Perceptual & Motor Skills: Motor Skills & Ergonomics 2015, 121, 1, 1-16. © Perceptual & Motor Skills 2015

POSTURAL CONTROL MECHANISMS IN HEALTHY ADULTS IN SITTING AND STANDING POSITIONS1 PILAR SERRA-AÑÓ AND LAURA LÓPEZ-BUENO Department of Physiotherapy, University of Valencia, Spain XAVIER GARCÍA-MASSÓ Department of Teaching of Musical, Visual and Corporal Expression, University of Valencia, Spain MARÍA TERESA PELLICE-CHENOLL AND LUIS-MILLÁN GONZÁLEZ Department of Physical Education and Sports, University of Valencia, Spain Summary.—This study explored differences in the center of pressure in healthy people in a sitting and standing position and with eyes open and closed. With this purpose, 32 healthy participants (16 men, 16 women; M age = 25.2 yr., SD = 10.0, range = 18–55) were measured with an extensiometric force plate. Using a two–way repeated-measures multivariate analysis of variance (MANOVA), the root mean square, velocity, range, and sway, in both visual conditions, had higher values in the standing task than in the sitting task. In the frequency domain, the low-frequency band had higher values during the standing task. For control mechanism variables, mean distance and time were greater when standing while mean peaks were greater when sitting. Thus, stability is worse in the standing position and more neuromuscular activity is required to maintain balance.

The central nervous system controls balance using sensory information from vestibular, visual, and proprioceptive feedback, which activates various relevant postural muscles, generating compensations and restricting deviations from the center of pressure (CoP; Isableu, Ohlmann, Crémieux, & Amblard, 1997). If some of these sensory inputs are disturbed, either by experimental manipulation or due to injury, a change in postural sway occurs (Diener & Dichgans, 1988). In fact, research has suggested that balance deficiencies in people with neurological problems can result from inappropriate interactions between the three sensory inputs that provide orientation information to the postural control system (Day, Guerraz, & Cole, 2002; Kaufman, Brey, Chou, Rabatin, Brown, & Basford, 2006). Previous balance training programs have also been developed to readjust these sensory alterations (Yelnik, Le Breton, Colle, Bonan, Hugeron, Egal, et al., 2008). Balance and its neuromuscular control can be quantified by several postural control measurements (Gribble & Hertel, 2004). Some of the Address correspondence to Pilar Serra-Añó, Department of Physiotherapy, University of Valencia, C/ Gascó Oliag, 5, 46010 Valencia, Spain or e-mail ([email protected]). 1

DOI 10.2466/26.25.PMS.121c10x4

ISSN 0031-5125

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parameters calculated in previous studies, such as the area covered by the CoP in both directions on the force plate surface (CoP area), describe the overall postural performance of the participants (Caron, Gelat, Rougier, & Blanchi, 2000). Other parameters, such as the mean CoP velocity, reflect the net neuromuscular activity required to maintain balance and characterize the postural control of the participants (Caron, et al., 2000). Furthermore, frequency domain analysis can assess the preferential involvement of short loops (proprioceptive, myotatic, and plantar cutaneous information) or long neuronal loops (vestibular information) in balance regulation when some perturbation is applied (Dietz, Mauritz, & Dichgans, 1980). The pathways of the different control loops are associated with different circuit delays, which underlie the frequencies of body sway. When the sensory information travels a long way to be processed and interpreted and the motor responses also follow a long displacement to reach the target muscles, the neurological delay (or circuit delay) is greater than in cases where the motor response is determined near to sensors and effectors. Spectral analysis of the frequency distribution of bodily oscillations allows the study of relative contributions of the different afferent systems to the regulation of balance function (Golomer, Dupui, & Bessou, 1994) as outlined below. In this regard, Dupui, Costes-Salon, Montoya, Séverac, Lazegues, Pagès, et al. (1990) analyzed the power spectra of the oscillations of the head and the body in participants standing on an unstable platform with vestibular, spastic, and cerebellar syndromes; they chose to cut the frequency spectrum into three bands with boundaries: below 0.5 Hz, which represents the band in which visual and vestibular contributors work; from 0.5 to 2 Hz, in which cerebellar contribution operates; and from 2 to 20 Hz, the band that covers proprioceptive contribution (Kapteyn, Bles, Njiokiktjien, Kodde, Massen, & Mol, 1983; Dupui, et al., 1990). On the other hand, it is important to check the effect of position on the neuronal processes involved in balance control. For this, an intermittent control mechanism has been considered, as suggested by Bottaro, Yasutake, Nomura, Casadio, and Morasso (2008). In this model, the postural control is performed by two different mechanisms: (i) the stiffness component of joint torque modulated by segmental reflexes (relatively stable motor commands) which avoids an incipient fall, and (ii) an anticipatory intermittent control that generates torque changes from one stable value to another. Sway density plot analysis has been proposed to establish the involvement of these mechanisms in static balance tasks (Baratto, Morasso, Re, & Spada, 2002; Jacono, Casadio, Morasso, & Sanguineti, 2004). Rehabilitation Understanding the underlying mechanisms of postural control is particularly necessary for achieving proper balance training when a patient is

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impaired. The design of such rehabilitation programs depends on which sensory contributor and control mechanism is affected and the extent to which it is affected. For this reason, it is necessary to assess the baseline condition of the contributors in different positions that could be used during the rehabilitation program and also to assess their evolution throughout the course of the treatment program. This enables clinicians to adapt the exercises of the balance rehabilitation program at different points in the program. Thus, it is possible that at the beginning of the rehabilitation, in which more comfortable and stable postures are used (i.e., sitting on a stable surface rather than standing), the exercises performed ought to be different from those performed in more unstable positions, because the contribution of each afferent system and control mechanism is different depending on the position. To be successful, all types of training require the application of the following basic training principles: (i) specificity, i.e., paying specific attention to the targeted function; (ii) progressive overload, i.e., providing a challenging overload to the physiological system through a certain level of intensity and regularity; and (iii) varied practice, i.e., promoting a variety of exercise conditions (Stergiou, Harbourne, & Cavanaugh, 2006; Kraemer, 2010). Consequently, such rehabilitation programs are gradual and begin with simpler exercises in easier postures, such as the sitting position, and grow increasingly complicated as the patient's strategies to maintain postural control and to perform standing or even monopodal activities improve. Rehabilitation of balance abilities requires a knowledge of the mechanisms of balance control. So far, data are available from time-domain analysis of balance of patients in a sitting position (Shirado, Kawase, Minami, & Strax, 2004; Vette, Masani, Sin, & Popovic, 2010) and a standing position (Collins & Luca, 1995; Caron, 2003; Mezzarane and Kohn, 2007; Nagy, Feher-Kiss, Barnai, Domján-Preszner, Angyan, & Horvath, 2007; Vette, et al., 2010), as well as from frequency-domain analysis in a standing position (Dietz, et al., 1980; Caron, 2003; Mezzarane & Kohn, 2007; Nagy, et al., 2007; Bizid, Jully, Gonzalez, François, Dupui, & Paillard, 2009; Vette, et al., 2010; Garkavenko, Gorkovenko, Kolosova, Korneyev, Mel'nichouk, & Vasilenko, 2012) and a sitting position (Karlsson, Norrlin, Silander, Dahl, & Lanshammar, 2000; Vette, et al., 2010). Studies of the neuronal process involved in maintaining balance have assessed the standing position (Lakie, Caplan, & Loram, 2003; Masani, Vette, & Popovic, 2006; Bottaro, et al., 2008; Loram, Gollee, Lakie, & Gawthrop, 2011; Suzuki, Nomura, Casadio, & Morasso, 2012) and the kneeling position (Mezzarane & Kohn, 2008). Nevertheless, apparently there is no evidence yet regarding differences in the contributions of the sensory systems and control mechanisms in healthy people in standing and sitting positions. Such an approach could be useful in prepar-

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ing a balance rehabilitation schedule during the initial stages of treatment, as it allows understanding of the contribution of each of the different systems involved in the postural maintenance of the sitting position (i.e., the position in which the program usually begins) and to adjust each program to allow for the compensatory mechanisms of the central nervous system, which is the basis of therapy (Konrad, Girardi, & Helfert, 1999). Research goal 1. To explore the CoP signal in the temporal domain to assess differences between the sitting and standing positions and between the eyes open and closed conditions. Research goal 2. To assess the patterns of sensory information from each contributor in the sitting and standing positions and between the eyes open and closed conditions by means of frequency analysis to identify potential future strategies for rehabilitation. Research goal 3. To assess the neuronal processes involved in balance control in the standing and sitting positions using as a reference an intermittent anticipatory control model. Hypothesis. The CoP pattern will reflect different involvement of the sensory contributors and control mechanisms depending on the position (i.e., sitting or standing position) and the visual condition of the participants. METHOD Participants The sample was obtained by a simple random sampling method in which all the possible participants of the accessible sample were numerated and then randomly selected. It was comprised of 32 healthy people (16 men, 16 women) with a mean age of 25.19 yr. (SD = 9.97, range = 18–55), a mean weight of 68.7 kg (SD = 14.2; range = 46.5–100.3), and a mean height of 1.69 m (SD = 0.75, range = 1.55–1.85). The presence of a risk of orthostatic hypotension, vestibular disorder, a pronounced visual deficiency, or any other disorder affecting the central or peripheral nervous systems was self-reported and was considered a contra-indication for participation in the study. When these exclusion criteria had been checked, all the selected participants signed the informed consent and all of the procedures were conducted in accordance with the principles of the World Medical Association's Declaration of Helsinki and were approved by the ethics committee of the University of Valencia. Measures One extensiometric force plate (DINASCAN®, IBV, Valencia, Spain) was used to assess sitting and standing balance in each participant. The

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platform consisted of a 600 × 370 mm plate with an active area and 100 mm height with four force transducers. The platform was placed on a stable surface on the floor to avoid distortion and noise in the signal. A portable, specially made stool, 70 cm in height, without a backrest and with the same base dimensions as the supportive base of the platform, was designed to perform the sitting assessment. This stool had adjustable footrests to assure a 90 º knee flexion in the sitting posture. The height of the stool footrest was taken into account for the calculations. Procedure The first part of the test consisted of a sitting task in two different conditions: one with eyes open and the other with eyes closed. The participants had to maintain their sitting position on the stool for 30 sec. with their arms relaxed alongside the trunk. They were asked to sit centered on the stool, with the sacrum at a 1 cm distance from the posterior edge of the stool. The second part of the test consisted of a standing task. The participants stood barefoot and still in a relaxed manner with their arms unfolded by their sides. The feet were placed according to the manufacturer's specifications (i.e., heels separated 2 cm and toes pointing outward at an angle of 20 º from the sagittal midline). In both tasks (sitting task and standing task), during the eyes-open condition, they looked at a mark placed at 1 m distance and at eye height. After a 1 min. rest, the eyes-closed condition was started. Two repetitions of each condition were conducted with 30 sec. of rest between them. All procedures were practiced before the assessment to be familiar with the tests. Analyses Signals were recorded at a frequency of 40 Hz using an amplified analog-to-digital converter. The data acquisition system recorded the center of pressure (CoP) patterns on the force plate during the tests performed by means of the software NedSVE/IBV (IBV, Valencia, Spain). Calculations of the sitting task were performed with the consideration that the center of pressure was recorded on the platform at a height of 70 cm (Fig. 1). MATLAB (2010b; MathWorks, Inc., Natick, MA, USA) was used for digital signal processing. Signals were filtered digitally using a fourth-order Butterworth low-pass filter with a 6-Hz cutoff frequency. The first and last 5 sec. of each attempt were discarded. In the temporal domain, the resultant distance was computed as a combination of the anterior-posterior (AP) and medial-lateral (ML) signals, i.e., resultant distance = √(AP2 + ML2). Then, the total range of movement of the CoP (range) as well as the mean velocity (MVel) were calculated. Additionally, the average energy of the signal was also calculated with the root mean square (RMS). The sway area [the area enclosed by the CoP

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8

6

AP Displacement (mm)

AP Displacement (mm)

8

4 2 0 –2 –4 –6

6 4 2 0 –2 –4 –6

–8 –8 –6 –4 –2

0

2

4

6

–8 –8 –6 –4 –2

8

7 6 5 4 3 2 1 0

10

15

20

Time (sec.)

4

6

8

7 6 5 4 3 2

0

25

5

10

15

20

Time (sec.)

25

1.0

12

PSD (mm /Hz)

10

0.8

2

2

2

8

1 5

14

PSD (mm /Hz)

0

ML displacement (mm)

8

RD Displacement (mm)

RD Displacement (mm)

ML displacement (mm)

8 6

0.6 0.4

4 0.2

2 0

0.0

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

Frequency (Hz)

3.5

3.5

3.0

3.0

SDP (sec.)

SDP (sec.)

Frequency (Hz)

2.5 2.0 1.5

2.5 2.0 1.5

1.0

1.0

0.5

0.5

0.0

0.0 5

10

15

20

Time (sec.)

25

5

10

15

20

Time (sec.)

25

FIG. 1. Experimental set-up and example of the signals recorded during an attempt with eyes open. From top to bottom: participant position over the platform; AP displacement = statokinesigram; RD = resultant distance; PSD = power spectral density of the signal; SDP = sway density plot.

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path per unit of time (sway)] was also calculated (Prieto, Myklebust, Hoffmann, Lovett, & Myklebust, 1996). In the frequency domain, power spectral density was calculated between 0.15 and 6 Hz (resolution = 0.039 Hz) using the Fast Fourier Transform algorithm (MATLAB “Periodogram” function; rectangular window of 1,024 data points; non-overlap). The 95% power frequency (95% frequency) of the spectrum and the median frequency were calculated. The percentage distribution of the spectral energy in three frequency bands was also calculated, as follows (Bizid, et al., 2009): low frequency, 0.15– 0.5 Hz; intermediate frequency, 0.5–2 Hz; and high frequency, 2–6 Hz. Finally, a sway density plot analysis was conducted as described by Jacono, et al. (2004) and Baratto, et al. (2002). The number of consecutive samples during which the CoP remained inside a 1 mm radius was computed. A 1 mm radius was used instead of a 3 mm radius (recommended) because in the sitting position a large radius meant that all the data points of each trial were inside the circle. The sample count was multiplied by the sample period to obtain a time versus time curve, representing the evolution of the stability of the CoP over time. Once the sway density plot was computed, it was filtered in both direct and reverse directions using a fourth order Butterworth filter with a 2.5 Hz cutoff frequency. The peaks of the sway density plot were then averaged to compute the Mean Peak. Moreover, the Mean Distance and Mean Time between consecutive peaks were computed. Statistical analysis was performed using PASW Version 18 (SPSS, Inc., Chicago, IL, USA). The normality assumption was checked with the Kolmogorov–Smirnov test and homoscedasticity was calculated by Levene's test values. When homoscedasticity was assumed, the F test ratio was used. In the case of heteroscedasticity, the Satterthwaite approximation was used to adjust the degrees of freedom. The effects of position (i.e., sitting and standing tasks) and visual condition (i.e., eyes open and eyes closed) were statistically analyzed using two-way repeated-measures multivariate analyses of variance (MANOVA). Multiple comparison techniques were requested using the Bonferroni correction. A p value of .05 was considered statistically significant. RESULTS Once normal distribution and the homoscedasticity of the variance were confirmed, multivariate contrasts were performed, in which all the variables are taken into consideration. The main effects of task (F11, 21 = 12.99, p < .001, η2p = 0.87) and the main effects of visual condition (F11, 21 = 18.05, p < .001, η2p = 0.90) were significant. There was also an interaction effect between task and visual condition (F11, 21 = 7.34, p < .001, η2p = 0.79).

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Univariate contrasts revealed a major effect of the task (sitting and standing) on some variables. Specifically, maintaining the balance while standing generated significantly higher values for RMS, MVel, Sway, and range (Fig. 2). As regards frequency domain variables, the low frequency was higher in the standing than in the sitting task, while intermediate frequency, high frequency, median power frequency and 95% power frequency were lower in the standing than in the sitting task (Table 1). Finally, regarding sway density plot variables, Mean Distance, and Mean Time were higher in the standing than in the sitting task, but Mean Peak was lower in the standing than in the sitting task (Table 2).

FIG. 2. Differences between the sitting and standing tasks in average spectral power (RMS), MVel, range, and sway. The bars represent the mean and the error bars the SEM. RMS = root mean square; MVel = mean velocity. *Significant differences between the sitting (SI) and standing (ST) tasks.

Regarding the effect of visual condition, it was found that the lack of visual input generated significantly higher values for the following variables: RMS (F1, 31 = 18.20, p < .01, η2p = 0.37), MVel (F1, 31 = 158.18, p < .01, η2p = 0.84),

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POSTURAL CONTROL MECHANISMS TABLE 1 DIFFERENCES BETWEEN THE SITTING AND STANDING POSITIONS IN FREQUENCY DOMAIN VARIABLES Variable

Sitting Position

Low frequency band, %

Standing Position

M

SEM

M

SEM

37.9*

1.58

67.48

1.48

F1, 31

p

η2p

95.40

< .001

0.71

Intermediate frequency band, %

52.5*

1.35

30.87

1.35

80.11

< .001

0.72

High frequency band, %

9.61*

0.54

1.66

0.24

74.25

< .001

0.71

Median power frequency, Hz

0.81*

0.03

0.36

0.01

87.12

< .001

0.74

95% power frequency, Hz 2.31* 0.05 1.35 0.04 79.36 < .001 0.72 Note.—Data are expressed as means and SEM. *Significant differences related to the standing position.

range (F1, 31 = 18.78, p < .01, η2p = 0.38), Mean Distance (F1, 31 = 151.93, p < .001, η2p = 0.83), and Mean Time (F1, 31 = 7.97, p = .008, η2p = 0.2). Finally, these contrasts revealed an effect of the position × visual condition interaction on RMS (F1, 31 = 10.45, p = .003, η2p = 0.25), MVel (F1, 31 = 48.12, p < .001, η2p = 0.61), range (F1, 31 = 24.38, p < .001, η2p = 0.44), 95% power frequency (F1, 31 = 12.59, p < .01, η2p = 0.29), Mean Distance (F1, 31 = 44.96, p < .001, η2p = 0.59), and Mean Peak (F1, 31 = 8.05, p = .008, η2p = 0.21). The pairwise comparisons related with these interaction effects can be seen in Fig. 3. The differences in all the variables between the sitting and standing tasks were significant for both the eyes-closed and eyes-open conditions (ps < .05). Furthermore, these differences were larger when the participants maintained their eyes closed: average signal power (RMS), Mvel, Range, and Mean Distance were larger in the eyes-closed than in the eyesopen condition in the standing task but not in the sitting task. Nevertheless, 95% power frequency and Mean Peak were larger in eyes-open than in the eyes-closed condition when the participants were in the standing position, and 95% power frequency was lower in the eyes-open than in the eyes-closed condition when the participants were in the sitting position. TABLE 2 DIFFERENCES BETWEEN THE SITTING AND STANDING POSITIONS IN SWAY DENSITY PLOT VARIABLES Variable

Sitting Position M

SEM

Standing Position M

SEM

F1, 31

p

η2p

M distance, mm

0.83*

0.12

3.14

0.19

50.33 < .001

0.62

M peaks, sec.

1.27*

0.10

0.59

0.04

19.36 < .01

0.38

M time, sec. 0.47* 0.004 0.50 0.004 14.54 .01 0.32 Note.—Data are expressed as means and SEM. *Significant differences related to the standing position.

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FIG. 3. Interaction effect between task and visual condition in postural control variables. Squares represent the mean and the error bars the SEM. *Significant differences between eyes-open and eyes-closed conditions (p < .05). RMS = average spectral power; SI: sitting task; ST: standing task.

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DISCUSSION The goal of this study was to analyze, through the time and frequency domains (i.e., spectral characteristics), the patterns of the CoP signals, as well as the underlying postural control mechanism by means of a sway density plot, in two different common positions: standing and sitting. These different postures induced differential changes in both domains and in the control mechanisms involved. In this respect, it was shown that in the standing task the postural performance was worse and more neuromuscular activity would be required to maintain balance. The contribution of the different mechanisms involved in the maintenance of postural control in both positions has been established. The changes in the power spectral density could reflect the actions of the short- and long-term underlying processes of postural control. In the standing task, the anticipatory motor commands generate longer shifts of the CoP than in the sitting task (i.e., a higher Mean Distance). Moreover, in the standing task the CoP was stable in less time periods than in the sitting task (i.e., a lower Mean Peak). It was observed that the RMS was larger when standing with the eyes open than when sitting in the same visual condition. This pattern was also seen in the total range of CoP, which was 4.46 times greater during the standing task than the sitting task, in the MVel (1.56 times greater), and in the sway of the CoP (17.43 times greater). This pattern was also reproduced in the eyes-closed condition. These results were consistent with previous studies that assessed both positions (i.e., standing and sitting; Vette, et al., 2010), as well as other studies that found higher average signal power (RMS) while standing compared with the kneeling position (Mezzarane & Kohn, 2008). The decrease in postural sway in the sitting task, as well as the decreases in the other parameters, could have been expected due to the altered biomechanics of this position. For example, the location of the center of mass nearer to the surface onto which the CoP was applied (compared to the standing task) decreased the instability of the biomechanical system (Mezzarane & Kohn, 2008). This could be one of the reasons why clinicians tend to begin balance rehabilitation programs from this position, which is more easily maintained by patients. The results obtained showed that the sensorial contributors (i.e., visual and vestibular) represented by the lower frequency bands (< 0.5 Hz) showed higher values in the standing task than in the sitting task. Nevertheless, the cerebellum contributor, represented by the intermediate frequency band and the higher frequency band to which the proprioceptive contributor belongs, showed lower values in the standing task than in the sitting task. There were no significant differences in variables between frequency bands depending on the visual condition. That could be because the rest of the afference included in the low frequency band (i.e., vestibular) com-

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pensate for the absence of the visual input. However, some parameters, such as average signal power (RMS), mean velocity, range, and 95% power frequency, are increased in tests performed with eyes closed, which indicates that, although the afferences compensate for this lack of vision, it is more difficult to maintain one's balance without visual information. These results are consistent with previous studies in which the effect of visual input has been explored (Bizid, et al., 2009). However, although some previous studies explored tendencies among the contributions of different sensorial systems to maintain postural control while standing by means of frequency domain analysis (Dietz, et al., 1980; Caron, 2003; Bizid, et al., 2009; Vette, et al., 2010; Cabeza-Ruiz, García-Massó, Centeno-Prada, Beas-Jiménez, Colado, & González, 2011; Garkavenko, et al., 2012), no study to date has investigated the differences between sitting and standing positions with respect to the contribution of these systems (represented by the three frequency bands) in able-bodied people. As Paillard and colleagues explained in a previous study (Paillard, Chaubet, Maitre, Dumitrescu, & Borel, 2010), the power in the higher band is usually low in healthy individuals while standing still, but can be seen with aging, in postural pathology, and in dynamic postural conditions. From those previous studies, only Bizid, et al. (2009), who assessed the monopodal balance of able-bodied people, reported results concerning the different frequency bands involved in postural control. Although they did not report exact values for the bands, these values could be obtained from their graphs that showed a frequency band distribution of approximately 4.5% for the high frequency band, 41.2% for the intermediate frequency band, and 54.3% for the low frequency band. Although these data were obtained for postural control over one foot (a harder position in which to maintain balance than sitting or standing) and they computed the magnitude of the Fast Fourier Transformation (more suitable for deterministic signals) instead of spectral power density which was calculated in this study (i.e., squared Fast Fourier Transformation normalized by the time interval), the values were similar. As reported in the current results, a greater contribution of the lower band frequency was obtained in the standing task than in the sitting task, which indicates that the visual and vestibular systems were more highly demanded in the former position. This could be due to the higher center of gravity placement of the participants in this position, which makes this position more complicated to maintain than sitting. This finding may be one of the reasons for starting rehabilitation programs in the sitting position, especially among patients with visual or vestibular disorders. However, lower values in the higher bands were observed in the standing position compared with the sitting position. It could be assumed that in the sitting position there was an absence of proprioceptive input

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from the sole of the foot and from the structures related to the ankle. Therefore, the postural control system in this position had to rely on the available somatosensory inputs it received from structures associated with the thighs and trunk muscles, as well as on the visual and vestibular inputs (Mezzarane & Kohn, 2008). This decrease in the somatosensory input in the central nervous system therefore produced too much demand on the available proprioceptive effectors. A similar observation has been made among people with spinal cord injuries when the afferent input from the spinal cord is reduced, resulting in a more complex processing of the remaining input and a stronger activation (Bruehlmeier, Dietz, Leenders, Roelcke, Missimer, & Curt, 2001). An additional possible physiological explanation for the higher relative power observed at the highest spectral frequencies during sitting relates to the fiber composition of the thigh muscles, which include fast fibers, in comparison with the soleus muscle, which is composed almost exclusively of slow fibers (Mezzarane & Kohn, 2008). Due to the fact that in a sitting position the proprioceptive afferent system is more demanded than when standing, it may be suitable to reinforce the implementation of proprioceptive exercises that work within this frequency band. Nevertheless, when standing position is used during the rehabilitation program the exercises should work on visual and vestibular afferences because the lower band increases in this position. Finally, the data revealed differences between the sitting and standing tasks in the control mechanism involved in maintaining balance. The Mean Peak variable (which provides information about the Mean Time interval in which the balance is maintained by joint stiffness modulated by spinal reflexes) was lower in the standing task than in the sitting task. Nevertheless, the Mean Distance variable was higher in the standing than in the sitting task: there was a higher anticipatory response torque in the standing task. Mezzarane and Kohn (2008) also found differences in the neural system mechanisms involved in maintaining balance in the standing and kneeling positions. Nevertheless, they used a continuous PID controller to model the neural system and implemented a parameter optimization method to determine the differences in proportional, derivative, and integral gain between the two positions (Mezzarane & Kohn, 2008). Limitations and Conclusions One must take into consideration that the sitting position was not arranged on a comfortable seat, such as a chair with a backrest, but rather on a 70-cm stool, which in itself could have destabilized the participants. The proprioceptive system would quickly become saturated if the difficulty of the postural task was increased. More complex postural tasks require a strong contribution from the proprioceptive system, so the maintenance

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of balance could be strongly disturbed. Therefore, a longer period of familiarization may be required when using a stool to measure postural control while sitting. The conditions of this test could also mediate the contribution of the cerebellum, incrementing its activity throughout the test. However, no measure was used to explore the contribution of the cerebellum, so the assumptions made are based on the division of the power spectra calculations of previous studies (Dupui, et al., 1990; Golomer, et al., 1994). It is important to note that there are limited data to support the relationships between sensory system regulation and frequency band energy content. Nevertheless, if future research demonstrates that this theory is wrong, the results presented in this paper will remain important. The differences in variables found among frequency bands for different tasks (body positions) could be used to improve rehabilitation processes once a compendium of exercises that improve the frequency content pattern has been established. The temporal domain parameters were greater in standing position; therefore, postural control and performance are more difficult in this position. The percentage of energy in the frequency bands involved in balance was different for sitting and standing positions. These results are useful in planning a rehabilitation program for improving balance in patients with balance impairments. Gradual exercises, normally starting from a sitting position, should consist mostly of proprioceptive exercise on unstable surfaces. After patients begin to perform exercises in a standing position, clinicians should offer some exercises during which visual and vestibular information could be modified. Nevertheless, a more complete study is needed to assess balance in healthy people. Such a study should add perturbations of the surface in both the sitting and standing positions to explore proprioceptive behaviors in order to plan more specific exercises. Finally, the neural control mechanisms involved in maintaining balance in the sitting and standing tasks were different. When standing, intermittent control by means of central neuronal commands produces a greater anticipatory muscle torque. Moreover, in this position the time for which balance can be maintained using passive joint stiffness and reflex modulation is briefer. REFERENCES

BARATTO, L., MORASSO, P. G., RE, C., & SPADA, G. (2002) A new look at posturographic analysis in the clinical context: sway-density vs. other parameterization techniques. Motor Control, 6, 246-270. BIZID, R., JULLY, J. L., GONZALEZ, G., FRANÇOIS, Y., DUPUI, P., & PAILLARD, T. (2009) Effects of fatigue induced by neuromuscular electrical stimulation on postural control. Journal of Science and Medicine in Sport, 12(1), 60-66. BOTTARO, A., YASUTAKE, Y., NOMURA, T., CASADIO, M., & MORASSO, P. (2008) Bounded stability of the quiet standing posture: an intermittent control model. Human Movement Science, 27(3), 473-495.

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