Simon A. Jobson

Controversies in the physiological basis of the ‘anaerobic threshold’ and their implications for clinical cardiopulmonary exercise testing

This article reviews the notion of the ‘anaerobic threshold’ in the context of cardiopulmonary exercise testing. Primarily, this is a review of the proposed mechanisms underlying the ventilatory and lactate response to incremental exercise, which is important to the clinical interpretation of an exercise test. Since such tests are often conducted for risk stratification before major surgery, a failure to locate or justify the existence of an anaerobic threshold will have some implications for clinical practice. We also consider alternative endpoints within the exercise response that might be better used to indicate a patient’s capacity to cope with the metabolic demands encountered both during and following major surgery.

Validity and reliability of the Wattbike cycle ergometer.

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

The Analysis and Utilization of Cycling Training Data

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

The cardiorespiratory, anthropometric, and performance characteristics of an international/national touring ballet company.

This study examined the cardiorespiratory and anthropometric indices of professional classical ballet dancers in relation to company seniority, gender, and supplemental training. Forty-nine participants from an international touring company carried out a peak VO2 test and vertical jump test. Anthropometric measurements and supplemental training activities were also recorded for each participant. Statistical analyses showed significant differences between gender and dancer seniority levels. Gender differences were seen for jump height (M = 52.7 ± 7.12 cm; = −37.6 ± 5.32 cm) and peak VO2(M = 49.32 ± 3.72 ml·kg-1·min-1;= −43.3 ± 5.16 ml·kg-1·min-1). Differences were also seen between dancer levels for peak VO2 (artist = 46.47 ± 4.67 ml·kg-1·min-1; first artist = 42.72 ± 5.81 ml·kg-1·min-1; soloist = 43.38 ± 7.14 ml·kg-1·min-1; principal = 49.04 ± 3.63 ml·kg-1·min-1) and jump height (artist = 42.0 ± 9.11 cm; first artist = 50.33 ± 11.65 cm; soloist = 45.6 ± 9.78 cm; principal = 44.67 ± 9.53 cm). Pairwise post hoc comparisons showed that corps and principals had significantly greater relative peak VO2 than first artists and soloists (p < 0.05), while soloists and first artists had significantly greater jump heights compared to principals and corps (p < 0.05). Analysis of covariance modeling indicated that the self-reported mode of supplemental training had no association with relative peak VO2 or the percentage at which ventilatory threshold occurred. The present study has provided further insight into the cardiorespiratory profiles of classical ballet dancers, where soloists have significantly greater power capacities compared to principals and corps, who in turn had significantly greater aerobic power. These data can help guide strength and conditioning intervention strategies that need to take into account the nuances of the different seniority levels within a dance company.

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

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

Reliability of Cycling Gross Efficiency Using the Douglas Bag Method

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

Controversies in the physiological basis of the ‘anaerobic threshold: Implications for cardiopulmonary exercise testing

This article reviews the notion of the ‘anaerobic threshold’ in the context of cardiopulmonary exercise testing. Primarily, this is a review of the proposed mechanisms underlying the ventilatory and lactate response to incremental exercise, which is important to the clinical interpretation of an exercise test. Since such tests are often conducted for risk stratification before major surgery, a failure to locate or justify the existence of an anaerobic threshold will have some implications for clinical practice. We also consider alternative endpoints within the exercise response that might be better used to indicate a patient’s capacity to cope with the metabolic demands encountered both during and following major surgery.

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

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

The Effects of Training on Gross Efficiency in Cycling: A Review

There has been much debate in the recent scientific literature regarding the possible ability to increase gross efficiency in cycling via training. Using cross-sectional study designs, researchers have demonstrated no significant differences in gross efficiency between trained and untrained cyclists. Reviewing this literature provides evidence to suggest that methodological inadequacies may have played a crucial role in the conclusions drawn from the majority of these studies. We present an overview of these studies and their relative shortcomings and conclude that in well-controlled and rigorously designed studies, training has a positive influence upon gross efficiency. Putative mechanisms for the increase in gross efficiency as a result of training include, muscle fibre type transformation, changes to muscle fibre shortening velocities and changes within the mitochondria. However, the specific mechanisms by which training improves gross efficiency and their impact on cycling performance remain to be determined.

A higher effort-based paradigm in physical activity and exercise for public health: making the case for a greater emphasis on resistance training

It is well known that physical activity and exercise is associated with a lower risk of a range of morbidities and all-cause mortality. Further, it appears that risk reductions are greater when physical activity and/or exercise is performed at a higher intensity of effort. Why this may be the case is perhaps explained by the accumulating evidence linking physical fitness and performance outcomes (e.g. cardiorespiratory fitness, strength, and muscle mass) also to morbidity and mortality risk. Current guidelines about the performance of moderate/vigorous physical activity using aerobic exercise modes focuses upon the accumulation of a minimum volume of physical activity and/or exercise, and have thus far produced disappointing outcomes. As such there has been increased interest in the use of higher effort physical activity and exercise as being potentially more efficacious. Though there is currently debate as to the effectiveness of public health prescription based around higher effort physical activity and exercise, most discussion around this has focused upon modes considered to be traditionally ‘aerobic’ (e.g. running, cycling, rowing, swimming etc.). A mode customarily performed to a relatively high intensity of effort that we believe has been overlooked is resistance training. Current guidelines do include recommendations to engage in ‘muscle strengthening activities’ though there has been very little emphasis upon these modes in either research or public health effort. As such the purpose of this debate article is to discuss the emerging higher effort paradigm in physical activity and exercise for public health and to make a case for why there should be a greater emphasis placed upon resistance training as a mode in this paradigm shift.