More than metrics: The role of socio-environmental factors in determining the success of athlete monitoring.

The perceived value of athlete monitoring systems (AMS) has recently been questioned. Poor perceptions of AMS are important, because where practitioners lack confidence in monitoring their ability to influence programming, and performance is likely diminished. To address this, researchers have primarily sought to improve factors related to monitoring metrics, e.g., validity rather than socio-environmental factors, e.g., buy-in. Seventy-five practitioners (response rate: n = 30) working with Olympic and Paralympic athletes were invited to take part in a survey about their perceptions of AMS value. Fifty-two per cent (n = 13) was confident in the sensitivity of their athlete self-report measures, but only 64% (n = 16), indicated their monitoring was underpinned by scientific evidence. A scientific base was associated with improved athlete feedback (rS (23) = 0.487, p =0.014*) and feedback correlated with athlete monitoring adherence (rS (22) = 0.675, p = <0.001**). If athletes did not complete their monitoring, 52% (n = 13) of respondents felt performance might be compromised. However, most respondents 56% (n = 14), had worked with internationally successful athlete(s) who did not complete their monitoring. While AMS can be a useful tool to aid performance optimisation, its potential value is not always realised. Addressing socio-environmental factors alongside metric-factors may improve AMS efficacy.

Athlete monitoring practices in elite sport in the United Kingdom.

Athlete monitoring systems (AMS) aid performance optimisation and support illness/injury prevention. Nonetheless, limited information exists on how AMS are employed across elite sports in the United Kingdom. This study explored how athlete monitoring (AM) data, in particular athlete self-report measures, were collected, analysed and disseminated within elite sports. Thirty elite sports practitioners representing 599 athletes responded to a survey on their AM methodologies. The majority, 83%, (n = 25) utilised an AMS, and a further 84% (n = 21) stated the collection of their AMS data was underpinned by a scientific rationale. Athlete self-report measures (ASRM) were the most commonly employed tool, with muscle soreness, sleep and energy levels amongst the most frequently collected measures. The ubiquitous use of custom single-item ASRM resulted in considerable variability in the questionnaires employed, thus potentially impacting questionnaire validity. Feedback processes were largely felt to be ineffective, with 44% (n = 11) respondents indicating that athletes did not receive sufficient feedback. Some respondents indicated that AMS data was never discussed with athletes and/or coaches. Overall, significant disparities exist in the use of athlete monitoring systems between research and elite sports practice, and the athlete, coach and practitioner experience of monitoring risks being poor if these disparities are not addressed.

Validity of Calculating Continuous Relative Phase during Cycling from Measures Taken with Skin-Mounted Electro-Goniometers.

The aim of this study was to assess the validity of electro-goniometers as a tool for recording continuous relative phase data at two joint couplings during cycling tasks at a range of cadences. Seven participants (4 male, 3 female, age: 29 ± 7 years, height: 1.76 ± 0.10 m, mass: 71.97 ± 11.57 kg) performed exercise bouts of 30 s at four prescribed cadences (60, 80, 100, 120 rev·min-1) on a stationary ergometer (Wattbike, Nottingham, UK). Measures were synchronously recorded by bi-axial electro-goniometers (Biometrics, UK) and a 12-camera motion-capture system (Qualisys, Gothenburg, Sweden), with both systems sampling at 500 Hz. Sagittal plane joint angle and joint angular velocity were recorded at the hip, knee and ankle and analysed for ten complete pedal revolutions per participant per condition. Data were interpolated to 100 time points and used to calculate mean continuous relative phase (CRP) per pedal revolution at two intra-limb couplings: (i) knee flexion/extension-ankle plantarflexion/dorsiflexion (KA) and (ii) hip flexion/extension-knee flexion/extension (HK). At the KA coupling, significant differences in mean CRP were found between measurement systems at 120 rev·min-1 (p = 0.006). At the HK coupling, significant differences in mean CRP were found between measurement systems at 80 rev·min-1 (p = 0.043) and 100 rev·min-1 (p = 0.028). ICC values for most comparisons were below 0.5, suggesting poor levels of agreement between systems. Significant differences in mean CRP per pedal revolution and poor levels of agreement between systems suggests that electro-goniometers are not a suitable alternative to motion-capture systems when attempting to record CRP during cycling.

The 3-minute all-out cycling test is sensitive to changes in cadence using the Lode Excalibur Sport ergometer.

This study investigated the effect cadence has on the estimation of critical power (CP) and the finite work capacity (W') during the 3-minute all-out cycling test. Ten participants completed 8 tests: 1) an incremental test to calculate gas exchange threshold (GET), maximal aerobic power (MAP) and peak oxygen uptake (V̇O2peak), 2-4) three time-to-exhaustion tests at 80, 100 and 105% MAP to calculate CP and W', 5-7) four 3-minute all-out tests to calculate end power (EP) and work done above EP (WEP) using cadences ranging from preferred -5 to preferred +10 rev·min-1 to set the fixed resistance. Significant differences were seen between CP and EP-preferred (267.5 ± 22.6 W vs. 296.6 ± 26.1 W, P < 0.001), CP and EP-5 (267.5 ± 22.6 W vs. 303.6 ± 24.0 W, P < 0.001) and between CP and EP+5 (267.5 ± 22.6 W vs. 290.0 ± 28.0 W, P = 0.002). No significant differences were seen between CP and EP+10 (267.5 ± 22.6 W vs. 278.1 ± 30.9 W, P = 0.331). Significant differences were seen between W' and WEP at all tested fixed resistances. EP is reduced when cycling at higher than preferred cadences, providing better estimates of CP.

The Reliability and Validity of the 3-min All-out Cycling Critical Power Test.

Research suggests that critical power (CP) can be estimated from a single 3-min bout of all-out cycling. The purpose of this study was to investigate the reliability and validity of the 3-min all-out cycling test when carried out at a constant cadence (isokinetic) and against a fixed resistance (linear). 12 participants completed 8 tests: 1) a ramp test; 2-4) 3 fixed power tests to calculate CP and W' using the 1/time mathematical model; 5-8) four 3-min all-out tests to calculate EP and WEP; 2 isokinetic and 2 linear tests. There was no significant difference between EP-isokinetic and CP (P=0.377). There were significant differences between EP-linear and CP (P=0.004), WEP-isokinetic and W' (P<0.001) and WEP-linear and W' (P<0.001). The coefficient of variation in EP-isokinetic, EP-linear, WEP-isokinetic and WEP-linear was 1.93, 1.17, 8.44 and 5.39%, respectively. The 3-min all-out isokinetic test provides a reliable estimate of EP and a valid estimate of CP. The 3-min all-out linear test provides a reliable estimate of EP, but not a valid estimate of CP. Furthermore, these results suggest that the 3-min all-out test should not be used to estimate W'.

Comparison of inter-trial recovery times for the determination of critical power and W' in cycling.

Critical Power (CP) and W' are often determined using multi-day testing protocols. To investigate this cumbersome testing method, the purpose of this study was to compare the differences between the conventional use of a 24-h inter-trial recovery time with those of 3 h and 30 min for the determination of CP and W'. METHODS: 9 moderately trained cyclists performed an incremental test to exhaustion to establish the power output associated with the maximum oxygen uptake (p[Formula: see text]max), and 3 protocols requiring time-to-exhaustion trials at a constant work-rate performed at 80%, 100% and 105% of p[Formula: see text]max. Design: Protocol A utilised 24-h inter-trial recovery (CP24/W'24), protocol B utilised 3-h inter-trial recovery (CP3/W'3), and protocol C used 30-min inter-trial recovery period (CP0.5/W'0.5). CP and W' were calculated using the inverse time (1/t) versus power (P) relation (P = W'(1/t) + CP). RESULTS: 95% Limits of Agreement between protocol A and B were -9 to 15 W; -7.4 to 7.8 kJ (CP/W') and between protocol A and protocol C they were -27 to 22 W; -7.2 to 15.1 kJ (CP/W'). Compared to criterion protocol A, the average prediction error of protocol B was 2.5% (CP) and 25.6% (W'), whilst for protocol C it was 3.7% (CP) and 32.9% (W'). CONCLUSION: 3-h and 30-min inter-trial recovery time protocols provide valid methods of determining CP but not W' in cycling.

Evaluating Changes in Mental Workload in Indoor and Outdoor Ultra-Distance Cycling.

Whilst increasing mental workload has been shown to have a detrimental effect on cycling performance and more generally to increase the risk of harm, no studies have measured how mental workload changes as a function of ultra-distance cycling, indoors or outdoors. Our objective was to measure the difference in mental workload, as indicated by changes in EEG theta power, components of HRV and psychomotor vigilance and as reported using the 'NASA Task Load Index questionnaire', before and after a 5 h indoor ride and outdoor ride completed at 65% of functional threshold power. Results of the NASA-TLX indicated the mental demand of outdoor cycling to be significantly less than that of indoor cycling. There were significant differences in the PVT results between the pre and the post outdoor ride average and median response times. The slowest 10% PVT responses were significantly slower pre than post the indoor ride. There were significant differences in HRV between pre and post outdoor and indoor rides, specifically, in the average RR intervals, RMSSD (ms2), LFPower (ms2), NN50. There were modest changes in indicators of mental workload during an ultra-distance cycle ride. As such, mental workload during ultra-distance cycling is unlikely to be a contributory factor to decreases in performance or to an increased likelihood of accident and injury.

The analysis and utilization of cycling training data.

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

Effect of the rotor crank system on cycling performance.

The aim of this study was to evaluate the impact of a novel crank system on laboratory time-trial cycling performance. The Rotor system makes each pedal independent from the other so that the cranks are no longer fixed at 180°. Twelve male competitive but non-elite cyclists (mean ± s: 35 ± 7 yr, Wmax = 363 ± 38 W, VO2peak = 4.5 ± 0.3 L·min(-1)) completed 6-weeks of their normal training using either a conventional (CON) or the novel Rotor (ROT) pedal system. All participants then completed two 40.23-km time-trials on an air-braked ergometer, one using CON and one using ROT. Mean performance speeds were not different between trials (CON = 41.7 km·h(-1) vs. ROT = 41.6 km·h(-1), P > 0.05). Indeed, the pedal system used during the time-trials had no impact on any of the measured variables (power output, cadence, heart rate, VO2, RER, gross efficiency). Furthermore, the ANOVA identified no significant interaction effect between main effects (Time-trial crank system*Training crank system, P > 0.05). To the authors' knowledge, this is the first study to examine the effects of the Rotor system on endurance performance rather than endurance capacity. These results suggest that the Rotor system has no measurable impact on time-trial performance. However, further studies should examine the importance of the Rotor 'regulation point' and the suggestion that the Rotor system has acute ergogenic effects if used infrequently. Key pointsThe Rotor crank system does not improve gross efficiency in well-trained cyclists.The Rotor crank system has no measurable impact on laboratory 40.23-km time-trial performance.A 6-week period of familiarisation does not increase the effectiveness of the Rotor crank system.

Training-Monitoring Engagement: An Evidence-Based Approach in Elite Sport.

PURPOSE: Poor athlete buy-in and adherence to training-monitoring systems (TMS) can be problematic in elite sport. This is a significant issue, as failure to record, interpret, and respond appropriately to negative changes in athlete well-being and training status may result in undesirable consequences such as maladaptation and/or underperformance. This study examined the perceptions of elite athletes to their TMS and their primary reasons for noncompletion. METHODS: Nine national-team sprint athletes participated in semistructured interviews on their perceptions of their TMS. Interview data were analyzed qualitatively, based on grounded theory, and TMS adherence information was collected. RESULTS: Thematic analysis showed that athletes reported their main reason for poor buy-in to TMS was a lack of feedback on their monitoring data from key staff. Furthermore, training modifications made in response to meaningful changes in monitoring data were sometimes perceived to be disproportionate, resulting in dishonest reporting practices. CONCLUSIONS: Perceptions of opaque or unfair decision making on training-program modifications and insufficient feedback were the primary causes for poor athlete TMS adherence. Supporting TMS implementation with a behavioral-change model that targets problem areas could improve buy-in and enable limited resources to be appropriately directed.

The Reliability and Validity of the PowerTap P1 Power Pedals Before and After 100 Hours of Use.

PURPOSE: To (1) evaluate agreement between the PowerTap P1 (P1) pedals and the Lode Excalibur Sport cycle ergometer, (2) investigate the reliability of the P1 pedals between repeated testing sessions, and (3) compare the reliability and validity of the P1 pedals before (P10) and after (P1100) ∼100 h of use. METHODS: Ten participants completed four 5-min submaximal cycling bouts (100, 150, 200, and 250 W), a 2-min time trial, and two 10-s all-out sprints on 2 occasions. This protocol was repeated after 15 mo and ∼100 h of use. RESULTS: Significant differences were seen between the P10 pedals and the Lode Excalibur Sport at 100 W (P = .006), 150 W (P = .006), 200 W (P = .001), and 250 W (P = .006) and during the all-out sprints (P = .020). After ∼100 h of use, the P1100 pedals did not significantly differ from the Lode Excalibur Sport at 100 W (P = .799), 150 W (P = .183), 200 W (P = .289), and 250 W (P = .183), during the 2-min time trial (P = .583), or during the all-out sprints (P = .412). The coefficients of variation for the P10 and P1100 ranged from 0.6% to 1.3% and 0.5% to 2.0%, respectively, during the submaximal cycling bouts. CONCLUSION: The P1 pedals provide valid data after ∼100 h of laboratory use. Furthermore, the pedals provide reliable data during submaximal cycling, even after prolonged use.

Validity of PowerTap P1 Pedals during Laboratory-Based Cycling Time Trial Performance.

The use of mobile power measuring devices has become widespread within cycling, with a number of manufacturers now offering power measuring pedals. This study aimed to investigate the validity of PowerTap P1 pedals by comparing them with the previously validated Wattbike ergometer. Ten trained cyclists performed three simulated 10-mile (16-km) time trials on a Wattbike, while using PowerTap P1 pedals. There were no statistically significant differences (p > 0.05) between PowerTap P1 pedals and a Wattbike for maximum, minimum, and mean power output, or for maximum, minimum, and mean cadence. There were good to excellent levels of agreement between the PowerTap P1 pedals and Wattbike (ICC > 0.8) for all measured variables except minimum cadence (ICC = 0.619). This suggests that PowerTap P1 pedals provide a valid measurement of power output.

Effect of gradient on cycling gross efficiency and technique.

PURPOSE: The purpose of this study was to determine the effect of gradient on cycling gross efficiency and pedaling technique. METHODS: Eighteen trained cyclists were tested for efficiency, index of pedal force effectiveness (IFE), distribution of power production during the pedal revolution (dead center size [DC]), and timing and level of muscle activity of eight leg muscles. Cycling was performed on a treadmill at gradients of 0% (level), 4%, and 8%, each at three different cadences (60, 75, and 90 rev·min). RESULTS: Efficiency was significantly decreased at a gradient of 8% compared with both 0% and 4% (P < 0.05). The relationship between cadence and efficiency was not changed by gradient (P > 0.05). At a gradient of 8%, there was a larger IFE between 45° and 225° and larger DC, compared with 0% and 4% (P < 0.05). The onset of muscle activity for vastus lateralis, vastus medialis, gastrocnemius lateralis, and gastrocnemius medialis occurred earlier with increasing gradient (all P < 0.05), whereas none of the muscles showed a change in offset (P > 0.05). Uphill cycling increased the overall muscle activity level (P < 0.05), mainly induced by increased calf muscle activity. CONCLUSIONS: These results suggest that uphill cycling decreases cycling gross efficiency and is associated with changes in pedaling technique.

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

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

Reliability of cycling gross efficiency using the Douglas bag method.

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

Whole-body efficiency is negatively correlated with minimum torque per duty cycle in trained cyclists.

The purpose of this study was to determine whether there is a causal relationship between pedalling "circularity" and cycling efficiency. Eleven trained cyclists were studied during submaximal cycling. Variables recorded included gross and delta efficiency and the ratio of minimum to peak torque during a duty cycle. Participants also completed a questionnaire about their training history. The most notable results were as follows: gross efficiency (r = -0.72, P < 0.05 at 250 W) was inversely correlated with the ratio of minimum to peak torque, particularly at higher work rates. There was a highly significant inverse correlation between delta efficiency and average minimum torque at 200 W (r = -0.76, P < 0.01). Cycling experience was positively correlated with delta efficiency and gross efficiency, although experience and the ratio of minimum to peak torque were not related. These results show that variations in pedalling technique may account for a large proportion of the variation in efficiency in trained cyclists. However, it is also possible that some underlying physiological factor influences both. Finally, it appears that experience positively influences efficiency, although the mechanism by which this occurs remains unclear.

The effect of crank inertial load on the physiological and biomechanical responses of trained cyclists.

The existing literature suggests that crank inertial load has little effect on the responses of untrained cyclists. However, it would be useful to be aware of any possible effect in the trained population, particularly considering the many laboratory-based studies that are conducted using relatively low-inertia ergometers. Ten competitive cyclists (mean VO(2max) = 62.7 ml x kg(-1) x min(-1), s = 6.1) attended the human performance laboratories at the University of Wolverhampton. Each cyclist completed two 7-min trials, at two separate inertial loads, in a counterbalanced order. The inertial loads used were 94.2 kg x m(2) (high-inertia trial) and 2.4 kg x m(2) (low-inertia trial). Several physiological and biomechanical measures were undertaken. There were no differences between inertial loads for mean peak torque, mean minimum torque, oxygen uptake, blood lactate concentration or perceived exertion. Several measures showed intra-individual variability with blood lactate concentration and mean minimum torque, demonstrating coefficients of variation > 10%. However, the results presented here are mostly consistent with previous work in suggesting that crank inertial load has little direct effect on either physiology or propulsion biomechanics during steady-state cycling, at least when cadence is controlled.