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Constraint-led changes in internal variability in running.

Introduction

Variability in human (motor) behavior and performance is still a two-sided affair. Although the advantage of variability has already been mentioned as early as in the 1960's by Russian pioneer Nikolai Bernstein Nikolai Aleksandrovich Bernstein (1896 - 1966) was a Russian neurophysiologist. Life
Bernstein was largely self-taught, though he was respected for his work by his colleagues.
 (1967), variability in the domain of sports (especially during movement production) has only recently been considered as an essential requirement. Traditional approaches saw variability or inconsistency in movement as noise or a problem to be reduced with training and practice (Bartlett et al., 2007; Davids et al., 2003). That neglected, however, its important functional role in motor behavior (i.e. variability that is beneficial to outcome performance) (Hamill et al., 1999; Hatze, 1986).

The development and successful integration of different perspectives (e.g. synergetics or dynamic system approaches, stochastic resonance Stochastic resonance (also known as SR) occurs when the signal-to-noise ratio of a nonlinear device is maximized for a moderate value of noise intensity. It often occurs in bistable and excitable systems with subthreshold inputs. , as well as natural and artificial neural networks) eventually contributed to a reconsideration of variability. The transfer of knowledge of, for example, information gained from networks within biological and computational science | Computational science (or scientific computing) is the field of study concerned with constructing mathematical models and numerical solution techniques and using computers to analyze and solve scientific, social scientific and engineering problems.  to sports highlights the importance of different experiential contents ensuring adaptation and flexibility (i.e. generalization) in task execution (Riley and Turvey, 2002; Schollhorn et al., 2009). Research in other domains such as disease or aging (e.g. loss of variability in gait due to aging and Huntington's disease (Van Emmerik and Van Wegen, 2002)) further supports the positive characteristics of movement-production variability as being not only non-interfering, but rather fundamental to achieving a consistent movement outcome (Heiderscheit et al., 2002; Schollhorn et al., 2009).

Given that most sport skills involve a large number of muscles and joints (i.e. many degrees of freedom), variability became an indicator reflecting readiness of these degrees of freedom to covary to achieve a required higher order macroscopic macroscopic /mac·ro·scop·ic/ (mak?ro-skop´ik) gross (2).

mac·ro·scop·ic or mac·ro·scop·i·cal
adj.
1. Large enough to be perceived or examined by the unaided eye.

2.
 movement outcome (Handford et al., 1997). This dynamic variability is the consequence of variations from the underlying nonlinearities in the system and emerges due to shape new emergence of coordination and control (Davids et al., 2005; Hatze, 1986; Van Emmerik and Van Wegen, 2002). Due to this indeterminacy in·de·ter·mi·na·cy  
n.
The state or quality of being indeterminate.

Noun 1. indeterminacy - the quality of being vague and poorly defined
indefiniteness, indefinity, indeterminateness, indetermination
 within sublevels in repetitive movements, the movement outcome as the result of a complex interplay of forces acting on the body (non-muscular forces or according to according to
prep.
1. As stated or indicated by; on the authority of: according to historians.

2. In keeping with: according to instructions.

3.
 Bernstein (1967) reactive phenomena) and those forces actively produced by the person itself (i.e. internal or produced muscle forces) will always inhere in·here  
intr.v. in·hered, in·her·ing, in·heres
To be inherent or innate.



[Latin inhaer
 a certain level of variability (Hatze, 1986).

In sports, analyses of even closed movements such as a free throw in basketball or treadmill running (e.g. Button et al., 2003; Verkerke et al., 1998; Wheat et al., 2005) illustrate that top athletes do, in fact, show variability in their execution levels (e.g. release angle in basketball (Button et al., 2003)). Despite this fact, they are still able to achieve the same movement outcome. This indicates that variability may be essential for producing skilled behavior (Bartlett et al., 2007; Wilson et al., 2007) and functionality for highly skilled athletes (pointing to an ability to co-vary) (Handford et al., 1997).

For training and development, this would further implement that adding variability by engaging athletes in complex and time-varying situations and settings may be adaptively advantageous in situations of environmental unpredictability (Fontanini and Katz, 2008). Several studies in various sports (e.g. volleyball (Spratte et al., 2007), soccer (Trockel and Schollhorn, 2003), speed skating speed skating

Sport of racing on ice skates. The blade of the speed skate is longer and thinner than that of the hockey or figure skate. Two types of track are used in international competition.
 (Savelsbergh et al., 2010), indoor hockey (Beckmann et al., 2008; Birklbauer et al., 2006) or athletics (Schollhorn et al., 2010)) support the positive effect of adding variability.

However, if, on the one hand, variability, induced by the set constraints or different executions or tasks, is too broad, the exercises may no longer be supportive for the actual task (i.e. no transfer of the exercises to the actual movement is possible); on the other hand, if there is no variability, the athlete is tightly constrained and it may be difficult to find the individual optimum (Schollhorn et al., 2009). Hence, the magnitude of variability must be attuned to remain within a functional bandwidth of variability (Birklbauer et al., 2006; Handford et al., 1997). Combining the aforementioned variability aspects, it is assumed that the induced variability (at least at a certain skill level) should remain within the movement skill or at least within its immediate vicinity, to maintain the basic structure of the movement pattern.

Against this background, the application of elastic tubes to provide resistance to the lower extremities was assumed to meet the requirements to create variability within an optimal boundary. As the given elastic constraint (due to its property) may influence loading of lower extremities and the resulting alteration in the moment of inertia of the leg contribute to the movement outcome (Martin, 1985), the upcoming question is how elastic tubes influence a well-established behavior as for instance running.

It is obvious that running with different tube positions increases variability during movement production and perhaps results in a variable movement outcome. That is because the permanently changing environmental constraints may require adaptation in muscle synergies to achieve the desired movement outcome and perform the requested movement pattern; however, it would be of interest to identify whether one single application can increase variability and if so, the extent to which it is increased and how performance can be adjusted to changes through this constraint.

Up to now, the field of application of elastic tubes was resistance and conditioning training (e.g. athletics) (Corn and Knudson, 2003) but not as a technique (and coordination) training device as is the case in this study. The tubes (attached between the hip and heel) influence the freedom of movement of the lower extremities and alter the forces and, thus, lead to variation within the reactive phenomena. The imposed perturbation perturbation (pŭr'tərbā`shən), in astronomy and physics, small force or other influence that modifies the otherwise simple motion of some object. The term is also used for the effect produced by the perturbation, e.g.  would result in supportive and counterproductive forces and for that reason muscle activations change accordingly with respect to an optimal pattern of coordination and performance. Consequently, it has been assumed that such a "variability" constraint increases variability on a muscular level. Running was chosen to be the investigated task because it is a routine and one of the most common types of locomotion locomotion

Any of various animal movements that result in progression from one place to another. Locomotion is classified as either appendicular (accomplished by special appendages) or axial (achieved by changing the body shape).
 (Abe et al., 2007).

Therefore, the aim of this study was to assess the acute effect of a one-time application of elastic constraints on variability of muscle activation variability during running. We wanted to determine whether it is possible to influence variability within the muscular components when running with elastic tubes. Specifically, it may be expected that there is a change and adaptation in variability over time during running with elastic tubes. Since participants were novices with the tubes, we expected to see an increase in muscle variability through the application of elastic tubes (and their sensitivity of initial condition) and also an after-effect, when switching back to running without tubes.

Methods

Participants

Eleven endurance-trained, male sports students (O 24.6 yrs; who were not endurance athletes, but could run the requested intervals without total exertion) were recruited for this study. The original sample size was 13, but due to missing data only eleven participants were included in data analyses. All participants had previous experience in running on a treadmill. Nonetheless, all participants were novices with the training device (Tendybelt, Salzburg, Austria). All participants were provided with a running shoe (Adidas Supernova supernova, a massive star in the latter stages of stellar evolution that suddenly contracts and then explodes, increasing its energy output as much as a billionfold. ) to wear during testing. Participants were informed of the aims of this study, procedures, anonymity of all data, and their right to withdraw at any time. They had to give their written informed consent prior to testing. The approval from the local review broad was received prior to participant recruitment.

Design

Prior to the study, participants were stratified stratified /strat·i·fied/ (strat´i-fid) formed or arranged in layers.

strat·i·fied
adj.
Arranged in the form of layers or strata.
 into two groups in respect to the initial condition as the order of presentation was counterbalanced across participants. Two intervals of 35 min each (including a standardized warm up of 5 min) had to be completed on a motorized mo·tor·ize  
tr.v. mo·tor·ized, mo·tor·iz·ing, mo·tor·iz·es
1. To equip with a motor.

2. To supply with motor-driven vehicles.

3. To provide with automobiles.
 treadmill (Marathon 2.0 V1, Sportgerate GmbH, Austria) set with no incline (Figure 1). A 30-min rest period in-between trials provided a recovery period so as to minimize the effects of fatigue. Each 35-min interval consisted of three blocks and a standardized warm-up, all at 2.9 m x [s.sup.-1]. The three main blocks represented 5 min running, 20 min running with (intervention condition) or without (control condition) tubes and another 5-min block of running.

[FIGURE 1 OMITTED]

Training device

The tubes were attached by plastic karabiners on a specially designed chest belt made of nylon with a certain number of loops on which to fix the tubes (Figure 2). Tied around the waist was a belt with loops at the front and back and a hook-and-loop fastener at the front to fix the belt properly. The shoulder straps were heavily padded for better comfort. The tubes used in this study were black thera tubes (Thera-Band [R] GmbH, Dornburg-Frickhofen, Germany), which were attached at the ilio-sacral joint and at the heel tab of the running shoes. Tube length was standardized at 40% of the individual leg length producing ~ 48 N at 100 % leg length.

[FIGURE 2 OMITTED]

Data collection

Following a 5 min warm-up, there was a direct transition to the 30-min interval and actual data collection (Figure 1). Data were captured in 2-min intervals (for data reduction) interspersed by 1-min breaks without measuring throughout the duration of the blocks, but starting at each transition from one block to another (i.e. data acquisition blocks started at min 0, 3, 5, 8, 11, 14, 17, 20, 23, 25 and 28). To attach and take off the elastic tubes, the treadmill was slowed down to 2.1 m x [s.sup.-1] to make it easier for participants to step aside from and back on the treadmill belt once the tubes were attached and removed, respectively. After slowing down to 2.1 m x [s.sup.-1], attaching or removing the tubes and speeding up to 2.9 m x [s.sup.-1] again, data collection continued as soon as 2.9 m x [s.sup.-1] were reached again to exclude possible influences of slower speed and restart of running on the calculated parameters, respectively.

To investigate motor behavioral variability muscle activity (EMG EMG
abbr.
electromyogram


Electromyography (EMG)
A diagnostic test that records the electrical activity of muscles.
) was measured. EMG recordings were obtained from surface electrodes (Skintact, Leonhard Lang GmbH, Innsbruck, Austria). Skin preparation and electrode placement was performed as internationally recommended (Hermens et al., 1999; Merletti and Parker, 2004). EMG recordings were taken from the right leg and were obtained from three muscles that play a role in running (Novacheck, 1998) and were assumed to be primarily influenced by the tubes: tibialis tibialis /tib·i·a·lis/ (tib?e-a´lis) [L.] tibial.

tibialis

[L.] tibial.
 anterior (TA), the lateral gastrocnemius gastrocnemius /gas·troc·ne·mi·us/ (gas?tro-ne´me-?s) (gas?trok-ne´me-us) see under muscle.

gas·troc·ne·mi·us
n. pl.
 (LG) and the rectus femoris rectus femoris
n.
A muscle with origin from the ilium and the acetabulum, with insertion into a tendon of the quadriceps muscle of the thigh.
 (RF). A reference electrode Reference electrode is an electrode which has a stable and well-known electrode potential. The high stability of the electrode potential is usually reached by employing a redox system with constant (buffered or saturated) concentrations of each participants of the redox reaction.  was placed on the tibia tibia: see leg. . A biaxial biaxial /bi·ax·i·al/ (-ak´se-al) having, pertaining to, or occurring in two axes.  accelerometer accelerometer

Instrument that measures acceleration. Because it is difficult to measure acceleration directly, the device measures the force exerted by restraints placed on a reference mass to hold its position fixed in an accelerating body.
 (Biovision, Werheim, Germany; bandwidth: DC-500 Hz) was mounted on the right heel to trigger heel strikes to calculate stride data of the recorded EMG data. Additionally, heart rate was recorded using a Polar S810 monitor (Polar Electro Polar Electro Oy is pioneered and leading manufacturer of personal Heart rate monitor registering and evaluation equipment. The company is based in Kempele, Finland. Founded in 1977 by University of Oulu professor Seppo Säynäjäkangas, who remains CEO today, Polar introduced the  Oy, Kempele, Finland) to estimate the actual exertion.

Data processing data processing or information processing, operations (e.g., handling, merging, sorting, and computing) performed upon data in accordance with strictly defined procedures, such as recording and summarizing the financial transactions of a  

EMG records were sampled with 2000 Hz. The raw signal was converted from analogue to digital (DAQ See data acquisition.  6024 A/D card, National Instruments, Austin, Texas, U.S.) and stored on a computer. The signal was preamplified at source (bandwith 10-500 Hz, 3dB) with a single differential amplifier Differential amplifier

An electronic circuit that is designed to amplify the difference between two voltages measured with respect to a common reference, usually designated as ground.
 (Biovision, Werheim, Germany; individually adjusted for best resolution after a short test run). Data were bandpass-filtered (10-300 Hz; Butterworth second order) and full-wave rectified to calculate iEMG of each muscle.

IEMG was calculated over each stride. To elicit variability in muscle activation, between stride standard deviation and the coefficient of variation Coefficient of Variation

A measure of investment risk that defines risk as the standard deviation per unit of expected return.
 (CV) of stride iEMG data were calculated. To normalize normalize

to convert a set of data by, for example, converting them to logarithms or reciprocals so that their previous non-normal distribution is converted to a normal one.
 the iEMG values, each participant's individual average iEMG value during running without tubes was calculated using all strides during the 30-min running without tubes and the first 5-min running block in the tube running interval (Figure 1). This average value was taken as iEMG reference value (i.e. 100 %-baseline iEMG). The calculated, normalized data (expressed as a percentage of this baseline value) were then used for statistical analyses. For graphical representation of mean stride muscle activation patterns, linear envelopes were created (10 Hz low-pass filter using a fourth order zero-lag digital Butterworth filter The Butterworth filter is one type of electronic filter design. It is designed to have a frequency response which is as flat as mathematically possible in the passband. Another name for them is 'maximally flat magnitude' filters. ).

Statistical analysis

Differences between the measurements collected during the intervals were performed using the statistical software package SPSS A statistical package from SPSS, Inc., Chicago (www.spss.com) that runs on PCs, most mainframes and minis and is used extensively in marketing research. It provides over 50 statistical processes, including regression analysis, correlation and analysis of variance.  16.1 (SPSS Inc., Chicago, IL., USA) and Office Excel 2007 (Microsoft Corporation (company) Microsoft Corporation - The biggest supplier of operating systems and other software for IBM PC compatibles. Software products include MS-DOS, Microsoft Windows, Windows NT, Microsoft Access, LAN Manager, MS Client, SQL Server, Open Data Base Connectivity (ODBC), MS Mail, , U.S.). Origin-Pro 8.0 (OriginLab Corporation, Northampton, USA) was used to graph the data. All significant differences reported are at p < 0.05. All data were checked for normality (Kolmogorov-Smirnov test In statistics, the Kolmogorov–Smirnov test (often called the K-S test) is used to determine whether two underlying one-dimensional probability distributions differ, or whether an underlying probability distribution differs from a hypothesized distribution, in either ) and sphericity, and means and standard deviations were calculated with conventional procedures. The differences in the two 20-min running blocks were compared using three 2 (situation) X 7 (data acquisition block) ANOVAs. Using 1 x 7 Repeated Measures ANOVAs, data were separately analyzed, particularly with respect to the change in variability within the 20 min of tube running and the change within the 20 min of running without tubes. To compare the last 2 min of tube running and the first 2 min of the final 5 min of normal running (i.e. de-adaptation), a 2 (situation) X 2 (data acquisition block) ANOVA anova

see analysis of variance.

ANOVA Analysis of variance, see there
 was applied. Additionally, effect size partial eta squared ([[eta].sub.p.sup.2]).

Results

With respect to the variability data, standard deviation represents the main parameter. A summary of the standard deviation and CV data is presented in Table 1. Overall comparisons: When comparing the two 20-min blocks, the 2 X 7 ANOVA for the stride-to-stride variability data showed significant effects for running condition as standard deviation was increased during tube running in all three measured muscles (Figure 3). The variability was significantly greater for RF with a 72.6 % -increase (p < 0.01; [[eta].sub.p.sup.2] = 0.75) and LG with a 36.5%-increase (p < 0.01; [[eta].sub.p.sup.2] = 0.72) through the tube application. Differences in variability for TA were not significant. Interactions to estimate the change in variability during running revealed significant results for all three muscles (p < 0.05; [[eta].sub.p.sup.2] > 0.20) as running without tubes maintained the same level but tube running variability decreased (p < 0.01; [[eta].sub.p.sup.2] > 0.30). Representative mean linear envelopes of each muscle are shown for one participant in Figure 4.

The increases in muscle activity ranged from 25 to 44 % for all three muscles for tube running. Mean iEMG was different in the two running conditions with statistically significant results for RF (p < 0.01; [[eta].sub.p.sup.2] = 0.70) and LG (p < 0.01; [[eta].sub.p.sup.2] = 0.66). Interactions for muscle activity revealed significant results only for RF (p < 0.05; [[eta].sub.p.sup.2] = 0.27). Separate post hoc post hoc  
adv. & adj.
In or of the form of an argument in which one event is asserted to be the cause of a later event simply by virtue of having happened earlier:
 analyses within the main 20-min blocks unveiled significant decreases for RF (p < 0.05; [[eta].sub.p.sup.2] = 0.24) and LG (p < 0.05; [[eta].sub.p.sup.2] = 0.30) in the tube running condition.

De-adaptation: With respect to variability, during de-adaptation (i.e. from tube running to running without tubes), a statistically relevant interaction was found only for RF (p < .01; [[eta].sub.p.sup.2] =.80; Figure 3). Significant decreases ranging from -10.9 % to -24.1 % were found for iEMG (p < .05; [[eta].sub.p.sup.2] [greater than or equal to] .30).

[FIGURE 3 OMITTED]

Comparing the five minutes following return to running without elastic tubes to the five minutes before the tube running interval, no significant differences were found for both variability and iEMG; however, iEMG variability after a 20-min tube interval increased with effects ranging from [[eta].sub.p.sup.2] = 0.11 to [[eta].sub.p.sup.2] = 0.22 compared to running without tubes before tube running.

[FIGURE 4 OMITTED]

Heart rate data used to measure exertion demonstrated significant differences neither between the two situations nor for de-adaptation (p > 0.20; [[eta].sub.p.sup.2] < 0.21).

Discussion

The purpose of the study was to examine the variability of muscle activity during treadmill running with and without elastic tubes. Up to now, elastic tube systems generally have been used in the training process with respect to strength and conditioning only, their primary field of use being the application as a resistance constraint (e.g. athletics) (Corn and Knudson, 2003). In this instance, the tube system was not applied as a conditioning training tool but as a technique (and coordination) training device that could be implemented in various sports and directly within their skills and movements. It was hypothesized that running with tubes may lead to an increase in EMG variability by the altering reactive phenomena. By creating enhanced variability within the movement skill, experiences may be conceived of as emergent and self-organizing higher-order patterns due to interaction of neuro-musculoskeletal subcomponents (Davids, et al., 2005). This should permit more flexibility to achieve a desired movement outcome and therefore enable a stable movement outcome regardless of the given perturbations (Handford, et al., 1997).

Our analyses showed a significant increase in iEMG variability in all three measured lower extremity lower extremity
n.
The hip, thigh, leg, ankle, or foot. Also called inferior limb, pelvic limb.
 muscles during the elastic constraint application. Analyses also revealed increases for the average iEMG values during tube running compared to running without tubes; however, the changes in variability were higher (also evident in an increased coefficient of variation; see Table 1).

The measured changes in EMG variability (i.e. comparing EMG variability when running without tubes to tube running) may on the one hand seem surprising given that treadmill running is known to constrain the behavior and is less variable than overground O´ver`ground´

a. 1. Situated over or above ground; as, the overground portion of a plant s>.
 running (Dingwell et al., 2001; Jordan et al., 2007). And the increase in EMG variability may be even more surprising as stride duration was similar in both situations (running without tubes: 0.75 sec [+ or -] 0.03; running with tubes: 0.76 sec [+ or -] 0.03).

Then again, such EMG variability increase in the tube running condition was expected since a rescaling of the parameters to adjust to this new unfamiliar constraint was required (Sanders et al., 2009).

During tube running, the system's variability (i.e. variability in muscle activity) increased as a consequence of permanent adaptation (Bernstein, 1967). Since movements such as running are affected not only by the input of the nervous system but also by the segments' biomechanical properties (the loading and inertial characteristics) all these factors also contribute to the movement outcome (Martin, 1985). And as the tubes' influences alternately increase or decrease due to the changing initial conditions, the alteration in the variability of reactive phenomena results in an increased variability of muscle activity (i.e. due to enhanced co-variation). So, the increased variability signifies the ability of a compensatory mechanism between active muscle forces and reactive phenomena (Bernstein, 1967). This provides a dynamic system that properly adapts to changing environmental and behavioral constraints. The decrease in muscle activity variability indicates adaptation to the tubes (Wilson et al., 2008).

On the contrary, the increased variability may also be due to the participants' inability to use the occurring and impinging reactive phenomena. The constraints (i.e. the combination of induced and natural interacting constraints) then resulted in an intervention-induced variability that was no longer supportive, but rather asked too much of the runners. Because of the tubes' properties and their application, participants were not able to co-vary by assembling other and more functional coordination patterns and, thus, control the altered forces (Muller and Sternad, 2009). The set boundaries in the form of the tubes overstrained the runners being not able to either handle or use the tubes (and their forces) complicated by their sensitivity. The prevailing variability in muscle activity may, therefore, be more unstructured and random.

However, different forms and especially the structuredness of the assessed variability should be considered (Riley and Turvey, 2002). Our analyses did not allow an estimation of this issue, which is the aim of subsequent evaluations.

Analyzing the change in variability within the unfamiliar tube running situation, we found a decrease of variability over the 30 min. According to Bertenthal (1999) and his learning-related U-shaped variability function (i.e. variability is high, decreases and increases again to a high(er) level), this suggests that runners were at least at the beginning not able to cope with the situation since they were novices with the tubes. At that time, runners explored different running patterns to find possibilities to deal with this unfamiliar constraint. The decrease in variability indicates that appropriate rescaling of the parameters of the muscle synergies during practice occurred. We postulate postulate: see axiom.  that an increase in functional variability (Bertenthal, 1999; Wilson et al., 2008) may occur at a later stage of tube running, for which the 30 min of running were not sufficient. Stable and flexible patterns of coordination due to the advanced ability to co-vary can then emerge (e.g. Bernstein, 1967) so as to fine tune the performance. In addition, the decrease in variability and therefore adaptation would be in line with the runners' feedback and hints towards a "normalization In relational database management, a process that breaks down data into record groups for efficient processing. There are six stages. By the third stage (third normal form), data are identified only by the key field in their record. " of the tube running situation.

Nevertheless, it is rather surprising that despite muscle activity and its variability were influenced by the tubes, stride duration remained almost unaltered. It could be assumed that the runners tried to maintain a preferred running pattern (Nigg, 2001). The increase in variability on muscular level is therefore the consequence of restructuring the sublevels to maintain this pattern. This indicates that the unfamiliar constraint did not demand a complete change in the fundamental movement pattern but rather requires some practice to appropriately rescale Verb 1. rescale - establish on a new scale
resize - change the size of; make the size more appropriate

scale down - reduce proportionally; "The model is scaled down"

scale up - increase proportionally; "scale up the model"
 the parameters of the muscle synergies (Sanders et al., 2009).

Due to the fact that we analyzed the stride, no conclusions can be made on the within-stride behavior and variability. Consequently, within-stride parameters may demonstrate a different behavior than the superior stride level (e.g. a shift in stance-swing time (Martin, 1985) or increased / decreased muscle activity during stance or swing).

Another observation in our analyses was a small shift in switch on and switch off times of muscle activity on a descriptive level. All runners appeared to activate all three measured muscles slightly earlier in the tube running condition (Figure 4). This shift was also obtained in other studies where participants initiated their movements earlier when they knew that their movements were perturbed per·turb  
tr.v. per·turbed, per·turb·ing, per·turbs
1. To disturb greatly; make uneasy or anxious.

2. To throw into great confusion.

3.
 (Button et al., 2002). Since our runners could not anticipate the tubes' influence due to their properties, they could not anticipate the kind of perturbation applied to their behavior (i.e. supportive or restrictive).

Participants probably initiated their movements earlier to react to possible perturbations and coordinate their behavior under the given constraints within their neurobiological neu·ro·bi·ol·o·gy  
n.
The biological study of the nervous system or any part of it.



neuro·bi
 systems (Glazier and Davids, 2009). Especially for TA and LG an increase in activity was measured. This antagonistic co-contraction may be a strategy for the runners to make the motor system more controllable (Hossner and Ehrlenspiel, 2010) and to pre-stabilize prior to heel strike heel strike Heel contact The beginning of stance phase, at the point of heel strike there is zero reaction. Immediately after contact there is an ↑ in ground reaction, known as heel strike transient, which pre-empts the major ↑ in ground reaction  because of possible tube perturbations (Novacheck, 1998).

Another explanation for the increased muscle activity may be a shift of attention to an internal focus. Since running was no more the routine behavior, an attention shift to the lower extremities could also be the reason for the increased EMG activity. Several studies reported an increase in EMG activity under internal focus conditions when performing biceps curls (Zachry et al., 2005), during basketball shooting (Hossner and Ehrlenspiel, 2010; Vance et al., 2004) or dart throwing (Lohse et al., 2010). More so, the applied constraint disrupted the acquired automaticity of implicit motor control during running and resulted in the observed increased muscle activity and variability to regain the impaired motor control (Hossner and Ehrlenspiel, 2010).

As our study only investigated performance, no conclusions for learning

can be made. However, that elastic tubes do provide a stimulus for learning and motor behavior was shown in a study on the volleyball smash, wherein variable practice with elastic tubes according to the differential learning approach was superior in variable, competition-like situations compared to traditional learning (Haudum et al., 2011). Another study was done in the ski touring ski touring
n.
Cross-country skiing for pleasure rather than competition.



ski tourer n.
 (Haudum et al., 2012). Here the application of tubes and the increase of variability were found to have positive effects on performance as less energy was required during walking with tubes compared to walking without tubes.

Nevertheless, limitations of the current study were that only muscle activity was measured and no additional information (e.g. lower extremity kinematics, kinetics or physiological parameters) is available. The combination of those would allow better demonstration of the actual influences. With respect to the type I error, there is also a small chance of its inflation beyond the p < 0.05 standard given that five statistical tests were performed on the same EMG data.

Conclusion

Concerning the current study, a one-time application of this constraint was analyzed and longitudinal studies should be conducted to confirm these results and to unveil changes in coordination variability after familiarization fa·mil·iar·ize  
tr.v. fa·mil·iar·ized, fa·mil·iar·iz·ing, fa·mil·iar·iz·es
1. To make known, recognized, or familiar.

2. To make acquainted with.
 with the tubes. Although further work is required, it can be concluded that the tubes provide an increase in variability that may induce some kind of exploratory behavior for the athlete (Newell and Ranganathan, 2009; Schollhorn et al., 2009). However, an intervention like this could certainly enable a search process in areas outside the natural variability that would otherwise not have occurred (Corbetta and Vereijken, 1999). Therefore, an intervention study to analyze the process of adaptation or differences after having adjusted to tube running may be of interest.

Key points

* The elastic constraints led to an increase in iEMG variability but left stride duration variability unaltered.

* Runners adapted to the elastic cords, evident in an iEMG variability decrease over time towards normal running.

* Hardly any aftermaths were observed in the iEMG analyses when comparing normal running after the constrained running block to normal running.

Received: 04 August 2011 / Accepted: 02 November 2011 / Published (online): 01 March 2012

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In the investing world, a totally new way of doing things that has a huge effect on business.

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AUTHORS BIOGRAPHY

Anita HAUDUM

Employment

PhD candidate at the Department of Sport

Science and Kinesiology kinesiology

Study of the mechanics and anatomy of human movement and their roles in promoting health and reducing disease. Kinesiology has direct applications to fitness and health, including developing exercise programs for people with and without disabilities, preserving
, and CD-Laboratory

"Biomechanics in Skiing", University of Salzburg The University of Salzburg, or Paris Lodron University (German Universität Salzburg) after its founder, the Prince Archbishop Paris Lodron, is located in the Austrian city of Salzburg, home of Mozart.

Founded in 1622, it today has c. 11,000 students and c.
, Austria.

Degrees

MSc

Research interests

Motor control and motor learning, movement variability, motor and performan ce development.

E-mail: anita.haudum@sbg.ac.at

Jurgen BIRKLBAUER

Employment

Assistant and PhD candidate at the Department of Sport Science and Kinesiology, and CD-Laboratory "Biomechanics in Skiing", University of Salzburg, Austria.

Degrees

MSc

Research interests

Motor control and motor learning, movement

variability, performance development, neuroscience.

E-mail: juergen.birbklbauer@sbg.ac.at

Josef KROLL

Employment

PostDoc at the Department of Sport Science and Kinesiology, and CD-Laboratory "Biomechanics in Skiing", University of Salzburg, Austria.

Degree

PhD

Research interests

Applied biomechanics, muscle function and motor learning.

E-mail: josef.kroell@sbg.ac.at

Erich MULLER

Employment

Head of the Department of Sport Science and Kinesiology, and the CD-Laboratory "Biomechanics in Skiing", University of Salzburg, Austria.

Degrees

PhD, Prof

Research interests

Biomechanics, training and coaching and motor learning.

E-mail: erich.mueller@sbg.ac.at

Anita Haudum (1,2) [mail] Jurgen Birklbauer (1,2), Josef Kroll (1,2) and Erich Muller (1,2)

(1) Department of Sport Science and Kinesiology, University of Salzburg, (2) Christian Doppler Johann Christian Andreas Doppler (November 29, 1803 – March 17, 1853) was an Austrian mathematician and physicist, most famous for the hypothesis of what is now known as the Doppler effect which is the apparent change in frequency and wavelength of a wave that is perceived  Laboratory 'Biomechanics in Skiing', Salzburg, Austria

[mail] Anita Haudum

Christian Doppler Laboratory "Biomechanics in Skiing", Department of Sport Science and Kinesiology, University of Salzburg, Rifer Schlossallee 49, A-5400 Hallein, Austria
Table 1. Summary of the mean variability changes [%] with respect
to the standard deviation (left) and coefficient of variation
(right). RF stands for rectus femoris, TA for tibialis anterior
and LG for lateral gastrocnemius.

                                         Standard deviation

Comparison              Muscle   Changes in
                                 variability
                                     [%]

Difference running        RF        72.6       [F.sub.1,8] = 23.8
  without tubes vs.       TA        74.2        [F.sub.1,8] = 2.5
  tube running            LG        36.5       [F.sub.1.9] = 22.7
Running without           RF                   [F.sub.6,48] = 2.3
  tubes by tube           TA                   [F.sub.6,48] = 4.0
  running interaction     LG                   [F.sub.6,54] = 6.1
Separate analyses         RF                   [F.sub.6,48] = 3.6
  for tube running        TA                   [F.sub.6,48] = 4.4
                          LG                   [F.sub.6,54] = 11.3
De-adaptation             RF        -31.9      [F.sub.6,48] = 11.7
                          TA        -2.9       [F.sub.6,48] =  0.9
                          LG        -12.3      [F.sub.6,54] =  0.4

                                  Standard deviation    Coefficient
                                                        of variation

Comparison              Muscle     p      [[eta].sub.    Changes in
                                           p.sup.2]     variability
                                                            [%]

Difference running        RF     < .01        .75           18.4
  without tubes vs.       TA     = .06        .24           15.2
  tube running            LG     < .01        .72           8.1
Running without           RF     < .05        .22
  tubes by tube           TA     < .01        .33
  running interaction     LG     < .001       .40
Separate analyses         RF     < .01        .31
  for tube running        TA     < .01        .36
                          LG     < .001       .56
De-adaptation             RF     < .01        .60           -8.8
                          TA     < .30        .10           15.3
                          LG     < .40        .08           0.2

                                 Coefficient of variation

Comparison              Muscle                          p

Difference running        RF     [F.sub.1,8] = 16.1   < .01
  without tubes vs.       TA     [F.sub.L8] = 11.5    < .01
  tube running            LG     [F.sub.1,9] = 3.1    = .10
Running without           RF     [F.sub.6,54] = 0.9   > .50
  tubes by tube           TA     [F.sub.6,48] = 2.8   < .05
  running interaction     LG     [F.sub.6,54] = 5.7   < .001
Separate analyses         RF     [F.sub.1,8] = 1.7    > .10
  for tube running        TA     [F.sub.1,8] = 4.4    < .01
                          LG     [F.sub.19] = 10.6    < .001
De-adaptation             RF     [F.sub.1,8] = 1.0    > .30
                          TA     [F.sub.1,8] =  1.0   > .30
                          LG     [F.sub.1,9] =  1.0   > .30

                                 Coefficient
                                of variation

Comparison              Muscle   [[eta].sub.
                                  p.sup.2]

Difference running        RF         .67
  without tubes vs.       TA         .59
  tube running            LG         .26
Running without           RF         .09
  tubes by tube           TA         .26
  running interaction     LG         .37
Separate analyses         RF         .20
  for tube running        TA         .35
                          LG         .54
De-adaptation             RF         .11
                          TA         .11
                          LG         .10
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Title Annotation:Research article
Author:Haudum, Anita; Birklbauer, Jurgen; Kroll, Josef; Muller, Erich
Publication:Journal of Sports Science and Medicine
Article Type:Report
Date:Mar 1, 2012
Words:6187
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