muscle coordination patterns for efficient cycling.21

Muscle Coordination Patterns forEfficient Cycling

OLLIE M. BLAKE1, YVAN CHAMPOUX2, and JAMES M. WAKELING1

1Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, CANADA;and 2VelUS, Mechanical Engineering Department, Universite de Sherbrooke, Sherbrooke, Quebec, CANADA

ABSTRACT

BLAKE, O. M., Y. CHAMPOUX, and J. M. WAKELING. Muscle Coordination Patterns for Efficient Cycling.Med. Sci. Sports Exerc.,

Vol. 44, No. 5, pp. 926–938, 2012. Introduction/Purpose: Cycling is a repetitive activity using coordinated muscle recruitment patterns

to apply force to the pedals. With more muscles available for activation than required, some patterns produce high power, whereas some

are more efficient. The purpose of this study was to identify relationships between muscle coordination and factors affecting muscle

coordination to explain changes in overall mechanical efficiency (GO). Methods: Surface EMG, kinematics, and pedal forces were

measured at 25%, 40%, 55%, 60%, 75%, and 90% VO2max. Principal component analysis was used to establish muscle coordination,

kinematic, and pedal force patterns associated with high and low GO. Results: At 55%–60% VO2max, GO was maximized and was highly

related to the muscle coordination patterns. At high GO, there was more medial and lateral gastrocnemii and soleus; less gluteus maximus,

rectus femoris, and tibialis anterior; later medial and lateral vastii and biceps femoris; and earlier semitendinosus muscle activity resulting

in an even distribution and synchronization of peak activity. Also, the ankle was more plantar flexed through the top and downstroke

of the pedal cycle and more dorsiflexed during the upstroke for high GO. The GO was independent of the pedal force application.

Conclusions: The results indicate that increased GO is achieved through the coordination of muscles crossing the same joint, sequential

peak activation from knee to hip to ankle, and reliance on multiple muscles for large joint torques. Also, muscle activity variability across

the top and bottom of the cycle indicates that left and right leg muscle coordination may play a significant role in efficient cycling. These

findings imply that cycling at 55%–60% VO2max will maximize the rider’s exposure to high efficient muscle coordination and kine-

matics. Key Words: EMG, KINEMATICS, PEDAL FORCES, POWER OUTPUT

Cycling is a repetitive activity using coordinated com-binations of leg muscles to apply force to the pedals.The muscle activity and coordination can vary dra-

matically between people throughout a single pedal cycle andbetween different pedal cycles of the same person (14,17).Even among elite cyclists in whom training backgroundsand physical attributes are similar, there is a variation in themuscle coordination used to complete a pedal cycle (14).

Muscle efficiency is the ratio of mechanical work to thetotal metabolic costs to produce the work (39). The coordi-nation of leg muscle activation affects the direction, mag-nitude, and duration of force applied to the pedal, whichis reflected in the mechanical work and power output of acyclist. Because the amount of muscle activity reflects themetabolic costs of cycling (1,2), comparisons of muscle ac-tivity to factors affecting mechanical work may provide val-uable insight into cycling efficiency and performance.

The purpose of this study was to identify relationshipsbetween muscle coordination patterns and factors known toaffect muscle coordination such as resistance and cadence tobetter understand changes in cycling efficiency.

Muscle timing and coordination. Given that cyclingis a constrained movement, the timing of muscle activationis typically referenced to 360- of a pedal cycle. The musclecoordination patterns of a pedal cycle have been describedand are dependent on the subjects’ cycling background, pedalsand shoes, load, cadence, fatigue, and body position (see thereview of Hug and Dorel [15]).

The timing and coordination of muscle activation play asignificant role in the amount of muscle activity used duringa pedal cycle. For example, longer durations of activationand more muscle cocontraction contribute to increased mus-cle activity. During sustained submaximal running, differentleg muscle coordination patterns have been identified forsimilar running styles (38). One suggested explanation wasthe compensation of agonistic muscles resulting in similarmuscle force patterns with differing recruitment patterns(38). In elite cyclists, there is considerable variation betweensubjects during incremental exercise to exhaustion for manykey leg muscles (14). For particular muscles, the gluteusmaximus (GM), vastus lateralis (VL), and vastus medialis(VM) have shown the lowest variation in the timing of acti-vation, whereas the rectus femoris (RF), biceps femoris (BF),and semitendinosus (ST) have exhibited the highest levels

Address for correspondence: Ollie M. Blake, Department of BiomedicalPhysiology and Kinesiology, Simon Fraser University, 8888 UniversityDrive, Burnaby, British Columbia, Canada V5A1S6; E-mail: [email protected] for publication May 2011.Accepted for publication October 2011.

0195-9131/12/4405-0926/0MEDICINE & SCIENCE IN SPORTS & EXERCISE�Copyright � 2012 by the American College of Sports Medicine

DOI: 10.1249/MSS.0b013e3182404d4b

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Copyright © 2012 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.

of variation during cycling (28). These studies highlight theenormous potential of variation in muscle activity throughaltered coordination despite the controlled cyclical motionof cycling.

Cycling cadence also affects the timing of muscle acti-vation relative to a pedal cycle (23,31). Earlier activation ofthe RF, BF (23,31), tibialis anterior (TA), GM (20,23), VL(20,31), medial gastrocnemius (MG), VM, semimembranosus(23), and lateral gastrocnemius (LG) (31) results from in-creased cadence. This may be a strategy to overcome theelectromechanical delay, which is the delay in time betweenthe onset of EMG and the application of force, to applytorque to the crank arm at a consistent position within eachpedal cycle, despite the cadence (23,31).

Quantity of muscle activity. Along with timing, theamplitude of leg muscle activity has a major effect on en-ergy consumption and cycling efficiency. Increases in muscleactivity may or may not be beneficial to cycling efficiencydepending upon which muscles are active, the level of ac-tivity, and the timing and duration of activation. For exam-ple, trained cyclists prefer high cadences that minimizeneuromuscular fatigue regardless of the elevated metaboliccost (31,32). Also potentially confusing the efficiency equa-tion is the cocontraction of agonist/antagonist muscle pairsthat occur in cycling (34). It has been suggested that thisstrategy stabilizes the joints for energy transfer reducingmuscle stress and mechanical energy expenditure (15,17).

The primary muscles involved in cycling power produc-tion are VL and VM (3,28). They display the highest peakactivity levels relative to the maximum voluntary contraction,whereas GM and RF show the lowest peak activity levels(13). Activation levels between different subjects vary con-siderably, yet within the same subject, the activity level isconsistent for different trials (8). These discrepancies betweendifferent subjects and muscles help explain differences in thequantity of muscle activity but do not clarify how changesin workload affect the muscle activity or how this activitycontributes to more efficient cycling.

The muscle activity is highly influenced by workload.As workloads increase, the activity levels of the VM, MG,soleus (Sol), GM, gluteus medius, RF, BF, and ST also in-crease (13). In addition, both power output and VL activityincreased throughout a 40-km time trial (3), whereas in a30-min time trial, with no time effect on power output, therewas no significant change in the activity level of VM, RF,BF, or MG (11). Thus, the response to altered workloads isnot uniform across all muscles. Muscles such as GM aremore susceptible to changes in load, whereas others like VL,VM, MG, and LG show less variation with workload (12,13).

Pedal forces. One approach to maximize cycling effi-ciency is to increase mechanical work without increasingmuscle activity. Force applied to the pedals in a directionperpendicular to the crank arm at every point in the pedalcycle would produce more power; however, anatomical con-straints suggest that muscles may be more effective in de-livering forces in nonoptimal directions relative to the pedals

(34). In short sprints at maximal power output, cyclists dis-play a positive contribution of force during the upstroke (21),whereas they show a negative force in the upstroke duringprolonged cycling (30). This implies that when power outputis the primary goal, forces applied perpendicular to the crankarm for more of the pedal cycle are more effective, whereaswhen efficiency is important, other force application strate-gies are more effective. Therefore, there exists a balance be-tween muscle activity and the direction and magnitude ofthe applied force that will produce a high power output withminimal energy cost thereby maximizing cycling efficiency.

Instrumented pedals are used to determine the total, effec-tive, and ineffective pedal forces. The effective force is theforce applied normal to the crank arm and is often used tocalculate pedal effectiveness (GP), which is the ratio of theeffective force to the total force. In cycling, the primaryapplication of force that contributes to positive work occursin the downstroke (from 0-, when the crank arm is at the topdead center (TDC) of the rotation, to 180-) (5,16,29,40). In-vestigations of the downstroke reveal consistency in the forcepattern between subjects (5,16,26,29) independent of train-ing status. During the upstroke (from 180- back to 0-), somecyclists display resistive forces (16,25–27,29,40), others con-tribute very little or no force (5,29), and some show in-creased positive work (5,40). The variation in pedal forceapplication requires different contributions of muscle activ-ity and should be detected in the muscle coordination pat-terns, which will have an effect on the mechanical poweroutput and the metabolic costs. Mornieux et al. (22) foundthat net muscular efficiency increased with GP of the down-stroke during steady-state cycling. Because they measuredoxygen consumption without including muscle activity, therelationships between muscle coordination and either GP ormuscular efficiency cannot be determined. In addition, thelocation in the pedal cycle where GP changed cannot beestablished because the downstroke was examined as a sin-gle unit.

Cyclists tend to intensify the angular impulse (5,19,29,30,40) and vertical forces (5,19,29) during the downstrokeas workload increases. The increased vertical forces useGM, VL, and VM during the downstroke where GP is at itsmaximum potentially reducing the muscle activity requiredfor a given power output. In addition, in cycling trials to ex-haustion, decreased effective force during the upstroke andincreased dorsiflexion in the ankle resulted in an earlierswitch to pulling back on the pedal at the end of the test (30).Some cyclists even showed increased effectiveness aroundthe top of the pedal cycle (19,29). This and the earlier tran-sition from pushing down to pulling back on the pedal arepartially explained by an increased dorsiflexed ankle caus-ing a greater horizontal force around the top of the cycle andduring the downstroke (19,30). This results in more GM andknee extensor activity around the top of the pedal cycleand allows for force application for more of the downstroke.

Muscle activation, pedal forces, and kinematics.Few studies have compared and connected variations in

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muscle activity to changes in efficiency during cycling. Inparticular, research relating muscle activity to the factorsaffecting mechanical work such as pedal forces and kine-matics is limited. This is an important area of research tofurther the understanding of how muscles work together ina coordinated fashion to produce movement. Hug et al. (16)showed that a large amount of variation in the muscle activ-ity between different subjects is not accompanied by signif-icant amounts of variation in the pedal forces. This impliesthat different combinations of muscle activity can producesimilar forces on the pedals but does not clarify which re-cruitment patterns maximize the cycling efficiency ratio byminimizing the metabolic costs. Muscles such as RF and BF,which display much variability in the timing of muscle ac-tivation (28), may help explain differences in metaboliccosts as they explain the disparity in total leg muscle activ-ity. Also, shifts in the timing of LG, MG, and TA relativeto the pedal cycle, as they fatigue in exhaustive cycling (9),may provide some insight because this results in limitedrange of motion and increased ankle dorsiflexion altering thedirection of force application on the pedals (30).

It is unclear how these changes in muscle activity andtheir associated pedal forces combined to affect cycling ef-ficiency. Muscle efficiency has been shown to increase withGP during the downstroke in steady-state cycling (22). Also,GP increases with power output primarily in the upstrokeduring short sprint cycling (21), whereas it decreases largelyin the upstroke during prolonged cycling (30). This impliesthat there is more relative muscle activation during the up-stroke at the highest power outputs, whereas there may berelatively more muscle activity of the opposite leg (whichis in the downstroke portion of the cycle) to overcome theresistive forces in longer duration cycling. Maximum pedalforce effectiveness and minimum amount and duration ofmuscle activity may be effective for high–power output andlong-duration cycling, respectively, but they are unlikely tooccur simultaneously in cycling. This study aims to identifythe balance of muscle timing and coordination, pedal forceapplication, and total muscle activity that maximizes cyclingefficiency.

METHODS

Nine experienced competitive male cyclists (mean TSEM: age = 41.8 T 2.7 yr, mass = 77.2 T 2.2 kg, height =1.81 T 0.01 m, maximal oxygen consumption (VO2max) =64.65 mLIkgj1Iminj1, yearly mileage = 9428 T 1913 km)participated in the study. The participants gave their in-formed written consent, and all procedures were approvedby the ethics committee in accordance with the Office ofResearch Ethics at Simon Fraser University.

One week before the main testing date, the participantsperformed an incremental cycling test to exhaustion to de-termine VO2max. Oxygen and carbon dioxide gas exchangewere sampled breath by breath using a metabolic cart (Vmax

229; SensorMedics, Yorba Linda, CA), and the participantswere instructed to maintain a constant freely chosen cadencethroughout the test, which was used in the main testingprotocol.

For the main test, the participants cycled in clipless ped-als at power outputs representing 25%, 40%, 55%, 60%,75%, and 90% VO2max on an indoor trainer (SRM, Julich,Germany). Resistances were presented in two groups (group1 = 25%, 40%, and 55% VO2max and group 2 = 60%, 75%,and 90% VO2max) and repeated in two blocks (block 1 =group 1, group 2 and block 2 = group 2, group 1). Resis-tances within the groups were presented in a random orderfor 3 min each, and the blocks were separated by a 5-min restperiod. The final 30 s of data from each 3-min trial was usedfor analysis.

Oxygen and carbon dioxide gas exchange, respiratoryquotient (RQ = volume of carbon dioxide released / volumeof oxygen consumed), power output, cadence, HR, kine-matics, and EMG were continuously monitored. EMG wasmeasured from the TA, MG, LG, Sol, VM, RF, VL, ST, BF,and GM using bipolar Ag/AgCl surface electrodes (10-mmdiameter, 21-mm spacing). The EMG electrodes were placedon the right leg after the removal of hair and cleaning withisopropyl wipes. EMG signals were amplified by a factorof 1000, filtered through a band-pass filter (bandwidth =10–500 Hz; Biovision, Wehrheim, Germany) and recordedthrough a 16-bit analog/digital converter (USB-6210; Na-tional Instruments, Austin, TX). Normal and tangential forcesapplied to the pedals were measured using a pedal dyna-mometer (VelUS, Department of Mechanical Engineering,Universite de Sherbrooke, Sherbrooke, Canada [10]) and re-solved into normal and tangential forces relative to the crankarm. Cycling cadence, total power output, and the poweroutput from each pedal were also calculated from the pedaldynamometers.

Sagittal kinematics of the right leg and foot were measuredat 100 Hz using an optical motion capture system (Optotrak;Northern Digital, Inc., Waterloo, Canada). Two sets of threelight-emitting diode (LED) markers were placed on rigidbodies that were attached to the thigh and shank, and threeLED markers were placed on the cycling shoe. LED markerswere then placed on the lateral femoral condyle and lateralmalleolus for a standing trial before the test to obtain theirorientation relative to the rigid bodies; these markers wereremoved for the test, and the position of the knee and anklejoints was tracked with virtual markers. Hip flexion was mea-sured using a twin-axis goniometer (SG150; Penny and GilesBiometrics, Ltd., Cwmfelinfach, Gwent, United Kingdom)attached above and below the greater trochanter. Heartbeatswere measured using a Polar T31 transmitter and a wirelessreceiver (Polar Electro Oy, Kempele, Finland). The EMG,pedal force, hip kinematics, and HR data were recorded at2000 Hz and synchronized to the ankle and knee kinematics.

Data analysis. The EMG signals were resolved intoEMG intensities by wavelet techniques using 10 wavelets(k = 1–10) (33), which act as a band-pass filter (bandwidth

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approximately 11–432 Hz). The EMG intensity across thisfrequency band (33) was interpolated to 100 evenly spacedpoints for each pedal cycle, starting at TDC. EMG intensitywas normalized to the mean intensity for each participantfor each muscle. The total EMG intensity (Itot) was given bythe sum of the EMG intensities across all muscles for eachpedal cycle. For ease of description, the pedal cycle wasbroken into four segments: quadrant 1 (Q1) was the first 90-of forward pedaling starting at TDC, quadrant 2 (Q2) wasfrom 90- to 180- of the crank arm rotation, quadrant 3 (Q3)was from 180- to 270-, and quadrant 4 (Q4) was from 270-

back to TDC (Fig. 1). The downstroke comprises Q1 and Q2,and the upstroke comprises Q3 and Q4.

Because of the large multivariate data sets obtained in thisexperiment, principal component (PC) analysis was used toreduce the number of variables and extract the importantfeatures. The EMG intensities from all 10 muscles were usedto construct a pattern of muscle coordination for each pedalcycle, and PC analysis was used to identify predominant co-ordination patterns during the cycling (37). In short, data fromall the cycles were placed into a P � N matrix A, whereP = 1000 samples per pattern (10 muscles � 100 EMG

FIGURE 1—PC weightings for the EMG intensity, pedal forces, and kinematics. The first row represents the mean EMG intensity; ankle (green dashedline), knee (red dashed line), and hip (solid blue line) joint angles; and effective (force applied normal to the crank arm, solid light blue line) andineffective (force tangential to the crank arm, orange dashed line) pedal forces per pedal cycle. Subsequent rows are visual representations of the firstfour PC weightings (PC,W). Figure legends and scales are shown at the bottom.


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intensities per cycle) and N = the number of pedal cyclesanalyzed (all subjects and all trials). The covariance matrixB was calculated from the data of matrix A, and the PCweightings (IPC,W) were determined from the eigenvectorsX of covariance matrix B. The importance of each PC wasgiven by the eigenvalue for each eigenvector–eigenvalue pairwith the greatest absolute eigenvalues corresponding to themain PCs. The relative proportion of the coordination pat-terns explained by each PC was given by X¶BX. The loadingscores for each PC (IPC,LS) for the N pedal cycles were givenby X¶A.

Effective and ineffective pedal forces (F) relative to thecrank arm and the limb kinematics (hip, knee, and hipangles) were interpolated into 100 evenly spaced points perpedal cycle. The GP (also known as the index of effective-ness) was determined as the ratio of effective to resultantforce on the crank arm. PCs for the pedal forces (FPC), forthe GP (GP,PC), and for the limb kinematics (KPC) were cal-culated in a similar manner to the PC analysis for the EMGintensities. A significant positive relationship between Itotand the metabolic power using the same methods has previ-ously been reported (r = 0.86) (36), so Itot was used as aninstantaneous cycle-by-cycle estimate of metabolic power.The overall mechanical efficiency (GO) is the ratio of me-chanical power output to the metabolic power per pedalcycle and so was estimated by the ratio of the mechanicalpower output to Itot. The mechanical power output used bothin calculating GO and in the statistical analysis was normal-ized to the mean power output for each subject.

Statistics. The most important features of the coordina-tion patterns for the EMG intensities were explained by their

most principal components. Using general linear multivar-iate ANCOVA (MANCOVA), the relationships were deter-mined between the muscle coordination patterns and thefollowing factors: subject, block, trial, cadence, pedal forces,limb kinematics, and GP. Also, the effect of subject, trial,block, cadence, IPC,LS values, PC loading scores for GP

(GP,PC,LS), PC loading scores for kinematics (KPC,LS), and PCloading scores for pedal forces (FPC,LS) on power output,Itot, and GO was determined using MANCOVA. The first 10IPC,LS values (IPC1,LS – IPC10,LS) were tested individually asthe dependent variables with subject as a random factor, trialand block as fixed factors, and cadence, GP,PC values, KPC

values, and FPC values as covariates. In addition, poweroutput, Itot, and GO were analyzed individually as dependentvariables with subject as a random factor, trial and block asfixed factors, and power output, cadence, IPC,LS values,GP,PC,LS values, Itot, KPC,LS values, and FPC,LS values ascovariates (with the exception that the dependent variablewas not included as an independent variable). Statisticaltests were considered significant at P e 0.05.

The EMG intensities were reconstructed from the sum ofthe products of the PC weights and their loading scores(~IPC,W IPC,LS) for each pedal cycle, using the first 10 PCs.The effect of the muscle coordination on the power output,Itot, and GO was visualized in the following manner. TheEMG intensity patterns were reconstructed using the first 10PCs that describe the major features of the coordination. Foreach mechanical factor (power output, Itot, or GO), if theIPC,LS had no significant effect on the mechanical factor(as determined from the MANCOVA), then the mean IPC,LSwas used. Alternatively, if the IPC,LS showed significant

FIGURE 2—Itot for each condition (25%, 40%, 55%, 60%, 75%, and 90% VO2max) and each muscle (TA, MG, LG, Sol, VM, RF, VL, ST, BF, andGM). The top rows show the mean T SEM power output, cadence, total muscle intensity, overall mechanical efficiency, and respiratory quotient (RQ)for each condition.


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covariance with the mechanical factor, then the IPC,LS val-ues were ranked by that factor, and the mean IPC,LS fromeither the top or bottom 100 cycles were taken from the rank.Thus, the major features of the muscle coordination that oc-curred with the highest or lowest of each factor (e.g., poweroutput) were reconstructed. Patterns of pedal forces, GP, andlimb kinematics were similarly reconstructed from their firstfive PCs. Values are reported as mean T SEM.

RESULTS

The mean cadence across all subjects and trials was 94.95 T0.08 rpm, and there was no significant difference in meancadence between conditions or between blocks. EMG inten-sities varied in timing and amplitude with each pedal cycle.HR, EMG intensities, and mechanical power output increasedin conjunction with resistance.

The 25%, 40%, 55%, and 60% VO2max trials had RQvalues less than 1, whereas the 75% and 90% VO2max trialswere above 1 indicating a significant contribution of the an-aerobic energy system for the group 2 trials (Fig. 2). Despitethis, the participants completed the protocol and therefore didnot experience failure due to fatigue. There was a significantdecrease in RQ and a significant increase in HR from block1 to block 2 yet no significant difference in power output,Itot, or GO between blocks.

The first 10 IPC,W values (IPC1,W – IPC10,W) explained morethan 81% of the coordination patterns. IPC1,W explained55.4% of the signal and was similar to the mean muscle in-tensity pattern (Fig. 1). IPC1,LS was correlated with the me-chanical power output (r = 0.84; Fig. 3A), Itot (r = 0.98;Fig. 3B), GO (r = –0.60; Fig. 3C), and GP (r = 0.76). IPC2,Wexplained 7% of the coordination patterns and showed apositive intensity for all muscles earlier than each muscle’speak intensity in IPC1,W and a negative intensity at or afterthe peak intensity of IPC1,W (Fig. 1). IPC2,LS was correlatedwith cadence (r = 0.94). GM had the largest EMG intensityrange between conditions followed by RF, BF, VM, and VL(Fig. 2). MG showed very little change in EMG intensitybetween conditions with LG, Sol, and TA displaying only aslightly greater range (Fig. 2).

Power output. The power output from the right pedalwas an excellent predictor of the total mechanical poweroutput (r = 0.99), and so, the results below focus primarilyon the power output from the right pedal (normalized) be-cause the EMG and kinematics were measured from this leg.IPC1,W accounted for the general muscle coordination patternand was very similar to the mean coordination pattern (Fig. 1).IPC4,W showed a general positive weight for EMG intensityfor TA, MG, LG, Sol, ST, and BF and negative weights forGM and RF (Fig. 1). IPC4,LS was negatively correlated with

FIGURE 3—Relationships between power output, total muscle intensity, GO (power output / total muscle intensity) and the loading scores for the EMGintensities. The ratio of IPC4,LS/IPC1,LS (D, E, and F) show the relative contribution of IPC4,LS to IPC1,LS.


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FIGURE 4—EMG intensities reconstructed from the PC analysis for the TA, MG, LG, Sol, VM, RF, VL, ST, BF, and GM with respect to high (solidgray line) and low (black dashed line) states of power output, Itot, and GO. The first 10 PCs were used for each reconstruction.


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power output (r =j0.84), and the ratio of IPC4,LS/IPC1,LS alsodecreased at higher powers (r = j0.81; Fig. 3D). Thus, atlower powers, there was a relatively smaller contribution ofthe GM and RF and a relatively greater contribution of theTA, MG, LG, Sol, ST, and BF to the reconstructed coordi-nation pattern; this situation was reversed at higher powers(Fig. 4). In addition, the reconstructed coordination patternsfor higher power outputs were larger in amplitude with VM,VL, and BF displaying a shift to earlier peak intensities thanfor lower power outputs (Fig. 4).

The first three PC weights for pedal forces (FPC1,W,

FPC2,W, and FPC3,W) explained more than 98% of the signal(Fig. 1) with their corresponding loading scores, FPC1,LS,FPC2,LS, and FPC3,LS correlated with power output (r = 0.74,0.61, and 0.70, respectively). The reconstructed forces showedthat higher power outputs were associated with higher effec-tive force during the downstroke and more negative effective

force during the upstroke (opposite of the direction of crankarm rotation) (Fig. 5). The mean GP for the pedal cycle hada strong positive relationship with power output (r = 0.89).The first PC weight for GP (GP,PC1,W) explained 93% of theGP signal; however, the first PC loading score (GP,PC1,LS)showed a low correlation with power (r = 0.22). Thereconstructed GP showed that the increased GP at higherpowers resulted from an increased GP during the upstrokeand across TDC (Fig. 5).

The reconstructed hip angles for the high power outputwere smaller in Q4 and Q1 and larger in Q2 and Q3 than forthe low power output, whereas the knee angles were simi-lar throughout (Fig. 5). The ankle angles for the high poweroutputs were 5-–14- more dorsiflexed than for the low poweroutputs throughout the pedal cycle (Fig. 5).

Itot. Itot correlated strongly with the power output (r = 0.86;Fig. 3G), and so many of the relations between muscle power

FIGURE 5—Pedal forces, GP, and sagittal plane joint angles reconstructed from the PC analysis with respect to high (solid gray line) and low (blackdashed line) states of power output, Itot, and GO. The first five PCs were used for each reconstruction.


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output and muscle coordination were matched by similarrelations between Itot and coordination. Itot correlated posi-tively with IPC1,LS (r = 0.98; Fig. 3B) and negatively withboth IPC4,LS and the ratio IPC4,LS/IPC1,LS (r = j0.66 andj0.74, respectively; Fig. 3E). The reconstructed coordinationpatterns showed greater EMG intensity for all muscles whenat high Itot, with the greatest increase being in RF and GM(Fig. 4). In addition, high Itot was associated with timingadvances for VM and VL in the downstroke and in BF at thebottom of the pedal cycle (Fig. 4).

High Itot was associated with a larger peak effective forceduring the downstroke and a similar effective force duringthe upstroke (Fig. 5). The ineffective force was more nega-tive in Q1, more positive in Q2 (when it reached its maxi-mum values), and smaller throughout the upstroke (Fig. 5).The primary difference in GP occurred at the top of the pedalcycle where GP was elevated for higher total intensity (Fig. 5).

Hip angles were larger throughout the pedal cycle forhigher Itot compared with lower Itot with the largest differ-ence of approximately 6- during the downstroke portion ofthe pedal cycle (Fig. 5). Knee angles were similar for highversus low Itot (Fig. 5). With higher Itot, the ankle was moredorsiflexed in Q1, Q2, and Q4 and more plantar flexed in Q3relative to lower Itot (Fig. 5).

GO. The 55% and 60% VO2max trials showed the highestlevels of GO with the 90% and 25% VO2max trials having thelowest (Fig. 2). As GO increased, Itot decreased (Fig. 3J),whereas power output remained reasonably constant (Fig. 3H),which was particularly apparent in the 90% VO2max condi-tion. There was a significant correlation between GO andIPC1,LS (r =j0.60; Fig. 3C) and also IPC4,LS, IPC5,LS, IPC7,LS,and IPC8,LS, and therefore, the overall efficiency wasstrongly related to the muscle coordination patterns. At lowGO, there were lower EMG intensities for MG, LG, and Sol;a greater EMG intensity for GM in the second half of Q2;greater RF and TA intensities across the top of the pedalcycle; and earlier peak VM, VL, and BF and later peak STintensities (Fig. 4).

There was little difference in pedal forces and no differ-ence in GP between high and low GO (Fig. 5). The hip andknee angles showed a negligible difference between highand low GO. The ankle angles were more plantar flexed dur-ing the downstroke and more dorsiflexed during the up-stroke for the high GO (Fig. 5).

DISCUSSION

This study has shown that there are significant associa-tions between the muscle coordination, forces acting on thepedals, kinematics, mechanical crank power, Itot, and GO.

GP and pedal forces. As with previous studies, themean GP for the entire pedal cycle increased with workloads(27,29); however, it was not a good indicator of GO. With thelarge time-varying fluctuation in effective force throughoutthe pedal cycle, instantaneous GP from the GP,PC values wasused in the analysis of total muscle intensity, power output,

and GO. FPC1,W explained 98% of the pedal forces (F), andso, the pattern of pedal force application was very consistentamong cyclists (14), with the primary differences arisingfrom changes in amplitude. This study supported previousreports because as workload increased, there was an increasein the effective force during the downstroke (5,19,29), a morenegative effective force during the upstroke, and a morepositive ineffective force in Q2, Q3, and Q4 (positive inef-fective force is directed away from the center of crank armrotation) (Fig. 5) (5).

Muscle activation. The primary muscle coordinationpattern (IPC1,W) related to the general coordination patternrequired for cycling and was similar to the mean coordinationpattern for all participants in all trials (Fig. 1). The significantcorrelation between IPC1,LS and Itot indicated that the EMGintensity showed general increases as the trials intensified(Fig. 3B). In terms of specific muscles, GM had the largestrise in muscle activity from low to high resistance (Fig. 2)and played a significant role in high power production thatwas largely due to the relative decrease in IPC4,LS (Fig. 3D).Ericson (12) found GM to have low peak activity levels rel-ative to maximum voluntary contractions (approximately 10%and 40% for 120-W and 240-W conditions, respectively)thereby not contributing as much to power production asfound here. In this study, the mean power output for the 90%VO2max condition was substantially higher at 326 T 9 W, andthere was a considerable rise in GM intensity between allconditions (Fig. 2). Therefore, there is evidence that the GMwas active at a high percentage of its maximum and supportsits role as a contributor to power production (28).

The RF and TA also showed increases in EMG intensityacross the top and early part of the pedal cycle with risingpower outputs (Fig. 4), although given the electromechanicaldelay, their primary application of force would occur duringthe first half of Q1. These muscles play a role in movingthe pedal over the top and at the start of the new pedal cycleto apply force to the pedal during the downstroke (12,23).The RF crosses two joints and performs dual functions offlexion of the hip and extension of the knee, whereas the TAacts to dorsiflex the ankle. Muscles crossing two joints arethought to transfer force between the joints and control thedirection of force application on the pedal (34). Given thatthe RF and TA were the only muscles demonstrating no-ticeable amounts of muscle activity across TDC and throughthe early portion of Q1 (Fig. 1), there is evidence that bothwere controlling the direction of force. The kinematic tracesshowed that the hip was undergoing a small amount offlexion, the knee was extending, and the ankle angle was notchanging across TDC (Fig. 1), yet there was very little forceacting on the pedal (Fig. 1). Considering the lack of muscleactivation in VM and VL, RF could have been acting to bothflex the hip to avoid adding resistance to the pedals fromthe weight of the leg and extend the knee. As the knee ex-tends in Q1, TA maintained ankle dorsiflexion to orient thepedal in a direction that will allow the force to be applied tothe crank during the downstroke. Because the EMG intensities


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are normalized for each muscle, the EMG intensity can onlybe determined relative to itself. This means that the highlevels of EMG intensity for the TA and RF at TDC and inQ1 may not result in much force but were their maximumvalues in the pedal cycle. Although, as with GM, RFexhibits significantly more muscle activity relative to max-imum voluntary contraction from 120 to 240 W (12), andaside from GM, RF muscle activity increased more than anyother muscle from the 25% to the 90% VO2max condition(Fig. 2). This suggests that RF may have been contributingsignificant amounts of force to the early portion of the pedalcycle at high resistances. Whether they produce a lot or verylittle force, RF and TA seem to be the keys to power pro-duction by initiating knee extension and maintaining ap-propriate joint angles.

VL and VM have been shown to be large power-producing muscles during the downstroke (3,12,28). Thisstudy did not show the same increase in EMG intensity forVL and VM as for GM and RF at higher workloads, yet bothwere highly active during the downstroke when most of thepower was being produced (Fig. 4). This adds more evidencethat these muscles are big contributors to the power outputregardless of the resistance (12). At the bottom of the pedalcycle during the transition from the downstroke to the up-stroke, the ankle plantar flexors (MG, LG, and Sol) as wellas the knee flexors (ST and BF) showed the most activity inthe primary muscle coordination pattern (IPC1,W; Fig. 1). Soldisplayed the greatest EMG intensity in the second half ofQ2 before the bottom of the pedal cycle when GM was alsoat its maximum, whereas LG and MG showed the greatestEMG intensity just after the bottom. This could be becauseSol helped to stabilize the ankle joint and transfer powerproduced by GM to the pedal, similar to the simulation studyfindings by Neptune et al. (24). Sol is a single-joint musclewith a higher percentage of slow-type muscle fibers thanMG and LG (18) and is used more at high resistances (37).It is therefore better suited to stabilize and plantar flex theankle joint to contribute to the pedal force through the lowerportion of the downstroke. Given the electromechanical de-lay, GM and Sol primarily contributed force to the pedal inthe second half of Q2, across the bottom of the pedal cycle,and through the first half of Q3, which explains the peakineffective pedal force (away from the center of crank rotation)occurring near the bottom dead center of rotation (Fig. 1).

Mechanical power output, total muscle intensity,and overall efficiency. The MANCOVA showed thatIPC4,LS and IPC7,LS were the most important covariates withGO. The common features of IPC4,W and IPC7,W includedchanges to GM and the muscles acting across TDC (TA andRF) and the bottom of the pedal cycle (Sol, ST, and BF).When GO was at its maximum (55% and 60% VO2max trials),the ratio IPC4,LS/IPC1,LS was only in its midrange (Fig. 3F);therefore, GO was very sensitive to IPC4,LS/IPC1,LS with ratiostoo high or too low being associated with low GO (90% and25% VO2max conditions). It is possible that the coordinationof muscle activity between left and right legs at the top and

bottom of the cycle played an important role in GO. Becausethe left and right crank arms are mechanically linked, thecoordination patterns of a single leg and the extrapolation totwo legs may be insufficient to fully understand the influ-ence of muscle coordination on GO.

The reconstructed EMG intensities for high and low GO

highlight some of the differences in muscle activation (Fig. 4).The coordination pattern for high GO had a more even dis-tribution of muscle activity across all muscles, whereas thecoordination pattern for low GO displayed much activity inGM, RF, and, to a lesser extent, short bursts of VM and VL.At high GO, the intensities for the key power-producingmuscles—VM, VL, and GM—were much more evenly dis-tributed during the downstroke (Fig. 4). Also, high GO wasassociated with a regular progression of activity between themuscles: initially, VM and VL synchronously, followed byGM, then Sol, and then LG, MG, ST, and BF synchronously.Conversely, with low GO, the muscle groups were activatedless in concert (between VM and VL and between LG, MG,ST, and BF), and the EMG intensities of VL and GM werealmost 90- apart. The timing of activation for GM and Solwas earlier at high GO, which, given the electromechanicaldelay, implies that these muscles provided force on the pedalfor more of the power-producing downstroke. Given thesmooth and even distribution of EMG intensity and syn-chronization of peak muscle activity between muscles actingacross the same joint, it is possible that the coordinationpattern for high GO results in smoother shifts from net kneejoint moments to net hip joint moments that occur during thedownstroke of the pedal cycle (34).

Because the 90% VO2max condition was the most ineffi-cient of all trials, it had a large weighting on low GO. Be-cause GM had the largest range of activity between allconditions (Fig. 2), it was a dominant feature of the 90%VO2max condition compared with the 55% and 60% VO2max

trials, and this contributed to the differences in EMG intensityfor GM between high and low GO. When the pedal cyclesfrom the 90% VO2max condition were excluded, there wasstill an uneven distribution of muscle activity in the recon-structed coordination patterns for low GO with more weighton muscles acting on the ankle joint (TA, MG, and LG) aswell as RF and BF. Despite the successful completion of thetesting protocol by all participants, the 90% VO2max condi-tion may have fatigued MG and LG (4,7), resulting in agreater reliance on GM and the knee extensor muscles tomaintain the required power output. However, Bini et al. (4)found that the mean ankle angle decreased with fatigue,whereas in this study, the mean ankle angle was reduced forhigher GO. This indicates that fatigue in the ankle plantarflexors may not have been present or that the occurrence offatigue in these muscles resulted in elevated efficiency whenall muscles were considered, although this study did not in-vestigate fatigue indicators of individual muscles across trials.

Methodological considerations. The randomized blockdesign was used to minimize bias on the EMG due to fac-tors that are known to influence the signals such as muscle


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temperature and fatigue (6). Lack of a significant differencebetween blocks for Itot was evidence of successful imple-mentation. When interpreting the coordination patterns, itis also important to account for the electromechanical de-lay. Assuming an electromechanical delay of 88 ms (34), thechanges in muscle force would occur at approximately 50- ofpedal rotation after the EMG signal at a cadence of 95 rpm. Thetiming of the EMG relative to the pedal cycle is very sensitiveto the pedal cadence because the electromechanical delay con-stitutes a larger proportion of the pedal cycle at higher ca-dences, and so, excitation is required earlier in the pedal cycleto apply force at a similar location in the cycle (23). In thisstudy, there was no significant difference in cadence acrossconditions or blocks. However, similar to a previous work,cadence was still a major source of variability, and the PCthat explained timing shifts (IPC2) accounted for a substantialproportion of the coordination patterns (37). IPC2,LS was sig-nificantly correlated with cadence, and by including it as acovariate in the MANCOVA, any bias due to cadence wasremoved from the remaining results.

The results of this study rely on Itot from only 10 legmuscles being an appropriate estimate of metabolic powerduring cycling. Wakeling et al. (36) used oxygen consump-tion to identify the relationship between metabolic power andItot of 10 leg muscles at workloads eliciting a respirationquotient above and below one. This means that metabolicpower was underestimated because it did not consider an-aerobic sources that become more significant at the highestworkloads. This accounts for the nonlinear relationship be-tween metabolic power and Itot at the highest workloadsfound by Wakeling et al. (36) and indicates that a linear re-lationship would exist if anaerobic energy was considered.It is important to note that this study examines changes anddifferences in relative and not absolute values of efficiency.Therefore, the GO used provides valuable insight into the rel-ative muscle activity and energy costs of the mechanical work.

CONCLUSIONS

In a previous study, we showed that coordinated musclerecruitment was a key factor in determining the mechani-cal efficiency of limb movement (35). Similarly, this studydemonstrates that GO in cycling is dependent on the activa-tion levels, timing, and coordination of all of the active legmuscles and not any one muscle in particular. With consis-tency in pedal force application, GO was independent of thedirection of applied force yet could be seen through thechanges in the amplitude of the force. In addition, this studyshows that there exists a trade-off between power and effi-ciency in cycling because the highest mechanical efficien-cies did not occur at the highest power outputs. Increased GO

was achieved through coordinated contraction of muscles act-ing across the same joint, such as VL and VM; peak mus-cle activity occurring sequentially from knee to hip to ankle;and the reliance on multiple muscles to produce large jointtorques. Given that both the left and right legs are used forcycling and the crank arms are not independent, coordinated

recruitment between the muscles of the left and right legscould play an important role in efficient cycling. The moststriking evidence of this was the significant relationshipbetween GO and the variability in coordination of musclesacross the top and bottom of the pedal cycle. Future musclecoordination studies in cycling should include the coordi-nation between both legs due to the mechanical dependenceof the crank system.

In practical terms, this study may have implications ontraining techniques for cycling. Training in specific condi-tions would maximize the use of muscle coordination pat-terns realized in competition given that the coordinationpatterns vary with workload. For example, the trade-off be-tween power output and overall efficiency indicates that shortsprint cycling events that require high power outputs wouldsacrifice efficiency for power output. These cyclists wouldbenefit from training as much as possible at these high poweroutputs to maximize their exposure to the corresponding mus-cle coordination patterns. More research is needed to verifythe specific applications from this study, but the coordina-tion patterns found here suggest that training to keep theankle dorsiflexed across the TDC of the pedal cycle andearly activation of the RF and GM would be a good strategyto improve power output for sprint cycling. This strategywould also be beneficial for efficiency because the power-producing muscles would be active for more of the down-stroke resulting in a reduced demand on less efficient muscleactivation during the upstroke.

Also, cycling to maximize overall efficiency at 55%–60%VO2max would promote a different set of muscle coordina-tion patterns, emphasizing and strengthening different mus-cles, that would be more suited to multistage cycling raceswhere overall efficiency is more important. The coordinationpatterns indicate that training to develop better efficiencywould involve a reduction in the range of motion of theankle combined with prolonged TA activity during the firstportion of the downstroke. This may provide a more stableplatform to transfer power to the pedal, allowing for moresynchronized muscle recruitment shown during the down-stroke for highly efficient cycling. Further investigation isneeded to establish if muscle coordination in a single leg orimproved coordination between the left and right legs is adetermining factor in these changes at the top of the pedalcycle. Training at these workloads would aim at increasingthe percentage of pedal cycles made with highly efficient co-ordination patterns, whereas racing at these workloads wouldbe a strategy to maximize power output while reducing therisk of early fatigue.

The authors thank Sabrina Lee and Manku Rana for their help withdata collection, Max Donelan for the use of the metabolic cart, DanMarigold for the use of the electronic goniometer, and the NaturalSciences and Engineering Research Council of Canada for financialsupport to James M. Wakeling.

There are no conflicts of interest with any of the authors of thisarticle.

The results of this study do not constitute endorsement by theAmerican College of Sports Medicine.


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INDEX OF ABBREVIATIONS

TDC Top dead center of pedal rotationQ1 The first 90- of forward pedaling starting at TDCQ2 From 90- to 180- of the crank arm rotationQ3 From 180- to 270- of the crank arm rotationQ4 From 270- back to TDC of crank arm rotation

GO Overall mechanical efficiencyItot Total EMG intensity across all muscles for each pedal cycleIPC PCs for EMG muscle intensity

IPC,LS PC loading scores for EMG intensityIPC,W PC weights for EMG intensity

(continued)

F Effective and ineffective pedal forcesFPC PCs for pedal forcesFPC,LS PC loading scores for pedal forcesFPC,W PC weights for pedal forces

K Hip, knee, and ankle joint kinematicsKPC PCs for kinematicsKPC,LS PC loading scores for kinematics

GP Pedal effectiveness (effective pedal force / total pedal force)GP,PC PCs for GP

GP,PC,LS PC loading scores for GP


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muscle coordination patterns for efficient cycling.21

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