Gait & Posture 40 (2014) 556–560
Contents lists available at ScienceDirect
Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost
Visual availability, balance performance and movement complexity in dancers Ruth Muelas Pe´rez a, Rafael Sabido Solana b,*, David Barbado Murillo b, Francisco Javier Moreno Herna´ndez b a b
Professional Dance School of Castilla y Leo´n, Burgos, Spain Sport Research Centre, Miguel Herna´ndez University of Elche, Elche, Spain
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 February 2013 Received in revised form 12 May 2014 Accepted 30 June 2014
Research regarding the complex fluctuations of postural sway in an upright standing posture has yielded controversial results about the relationship between complexity and the capacity of the system to generate adaptive responses. The aim of this study is to compare the performance and complexity of two groups with different levels of expertise in postural control during a balance task. We examined the balance ability and time varying (dynamic) characteristics in a group of 18 contemporary dancers and 30 non-dancers in different visual conditions. The task involved maintaining balance for 30 s on a stability platform with opened or closed eyes. The results showed that dancers exhibited greater balance ability only in open eyes task than non-dancers. We also observed a lower performance in both groups during the test with closed eyes, but only dancers reduced their complexity in closed eyes task. The main conclusion is that the greater postural control exhibited by dancers depends on the availability of visual information. ß 2014 Elsevier B.V. All rights reserved.
Keywords: Balance ability Complexity Dance Visual information
1. Introduction Postural control during the maintenance of an upright standing posture is a fundamental motor act that provides the basis for locomotion and most other movement tasks [1]. The postural control system regulates the body’s postural sway during upright standing through the complex interaction of somatosensory, visual and vestibular sensory feedback networks, numerous brain regions, and the musculoskeletal system [2–4]. Complexity is defined as the number of system components and coupling functions (interactions) among the components [1]. This complexity can be observed in the upright standing posture through fluctuations of postural sway [4–6] and has increasingly led scientists to analyze postural stability through non-linear mathematical tools [7–11]. The results observed through this type of analysis have allowed scientists to relate lower complexity levels to a worse performance [12] related to aged and unhealthy systems [13]. Haran and
* Corresponding author at: Sport Research Centre of Elche, Miguel Herna´ndez University, Avda. de la Universidad s/n, 03202 Elche, Spain. Tel.: +34 965 22 24 37; fax: +34 965 22 24 56. E-mail address: [email protected] (R. Sabido Solana). http://dx.doi.org/10.1016/j.gaitpost.2014.06.021 0966-6362/ß 2014 Elsevier B.V. All rights reserved.
Keshner showed the benefits of a balance-training program, in which the unhealthy participants improved their postural control, and the complexity of postural sway was increased [14]. By applying different levels of difficulty, depending on the availability of visual information, studies have shown a reduced performance associated with less postural sway complexity when the subjects kept their eyes closed [11,15]. However, some authors state that, depending on the specialization of the sample in proprioceptive tasks, the decrease in performance will generally be significant. Some results have shown that gymnasts only worsen their performance on stability tasks with closed eyes compared to another group of athletes [16]. However, other studies appear to be inconsistent with this relationship between performance and system complexity. Some age-related studies have observed a greater complexity of the system related to worse performances on postural control tasks [9,10]. High levels of complexity may indicate that the system is becoming less sustainable. This assumption is close to the traditional interpretation of variability as a measure of disorder and noise [10]. The relationship between complexity and performance in balance tasks has been previously analyzed in dancers. Stin et al. have observed that young dancers exhibit greater postural control with greater complexity compared to other groups of
R. Muelas Pe´rez et al. / Gait & Posture 40 (2014) 556–560
participants without experience in balance skills [7]. However, Schmit et al. did not observe differences in postural control between dancers and track athletes [11]. These authors observed differences in behavioral complexity between both groups with greater complexity or irregularity in the postural stability of dancers. Schmit et al. argued that there is a qualitative difference, rather than quantitative, in the balance task between these two groups [11]. Because of the controversy in the results, the aim of this study was to evaluate the relationship between complexity and performance through a comparison of two groups of different levels of expertise in postural control (dancers and non-dancers), and two levels of availability of visual information during a balance task. The following are the hypotheses of this study: (1) expert dancers will show greater complexity and a better performance during the balance task than non-dancers in both visual conditions, and (2) both groups will show a greater complexity with a better performance in the balance task when visual information is available.
2. Method 2.1. Participants Eighteen undergraduate dancers (all females) from the Spanish Royal Conservatory of Dance and thirty healthy young women without any experience in dance participated in the study. All dancers were specifically trained in contemporary dance and ballet for a minimum of five years. The remaining thirty women served as a control group and were not explicitly trained in balance tasks. The participants signed a written informed consent document prior to the experimental session. Table 1 shows the descriptive data, including age, weight and height of the sample. The study protocol was approved by the ethics committee of Extremadura University. 2.2. Performance task and apparatus The performance task used to measure balance ability was a stabilometer (Model 16020, Lafayette Instrument Inc., Lafayette, IN), in which the tilt angle, recorded by a SMEG330 electrogoniometer (1-KHz data collection rate) represented the criterion measure. The tilt angle was the participant’s error score reflecting deviation (medio-lateral) from the target horizontal platform position (08). The stabilometer platform (0.66 m 1.08 m 0.025 m) was placed 0.16 m from the frame and 0.22 m from the floor. The range of the stabilometer was set to 208 from a horizontal position. The stabilometer task has been shown as a valid and reliable measure of balance [17]. 2.3. Procedure The participants were asked to stand barefoot on the platform maintaining stability from the horizontal position for 30 s in two visual conditions: open eyes (OE) and closed eyes (CE). The order of the conditions was randomized between the participants. Table 1 Descriptive and anthropometric data of all participants. Group
Age (years) (average SD)
Weight (kg) (average SD)
Height (cm) (average SD)
Dancers Non-dancers
23.32 2.58 22.23 1.79
65.73 7.96 65.94 10.53
171.91 7.02 169.97 7.56
557
The participants were instructed to adopt a shoulder-width stance with their arms held at their sides. The participants were further directed not to speak during the trials. At the beginning of each trial, the participants assumed the aforementioned stance. Data collection was initiated after the participants felt comfortable and ready. The participants were allowed to rest for 3 min between the conditions. To ensure that there was no rest of vision during the closed eyes condition, all participants placed an ocular mask on their face. 2.4. Data analysis and reduction Data obtained from electrogoniometer was subsampled to 100 Hz. To evaluate the performance in postural stability, the absolute error of the tilt platform was measured as the average of the absolute distance (AE) to the horizontal angle of the platform. In addition, we assessed participant’s balance control trough the standard deviation (SD) and mean velocity (MV). Non-linear time series analysis was applied to the angular displacement of the platform. The complexity of the postural sway dynamics was calculated by two methods: Sample Entropy (SampEn) and Permutation Entropy (PE). Higher values of SampEn thus represent lower repeatability of vectors of length m to that of m + 1, which marks a lower predictability of future data points and a greater irregularity within the time series. SampEn was performed using the following input parameters for the analysis algorithms: 0.15 for tolerance (r) (in proportion to the SD of the signal) and 2 for vector length (m). The selection of these values was based on the procedures suggested by Cavanaugh et al. [18]. We have included PE to reduce the influence of the magnitude of the time series, and therefore the influence of the tolerance window parameter. Permutation entropy is independent of the data magnitude because it measures the entropy of sequences of ordinal patterns derived from m-dimensional delay embedding vectors [19]. PE was performed using 5 for vector length (m). In order to assess the robustness of SampEn and PE method, we have applied them modifying input parameter: SampEn was applied on angular displacement signal using different r (0.15, 0.20 and 0.25 in proportion to the SD of the signal) and m (2, 3 and 4). PE was applied modifying m from 4 to 6. Higher r and m values increased entropy output. Nevertheless its influence seems similar in all conditions and does not affect result interpretation. 2.5. Statistical analysis Normality of the data distribution was evaluated by calculating asymmetry, kurtosis and the Kolmogorov–Smirnov method with the Lilliefors correction. All variables were normally distributed. A mixed-design analysis of variance (ANOVA) was performed to test the mean differences between the two groups (dancers and control), vision conditions (repeated measures factors) and interactions. A post hoc analysis with a Bonferroni adjustment was used for multiple comparisons. Pearson’s correlation coefficient tested for correlations between the variables of the present study. Significance was established at p < 0.05. SPSS 16.0 (SPSS Inc, Chicago, IL, USA) was used for all statistical procedures.
3. Results An example of a medio-lateral deviation of the platform tilt angle from the target horizontal position (08) for dancers and non-dancers in both visual conditions is shown in Fig. 1. Higher medio-lateral deviations can be observed in the CE condition compared to OE condition. The data from the dancer performance show lower deviation than non-dancer mainly in the OE condition. Table 2 shows the relationship between the AE, SD and MV of the tilt platform and the vision conditions. All the participants performed significantly better in the balance task in the OE condition compared to the CE condition. Both groups
[(Fig._1)TD$IG]
R. Muelas Pe´rez et al. / Gait & Posture 40 (2014) 556–560
558
Fig. 1. An example of the medio-lateral deviation of the platform tilt angle from the target horizontal position (08) for dancers and non-dancers in both visual conditions. increased their values in AE, SD and MV when standing on the platform in the CE condition. Regarding the differences between the groups (Table 3), the dancers showed better postural stability (lower absolute error and standard deviation) than non-dancers only in the OE condition (p < 0.05). Furthermore, MV was higher in dancers in both conditions (p < 0.05).
Table 2 Mixed ANOVA testing mean differences between the dancers and non-dancers (group) and the repeated measures factors of visual condition (vision) and interaction. F1,46
P
Vision Group Vision group
48.187 3.313 2.256
.001 .075 .140
SD
Vision Group Vision group
37.538 6.758 6.845
.001 .013 .012
MV
Vision Group Vision group
7.793 7.962 .120
.008 .007 .731
SampEn
Vision Group Vision group
19.108 2.288 5.804
.000 .137 .020
PE
Vision Group Vision group
3.064 13.324 24.708
.087 .000 .000
AE
AE, absolute error; SD, standard deviation; MV, mean velocity; SampEn, sample entropy; PE, permutation entropy.
The analysis of the complexity of the tilt platform excursion showed significant main effects for the visual condition in SampEn and for group in PE (Table 2). Nevertheless, the interaction effects indicate that the visual conditions affect the groups differently. Pairwise comparisons showed lower entropy values for dancers in CE condition compared with OE condition. In addition, dancers exhibited lower entropy than non-dancers in CE condition. The results indicate that dancers exhibit
Table 3 Mean and standard deviations of absolute error along the medio-lateral axis (AE), standard deviation (SD), mean velocity (MV), sample entropy (SampEn) and permutation entropy (PE) for dancers and non-dancers in the two visual conditions. N
Open eyes
Closed eyes
AE
ND D
30 18
11.45 1.11a 10.61 1.13
12.57 1.30B 12.34 1.24B
SD
ND D
30 18
12.85 1.60A 11.45 1.02
13.74 1.61B 13.41 0.81B
MV
ND D
30 18
40.82 11.69a 33.67 5.86
43.94 11.11A 36.01 3.45b
SampEn
ND D
30 18
0.094 0.030 0.096 0.028
0.082 0.037a 0.058 0.024B
PE
ND D
30 18
0.58 0.12 0.54 0.06
0.60 0.10A 0.44 0.07B
a Different from the dancers using the Bonferroni correction. Pairwise comparison, p < .05. A Different from the dancers using the Bonferroni correction. Pairwise comparison, p < .01. B Different from the open eyes condition using the Bonferroni correction. Pairwise comparison, p < .01.
R. Muelas Pe´rez et al. / Gait & Posture 40 (2014) 556–560 lower complexity, lower entropy values, when visual information is not available (Table 3). The results of the correlation analysis performed on the sample of non-dancers showed that there is a positive relationship between AE (r = .465, p < .05), SD (r = .504, p < .01) and MV (r = .786, p < .01) in the OE and CE conditions. Thus, the higher the performance stability test in OE condition, the greater the performance stability test in CE condition. However, the performance of dancers between the two stability tests is not significantly correlated. The better performance of the dancers in the OE condition is not associated with a higher yield in the test with CE. Comparing the absolute error of the platform angle with the complexity measured through SampEn, the results in the dancer group showed negative correlations between the AE and complexity of the tilt angle for both conditions. This behavior is exhibited in both the OE (r = .605, p .01) and CE conditions (r = .711, p .01). Similar correlations has been observed between SampEn and SD in OE condition (r = . 483, p < .05) and CE condition (r = .807, p < .01). Thus, as the complexity increases, the performance improves, regardless of the availability of visual information. The non-dancers also showed these negative correlations in both conditions between AE and SampEn (OE: r = .579, p < .01; CE: r = .594, p < .01), and between SD and SampEn (OE: r = .602, p < .01; CE: r = .648, p < .01).
4. Discussion The results are consistent with other studies in which performance on the stability test in the OE condition was better than the performance in the CE condition [11,15,20]. Moreover, and partially confirming the first hypothesis, the group of dancers performed better only on the balancing task with OE. These results may be because of the increased specialization of dancers in tasks in which postural control is regulated through visual feedback [11,21,22]. Rist states that dancers are used to regulate their posture through visual information from the initial moment of learning [23]. Teasdale et al. claim that a specialization in learning leads to a specialization in the information channel used in execution [24]. Thus, the greater the visual information used during the learning process, the greater the decrease in performance when vision is removed [25]. This idea was also observed by Gerbino et al., who observed highly significant reductions in the balance performance of dancers in the CE condition [26]. Our results show that the deprivation of visual information matches the performance of both groups in the balance task. These results confirm the dependence of dancers on visual information to regulate their posture, as argued by other authors in several studies [21,27]. Kavounoudias et al. suggested that superficial plantar mechanoreceptors provide the CNS with information relative to the position of the body with respect to the vertical reference [28]. Exclusion of the vision would render the CNS rely on vestibular and proprioception systems. Perrin et al. argued that dance training strengthens the accuracy of proprioceptive inputs in a lower way than other sports [27], developing specific modalities of balance not transferable to posture control in CE situations [21]. In relation to the entropy values, opposed to the hypothesis, the group of dancers shows lower behavioral complexity than nondancers during the tasks only in the CE condition. Indeed, comparing both visual conditions, only the dancers entropy values decrease in the CE condition, which is in contrast to the group of non-dancers, in which no difference was observed. Movement instabilities, in the form of critical fluctuations, or temporary losses in stability, are exhibited when a transition from one coordination pattern to another may be about to occur [29]. As argued previously, under CE condition dancers exhibit a shift in sensorimotor dominance from vision to proprioception reflected in a different coordination pattern. The reduced complexity observed in the dancer system may reflect an altered coordination strategy characterized by lower degrees of freedom in an adapting process to an unfamiliar task [30]. Consistent with the above-mentioned, Schmidt et al. argue that differences in the postural stability behavior of a dancer compared to other (physically active) groups are not in their better
559
performance but in their system flexibility to adapt to different situations of postural control [11]. Thus, the differences would be more qualitative than quantitative. For this reason, we might have only observed differences between both groups in their behavioral complexity during the balance tasks in CE conditions, where difference in the performance between the groups practically disappears. Sensory deprivation increases the difficulty of the task, and the group of dancers changed their behavior, freezing their degrees of freedom in a reduced effectiveness of their strategy to stabilize and prevent falls, thus resulting in lower values of entropy [31]. Regarding the relationship between complexity and performance, we have found contradictory results. On the one hand, correlation analysis in both groups showed that the participants with higher complexity exhibited higher performance regardless of the availability of visual information. On the other hand, even though dancers exhibited better performance than non-dancers in OE, they there was not differences in complexity between groups. In addition, the decrease in performance of non-dancers in the CE condition compared to OE condition was not related to lower behavioral complexity. In this regard, the relationship between performance and complexity is not linear or is mediated by other variables [1]. Some authors suggested that an increase in entropy variables is not necessarily synonymous with an increase in physiologic complexity [13] and the relationship between entropy variables and physiologic complexity is not linear. In this sense, according with Stergiou and Decker, there would be inverse Ushape relationship between entropy variables and complexity [32]. In summary, the differences in balance ability between dancers and non-dancers are only meaningful in the OE condition, thus indicating that the best performance in the former group is coupled with the use of information from vision to control balance adjustments. The deprivation of visual information increases the difficulty of the task, which is more significant in dancers because of their specialization in learning through visual information. Decreases in the complexity of postural control in the dancers entail a decrease in performance. These results may be observed because of specific strategies developed to control a novel balance tasks. The non-linear analysis methods allow us to explain both the differences observed between the different levels of difficulty in the balance tasks and those observed between the different levels of expertise in the participants.
Conflict of interest statement There are no financial or personal relationships between any of the authors and other people or organizations that could inappropriately influence this study.
References [1] Vaillancourt DE, Newell KM. Changing complexity in human behavior and physiology through aging and disease. Neurobiol Aging 2002;23:1–11. [2] Winter DA, Patla AE, Frank JS. Assessment of balance control in humans. Med Prog Technol 1990;16:31–51. [3] Palmieri RM, Ingersoll CD, Stone MB, Krause BA. Center-of-pressure parameters used in the assessment of postural control. J Sport Rehabil 2003;11:51– 66. [4] Manor B, Costa MD, Hu K, Newton E, Starobinets O, Kang HG, et al. Physiological complexity and system adaptability: evidence from postural control dynamics of older adults. J Appl Physiol 2010;109:1786–91. [5] Lipsitz LA. Dynamics of stability: the physiologic basis of functional health and frailty. J Gerontol Ser A: Biol Sci Med Sci 2002;57:115–25. [6] Thurner S, Mittermaier C, Ehrenberger K. Change of complexity patterns in human posture during aging. Audiol Neurootol 2002;7:240–8. [7] Stins JF, Michielsen ME, Roerdink M, Beek PJ. Sway regularity reflects attentional involvement in postural control: effects of expertise, vision and cognition. Gait Posture 2009;30:106–9.
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[8] Roerdink M, Haart MD, Daffertshofer A, Donker SF, Geurts ACH, Beek PJ. Dynamical structure of center of pressure trafectories in patients recovering from stroke. Exp Brain Res 2006;174:256–69. [9] Duarte M, Sternad D. Complexity of human postural control in young and older adults during prolonged standing. Exp Brain Res 2008;191:265–76. [10] Borg FG, Laxa˚back G. Entropy of balance – some recent results. J Neuroeng Rehabil 2010;7:38. [11] Schmit JM, Regis DI, Riley MA. Dynamic patterns of postural sway in ballet dancers and track athletes. Exp Brain Res 2005;163(3):370–8. [12] Lamouth CJ, Van Lummel RC, Beek PJ. Athletic skill level is reflected in body sway: a test case for accelometry in combination with stochastic dynamics. Gait Posture 2009;29(4):546–51. [13] Goldberger AL, Peng CK, Lipsitz LA. What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging 2002;23(1):23–6. [14] Haran FJ, Keshner EA. Sensory reweighting as a method of balance training for labyrinthine loss. J Neurol Phys Ther 2008;32:186–91. [15] Donker SF, Lebdet A, Roerdink M, Savelsberg JP, Beck PJ. Children with cerebral palsy exhibit greater and more regular postural sway than typically developing children. Exp Brain Res 2008;184(3):363–70. [16] Vuillerme N, Teasdale N, Nougier V. The effects of expertise in gimnastics on propioceptive sensory integration in human subjects. Neurosci Lett 2001;311(2):73–6. [17] Murray JF. Construction of a stabilometer capable of indicating the variability of non-level performance. Percept Mot Skills 1982;55:1211–5. [18] Cavanaugh JT, Mercer VS, Stergiou N. Approximate entropy detects the effect of a secondary cognitive task on postural control in healthy young adults: a methodological report. J Neuroeng Rehabil 2007;4:42. [19] Frank B, Pompe B, Schneider U, Hoyer D. Permutation entropy improves fetal behavioural state classification based on heart rate analysis from biomagnetic recordings in near term fetuses. Med Biol Eng Comput 2006;44:179–87. [20] Rogind H, Simonsen H, Era P, Bliddal H. Comparison of Kistler 9861A. Force Platform and Chattecx Balance System for measurement of postural sway: correlation and test–retest reliability. Scand J Med Sci Sport 2003;13(2):106–14.
[21] Hugel F, Cadopi M, Kohler F, Perrin P. Postural control of ballet dancers: a specific use of visual input for atistic purposes. Int J Sports Med 1999;20(2):86–92. [22] Schanfein L, Rietdyk S. The relationship between center of pressure displacement and estimated instability of dancers and non-dancers while in a moving room. In: Proceedings for the North American congress on biomechanics. MI: Ann Arbor; 2008, August. [23] Rist R. Dance science. The Dancing Times 1991, December;243. [24] Teasdale N, Stelmach GE, Breunig A. Postural sway characteristics of the elderly under normal and altered visual and support surface conditions. J Gerontol 1991;46B:238–44. [25] Proteau L. On the specificity of learning and the role of visual information in movement control. In: Proteau L, Elliot D, editors. Vision and Motor Control, North Holland: Amsterdam; 1992. p. 67–103. [26] Gerbino PG, Griffin ED, Zurakowski D. Comparison of standing balance between female collegiate dancers and soccer players. Gait Posture 2007;26(4): 501–7. [27] Perrin P, Deviterne D, Hugel F, Perrot C. Judo, better than dance, develops sensorimotor adaptabilities involved in balance control. Gait Posture 2002;15: 187–94. [28] Kavounoudias A, Roll R, Roll JP. The plantar sole is a ‘‘dynamometric map’’ for human balance control. NeuroReport 1998;9:3247–52. [29] Scho¨ner G, Haken H, Kelso JAS. A stochastic theory of phase transition in human hand movement. Biol Cybern 1987;53:247–57. [30] Davids K, Button C, Bennet S. Dinamical skill acquisition. A constraints-led approach. Champaign: Human Kinetics; 2007. [31] Amoud H, Snoussi H, Hewson DJ, Ducheˆne J. Intrinsic Mode Entropy for postural steadiness analysis. In: Proceedings for the 4th European conference of the international federation for medical and biological engineering, vol. 22; 2008. p. 212–5. [32] Stergiou N, Decker LM. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci 2011;30(5):869–88.
Contents lists available at ScienceDirect
Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost
Visual availability, balance performance and movement complexity in dancers Ruth Muelas Pe´rez a, Rafael Sabido Solana b,*, David Barbado Murillo b, Francisco Javier Moreno Herna´ndez b a b
Professional Dance School of Castilla y Leo´n, Burgos, Spain Sport Research Centre, Miguel Herna´ndez University of Elche, Elche, Spain
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 February 2013 Received in revised form 12 May 2014 Accepted 30 June 2014
Research regarding the complex fluctuations of postural sway in an upright standing posture has yielded controversial results about the relationship between complexity and the capacity of the system to generate adaptive responses. The aim of this study is to compare the performance and complexity of two groups with different levels of expertise in postural control during a balance task. We examined the balance ability and time varying (dynamic) characteristics in a group of 18 contemporary dancers and 30 non-dancers in different visual conditions. The task involved maintaining balance for 30 s on a stability platform with opened or closed eyes. The results showed that dancers exhibited greater balance ability only in open eyes task than non-dancers. We also observed a lower performance in both groups during the test with closed eyes, but only dancers reduced their complexity in closed eyes task. The main conclusion is that the greater postural control exhibited by dancers depends on the availability of visual information. ß 2014 Elsevier B.V. All rights reserved.
Keywords: Balance ability Complexity Dance Visual information
1. Introduction Postural control during the maintenance of an upright standing posture is a fundamental motor act that provides the basis for locomotion and most other movement tasks [1]. The postural control system regulates the body’s postural sway during upright standing through the complex interaction of somatosensory, visual and vestibular sensory feedback networks, numerous brain regions, and the musculoskeletal system [2–4]. Complexity is defined as the number of system components and coupling functions (interactions) among the components [1]. This complexity can be observed in the upright standing posture through fluctuations of postural sway [4–6] and has increasingly led scientists to analyze postural stability through non-linear mathematical tools [7–11]. The results observed through this type of analysis have allowed scientists to relate lower complexity levels to a worse performance [12] related to aged and unhealthy systems [13]. Haran and
* Corresponding author at: Sport Research Centre of Elche, Miguel Herna´ndez University, Avda. de la Universidad s/n, 03202 Elche, Spain. Tel.: +34 965 22 24 37; fax: +34 965 22 24 56. E-mail address: [email protected] (R. Sabido Solana). http://dx.doi.org/10.1016/j.gaitpost.2014.06.021 0966-6362/ß 2014 Elsevier B.V. All rights reserved.
Keshner showed the benefits of a balance-training program, in which the unhealthy participants improved their postural control, and the complexity of postural sway was increased [14]. By applying different levels of difficulty, depending on the availability of visual information, studies have shown a reduced performance associated with less postural sway complexity when the subjects kept their eyes closed [11,15]. However, some authors state that, depending on the specialization of the sample in proprioceptive tasks, the decrease in performance will generally be significant. Some results have shown that gymnasts only worsen their performance on stability tasks with closed eyes compared to another group of athletes [16]. However, other studies appear to be inconsistent with this relationship between performance and system complexity. Some age-related studies have observed a greater complexity of the system related to worse performances on postural control tasks [9,10]. High levels of complexity may indicate that the system is becoming less sustainable. This assumption is close to the traditional interpretation of variability as a measure of disorder and noise [10]. The relationship between complexity and performance in balance tasks has been previously analyzed in dancers. Stin et al. have observed that young dancers exhibit greater postural control with greater complexity compared to other groups of
R. Muelas Pe´rez et al. / Gait & Posture 40 (2014) 556–560
participants without experience in balance skills [7]. However, Schmit et al. did not observe differences in postural control between dancers and track athletes [11]. These authors observed differences in behavioral complexity between both groups with greater complexity or irregularity in the postural stability of dancers. Schmit et al. argued that there is a qualitative difference, rather than quantitative, in the balance task between these two groups [11]. Because of the controversy in the results, the aim of this study was to evaluate the relationship between complexity and performance through a comparison of two groups of different levels of expertise in postural control (dancers and non-dancers), and two levels of availability of visual information during a balance task. The following are the hypotheses of this study: (1) expert dancers will show greater complexity and a better performance during the balance task than non-dancers in both visual conditions, and (2) both groups will show a greater complexity with a better performance in the balance task when visual information is available.
2. Method 2.1. Participants Eighteen undergraduate dancers (all females) from the Spanish Royal Conservatory of Dance and thirty healthy young women without any experience in dance participated in the study. All dancers were specifically trained in contemporary dance and ballet for a minimum of five years. The remaining thirty women served as a control group and were not explicitly trained in balance tasks. The participants signed a written informed consent document prior to the experimental session. Table 1 shows the descriptive data, including age, weight and height of the sample. The study protocol was approved by the ethics committee of Extremadura University. 2.2. Performance task and apparatus The performance task used to measure balance ability was a stabilometer (Model 16020, Lafayette Instrument Inc., Lafayette, IN), in which the tilt angle, recorded by a SMEG330 electrogoniometer (1-KHz data collection rate) represented the criterion measure. The tilt angle was the participant’s error score reflecting deviation (medio-lateral) from the target horizontal platform position (08). The stabilometer platform (0.66 m 1.08 m 0.025 m) was placed 0.16 m from the frame and 0.22 m from the floor. The range of the stabilometer was set to 208 from a horizontal position. The stabilometer task has been shown as a valid and reliable measure of balance [17]. 2.3. Procedure The participants were asked to stand barefoot on the platform maintaining stability from the horizontal position for 30 s in two visual conditions: open eyes (OE) and closed eyes (CE). The order of the conditions was randomized between the participants. Table 1 Descriptive and anthropometric data of all participants. Group
Age (years) (average SD)
Weight (kg) (average SD)
Height (cm) (average SD)
Dancers Non-dancers
23.32 2.58 22.23 1.79
65.73 7.96 65.94 10.53
171.91 7.02 169.97 7.56
557
The participants were instructed to adopt a shoulder-width stance with their arms held at their sides. The participants were further directed not to speak during the trials. At the beginning of each trial, the participants assumed the aforementioned stance. Data collection was initiated after the participants felt comfortable and ready. The participants were allowed to rest for 3 min between the conditions. To ensure that there was no rest of vision during the closed eyes condition, all participants placed an ocular mask on their face. 2.4. Data analysis and reduction Data obtained from electrogoniometer was subsampled to 100 Hz. To evaluate the performance in postural stability, the absolute error of the tilt platform was measured as the average of the absolute distance (AE) to the horizontal angle of the platform. In addition, we assessed participant’s balance control trough the standard deviation (SD) and mean velocity (MV). Non-linear time series analysis was applied to the angular displacement of the platform. The complexity of the postural sway dynamics was calculated by two methods: Sample Entropy (SampEn) and Permutation Entropy (PE). Higher values of SampEn thus represent lower repeatability of vectors of length m to that of m + 1, which marks a lower predictability of future data points and a greater irregularity within the time series. SampEn was performed using the following input parameters for the analysis algorithms: 0.15 for tolerance (r) (in proportion to the SD of the signal) and 2 for vector length (m). The selection of these values was based on the procedures suggested by Cavanaugh et al. [18]. We have included PE to reduce the influence of the magnitude of the time series, and therefore the influence of the tolerance window parameter. Permutation entropy is independent of the data magnitude because it measures the entropy of sequences of ordinal patterns derived from m-dimensional delay embedding vectors [19]. PE was performed using 5 for vector length (m). In order to assess the robustness of SampEn and PE method, we have applied them modifying input parameter: SampEn was applied on angular displacement signal using different r (0.15, 0.20 and 0.25 in proportion to the SD of the signal) and m (2, 3 and 4). PE was applied modifying m from 4 to 6. Higher r and m values increased entropy output. Nevertheless its influence seems similar in all conditions and does not affect result interpretation. 2.5. Statistical analysis Normality of the data distribution was evaluated by calculating asymmetry, kurtosis and the Kolmogorov–Smirnov method with the Lilliefors correction. All variables were normally distributed. A mixed-design analysis of variance (ANOVA) was performed to test the mean differences between the two groups (dancers and control), vision conditions (repeated measures factors) and interactions. A post hoc analysis with a Bonferroni adjustment was used for multiple comparisons. Pearson’s correlation coefficient tested for correlations between the variables of the present study. Significance was established at p < 0.05. SPSS 16.0 (SPSS Inc, Chicago, IL, USA) was used for all statistical procedures.
3. Results An example of a medio-lateral deviation of the platform tilt angle from the target horizontal position (08) for dancers and non-dancers in both visual conditions is shown in Fig. 1. Higher medio-lateral deviations can be observed in the CE condition compared to OE condition. The data from the dancer performance show lower deviation than non-dancer mainly in the OE condition. Table 2 shows the relationship between the AE, SD and MV of the tilt platform and the vision conditions. All the participants performed significantly better in the balance task in the OE condition compared to the CE condition. Both groups
[(Fig._1)TD$IG]
R. Muelas Pe´rez et al. / Gait & Posture 40 (2014) 556–560
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Fig. 1. An example of the medio-lateral deviation of the platform tilt angle from the target horizontal position (08) for dancers and non-dancers in both visual conditions. increased their values in AE, SD and MV when standing on the platform in the CE condition. Regarding the differences between the groups (Table 3), the dancers showed better postural stability (lower absolute error and standard deviation) than non-dancers only in the OE condition (p < 0.05). Furthermore, MV was higher in dancers in both conditions (p < 0.05).
Table 2 Mixed ANOVA testing mean differences between the dancers and non-dancers (group) and the repeated measures factors of visual condition (vision) and interaction. F1,46
P
Vision Group Vision group
48.187 3.313 2.256
.001 .075 .140
SD
Vision Group Vision group
37.538 6.758 6.845
.001 .013 .012
MV
Vision Group Vision group
7.793 7.962 .120
.008 .007 .731
SampEn
Vision Group Vision group
19.108 2.288 5.804
.000 .137 .020
PE
Vision Group Vision group
3.064 13.324 24.708
.087 .000 .000
AE
AE, absolute error; SD, standard deviation; MV, mean velocity; SampEn, sample entropy; PE, permutation entropy.
The analysis of the complexity of the tilt platform excursion showed significant main effects for the visual condition in SampEn and for group in PE (Table 2). Nevertheless, the interaction effects indicate that the visual conditions affect the groups differently. Pairwise comparisons showed lower entropy values for dancers in CE condition compared with OE condition. In addition, dancers exhibited lower entropy than non-dancers in CE condition. The results indicate that dancers exhibit
Table 3 Mean and standard deviations of absolute error along the medio-lateral axis (AE), standard deviation (SD), mean velocity (MV), sample entropy (SampEn) and permutation entropy (PE) for dancers and non-dancers in the two visual conditions. N
Open eyes
Closed eyes
AE
ND D
30 18
11.45 1.11a 10.61 1.13
12.57 1.30B 12.34 1.24B
SD
ND D
30 18
12.85 1.60A 11.45 1.02
13.74 1.61B 13.41 0.81B
MV
ND D
30 18
40.82 11.69a 33.67 5.86
43.94 11.11A 36.01 3.45b
SampEn
ND D
30 18
0.094 0.030 0.096 0.028
0.082 0.037a 0.058 0.024B
PE
ND D
30 18
0.58 0.12 0.54 0.06
0.60 0.10A 0.44 0.07B
a Different from the dancers using the Bonferroni correction. Pairwise comparison, p < .05. A Different from the dancers using the Bonferroni correction. Pairwise comparison, p < .01. B Different from the open eyes condition using the Bonferroni correction. Pairwise comparison, p < .01.
R. Muelas Pe´rez et al. / Gait & Posture 40 (2014) 556–560 lower complexity, lower entropy values, when visual information is not available (Table 3). The results of the correlation analysis performed on the sample of non-dancers showed that there is a positive relationship between AE (r = .465, p < .05), SD (r = .504, p < .01) and MV (r = .786, p < .01) in the OE and CE conditions. Thus, the higher the performance stability test in OE condition, the greater the performance stability test in CE condition. However, the performance of dancers between the two stability tests is not significantly correlated. The better performance of the dancers in the OE condition is not associated with a higher yield in the test with CE. Comparing the absolute error of the platform angle with the complexity measured through SampEn, the results in the dancer group showed negative correlations between the AE and complexity of the tilt angle for both conditions. This behavior is exhibited in both the OE (r = .605, p .01) and CE conditions (r = .711, p .01). Similar correlations has been observed between SampEn and SD in OE condition (r = . 483, p < .05) and CE condition (r = .807, p < .01). Thus, as the complexity increases, the performance improves, regardless of the availability of visual information. The non-dancers also showed these negative correlations in both conditions between AE and SampEn (OE: r = .579, p < .01; CE: r = .594, p < .01), and between SD and SampEn (OE: r = .602, p < .01; CE: r = .648, p < .01).
4. Discussion The results are consistent with other studies in which performance on the stability test in the OE condition was better than the performance in the CE condition [11,15,20]. Moreover, and partially confirming the first hypothesis, the group of dancers performed better only on the balancing task with OE. These results may be because of the increased specialization of dancers in tasks in which postural control is regulated through visual feedback [11,21,22]. Rist states that dancers are used to regulate their posture through visual information from the initial moment of learning [23]. Teasdale et al. claim that a specialization in learning leads to a specialization in the information channel used in execution [24]. Thus, the greater the visual information used during the learning process, the greater the decrease in performance when vision is removed [25]. This idea was also observed by Gerbino et al., who observed highly significant reductions in the balance performance of dancers in the CE condition [26]. Our results show that the deprivation of visual information matches the performance of both groups in the balance task. These results confirm the dependence of dancers on visual information to regulate their posture, as argued by other authors in several studies [21,27]. Kavounoudias et al. suggested that superficial plantar mechanoreceptors provide the CNS with information relative to the position of the body with respect to the vertical reference [28]. Exclusion of the vision would render the CNS rely on vestibular and proprioception systems. Perrin et al. argued that dance training strengthens the accuracy of proprioceptive inputs in a lower way than other sports [27], developing specific modalities of balance not transferable to posture control in CE situations [21]. In relation to the entropy values, opposed to the hypothesis, the group of dancers shows lower behavioral complexity than nondancers during the tasks only in the CE condition. Indeed, comparing both visual conditions, only the dancers entropy values decrease in the CE condition, which is in contrast to the group of non-dancers, in which no difference was observed. Movement instabilities, in the form of critical fluctuations, or temporary losses in stability, are exhibited when a transition from one coordination pattern to another may be about to occur [29]. As argued previously, under CE condition dancers exhibit a shift in sensorimotor dominance from vision to proprioception reflected in a different coordination pattern. The reduced complexity observed in the dancer system may reflect an altered coordination strategy characterized by lower degrees of freedom in an adapting process to an unfamiliar task [30]. Consistent with the above-mentioned, Schmidt et al. argue that differences in the postural stability behavior of a dancer compared to other (physically active) groups are not in their better
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performance but in their system flexibility to adapt to different situations of postural control [11]. Thus, the differences would be more qualitative than quantitative. For this reason, we might have only observed differences between both groups in their behavioral complexity during the balance tasks in CE conditions, where difference in the performance between the groups practically disappears. Sensory deprivation increases the difficulty of the task, and the group of dancers changed their behavior, freezing their degrees of freedom in a reduced effectiveness of their strategy to stabilize and prevent falls, thus resulting in lower values of entropy [31]. Regarding the relationship between complexity and performance, we have found contradictory results. On the one hand, correlation analysis in both groups showed that the participants with higher complexity exhibited higher performance regardless of the availability of visual information. On the other hand, even though dancers exhibited better performance than non-dancers in OE, they there was not differences in complexity between groups. In addition, the decrease in performance of non-dancers in the CE condition compared to OE condition was not related to lower behavioral complexity. In this regard, the relationship between performance and complexity is not linear or is mediated by other variables [1]. Some authors suggested that an increase in entropy variables is not necessarily synonymous with an increase in physiologic complexity [13] and the relationship between entropy variables and physiologic complexity is not linear. In this sense, according with Stergiou and Decker, there would be inverse Ushape relationship between entropy variables and complexity [32]. In summary, the differences in balance ability between dancers and non-dancers are only meaningful in the OE condition, thus indicating that the best performance in the former group is coupled with the use of information from vision to control balance adjustments. The deprivation of visual information increases the difficulty of the task, which is more significant in dancers because of their specialization in learning through visual information. Decreases in the complexity of postural control in the dancers entail a decrease in performance. These results may be observed because of specific strategies developed to control a novel balance tasks. The non-linear analysis methods allow us to explain both the differences observed between the different levels of difficulty in the balance tasks and those observed between the different levels of expertise in the participants.
Conflict of interest statement There are no financial or personal relationships between any of the authors and other people or organizations that could inappropriately influence this study.
References [1] Vaillancourt DE, Newell KM. Changing complexity in human behavior and physiology through aging and disease. Neurobiol Aging 2002;23:1–11. [2] Winter DA, Patla AE, Frank JS. Assessment of balance control in humans. Med Prog Technol 1990;16:31–51. [3] Palmieri RM, Ingersoll CD, Stone MB, Krause BA. Center-of-pressure parameters used in the assessment of postural control. J Sport Rehabil 2003;11:51– 66. [4] Manor B, Costa MD, Hu K, Newton E, Starobinets O, Kang HG, et al. Physiological complexity and system adaptability: evidence from postural control dynamics of older adults. J Appl Physiol 2010;109:1786–91. [5] Lipsitz LA. Dynamics of stability: the physiologic basis of functional health and frailty. J Gerontol Ser A: Biol Sci Med Sci 2002;57:115–25. [6] Thurner S, Mittermaier C, Ehrenberger K. Change of complexity patterns in human posture during aging. Audiol Neurootol 2002;7:240–8. [7] Stins JF, Michielsen ME, Roerdink M, Beek PJ. Sway regularity reflects attentional involvement in postural control: effects of expertise, vision and cognition. Gait Posture 2009;30:106–9.
560
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[8] Roerdink M, Haart MD, Daffertshofer A, Donker SF, Geurts ACH, Beek PJ. Dynamical structure of center of pressure trafectories in patients recovering from stroke. Exp Brain Res 2006;174:256–69. [9] Duarte M, Sternad D. Complexity of human postural control in young and older adults during prolonged standing. Exp Brain Res 2008;191:265–76. [10] Borg FG, Laxa˚back G. Entropy of balance – some recent results. J Neuroeng Rehabil 2010;7:38. [11] Schmit JM, Regis DI, Riley MA. Dynamic patterns of postural sway in ballet dancers and track athletes. Exp Brain Res 2005;163(3):370–8. [12] Lamouth CJ, Van Lummel RC, Beek PJ. Athletic skill level is reflected in body sway: a test case for accelometry in combination with stochastic dynamics. Gait Posture 2009;29(4):546–51. [13] Goldberger AL, Peng CK, Lipsitz LA. What is physiologic complexity and how does it change with aging and disease? Neurobiol Aging 2002;23(1):23–6. [14] Haran FJ, Keshner EA. Sensory reweighting as a method of balance training for labyrinthine loss. J Neurol Phys Ther 2008;32:186–91. [15] Donker SF, Lebdet A, Roerdink M, Savelsberg JP, Beck PJ. Children with cerebral palsy exhibit greater and more regular postural sway than typically developing children. Exp Brain Res 2008;184(3):363–70. [16] Vuillerme N, Teasdale N, Nougier V. The effects of expertise in gimnastics on propioceptive sensory integration in human subjects. Neurosci Lett 2001;311(2):73–6. [17] Murray JF. Construction of a stabilometer capable of indicating the variability of non-level performance. Percept Mot Skills 1982;55:1211–5. [18] Cavanaugh JT, Mercer VS, Stergiou N. Approximate entropy detects the effect of a secondary cognitive task on postural control in healthy young adults: a methodological report. J Neuroeng Rehabil 2007;4:42. [19] Frank B, Pompe B, Schneider U, Hoyer D. Permutation entropy improves fetal behavioural state classification based on heart rate analysis from biomagnetic recordings in near term fetuses. Med Biol Eng Comput 2006;44:179–87. [20] Rogind H, Simonsen H, Era P, Bliddal H. Comparison of Kistler 9861A. Force Platform and Chattecx Balance System for measurement of postural sway: correlation and test–retest reliability. Scand J Med Sci Sport 2003;13(2):106–14.
[21] Hugel F, Cadopi M, Kohler F, Perrin P. Postural control of ballet dancers: a specific use of visual input for atistic purposes. Int J Sports Med 1999;20(2):86–92. [22] Schanfein L, Rietdyk S. The relationship between center of pressure displacement and estimated instability of dancers and non-dancers while in a moving room. In: Proceedings for the North American congress on biomechanics. MI: Ann Arbor; 2008, August. [23] Rist R. Dance science. The Dancing Times 1991, December;243. [24] Teasdale N, Stelmach GE, Breunig A. Postural sway characteristics of the elderly under normal and altered visual and support surface conditions. J Gerontol 1991;46B:238–44. [25] Proteau L. On the specificity of learning and the role of visual information in movement control. In: Proteau L, Elliot D, editors. Vision and Motor Control, North Holland: Amsterdam; 1992. p. 67–103. [26] Gerbino PG, Griffin ED, Zurakowski D. Comparison of standing balance between female collegiate dancers and soccer players. Gait Posture 2007;26(4): 501–7. [27] Perrin P, Deviterne D, Hugel F, Perrot C. Judo, better than dance, develops sensorimotor adaptabilities involved in balance control. Gait Posture 2002;15: 187–94. [28] Kavounoudias A, Roll R, Roll JP. The plantar sole is a ‘‘dynamometric map’’ for human balance control. NeuroReport 1998;9:3247–52. [29] Scho¨ner G, Haken H, Kelso JAS. A stochastic theory of phase transition in human hand movement. Biol Cybern 1987;53:247–57. [30] Davids K, Button C, Bennet S. Dinamical skill acquisition. A constraints-led approach. Champaign: Human Kinetics; 2007. [31] Amoud H, Snoussi H, Hewson DJ, Ducheˆne J. Intrinsic Mode Entropy for postural steadiness analysis. In: Proceedings for the 4th European conference of the international federation for medical and biological engineering, vol. 22; 2008. p. 212–5. [32] Stergiou N, Decker LM. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci 2011;30(5):869–88.
