athlete, a number of potential markers are available for use. However, very few of these markers have strong scientific evidence supporting their use, and there is yet to be a single, definitive marker described in the literature. Research has investigated a number of external load quantifying and monitoring tools, such as power output measuring devices, time-motion analysis, as well as internal load unit measures, including perception of effort, heart rate, blood lactate, and training impulse. Dissociation between external and internal load units may reveal the state of fatigue of an athlete. Other monitoring tools used by high-performance programs include heart rate recovery, neuromuscular function, biochemical/hormonal/immunological assessments, questionnaires and diaries, psychomotor speed, and sleep quality and quantity. The monitoring approach taken with athletes may depend on whether the athlete is engaging in individual or team sport activity; however, the importance of individualization of load monitoring cannot be over emphasized. Detecting meaningful changes with scientific and statistical approaches can provide confidence and certainty when implementing change. Appropriate monitoring of training load can provide important information to athletes and coaches; however, monitoring systems should be intuitive, provide efficient data analysis and interpretation, and enable efficient reporting of simple, yet scientifically valid, feedback.

Validity and reliability of the look Keo power pedal system for measuring power output during incremental and repeated sprint cycling

Power meters have traditionally been integrated into the crank set, but several manufacturers have designed new systems located elsewhere on the bike, such as inside the pedals.

PURPOSE

This study aimed to determine the validity and reliability of the Keo power pedals during several laboratory cycling tasks.

METHODS

Ten active male participants (mean ± SD age 34.0 ± 10.6 y, height 1.77 ± 0.04 m, body mass 76.5 ± 10.7 kg) familiar with laboratory cycling protocols completed this study. Each participant was required to complete 2 laboratory cycling trials on an SRM ergometer (SRM, Germany) that was also fitted with the Keo power pedals (Look, France). The trials consisted of an incremental test to exhaustion followed by 10 min rest and then three 10-s sprint tests separated by 3 min of cycling at 100 W.

RESULTS

Over power ranges of 75 to 1147 W, the Keo power-pedal system produced typical error values of 0.40, 0.21, and 0.21 for the incremental, sprint, and combined trials, respectively, compared with the SRM. Mean differences of 21.0 and 18.6 W were observed between trials 1 and 2 with the Keo system in the incremental and combined protocols, respectively. In contrast, the SRM produced differences of 1.3 and 0.6 W for the same protocols.

CONCLUSIONS

The power data from the Keo power pedals should be treated with some caution given the presence of mean differences between them and the SRM. Furthermore, this is exacerbated by poorer reliability than that of the SRM power meter.

Variability in power output during cycling in international Olympic-distance triathlon

PURPOSE

The patterns of power output in the ~1-h cycle section of Olympic-distance triathlon races are not well documented. Here the authors establish a typical cycling-race profile derived from several International Triathlon Union elite-level drafting-legal triathlon races.

METHODS

The authors collated 12 different race power profiles from elite male triathletes (N = 5, age 25 ± 5 y, body mass 65.5 ± 5.6 kg; mean ± SD) during 7 international races. Power output was recorded using SRM cranks and analyzed with proprietary software.

RESULTS

The mean power output was 252 ± 33 W, or 3.9 ± 0.5 W/kg in relative terms, with a coefficient of variation of 71% ± 13%. Normalized power (power output an athlete could sustain if intensity were maintained constant without any variability) for the entire cycle section was 291 ± 29 W, or 40 ± 13 W higher than the actual mean power output. There were 34 ± 14 peaks of power output above 600 W and ~18% time spent at >100% of maximal aerobic power.

CONCLUSION

Cycling during Olympic-distance triathlon, characterized by frequent and large power variations including repeat supramaximal efforts, equates to a higher workload than cycling at constant power.

A comparison of methods for quantifying training load: relationships between modelled and actual training responses

PURPOSE

To assess the validity of methods for quantifying training load, fitness and fatigue in endurance athletes using a mathematical model.

METHODS

Seven trained runners (VO2max: 51.7 ± 4.5 mL kg(-1) min(-1), age: 38.6 ± 9.4 years, mean ± SD) completed 15 weeks of endurance running training. Training sessions were assessed using a heart rate (HR), running pace and rating of perceived exertion (RPE). Training dose was calculated using the session-RPE method, Banisters TRIMP and the running training stress score (rTSS). Weekly running performance (1,500-m time trial), fitness (submaximal HR, resting HR) and fatigue [profile of mood states, heart rate variability (HRV)] were measured. A mathematical model was applied to the training data from each runner to provide individual estimates of performance, fitness and fatigue. Correlations assessed the relationships between the modelled and actual weekly performance, fitness and fatigue measures within each runner.

RESULTS

Training resulted in 5.4 ± 2.6 % improvement in 1,500-m performance. Modelled performance was correlated with actual performance in each subject, with relationships being r = 0.70 ± 0.11, 0.60 ± 0.10 and 0.65 ± 0.13 for the rTSS, session-RPE and TRIMP input methods, respectively. There were moderate correlations between modelled and actual fitness (submaximal HR) for the session-RPE (-0.43 ± 0.37) and TRIMP (-0.48 ± 0.39) methods and moderate-to-large correlations between modelled and actual fatigue measured through HRV indices for both session-RPE (-0.48 ± 0.39) and TRIMP (-0.59 ± 0.31) methods.

CONCLUSIONS

These findings showed that each of the training load methods investigated are appropriate for quantifying endurance training dose and that submaximal HR and HRV may be useful for monitoring fitness and fatigue, respectively.

Rationale and resources for teaching the mathematical modeling of athletic training and performance

A number of professions rely on exercise prescription to improve health or athletic performance, including coaching, fitness/personal training, rehabilitation, and exercise physiology. It is therefore advisable that the professionals involved learn the various tools available for designing effective training programs. Mathematical modeling of athletic training and performance, which we henceforth call "performance modeling," is one such tool. Two models, the critical power (CP) model and the Banister impulse-response (IR) model, offer complementary information. The CP model describes the relationship between work rates and the durations for which an individual can sustain them during constant-work-rate or intermittent exercise. The IR model describes the dynamics by which an individual's performance capacity changes over time as a function of training. Both models elegantly abstract the underlying physiology, and both can accurately fit performance data, such that educating exercise practitioners in the science of performance modeling offers both pedagogical and practical benefits. In addition, performance modeling offers an avenue for introducing mathematical modeling skills to exercise physiology researchers. A principal limitation to the adoption of performance modeling is a lack of education. The goal of this report is therefore to encourage educators of exercise physiology practitioners and researchers to incorporate the science of performance modeling in their curricula and to serve as a resource to support this effort. The resources include a comprehensive review of the concepts associated with the development and use of the models, software to enable hands-on computer exercises, and strategies for teaching the models to different audiences.

Salivary cortisol and testosterone responses to high-intensity cycling before and after an 11-day intensified training period

This study examined salivary cortisol and testosterone responses to two, different high-intensity, ∼30-min cycles separated by 2 h rest before and after an 11-day intensified training period. Twelve recreationally active, healthy males completed the study. Saliva samples were collected before, immediately after and 30 min after both bouts with salivary cortisol and testosterone concentrations assessed. Compared with pre-training blunted exercise-induced salivary cortisol, testosterone and cortisol/testosterone responses to both bouts post-training were observed (P < 0.05 for all). Comparing pre- with post-training the absolute exercise-induced salivary cortisol, testosterone and cortisol/testosterone decreased from 11.1 to 3.1 and 7.0 to 4.4 nmol · L⁻¹ (cortisol), from 407 to 258 and from 473 to 274 pmol · L⁻¹ (testosterone) and from 12 to 4 and 7 to 5 (cortisol/testosterone) for the first and second bouts, respectively (P < 0.05). No differences in the pre- and post-training rating of perceived exertion (RPE) and heart rate (HR) responses during the cycles or times to fatigue were found (P > 0.05). Fatigue and Burnout scores were higher post- compared with pre-training (P < 0.05). These high-intensity exercise bouts can detect altered hormonal responses following intensified training. This test could assess an athlete's current hormonal status, reductions in salivary cortisol and testosterone responses suggestive of increased fatigue.

Guidelines to Classify Subject Groups in Sport-Science Research

Purpose:

The aim of this systematic literature review was to outline the various preexperimental maximal cycle-test protocols, terminology, and performance indicators currently used to classify subject groups in sportscience research and to construct a classification system for cycling-related research.

Methods:

A database of 130 subject-group descriptions contains information on preexperimental maximal cycle-protocol designs, terminology of the subject groups, biometrical and physiological data, cycling experience, and parameters. Kolmogorov-Smirnov test, 1-way ANOVA, post hoc Bonferroni (P < .05), and trend lines were calculated on height, body mass, relative and absolute maximal oxygen consumption (VO2max), and peak power output (PPO).

Results:

During preexperimental testing, an initial workload of 100 W and a workload increase of 25 W are most frequently used. Three-minute stages provide the most reliable and valid measures of endurance performance. After obtaining data on a subject group, researchers apply various terms to define the group. To solve this complexity, the authors introduced the neutral term performance levels 1 to 5, representing untrained, recreationally trained, trained, well-trained, and professional subject groups, respectively. The most cited parameter in literature to define subject groups is relative VO2max, and therefore no overlap between different performance levels may occur for this principal parameter. Another significant cycling parameter is the absolute PPO. The description of additional physiological information and current and past cycling data is advised.

Conclusion:

This review clearly shows the need to standardize the procedure for classifying subject groups. Recommendations are formulated concerning preexperimental testing, terminology, and performance indicators.

Mechanical work and physiological responses to simulated cross country mountain bike racing

The purpose was to assess the mechanical work and physiological responses to cross country mountain bike racing. Participants (n = 7) cycled on a cross country track at race speed whilst VO2, power, cadence, speed, and geographical position were recorded. Mean power during the designated start section (68.5 ± 5.5 s) was 481 ± 122 W, incurring an O2 deficit of 1.58 ± 0.67 L - min(-1) highlighting a significant initial anaerobic (32.4 ± 10.2%) contribution. Complete lap data produced mean (243 ± 12 W) and normalised (279 ± 15 W) power outputs with 13.3 ± 6.1 and 20.7 ± 8.3% of time spent in high force-high velocity and high force-low velocity, respectively. This equated to, physiological measures for %VO(2max) (77 ± 5%) and % HR(max) (93 ± 2%). Terrain (uphill vs downhill) significantly (P < 0.05) influenced power output (70.9 ± 7.5 vs. 41.0 ± 9.2% W(max)),the distribution of low velocity force production, VO2 (80 ± 1.7 vs. 72 ± 3.7%) and cadence (76 + 2 vs. 55 ± 4 rpm) but not heart rate (93.8 ± 2.3 vs. 91.3 ± 0.6% HR(max)) and led to a significant difference between anaerobic contribution and terrain (uphill, 6.4 ± 3.0 vs. downhill, 3.2 ± 1.8%, respectively) but not aerobic energy contribution. Both power and cadence were highly variable through all sections resulting in one power surge every 32 s and a supra-maximal effort every 106 s. The results show that cross country mountain bike racing consists of predominantly low velocity pedalling with a large high force component and when combined with a high oscillating work rate, necessitates high aerobic energy provision, with intermittent anaerobic contribution. Additional physical stress during downhill sections affords less recovery emphasised by physiological variables remaining high throughout.