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

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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
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  Review 845  Hopker J et al. The E ff  ects of Training on Gross E ffi  ciency in Cycling … Int J Sports Med 2009; 30: 845 – 850 accepted after revision  July 20, 2009 Bibliography DOI 10.1055/s-0029-1237712 Published online: November 25, 2009 Int J Sports Med 2009; 30: 845 – 850 © Georg Thieme Verlag KG Stuttgart · New York ISSN 0172-4622 Correspondence   Dr. James Hopker   Centre for Sport Studies University of Kent Chatham Maritime Kent ME4 4AG Tel.: + 44 / 1634 / 88 88 14 Fax: + 44 / 1634 / 88 88 09  j.g.hopker@kent.ac.uk Key words   ● ▶  endurance training ● ▶  economy ● ▶  cycling ● ▶  delta e ffi  ciency The E ff  ects of Training on Gross E ffi  ciency in Cycling: A Review Considered in full, the extant literature also pro-vides some clues regarding the possible mecha-nisms and adaptations that are responsible. The purpose of this review is to provide: a summary of the di ff  erent methods used to calculate e ffi  -ciency, a discussion of the requirements for reli-able measurement of e ffi  ciency during cycling, an overview of the published literature relating to acute and training-related e ff  ects on gross e ffi  -ciency, and suggestions for future research.  Terminology &   During steady-state cycle ergometry, e ffi  ciency has been extensively used to provide a convenient index of the e ff  ectiveness with which an individ-ual can convert chemical energy into mechanical power [4, 23, 28, 42, 52, 66] . The most commonly used measure of e ffi  ciency is gross e ffi  ciency ([work accomplished / energy expended] × 100). In calculating gross e ffi  ciency, the caloric equiva-lent of steady state ú V O 2  and the respiratory exchange ratio (RER) are used to calculate energy expenditure. The term ‘ gross e ffi  ciency ’ is nor-mally reported as a percentage of total energy expenditure [15] . Introduction &   Gross e ffi  ciency is de Þ ned as the ratio of power output to energy expenditure and is a key deter-minant of endurance cycling performance [14, 17, 18, 36, 49, 53] . Despite this, previous studies investigating gross e ffi  ciency during cycling have found no di ff  erences between trained and untrained cyclists [6, 45, 50, 52] . The results of these studies supported the recently challenged assumption that training has no e ff  ect on a cyclist ’ s gross e ffi  ciency [34] . These observations of non-signi Þ cant di ff  erences between trained and untrained individuals using cross-sectional research designs have limited the extent of inves-tigation into the mechanisms that could be responsible for changes in e ffi  ciency with train-ing. Recently, using more rigorous experimental designs researchers have provided evidence that gross e ffi  ciency can increase with training and is higher in a trained population [33, 34, 60] . None-theless, one recent study documenting improve-ments in cycling e ffi  ciency in a Grand Tour Champion [19] has provoked much debate within the scienti Þ c literature [25, 47, 61] . Currently, researchers are beginning to identify the speci Þ c circumstances under which training can lead to an increase in gross e ffi  ciency during cycling. Authors  J. Hopker 1  , L. Pass Þ eld 1  , D. Coleman 2  , S. Jobson 1  , L. Edwards 3  , H. Carter 4  A ffi  liations   1  Centre for Sport Studies, University of Kent, England 2  Department of Sports Science, Leisure and Tourism, Canterbury Christ Church University, Kent, England 3  Department of Physiology, Anatomy and Genetics, University of Oxford, England 4  Chelsea School, University of Brighton, England  Abstract &   There has been much debate in the recent scien-ti Þ c literature regarding the possible ability to increase gross e ffi  ciency in cycling via training. Using cross-sectional study designs, researchers have demonstrated no signi Þ cant di ff  erences in gross e ffi  ciency between trained and untrained cyclists. Reviewing this literature provides evi-dence to suggest that methodological inad-equacies may have played a crucial role in the conclusions drawn from the majority of these studies. We present an overview of these stud-ies and their relative shortcomings and conclude that in well-controlled and rigorously designed studies, training has a positive in ß uence upon gross e ffi  ciency. Putative mechanisms for the increase in gross e ffi  ciency as a result of train-ing include, muscle Þ bre type transformation, changes to muscle Þ bre shortening velocities and changes within the mitochondria. However, the speci Þ c mechanisms by which training improves gross e ffi  ciency and their impact on cycling per-formance remain to be determined.  Review 846  Hopker J et al. The E ff  ects of Training on Gross E ffi  ciency in Cycling … Int J Sports Med 2009; 30: 845 – 850 Other indices of e ffi  ciency used in the literature include work, net and delta e ffi  ciency. The calculation of net e ffi  ciency requires the energy expenditure at rest to be measured (and assigned to the performance of internal work), which is then subtracted from the total amount of energy expended for the external work accomplished. Thus, net e ffi  ciency does not take into account the cost of moving the involved limbs. Gaesser and Brooks [23] suggested that for a valid measurement this cost should be taken into account. For example, the energetic cost of cycling on an ergometer, but with a work rate of 0W (i. e. unloaded pedalling) would need to be subtracted from the total energy expenditure for the measured work rate. The calculation of work e ffi  ciency accounts for the additional cost of moving the legs, but this measurement of zero load in cycling can be problematic. Kautz and Neptune [40] have argued that zero load cycling does not provide a valid baseline for refer-ence to a range of work intensities. Gaesser and Brooks [23] pro-posed a ß oating baseline measure for the physiological and external energy cost of exercise, i. e. delta e ffi  ciency (DE). Here energy expenditure at a lower work rate is subtracted from the energy expenditure at a higher work rate ([delta work accomplished / delta energy expended] × 100). Coyle et al. [16] suggested that this is the most valid calculation of whole-body e ffi  ciency as it attempts to partial out the in ß uence of unmea-sured work. However, the linear relationship that is produced between energy cost and work rate does not necessarily mean that it is a valid measure (i. e. it is independent of the work rate used). For e ffi  ciency to increase with power output [12, 23] energy expenditure must increase non-linearly due to the decreasing relative contribution of non-propulsive factors (e. g. basal metabolism and moving the legs). The use of DE may also be limited as both Moseley and Jeukendrup [49] and Hopker et al. [33] have shown it to have greater day-to-day variability than gross e ffi  ciency. The issues involved in these measures have been extensively dis-cussed by Gasser and Brooks [23] , Stainsby et al. [64] , Cavanagh and Kram [11] and Kautz and Neptune [40] . Due to the various criticisms of base-line subtraction methods outlined above, it is unrealistic to attempt to attribute a portion of the total body energy cost to muscle work during a whole body exercise such as cycling. Therefore we will focus on gross e ffi  ciency for the rest of this review.  The measurement of e ffi  ciency and other methodological issues &   Steady state testing In order to measure e ffi  ciency accurately all gas collection must take place under steady state exercise conditions, otherwise measured pulmonary ú V O 2  may not adequately re ß ect muscle O 2  consumption [55] . At the onset of exercise or in the transition from one work rate to another, during light to moderate intensi-ties, pulmonary ú V O 2  increases in a mono-exponential manner to reach a steady state within 2 – 3 min [75] . Heavy exercise intensi- ties, characterised by a sustained metabolic acidosis, have been found to result in a delayed steady state [74] . Consequently, the relationship between ú V O 2  and power output presents a marked deviation from linearity at higher intensities when examined carefully [26, 78] . The exercise intensities eliciting this additional O 2  consumption, or ú V O 2  slow component, should not be used to determine e ffi  ciency values. The calculated energy equivalent for a given ú V O 2  depends upon the equivalence of RER and mus-cle RQ. A decrease of 0.05 in RER reduces calculated energy expenditure by 1.3 % typically increasing GE by 0.4 % . Therefore as ú V CO 2  a ff  ects RER it is important to ensure its stability prior to taking e ffi  ciency measurements. During steady state exercise resulting in minimal metabolic acidosis, ú V CO 2  may take consid-erably longer (at least 4 min) to reach steady state [13, 72] . For these reasons, long work stages ( ≥  5 min) should be used when collecting gas data for the calculation of e ffi  ciency. Boning et al. [6] and Moseley et al. [50] used 3 min stage durations for their studies of cycling e ffi  ciency. Therefore they may have failed to allow su ffi  cient time to achieve a steady state when investigat-ing di ff  erences in e ffi  ciency between trained and untrained cyclists. Exercise intensity A particular criticism of past research is the low exercise intensi-ties used for assessing e ffi  ciency during cycling. Researchers have previously sought to investigate optimal levels of energy expenditure during cycling using untrained participants [23, 62, 66, 77] . As a result, it has only been possible to study responses to low work rates. These values are typically very low if inferences from such Þ ndings are made to trained cyclists who commonly race at much higher work rates. E ffi  ciency has been shown to increase with work rate [12, 23] . It is thought that this is largely due to the unmeasured work (i. e. that required to sustain basal metabolism and body position on the bike) forming a smaller percentage of total energy expendi-ture at higher work rates [64] . However, more recently Moseley and Jeukendrup [49] have demonstrated that a plateau in gross e ffi  ciency occurs at the higher work rates ( > 240W) used by trained cyclists. Ideally, the work rate used for the determination of gross e ffi  ciency should encompass the functional range of the population of interest. However, when trained cyclists are con-sidered this is potentially di ffi  cult due to the ú V O 2  slow compo-nent associated with higher racing intensities. Standardisation of other factors Most authors agree that changes in muscle shortening velocity (i. e. pedal cadence) markedly a ff  ect e ffi  ciency. For a given power output, increasing cadence has been shown to decrease gross e ffi  ciency [12, 28, 45, 63] . Therefore, cadence must be standard-ised when conducting repeated measurements of e ffi  ciency in the same individual. Changes in riding position may also in ß u-ence the e ffi  ciency values obtained. Alterations in seat tube angle and saddle height have been shown to change gross e ffi  -ciency [30, 56] . Price and Donne [56] found an energetic opti-mum for combinations of seat tube angle and seat height (70 ° seat tube angle; 100 % trochanteric height). Alterations in the muscle length-tension relationships (quadriceps versus ham-strings) and ankling patterns could account for di ff  erences in e ffi  ciency found with increased seat tube angles and heights. Thus, the dimensions of a cyclist ’ s bicycle should be replicated when using a cycle ergometer and maintained during any subse-quent tests. High ambient temperatures (35.5 ° C) have been shown to cause decreases in gross e ffi  ciency [31] . With hyperthermia the energy cost of the exercise may increase due to greater circulation, sweating and ventilation. This in turn may reduce e ffi  ciency as the work accomplished would remain unchanged. Therefore ambient temperature should be tightly controlled, especially  Review 847  Hopker J et al. The E ff  ects of Training on Gross E ffi  ciency in Cycling … Int J Sports Med 2009; 30: 845 – 850 when repeating tests during di ff  erent phases of a season under varied climatic conditions. Finally, the control of cyclists ’ pre-testing regimen presents a further methodological issue. Racing cyclists often train and compete for several hours at intensities of 60 % ú V O 2 peak  and above [44] . A gradual ú V O 2  drift is commonly observed during such prolonged exercise. This ú V O 2  drift manifests as a reduction in gross e ffi  ciency and is apparent even in trained cyclists when exercising for 75 min at 60 % ú V O 2 peak  [54] . Pass Þ eld and Doust [54] further demonstrated that this acute reduction in gross e ffi  -ciency was signi Þ cantly correlated with a lower 5 min time-trial performance. Similarly, there is also some evidence to suggest that muscle damage from high intensity training might decrease e ffi  ciency during subsequent exercise performance [43, 71] . Therefore, it is important to monitor training in the days prior to testing athletes for e ffi  ciency. The time course for restoration of gross e ffi  ciency after exercise has not been established. The ability to detect meaningful changes in cycling performance over time, and to study the e ff  ects of selected interventions, requires careful consideration of the validity and reliability of testing methods and equipment [3, 35] . If reliability of repeated measurements is not ensured then there will be increased chances of falsely rejecting the null hypothesis in any research study. Therefore, it is critical that appropriate methods are employed which minimise the variability of within steady state ú V O 2  and ú V CO 2  data, exercise intensities utilised, ergometer set-up and participant pre-test preparation. Chronic changes in gross e ffi  ciency with training &   A question which has been addressed by several researchers is whether cycling e ffi  ciency can be increased by training. Previous cross-sectional studies have failed to Þ nd any di ff  erences in e ffi  -ciency between trained and untrained cyclists [6, 45, 46, 50, 52] . However a number of these investigations can be criticized on the basis of their methods and their failure to address the risk of committing a type 2 statistical error. Speci Þ cally, a lack of statis-tical power in past research studies has meant an inability to detect signi Þ cant di ff  erences between study populations. Other confounding factors are the usage of short stage protocols and arti Þ cially imposed cadences. ● ▶   Table 1  shows the main Þ nd-ings of research studies that have sought to investigate di ff  er-ences in e ffi  ciency between participants of varied cycling ability. To illustrate the likely chance of a type 2 statistical error we have calculated the e ff  ect size and statistical power using post hoc methods [5, 69] . E ff  ect size was calculated by the division of the mean di ff  erence between the two groups ( μ   1  and μ   2  ) by the pooled standard deviation (SD) [69] ; ES   = ( μ   1   −   μ   2  ) / SD   (the value for non centrality) and achieved statistical power was then calculated using GPower software [22] . Gross e ffi  ciency during cycling has been reported to be in the range of 18 – 23 % [16] . An improvement in e ffi  ciency implies an increase in mechanical power output for a given metabolic cost [53] . Using the extremes of the 18 – 23 % range suggests that for the same rate of metabolic energy expenditure, an e ffi  cient cyclist would produce 28 % more power than a less e ffi  cient cyclist ((23 – 18) / 18 = 28 % ). Evidence of longitudinal changes in gross e ffi  ciency was provided by Coyle [19] , who reported an 8.8 % increase in the gross e ffi  ciency of a Grand Tour Champion over a 7-year-period. Unfortunately, it was not possible to tightly control all aspects of data collection in this case study. Conse-quently, this study has been the subject of repeated criticism for its design, method and analysis [25, 47, 61] . Sassi et al. [60] have reported seasonal changes (albeit not signi Þ cant) in gross e ffi  -ciency in a small group of competitive cyclists. Seasonal varia-tion was not controlled in Coyle ’ s [19] study and therefore could account for the majority of the improvement he reports. Using a longitudinal study design Hopker et al. [34] have demonstrated that competitive cyclists can increase their gross e ffi  ciency by as much as 5 % . These increases in gross e ffi  ciency were signi Þ -cantly correlated with the volume and intensity of training com-pleted. More speci Þ cally, high intensity training was most strongly related to increases in gross e ffi  ciency. Even though the studies outlined above have demonstrated that gross e ffi  ciency can increase as a result of training, it is still unclear whether such chronic changes will actually impact on performance. Currently, the e ff  ect of training-induced increases in e ffi  ciency on cycling performance has not been assessed. Table 1 Methods and Þ ndings of cross sectional research studies that have investigated di ff  erences in e ffi  ciency between participants of varied cycling ability. The table is restricted to studies which present enough data to estimate e ff  ect size and statistical power. Author(s) Sample Size Stage Duration Power Outputs used E ffi  ciency measure Cadence imposed E ff  ect Size / Statistical Power E ffi  ciency Þ nding Boning et al., (1984) 15 (9 trained cy-clists; 6 untrained) 3 mins 50, 100, 200W GE & NE yes (40, 60, 70, 80, 100 rev.min −  1  ) 0.87 / 0.33 at 200W GE (  p  < 0.05) & NE (both 1 % mean di ff  erence) Nickleberry & Brooks (1996) 12 (6 competitive; 6 recreational) 4 mins 50 – 200W GE & DE yes (50 and 80 rev.min −  1  ) 0.26 / 0.07 at 200W no signi Þ cant di ff  erence ( > 1 % di ff  erence between groups) Marsh et al., (2000) 31 (11 competi-tive cyclists; 10 trained runner; 10 non-cyclists) 5 mins trained cyclists; 100, 150, 200W Untrained 75, 100, 150W DE yes (50, 65, 80, 95, 110 rev.min −  1  ) 1.25 / 0.77 at 80 rev.min −  1  no signi Þ cant di ff  erence ( ~ 1 % di ff  erence between groups at 80 & 95rev.min −  1  ) Moseley et al., (2004) 69 trained cyclists (divided on ú V O 2  peak, low, medium and high) 3 mins 95W increasing by 35W GE & DE yes (80 – 90 rev.min −  1  ) 2.33 / 1.00 between low and high ú V O 2  peak groups GE & DE no signi Þ cant di ff  erence (GE 0.9 % dif-ference Med-High groups; DE 1.2 % di ff  erence Low to High groups) Hopker et al., (2007) 30 (14 trained cyclists; 16 recrea-tional) 10 mins 150W, 50 & 60 % W max  GE preferred cadence used 1.51 – 1.54 / 0.98 across intensities used GE signi Þ cantly higher in trained group (mean + 1.4 %  p  < 0.05)  Review 848  Hopker J et al. The E ff  ects of Training on Gross E ffi  ciency in Cycling … Int J Sports Med 2009; 30: 845 – 850 Horowitz et al. [36] have shown that cyclists with a high gross e ffi  ciency (21.9 % ) were able to sustain a signi Þ cantly higher power output (27W) during a 1-hour cycle time-trial perform-ance than a group with a lower gross e ffi  ciency (20.4 % ). Jeuken-drup et al. [39] used a mathematical modeling approach to predict that a 1 % increase in a cyclist ’ s gross e ffi  ciency would result in a 63-s improvement in 40 km time-trial time. It is there-fore quite possible that the changes in gross e ffi  ciency following training described by Hopker et al. [34] and Sassi et al. [60] will a ff  ord a performance advantage. Potential mechanisms for chronic changes in gross e ffi  ciency &   Muscle Þ bre type transformation Previous work has demonstrated that there is a positive correla-tion between the percentage of type I Þ bres and gross e ffi  ciency [16, 36] . Horowitz et al. [36] have shown that cyclists with a higher percentage of type I Þ bres also had a signi Þ cantly higher e ffi  ciency and were able to maintain a 9 % higher power output during a one-hour performance trial. Both studies largely pro-vide correlation based evidence, and intervention studies are required to evaluate this relationship more thoroughly. Coyle et al. [15] reported a positive relationship between years of endurance training and percentage of type I Þ bres (r = 0.75). Again, due to the cross-sectional study design, it was not possi-ble to determine whether the percentage of type I Þ bres were a response to years of training, or that those cyclists with more type I Þ bres continue to train and race longer.  Jansson and Kaijser [38] compared metabolic responses in trained cyclists and untrained individuals exercising at 65 % ú V O 2peak  . The trained cyclists had a signi Þ cantly higher per-centage of type I Þ bres (70 % vs. 40 % ) and a greater gross e ffi  -ciency (22 % vs. 19 % ). Coyle [19] suggested that much of the 8 % increase in e ffi  ciency observed in a Grand Tour Champion was the result of an increased percentage of type I muscle Þ bres caused by prolonged intense endurance training and / or pro-longed exposure to high altitude conditions. Coyle attributed much of the improvement in e ffi  ciency to muscle Þ bre type adaptations (speculating that the rider ’ s fast twitch Þ bres took on more of a slow twitch role), even though no muscle biopsies were taken. In contrast, Martin et al. [47] suggested that modi Þ -cations in diet, training and chemotherapy (with a resultant loss of body and leg mass), were as likely to be responsible. When training for several hours per day at low force and move-ment speeds, as in cycling, the possibility of low frequency stim-ulation-induced transitions from type IIB to type IIA and ultimately type I might be expected [37, 59, 76] . This has led to the formulation of the ‘ adaptive range ’ concept, which describes the adaptive possibilities for each muscle Þ bre type [27, 73, 76] . As satellite cells have been shown to be predetermined to end up as a speci Þ c muscle Þ bre type within a certain adaptive range, local genetic factors have been suggested as unimportant [2] in Þ bre type alteration. There is a signi Þ cant di ff  erence in tension cost (how much ATP is consumed per unit force generated during an isometric contrac-tion) between human type I and type II Þ bres [65] . However, during active shortening, thermodynamic e ffi  ciency is the same (21 – 27 % ), although peak e ffi  ciency is reached at di ff  erent veloc-ities (at around 15 % of maximum shortening velocity in both cell types) [29] . Thus, the e ff  ect of Þ bre-type distribution on whole-body e ffi  ciency when cycling is probably not due to dif-ferences in contractile e ffi  ciency between myosin isoforms. Rather, it may be due to the shortening velocities during cycling being closest to those associated with peak e ffi  ciency in type I Þ bres (as has been suggested elsewhere [16] ). Interestingly, marathon training increases the peak shortening velocity of type I Þ bres [70] , and therefore, presumably, the shortening velocity at which peak e ffi  ciency is attained. This would improve e ffi  -ciency during running if the new, most e ffi  cient, shortening velocity was better matched with the actual shortening velocity during running. Thus, there is a mechanism by which muscle cells could provide improved contractile e ffi  ciency in response to training, without any change in metabolic e ffi  ciency. Factors in ß uencing e ffi  ciency in the muscle cell &   Oxidative phosphorylation is the main process by which ATP is produced under aerobic conditions. Changes in the e ffi  ciency of oxidative phosphorylation will therefore a ff  ect cycling e ffi  ciency Adaptations that might a ff  ect e ffi  ciency which are detectable early in a training programme may be related to the myosin ATP supply. Mitochondrial volume and aerobic capacity increase greatly within the Þ rst 4 – 6 weeks, especially in type II Þ bres, whilst anaerobic capacity decreases [32, 37, 59] . It could also be suggested that decreases in submaximal oxygen uptake may be due to changes in the working muscle ’ s oxidative capacity and metabolic processes, represented by an increase in activity of the mitochondrial enzymes [10] . A precursor for the aerobic adaptations seen as a result of train-ing is the production of adenosine monophosphate-activated protein kinase. This enzyme is released as a result of intensive training [24, 41] , and may cause the up-regulation of PGC-1 α  . This in turn is thought to regulate mitochondrial biogenesis in type I, IIa and IIx Þ bers [58, 67, 68] . However, these observations are limited to low and moderately trained individuals, who demonstrate marked physiological adaptations and improve-ments in Þ tness. Whether the same adaptations are typical of a trained population remains to the determined. Key questions also remain unanswered regarding the e ffi  ciency of energy transfer within the mitochondria and the possible role of the uncoupling of oxidative phosphorylation. Energy liber-ated from respiratory chain reactions are used to translocate protons across the inner mitochondrial membrane, creating an electrochemical potential [7] . This potential is subsequently used to drive the endergonic re-phosphorylation of ADP to ATP by the ATP-synthase, a reaction which is stoichiometric [8] . However, protons are able to leak back across the inner mem-brane without driving the ATP-synthase, a phenomenon known as uncoupling. In skeletal muscle, the most important proteins mediating this process appear to be the adenine nucleotide translocase (ANT) and uncoupling protein-3 (UCP3). Thus increases in the content or activity of these proteins might have adverse e ff  ects on cycling e ffi  ciency. Uncoupling accounts for around 50 % of resting oxygen con-sumption in rodent muscle [57] . Its contribution to e ffi  ciency during exercise is unclear, particularly as proton leak is of dimin-ishing importance as mitochondrial respiratory rate rises [9] . In the only study of UCP3 content and e ffi  ciency in cyclists to date, muscle UCP3 content and work e ffi  ciency were negatively correlated in a cohort of mixed-ability cyclists [48] . UCP3 con-tent was greater in untrained compared to trained individuals.  Review 849  Hopker J et al. The E ff  ects of Training on Gross E ffi  ciency in Cycling … Int J Sports Med 2009; 30: 845 – 850 Therefore, the process of training might have reduced the expres-sion of UCP3, increased the coupling of oxidative phosphoryla-tion, and improved gross e ffi  ciency when cycling. However, this mechanism is yet to be observed following a period of training. The contribution of ANT activity to whole-body e ffi  ciency remains unexplored. Longitudinal or interventional studies are needed to illuminate the contribution of mitochondrial uncou-pling to exercise e ffi  ciency in humans. Conclusions &   Until recently it has been assumed that training had no impact on cycling e ffi  ciency. This was largely based on the results of investigations that did not Þ nd a di ff  erence between trained and untrained cyclists. It now appears that insu ffi  cient methods (e. g. small sample sizes and inappropriate exercise intensities) may have resulted in type II errors in many of these studies. To enable the identi Þ cation of training related changes and obtain consist-ency in e ffi  ciency measurements pre-test preparation, ergo-meter set-up and methods used for data collection must all be valid and reliable. Recent evidence suggests that training (espe-cially at high intensities) improves gross e ffi  ciency in cycling. Potential mechanisms which might be responsible for training related increases in GE include muscle Þ bre type transformation, aerobic enzyme capacity within the muscle and the expression of PGC1 α  , ANT and UCP3. Future studies should seek to fully investigate the mechanisms responsible for determining gross e ffi  ciency via the use of intervention methodologies and inva-sive biopsy techniques. Further research is required to clarify the role of speci Þ c training regimes on the development of gross e ffi  ciency and the underpinning changes. References 1  Andreyev    ATO  , Bondareva   TO  , Dedukhova   VI   , Mokhova   EN   ,  Skulachev   VP   , Tso  Þ na   LM   , Volkov   NI   , Vvgodina   TV   . The ATP/ADP-antiporter is involved in the uncoupling e ff  ect of fatty acids on mitochondria . Eur  J Biochem 1989 ; 182 : 585 – 592 2  Astrand   P   , Rodahl   K   , Dahl   H   ,  Stromme    S   . Textbook of Work Physiology: Physiological Bases of Exercise (4 th  Ed.) . Illinois: Human Kinetics ; 2003 3  Atkinson   G  , Nevill    A  . Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine . 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