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An overview of age-related changes in postural control during quiet standing tasks using classical and modern stabilometric descriptors
Journal of Electromyography and Kinesiology 19 (2009) e513–e519
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An overview of age-related changes in postural control during quiet standing tasks using classical and modern stabilometric descriptors Taian de Mello Martins Vieira a,b,*, Liliam Fernandes de Oliveira b, Jurandir Nadal c a
LISiN, Electronics Department, Polytechnic of Turin, Corso Duca degli Abruzzi 24, Torino 10129, Italy Biomechanics Laboratory, Bioscience Department, Federal University of Rio de Janeiro, Brazil c LAPIS, Biomedical Engineering Department, Federal University of Rio de Janeiro, Brazil b
a r t i c l e
i n f o
Article history: Received 13 June 2008 Received in revised form 27 October 2008 Accepted 27 October 2008
Keywords: Postural balance Aging Stabilometric descriptors Signal processing
a b s t r a c t Age-related changes in postural control during quiet standing likely result from underlying pathological conditions or from the low specificity of classical stabilometric parameters, which are vulnerable to base of support configurations and anthropometric differences. This study focuses on the identification of changes in postural control with natural aging by using conventional and recent stabilometric analysis, and on the interpretation of the stabilometric parameters according to a recently proposed framework of postural control. Quiet standing stabilometric tests were applied to 57 subjects equally divided into young, middle-aged and aged groups (19–29, 38–51 and 65–73 years, respectively) with eyes open and closed conditions. In addition to estimation of classical descriptors, center of pressure time series were approached according to a diffusion-like process and the recently proposed sway density curve. Two out of 10 estimated descriptors identified between-group differences. Aged subjects exhibited higher sway frequencies, possibly resulting from the increase of torque bursts produced by the plantar flexors, and stronger negative correlation between consecutive center of pressure displacements observed for long time intervals, likely due to higher amplitude of plantar flexors torque. Aging itself does not result in major changes of postural stability, but reflects a small increase in plantar flexion torque amplitude and frequency of torque adjustments, probably to compensate for the lower stiffness of calf muscle tendon in aged subjects. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction The lack of postural stability may severely reduce the functional mobility in aged people, leading to social, physiological and psychological impairments and an increased risk of fall (Daley and Spinks, 2000). Loss of sensory information and decrease in muscle strength, generally observed in frail older adults, could be major causes of postural instability (Prieto et al., 1996; Romero and Stelmach 2003; Shumway-Cook et al., 1997). On the other hand, sensory and musculo-skeletal constraints likely result from pathological conditions or catastrophes instead of being a natural consequence of aging, so that the response of older patients to a same destabilizing stimulus depends on the underlying neuromuscular disease (Shupert and Horak, 1999). Divergent results concerning body sway stabilization in the elderly, by means of center of pressure (COP) assessment during quiet standing, possibly reflect the between-subject variability to
* Corresponding author. Address: LISiN, Electronics Department, Polytechnic of Turin, Corso Duca degli Abruzzi 24, Torino 10129, Italy. Tel.: +39 011 4330476; fax: +39 011 4330404. E-mail address: [email protected] (T. d. M. M. Vieira). 1050-6411/$34.00 Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2008.10.007
compensate for center of gravity (COG) spontaneous sways (Prieto et al., 1996; Benjuya et al., 2004; Choy et al., 2003), or the low specificity of classical stabilometric descriptors (Lafond et al., 2004). Furthermore, the interpretation of stabilometric data measured from a quiet standing task is so far subjective, and covers an assortment of alternatives to account for the postural control mechanisms, ranging from reflexes (Schieppati et al., 2001) to a predictive trial-and-error controller (Loram et al., 2005). Therefore, care should be taken when associating postural instability with aging. Measuring individual responses during perturbed stance is an alternative to assess balancing abilities (Horak and Hlavacka, 2001), however, such a protocol may be hazardous, costly, time consuming and may not expose the intrinsic control mechanisms elicited during unperturbed stance (Baratto et al., 2002). Concerning the choice of COP sway descriptors to assess postural control during quiet standing, some studies focused on the identification of: (1) reliable and non-redundant stabilometric descriptors, providing recommendations to minimize the influence of anthropometry and biomechanical factors and increase the intraclass correlation coefficient (Lafond et al., 2004; Chiari et al., 2002), (2) stabilometric parameters physiologically related to the controlling mechanisms (Prieto et al., 1996) and (3) meaningful
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physiological information by modeling the COP experimental data according to a sway density curve (Baratto et al., 2002; Jacono et al., 2004) or to a diffusion-like process (Collins and DeLuca, 1993). If changes in postural stability with aging result from a common underlying pathology, the assessment of stabilometric data in healthy young, middle-aged and older adults, according to the above mentioned proposals, may provide reliable insights about the natural time course of postural control system. Besides the use of reliable stabilometric descriptors, the interpretation of COP data recorded from quiet standing tests may be critical for assessing changes in the postural control system with aging. The role of reflex control, for example, seems to be marginal or even absent during orthostatic posture (Loram et al., 2005), so that postural sways may not result from reactive and corrective commands triggered by the postural control system, but from a trial-and-error mechanism for producing intermittent and anticipatory bursts of torque to compensate for a compliant linkage, the Achilles tendon (Lakie et al., 2003; Loram et al., 2005). This study aims to identify changes in the postural control system with healthy aging by means of: (1) the estimation of classical and modern stabilometric descriptors that are reliable and not affected by anthropometric effects, (2) the interpretation of our findings according to the recently proposed trial-and-error paradigm of postural control. By considering this paradigm, we hypothesized that natural aging would lead to minor changes of balancing abilities, mostly due to changes in the stiffness of the Achilles tendon rather than a generalized sensorial loss. 2. Methods 2.1. Subjects Fifty seven volunteers equally divided into three groups, young, middle-aged and aged adults (Table 1), were enrolled in the study after providing informed consent and with approval from the institutional ethical committee. Subjects reporting circulatory, respiratory, musculo-skeletal, peripheral or central neurological dysfunctions, fall history, abusive use of alcohol or intake of drugs that affect balance, like benzodiazepine, were excluded from the experiment. Functional equivalence between groups was evaluated by means of an extensive questionnaire that included issues related to individual performance during daily life activities (i.e. carrying baggage, climbing stairs). 2.2. Experimental setup Subjects were instructed to stand barefoot on a forceplate for 60 s with arms along the body, heels separated by six centimeters, 10° of feet abduction and facing a wall two meters distant to avoid foot positioning and visual field effects on the measurements (Chiari et al., 2002; Stoffregen et al., 2000). Each subject repeated this procedure ten times, five with eyes open (EO) and five with eyes closed (EC), resting three minutes between trials. Center of pressure time series was measured using a tri-axial forceplate (AccusWayPLUSS, AMTI, Watertown, EUA) with integrated A/D converter (16 bits resolution, ±10 V dynamic range).
Table 1 Sample description, mean (SD).
Age (years) Weight (kg) Height (m) *
COP data were sampled at 100 Samples/s using the software Balance Clinic supplied by the manufacturer, and stored on a personal computer. 2.3. Classical stabilometric descriptors The first 15 s of each record was discarded to avoid transients and the remaining data was low-pass filtered, in forward and backward directions to eliminate phase lag, by using a 2nd order Butterworth filter with 12.5 Hz cutoff frequency. Mean velocity (MV) and the frequency band (F80) that encompasses 80% of the area under COP power density spectrum (Baratto et al., 2002), were estimated for COP data along the anterior–posterior axis. Amplitude spectra were estimated by partitioning the entire 45 s record into seven 50% overlapped epochs of 11.25 s (using the hanning winow), with spectral resolution of 0.08 Hz and reduced variance due to segmentation (Shiavi, 1999). The elliptic area (area) encompassing 85% of COP samples in 45 s was evaluated by using Principal Component Analysis to estimate ellipse axes, as proposed by Oliveira et al. (1996). 2.4. Modern stabilometric descriptors In addition to the classical descriptors MV, F80 and area, COP time series were evaluated according to the recently proposed sway density curve (SDC) (Jacono et al., 2004) and with the assumption that COP displacement resembles a fractal Brownian motion (Collins and DeLuca, 1993). The SDC consists in a time-dependent graphical representation of the stabilometric record, created by counting the number of consecutive COP samples falling inside a circle of 2.5 mm radius and centered at each COP sample (Fig. 1a). Peaks of the resultant curve (Fig. 1b) correspond to time instants of momentary COG stabilization (i.e. ankle torque breaks a forward sway, slowing down COG displacements), while valleys relate to shifts between these stabilization events. SDC parameterization provides three descriptors: (1) the mean amplitude of peaks (MP) describes the degree of postural stability, (2) the mean distance between successive peaks (MD) relates to the amount of change in torque required for stabilization and (3) the mean time interval between successive peaks (MT) is how often torque bursts are produced. Normalizing the SDC with respect to sampling frequency confers time dimension to MP, thus representing the mean time spent by COP inside the given circle. The correlation between successive COP displacements and the degree of randomness (noise) within the postural control system were assessed by considering COP as a random walker. Features of the random walker are described by the log–log stabilogram diffusion plot (SDF), where COP mean square displacements are plotted versus the duration of its actual displacements in a logarithmic scale (Chiari et al., 2000). was computed by averaging the squared distance between consecutive COP samples observed for time intervals equal to mDt (Fig. 1c), where m is an integer ranging from 1 to 1000 and Dt is the sampling period (0.01 s). The slope H and intercept K of the best line fitted in a least square sense to the experimental log–log SDF (Fig. 1d), according to Chiari et al. (2000), gives the correlation between COP displacements and the stochastic degree of postural control for short (Hs and Ks) and long (Hl and Kl) time intervals, respectively. 2.5. Height normalization and statistical analysis
Younger (n = 19)
Middle-aged (n = 19)
Aged (n = 19)
22.78 (2.3) 72.58 (12.5) 1.73 (0.08)
44.00 (4.6) 72.14 (11.8) 1.70 (0.08)
68.6 (2.4) 67.0 (16.7) 1.59 (0.11)*
Non parametric ANOVA Kruskal–Wallis test (p = 0.002).
As young and middle-aged groups were significantly taller than older adults, height effect on the stabilometric parameters was suppressed by normalizing the stabilometric descriptors significantly correlated to height (Chiari et al., 2002). The normalization
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Fig. 1. Schematics of SDC and log–log diffusion plot computations. (a) Statokinesigram of a single subject from 15 (d) to 25 s (j) with a circle centred on the sample S. Note that only consecutive samples inside the circle are counted (thicker line). (b) SDC computed from the record in (a), normalized to sampling frequency, and the peak produced at sample S (21.3s). (c) COP displacements (Dr in millimetres) used to compute the log–log plot (d) of COP mean square displacements versus time interval (Dt in seconds), ranging from 0.01 to 10 s. The slopes (Hs,l) and intercepts (Ks.l) are estimated from the two characteristic regions of log–log plot.
procedure, as proposed by O’Malley (1996), consisted in: (1) computing the mean value for the stabilometric descriptors statistically correlated to height, (2) estimating the regression line between each stabilometric descriptors and height, (3) subtracting the regression line from the observed data for each descriptor and (4) adding the mean value computed in step 1 to the resulting data. This procedure avoids residual correlation and rescales the data to its actual range. The values estimated for each stabilometric descriptor from the five trials performed by each subject, for eyes open and eyes closed conditions separately, were averaged to improve reliability (Lafond et al., 2004), thus, resulting into a single value. Since the data distribution for each descriptor was Gaussian (Lilliefors test, p > 0.05 in all cases), the parametric ANOVA design (3 groups, 2 visual conditions), with visual condition as repeated measures (DawsonSaunders and Trapp, 2000), was applied to assess age and visual condition effects and possibly interaction between both factors. 3. Results
Fig. 2. Scatter plot of COP elliptic area (area) versus height for eyes open condition (aged = j, r = 0.51, p < 0.05 and young subjects = h, r = 0.52, p < 0.05).
area values uncorrelated to height and scaled to its actual range for young and elderly (Table 2).
3.1. Height effect Either with EO or EC condition, a significant correlation coefficient was observed only between height and area descriptor for young and aged groups, but not for middle-aged group, although with an opposite relation (Fig. 2). Elliptic area values exhibited a significant increase with height in the young group with EO (r = 0.52, p = 0.02) and an increasing trend with EC (r = 0.42, p = 0.07), however, in the aged group height and area were negatively correlated, either with EO (r = 0.51, p = 0.02) or EC (r = 0.48, p = 0.04). The normalization procedure provided elliptic
Table 2 Mean (SD) of COP elliptic area and correlation coefficient before (area, r) and after (arean, rn) height normalization in young and aged subjects, for EO and EC conditions.
Young Aged
*
EO EC EO EC
area (mm)
arean (mm)
r
rn
254.9 268.6 225.3 252.2
255.6 (128.9) 212.7 (143.4) 270.4(118.6) 226.6 (134.1)
0.42 0.52* 0.51* 0.48*
0.12 0.18 0.3
(125.2) (173.6) (103.9) (170.6)
Significant difference at p < 0.05.
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3.2. Visual condition differences
3.3. Age effect
Comparing each group according to visual conditions, F80, MV, MD, Ks and Kl increased when changing from EO to EC (p < 0.001, p = 0.002, p = 0.005, p = 0.012 and p = 0.033, respectively), while MP and Hl values decreased from EO to EC (p = 0.025 and p < 0.001, respectively) as depicted in Table 3. Eyes closed effect was the same for all groups (Fig. 3), suggesting that inhibition of visual information did not elicit changes in the stabilization mechanisms due to aging.
Two out of 10 stabilometric descriptors identified changes in postural control between groups (Table 3). Among the classical stabilometric parameters, F80 increased significantly (p = 0.002) from young and middle-aged to older adults when comparing groups with eyes open (Fig. 4a), reflecting the increase of sway frequencies with aging. Regardless of visual conditions Hl values were statistically lower (p = 0.012) in the aged group (Fig. 4b). In all cases Hl was less than 0.5 (Table 3), which corresponds to a negative correlation between successive COP displacements for time intervals longer than 1 s. The parametric test also indicated a significant, albeit relatively small (2%), decrease of Hs from the middle-aged to the aged group (p = 0.024). Even after normalization, the elliptic area values were not different between groups or visual conditions, as observed for MT and Hs as well.
Table 3 Mean (SD) of all stabilometric descriptors estimated for young, middle-aged and aged groups, with eyes open (EO) and eyes closed (EC). Each value corresponds to the average of five trials across 19 subjects for each group (n = 95). Young
Middle-aged
Aged
EC
EO
EC
EO
EC
9.95 (3.10) 212.7 (143.4) 0.37 (0.10)
8.90 (2.06) 265.9 (129.6) 0.26 (0.11)
11.35 (3.27) 261.6 (106.0) 0.43 (0.11)
9.84 (2.64)
11.28 (3.37) 226.6 (134.1) 0.43 (0.10)
0.59 (0.03) 2.82 (0.92) 1.73 (0.81)
0.59 (0.02) 3.39 (1.27) 1.53 (0.83)
0.58 (0.03) 2.77 (0.59) 1.63 (0.76)
0.58 (0.02) 3.75 (1.09) 1.25 (0.67)
0.57 (0.02)
log–log SDF Hs 0.76 (0.02) 0.21 Hl*,a (0.11) a 1.53 Ks (0.28) a Kl 1.33 (0.28)
0.76 (0.02) 0.15 (0.08) 1.66 (0.32) 1.44 (0.31)
0.76 (0.01) 0.22 (0.14) 1.59 (0.23) 1.37 (0.19)
0.77 (0.02) 0.11 (0.13) 1.80 (0.28) 1.52 (0.23)
0.76 (0.02)
EO Classical 8.60 MVa (2.36) area 255.6 (128.9) F80*,a 0.25 (0.10) SDC MT MDa MP
*
a
4. Discussion
Significant difference for the aged group (p < 0.05). EC effect for all groups (p < 0.05).
a
270.4(118.6) 0.35 (0.10)
3.07 (1.16) 1.47 (0.62)
0.12 (0.08) 1.63 (0.29) 1.45 (0.26)
0.58 (0.03) 3.54 (1.68) 1.31 (0.60) 0.75 (0.03) 0.07 (0.07) 1.73 (0.32) 1.52 (0.3)
By matching groups with respect to daily life activities and absence of balance-related disorders, we expected to identify natural changes in quiet standing stabilization mechanisms with aging, by means of reliable stabilometric descriptors and in light of the trialand-error postural control paradigm, proposed by Loram et al. (2005), to interpret our findings. 4.1. Reliability of stabilometric descriptors Concerning the reliability of stabilometric descriptors, five 45 s quiet standing trials provide intraclass correlation coefficients higher than 0.5 for MV and area but not for frequency descriptors (Lafond et al., 2004). However, the spectral averaging procedure applied in this study greatly reduces COP spectrum variance (Shiavi, 1999), probably increasing F80 reliability. Although Collins and DeLuca (1993) obtained reliable descriptors averaging a large ensemble of stabilogram diffusion plots for each subject, Chiari et al. (2000) obtained reliable estimates of H and K by selecting the appropriate boundaries for the short and long time interval regions in the log–log stabilogram diffusion plot, without ensemble
Fig. 3. Mean and standard deviation of MP, MV, MD, Ks and Kl stabilometric descriptors (see methods) estimated for young (Y), middle-aged (M) and aged (A) subjects, grouped according to eyes open (gray) and eyes closed (black) conditions. Note that vision effect between groups was the same for all descriptors. In all cases visual condition effect was significant (p < 0.02).
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Fig. 4. (a) COP power spectrum (seven 11.25 s epochs with 50% overlapping) and F80 descriptor estimated for each group and visual condition (EO = d; EC = s). b) Long time interval (Dt > 1 s) region of the log–log diffusion plot and the best linear fit estimated for each group in EO condition. Note the decreased slope (Hl) in the aged group. Spectra and log–log plots are averaged estimates of 95 realizations for each group (5 trials 19 subjects). * significant difference for aged group (F80, p = 0.002; Hl, p = 0.012).
averaging. Reliability of SDC parameters was adequately addressed elsewhere (Baratto et al., 2002). By using reliable stabilometric descriptors, any difference between young, middle-aged and aged groups would likely be attributable to age effects rather than random fluctuation of these descriptors. 4.2. Changes in postural control with age Among the classical stabilometric descriptors estimated in this study, only F80 identified age-related differences in postural control, contradicting previous studies that observed significant changes of other classical descriptors with aging (Prieto et al., 1996; Benjuya et al., 2004; Choy et al., 2003). Prieto et al. (1996), for example, observed a significant increase of COP mean velocity and elliptic area with age, suggesting an increased level of neuromuscular activity to regulate quiet standing posture. Probably, these differences could be accounted for by the base of support effect (Chiari et al., 2002) and unreliable descriptors estimated from a single 30 s trial (Lafond et al., 2004). Furthermore, Prieto et al. (1996) speculated about different control mechanisms to compensate for the absence of vision, since differences between EO and EC were identified by stabilometric parameters in time and hybrid (time-space) domain, respectively, in young and elderly. Choy et al. (2003) associated a significant increase of MV in women older than 60 years to postural instability, more evident with eyes closed than open, indicating an augmented reliance on visual information with age. Unlikely, the MV values reported in Table 3 exhibited an increasing trend with age, and a relative increase from EO to EC condition higher in young (15.4%) and middle-aged group (27.5%) than in the elderly (14.6%), corroborating previous findings (Benjuya et al., 2004). Benjuya et al. (2004) suggested that young adults respond to the lack of vision with remaining sensorial information while elderly increase ankle stiffness with cocontraction of lower limb muscles, leading to a reduced COP area, which was not confirmed in this study even after height normalization. In addition, eyes closure effect on the estimated variables did not change with age (Fig. 3), ruling out the contribution of age-dependent mechanisms to compensate for eyes closed condition.
It is tempting to delegate the loss of balancing ability to the absence of visual input, however, the suppression of sensory modalities seems to reduce the accuracy of adjustments in the ankle torque rather than affect the controlling mechanisms (e.g. the timing of shortenings of the calf muscles is not affected by independent sensory inputs, Lakie and Loram, 2006). Therefore, the increase of MD, MV, Ks and Kl with EC (Fig. 3) indicates higher torque level and neuromuscular activity to correct center of gravity (COG) sways, inducing a slight decrease of postural stability as detected by MP but not area parameter (Table 3). Such changes in torque level could possibly result from the diminished accuracy of modulations in the ankle torque when standing with eyes closed (Lakie and Loram, 2006). 4.3. Modulation of plantar flexors torque to correct body sways As the lower band of COP spectrum (<0.4 Hz) mainly reflects COG sways, changes in energy distribution toward higher frequencies accounts for changes in the ankle torque pattern (Baratto et al., 2002; Winter et al., 1998), so that the increased F80 values in aged group likely relates to increased ankle stiffness. Indeed, asking subjects to balance a real inverted pendulum Loram et al. (2001) observed that ankle stiffness increases according to sway frequency, so that ‘‘ankle impedance” is preferred to ankle stiffness. However, such increase may derive from calf muscle EMG modulations instead of a co-contraction strategy in healthy elderly, assuming that muscle tendon mechanical properties decrease with aging. Since stabilization of quiet standing posture results from impulsive bursts of plantar flexion torque to set Achilles tendon stiffness (Loram et al., 2001), and the amount of torque increases with tendon compliance (Lakie et al., 2003), the reduced stiffness of calf muscles tendon due to aging would require larger and faster changes in plantar flexion torque instead of co-contraction of tibialis anterior muscle. The question is, are there evidences for a decrease in tendon stiffness with aging? Very recently, Onambele and co-workers (2006) measured in vivo changes of Achilles tendon stiffness with aging, revealing a decrease of circa 20 N/mm from younger to older subjects. However, this decrease did not affect postural sway during bipedal stance, probably due to the COP size parameter used (i.e. area
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and RMS of COP displacements enclose equivalent features of a stabilometric record) or support base effects (Chiari et al., 2002), not mentioned in the study. Decomposing the ankle torque into passive and active fractions, Jacono et al. (2004) observed that SDC peaks are related to the active torque component, essential to increase ankle impedance in opposition to the toppling torque of gravity. Thus, the lack of statistical difference in MP values with age indicates small changes of postural control activity, which is strengthened by similar degrees of COP stochastic activity (Ks and Kl) reported on Table 3. The significant increase in COP anti-persistence motion (i.e. COP tendency to return to its initial position) indicated by stronger negative correlation (Hl) between successive COP displacements in the aged group (Fig. 4b), possibly results from the neural modulation of ankle torque to adjust tendon stiffness, as previously discussed. The hypothesis of ankle torque modulation to compensate for a compliant linkage in older adults could be clarified on the basis of co-activation and substitution among calf muscle synergists. McLean and Goudy (2004) observed that during low level (10% MVC) and long duration (60 min) isometric contraction of the plantar flexors, medial gastrocnemius and soleus were co-activated while medial gastrocnemius EMG activity was negatively correlated with soleus and lateral gastrocnemius EMG. Such strategies could be exaggerated with age, in order to minimize fatigue and assure the required amount of ankle torque to preserve postural stability, thus increasing the frequency of torque bursts. 4.4. Limitations Age-related differences observed in this study result from reliable estimates and the use of physiologically meaningful parameters (SDC and random walker approach) provided a substantial overview of postural stability, although EMG recordings could have confirmed the lack of co-contraction during quiet standing with aging. Since the parametric test revealed between-group differences only for the older subjects, as reflected by changes in Hl and F80, the effects of aging on stabilometric descriptors seems to be quite small, so that the assessment of changes in postural control with natural aging would benefit from a larger sample of young, middle-aged and aged subjects. The general limitation of studies assessing postural stability during quiet standing concerns the uncertainty about postural control underlying mechanisms, which comprise a number of different frameworks from which the trial-and-error is the most recently proposed (Loram et al., 2005).
5. Conclusions Aging itself does not result in significant changes of postural stability, as indicated by the sway density analysis. According to the predictive trial-and-error mechanism of postural control, the increase in COP frequencies and the stronger negative correlation between COP displacements in the older group may reflect a slight increase in the compliance of calf muscle tendon with age, rather than co-contraction of ankle flexors. Acknowledgements The authors are grateful to Prof. Roberto Merletti for his substantial comments and suggestions. This study was partially supported by the Brazilian Research Council (CNPq) and Brazilian Foundations FUJB and FAPERJ. The first author acknowledges the scholarships given by FAPERJ and CNPq Foundations.
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Taian de Mello Martins Vieira is graduated in Physical Education and obtained his Master of Science in Biomedical Engineering from the Federal University of Rio de Janeiro, Rio de Janeiro, Brazil, in January 2005. He taught courses in Biomechanics, Statistics and Postural Control as a fellow of the Laboratory of Biomechanics in Rio de Janeiro. He is PhD candidate at Polytechnic of Turin with a scholarship provided by the Brazilian Council of Technology and Research. His main research interests concern postural control, muscular mechanics and biological signal and image processing.
T.d.M.M. Vieira et al. / Journal of Electromyography and Kinesiology 19 (2009) e513–e519 Liliam Fernandes de Oliveira is graduated in Physical Education (1981) and Physical Therapy (1992), specialised in Human Performance Sciences (1982), with M.Sc. (1987) and D.Sc. (1997) degrees in Biomedical Engineering from the Federal University of Rio de Janeiro, Brazil. Developed one year project in the Biomechanics Lab. of the London University supervised by Dr. Donald Grieve (1988). She is Associate Professor and Head of the Biosciences Department and the Biomechanics Lab. of the Physical Education and Sports Institute at the Federal University of Rio de Janeiro, since 2000. Her initial interest is in muscle mechanics, electromyography and noninvasive postural evaluation, then postural control and stabilometry. Recently, she has started focusing on muscle morphology analysis with ultrasound image technique.
Jurandir Nadal is graduated in Electronic Engineer in 1979 from the Pontifical Catholic University of Rio de Janeiro, Brazil, and obtained M.Sc. and D.Sc. degrees in Biomedical Engineering from the Biomedical Engineering Program, Post-Graduate Engineering Institute, Federal University of Rio de Janeiro (COPPE/ UFRJ), Brazil, in 1981 and 1991, respectively. He joined the Biomedical Engineering Program at COPPE/UFRJ as a lecturer in 1985, where he was Head of Department (1995–1997) and is now Associate Professor (2006). He was Chairman of the Scientific Program Committee for the World Congress of Medical Physics and Biomedical Engineering (Rio, 1994) and Co-Chairman of the Publication Committee of the Annual International Meeting of the IEEE Engineering in Medicine and Biology Society (Cancu´n, 2003). He is member of the IEEE/EMBS and President
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(2006–2008) of the Brazilian Society of Biomedical Engineering (Vice-president, 2000–2002). He was also Editor in Chief of the Brazilian Journal of Biomedical Engineering (1996–2006). His current research interests are digital signal processing of biological signals, with particular interests in non-invasive cardiology (cardiovascular control and methods for monitoring and diagnostics), and biomechanics (body sway control and gait analysis).
Contents lists available at ScienceDirect
Journal of Electromyography and Kinesiology journal homepage: www.elsevier.com/locate/jelekin
An overview of age-related changes in postural control during quiet standing tasks using classical and modern stabilometric descriptors Taian de Mello Martins Vieira a,b,*, Liliam Fernandes de Oliveira b, Jurandir Nadal c a
LISiN, Electronics Department, Polytechnic of Turin, Corso Duca degli Abruzzi 24, Torino 10129, Italy Biomechanics Laboratory, Bioscience Department, Federal University of Rio de Janeiro, Brazil c LAPIS, Biomedical Engineering Department, Federal University of Rio de Janeiro, Brazil b
a r t i c l e
i n f o
Article history: Received 13 June 2008 Received in revised form 27 October 2008 Accepted 27 October 2008
Keywords: Postural balance Aging Stabilometric descriptors Signal processing
a b s t r a c t Age-related changes in postural control during quiet standing likely result from underlying pathological conditions or from the low specificity of classical stabilometric parameters, which are vulnerable to base of support configurations and anthropometric differences. This study focuses on the identification of changes in postural control with natural aging by using conventional and recent stabilometric analysis, and on the interpretation of the stabilometric parameters according to a recently proposed framework of postural control. Quiet standing stabilometric tests were applied to 57 subjects equally divided into young, middle-aged and aged groups (19–29, 38–51 and 65–73 years, respectively) with eyes open and closed conditions. In addition to estimation of classical descriptors, center of pressure time series were approached according to a diffusion-like process and the recently proposed sway density curve. Two out of 10 estimated descriptors identified between-group differences. Aged subjects exhibited higher sway frequencies, possibly resulting from the increase of torque bursts produced by the plantar flexors, and stronger negative correlation between consecutive center of pressure displacements observed for long time intervals, likely due to higher amplitude of plantar flexors torque. Aging itself does not result in major changes of postural stability, but reflects a small increase in plantar flexion torque amplitude and frequency of torque adjustments, probably to compensate for the lower stiffness of calf muscle tendon in aged subjects. Ó 2008 Elsevier Ltd. All rights reserved.
1. Introduction The lack of postural stability may severely reduce the functional mobility in aged people, leading to social, physiological and psychological impairments and an increased risk of fall (Daley and Spinks, 2000). Loss of sensory information and decrease in muscle strength, generally observed in frail older adults, could be major causes of postural instability (Prieto et al., 1996; Romero and Stelmach 2003; Shumway-Cook et al., 1997). On the other hand, sensory and musculo-skeletal constraints likely result from pathological conditions or catastrophes instead of being a natural consequence of aging, so that the response of older patients to a same destabilizing stimulus depends on the underlying neuromuscular disease (Shupert and Horak, 1999). Divergent results concerning body sway stabilization in the elderly, by means of center of pressure (COP) assessment during quiet standing, possibly reflect the between-subject variability to
* Corresponding author. Address: LISiN, Electronics Department, Polytechnic of Turin, Corso Duca degli Abruzzi 24, Torino 10129, Italy. Tel.: +39 011 4330476; fax: +39 011 4330404. E-mail address: [email protected] (T. d. M. M. Vieira). 1050-6411/$34.00 Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jelekin.2008.10.007
compensate for center of gravity (COG) spontaneous sways (Prieto et al., 1996; Benjuya et al., 2004; Choy et al., 2003), or the low specificity of classical stabilometric descriptors (Lafond et al., 2004). Furthermore, the interpretation of stabilometric data measured from a quiet standing task is so far subjective, and covers an assortment of alternatives to account for the postural control mechanisms, ranging from reflexes (Schieppati et al., 2001) to a predictive trial-and-error controller (Loram et al., 2005). Therefore, care should be taken when associating postural instability with aging. Measuring individual responses during perturbed stance is an alternative to assess balancing abilities (Horak and Hlavacka, 2001), however, such a protocol may be hazardous, costly, time consuming and may not expose the intrinsic control mechanisms elicited during unperturbed stance (Baratto et al., 2002). Concerning the choice of COP sway descriptors to assess postural control during quiet standing, some studies focused on the identification of: (1) reliable and non-redundant stabilometric descriptors, providing recommendations to minimize the influence of anthropometry and biomechanical factors and increase the intraclass correlation coefficient (Lafond et al., 2004; Chiari et al., 2002), (2) stabilometric parameters physiologically related to the controlling mechanisms (Prieto et al., 1996) and (3) meaningful
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physiological information by modeling the COP experimental data according to a sway density curve (Baratto et al., 2002; Jacono et al., 2004) or to a diffusion-like process (Collins and DeLuca, 1993). If changes in postural stability with aging result from a common underlying pathology, the assessment of stabilometric data in healthy young, middle-aged and older adults, according to the above mentioned proposals, may provide reliable insights about the natural time course of postural control system. Besides the use of reliable stabilometric descriptors, the interpretation of COP data recorded from quiet standing tests may be critical for assessing changes in the postural control system with aging. The role of reflex control, for example, seems to be marginal or even absent during orthostatic posture (Loram et al., 2005), so that postural sways may not result from reactive and corrective commands triggered by the postural control system, but from a trial-and-error mechanism for producing intermittent and anticipatory bursts of torque to compensate for a compliant linkage, the Achilles tendon (Lakie et al., 2003; Loram et al., 2005). This study aims to identify changes in the postural control system with healthy aging by means of: (1) the estimation of classical and modern stabilometric descriptors that are reliable and not affected by anthropometric effects, (2) the interpretation of our findings according to the recently proposed trial-and-error paradigm of postural control. By considering this paradigm, we hypothesized that natural aging would lead to minor changes of balancing abilities, mostly due to changes in the stiffness of the Achilles tendon rather than a generalized sensorial loss. 2. Methods 2.1. Subjects Fifty seven volunteers equally divided into three groups, young, middle-aged and aged adults (Table 1), were enrolled in the study after providing informed consent and with approval from the institutional ethical committee. Subjects reporting circulatory, respiratory, musculo-skeletal, peripheral or central neurological dysfunctions, fall history, abusive use of alcohol or intake of drugs that affect balance, like benzodiazepine, were excluded from the experiment. Functional equivalence between groups was evaluated by means of an extensive questionnaire that included issues related to individual performance during daily life activities (i.e. carrying baggage, climbing stairs). 2.2. Experimental setup Subjects were instructed to stand barefoot on a forceplate for 60 s with arms along the body, heels separated by six centimeters, 10° of feet abduction and facing a wall two meters distant to avoid foot positioning and visual field effects on the measurements (Chiari et al., 2002; Stoffregen et al., 2000). Each subject repeated this procedure ten times, five with eyes open (EO) and five with eyes closed (EC), resting three minutes between trials. Center of pressure time series was measured using a tri-axial forceplate (AccusWayPLUSS, AMTI, Watertown, EUA) with integrated A/D converter (16 bits resolution, ±10 V dynamic range).
Table 1 Sample description, mean (SD).
Age (years) Weight (kg) Height (m) *
COP data were sampled at 100 Samples/s using the software Balance Clinic supplied by the manufacturer, and stored on a personal computer. 2.3. Classical stabilometric descriptors The first 15 s of each record was discarded to avoid transients and the remaining data was low-pass filtered, in forward and backward directions to eliminate phase lag, by using a 2nd order Butterworth filter with 12.5 Hz cutoff frequency. Mean velocity (MV) and the frequency band (F80) that encompasses 80% of the area under COP power density spectrum (Baratto et al., 2002), were estimated for COP data along the anterior–posterior axis. Amplitude spectra were estimated by partitioning the entire 45 s record into seven 50% overlapped epochs of 11.25 s (using the hanning winow), with spectral resolution of 0.08 Hz and reduced variance due to segmentation (Shiavi, 1999). The elliptic area (area) encompassing 85% of COP samples in 45 s was evaluated by using Principal Component Analysis to estimate ellipse axes, as proposed by Oliveira et al. (1996). 2.4. Modern stabilometric descriptors In addition to the classical descriptors MV, F80 and area, COP time series were evaluated according to the recently proposed sway density curve (SDC) (Jacono et al., 2004) and with the assumption that COP displacement resembles a fractal Brownian motion (Collins and DeLuca, 1993). The SDC consists in a time-dependent graphical representation of the stabilometric record, created by counting the number of consecutive COP samples falling inside a circle of 2.5 mm radius and centered at each COP sample (Fig. 1a). Peaks of the resultant curve (Fig. 1b) correspond to time instants of momentary COG stabilization (i.e. ankle torque breaks a forward sway, slowing down COG displacements), while valleys relate to shifts between these stabilization events. SDC parameterization provides three descriptors: (1) the mean amplitude of peaks (MP) describes the degree of postural stability, (2) the mean distance between successive peaks (MD) relates to the amount of change in torque required for stabilization and (3) the mean time interval between successive peaks (MT) is how often torque bursts are produced. Normalizing the SDC with respect to sampling frequency confers time dimension to MP, thus representing the mean time spent by COP inside the given circle. The correlation between successive COP displacements and the degree of randomness (noise) within the postural control system were assessed by considering COP as a random walker. Features of the random walker are described by the log–log stabilogram diffusion plot (SDF), where COP mean square displacements
Younger (n = 19)
Middle-aged (n = 19)
Aged (n = 19)
22.78 (2.3) 72.58 (12.5) 1.73 (0.08)
44.00 (4.6) 72.14 (11.8) 1.70 (0.08)
68.6 (2.4) 67.0 (16.7) 1.59 (0.11)*
Non parametric ANOVA Kruskal–Wallis test (p = 0.002).
As young and middle-aged groups were significantly taller than older adults, height effect on the stabilometric parameters was suppressed by normalizing the stabilometric descriptors significantly correlated to height (Chiari et al., 2002). The normalization
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Fig. 1. Schematics of SDC and log–log diffusion plot computations. (a) Statokinesigram of a single subject from 15 (d) to 25 s (j) with a circle centred on the sample S. Note that only consecutive samples inside the circle are counted (thicker line). (b) SDC computed from the record in (a), normalized to sampling frequency, and the peak produced at sample S (21.3s). (c) COP displacements (Dr in millimetres) used to compute the log–log plot (d) of COP mean square displacements
procedure, as proposed by O’Malley (1996), consisted in: (1) computing the mean value for the stabilometric descriptors statistically correlated to height, (2) estimating the regression line between each stabilometric descriptors and height, (3) subtracting the regression line from the observed data for each descriptor and (4) adding the mean value computed in step 1 to the resulting data. This procedure avoids residual correlation and rescales the data to its actual range. The values estimated for each stabilometric descriptor from the five trials performed by each subject, for eyes open and eyes closed conditions separately, were averaged to improve reliability (Lafond et al., 2004), thus, resulting into a single value. Since the data distribution for each descriptor was Gaussian (Lilliefors test, p > 0.05 in all cases), the parametric ANOVA design (3 groups, 2 visual conditions), with visual condition as repeated measures (DawsonSaunders and Trapp, 2000), was applied to assess age and visual condition effects and possibly interaction between both factors. 3. Results
Fig. 2. Scatter plot of COP elliptic area (area) versus height for eyes open condition (aged = j, r = 0.51, p < 0.05 and young subjects = h, r = 0.52, p < 0.05).
area values uncorrelated to height and scaled to its actual range for young and elderly (Table 2).
3.1. Height effect Either with EO or EC condition, a significant correlation coefficient was observed only between height and area descriptor for young and aged groups, but not for middle-aged group, although with an opposite relation (Fig. 2). Elliptic area values exhibited a significant increase with height in the young group with EO (r = 0.52, p = 0.02) and an increasing trend with EC (r = 0.42, p = 0.07), however, in the aged group height and area were negatively correlated, either with EO (r = 0.51, p = 0.02) or EC (r = 0.48, p = 0.04). The normalization procedure provided elliptic
Table 2 Mean (SD) of COP elliptic area and correlation coefficient before (area, r) and after (arean, rn) height normalization in young and aged subjects, for EO and EC conditions.
Young Aged
*
EO EC EO EC
area (mm)
arean (mm)
r
rn
254.9 268.6 225.3 252.2
255.6 (128.9) 212.7 (143.4) 270.4(118.6) 226.6 (134.1)
0.42 0.52* 0.51* 0.48*
0.12 0.18 0.3
(125.2) (173.6) (103.9) (170.6)
Significant difference at p < 0.05.
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3.2. Visual condition differences
3.3. Age effect
Comparing each group according to visual conditions, F80, MV, MD, Ks and Kl increased when changing from EO to EC (p < 0.001, p = 0.002, p = 0.005, p = 0.012 and p = 0.033, respectively), while MP and Hl values decreased from EO to EC (p = 0.025 and p < 0.001, respectively) as depicted in Table 3. Eyes closed effect was the same for all groups (Fig. 3), suggesting that inhibition of visual information did not elicit changes in the stabilization mechanisms due to aging.
Two out of 10 stabilometric descriptors identified changes in postural control between groups (Table 3). Among the classical stabilometric parameters, F80 increased significantly (p = 0.002) from young and middle-aged to older adults when comparing groups with eyes open (Fig. 4a), reflecting the increase of sway frequencies with aging. Regardless of visual conditions Hl values were statistically lower (p = 0.012) in the aged group (Fig. 4b). In all cases Hl was less than 0.5 (Table 3), which corresponds to a negative correlation between successive COP displacements for time intervals longer than 1 s. The parametric test also indicated a significant, albeit relatively small (2%), decrease of Hs from the middle-aged to the aged group (p = 0.024). Even after normalization, the elliptic area values were not different between groups or visual conditions, as observed for MT and Hs as well.
Table 3 Mean (SD) of all stabilometric descriptors estimated for young, middle-aged and aged groups, with eyes open (EO) and eyes closed (EC). Each value corresponds to the average of five trials across 19 subjects for each group (n = 95). Young
Middle-aged
Aged
EC
EO
EC
EO
EC
9.95 (3.10) 212.7 (143.4) 0.37 (0.10)
8.90 (2.06) 265.9 (129.6) 0.26 (0.11)
11.35 (3.27) 261.6 (106.0) 0.43 (0.11)
9.84 (2.64)
11.28 (3.37) 226.6 (134.1) 0.43 (0.10)
0.59 (0.03) 2.82 (0.92) 1.73 (0.81)
0.59 (0.02) 3.39 (1.27) 1.53 (0.83)
0.58 (0.03) 2.77 (0.59) 1.63 (0.76)
0.58 (0.02) 3.75 (1.09) 1.25 (0.67)
0.57 (0.02)
log–log SDF Hs 0.76 (0.02) 0.21 Hl*,a (0.11) a 1.53 Ks (0.28) a Kl 1.33 (0.28)
0.76 (0.02) 0.15 (0.08) 1.66 (0.32) 1.44 (0.31)
0.76 (0.01) 0.22 (0.14) 1.59 (0.23) 1.37 (0.19)
0.77 (0.02) 0.11 (0.13) 1.80 (0.28) 1.52 (0.23)
0.76 (0.02)
EO Classical 8.60 MVa (2.36) area 255.6 (128.9) F80*,a 0.25 (0.10) SDC MT MDa MP
*
a
4. Discussion
Significant difference for the aged group (p < 0.05). EC effect for all groups (p < 0.05).
a
270.4(118.6) 0.35 (0.10)
3.07 (1.16) 1.47 (0.62)
0.12 (0.08) 1.63 (0.29) 1.45 (0.26)
0.58 (0.03) 3.54 (1.68) 1.31 (0.60) 0.75 (0.03) 0.07 (0.07) 1.73 (0.32) 1.52 (0.3)
By matching groups with respect to daily life activities and absence of balance-related disorders, we expected to identify natural changes in quiet standing stabilization mechanisms with aging, by means of reliable stabilometric descriptors and in light of the trialand-error postural control paradigm, proposed by Loram et al. (2005), to interpret our findings. 4.1. Reliability of stabilometric descriptors Concerning the reliability of stabilometric descriptors, five 45 s quiet standing trials provide intraclass correlation coefficients higher than 0.5 for MV and area but not for frequency descriptors (Lafond et al., 2004). However, the spectral averaging procedure applied in this study greatly reduces COP spectrum variance (Shiavi, 1999), probably increasing F80 reliability. Although Collins and DeLuca (1993) obtained reliable descriptors averaging a large ensemble of stabilogram diffusion plots for each subject, Chiari et al. (2000) obtained reliable estimates of H and K by selecting the appropriate boundaries for the short and long time interval regions in the log–log stabilogram diffusion plot, without ensemble
Fig. 3. Mean and standard deviation of MP, MV, MD, Ks and Kl stabilometric descriptors (see methods) estimated for young (Y), middle-aged (M) and aged (A) subjects, grouped according to eyes open (gray) and eyes closed (black) conditions. Note that vision effect between groups was the same for all descriptors. In all cases visual condition effect was significant (p < 0.02).
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Fig. 4. (a) COP power spectrum (seven 11.25 s epochs with 50% overlapping) and F80 descriptor estimated for each group and visual condition (EO = d; EC = s). b) Long time interval (Dt > 1 s) region of the log–log diffusion plot and the best linear fit estimated for each group in EO condition. Note the decreased slope (Hl) in the aged group. Spectra and log–log plots are averaged estimates of 95 realizations for each group (5 trials 19 subjects). * significant difference for aged group (F80, p = 0.002; Hl, p = 0.012).
averaging. Reliability of SDC parameters was adequately addressed elsewhere (Baratto et al., 2002). By using reliable stabilometric descriptors, any difference between young, middle-aged and aged groups would likely be attributable to age effects rather than random fluctuation of these descriptors. 4.2. Changes in postural control with age Among the classical stabilometric descriptors estimated in this study, only F80 identified age-related differences in postural control, contradicting previous studies that observed significant changes of other classical descriptors with aging (Prieto et al., 1996; Benjuya et al., 2004; Choy et al., 2003). Prieto et al. (1996), for example, observed a significant increase of COP mean velocity and elliptic area with age, suggesting an increased level of neuromuscular activity to regulate quiet standing posture. Probably, these differences could be accounted for by the base of support effect (Chiari et al., 2002) and unreliable descriptors estimated from a single 30 s trial (Lafond et al., 2004). Furthermore, Prieto et al. (1996) speculated about different control mechanisms to compensate for the absence of vision, since differences between EO and EC were identified by stabilometric parameters in time and hybrid (time-space) domain, respectively, in young and elderly. Choy et al. (2003) associated a significant increase of MV in women older than 60 years to postural instability, more evident with eyes closed than open, indicating an augmented reliance on visual information with age. Unlikely, the MV values reported in Table 3 exhibited an increasing trend with age, and a relative increase from EO to EC condition higher in young (15.4%) and middle-aged group (27.5%) than in the elderly (14.6%), corroborating previous findings (Benjuya et al., 2004). Benjuya et al. (2004) suggested that young adults respond to the lack of vision with remaining sensorial information while elderly increase ankle stiffness with cocontraction of lower limb muscles, leading to a reduced COP area, which was not confirmed in this study even after height normalization. In addition, eyes closure effect on the estimated variables did not change with age (Fig. 3), ruling out the contribution of age-dependent mechanisms to compensate for eyes closed condition.
It is tempting to delegate the loss of balancing ability to the absence of visual input, however, the suppression of sensory modalities seems to reduce the accuracy of adjustments in the ankle torque rather than affect the controlling mechanisms (e.g. the timing of shortenings of the calf muscles is not affected by independent sensory inputs, Lakie and Loram, 2006). Therefore, the increase of MD, MV, Ks and Kl with EC (Fig. 3) indicates higher torque level and neuromuscular activity to correct center of gravity (COG) sways, inducing a slight decrease of postural stability as detected by MP but not area parameter (Table 3). Such changes in torque level could possibly result from the diminished accuracy of modulations in the ankle torque when standing with eyes closed (Lakie and Loram, 2006). 4.3. Modulation of plantar flexors torque to correct body sways As the lower band of COP spectrum (<0.4 Hz) mainly reflects COG sways, changes in energy distribution toward higher frequencies accounts for changes in the ankle torque pattern (Baratto et al., 2002; Winter et al., 1998), so that the increased F80 values in aged group likely relates to increased ankle stiffness. Indeed, asking subjects to balance a real inverted pendulum Loram et al. (2001) observed that ankle stiffness increases according to sway frequency, so that ‘‘ankle impedance” is preferred to ankle stiffness. However, such increase may derive from calf muscle EMG modulations instead of a co-contraction strategy in healthy elderly, assuming that muscle tendon mechanical properties decrease with aging. Since stabilization of quiet standing posture results from impulsive bursts of plantar flexion torque to set Achilles tendon stiffness (Loram et al., 2001), and the amount of torque increases with tendon compliance (Lakie et al., 2003), the reduced stiffness of calf muscles tendon due to aging would require larger and faster changes in plantar flexion torque instead of co-contraction of tibialis anterior muscle. The question is, are there evidences for a decrease in tendon stiffness with aging? Very recently, Onambele and co-workers (2006) measured in vivo changes of Achilles tendon stiffness with aging, revealing a decrease of circa 20 N/mm from younger to older subjects. However, this decrease did not affect postural sway during bipedal stance, probably due to the COP size parameter used (i.e. area
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and RMS of COP displacements enclose equivalent features of a stabilometric record) or support base effects (Chiari et al., 2002), not mentioned in the study. Decomposing the ankle torque into passive and active fractions, Jacono et al. (2004) observed that SDC peaks are related to the active torque component, essential to increase ankle impedance in opposition to the toppling torque of gravity. Thus, the lack of statistical difference in MP values with age indicates small changes of postural control activity, which is strengthened by similar degrees of COP stochastic activity (Ks and Kl) reported on Table 3. The significant increase in COP anti-persistence motion (i.e. COP tendency to return to its initial position) indicated by stronger negative correlation (Hl) between successive COP displacements in the aged group (Fig. 4b), possibly results from the neural modulation of ankle torque to adjust tendon stiffness, as previously discussed. The hypothesis of ankle torque modulation to compensate for a compliant linkage in older adults could be clarified on the basis of co-activation and substitution among calf muscle synergists. McLean and Goudy (2004) observed that during low level (10% MVC) and long duration (60 min) isometric contraction of the plantar flexors, medial gastrocnemius and soleus were co-activated while medial gastrocnemius EMG activity was negatively correlated with soleus and lateral gastrocnemius EMG. Such strategies could be exaggerated with age, in order to minimize fatigue and assure the required amount of ankle torque to preserve postural stability, thus increasing the frequency of torque bursts. 4.4. Limitations Age-related differences observed in this study result from reliable estimates and the use of physiologically meaningful parameters (SDC and random walker approach) provided a substantial overview of postural stability, although EMG recordings could have confirmed the lack of co-contraction during quiet standing with aging. Since the parametric test revealed between-group differences only for the older subjects, as reflected by changes in Hl and F80, the effects of aging on stabilometric descriptors seems to be quite small, so that the assessment of changes in postural control with natural aging would benefit from a larger sample of young, middle-aged and aged subjects. The general limitation of studies assessing postural stability during quiet standing concerns the uncertainty about postural control underlying mechanisms, which comprise a number of different frameworks from which the trial-and-error is the most recently proposed (Loram et al., 2005).
5. Conclusions Aging itself does not result in significant changes of postural stability, as indicated by the sway density analysis. According to the predictive trial-and-error mechanism of postural control, the increase in COP frequencies and the stronger negative correlation between COP displacements in the older group may reflect a slight increase in the compliance of calf muscle tendon with age, rather than co-contraction of ankle flexors. Acknowledgements The authors are grateful to Prof. Roberto Merletti for his substantial comments and suggestions. This study was partially supported by the Brazilian Research Council (CNPq) and Brazilian Foundations FUJB and FAPERJ. The first author acknowledges the scholarships given by FAPERJ and CNPq Foundations.
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Taian de Mello Martins Vieira is graduated in Physical Education and obtained his Master of Science in Biomedical Engineering from the Federal University of Rio de Janeiro, Rio de Janeiro, Brazil, in January 2005. He taught courses in Biomechanics, Statistics and Postural Control as a fellow of the Laboratory of Biomechanics in Rio de Janeiro. He is PhD candidate at Polytechnic of Turin with a scholarship provided by the Brazilian Council of Technology and Research. His main research interests concern postural control, muscular mechanics and biological signal and image processing.
T.d.M.M. Vieira et al. / Journal of Electromyography and Kinesiology 19 (2009) e513–e519 Liliam Fernandes de Oliveira is graduated in Physical Education (1981) and Physical Therapy (1992), specialised in Human Performance Sciences (1982), with M.Sc. (1987) and D.Sc. (1997) degrees in Biomedical Engineering from the Federal University of Rio de Janeiro, Brazil. Developed one year project in the Biomechanics Lab. of the London University supervised by Dr. Donald Grieve (1988). She is Associate Professor and Head of the Biosciences Department and the Biomechanics Lab. of the Physical Education and Sports Institute at the Federal University of Rio de Janeiro, since 2000. Her initial interest is in muscle mechanics, electromyography and noninvasive postural evaluation, then postural control and stabilometry. Recently, she has started focusing on muscle morphology analysis with ultrasound image technique.
Jurandir Nadal is graduated in Electronic Engineer in 1979 from the Pontifical Catholic University of Rio de Janeiro, Brazil, and obtained M.Sc. and D.Sc. degrees in Biomedical Engineering from the Biomedical Engineering Program, Post-Graduate Engineering Institute, Federal University of Rio de Janeiro (COPPE/ UFRJ), Brazil, in 1981 and 1991, respectively. He joined the Biomedical Engineering Program at COPPE/UFRJ as a lecturer in 1985, where he was Head of Department (1995–1997) and is now Associate Professor (2006). He was Chairman of the Scientific Program Committee for the World Congress of Medical Physics and Biomedical Engineering (Rio, 1994) and Co-Chairman of the Publication Committee of the Annual International Meeting of the IEEE Engineering in Medicine and Biology Society (Cancu´n, 2003). He is member of the IEEE/EMBS and President
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(2006–2008) of the Brazilian Society of Biomedical Engineering (Vice-president, 2000–2002). He was also Editor in Chief of the Brazilian Journal of Biomedical Engineering (1996–2006). His current research interests are digital signal processing of biological signals, with particular interests in non-invasive cardiology (cardiovascular control and methods for monitoring and diagnostics), and biomechanics (body sway control and gait analysis).