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Use of bracing and EMG biofeedback to investigate the relationship between soleus and gastrocnemius excitation.. Wilkes, Julia 2008-03-04

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Use of bracing and EMG biofeedback to investigate the relationship between soleusand gastrocnemius excitation during cyclingbyJulia WilkesB.P.H.E., B.Sc., Queen’s University, 2006A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate Studies(Human Kinetics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)AUGUST 2008© Julia Wilkes, 2008AbstractIntroductionOur basic understanding of muscle synergies is incomplete. The interaction among thetriceps surae muscles (medial and lateral gastrocnemius, soleus) is still underinvestigation. Studies have shown that these muscles respond differently to cadencemanipulations. These excitation differences might relate to the role of gastrocnemius inknee flexion, a role that soleus does not serve.PurposeThe purpose was to investigate the response of the medial and lateral gastrocnemiusmuscles when soleus excitation was eliminated using bracing and biofeedback duringcycling.MethodsParticipants cycled under braced and unbraced conditioms over two sessions. During eachsession, cycling protocol involved a normalization ride with no brace and no feedback,followed by a second ride without feedback, and a prolonged ride with feedback. Thebiofeedback consisted of a moving bar graph representing the average soleus excitationfor the first half of the pedal cycle and was updated with every pedal stroke.Electromyography of seven muscles was collected and analyzed.ResultsIn the unbraced condition, soleus excitation was not modified with visual biofeedback.While wearing the brace, the integrated electromyography (iEMG) of all triceps suraemuscles decreased by 30%. With the addition of EMG biofeedback, soleus and lateralgastrocnemius iEMG decreased a further 26% and 21% respectively by the end of thefeedback period while medial gastrocnemius excitation did not change. Tibialis anteriorexcitation was significantly increased while rectus femoris and biceps femoris excitationdid not change. Gluteus maximus iEMG decreased with bracing and biofeedback.ConclusionsWhile unbraced, soleus excitation was not reduced with biofeedback as it is difficult tomodify joint position in this learned motor task. When the known task was modified byapplying an ankle-foot orthosis, participants successfully modified soleus excitationunder less-familiar task requirements. Participants voluntarily activated the tibialis11anterior muscle, and it is proposed that through reciprocal inhibitionpathways, soleusEMG was reduced. Lateral gastrocnemius excitation also decreased. With soleusexcitation decreased, medial gastrocnemius excitation wasunchanged likely due to its ongoing role in knee flexion. This effect was localized to the ankle jointas proximalmuscles were unaffected by bracing and by voluntary changesin soleus excitation.111Table of ContentsAbstract.iiTable of Contents ivList of Figures viList of Tables viiiAcknowled2emeats ixINTRODUCTION 11.1 Statement of the Problem 21.2 Brief Methods 21.3 Hypothesis 32 REVIEW OF LITERATURE 42.1 Triceps Surae Anatomy 42.1.1 Anatomy of the lower limb: origin, insertion, and action (Drake, 2005) 42.1.2 Evidence for differential activation of the triceps surae 52.2 Triceps Surae in Cycling72.2.1 Characteristic cycling action72.2.2 Evidence for differential activation of the triceps surae in cycling102.2.3 Manipulation of the ankle joint during cycling 112.3 Tools to Reduce Soleus Excitation 132.3.1 Bracing and biofeedback 132.3.2 Theories of biofeedback function 142.3.3 Previous use of EMG biofeedback 152.3.4 Selection of biofeedback displays 152.3.5 Multiple types of biofeedback 162.4 Interaction Among Motor Neuron Pools 172.4.1 Measuring excitability 182.4.2 Sources of inhibition on the motor neuron pool 192.4.3 Influence of single muscle excitation on soleus excitability 202.4.4 Excitation of multiple muscles 202.4.5 Influence of the non-test limb 222.4.6 Soleus excitability in cycling 222.4.7 Other influential factors 232.5 Summary 243 METHODS 253.1 Participants 253.2 Instrumentation 253.3 Procedures 293.4 Data Analysis 303.5 Statistical Analyses 30iv4 RESULTS.324.1 Description of Participants324.2 Cadence324.3 Integrated Electromyography334.3.1 Unbraced334.3.2 Braced354.4 Time-to-peak EMG384.5 Repeatability404.6 Median Power Frequency404.7 Soleus EMG Biofeedback42S DISCUSSION435.1 Overview435.2 Support for the Hypotheses445.3 Mechanism for Reducing Soleus EMG445.4 Effects of Reduced Soleus Excitation on Medial Gastrocnemius Excitation465.5 Ability to Use Biofeedback505.6 Localized Effect535.7 Methodological Considerations 546 CONCLUSION587 REFERENCES598 APPENDICES65Appendix A: UBC Research Ethics Board Certificates of Approval65Appendix B: Individual Cadence Data67Appendix C: Statistical Tests69Appendix D: Individual Participant Data75VList of FiguresFigure 2.1. Change in T2 times in distal and proximalsegments of four lower leg muscles(Adapted from Segal & Song, 2005)6Figure 2.2. Pedal cycle with clockwise pedal movement7Figure 2.3. Ankle and knee angles throughout a pedal cycle ata cadence of 80rpm(Sanderson et al., 2006)8Figure 2.4. Muscle excitation of SOL, LG, and TA for onepedal cycle (TDC to TDC).The grey shading indicates standard deviation across participants (AdaptedfromChapman et al., 2006)9Figure 2.5. Talocrural position during cycling while attemptingto hold the joint indorsiflexion or plantarfiexion (Cannon et al., 2007)12Figure 2.6. Descending control of reciprocal inhibition. Activation of the flexormusclespindles excites the flexor muscle and inhibits the extensor muscle (Dale, 2004)17Figure 3.1. Biofeedback display screen27Figure 3.2. Examples of SOL EMG biofeedback. The bars (from leftto right) display1 00+% excitation, 50% excitation, and 10% excitation28Figure 3.3. Testing Procedure (B = braced, NB = unbraced, F = feedback, NF= nofeedback). During four minute trials, data were recorded duringthe last 1 Os; during the20 minute trials, data were recorded for 1 Os every minute29Figure 4.1. Mean participant (SD) SOL and TA iEMG from four conditions averagedacross ten pedal cycles during the unbraced condition34Figure 4.2. Mean participant (SD) SOL, LG, and MG iEMG from four conditionsaveraged across ten pedal cycles during the unbraced condition34Figure 4.3. Mean participant (SD) SOL and TA iEMG from four trialsaveraged acrossten pedal cycles during the braced condition36Figure 4.4. Mean participant (SD) SOL, LG, and MG iEMG from four trials averagedacross ten pedal cycles during the braced condition36viFigure 4.5. Mean time-to-peak EMG in the LG and MG muscles averaged across tenpedal cycles in the braced condition 39Figure 4.6. Mean participant (SD) median power frequency of the SOL, LG, MG and TAMPF from four trials averaged across ten pedal cycles during the braced condition 41Figure 4.7. Mean participant (SD) BF, RF, and GM MPF from four trials averaged acrossten pedal cycles during the braced condition 41Figure 4.8. Average SOL EMG across the entire testing session in the braced conditionfrom an individual participant (Participant 06). Data were filtered with a 2nd order lowpass Butterworth filter with a 10Hz cutoff frequency 42Figure 5.1. Four potential organizations of the triceps surae motor neuron poo1 includingthe two extremes, shared or distinct. Two examples of other possible organizationsinclude divided, in this case shared motor neurons between the LG and MG and distinctfrom SQL, or hybrid, some degree of shared and distinct motor neurons between all threemuscles 48viiList of TablesTable 2.1. Effect of cadence manipulations during cycling on triceps suraeexcitation... 10Table 3.1. Pairwise comparisons computed when 1x4 ANOVA was significant31Table 4.1. Mean cadence and standard deviation across participants in the four analyzedcollection periods, with and without the brace32Table 4.2. Normalized iEMG (to the NB-NF condition) for each muscle averaged acrossparticipants for three different trials in the unbraced condition35Table 4.3. Normalized iEMG (to the NB-NF condition) for each muscle averaged acrossparticipants for three different trials in the braced condition37viiiAcknowledgementsFirst off, I’d like to thank Dr. Dave Sanderson, my supervisor, for demandingindependent thought, for continually challenging me, and foronly accepting my mostaccomplished work. Thank you for your constant support evenafter I decided to partways with science. I am grateful for of all of the skills that I havedeveloped incompleting my masters degree, most of which I could not have gainedwithout you.Thank you to my committee members, Dr Romeo Chua and Dr Tim Inglis, forbeing available whenever I needed you. A special thanks to Romeo for allof your handson work with LabView fulfilling your self-proclaimed tech role.To my labmates, Scott, Ryan, Karine, and Lexi, and those in the War Memorialbasement (especially Mel Roskell who dealt with me both at home and in the lab), Icouldn’t have finished without you. Thank you for the discussions, both scientific andotherwise, and for your endless support and friendship.To Mum and Dad, thank you for always encouraging me. To big brother Dave,thank you for the frequent visits and life conversations. To everyone else who hascontributed to the successful completion of my master’s degree, I sincerely appreciateyour friendship, honesty, and guidance.ix1 INTRODUCTIONThe triceps surae is a muscle group consisting of the soleus (SOL), lateralgastrocnemius (LG), and medial gastrocnemius (MG) muscles. Given their anatomicalstructures, it was once believed that these muscles worked in unison but imagingtechnology has shown that this is not always the case (Yanagisawa et al., 2003,Segal andSong, 2005); instead, researchers suspect that these muscles are differentially controlledleading to a load sharing relationship among the triceps surae complex.Previous work in cycling has shown that while SOL excitation, as measuredbysurface EMG, was unaffected by cadence manipulations, MG excitation increasedascadence increased (Marsh and Martin, 1995). A series of experiments by Sanderson andcolleagues (Sanderson and Kenyon, 2005, Sanderson et al., 2006) haveattempted todetermine why the muscles of the triceps surae respond differently to cadencemanipulations. In extending the protocol of Marsh and Martin (1995), Sandersonet al.(2006) recorded muscle lengths in addition to muscle excitations while cycling at arangeof cadences. They suggested that differential responses to cadence manipulationsbetween the SOL and MG muscles might be associated with mechanical properties ofthemuscles, primarily muscle length (Sanderson et al., 2006).Because motion at the ankle and knee joints occurs simultaneously duringcycling, it is difficult to partition the excitation of the LG and MG muscles todeterminethe relative contribution of ankle and of knee motion. One way to measure the relativecontribution is to eliminate movement at one of the two joints, for example, the anklejoint. By de-recruiting the SOL muscle, we presume that the involvement of the anklejoint motion is removed and that all remaining LG and MG excitation is directedby kneemotion. In a previous study designed to investigate the differing relationships withcadence and to separate the muscles’ contributions to actions at the knee and ankle joints,participants wore an ankle brace that positioned the ankle joint at 90°. The cadencesensitivity in MG remained while SOL remained insensitive to cadence below85 rpm(Sanderson and Kenyon, 2005). Because the brace eliminated plantarfiexion anddorsiflexion motion, and thus plantarfiexor torque could not contribute to the cyclingaction, one might have expected that the SOL muscle would become silent; however,substantial muscle excitation remained. Since SOL could not contribute to the production1of pedal forces, it was proposed that participants could be trained to reduce SOLexcitation during cycling. Visual biofeedback of overly activemuscles has been effectivein guiding individuals to reduce muscle excitation, particularly in trapezius musclerelaxation exercises (van Dijk and Hermens, 2006). As a preliminaryquestion, thecurrent study determined if cyclists could use electromyography(EMG) biofeedback ofthe soleus muscle to voluntarily inhibit its excitation. The primary purposewas todetermine how the medial and lateral gastrocnemius muscles respondedwhen soleusexcitation was eliminated.1.1 Statement of the ProblemThe current study addressed three questions with the primary research questiondesigned to investigate the differential activation of the triceps suraemuscles duringcycling:1. Can biofeedback be used in isolation to induce a reduction in soleus excitation(biofeedback only)?2. Is biofeedback an effective tool to reduce soleus excitation beyond bracing (bracingand biofeedback)?3. When soleus excitation was significantly reduced, how did the recruitment of themedial and lateral gastrocnemius muscles change?1.2 Brief MethodsTo address these questions, participants were asked to cycle under two conditions:unbraced and braced. The unbraced condition involved normal cycling with noconstraints while the braced condition referred to cycling with an ankle-foot orthoses, aplastic L-shaped brace covering the foot and lower leg. Across two days, participantscycled braced and unbraced in conditions with and without biofeedback. The averagesoleus EMG from the first half of the pedal cycle (0-180° with 0° indicating the top ofthepedal stroke) was displayed during a twenty-minute ride while participants attempted tosilence soleus excitation. Electromyography was recorded from seven muscles: soleus,lateral gastrocnemius, medial gastrocnemius, tibialis anterior, biceps femoris, rectusfemoris, and gluteus maximus.21.3 HypothesisWhen wearing an ankle-foot orthosis, soleus recruitment will be reduced usingfeedback of surface EMG in real-time; however, participants will be unable to reduceSOL EMG in the unbraced ankle condition. EMG biofeedback will allow participantstobecome consciously aware of their soleus excitation; through visual integrationof thisfeedback, braced participants will be able to voluntarily decrease recruitmentof SOL.Cannon et al. (2007) found that cyclists can modify mean ankle joint positionwhilecycling without braces; however, while trying to maintain maximal dorsiflexion, themaximal plantarfiexion angle did not change although the time in plantarfiexionwasslightly reduced. Thus, it is expected that during unbraced cycling with feedback,SOLEMG will not change significantly as plantarfiexor torque will not be limited.Medial and lateral gastrocnemius muscle excitation will decrease during SOLEMG biofeedback in the braced condition. Momieux et al. (2007) found thatthe anklemoment contributed 21% to the total moment in cycling. Sandersonand Kenyon (2005)showed that while wearing an ankle brace, the excitation of both SOL and MG decreased.The reduction in LG and MG excitation from the elimination of active plantarflexortorque in the braced condition will exceed any increased excitation in these musclesdueto increased knee flexor torque requirement. Thus, LG and MG excitation will decreasealong with SOL excitation during bracing and biofeedback.32 REVIEW OF LITERATUREThis literature review focuses on four essential areas of research: understandinghow the triceps surae group functions both generally and in cycling, howone usesbiofeedback, and how soleus excitation may be influenced by other leg musculature.2.1 Triceps Surae Anatomy2.1.1 Anatomy of the lower limb: origin, insertion,and action (Drake, 2005)The gastrocnemius and soleus muscles are together referred to as the tricepssuraemuscle group. The gastrodnemius (GAS) has two parts, the medial head (MG)and thelateral head (LG). Since many authors did not differentiate between the MG andLG, theterm GAS is used when no specifics are available. The lateralhead originates from theupper posterolateral surface of lateral femoral condyle while the medial head originatesfrom the posterior surface of distal femur just superior to the medial condyle.The soleus(SOL) muscle lies deep to the gastrodnemius. It originates from two areas:fibular (theposterior aspect of the head and neck and upper shaft of the fibula) and tibial (thesolealline and adjacent medial border of the tibia); it has a ligamentous arch between thefibularand tibial attachments and, unlike GAS, is considered a single muscle. The SOL, LG,and MG insert on the Achilles tendon which attaches to the posterior surface of thecalcaneus.SOL, LG, and MG are innervated by the tibial nerve with nerves originating fromthe Si and S2 spinal segments for the gastrocnemius and soleus respectively. Thebiarticular LG and MG cross both the knee and ankle joints while the monoarticularSQLcrosses only the ankle joint. At the ankle joint, the muscles work as synergists,musclesthat actively provide an additive contribution to a particular function duringa contraction(Basmajian and DeLuca, 1985). These muscles act to plantarfiex or control dorsiflexionof the foot while GAS also flexes or controls extension of the knee. There are othermuscles in the posterior compartment of the lower leg that contribute to plantarfiexortorque including the plantaris, tibialis posterior, flexor hallucislongus, and peroneusbrevis; however, these other muscles are not considered in the current researchas the4triceps surae muscles account for 60-80% of plantarfiexor torque (Sale et al., 1982)andthe specific aim is to investigate the relationship between the SOL, LG, andMG muscles.The primary antagonist muscle, the tibialis anterior (TA), originates from theupper two-thirds of the lateral surface of the shaft of the tibia and adjacent surfaceof theinterosseous membrane as well as the deep fascia. The muscle fibres convergein thelower third of the leg to form a tendon which descends into the medial side of thefootand attaches to the medial and inferior surfaces of the medial cuneiform andfirstmetatarsal. TA acts to dorsiflex the foot and is innervated by the deep peronealnerve, abranch of the common fibular nerve.2.1.2 Evidence for differential activation of the triceps suraeIt was once believed that all muscles forming the triceps surae group acted inunison, and while it has been known for some time that this beliefis incorrect, directevidence for the differential activation of triceps surae came only recently fromdynamicmagnetic resonance imagery (Yanagisawa et a!., 2003, Segal and Song, 2005). Whenalimb is scanned in a magnetic resonance scanner, atoms with an odd number ofprotonsspin along the axis of the scanner. A brief radiofrequency causes the protonsto spin off-axis. The time over which a proton moves away from the bore axis iscalled the T2 time.By measuring the T2 times during different conditions (suchas pre- and post-exercise),one can determine the relative activity within and between muscles; a longer T2timeindicates a more active muscle or segment of muscle (Segal and Song, 2005).Using a heel-raise task, Yanagisawa et al. (2003) found that the T2 timesof allplantarfiexors increases. While SOL activity increased after plantarfiexion,thepercentage increase in activity was much higher in MG. Further, researchers havedetermined that sub-volumes of the muscles of the triceps surae are differentially activeduring unilateral heel raises. As displayed in Figure 2.1, the spatial distribution of T2time changes was not homogenous throughout the SOL, MG, and LG; instead,theproximal components were more active than the distal ones (Segal and Song,2005).These fmdings suggested a possible compartmentalization to recruitment and showedbetween and within muscle differences in the triceps surae during dynamic activities.52520a)E15I—ciCC)50MuscleFigure 2.1. Change in T2 times in distal and proximal segments of four lower leg muscles (Adaptedfrom Segal & Song, 2005).*DistaliiProximal**LG MG SOL6Further differences in activation of the triceps surae have been illuminatedthrough factors such as weight bearing and activation level. Fiebert et al. (2000)collected surface EMG during weight-bearing isometric plantarfiexion contractionsat 30,50, 70, and 100% of body mass. They reported that excitation was greaterin MG thanLG for all conditions but that the excitation difference between muscles decreasedas thedegree of weight bearing increased (Fiebert et al., 2000). Compared to resting levels,Giordano and Segal (2006) found that neither the LG, MG, nor SQL had increasedT2times from resting to isometric contraction at 25% maximum voluntary contraction(MVC); however, at 65% MVC, T2 times increased in all three muscles, withproximalsegments of the MG and LG more active than distal ones.Clearly, the triceps surae group does not always act as a single muscle; instead,regional differences between and within muscles are apparent with factorssuch as weightbearing and activation level influencing the extent of excitation of each muscle.Giventhese differences during isometric conditions, the dynamic load sharing relationshipbetween these muscles is likely to differ between activities, suchas walking and cycling,and within an activity, across changes in workload and cadence.2.2 Triceps Surae in Cycling2.2.1 Characteristic cycling actionDue to the constrained nature of cycling, the generalpattern of ankle and knee joint movement has been shown tobe relatively consistent. As shown in Figure 2.2, a pedalcycle has been divided into two phases: the power phaselasting from top dead centre (TDC, 0° crank angle) tobottom dead centre (BDC, 180°) and the recovery phasefrom BDC back to TDC (180° to 360°, or 0° in thesubsequent cycle).Standard movement patterns have been described bySanderson et al. (2006). Ankle joint dorsiflexion occurred from TDC to a 45° crankposition. Ankle plantarfiexion lasted from 45° until just prior to BDC. The maximumplantarfiexion angle was 3-7° beyond neutral (90°) and occurred near BDC. The ankleTDC:013600BDC:1800Figure 2.2. Pedal cycle withclockwise pedal movement.7remained in dorsiflexion throughout the recovery phase. Generally, the kneejointunderwent extension throughout the power phase and flexion during the recoveryphase.At TDC, the knee joint was at 100° of flexion and extendeduntil reaching a 30° jointangle prior to BDC. Knee flexion occurred until the 330° crank position when kneeextension began (Figure 2.3).1v.tDorsiflexion -- -80 rpm,, —-,.%F‘90 — -‘S. —80IThese general patterns of ankle and kneejoint motion are influenced by factors such ascadence. Sanderson et al. (2006) showed that ascadence increases, the ankle joint becomes moreplantarfiexed, the knee joint becomes more flexed,and the range of motion decreases.In addition to kinematic analyses,previous researchers have focused on muscleexcitation in cycling. A single peak was seen inSOL excitation (at 90° crank angle) while GASexcitation peaked twice and remained active forlonger (Amoroso, 1994, Chapman et al., 2006,Sanderson et al., 2006). After peaking at a 90°crank angle, SOL excitation subsided quickly tobaseline by 180°. LG had two activation peaks,the larger occurring at 100° crank angle and thesmaller shortly after BDC. With increasedcadence, the amplitude of the normalized FMG of the first peak increased whilethesecond peak and the SOL peak remained relatively constant (Sandersonet al., 2006).Similarly, researchers found that LG was active from 32±10° to 230±30°while SOLbecame active at 20-30°, peaked at 90°, and subsided rapidly (Figure 2.4, Chapmanet al.,2006). Sanderson et al. (2006) proposed a potential difference in functionwith SOLinvolved in generating initial propulsive forces and GAS overlapping thatexcitation andprolonging excitation later in the cycle to provide continual forceat the pedal.11010044U,10080Fd1I-Flexion tI• FI11-‘ -V0 90 180 270 360Crank Angle (degrees)Figure 2.3. Ankle and knee anglesthroughout a pedal cycle at a cadence of80rpm (Sanderson et al., 2006).8SOL0/360TAn0 360 180Crank AngleFigure 2.4. Muscle excitation of SOL, LG, and TA for one pedal cycle (TDC toTDC). The greyshading indicates standard deviation across participants (Adapted from ChapmanCt al., 2006).180Crank Angle92.2.2 Evidence for differential activation of the triceps surae in cyclingMuch of the evidence to suggest that the triceps surae muscles function differentlyduring cycling came from studies of cadence manipulations. With few exceptions,researchers found that SOL was insensitive to cadence manipulations while GASexcitation increases as cadence increases (Table 2.1).Table 2.1. Effect of cadence maniDulations during cycling on triceps surae excitation.Authors Cadences Power SQL response toGAS response to(rpm) Output (W) cadence increase cadence increaseEricson et al. 40, 60, 80, 100 120 No change Linear increase(1985)Duchateau et 30, 55, 80, iON No change or Linear increaseal. (1986) 110, 140, 170 slight decreaseMarsh and 50, 65, 80, 95, 200 No change Increase (MG)Martin(1995) 110MacIntosh et 50, 60, 80, 100, 200, 100, 200, 300 W: 100, 200 W:al. (2000) 100, 120 300, 400 No change Linear increase400W: Increase 300, 400W:Quadratic increaseSanderson et 50, 65, 80, 95, 200 No change Increase (MG)al.(2006) 110Marsh and Martin (1995) proposed many explanations such as fibre type andmuscle length changes to explain these differential responses to cadence manipulations.SQL is predominantly formed by slow-twitch fibres so it may have been less effective atdeveloping force as movement speed increased. It was also proposed that SQLunderwent greater muscle length changes from TDC to BDC when SQL wasmost active;thus, SQL was rendered less effective due to the increased shortening and lengtheningvelocity changes across cadence manipulations.Sanderson et al. (2006) replicated the procedure of Marsh and Martin (1995)while collecting both kinematic and EMG data to decipher the relationship of musclelength and muscle excitation across cadence. They provided a potential explanation forthe differing cadence sensitivities focused on the force-length-velocity relationship.There were times during the pedal cycle when one of the triceps surae muscles was activewhile another was silent; for example, directly after BDC, MG excitation peaked whileSQL remained relatively quiet. Additionally, Sanderson and colleagues found periods inwhich SOL was concentrically contracting (shortening) while GAS was eccentrically10contracting (lengthening). They showed evidence of the stretch-shorten cyclein bothmuscles; the extent of lengthening decreased and extent of shortening increasedascadence increased while the velocity of both shortening and lengthening increasedacrosscadence. The researchers proposed that the muscle excitation was dictatedby the musclelength and velocity relationship and that optimal force production occurred withinacertain operating length (Sanderson et al., 2006).Sanderson and Kenyon (2005) suggested that a difference in mechanicalproperties of SOL and MG led to their different responsesto cadence manipulations. Toeliminate the effect of the ankle joint, they braced the ankle at900of flexion and againreplicated the procedure of Marsh and Martin (1995). Movementat the ankle joint waseffectively reduced, limited to30as dictated by the flex of the rigid plastic brace. Asexpected, SOL excitation was reduced, on average by 30% from the baseline, unbracedcondition. MG excitation decreased by 33%, a surprising finding as excitationwasexpected to increase to compensate for the reduction in SOL excitation. Since theanklejoint contributed 21% to the total moment during cycling (Mornieuxet al., 2007), it wasexpected that the moments at the knee and hip would increase to compensate;however, itwas apparent that the MG did not fill this compensatory role. Surprisingly, SOL was stillhighly active; by eliminating plantarfiexion, its major action, its ability to contributetopedal forces was removed. The remaining SOL excitation could not generatepurposefulmovement and may have acted to stabilize the joint and control dorsiflexion.2.2.3 Manipulation of the ankle joint during cyclingMany investigations have explored cycling efficiency through modifications inpedalling cadence, crank length, and seat height, but only Cannonet al. (2007) focusedon manipulation of the position of the talocrural joint (more often referredto as the anklejoint). Joint position was measured by an electrogoniometer attached to the lateralborderof the ankle and anchored along the axis of the tibia and metatarsals. Usinga one-weektraining protocol, participants were instructed to pedal with maximal dorsiflexionormaximal plantarfiexion throughout the entire pedal cycle at 90 rpm anda power outputthat elicited 80% of maximal oxygen uptake. Compared to the mean joint angleheld incontrol trials, cyclists maintained a mean joint angle of 7.1° of dorsiflexion and6.9° ofplantarfiexion (Figure 2.5).11Figure 2.5. Taiocrural position during cycling while attempting to hold the joint in dorsiflexionorplantarfiexion (Cannon et al., 2007)..200-,Dorsiflexion—— Plantarfiexion///PlantarfiexCrank PositionDorsiflex12Participants were unable to eliminate dorsiflexion or plantarfiexion. However, inthe plantarfiexion condition, the maximal dorsiflexion angle andtotal time in dorsiflexionwere reduced; in the dorsiflexion condition, the overall time inplantarfiexion wasreduced while the maximal plantarfiexion angle remained constant. With respect toEMG, TA showed no statistically significant changes but comparedto the controlcondition, mean TA excitation tended to decrease in plantarflexion and increase indorsiflexion. GAS EMG increased significantly in the dorsiflexedcondition compared tothe control condition but no difference was found in the plantarflexed condition. Duringthe dorsiflexion trials, the increased GAS excitation could have beendue to increasedknee flexion. Additionally, it was not surprising that there was no reduction in GASEMG since there was no reduction in maximal plantarfiexion angle.Due to an inaccurate definition of TDC, the authors’ interpretation was based on apedal cycle shifted by an unknown number of degrees and led to erroneous conclusions;however, this study provided an example of successful manipulation of cycling techniquefrom control of the ankle joint and exposed potential muscular changes with voluntarycontrol of ankle position. Through instructions to adopt a certain ankle position andverbal feedback from the researcher monitoring joint angle, adaptation to normal cyclingtechnique was shown possible (Cannon et al., 2007).2.3 Tools to Reduce Soleus Excitation2.3.1 Bracing and biofeedbackThe ankle bracing technique provided evidence for the ability to reduce SOL EMGthrough external means (Sanderson and Kenyon, 2005). However, since ankle bracingalone did not remove SOL excitation entirely, other tools were pursued. Cannon et al.(2007) showed that an individual could voluntarily manipulate the position of the anklejoint during cycling but had no recording from the SOL muscle. It remained uncertainwhether one could voluntarily modify the known skill of cycling to reduce SOLexcitation.During performance of a motor skill, individuals are known to receive task-intrinsicfeedback, the sensory-perceptual information that is a natural part of performing the skill.This might include visual, proprioceptive, and auditory feedback. While learning to13modifi known skills, attending to and understanding this feedback can be difficultsoenhancing sensory feedback can facilitate goal achievement.Augmented feedbackinvolves adding to or enhancing task-intrinsic feedback (Magill, 2001) andcan aid skillacquisition. While one receives intrinsic feedback about muscle excitation,determiningone’s exact level of muscle excitation is difficult without an augmented feedbackdisplay.Previously, Sanderson (1986) used visual feedback ofpedal force application to modifycycling technique. Using a one-week training protocol, he showed that feedbackcaneffectively aid in alteration of pedalling technique.Biofeedback has been defined as “the use of instrumentation to make covertphysiological processes more overt” (Huang et al., 2006). Itallows individuals to gainvoluntary control over a psychophysiological process that is consideredbeyond consciousawareness (Blumenstein et al., 2002). The basis of EMG biofeedback iselectromyography, a tool that measures electrical activity preceding muscle contraction.Visual EMG biofeedback displays present the activity of one or more musclesallowingindividuals to understand their activation levels or patterns and makeadjustments asrequired.2.3.2 Theories of biofeedback functionIndividuals who suffer from sensorimotor deficits have been shownto adapt theirmuscle excitation as they become cognizant of the EMG signal through biofeedback.Basmajian (1982) posited two potential neurological mechanisms to explain the enhancedability to voluntarily control muscle activation in diseased or injured individuals.Hestated that either new pathways were developed or auxiliary feedback loops recruitedexistent but dormant cerebral and spinal pathways. Fitting with thesecond rationale,Wolf (1983) suggested that in executing motor commands, both visual andauditoryfeedback activate unused or underused synapses.Having tested healthy individuals, Palmerud et al. (1995) presented two rationalesfor trapezius muscle silence during a biofeedback relaxation task. First, there mighthavebeen a load shift to other muscles compensating for the reduced excitationof thetrapezius. Second, there might have been an initial over-activity in the trapeziusand itsantagonists leading to overstabilization of the shoulder joint. They used single wireintramuscular EMG on five shoulder muscles and determined that there wasno14concomitant increase in synergist muscles when trapezius muscle excitation decreased,suggesting that the latter hypothesis was more valid (Palmerud et al., 1995). It is notcertain why individuals activated muscles that were not requiredto effectively performthe task. When attempting to decrease muscle activity, healthy individuals viewing EMGbiofeedback were able to decrease recruitment of active muscles by reducing seeminglyunnecessary muscle excitation.2.3.3 Previous use of EMG biofeedbackEMG biofeedback has proven successful in altering muscle patterns. In clinicalgait research, EMG biofeedback was used to correct abnormal movement patterns byoverlaying target levels of muscle excitation on patients’ muscle excitation traces(Colborne et al., 1994, Petrofsky, 2001, Aiello et al., 2005). These three gait studiesprovided examples of the successful implementation of EMG biofeedback to alterexcitation of select muscles during dynamic tasks. While these tasks have frequentlyinvolved an attempt to increase muscle excitation, the aim to voluntarily reduce muscleexcitation is not a novel task. A much-investigated area of EMG biofeedback targetedrelaxation of postural muscles and provided a useful model. Both qualitative andquantitative studies have shown that providing biofeedback of the trapezius muscleinduced a reduction in muscle excitation during static and dynamic tasks in both injuredand healthy participants (Nord et al., 2001, van Dijk et al., 2005). These studies haveprovided evidence for the successful use of EMG biofeedback to reduce excitation inselect muscles.2.3.4 Selection of biofeedback displaysResearchers and clinicians must determine the best way to deliver biofeedbackincorporating considerations such as timing, frequency, and presentation type. EMGbiofeedback has been presented as concurrent or terminal feedback. Concurrent feedbackoccurs in real-time while the muscle is active whereas terminal feedback occurs after taskcompletion (van Dijk and Hermens, 2006). In a simple bottle movement task, individualsaiming to reduce trapezius EMG saw performance improvements (compared to a nofeedback trial) regardless of timing condition, perhaps due to the simplicity of the taskand the focus on a single muscle (van Dijk and Hermens, 2006). In quick, discrete tasks,15the short latency of terminal feedback reduced the differencein performance betweenconcurrent and terminal feedback; however, as the lengthand complexity of the taskincreased, terminal feedback might occur too late to allow individuals to effectivelyintegrate feedback (Schmidt et a!., 1990) and alter muscle excitation.Feedback presented too frequently might also be detrimental to optimalperformance. With auditory feedback provided at one of threetime periods (5, 10, or 20seconds), the frequency of information which led to the greatest reduction in trapeziusexcitation was the ten-second interval (Voerman et al., 2004).Both the timing andfrequency of the feedback are important considerations in presenting theappropriateamount of feedback.Previous researchers have implemented many types of EMG biofeedbackdisplays, primarily visual or auditory. In visual EMG biofeedback, the display mighthave consisted of a simple on/off bar, a single value such as average EMG calculatedover a select time interval, or an online EMG trace (Blumensteinet a!., 2002). Othershave used an auditory tone presented if muscle excitation was above threshold ormultipletones presented with different tones relating to different excitation levels (Lamand Dietz,2004). The feedback display must be designed balancing the need for completenesswiththat of simplicity. Many muscle relaxation studies have employed a visualEMG trace(Palmerud et al., 1995, van Dijk and Hermens, 2006) but the best form of presentationishighly dependent on the task. In the case of a cyclical activity in which themuscle ofinterest has a single active period, an EMG trace allowing for participants to understandthe timing of muscle activity bursts might become unnecessary (Madeleine et al., 2006).A waveform could be replaced by an on/off feedback display or bar chart which requiresminimal integration and processing.2.3.5 Multiple types of biofeedbackWhen two types of biofeedback are presented simultaneously, individuals mustdecide which feedback display to attend. The use of two types of feedback hasprovensuccessful. Many studies have been conducted that required participants to ramp upplantarfiexion or dorsiflexion torque to a set level and then, while maintaining EMGactivity, co-contract to bring the torque level back to zero. This required thesimultaneous presentation of EMG and torque biofeedback, and researchersnoted that,16even though they were required to constantly monitor both levels,participants were ableto complete the task without difficulty within minutes of training(Nielsen et al., 1994).In a cycling study, Chu (2006) used augmented feedbackto train cadence. Participantstrained 20 minutes a day for 10 days at a predetermined optimal cadence. Cadencefeedback was withheld from the control group while the experimentalgroup was givenfeedback at set intervals. The amount of feedback decreased acrossdays, and testingoccurred without feedback. Both the cadence consistency and variabilitywere effectivelyreduced in the feedback group. During cycling with feedback, participants were able tomaintain the desired cadence throughout the display period (Chu, 2006).Duringpresentation of both EMG and cadence feedback, participants can be instructed to attendsporadically to cadence feedback while focusing on the EMG biofeedback, without toomuch concern for maintenance of appropriate cadence.2.4 Interaction Among Motor Neuron PoolsMotor neuron pools are acollection of motor neurons whichinhibitory inhibitioninnervate a single skeletal muscle. Theya afferentare located in the spinal cord; generally,motor neuron pools for distal musclesalpha motor neuronsare located more laterally in the ventralmusc’ehorns. They have been shown to beInhibitioninfluenced primarily by the descendingStimuIationfibres of the lateral corticospinal andreticulospinal pathways and may beFigure 2.6. Descending control of reciprocal inhibition.Activation of the flexor muscle spindles excites the flexorinfluenced by the vestibulosprnalmuscle and inhibits the extensor muscle (Dale, 2004).pathways as well (Rossignol et al., 2006). A motor neuron pool from one musclecaninfluence the excitability of other muscles such that input to a motor neuron, andeventually to a muscle fibre, does not come from a single source. Instead, antagonists,synergists, and even muscles from the contralateral limb affect the firing of a motorneuron. Voluntary reductions in SOL muscle excitation may be affectedby the ipsilateralTA and GAS and the contralateral SOL and thus their connections must be examined(Figure 2.6).172.4.1 Measuring excitabilityBecause there is no direct measure of the effect of one motor neuron pooi onanother, motor neuron pooi excitability before and after stimulation of group Ia afferentshas been used as an indirect measure. Researchers employedthe Hoffman reflex (Hreflex) technique, an artificially elicited response that tests the efficiency of transmissionof a stimulus passing from the afferent fibres to the efferent fibres, through themotorneuron pool (Enoka, 2002). A change in the excitability of the motor neuronsinfluencesthe H-reflex; an inhibitory post-synaptic potential (IPSP) elicitedby stimulation of anantagonist Ia afferent transiently hyperpolarized the motor neuron resultingin a decreasein the size of the H-reflex (Nielsen, 2004). Stimulation of the Ia afferents from theTA(via the common peroneal nerve) depressed the SOL H-reflex.A depression in the H-reflex indicated decreased motor drive and led to a decreased muscleexcitation.Influences from synergists and muscles on the opposite limb can be tested inthe sameway simply by stimulating a different nerve. H-reflexes within lower leg musclescan beevoked by stimulation of the tibial nerve for SOL reflex, common peroneal nervefor TA,and medial gastrocnemius motor nerve for GAS (Nielsen and Kagamihara,1993). A highlevel of SOL H-reflex inhibition from any of these sources would resultin lowexcitability of the SOL muscle.The H-reflex, while a useful tool, is not an unambiguous measure of motor neuronexcitability (Zehr, 2002, Misiaszek, 2003). Through altered presynaptic inhibition,theamplitude of the H-reflex changed without a corresponding change in the postsynapticmembrane, including the motor neuron (Zehr, 2002). Unless the pre- and post-testH-reflexes were collected with the same postural orientation, intention, level of muscleexcitation, and while stationary, presynaptic inhibition will likely influence the H-reflex.This presynaptic inhibition can arise from descending supraspinal commands as wellasfrom afferent feedback from peripheral receptors such as Golgi tendon organs,musclespindles, and cutaneous mechanoreceptors. These factors can only be ruled out bycontrolling the posture and intention of the participant. The amplitude of the H-reflexwas also sensitive to mechanisms that directly affect neurotransmitter release fromthe Taafferent terminals (Zehr, 2002, Misiaszek, 2003). The H-reflex technique is confoundedby influences other than group Ia afferents and must be interpreted with caution.182.4.2 Sources of inhibition on the motor neuron pooiGroup Ia afferents leave a muscle and branch to the motor neuron associated withthe agonist muscle and to an mterneuron associated with the antagonist muscle.Thesynapse with an inhibitory interneuron allows for IPSPs to the motor neuron oftheantagonist. It lowers the excitability of the antagonist motor neuron and is knownas thereciprocal-inhibition reflex (Enoka, 2002). Activation of muscle spindles in TAelicits anexcitation of the agonist motor neurons but inhibits those of the antagonistic SOL.Sincethe net muscle excitation about a joint results from the difference in excitationof theagonist-antagonist pair, reciprocal inhibition increases the likelihood thata stimulus toactivate a muscle will elicit a meaningful response in that muscle. Musclesof the lowerleg are also influenced by reciprocal connections with muscles above the knee(Misiaszek, 2003, Wilmink and Nichols, 2003). The quadriceps (rectus femorisand vastimuscles) has a purely inhibitory influence on GAS while the influenceon SOL is eitherexcitatory or inhibitory.Another source of inhibition that has been demonstrated is recurrentinhibitionresulting from the neural connection of synergists. It occurred through twointerneurons,rather than a single one in reciprocal inhibition. Windhorst (2007) explained that groupIa afferents from a synergist muscle synapse on Renshaw cells which synapseon Iainhibitory interneurons. The main source of the long-lasting inhibitionon SOL motorneurons came from activity from the Renshaw cells of the MG (Rossi et al., 1994).Thus,isolated activation of the MG had an inhibitory effect on SOL muscle excitation.Lastly, crossed inhibition involved inhibition from the muscles on the oppositeleg. Crossed inhibition has been found to be mediated through the same neuralpathwayas reciprocal and recurrent inhibition (Cheng et al., 1998). Group Ia muscle afferentshave been reported to contralaterally modulate reflex responses and motorneuronalactivities in humans (Cheng et al., 1998). In a cyclical action, activation of the rightSOLmuscle inhibits the left SOL. Potential sources of inhibition onSOL excitation includedreciprocal inhibition from the ipsilateral TA, recurrent inhibition from the ipsilateralGAS, and crossed inhibition from the contralateral SOL.192.4.3 Influence of single muscle excitation on soleus excitabilityDuring voluntary movement, reciprocal inhibition on the activated motor neuronwas reduced with respect to passive movement thus increasingthe H-reflex magnitude ofthe active muscle (Crone et al., 1985, lies, 1986). In simple plantarfiexion anddorsiflexion movements, voluntary activation of TA led toincreased reciprocal inhibitionon the SOL muscle compared to rest; further, inactivation of SOL led to decreasedreciprocal inhibition on TA (Tanaka, 1974, Pyndt et al., 2003, Nielsen, 2004).Withvoluntary activation, there was a general increase in the excitability of the TAmotorneuron pool resulting in sub-threshold depolarization ofa number of motor neuronswithin the pool. Thus, with the same afferent dischange, more motor neuronswere ableto generate action potentials (Latash, 1998). Increasing activation of TA led to anincreased excitability of TA and a decreased SOL excitability.Isometric leg flexion by tonic activation of GAS without activation ofSOLinduced an inhibition of SOL H-reflex even at very low levelsof GAS EMG (Gritti andSchieppati, 1989). Tonic voluntary contraction of the SOL at less than 20% MVCabolished the inhibitory effect on the H-reflex of both isolated stimulation inTA andGAS as well as in combined stimulations (Schieppati et al., 1990). Thus,excitation ofSOL increased the SOL H-reflex while excitation of TA and GAS led to a decreaseinSOL H-reflex.2.4.4 Excitation of multiple musclesCo-contraction of agonist/antagonist pairsIn a more complex situation with excitation of both TA and SOL (co-contraction),the SOL H-reflex was decreased with respect to isolated plantarflexionthus cocontraction decreased excitability (Nielsen et al., 1994). During activation ofbothmuscles, the reciprocal inhibition was greater than with isolated activation of theagonistbut smaller than isolated activation of the antagonist.Interaction between reciprocal and recurrent inhibitionSince concurrent activation of TA and GAS occurs during cycling, it is importantto determine how reciprocal and recurrent inhibitions interact. Throughout thepedalcycle, the main period of co-contraction occurred from approximately 160-190degrees20on crank rotation (Chapman et a!., 2006). Schieppati et al. (1990) investigatedtheinteraction inhibition from TA and GAS on SOL H-reflex through isolated and combinedstimulation of the nerves and found that combined stimulation significantly reducedtheH-reflex in SOL beyond the inhibition in either isolated case. This suggestedconvergence of Ta fibres from synergistic and antagonistic muscles onto commoninhibitory interneurons. Reduction of the SOL H-reflex appeared to have aceiling level;with stimulus intensity at 1.3 times motor threshold in both the TA and GAS, thecombined stimulus did not further inhibit the H-reflex suggestinga saturation effect(Schieppati et al., 1990). The concurrent excitation of TA and GAS (up to a certainthreshold level) inhibited SOL excitability to a greater extent than isolated activationofeither muscle.Tnfluence of TA and SOL on GAS excitabilityNielsen and Kagamihara (1993) tested the SOL, TA, and GAS H-reflexes duringplantarflexion, dorsiflexion, and co-contraction. Maintaining a constant level of EMGactivity in the agonist muscle, the SOL and TA H-reflexes were found to be smallerduring co-contraction than during an isolated agonist contraction. Contrarily,the medialGAS H-reflex was the same size during co-contraction as during isolated plantarfiexion.Thus, it appears that TA excitation differentially affects SOL and GAS excitability.While TA clearly provides reciprocal inhibition to the SOL muscle, reciprocal inhibitionto GAS was small if present. This was confirmed by PSTH which showed a depressionof the peak excitation in SOL motor units but not in medial GAS motor units duringactivation of TA (Nielsen and Kagamihara, 1993).There appears to be directionality of recurrent inhibition between SOL and GAS.Synergism, observed through inhibitory reflexes among SOL, medial GAS, and lateralGAS, was apparent only at low to moderate forces in cats. An electrically evoked crossextension reflex showed a unidirectional inhibitory reflex from both LG and MG to SOLand showed an increased inhibition with increased force in GAS (Nichols, 1989).Byinhibiting SOL H-reflex, LG and MG might reduce redundant excitation in theplantarfiexors. It appeared that recurrent inhibition from GAS to SQL was not bidirectional and, like TA, SQL excitation did not induce inhibition of the GAS H-reflex.212.4.5 Influence of the non-test limbPassive cyclic movement of one leg induced SOL H-reflex inhibition in the statictest limb (Mcllroy et al., 1992, Collins et at., 1993). This inhibition was attributed toreciprocal inhibition from the contralateral TA (crossed reciprocal inhibition) and wasweaker than ipsilateral reciprocal inhibition in the moving limb (Cheng et al., 1998). Itwas found that when both legs are moving, inhibition of the SOL H-reflex was notincreased over single, ipsilateral leg movement. This redundant system had nosummation of ipsilateral and contralateral inhibition but rather a degree of overlappinginhibition (Mcllroy et al., 1992). If both TA muscles worked simultaneously, the crossedreciprocal inhibition would be entirely redundant; however, in cycling, the legs are1800out of phase so the contralateral TA is most active when the ipsilateral SOL is active.Since tonic activity in the agonist muscle decreased reciprocal inhibition on itself (Croneet al., 1985, Iles, 1986), the heightened H-reflex from the active SOL would likely erasethe depression in H-reflex caused by the crossed reciprocal inhibition. Thus, it appearedthat cycling was substantially directed by a single limb suggesting that the vast majorityof contralateral controls were redundant and could be ignored when determining SOLexcitability (Boylls et al., 1984).2.4.6 Soleus excitability in cyclingBrooke et al. (1992) suggested that there are three movement features with a clearinfluence in determining H-reflex magnitude: limb joint angles (percentage in the cycle),SOL contraction (with voluntary contraction decreasing inhibition of SOL), and TAcontraction (with voluntary contraction facilitating inhibition of SOL). At each point inthe pedal cycle, the level of TA and SOL activation interacted resulting in isolatedcontraction of one of the two muscles or co-contraction. The highest reciprocal inhibitionon SOL occurred during recovery (specifically 225 to 270°) when TA was most active;within the power phase, reciprocal inhibition was higher in the second half(90° - 180°)(Pyndt et al., 2003). In another study, SOL H-reflex was found to differ between four setcrank positions (55, 140, 250, 330 degrees) with H-reflexes most inhibited at a 330° crankangle (Brooke et al., 1992, Mcllroy et al., 1992, Collins et al., 1993). Brooke et at.(1992) and Mcllroy et at. (1992) found that SOL H-reflex magnitudes were largest during22the power producing phase and were reduced to near zeroduring the recovery phase.These findings were congruent with the idea that the highestdegree of reciprocalinhibition on SOL occurred when TA, the antagonist muscle,was active (Pyndt et al.,2003, Nielsen, 2004). The three factors proposed by Brookeet al. (1992) interacted tocontrol SOL activation and neural drive with the amount of inhibition changingthroughout the pedal cycle.2.4.7 Other influential factorsWorkloadPyndt et al. (2003) considered multiple workloads finding that as a percentageofbackground EMG, as load increased, the reciprocal inhibition of SOL by TA decreased.As the SOL was required to increase activation to meet the demands of anincreasedworkload, there was a gradual decline in reciprocal inhibition from TA. This wasconfirmed by Nielsen and Kagamihara (1993) in a plantarfiexion, dorsiflexion, andcocontraction task in which the level of contraction was varied.With increasing levels ofplantarfiexion, SOL H-reflex size increased while TA H-reflexsize decreased; theopposite occurred with increasing dorsiflexion levels (Nielsen and Kagamihara,1993).This confounding factor is easily eliminated by maintaining a constant workloadthroughout the testing protocol.Cadence manipulationsWhile many researchers have investigated the effects of cadence changeson SOLH-reflexes, few have maintained a constant power output making it difficultto separateout the effects of cadence from those of increased workload. Mcllroy et al. (1992)reported a negative linear relationship between cadence and SOL H-reflexmagnitudeduring passive cycling but saw the magnitude plateau between 30-60rpmindicating thatall cadences beyond that point resulted in the same SOL H-reflex magnitude. Otherpassive movement studies indicate that as the velocity of passive cyclic rotationof the legincreased, the H-reflex in the stationary contralateral limb became progressivelyinhibited(Cheng et al., 1998). The opposite relationship was found as Pyndt et al. (2003) studiedthe effect of cadence on reciprocal inhibition during an active cycling period. Ascadenceincreased, reciprocal inhibition as a percentage of background EMG decreased. Whilethe relationship differed from passive cycling, active cycling studies showed thatthe SOL23H-reflex magnitude was influenced by cadence, with inhibitiondecreasing as cadenceincreased.Ankle bracingMcllroy et al. (1992) considered the possibility that theuse of an ankle footorthosis to position the ankle joint at900might lead to a differential discharge ofcutaneous receptors which could influence H-reflex modulation. Theauthors contendedthat any contribution specific to the AFO would be limitedbased on the similarities in H-reflex between active pedalling with and without the brace.2.5 SummaryThe preceding literature review highlighted previous researchwhich showed thatin certain conditions, parts of the triceps surae group responded differentlytoperturbations both in cycling and other activities (Segal andSong, 2005, Sanderson et al.,2006). Using a braced cycling paradigm, attempts to identifythe relative contribution ofthe LG and MG to each of ankle plantarfiexion and knee flexion were unsuccessful(Sanderson and Kenyon, 2005). The current study aimed to employ bracingand EMGbiofeedback to successfully eliminate plantarflexion from thecycling motion and isolateknee flexion as the only action of the gastrocnemius muscle. Thepotentialneurophysiology behind the ability to accomplish this taskwas described, with reductionsin soleus excitation related to inhibitory influences froma number of leg musclesprimarily tibialis anterior and gastrodnemius.243 METHODS3.1 ParticipantsA study group of 13 individuals, recruited primarily fromthe university graduatestudent population, gave their informed consent to participate.All participants were 19years of age or older. Potential participants were excluded ifthey had any lower-limbinjuries or neurological conditions or if they felt that theymay be unable to maintain therequired workload. Cyclists were asked to refrain from exercising on testday toeliminate the effects of fatigue.3.2 InstrumentationPreformed ankle-foot orthoses (AFOs) were fitted to the participant. TheAFOswere L-shaped braces made from plastic polypropylene coveringsections of the front andback of the lower leg and underneath the foot. Padding wasadded in areas where thebrace did not contact the foot comfortably. The AFOs wereslipped into appropriatelysized cycling shoes which were usually one size largerthan the participants’ normalshoes.Sagittal-view kinematics of the left lower limb were collected at 60Hzusing aPanasonic video camera (WDV 5100, Okayama-City Okayama,Japan). Reflectivemarkers were placed on the left side over the greater trochanter, lateral midlineof theknee, lateral malleolus, and on the cycling shoes at the base of the calcaneus andthe headof the fifth metatarsal. Additionally, a reflective marker was positioned overthe lateralmalleolus on the left AFO for braced trials. Two points in thepedal cycle were recordedusing a 1024-step optical encoder and foil switches to record top dead centre (TDC)and amagnetic reed switch at bottom dead centre (BDC).Surface EMG data were collected at 600Hz from seven muscles. Theskin overthe muscles was shaved, abraded, and cleaned to prepare forattachment of pre-amplifiedsurface electrodes (Therapeutics Unlimited, Model 544, IowaCity, LA, USA, gain =35).Muscle excitation was recorded from the left soleus (SOL), medial gastrocnemius(MG),lateral gastrocnemius (LG), tibialis anterior (TA), biceps femons(BF), rectus femoris25(RF), and gluteus maximus (GM). Electrodes were placed in accordance withBasmanjian and De Luca (1985) and were positioned to avoid contact with the brace.With electrodes and markers in place, bicycle seat height was adjusted to 100%trochanteric length (measured from the greater trochanter to the floor while standingbarefoot). Participants rode on a medium size standard frame mounted on a SchwinnVelodyne electronically braked cycle ergometer which controlled power output. ACateye cadence monitor attached to the handlebars helped participants maintain the targetcadence.A heart-rate strap was worn by participants to monitor how heart rate changedduring the cycling period. Heart rate, an indirect measure of fatigue, was collected toensure that fatigue did not influence muscle excitation during prolonged cycling withbiofeedback. Due to cardiac drift, heart rate increases over a prolonged cycling bout.Lepers et al. (2000) reported a 7.3% and a 12.7% increase in heart rate over the first 50and 100 minutes of a 120 minute cycling bout at a power output corresponding to 65% ofmaximal aerobic power. If we assume that our protocol occurred at 65% of maximalpower and that the cardiac drift occurred evenly throughout the entire hour, we wouldexpect a 2.9% increase in heart rate over our twenty-minute cycling period. It wasexpected that heart rate would increase with time, but any large increase in this measurecould indicate neuromuscular fatigue.EMG biofeedback from the left SQL was provided during designated feedbacktrials. As shown in Figure 3.1, participants watched a computer screen with an activelyupdating real-time bar graph representing average rectified SQL EMG over the powerphase, from TDC to BDC (screen developed in LabView Version 5.0, NationalInstruments, Austin, TX). The participants were given the following instructions:You will continue cycling at 150W and 80 rpm. After you have reached thedesired cadence, I will turn on the biofeedback and start the prolonged trial. Each timethat the left pedal reaches the top of the cycle, the bar graph will update. This barindicates your average soleus muscle activity from the first half of the pedal stroke. Youraim is to silence the muscle — a completely unfilled bar indicates no muscle excitation.At various times throughout the cycling period, I will collect data. You will not beinformed when I am collecting so please try to maintain minimal muscle activity at alltimes during the testing period. Try to attend to the biofeedback constantly but be sure tocheck your cadence regularly and keep it steady at 80 rpm. Try to find a comfortableposition for your arms on the handlebars and please do not change positions during thetrials.26Julia Feedback XEk It3tSFigure 3.1. Biofeedback display screen.27The maximum height of the biofeedback bar was set to the averagerectified SOLEMG across 20 pedal cycles during the baseline, no feedback condition.This collectionoccurred during the second half of the four minute trial and normalizedthe display outputbetween participants.To ensure that participants always had high-resolution feedback allowingthem tovisualize minor changes in SOL EMG, the bar was colouredin two parts. As displayedin Figure 3.2, when the EMG was greater than 100%, thebar was fully coloured with thetop part in blue and the bottom in red. The line separating the twosegments of the barwas set to 20% of the full height of the bar, or 20% of the baselineSOL EMG. As theEMG fell, the height of the coloured bar decreased. When EMGwas greater than 20%,the red portion remained saturated. The height of the bar was recorded onlineto generatea profile of how the feedback display changed over time.On a second computer, data from four lower leg muscles (SOL, LG, MG, and TA)were captured at a sampling frequency of 1000Hz across the entire feedback period.Intrials without biofeedback, participants were asked to direct theirattention forwardtowards to computer screen.ojFigure 3.2. Examples of SOL EMG biofeedback. The bars (from left to right)display 100+%excitation, 50% excitation, and 10% excitation.283.3 ProceduresData were collected at the University of British Columbia’s BiomechanicsLaboratory. The protocol was performed over two days. Onday one, participants wererandomly assigned to a starting condition (braced or unbraced).Of the nine completedata sets, five participants completed the unbraced condition onday one.The testing procedure is illustrated in Figure 3.3. In both conditions, participantswarmed up for 10 minutes at a comfortable, self-selected power output andcadence.Next, they completed a four minute no brace-no feedback (NB-NF) trial to allowfornormalization across days, with data collection occurring during the final1 Os of cycling.For all trials, participants were required to maintain a fixed power output (150W)at afixed cadence (80 rpm). In the braced condition, participants dismounted fromthe cycleergometer and applied the braces bilaterally. After mountingthe bike, they completed afour minute braced-no feedback (B-NF) trial. To ensure that cycling andrest time werekept constant between conditions, during the unbraced condition, cyclists restedfor thesame length of time as it took to dismount, slip on the braces,and climb back onto thebicycle; then, participants completed a second four minute no brace-nofeedback (NBNF2) trial. Next, in both conditions, participants were presented with EMGbiofeedbackfor 20 minutes. Data were collected for lOs at the end of every minute. Oneweek later,participants returned to the lab to complete the other condition. Duringeach session,total riding time was 38 minutes while total testing time was about twohours.Warm Up: 4 MIN:4 MIN: 20 MIN:Unbraced (NB)NB-NF NB-NF NB-NF2NB-F1 I Warm Up: I I MIN: I I 4 MIN:I I20 MIN:Braced (B)fl__NB-NF__flNB-NFflB-NFflB-FFigure 3.3. Testing Procedure (B = braced, NB = unbraced, F = feedback, NF = nofeedback). Duringfour minute trials, data were recorded during the last lOs; during the 20 minute trials,data wererecorded for lOs every minute.293.4 Data AnalysisFor each trial, EMG data from ten pedal cycles were analysed. EMGdata wererectified and integrated over each pedal cycle and a mean was calculated. Foreach trial,the mean integrated EMG (iEMG) was normalized to the mean iEMG fromthe ten pedalcycles in the first NB-NF condition of each testing session. For repeatabilitycalculations,root mean square (RMS) EMG with a 50 ms window for each of the seven muscleswascalculated for the NB-NF and NB-NF2 conditions.A power spectrum of each of theseven muscles for each of the data collection periods was computed and the medianpower frequency (MPF) was determined. Additionally, peakEMG was calculated as thehighest 5Oms window of rectified, filtered, normalized EMGfor each trial. Then, thetime-to-peak EMG was calculated for each pedal cycle and averaged to createtrialmeans; the time-to-peak data were analyzed as a relative measure, in percent ofpedalcycle.3.5 Statistical AnalysesA power analysis estimating necessary sample size was calculated usingG*Power3.0.8 (Erdfelder et al., 1996) with conservative estimates of power, effect size,andcorrelation between repeated measures. Data from the braced measures in SandersonandKenyon (2005) were used in these estimates.For each of the bracing conditions, a 7x4 (muscle x time points) repeated measuresanalysis of variance (ANOVA) tests was conducted. In the unbraced condition,the fourtime points were the two no-brace, no-feedback conditions (NB-NF and NB-NF2)and thefirst and last minute of feedback (NB-Fl and NB-F20). Similar collection periodswerechosen in the braced condition with the NB-NF2 trial replacedby B-NF; thus, analysisoccurred on NB-NF, B-NF, B-Fl, and B-F20. When Mauchly’s test of sphericitywassignificant, p-values were corrected by using the Greenhouse-Geisseradjusted values.The alpha level was set a priori to 0.05.When the interaction term of the 7x4 ANOVA test was significant, plannedposthoc analyses were conducted. First, pairwise comparisons between the three tricepssuraemuscles were calculated using a Bonferroni correction for multiple comparisons.Next, a1x4 ANOVA was conducted for each muscle. When the 1x4 ANOVA test was30significant, all possible pairwise comparisons between conditions wereconductedresulting in six comparisons (Table 3.1). The Bonferroni correction factorwas againapplied to all comparisons.Table 3.1. Pairwise comparisons computed when 1x4 ANOVA was significantNB-NFB-NF 1B-Fl 2BF-20 3To test for fatigue, the median power frequency of each muscle was computed onthe demeaned EMG data and the four time points of interest were comparedwith a seriesof 1x4 ANOVA tests. Similar analyses were conducted on time-to-peakEMG data todetermine if a shift in the temporal structure of the responses occurred.When theANOVA tests were significant, post hoc contrast analyses were conducted comparingeach time point to the baseline, NB-NF, condition as well as to the previous condition (ie.B-NF to NB-NF, B-Fl to B-NF, and B-F20 to B-Fl).To test the repeatability of EMG data during cycling, paired samplest-tests wereused to compare the NB-NF and NB-NF2 trials for each muscle. RMS EMG was usedtoallow for direct comparison with Dorel et al. (2007) who recently reported that therewereno differences in RMS EMG of ten lower limb muscles across testing periods separatedby a one hour non-fatiguing protocol.B-NF B-Fl BF-20314 RESULTS4.1 Description of ParticipantsThirteen participants, eight males and five females, volunteered to participateinthis investigation. Three female participants were unable to maintain the requiredworkload at the testing cadence, so data collection was stopped and their datawereconsidered incomplete. Due to equipment failure, one male dataset from the bracedcondition was lost leaving nine complete sets of data. The nine participantsincluded inthe analysis had a mean (SD) age of 25.67(±1.32) years, mean height of 1.78(±0.05) m,and mean mass of 74.28(±9.81) kg.4.2 CadenceSince prior studies have shown that SOL and GAS respond differentlyto cadencemanipulations, it was important to confirm that participants cycled at the standardized80rpm across the entire testing period. Table 4.1 shows the average cadencemaintainedduring the ten cycles analyzed from the data collection periodduring select trials. Thefour trials used in all analyses include the two no-feedback trialsas well as the first andlast minute of the feedback period. There was no significant difference in cadencewithinthe unbraced(p = 0.63, F3,24 = 0.59, MS = 0.972, ifr2 = 0.07) or braced (p = 0.91, F3,240.18, MS = 0.47, r2 = 0.02) conditions as calculated using two separate 1x4 repeatedmeasures ANOVAs. Individual cadence data are provided in Appendix B.Table 4.1. Mean cadence and standard deviation across participants in the four analyzed collectioneriods. with and without the brace.No Brace Average Standard Brace AverageStandardCadence (rpm) Deviation Cadence (rpm) DeviationNB-NF 81.34 1.21 NB-NF 80.730.77NB-NF2 80.94 1.24 B-NF 80.291.52NB-Fl 80.55 1.86 B-Fl 80.651.63NB-F20 81.07 1.21 B-F20 80.302.22Mean 80.98 1.38 Mean 80.491.56324.3 Integrated ElectromyographyChanges in iEMG of each of the seven muscles in responseto different conditionswere the major variables of interest in the current study. The followingresults presentonly the p-values in the discussion of significance. When means are presentedfollowedby the symbol ± and another number, the latter number refers to the standard deviation.Full results of the statistical tests are presented in AppendixC: for ANOVA and post hoccontrasts - F-value, degrees of freedom, mean sum of squares,effect size, p-value; forpairwise comparisons - mean difference, standard error,lower and upper bounds of 95%confidence interval, p-value.4.3.1 UnbracedANOVAIn the unbraced condition, there was no significant muscle by conditioninteraction effect (p = 0.07) nor was there a significant maineffect of condition(p =0.17). There was a significant main effect of muscle(p = 0.04) indicating that the iEMGdiffered between muscles. The main effect of muscle wasnot a primary concern withoutan interaction effect showing differences across conditions, so no further analyseswereconducted.The largest decrease in SQL iEMG was 18±27% at NB-F20 comparedto the NBNF condition. Although it was likely due to participants’ inabilityto use biofeedbackwithout the brace, another potential reason for the non-significant ANOVA testcouldhave been the large variability within certain muscles; for example, withinthe TA iEMG,there were large increases of up to 197±267% (NB-NF vs NB-Fl) butthe variabilitymasked this increase in the statistical tests. GM iEMG decreasedby 16±14% from theNB-NF to the NB-F20 condition. Figures 4.1 and 4.2 display the SOL andTA iEMG andthe SOL, LG, and MG iEMG across conditions while all mean(SD) data from eachmuscle are shown in Table 4.2.33SOL and TA IEMG (Unbraced)654CDz21•0IC’_______________________________________________________________________NB—NF NB-NF2 NB—Fl NB-F20Condition•SOL OTAFigure 4.1. Mean participant (SD) SOL and TA iEMG from four conditions averagedacross tenpedal cycles during the unbraced condition.1.5 Triceps Surae iEMG (Unbraced) •o.z0. NB-NF2 NB—Fl NB—F20Condition•SOL 0LG •MGFigure 4.2. Mean participant (SD) SOL, LG, and MG iEMG from four conditions averaged acrossten pedal cycles during the unbraced condition.34Table 4.2. Normalized iEMG (to the NB-NF condition) for each muscleaveraged across participantsfor three different trials in the unbraced condition.NB-NF2 NB-Fl NB-F20Mean SD Mean__L SD Mean SDSOL1.02 0.17 0.93 0.33 0.82 0.27LG1.06 0.06 0.91 0.23 1.06 0.24MG1.02 0.04 1.03 0.14 1.03 0.07TA0.90 0.17 2.97 2.67 2.19 2.21BF1.04 0.12 1.02 0.34 1.18 0.41RF0.92 0.09 1.09 0.26 1.04 0.39GM1.05 0.17 0.92 0.10 0.84 BracedANOVAIn the braced condition, the 7x4 (muscle x time points) repeated measuresANOVA test showed a significant muscle by condition interaction(p = 0.01) as well as asignificant main effect of muscle(p < 0.001). There was no main effect of condition (p =0.29).Having revealed a significant interaction effect, seven 1 x4 ANOVA tests wereconducted to investigate trends within a single muscle. All three triceps surae musclesshowed significant decreases in iEMG (SOL, LG, MG:p < 0.00 1) while TA iEMGincreased significantly (p = 0.03). Neither the iEMG of BF(p = 0.11) norRF (p = 0.64)changed with feedback during the braced condition. There was a significant decreaseinGM iEMG in the braced condition(p < 0.01). Figures 4.3 and 4.4 display the iEMG ofthe SOL and TA and the SOL, LG, and MG respectively during the braced conditionswhile all mean (SD) data are shown in Table 4.3.356SQL and TA iEMG (Braced)54CDui3z2N8-NF B-NF 8-Fl 8-F20Condition•SOL0 TAFigure 4.3. Mean participant (SD) SOL and TA 1EMG from four trials averaged acrossten pedalcycles during the braced condition.Triceps Surae iEMG (Braced)1 •o.—NF B—NE B-Fl 8-F20Condition•SOL OLG •MGFigure 4.4. Mean participant (SD) SOL, LG, and MG iEMG from four trials averaged acrosstenpedal cycles during the braced condition.36B-NF B-Fl B-F20Mean__[SD Mean SD Mean SDSQL0.69 0.18 0.46 0.19 0.43 0.20LG0.75 0.17 0.60 0.33 0.54 0.25MG0.68 0.15 0.70 0.27 0.69 0.19TA1.51 0.54 2.81 1.42 3.09 2.51BF1.10 0.18 1.34 0.49 1.43 0.701=F1.04 0.31 1.16 0.41 1.00 0.45GM1.05 0.22 0.89 0.17 0.80 0.15Pairwise ComparisonsIn order to draw conclusions about the effect of bracing and biofeedback betweenthe triceps surae muscles, pairwise comparisons were necessary. The Bonferronicorrection factor was applied manually so the p-value was significant at 0.017.The testsshowed that there were no significant differences between SQLand LG (p = 0.08) or LGand MG (p = 0.27); however, there was a significant difference between iEMGof theSQL and MG muscles (p = 0.0 17).For each of the five muscles that revealed a significant ANQVA test, six pairwisecomparisons were conducted to determine in which conditions iEMG differed.Compared to the NB-NF condition, there was a significant reduction in iEMGin all threeof the other conditions (B-NF, B-Fl, and B-F20) in SQL(p <0.0i,p<O.OO1,p < 0.001respectively) and LG (p=O.Ol,p=O.O4.,p < 0.01). There were also significant iEMGreductions in MG in the B-NF and B-F20 conditions(p < 0.01,p< 0.01) compared to theNB-NF condition but not when compared to the B-Fl condition (p 0.06). Theadditionof the brace without feedback (B-NF) led to a 3 1±18% decreasein SOL iEMG, a25±17% decrease in LG iEMG, and a 32±15% decrease in MG iEMG. When feedbackwas first presented, there was a significant decrease in SQL iEMG (B-NF vs B-Fl,p =0.03) but there was no significant decrease in iEMG of LG (p = 0.41) or MG (p= 1.00).However, there was a significant decrease in both SQL iEMG (p = 0.03)and LG iEMG(p = 0.02) from the B-NF to the B-F20 condition while there was stillno change with MGiEMG (p = 1.00). With bracing and biofeedback SQL iEMG was 54±19% lowerthan theinitial condition, and 23% lower than with bracing alone. The B-Fl and B-F20Table 4.3. Normalized iEMG (to the NB-NF condition) for eachmuscle averaged across participantsfor three different trials in the braced condition.37conditions were compared to test for learning across the feedback period. Therewas noreduction in iEMG across the feedback period in SOL, LG,or MG (p = 1.00 for all).TA iEMG did not increase significantly from the NB-NF condition in the B-NFcondition (51±54%,p 0.13) or B-F20 condition(2O9±25l%,p = 0.223). However, TAiEMG increased significantly in the B-Fl condition comparedto the NB-NF conditionwith increases of 181±142% in the B-Fl condition(p = 0.03). Opposite the decrease seenin SOL iEMG, there was a significant increase in TA iEMGwith feedback (B-NF vs B-Fl,p= 0.04). There was no change in TA iEMG across the feedback period (B-Fl vsBF20, p = 1.00). The large variability in the B-F20 condition likely contributedto the nonsignificant increases from the NB-NF and B-NF conditions.The significant reduction in GM iEMG was evident between the NB-NF conditionand the B-F20 condition(p = 0.02) but not when contrasted with the B-NF (p = 1.00) orB-Fl (p = 0.46) conditions. There was a significant reductionin GM iEMG withfeedback (p < 0.01) but no significant reduction across the feedback period(p = 0.17).4.4 Time-to-peak EMGThe time-to-peak EMG was determined in the two gastrocnemius muscles toinvestigate one aspect of the temporal structure of the pedal cycle and how it mightchange across conditions. In the unbraced condition, the ANOVA testdetermined thatthere were no significant differences in time-to-peak EMG in the LG or MGmuscles (p =0.96 1 andp = 0.154 respectively). However, in the braced condition, the time-to-peakEMG changed in both LG and MG (p < 0.01 andp = 0.05). Post hoc contrastanalysisrevealed differences in time-to-peak LG EMG from the NB-NF trial to the B-Fl (p =0.02) and B-F20 (p = 0.02) as well as from B-NF to B-Fl (p =0.03). The time-to-peakMG EMG was lengthened in the B-NF and B-F20 trials compared to the NB-NFtrial (p =O.Ol,p < 0.01) and in the B-F20 compared to the B-Fl(p = 0.02). From the NB-NF tothe B-NF and further to the B-F20 trial, the time-to-peak EMG increasedfrom37.7±10.2% to 42.9±8.9% to 47.6±10.0% in LG and from 3 1.6±3.3% to 33.2±2.9%to36.5±5.6% in MG. Figure 4.5 displays the mean time-to-peak EMG for theLG and MGmuscles across four trials in the braced condition. Full results of the statistical tests arepresented in Appendix C.38Time to Peak GAS EMG (Braced)6055Ci500I-.41) 45C)>‘0(Va,a. 400Ca,041,0. 35302NB—NF B—NF B-Fl B—F20Condition0LG • MGFigure 4.5. Mean time-to-peak EMG in the LG and MG muscles averagedacross ten pedal cycles inthe braced condition.394.5 RepeatabilityThe two NB-NF trials conducted minutes apart allowed testing of the repeatabilityof muscle excitation during cycling. Paired samples t-tests showed no significantdifferences in SOL (p = 0.92), LG(p 0.78), MG (p = 0.35), TA (p = 0.21), BF (p0.33), or GM (p = 0.51). A significant difference in RF RMS EMG was foundbetweenthe NB-NF and NB-NF2 trials(p = 0.04); mean RMS EMG decreased from the first trialto the second trial. Full statistical results (t-statistic, degrees of freedom, p-value)arepresented in Appendix C.4.6 Median Power FrequencyMedian power frequency (MPF) of EMG is often used as a measure of muscularfatigue. In the unbraced condition, across the four analyzed timepoints, there weresignificant differences in the median power frequency of two ofthe seven tested muscles.Significant ANOVA tests showing differences in SOL (p = 0.02) and LG(p = 0.05) werefollowed up with contrast analyses which showed significantly decreasedMPF betweenthe NB-NF2 condition and the NB-Fl condition (SOL:p= 0.01; LG:p = 0.01). The SOLand LG MPF returned to the levels of NB-NF2 by the NB-F20 condition,so this initialreduction did not appear to be representative of muscularfatigue resulting fromprolonged exercise. In the braced condition, there was again significantly differentMPFin SOL EMG (p = 0.03) and LG EMG (p = 0.01). Decreases in SOL MPF wereseenbetween the B-NF trial and the B-F20 trial (p = 0.03) and in LG MPF between theNBNF trial and B-F20 trial (p = 0.01). Additionally, the GM MPF (p = 0.02) increasedfromthe NB-NF to the B-F20 condition (p = 0.02). Figures 4.6 and 4.7 display the MPFoflower leg muscles and upper leg muscles respectively for the braced condition.Completeresults (F-value, degrees of freedom, mean sum of squares, effect size, p-value)of thestatistical tests are presented in Appendix C.40Median Power Frequency of Lower Leg Muscles (Braced)180160140120NL10100806040NB-NF B-NF B-Fl B-F20Condition•SOL °LG •MG OTAFigure 4.6. Mean participant (SD) median power frequency of the SOL,LG, MG and TA MPF fromfour trials averaged across ten pedal cycles during the braced condition.Median Power Frequency of Upper Leg Muscles (Braced)100908070N.. 600..I30NB-NF B-NF B—Fl B-F20Condition•BF ORF •GMFigure 4.7. Mean participant (SD) BF, RF, and GM MPF from four trials averaged across tenpedalcycles during the braced condition.414.7 Soleus EMG BiofeedbackRaw SOL EMG was recorded continuously throughout the testing period. ThisEMG was rectified and averaged and used to provide the visual feedbackto the rider.Figure 4.8 shows the rectified, averaged, filtered SOL EMGfor each power phase (fromTDC to BDC) of one participant. Average SOL EMG decreased from theNB-NFcondition to the B-NF condition and then further decreased in the B-Fl condition.Afterthis initial decrease with feedback, average SOL EMG did not improve overtime. Asreported above, there was no significant difference in SOLiEMG across the feedbackperiod (B-Fl and B-F20) indicating that there was no learning across this singletestingsession.00 200Time (s)Figure 4.8. Average SOL EMG across the entire testing session in the braced conditionfrom anindividual participant (Participant 06). Data were filtered with a 2nd order low pass Butterworthfilter with a 10Hz cutoff frequency.1.2NB-NFAverage Soleus EMG throughout the Braced Condition - Subject060.8wz0,6Feedback ON0.40.2400 600 800 1000 1200 1400 1600425 DISCUSSION5.1 OverviewThe most important fmding of the present study wasthat using only the bracingmethod, each of the triceps surae muscles showed a similar reduction of iEMG,approximately 30% compared to baseline; however, with the combined applicationofbracing and biofeedback, SQL iEMG was immediately reducedby a further 23% whileMG and LG excitation did not change. However, by the end ofthe feedback period, LGiEMG was significantly reduced by 21% whereas the MGiEMG did not change. Thisfinding suggested that the MG iEMG reached an excitation plateau, unableto furtherreduce its excitation potentially due to its role as a knee flexor.LG iEMG decreasedalong with SQL iEMG and may have a greater role in ankle plantarfiexion.The present study provided evidence to suggest that SOL and GAS excitationsdonot always change in the same direction with the same function, leading to theconclusionthat they function differentially. The biarticular nature of the gastrocnemius (bothmedialand lateral heads) leads to its function as both an ankle plantarfiexor and a kneeflexor butthe relative contribution of its excitation to one function or the other remainedunclear.Soleus, meanwhile, has no involvement at the knee but shares the function of ankleplantarfiexion. While motor neurons are anatomically distinct and direct theactivation ofa single muscle, due to their role as synergists, it is possible that SQL and GAS couldhave functionally overlapping motor neuron pools. Conversely, the motor neuronpoolscould be both anatomically and functionally distinct but activated simultaneously.Toisolate the role of GAS at the knee, a combination of ankle bracing and SQLEMGbiofeedback was used to passively and actively eliminate plantarfiexion; by recordingfrom SQL, an indirect measure of the reduction in the role of GAS as a plantarfiexorwasdetermined.Because bracing and biofeedback were tools employed to help reduce SQL EMG,comparisons between the braced and unbraced condition were not of concern. Theunbraced trials were designed to show whether a mechanical tool was necessaryto inducechanges in SOL EMG. We found that there was no significant effect of conditiononmuscle excitation during the unbraced trials and thus participants were not ableto use the43biofeedback alone effectively; this suggested thatthere was something inherent to thecycling motion that necessitated the use of the ankleplantarfiexors. While one mightargue that the feedback image was inadequate to inducethe desired modification, itssuccessful implementation during the braced condition madethis proposition lessprobable. The remainder of the discussion focuses on thebraced condition in which thedesired modification of SOL EMG was induced.5.2 Support for the HypothesesThe first hypothesis contained two parts, both of which were accepted. EMGbiofeedback was a useful tool in the reduction of SOL EMGbeyond that seen in bracing.Additionally, biofeedback was not useful in modifying SOL EMG without theapplicationof a mechanical brace.The second hypothesis was rejected for the medial gastrocnemius andaccepted forthe lateral gastrocnemius. MG excitation did not decrease during SOL EMG biofeedbackwhile LG EMG decreased by the end of the biofeedback protocol in the bracedcondition.5.3 Mechanism for Reducing Soleus EMGSanderson and Kenyon (2006) showed that bracing the ankle with a pre-formedankle-foot orthosis reduced SOL excitation by an average of 30% during cycling.Thecurrent study confirmed these results and sought to further this reduction using SOLEMG biofeedback. Biofeedback was successful in inducing a voluntaryreduction inSOL iEMG beyond that seen in bracing. When shown visual feedback, in boththebraced and unbraced conditions, participants attempted different strategies to minimizethe displayed EMG. While some participants reported usinga pull-up strategy in whichthey focused on pulling up with the quadriceps muscles, one strategy that participantsconsistently reported using in order to reduce SOL excitation wasactively contracting theantagonist TA. This was termed the TA excitation strategy, and the neural implicationsof this strategy must be understood. Given the large increases in TA excitation ofnearly210%, the likely mechanism through which the decrease in SOL excitationoccurred wasthrough heightened reciprocal inhibition from the TA. During voluntary movement,inhibition on the agonist muscle decreased to facilitate its action while inhibitionofantagonist muscles increased to promote their silence and to ensure that nounwanted44stretch reflex activity was evoked (Nielsen, 2004, Windhorst,2007). In the currentinvestigation, by voluntarily ramping up TA excitation, projections to SOLthroughinterneurons in the spinal cord may have created an inhibitionin the SOL muscle as hasbeen shown previously by Tanaka (1974), Pyndt et al. (2003), and Nielsen(2004).We suggested that a prolonged TA excitation strategy was not sustainable as TAis predominantly a fast-twitch muscle and presumably would fatigue throughout theprotocol with constant excitation. Under normal conditions duringa pedal cycle, TA hadtwo activation bursts, with both peaks occurring in the recovery phase of cycling(180-360°), while SOL had a single excitation burst with a peak at90° (Chapman et al., 2006).Thus, to counter the SOL excitation burst, the TA excitationstrategy would require themuscle to be active during the power phase, in additionto its normal function in therecovery phase, and this might result in fatigue. The TA muscle excitation patterndidchange with bracing and feedback; in addition to the two bursts correspondingtodorsiflexion, there was a burst during the early power phase. Because medianpowerfrequency of TA EMG did not change over the testing interval, it was concluded thatTAdid not experience muscular fatigue. It was proposed that participants activatedanddeactivated TA throughout the 20-minute protocol to avoid reaching muscularfatigue.Thus, from minute to minute, the TA EMG excitation trace may have beenquite variable.Across the prolonged feedback period, TA iEMG increased and decreased withnoconsistent time interval over which the changes occurred. The number of cyclesoverwhich TA was highly active varied between participants who were free to implementtheir own onloff timing; at any one time point, some participantsmay have beenimplementing the TA strategy while others may have been “resting” their TA.Thisaccounts for the wide variability seen in the TA iEMG data as the timing andlength ofactivation occurred differently across all participants. This wide variability mayalso helpexplain why the TA excitation strategy was not successfully employedin the unbracedcondition; between the braced and unbraced conditions, participants had similarTAiEMG increases in the feedback condition compared to the no feedback baselinebut thelarger variability in the unbraced condition led to non-significant statistical tests. Withoutthe brace neutralizing the position of the ankle joint, it was more difficult to voluntarilyeliminate plantarfiexion and continually activate the TA muscle.45The likely mechanism whereby SOL excitation was significantlydecreased in thebraced-biofeedback protocol was through reciprocal inhibition from TA. It is importantto note that TA also has reciprocal connections with GAS. However, it hasbeen shownthat TA excitation affects SOL and GAS differently witha greater reciprocal inhibitionbetween TA and SQL than TA and GAS (Nielsen and Kagamihara, 1993).5.4 Effects of Reduced Soleus Excitation on Medial Gastrocnemius ExcitationThe previously discussed experiment by Sanderson and Kenyon (2005) wasdesigned to determine the relative contribution of the MG muscle to plantarfiexioncompared to knee flexion; however, they did not differentiate between the two rolesbecause the changes in MG and SQL excitation were nearly identical, withthe bracingprotocol leading to an average 30% EMG decrease in both muscles. The currentaugmentation of the bracing protocol with a SQL EMG biofeedback display led to afurther 23% reduction in SQL iEMG with no concurrent reduction in MGiEMG. Theappearance of this excitation plateau suggested that MG excitation cannotbe furtherreduced because of its role as a knee flexor. Since MG iEMG wasunchanged withbiofeedback, one might speculate that the contribution of the MG atthe ankle joint wasremoved by bracing and the remaining excitation was related only to knee joint motion.However, since SOL excitation was not completely eradicated, it was not possible totease apart this relationship. This result provided evidence for the possibility that theremaining excitation corresponds to knee flexion, but a different methodology must beemployed in future studies toverifS’these results.Further evidence for the increased contribution of the kneejoint motion camefrom the shift in time-to-peak EMG. In the braced condition, there was a temporal shiftin GAS recruitment with peak EMG occurring later in the pedal cycle. The time-to-peakEMG was delayed from 37.7% of the pedal cycle in the NB-NF condition to 47.6% in theB-F20 condition in LG and from 31.6% to 36.5% in MG. During thepedal cycle, thebiarticular GAS often has two bursts of activity; the earlier one occurs during the powerphase and corresponds to ankle plantarfiexion while the latter one occurs during therecovery phase and corresponds to knee flexion. This temporal shift in time-to-peakEMG indicated that the GAS iEMG now reflected only its role in knee flexion. Withbracing and biofeedback, the time-to-peak EMG occurs later in the pedal cycle and when46combined with the fmding that MG iEMG does not decrease,it suggested that kneeflexion because the only role of MG as plantarfiexion and SQL EMG arereduced.The motor neuron pools for the LG and MG are directed by neuraldrives for bothknee flexion and ankle plantarfiexion, while that of SQL is directed onlybyplantarfiexion. In the following discussion about motor neuron poois,it is important tonote that we are not suggesting that individual motor neurons influencemultiple muscles;motor neuron pools are anatomically distinct. Instead, we suggestthat there is afunctional overlap of the motor neuron pools. Based on their differentresponses tobiofeedback, there must have been some isolation between the motor neuronpools ofSOL and MG. Four possible motor neuron pool organizations are displayedin Figure5.1.47Shared Motor Neuron Pools DistinctMotor Neuron PoolsSQLDivided Motor Neuron Pools Hybrid Motor Neuron PoolsFigure 5.1. Four potential organizations of the triceps surae motor neuron pooi including the twoextremes, shared or distinct. Two examples of other possible organizations include divided, in thiscase shared motor neurons between the LG and MG and distinct from SOL, or hybrid, some degreeof shared and distinct motor neurons between all three muscles.LG MG LGSQL LGMG48The current study suggested that a single, shared motor neuron poolfor the tricepssurae muscles cannot exist as comparisons show that the SOL and MG iEMGaresignificantly different. We suggest that the motor drive to MG differed toogreatly fromthat of SOL during biofeedback for a fully common input to exist. Distinctmotor neuronpoois activated simultaneously could result in these differential responses to biofeedback;however, a hybrid of the two ideas, with some motorneurons contributing to all musclesand others to one part of the triceps surae is also possible. Given the common responsetobracing, there are likely some common connections amongthe three muscles, but itremains to be seen whether this occurs at the motor neuron level. The relationshipbetween MG and LG is not easily deciphered with the present results. Statistically,bothmuscles show a decrease in iEMG with bracing and no initial change in iEMGwithbiofeedback; however, by minute twenty of the braced-biofeedback protocol,LG iEMGwas significantly decreased beyond bracing alone while MG iEMG did not changefromthe B-NF condition. In Figure 5.1, the two muscles are diagramed together(divided) orseparately (distinct); in both of these examples, the motor neuron poois are distinctfromSOL. These different responses to biofeedback indicated that there were some distinctmotor neurons within the LG and MG motor neuron poolsbut pairwise comparisonsshow no statistical difference between the two muscles when averagedacross allconditions and thus limit the ability to comment conclusively. From the currentresults,we suggest that there are varying levels of common motor neurons betweenSOL, LG,and MG, and a hybrid system most accurately depicts this possibility.Since LG decreased significantly by the end of the biofeedback protocol, it is likelythat SOL and LG have a greater common input than SOL and MG. McLeanand Goudy(2004) studied the effects of sustained contraction on surface EMG of SOL,LG, and MG.The correlation among RMS EMG was highest between SOL and LG (R2= 0.662) whichhad a coactivation synergism as opposed to SOL and MG (R2 = 0.114)and LG and MG(R2 = 0.155) which had a trade-off synergism. Knee joint angle was held constantso thissynergism only related to the action at the ankle joint. It appeared that SOLand LG weremore closely related than both the SOL/MG and LG/MG pairs, and theauthors suggestedthat this might relate to their common innervations. In 26 of 37 cadaver limbs,the SOLand LG nerves branched from a common tnrnk (Parratte et a!., 2002). Further, SirinandPatla (1987) previously conducted a similar submaximal sustained plantarfiexion49experiment at two knee angles (0 and1200).The predominant synergy was foundbetween SOL and LG not SOL/MG or LG/MG, with coactivation becomingstronger overtime. Evaluation of the pairwise comparisons shows that LG iEMG was not significantlydifferent than SOL or MG iEMG but the latter two musclesdiffered; we suggest that theresponse in the LG may indicate its intermediate role. The ideaof a hybrid system and anintermediate role of LG fits well with the partitioninghypothesis and ideas ofneuromuscular compartmentalization. English et al. (1993) and Windhorst etal. (1989)suggested that the gastrocnemius muscles were compartmentalized with fourneuromuscular compartments in LG and eight compartments in MG. Thiscompartmentalization might have led to the findings of a different degree ofcommoninput to the triceps surae muscles. We must also note that recordings fromthe SOLmuscle were taken from the lateral part of the muscle; had we recorded more medially,we may expect to see a greater common input with MG. LG might share morecommoninput with each of SOL and MG than SOL and MG do witheach other. In LG, theconcurrent decrease with SOL EMG during biofeedback provided evidence ofa possiblefunctional difference between LG and MG, with LG playing a greater relativerole inplantarfiexion while MG EMG remains heightened due to its larger role in kneeflexion.While it is established that all three muscles do not share identical motor neuron pools,the degree of overlap, if any, between each of the three pools remains uncertainandshould be investigated in future research.5.5 Ability to Use BiofeedbackThe mechanical brace was necessary to reduce SOL excitation using EMGbiofeedback. There are two potential ways in which bracing may have influencedtheability to modify SOL EMG with feedback. The first related to the level of experiencewith the task while the second related to the mechanical limitations imposedby the brace.All of the participants had prior cycling experience and were comfortable inperforming a consistent cycling action while unbraced. Cycling, like most motorskills,involves the coordination of multiple muscles working about multiple joints. Sincesynergist muscles execute the same function, the motor task could have been performedin numerous ways with different combinations of muscles leading to the generationof thesame joint torques. However, even with the possibility of different muscle excitations50both between individuals and between pedal cycles, healthyindividuals employ similarmuscle activation patterns while performing the same well-learned tasks (PrilutskyandZatsiorsky, 2002). Adults who have not cycled for years remember howto ride a bicyclesuggesting that cycling is a highly learned task, one that hasbeen trained through hoursof practice. It was suspected that under normal conditions,ankle plantarfiexion isincorporated into the cycling motor pattern; current results indicated thatthe muscleexcitation pattern for cycling was too well-established to be voluntarily modifiedby thebiofeedback within a single testing session. While the cycling action can beaccomplished in other ways, the use of the triceps surae musclesto transfer forces to thepedal and to help generate the necessary extensor moment was ingrained in themovement pattern.The application of the brace modified the requirements of the cycling taskcreating a new, but similar task. In studying the SOL H-reflex, bracing is oftenemployedto ensure that the geometry of the muscle does not change. Misiaszek (2003) notedthatthe shortcoming of this method was that the movement became unnatural creatinga noveland artificial motor task. We suggest that participants were able to immediatelymodifthis similar task and effectively use biofeedback because the muscle activationpattern isnot as well-defined. The current study employed the same perturbation intwo similartasks; however, the difference in experience with each task results in a differentresponseto the perturbation, with participants only able to use biofeedback in the less familiarbraced condition.The second, and perhaps the primary reason, for the difference in the ability tousebiofeedback related to the kinematic requirements of the task. Modification of thecycling task can change the movement pattern required during its performance. Forexample, lowering the seat height reduced or eliminated ankle plantarflexion and ledtochanges in muscle requirements of the task (Sanderson and Cawsey, personalcommunication). Participants were still able to perform the cycling task; however, itsmuscle activation patterns were altered. In the unbraced condition, the ankle joint movedfreely whereas in the braced condition, it was constrained to the neutral position. Whenparticipants were unable to eliminate plantarfiexion, SOL EMG was not reduced.Cannon et al. (2007) showed that when participants were instructed to maintainthe anklein maximal dorsiflexion throughout the pedal cycle, they did not decrease the maximal51plantarfiexion angle from normal cycling. In agreement with the aforementionedstudy, ittook the elimination of plantarfiexion through bracing to significantly reduceSOL EMG.It is suggested that participants were unable to reduce SOL EMG and use biofeedbackinthe unbraced condition because they could not restrict ankle joint motion without theaidof an external brace.By mechanically bracing the ankle joint, the primary action of the SOL wasremoved. Providing biofeedback allowed for an individual to visualize theonline SOLexcitation and modify pedalling technique to reduce SOL EMG. However, withinthebraced condition, participants were unable to eliminate SOL EMG. It is difficulttopredict the effect of previously learned skills on a similar taskbut it has been noted thatwhen the environmental context of two performance situations were similar but themovement characteristics required were different, negative transfer effects canoccur.This was particularly evident when it involved a change in spatial locationsof amovement or timing structure of a movement (Magill, 2001). It wasproposed that themovement and timing structure of the known skill of cycling may have interferedwithone’s ability to silence SOL with biofeedback. The residual excitation likelyarose fromthe similarity between the two tasks creating some skill transfer from the unbracedcondition. The SOL was actively involved in performing the unconstrained cyclingmotion and became active in the similar task. While biofeedback allows forparticipantsto modify the task that they are learning, the well-learned task of cycling interferes withtheir ability to entirely eradicate SOL excitation. It is important to note that in the bracedcondition, after the initial improvement with feedback, participants did not continuetoimprove over time. It is suspected that the twenty-minute biofeedback period wastooshort to allow for learning to occur. Two groups of individuals with incomplete spinalcord injury and who suffered from Trendelenburg gait were given biofeedbackto modifytheir gait. In this abnormal walking pattern, gluteus medius excitation istoo low; audiobiofeedback was provided to individuals when the muscle excitation was belowa certainthreshold. One group received feedback for 30 minutes per day while the other groupreceived it throughout the entire day. Over the two-month period, the limitedbiofeedback group reduced their hip drop by 50% while the constant feedback groupreturned to almost normal gait (Petrofsky, 2001). Over time, one might inducelong termchanges to supraspinal and sensory afferent inputs with biofeedback leading to52modification of the output from the motor cortex, potentially inboth the braced andunbraced conditions.While the braced motor task was likely influenced by unbraced cycling,it wasmodifiable by biofeedback. The way in which feedback modifiesthe motor pattern canonly be speculated. Perez et al. (2005) suggested that duringmotor learning, changes toreflex circuitry required changes in presynaptic inhibition ofsynapses between sensoryafferents and motor neurons. He proposed that visual input travels from thevisual cortexto alter the motor cortex, which sends certain commands from descending driveto spinalinterneurons. In the current study, those descending commandsdirect a reduction in SOLEMG. In their novel visuo-motor skill training task, Perezet al. (2005) found thatfeedback improved performance of the task and led to a significant immediatereductionof the SOL H-reflex recruitment curve. They contendedthat there were changes indescending drive to the interneurons conveying the inhibition andsuggested that tooptimize the motor pattern during skill acquisition, visual input and proprioceptiveinformation were centrally integrated. The visuomotor task required increasedattentionresulting in an increased motor cortical excitability, an adaptationin the motor cortex.The proposed pathway explains the successful modification inSOL EMG, and wesuggest that the descending drive to SOL was difficult to silence due toresidualexcitation from the known motor pattern.5.6 Localized EffectPrevious experiments by Sanderson and colleagues (Sanderson and Kenyon,2005, Sanderson et al., 2006) limited EMG collection to the triceps suraeand TAmuscles. The addition of muscle excitation recordings from more proximal musclesshowed that the effect of bracing and biofeedback may have been limitedto the musclescontrolling the ankle joint including both monoarticular and biarticularmuscles.Excitation of BF, RF, and GM which act at the knee and hip joints were notincreased tocompensate for the decreased contribution of the ankle muscles to the totalextensormoment. Instead, BF and RF excitation did not change while GM excitationdecreased.Thus, the recorded upper leg muscles were not responsible for compensating for thereduced ankle extensor moment. The compensatory increase in excitation mayhavecome from other proximal muscles from which we did not record. Comments from53participants suggested that the quadriceps muscles worked harder in the bracedconditioncompared to the unbraced condition. So while the present results suggested thatthemodifications induced by bracing and biofeedback were localized, with theeffectnarrowly focused on the muscles surrounding the ankle, it waslikely that the vasti muscleexcitation increased as the compensatory mechanism for decruitment of the tricepssuraemuscles. It was a novel finding that changes in the triceps surae group were independentof BF, RF, and GM.5.7 Methodological ConsiderationsA number of factors have to be controlled or considered in the interpretation ofthe results including cadence, repeatability, fatigue, cutaneous input, and individualdifferences. Because Sanderson et al. (2006) among others (Ericson et al., 1985,Duchateau et al., 1986, Marsh and Martin, 1995, MacIntosh et al., 2000) have shownthatcadence changes differentially affect SOL and MG, cadence must be held constantinorder to compare between trials. Statistical analysis revealed that cadence wasunchanged across the testing period. It was possible that monitoring cadence interferedwith participants’ abilities to modify SOL EMG. Given two different types of feedback,cadence and EMG, participants had to determine how frequently to focus on onetype orthe other. They were asked by the researcher to focus on the EMG biofeedback but toglance occasionally at the cadence feedback. If the researcher noticed theparticipantfocusing too much on the cadence monitor, she verbally encouraged a switchto thebiofeedback; however, there was no measure of how frequently this occurred. Futureresearch employing two types of biofeedback should have a cadence monitor appear onthe feedback display at regular intervals to ensure near constant attention is directedtothe EMG biofeedback. Given the current measures, it was possible that the participantswere focused on neither the biofeedback nor the cadence feedback. Qualitatively, manyparticipants mentioned that in the first few minutes, they attended continuously to theEMG biofeedback as they tested strategies to minimize SOL EMG; however, theysuggested that once they chose a strategy, they tend to pay less attention to thebiofeedback as they could identify the lowest level of SOL excitation by ‘feel’. Whilethe participants were asked to minimize SOL EMG and we expected them to continuallyattempt to further reduce SOL EMG throughout the protocol, it seemed as though many54participants employed a self-defined optimal strategy chosen within thefirst few minutes.They continually used this strategy throughout the protocol whichled to an excitationplateau and no further improvement with time. Devising a method toensure thatparticipants constantly work to reduce SOL excitation with EMG feedbackwould beuseful for future applications of biofeedback.By completing two NB-NF trials, data to test the repeatability of EMG patternsincycling were available. Among the seven muscles, only the RF is significantlydifferentbetween the two trials. The present data were compared to Dorel et al. (2007) wholooked at the natural physiological variability of EMG patterns whilecycling at 150W;current data were analyzed as RMS EMG over a 5Oms window to matchtheir analysis.Much like the previous study, these data showed that although therewere intra-individualdifferences, the group means were not significantly different (withthe exception of RF).Thus, the decision to normalize to a NB-NF baseline was deemed appropriate althoughthe RF data may not have a representative normalization and confidencein thesemeasures is reduced.Due to the length of the cycling period, muscular fatigue could have played a rolein altering muscle excitation. During sustained isometric contractions,a decrease in MPFhas been accepted as a sign of muscle fatigue. The decrease in MPF was proposed tobecaused by a decline in mean muscle fibre conduction velocity and dischargesynchronization of the motor units (Ament et al., 1996). In the braced condition, MPFdecreased in the SOL, LG, and GM with significant differencesbetween either the NBNF or B-NF condition and the B-F20 condition. Muscular fatigue in SOL and LG wassurprising given the aim to voluntarily reduce SOL excitation. GM is a powerfulhipextensor and because bracing of the ankle increased the need for hip extension(Sanderson and Kenyon, 2005), it might follow that muscular fatigue in this muscleoccurred; however, since the GM MPF increased, fatigue was not relatedto the changingMPF. A second, indirect measure of neuromuscular fatigue was heart rate. From thethird to the eighteenth minute, heart changes from 147.4±19.8bpm to 152.7±20.5bpminunbraced cycling and 145.0±1 8.4bpm to 153.6±18 .7bpm in bracedcycling, variations of3.5% and 5.9% respectively. Heart rate increased by 7.3% and 12.7% after 50 minutesand 100 minutes during a two hour cycling protocol (Lepers et a!., 2000) which we can55extract to a 2.9% change over twenty minutes. Heart rateincreased continually as testingprogressed, and this increase was within the normal rangeexpected due to cardiac drift.Cutaneous afferents have been shown to influence the motor neuron excitabilityinSOL (lies, 1996) so it was important to ensure that this wasnot the only influence.Cutaneous input from stimulation of branches of the commonperoneal nerve on thedorsum of the foot led to a significant reduction in presynaptic inhibitionof soleus Taafferents (lIes, 1996); however, it is suspected that on thefoot, the cutaneous input fromthe brace would be much like that of the shoe. Mcllroy (1992) argued thatthe addition ofan ankle-foot orthosis would not modulate the SOL H-reflexgreatly; if there had been nochange in the SOL, LG, or MG excitation during biofeedback,one might suggest that thechanges to the cutaneous inputs from the brace itself could have played a majorrole inthe reduction of muscle excitation. However, the biofeedback protocol induceda furtherdecrease in SOL and LG iEMG with no changes in cutaneousinput from the bracingalone protocol. Thus, decreased SOL excitation is not due to altered cutaneousinputalone but the extent of the influence of cutaneous afferents in the braced protocolremainsuncertain.In the unbraced condition, there was a non-significant increase in TA iEMG,withthe non-significant statistical test likely due to the variability inthe data. Participantsattempted to maintain a heightened TA excitation as they did with the brace, butstatistical analysis showed that they were unable to do so. While some participantswerecapable of lowering SOL excitation during unbraced cycling, individualvariation createda non-significant statistical result. One potential reason for the individual variationis thefitness level of participants. This study employed an absolute workload of150W ratherthan a relative workload established based on individual criterion suchas VO2max; thus,some participants were working harder relative to their maximum capabilities. Athigherworkloads, the reciprocal inhibition on SOL by TA decreased; to meet thedemands of anincreased workload, SOL excitation increased and there was a gradual declineinreciprocal inhibition from TA (Pyndt, 2003). While there were no recorded measuresoffitness in the present work, it was possible that more fit individuals werebetter able toreduce SOL excitation. Qualitatively, some participants commented that the workloadfelt more difficult than they might normally perform in self-designed workouts;conversely, other participants admitted that they were working well below theworkload56of their normal cycling sessions. While all participants wereable to complete thephysical cycling task, those who were more physically fit were likelybetter able toconcentrate on and use the feedback rather than simply focussingon completing thecycling task. Thus, the variation displayed in the muscle excitation datamight be relatedto the range in fitness levels among participants.576 CONCLUSIONThe present study tested the immediate effects of reduction of SOL EMG on MGand LG excitation during cycling. It confirmed the results of Sandersonand Kenyon(2005) that during braced cycling, all three muscles respondsimilarly to the perturbation.It builds on this previous study by showing that SOL excitationwas further reduced withbiofeedback. The application of SOL EMG biofeedback led to a selectivereduction inSOL and LG excitation while MG excitation was not significantly changed.Thesefindings provided evidence to suggest that there are different motor neuron pools forthetriceps surae muscles and that there may be some shared motor neurons creatingoverlapping pools. Even with biofeedback, individuals were unable to voluntarilyeliminate SOL excitation which might be due to the use of the learned cyclingskill. Theeffects of bracing and biofeedback were localized to the muscles acting at the anklejointand thus do not lead to changes in superior muscles acting above theankle at the knee orhip joints. Biofeedback was an effective tool to induce changes in SOL excitationin theshort term and could help to induce a complete elimination of SOL muscle excitationinunbraced and braced cycling during a long term learning study. 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Considerations for use of the Hoffmann reflexin exercise studies.European Journal of Applied Physiology. 86, 455-468.648 APPENDICESAppendix A: UBC Research Ethics Board Certificates of Approval65LJBC The University ofBritish ColumbiaOffice ofResearch ServicesBehavioural Research Ethics BoardSuite 102, 6190 Agronomy Road,Vancouver, B.C. V6T 1Z3CERTIFICATE OF APPROVAL - MINIMALRISKERINCIPAL INVESTIGATOR:UBCBREB NUMBER:)avid J. SandersonIUBC/Education!HumanKinetics1H07-02893INSTITUTION(S) WHERE RESEARCH WILL BE CARRIED OUT:InstitutionISiteUBC Vancouver (excludes UBC Hospital)Other locations where the research will be conducted:N/ACO-INVESTIGATOR(S):ulia Marjorie Wilkes;P0NsORING AGENCIES:ROJECT TITLE:Jse of EMG biofeedback to voluntarily reduce soleus excitation during cycling while the ankleis passively)racedCERTIFICATE EXPIRY DATE: February 20, 2009)OCUMENTS INCLUDED IN THIS APPROVAL: ATE APPROVED:IFebruary20, 2008)ocument NameIVersionIDateConsent Forms:Main Study Consent 2 February13, 2008Advertisements:Advertisement 2 February13, 2008The application for ethical review and the document(s) listed above have been reviewed and the proceduresvere found to be acceptable on ethical grounds for research involving human subjects.Approval is issued on behalfofthe Behavioural Research Ethics Boardand signed electronically by one ofthefollowing:Dr. M. Judith Lynam, ChairDr. Ken Craig, ChairDr. Jim Rupert, Associate ChairDr. Laurie Ford, Associate ChairDr. Daniel Sathani, Associate ChairDr. Anita Ho, Associate Chair66Appendix B: Individual Cadence DataAverage Cadence during 10 Pedal Cycles in Each DataCollection PeriodUnbraced_____Participant NumberCondition5 6 7 8 14 15 1617 18NB-NF 80.94 80.94 83.57 81.91 80.43 82.27 81.6279.26 81.15NB-NF2 81.60 80.69 79.79 82.51 81.10 81.08 82.7879.16 79.79NB-Fl 79.45 82.68 78.45 81.89 80.9082.59 79.53 77.61 81.88NB-F2 79.89 78.21 77.39 79.86 79.52 82.70 80.3281.36 81.45NB-F3 77.67 77.99 76.34 80.38 80.9282.97 81.14 80.94 81.39NB-F4 79.40 79.66 77.04 80.48 82.08 82.17 79.4079.17 81.50NB-F5 80.66 80.72 76.76 82.61 79.23 82.85 82.1779.05 80.63NB-F6 80.00 79.21 77.52 81.86 80.18 83.96 78.75 81.9281.21NB-F7 79.53 80.90 75.62 80.94 80.10 83.47 78.5279.49 80.43NB-F8 79.65 80.41 76.26 82.97 81.91 84.2578.55 80.65 81.54NB-F9 79.82 80.94 75.83 81.82 80.90 81.4778.69 80.43 81.93NB-FlU 79.33 78.83 76.46 81.10 80.4185.13 81.16 81.99 81.71NB-F11 82.55 79.86 76.21 81.78 80.7082.48 81.73 80.27 80.66NB-F12 81.71 79.56 79.35 81.25 79.9583.26 80.81 79.74 77.19NB-F13 80.45 79.28 78.59 82.80 78.21 82.7680.79 78.18 81.30NB-F14 80.86 80.47 79.89 81.58 80.8382.53 81.03 77.22 81.50NB-F15 78.38 78.79 76.55 84.65 80.47 82.66 80.1179.77 80.30NB-F16 82.53 79.47 75.84 81.04 79.0981.42 80.29 82.14 81.19NB-F17 81.20 79.47 78.02 81.50 80.6183.65 82.38 79.33 82.53NB-F18 80.07 81.37 79.47 81.30 80.2282.57 78.96 82.56 76.78NB-F19 79.74 79.45 80.81 81.34 81.6083.36 79.47 80.30 81.82NB-F20 83.14 79.45 81.03 81.47 79.65 81.97 80.5680.33 82.0867Braced_____Participant NumberCondition5 6 7 8 14 15 16 17j18NB-NFB-NPB-FlB-F2B-F3B-F4B-F5B-F6B-F7B-F8B-F9B-F 10B-FllB-F 12B-F 13B-F14B-F 15B-F 16B-F 17B-F 18B-F 19B-F2080.7281.3081.6780.9281.5080.9480.6880.5880.4180.9581.5680.7280.2280.2982.5581.3880.9683.0580.7980.3682.9382.0180.1678.2180.7783.4575.8480.8177.8978.8679.2478.8879.4580.2077.1080.9080.9778.6481.2183.9082.7281.1980.8778.1582.4082.7479.8480.0981.7180.7180.2482.0180.3678.0878.7880.1179.0274.1778.4179.9180.7778.8581.5279.8679.3576.7880.8881.1077.6479.7782.1882.2580.3682.3080.9282.1082.0382.1780.8881.6183.2482.8082.1282.2582.5182.4882.8283.0579.5679.8879.9879.7479.4479.8984.5781.0379.0480.1680.8781.1682.3880.0280.4077.9280.1480.1379.3279.5978.9077.7980.5078.6982.3878.3580.3479.9580.0477.1178.4580.0580.7282.6680.1578.9382.3480.9279.8379.7981.3679.8382.4682.3480.4581.0381.0180.6580.9180.0080.6480.4781.9582.1280.6182.9680.7481.2681.0180.7580.4581.5681.9180.9981.4381.3680.8878.6679.5679.8480.8583.2081.5678.2682.2782.4979.7481.6178.8981.9980.9083.1082.6178.1979.2583.2482.6880.5680.9781.0183.0380.7780.9082.3181.4382.0680.9581.6981.5881.2580.9581.8681.7181.4180.5683.1481.8482.1080.7780.6868Appendix C: Statistical TestsIntegrated EMG7x4 Repeated Measures ANOVAo Degrees of freedom, without Greenhouse-Geisser correction factor are:Muscle (6, 48) Condition (3, 24) Muscle x Condition(18, 144)UnbracedF df MSMuscle 5.62 1.190, 9.516 15.5020.036 0.413Condition 2.082 1.477, 11.818 2.192 0.1730.207Musclex 3.148 2.108, 16.862 11.538 0.0670.282ConditionBracedF df MSp up2Muscle 17.910 1.505, 12.038 35.858 <0.001 0.692Condition 1.358 1.547, 12.373 0.938 0.285 0.145Muscle x 6.128 1.825, 14.604 17.060 0.0130.434ConditionPost-hoc AnalysisNote: Post hoc analyses were conducted only in the braced conditionPairwise Comparisons between Muscles- Bonferroni correction: p 0.0 17MD SE Lower UpperpSOL vs LG -0.075 0.037 -0.159 0.009 0.075SOL vs MG -0.119 0.040 -0.211 -0.028 0.017LG vs MG -0.045 0.037 -0.13 1 0.042 0.267691x4 Repeated Measures ANOVAF df MSSOL 35.185 3,24 0.621 <0.001 0.815LG 15.485 3, 24 0.382 <0.001 0.659MG 12.324 3, 24 0.221 <0.001 0.606TA 5.165 1.445, 11.557 18.912 0.033 0.392BF 2.810 1.456, 11.650 0.757 0.111 0.260RF 0.569 2.570, 20.562 0.048 0.64 1 0.066GM 10.446 1.542, 12.339 0.229 0.003 0.566Pairwise Comparisons between ConditionsMD = Mean difference SE = Standard errorLower = Lower bound of 95% confidence intervalUpper = Upper bound of 95% confidence intervalNB-NF vs B-NFMD SE Lower UpperSOL 0.310 0.060 0.103 0.518 0.005LG 0.254 0.057 0.056 0.452 0.012MG 0.324 0.049 0.153 0.495 0.001TA -0.508 0.180 -1.133 0.117 0.133GM -0.050 0.074 -0.308 0.207 1.000NB-NF vs B-FlMD SE Lower UpperpSOL 0.540 0.063 0.320 0.759 <0.001LG 0.399 0.109 0.019 0.780 0.039MG 0.304 0.090 -0.009 0.617 0.058TA -1.807 0.473 -3.453 -0.161 0.031GM 0.114 0.056 -0.081 0.308 0.46370NB-NF vs B-F20MD SE Lower UpperpSQL 0.567 0.066 0.337 0.797<0.001LG 0.465 0.083 0.176 0.7530.003MG 0.311 0.063 0.093 0.5290.007TA -2.088 0.837 -4.999 0.824 0.223GM 0.204 0.05 0.03 1 0.3770.021B-NF vs B-FlMD SE Lower UpperpSOL 0.229 0.060 0.021 0.4380.030LG 0.145 0.069 -0.095 0.385 0.413MG -0.020 0.061 -0.234 0.193 1.000TA -1.299 0.362 -2.560 -0.038 0.043GM 0.164 0.025 0.075 0.252 0.001B-NF vs B-F20MD SE Lower UpperpSOL 0.527 0.067 0.022 0.491 0.031LG 0.210 0.053 0.026 0.394 0.024MG -0.013 0.046 -0.174 0.148 1.000TA -1.579 0.856 -4.556 1.397 0.613GM 0.254 0.046 0.093 0.415 0.003B-Fl vs B-F20MD SE LowerjUpperI________SQL 0.270 0.059 -0.177 0.232 1.000LG 0.065 0.057 -0.133 0.264 1.000MG 0.007 0.06 -0.201 0.215 1.000TA -0.281 0.729 -2.815 2.254 1.000GM 0.091 0.034 -0.027 0.208 0.16771Median Power Frequency1x4 Repeated Measures Analysis of VarianceIf df is not 3,24, the Greenhouse Geisser correction factorhas been used.Bold =p < 0.05UnbracedBracedF df MSSOL 5.401 1.387, 11.100 3705.915 0.032 0.403LG 4.856 2.344, 18.751 840.0350.009 0.378MG 1.485 1.391,11.129 1971.10 0.260 0.157TA 1.164 3,24 43.992 0.344 0.127BF 0.192 3, 24 14.980 0.901 0.023RF 0.87 1 3, 24 73.289 0.470 0.098GM 4.043 3, 24 216.560 0.018 0.336Post Hoc ContrastsUnbracedSOLNB-NF vs NB-NF2NB-NF2 vs NB-FlNB-Fl vsNB-F20F4.99312.1168.686MS190.902284.7761834. 123p0.0560.0080.52 12lp0.3840.6020.0192lp0.0560.558F0.47710.082df1, 81, 81, 8p0.5090.013.1F df MSp11p2SOL 4.120 3,24 463.964 0.017 0.340LG 3.123 3,24 384.467 0.045 0.281MG 1.225 3,24 240.135 0.3220.133TA 0.857 1.531, 12.251 623.375 0.4200.097BF 3.428 1.859, 14.873 496.542 0.0620.300RF 1.606 3,24 42.632 0.2230.167GM 1.622 3,24 65.204 0.211 0.169LGNB-NF vs NB-NF2NB-NF2 vs NB-Fldf1, 81, 8NB-Fl vsNB-F20MS33.2161161.6744.550 1, 8 1763.720 0.065 0.36372Braced — only the significant results are shownSOLIF df MSB-NF vs B-F20 7.336 1, 8 9150.3890.027 0.478LGIF df MS p lhlp2NB-NF vs B-F20 12.639 1, 8 4494.5400.007 0.612GMIF df MSplhlp2NB-NF vs B-F20 9.596 1, 8 1067.0240.015 0.545RepeatabifityNB-NF vs NB-NF2 with Paired Samples T-testt df p Cohen’sdSOL -0.108 8 0.916 0.809LG 0.287 8 0.781 1.257MG -0.997 8 0.348 0.427TA 1.366 8 0.209 1.682BF -1.032 8 0.332 3.921RF 2.541 8 0.035 18.586GM -0.686 8 0.5 12 0.779Time-to-peak EMG1x4 Repeated Measures Analysis of VarianceIf df is not 3,24, the Greenhouse Geisser correction factor hasbeen used.Bold =p < 0.05UnbracedF df MSpii2LG 0.097J3, 24 3.395 0.96 1 0.0 12MG 1.913jl.1699.355 25.533 0.154 0.19373BracedIFIdf 1M5IpIiip2LG 6.076 3, 24 198.2580.003 0.432MG 4.93 1 1.2 18, 9.745 93.2050.046 0.38 1Post Hoc ContrastsBracedLGF [df MS[2NB-NFvsB-NF 3.638 1,8 242.5810.093 0.313NB-NF vs B-Fl 9.163 1, 8 869.0900.016 0.534NB-NF vs B-F20 9.223 1, 8 876.9690.016 0.536B-NF vs B-Fl 7.154 1, 8 193.358 0.0280.472B-Fl vs B-F20 0.000 1, 8 0.018 0.9880.000MGF df MS pNB-NF vs B-NF 10.875 1, 825 .003 0.011 0.576NB-NFvsB-Fl 2.834 1,8 70.6160.131 0.262NB-NFvsB-F20 15.891 1,8 214.710 0.0040.665B-NF vs B-Fl 0.395 1,8 11.580 0.5470.047B-Fl vs B-F20 9.427 1, 8 39.0580.015 0.54174Appendix D: Individual Participant DataIntegrated EMGSOLUnbraced BracedNB-NF NB-NF2 NB-Fl NB-F205 1.00 0.98 0.59 0.586 1.00 1.06 0.93 0.937 1.00 1.39 1.43 1.06i 1.00 1.07 0.44 0.4714 1.00 0.91 0.99 0.89T 1.00 0.97 1.34 0.7716 1.00 0.77 0.65 0.4317 1.00 0.99 1.14 1.1318 1.00 1.02 0.86 1.11LGUnbracedNB-NF NB-NF2 NB-FlJNB-F205 1.00 1.15 0.77 1.186 1.00 1.13 0.85 1.087 1.00 1.05 0.80 0.748 1.00 1.05 0.56 0.8614 1.00 1.13 1.37 1.2715 1.00 0.99 1.12 0.8916 1.00 1.03 0.82 1.495T 1.00 1.03 0.94 0.8818 1.00 1.02 0.96 1.11[NB-NF B-NF B-Fl B-F205 1.00 0.77 0.30 0.336 1.00 0.59 0.62 0.477 1.00 0.86 0.70 0.438 1.00 0.78 0.26 0.1914 1.00 0.88 0.53 0.8615 1.00 0.51 0.33 0.2616 1.00 0.48 0.36 0.4917 1.00 0.89 0.74 0.5618 1.00 0.46 0.30 0.31BracedNB-NF B-NF B-Fl B-F205 1.00 0.69 0.53 0.546 1.00 0.69 0.48 0.677 1.00 0.84 0.79 0.548 1.00 0.70 0.24 0.30-j-:j-1.00 1.07 1.33 1.1115 1.00 0.68 0.67 0.5016 1.00 0.73 0.35 0.4817 1.00 0.87 0.70 0.4318 1.00 0.44 0.33 0.2575MGUnbraced BracedNB-NF NB-NF2 NB-Fl NB-.F20—:- 1.00 1.04 0.97 1.04i 1.00 1.09 1.17 1.03T 1.00 0.97 1.13 1.07i• 1.00 1.01 0.77 0.9014 1.00 0.97 0.95 1.0815 1.00 1.02 1.24 0.941.00 1.05 1.03 1.115T 1.00 0.97 0.97 1.0018 1.00 1.01 1.05 1.07NB-NF B-NF B-Fl B-F205 1.00 0.68 0.53 0.636 1.00 0.61 0.48 0.737 1.00 1.00 1.00 0.858 1.00 0.61 0.57 0.8414 1.00 0.75 1.21 0.9515 1.00 0.76 0.87 0.751.00 0.53 0.44 0.4117 1.00 0.62 0.67 0.6118 1.00 0.52 0.49 0.42TAUnhracedNB-NF NB-NF2 NB-Fl NB-F205 1.00 0.78 1.85 0.936 1.00 1.10 3.89 0.887 1.00 0.77 4.49 5.988 1.00 1.15 2.05 6.0514 1.00 0.86 2.24 1.7615 1.00 0.65 9.23 1.5016 1.00 1.04 0.60 0.2617 1.00 0.84 1.05 0.9418 1.00 0.91 1.29 1.41BracedNB-NF B-NF B-Fl B-F205 1.00 1.33 1.29 1.466 1.00 2.52 5.07 3.077 1.00 1.21 3.64 9.068 1.00 1.39 3.75 2.5414 1.00 1.87 3.73 3.5915 1.00 1.78 3.21 2.4716 1.00 0.74 0.63 0.1717 1.00 0.96 2.00 3.7918 1.00 1.79 1.94 1.6376BFUnbraced BracedNB-NF NB-NF2 NB-Fl NB-F205 1.00 1.17 0.63 1.646 1.00 1.21 1.59 1.917 1.00 0.94 0.97 1.258 1.00 0.85 0.72 0.8614 1.00 0.94 0.76 0.7515 1.00 1.05 1.21 0.9316 1.00 1.09 1.50 1.4817 1.00 1.00 0.82 0.9418 1.00 1.16 0.98 0.84RFUnbracedNB-NF NB-NF2JNB-Fl NB-F205 1.00 0.94 1.56 0.806 1.00 0.84 0.89 0.857 1.00 1.07 1.27 1.188 1.00 0.86 0.81 1.0114 1.00 0.95 0.91 0.8815 1.00 0.78 1.17 0.7916 1.00 0.93 1.23 2.0217 1.00 0.96 1.19 0.92•Th 1.00 0.95 0.77 0.91NB-NF B-NF B-Fl B-F205 1.00 0.77 1.05 1.896 1.00 1.31 2.30 2.777 1.00 1.15 1.43 2.028 1.00 1.10 0.63 0.7914 1.00 1.23 1.67 1.4115 1.00 1.00 1.43 1.1316 1.00 1.14 1.18 0.7717 1.00 0.93 0.87 0.6418 1.00 1.31 1.51 1.49BracedNB-NF B-NF JB-F1 B-F205 1.00 1.22 1.46 1.976 1.00 1.16 1.02 0.537 1.00 1.31 1.34 1.271 1.00 0.74 1.93 0.9714 1.00 0.70 0.52 0.7715 1.00 1.13 1.15 1.0816 1.00 0.68 1.08 0.7117 1.00 0.88 0.75 0.5618 1.00 1.56 1.15 1.1677GMUnbraced________ _______ ________BracedTime-to-peak EMGSOLUnbraced BracedNB-NF NB-NF2 NB-Fl NB-F20i 1.00 0.91 0.91 0.976 1.00 1.01 0.78 0.787 1.00 0.86 1.06 0.688 1.00 1.10 0.94 0.6814 1.00 0.95 0.82 0.8415 1.00 1.16 0.91 0.6916 1.00 0.93 0.81 0.94TT 1.00 1.10 1.04 0.9318 1.00 1.41 1.02 1.03NB-NF B-NF B-Fl B-F205 1.00 0.98 0.79 0.806 1.00 1.25 1.03 0.937 1.00 1.21 1.10 1.048 1.00 1.16 0.90 0.9214 1.00 1.21 1.00 0.7215 1.00 1.01 0.84 0.741.00 0.54 0.53 0.5417 1.00 0.94 0.85 0.7118 1.00 1.16 0.96 0.78NB-NF NB-NF2 NB-Fl NB-F205 20.18 19.03 15.80 17.516 14.17 11.11 18.65 15.557 18.05 19.49 16.48 14.978 20.77 20.21 17.22 27.5814 27.15 25.87 22.49 23.9015 18.33 16.92 15.63 18.8516 26.15 24.60 25.52 24.9317 22.62 24.23 25.32 23.9918 19.35 19.44 18.51 21.12NB-NF B-NF B-Fl B-F205 20.36 22.67 23.75 24.016 19.75 21.68 32.23 29.917 18.30 19.49 21.23 36.958 18.18 25.37 26.19 36.3014 26.76 27.04 26.09 25.8515 17.61 20.13 23.49 19.5016 27.21 24.21 27.63 47.1017 20.09 25.43 24.51 24.6318 21.01 28.22 25.68 24.9178C) CDC)00400Q(JiI’3 DUit’J4UiC-—D\DL’J‘3CLc)(I’)c0Pt\)C0Ui00C00‘Jk)LUiCDC00IC-cDcDLIIjCD—4\OCUi(.J004-UiCC-- OOUi0000000C00Ui00Uit’JCUi‘J00•1J4—i-C-Ui—1‘JC--40044-Q000UiUiUi.04DCCC00J00O00CC’t’JUi(3Ui00UiCUi‘J000Ui4CCCCUiC00004C-‘-.I-000Ui-00-O’Ui——————————z-tZc‘JIN)Ui-1Ui-‘JUi00C‘JDUi-40000Uii-UiL4Ui44LJ4004C00CQ0C-(0Ui4UiJkUiD—--© —-JD(Ui—t’JJUiUi4I‘.J44 ‘JT00oo—- 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