European Journal of Applied Physiology

, Volume 112, Issue 10, pp 3495–3502

The interaction between the location of lower extremity muscle fatigue and visual condition on unipedal postural stability

  • Manijeh Soleimanifar
  • Mahyar Salavati
  • Behnam Akhbari
  • Mojgan Moghadam
Original Article

DOI: 10.1007/s00421-012-2330-z

Cite this article as:
Soleimanifar, M., Salavati, M., Akhbari, B. et al. Eur J Appl Physiol (2012) 112: 3495. doi:10.1007/s00421-012-2330-z

Abstract

The purpose of this study was to investigate the effects of unilateral muscle fatigue induced on the hip flexors/extensors or the ankle plantar/dorsiflexors on unipedal postural stability under different visual conditions. Twenty-four healthy young women completed 2 testing sessions 1 week apart with a randomized order assigned according to the muscles tested. During each session, one set of muscle groups was fatigued using isokinetic contractions: ankle plantar/dorsi flexors or hip flexor/extensors. Postural stability was assessed during trials of unilateral stance on a force plate before and after the fatigue protocol. 10 s into the trial, subjects were asked to close their eyes. Mean velocity, the area of the 95% confidence ellipse, and standard deviation of velocity in anteroposterior and mediolateral directions of center of pressure displacements were calculated for two periods of 5 s, immediately before and 1 s after the eyes closure. The results of the repeated measures ANOVAs showed a significant fatigue-by-fatigue segment by visual condition interaction for the CoP parameters. When the vision was removed, the interaction between fatigue and fatigue segment was significant for the CoP parameters. In conclusion, fatigue in both proximal and distal musculature of the lower extremity yielded decreased postural stability during unipedal quiet standing in healthy young women. This effect was more accentuated when visual information was eliminated. Withdrawing vision following fatigue to the proximal musculature, led to a significantly greater impairment of postural stability compared to the fatigue of more distal muscles.

Keywords

Postural stability Local muscle fatigue Vision Lower extremity 

Introduction

Postural control is an essential requirement for successful performance of both activities of daily living and sporting activities (Murphy et al. 2003). Postural control is partly dependent on afferent inputs from the visual, vestibular, and proprioceptive systems, which the central nervous system (CNS) must weight relative to one another according to the varying task demands. During quiet standing, postural control system has to compensate any change in the reliability and/or availability of the sensory inputs, by a functional reweighting of the different sources of sensory information, to ensure appropriate motor commands (Massion 1994). It is well documented that postural stability generally decreases in the absence of visual input (Collins and De Luca 1995; Oie et al. 2002; Speers et al. 2002). Proprioceptive input from the muscles of the legs and trunk plays an important role in maintaining postural stability, as well (Allum et al. 1998).

Muscle fatigue is a common phenomenon of physical and daily activities known to have negative effects on proprioception (Forestier et al. 2002; Hiemstra et al. 2001; Taylor et al. 2000). Local fatigue of the lower limbs has been reported to decrease postural stability as well (Gribble and Hertel 2004a, b; Johnston et al. 1998; Joyce et al. 2001; Ochsendorf et al. 2000; Ramsdell et al. 2001; Rozzie and Lephart 1999; Salavati et al. 2007; Vuillerme et al. 2001, 2002; Yaggie and McGregor 2002). Gribble and Hertel (2004a, b) and Salavati et al. (2007) have found that fatigue of proximal musculature of the lower extremity has greater negative effects on postural stability than the more distal muscles. These findings imply that proximal musculature has a greater role in maintaining static posture compared to distal musculature. Furthermore, the role of proximal joint musculature increases with increasing levels of the task difficulty (Riemann et al. 2003). There is also some evidence that show proprioceptive inputs from the hip region are essential for postural control (Allum et al. 1998).

Increased contribution of vision to postural control following impairment of proprioceptive information due to local muscle fatigue has been documented in a few studies (Ledin et al. 2004; Vuillerme et al. 2001, 2006. Vuillerme et al. (2001) investigated the effect of vision on postural control following calf muscles fatigue. Before and after muscle fatigue, vision was suppressed or reinserted by eyes closure or opening during a static unipedal postural stability task. Results showed that the availability of visual information could compensate the destabilizing effects of muscular fatigue (2001). Ledin et al. (2004) reported that vision reduced postural sway following calf muscles fatigue with the most pronounced effect occurring in the sagittal plane during quiet standing.

In general, most published works have examined the effects of fatigue of the ankle muscles on postural control under different visual conditions, whereas the effects of fatigue induced at the more proximal muscles on the degree of visual dependency remained unclear. It can be speculated that different levels of inhibition of the afferent and efferent neuromuscular pathways consequent to fatigue of the hip and ankle musculature have different effects on the relative weighting of visual inputs for maintaining postural stability. The purpose of the present experiment was to investigate the combined effects of visual condition and local fatigue induced on the hip flexors/extensors or the ankle plantar/dorsiflexors on unipedal postural stability.

Methods

Subjects

Twenty-five healthy female college students (age: 22.5 ± 2.1 years, height: 159.8 ± 5.4 cm, weight: 63.3 ± 7.9 kg) volunteered to participate. None of the subjects presented any history of musculoskeletal problems, neurological disease, or vestibular impairment. All subjects also had normal or corrected-to-normal vision. Alcoholic beverages and sedative drugs were proscribed for 24 h preceding the testing (Johnston et al. 1998; Vuillerme et al. 2005). A convenient sample of eligible subjects gave their informed consent to participate in the experiment in accordance with the declaration of Helsinki.

The Institutional Review Board of “University of Social Welfare and Rehabilitation Sciences” approved the study.

Testing procedure

A repeated measures design was employed, in which participants completed single-leg static postural stability tests before and after fatiguing exercises involving either the ankle or hip muscles under different visual conditions. The factors were fatigue (pre-fatigue vs. post-fatigue), visual condition (Vision vs. No-Vision), and segment of muscle fatigue (distal vs. proximal).

There were two separate experimental sessions with a break period of at least 48 h between sessions to avoid residual effects of fatigue. The order of the testing sessions was randomized according to the segment of local muscle fatigue. Each session consisted of pre-fatigue and post-fatigue single-leg static postural stability measurements immediately before and after an isokinetic fatiguing task for the sagittal plane movers of the ankle or hip. All assessments and interventions were performed on the dominant leg.

Muscle fatigue

Peak torque measurements and fatigue protocols were performed using the Biodex System III isokinetic dynamometer (Biodex Inc., Shirley, NY, USA). In order to determine the initial peak torque (IPT) values, two sets of concentric/concentric ankle plantar/dorsiflexion movements were performed at 60 and 120°/s, respectively. The first set was a familiarization task and consisted of three submaximal and three maximal contractions. In the second set, three trials of maximal effort were performed with no rest. The highest peak torque of three repetitions was recorded as IPT. After a 2- to 3-min rest, the fatigue protocol was initiated, during which subjects performed continuous concentric/concentric plantar/dorsiflexion movements at 60 and 120°/s, respectively. Fatigue was judged to have occurred when the torque output in both directions dropped below 50% IPT for 3 consecutive movements. Flexors and extensors of the hip joint were fatigued by performing a similar fatigue protocol, at 60 and 90°/s (Gribble and Hertel 2004a, b; Joyce et al. 2001; Ochsendorf et al. 2000; Ramsdell et al. 2001; Salavati et al. 2007; Yaggie and McGregor 2002).

Verbal encouragement was given throughout all tests and fatigue protocols using standard procedures. After completing the fatigue protocols, subjects were removed from the dynamometer and tested for postural stability with a delay of no more than 30 s (Salavati et al. 2007; Schwendner et al. 1995).

Postural stability assessment

Subjects stood barefoot on their dominant leg with their both arms hanging relaxed at the sides and the other leg held in the position of hip neutral extension and 90° of knee flexion. The postural task consisted in maintaining a single stance on the test leg as immobile as possible for a total duration of 20 s. Subjects began the trial with their eyes open (Vision), while staring the intersection of a black cross (20 × 25 cm) placed onto the white wall 150 cm in front of them. 10 s into the trial, they were asked to close their eyes (No-Vision) and to keep their gaze in a straight-ahead direction. Three trials of 20 s were performed before and after the fatiguing task with 10 s of rest between trials, for 6 trials totally. Subjects were asked to maintain the test foot position on the forceplate during the resting periods (Gribble and Hertel 2004a, b; Ledin et al. 2004; Vuillerme et al. 2001, 2005, 2006).

Center of pressure (CoP) data were obtained using a strain gauge Bertec 4060-10 force platform and Bertec AM-6701 amplifier (Bertec Corp., Columbus, OH). Signals from the force platform were sampled at 100 Hz (12-bit A/D conversion), amplified and filtered with a second-order Butterworth filter (10 Hz low-pass cut-off frequency) and transformed to obtain CoP values. Force plate data obtained from three trials were averaged for each test condition and used to calculate the CoP parameters.

Center of pressure parameters calculated from the CoP signals were mean velocity (in cm/s), the area of the 95% confidence ellipse (AoE) (in cm2), and standard deviation (SD) of velocity in anteroposterior (AP) and mediolateral (ML) directions (in cm/s), for two periods of 5 s, immediately before and 1 s after the eyes closure (to prevent the effect of closing the eyes). The CoP mean velocity represents the total distance traveled by CoP by the time of the trial and constitutes a good index of the amount of activity required to maintain stability. It was calculated using the formula:
$$ \bar{v} = \frac{1}{T}\sum\limits_{1}^{T} {\sqrt {(x_{t + 1} - x_{t} )^{ 2} + (y_{t + 1} - y_{t} )^{ 2} } } $$
The elliptical area covered by the trajectory of the CoP with a 95% confidence interval is a measure of the CoP spatial variability and is calculated using the formula:
$$ A = 2\pi F_{0.05[2,N - 2]} \sqrt {\sigma_{x}^{2} \sigma_{y}^{2} \sigma_{xy}^{2} } $$
where \( \sigma_{xy} = \frac{{\left( {x_{i} - \bar{x}} \right)(y_{i} - \bar{y})}}{N - 1} \)
In order to provide the directional-specific information about the postural stability in sagittal and lateral directions individually, the SD of velocity in AP and ML directions has been calculated using the formulae:
$$ \sigma_{{v_{x} }} = \sqrt {\frac{{\sum {(v_{{x_{i} }} - \bar{v})^{2} } }}{N - 1}} {\text{ where }}v_{{x_{\text{i}} }} = \frac{{x_{i + 1} - x_{i} }}{{t_{i + 1} - t_{i} }} $$
$$ \sigma_{{v_{y} }} = \sqrt {\frac{{\sum {(v_{{y_{i} }} - \bar{v})}^{2} }}{N - 1}} {\text{ where }} v_{{y_{i} }} = \frac{{y_{i + 1} - y_{i} }}{{t_{i + 1} - t_{i} }} $$

During quiet standing, increased values of these above CoP parameters indicate a decreased postural control, whereas decreased values express an increased postural control (Doyle et al. 2007; Pinsault and Vuillerme 2009).

Statistics

One-sample Kolmogorov–Smirnov test was used to verify normal distribution of the data.

In order to determine the effects of fatigue, vision and fatigue segment on postural stability, 2 separate three-way within-subject analyses of variance (ANOVAs) with repeated measures for fatigue (pre-fatigue vs. post-fatigue), visual condition (Vision vs. No-Vision), and segment of muscle fatigue (distal vs. proximal) were conducted for the mean velocity, AoE, and SD of velocity in AP and ML directions. The alpha level for ANOVA was set at p = 0.05. In the presence of significant interactions, multiple comparisons were made. Bonferroni correction was applied to adjust for multiple comparisons with p < 0 0.0125 (0.05/4) indicating statistical significance.

Results

One-sample Kolmogorov–Smirnov test confirmed that the distribution of all variables was normal distribution (p > 0.05), so the parametric tests can be used for multiple comparisons of dependent variables.

Three-way repeated measures ANOVA revealed a significant fatigue × fatigue segment × visual condition interaction for mean velocity (F(1,24) = 54.55, p < 0.001), AoE (F(1,24) = 11.45, p = 0.002), and SD of velocity in AP (F(1,24) = 12.19, p = 0.002) and ML (F(1,24) = 60.01, p < 0.001) directions of CoP displacements (Table 1).
Table 1

Summary of analysis of variance for CoP parameters: F ratios and P values by variable

Independent variable

Mean velocity (cm/s)

Area (cm2)

SD of velocity (AP) (cm/s)

SD of velocity (ML) (cm/s)

F ratio

P

F ratio

p

F ratio

p

F ratio

p

Main effect

 Fatigue

120.99

<0.001

50.55

<0.001

96.97

<0.001

97.23

<0.001

 Fatigue segment

56.06

<0.001

9.90

0.004

18.84

<0.001

52.32

<0.001

 Visual condition

199.96

<0.001

148.76

<0.001

78.34

<0.001

109.84

<0.001

Interaction

Fatigue × fatigue segment

55.14

<0.001

11.97

0.002

11.82

0.002

60.85

<0.001

Fatigue × visual condition

121.58

<0.001

43.22

<0.001

61.99

<0.001

98.28

<0.001

Fatigue segment × visual condition

55.17

<0.001

8.96

0.006

17.42

<0.001

51.46

<0.001

Fatigue × fatigue segment × visual condition

54.55

<0.001

11.45

0.002

12.19

0.002

60.01

<0.001

In order to determine the basis of the significant three-way interaction, we performed two-way repeated measures ANOVAs (fatigue × segment) separately for Vision and No-Vision conditions. In Vision condition, there was no significant interaction between fatigue and fatigue segment for mean velocity (F(1,24) = 2.57, p = 0.12) (Fig. 1a), AoE (F(1,24) = 1.01, p = 0.32) (Fig. 2a), and SD of velocity in AP (F(1,24) = 0.08, p = 0.77) (Fig. 3a) and ML (F(1,24) = 0.49, p = 0.49) (Fig. 4a) directions of CoP displacements. However, when the vision was removed the interaction between fatigue and fatigue segment was significant for mean velocity (F(1,24) = 54.87, p < 0.001) (Fig. 1b), AoE (F(1,24) = 11.73, p = 0.002) (Fig. 2b), and SD of velocity in AP (F(1,24) = 12.07, p = 0.002) (Fig. 3b) and ML (F(1,24) = 60.57, p < 0.001) (Fig. 4b) directions of CoP displacements.
Fig. 1

Interactive effects of fatigue and segment on mean velocity for two conditions of: a Vision and b No-Vision. Error bars represent standard error of mean. The significant results (p < 0.05) of multiple comparisons of means are shown with an asterisk

Fig. 2

Interactive effects of fatigue and segment on AoE for two conditions of: a Vision and b No-Vision. Error bars represent standard error of mean. The significant results (p < 0.05) of multiple comparisons of means are shown with an asterisk

Fig. 3

Interactive effects of fatigue and segment on SD of velocity (AP) for two conditions of: a Vision and b No-Vision. Error bars represent standard error of mean. The significant results (p < 0.05) of multiple comparisons of mean are shown with an asterisk

Fig. 4

Interactive effects of fatigue and segment on SD of velocity (ML) for two conditions of: a Vision and b No-Vision. Error bars represent standard error of mean. The significant results (p < 0.05) of multiple comparisons of mean are shown with an asterisk

This significant two-way interaction effect was further analyzed using a simple main effects analysis of fatigue segment within each level of fatigue. In the pre-fatigue condition, no significant difference was detected between the CoP parameters recorded in two sessions [mean velocity; (F(1, 24) = 0.31, p = 0.58), AoE; (F(1, 24) = 0.52, p = 0.48), SD of velocity in AP direction; (F(1, 24) = 3.89, p = 0.06), SD of velocity in ML direction; (F(1, 24) = 0.23, p = 0.64)]. However, the CoP parameters were significantly greater after fatigue of the proximal muscles compared to fatigue of the distal muscles [mean velocity; (F(1,24) = 55.59, p < 0.001), AoE; (F(1, 24) = 10.93, p = 0.003), SD of velocity in AP direction; (F(1, 24) = 15.05, p = 0.001), SD of velocity in ML direction; (F(1, 24) = 56.72, p < 0.001)].

With both proximal and distal muscle fatigue, the value of post-fatigue scores was significantly higher for mean velocity [distal segment; F(1,24) = 71.65, p < 0.001, proximal segment; F(1,24) = 113.23, p < 0.001], AoE [distal segment; F(1,24) = 27.62, p < 0.001, proximal segment; F(1,24) = 40.46, p < 0.001], and SD of velocity in AP direction [distal segment; F(1,24) = 88.27, p < 0.001, proximal segment; F(1,24) = 44.06, p < 0.001]. For the SD of velocity in ML direction, however, the value of post-fatigue scores was significantly higher only with proximal muscle fatigue [distal segment; F(1,24) = 6.98, p = 0.014, proximal segment; F(1,24) = 83.63, p < 0.001].

Discussion

Several studies demonstrated that there was a greater effect of localized fatigue of the hip muscles compared to the ankle on maintenance of postural stability in single-leg stance (Bizid et al. 2009; Gribble and Hertel 2004a, b; Harkins et al. 2005; Salavati et al. 2007). Very little is known, however, about how the contribution of visual information in postural control is affected by muscular fatigue of hip compared to ankle musculature. The present experiment was designed to clarify if hip and ankle muscular fatigue may have different effects on the degree of visual dependency during a unipedal postural stability task. To this aim, mean velocity, AoE, and SD of velocity in AP and ML directions of CoP displacements were measured, before and after a fatiguing exercise of either ankle or hip muscles, under Vision and No-Vision conditions.

Our data suggest that localized muscle fatigue of the lower extremities reduces postural stability. Several previous studies have reported similar findings (Gribble and Hertel 2004a, b; Harkins et al. 2005; Johnston et al. 1998; Joyce et al. 2001; Ochsendorf et al. 2000; Ramsdell et al. 2001; Salavati et al. 2007; Yaggie and McGregor 2002). One possible explanation for these results might be proprioceptive deficiency associated with muscle fatigue and consequently the inappropriate efferent muscle responses (Forestier et al. 2002; Hiemstra et al. 2001; Taylor et al. 2000).

More interestingly, results of the present experiment may propose a functionally different reweighting of each sensory system in response to the new situations depending on the location of muscle fatigue. With fatigue of the ankle muscles, eyes closure did not lead to a significant decrement of the postural stability. In the presence of proximal muscle fatigue, however, withdrawing vision yielded a significantly greater disturbance of the postural control ability as indicated by the more increments of the CoP parameters.

With regard to the fatigue of ankle plantar/dorsiflexores, our data are to some extent consistent with the findings of Vuillerme et al. (2001) in which withdrawing vision resulted in a similar increase in CoP displacements in the two conditions of “No-Fatigue” and “Fatigue” of calf muscles. In the No-Fatigue condition, however, a rapid reorganization of postural control and switching from visual to proprioceptive control was observed. They also found that restoration of vision would largely compensate for the destabilizing effect of calf muscles fatigue (Vuillerme et al. 2001). In our experiment, of course, we did not aim to examine the effects of reinserting visual information and the adaptive processes occurred over time. Adding more postural assessment trials to reinsert the vision was not practical in our study, as the recovery time of the fatigue protocol was limited (Salavati et al. 2007; Schwendner et al. 1995).

Factors such as pathologic conditions, trauma, aging, muscle vibration, and muscular fatigue have been shown to decrease postural control, probably due to impaired proprioceptive information (Gribble and Hertel 2004a, b; Johnston et al. 1998; Joyce et al. 2001; Laughton et al. 2003; Ochsendorf et al. 2000; Ramsdell et al. 2001; Riemann et al. 2002; Salavati et al. 2007; Vuillerme et al. 2001, 2002, 2005; Yaggie and McGregor 2002). In our study, although the exact mechanism inducing deficits in postural stability is rather unknown, it is possible that joint proprioception was altered due to muscle fatigue. Considering the multisensory nature of postural control, it is expectable that decreased availability or reliability of inputs from a certain sensory system would force the CNS to increase the weighting of inputs from other sensory systems with more accurate information to maintain a stable posture (Horak and Macpherson 1996). It is well demonstrated that when somatosensory input is disrupted, as is the case of standing on an unstable surface, eliminating vision by closing the eyes results in a significantly greater postural sway compared to standing on a stable surface (Shumway-Cook and Woollacott 2001). Therefore, one way to compensate for an impaired neuromuscular control following the muscle fatigue might be to increase the reliance on an alternative sensory system such as vision. It seems that in the condition of muscle fatigue, closing eyes would eliminate the main reliable resource of sensory inputs from the environment, thus lead to an even more disturbed postural stability.

In addition, during maintaining a standing posture, corrective contractions in response to any perturbations are necessary. Decreased conduction velocity of afferent signal from the fatigued muscle may lead to slowed efferent signals, resulting in a lack of neuromuscular control and greater position changes. The greater variability in joint motion and lack of proper corrective muscle contractions may result in disturbed postural control, as indicated by greater COP parameters (Taylor et al. 2000). In the present study, it can also be speculated that several linked physiologic mechanisms might be impaired by neuromuscular fatigue.

Greater postural control disturbance by withdrawing vision following fatigue to hip musculature, observed in the present study, is supported to some extent by the literature that provide evidence of a major role for proprioceptive inputs from the hip region and a minor role for ankle inputs in triggering balance corrections. There has been some investigation into the role of proprioception in the control of posture, which have employed different approaches such as modulating proprioception in healthy subjects or evaluating postural control in patients with ‘selective’ sensory lesions. Interestingly, a common concept of “a proximally located proprioceptive trigger for balance corrections” appears to have emerged from these studies (Allum et al. 1998).

Therefore, it might be supposed that deficits in the proprioceptive inputs from hip area due to fatigue would lead to a greater degree of visual dependency, hence a greater disturbance of postural stability by eyes closure. This possible explanation would also be in congruence with the opinion of Vuillerme et al. (2006), who concluded that fatigue of the ankle muscles leads to less reliable ankle proprioceptive signals and compensates by increasing the gain at more proximal joints (e.g. hips and lumbar spine).

With regard to the directional-specific effects of muscle fatigue, our findings indicated that postural control in the ML direction is affected more significantly by fatigue of proximal musculature of the lower extremity compared to the more distal muscles. However, we observed that fatigue of hip and ankle muscles impaired postural control in similar ways in the AP direction. These results are in accordance with the findings of Gribble and Hertel (2004a). They also found that the postural control impairment in frontal plane was significantly larger for hip or knee flexor/extensor muscles fatigue than for ankle plantar/dorsiflexor muscles fatigue, but postural control in sagittal plane was deteriorated in similar ways after fatigue of either hip, knee or ankle muscles. Such findings suggest that the effects of fatigue on postural control are specific to the fatigue location and measures of postural control used.

In conclusion, results of the present experiment showed that fatigue in both proximal and distal musculature of the lower extremity yielded decreased postural stability during unipedal quiet standing in healthy young women. This effect was more accentuated when visual information was eliminated by eyes closure. Interestingly, it was demonstrated that withdrawing vision following fatigue to hip musculature, led to a much more prominent impairment of postural stability compared to the fatigue of more distal muscles. These results may provide new insights into deficits seen in pathologic conditions (aging, neurologic diseases, or orthopedic injuries) and training protocols to improve balance or prevent sports injuries. Future studies could be conducted to include kinematic and electromyographic measures during postural task performance in different visual conditions, before and after muscle fatigue. One limitation of the present study is that the results could not be generalized to male population as our sample included only females. This point can be considered in future similar studies including other age and sex samples, or pathological conditions.

Acknowledgments

The authors would like to acknowledge the Research Committee of University of Social Welfare and Rehabilitation Sciences, for ongoing support of their work.

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Manijeh Soleimanifar
    • 1
  • Mahyar Salavati
    • 1
  • Behnam Akhbari
    • 1
  • Mojgan Moghadam
    • 2
  1. 1.Department of Physical TherapyUniversity of Social Welfare and Rehabilitation SciencesTehranIran
  2. 2.Department of Physical Therapy, Faculty of Rehabilitation SciencesTehran University of Medical SciencesTehranIran

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