Variability of centre of pressure movement during gait in young and middle-aged women

Gait & Posture 40 (2014) 399–402

Contents lists available at ScienceDirect

Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost

Variability of centre of pressure movement during gait in young and middle-aged women Lucia Bizovska a,b, *, Zdenek Svoboda a , Patrik Kutilek c, Miroslav Janura a , Ales Gaba a , Zuzana Kovacikova a a Department of Natural Sciences in Kinanthropology, Faculty of Physical Culture, Palacky University Olomouc, Trida Miru 115, 771 11, Olomouc, Czech Republic b Department of Biophysics, Faculty of Science, Palacky University Olomouc, 17. listopadu 12, 771 46 Olomouc, Czech Republic c Department of Natural Sciences, Faculty of Biomedical Engineering, Czech Technical University in Prague, Nam. Sitna 3105, 272 01 Kladno, Czech Republic

A R T I C L E I N F O

A B S T R A C T

Article history: Received 7 October 2013 Received in revised form 14 May 2014 Accepted 23 May 2014

The variability of the centre of pressure (COP) movement is a tool that is often used for stability assessments during standing; however, this variable can provide relevant findings during dynamic conditions, which are more related to fall risks. The aim of this study was to investigate age-related differences in the variability of COP movement. Healthy young (younger group – 25 subjects, age 22.2
1.8 years) and middle-aged (elder group – 25 subjects, age 56.6
4.9 years) females participated in this study. The ground reaction forces and COP movement during walking at a self-selected speed were recorded using two force platforms. Each stance phase was divided into four subphases: loading response (LR), mid-stance (MSt), terminal stance (TSt) and preswing (PS). Standard deviations of the medial– lateral, anterior–posterior and total COP displacements were assessed. For statistical comparisons, oneway ANOVA and the Bonferroni post-hoc test were used. These results showed significantly higher COP movement variability in selected variables in the PS, LR and MSt subphases in the elder group (p < 0.05) compared with the younger group; no differences were found in the TSt subphase. A comparison of the subphases within the groups revealed significant differences (p < 0.001 for all cases and both groups) between the parameters in the LR  MSt, LR  TSt, MSt  PS and TSt  PS subphases. The LR and PS subphases showed significantly higher values for the variability parameters. ã 2014 Elsevier B.V. All rights reserved.

Keywords: Walking Stance phase Ground reaction force Fall risk

1. Introduction The most common causes of falls have been widely studied; however, different approaches have yielded different results. Rubenstein and Josephson [1] reported the most common causes of falls based on 12 large studies as the following: 31% of falls was related to the environment or were caused by accident, 17% was related to gait and balance disorders or weakness, and 13% was caused by dizziness or vertigo. Less frequent factors also included confusion, postural hypotension or drop attack. In addiEtion, reports of gait or balance deterioration-related falls increased with increasing age [2]. In the literature, there is evidence of a connection between fall risk and movement variability, which is one of the possibilities for the differentiation between fallers and

* Corresponding author at: Department of Natural Sciences in Kinanthropology, Faculty of Physical Culture, Palacky University, Trida Miru 115, Olomouc 77111, Czech Republic. Tel.: +420 777 830 724; fax: +420 585 412 899. E-mail address: [email protected] (L. Bizovska). http://dx.doi.org/10.1016/j.gaitpost.2014.05.065 0966-6362/ ã 2014 Elsevier B.V. All rights reserved.

non-fallers [3–5]. According to Hamacher et al. [6], the variability of temporal measures of stride, swing and stance time are the most suitable parameters for this purpose. When considering the centre of pressure (COP) movement variability, the connection to fall risk was found mainly under static conditions [7,8]. There is growing evidence of experimental approaches that have been developed to assess dynamic stability, such as functional and system tests [9], gait stability index computations using fuzzy logic [10], Lyapunov exponent assessments of local dynamic stability [11–13], Floquet multiplier assessments of orbital stability [14,15] and spatio-temporal parameters and velocities [16,17]. Indirect gait stability assessments include the variability of kinematic parameters [6] using the standard deviation (SD) or coefficient of variation. When considering a stance phase, COP excursion and velocity are often investigated. These variables can be assessed within a whole stance phase or within gait cycle subphases. In the literature, the COP trajectory variables within a stance phase were divided according to the acceleration and deceleration of different sections of the foot [18] or contact with the pressure plate

400

L. Bizovska et al. / Gait & Posture 40 (2014) 399–402

[19]. Another potential division may be according to gait phases, which can be defined by the vertical ground reaction force (vGRF) [20]. The reason for this division is that different phases are associated with different functional tasks. The loading response (LR) is involved in the performance of weight acceptance, which demands initial limb stability and shock absorption while simultaneously preserving the momentum of progression. The second functional task (single limb support) is associated with mid-stance (MSt) and terminal stance (TSt). Preswing (PS) is a transitional phase and may be considered as a final part of singlelimb support and the first part of the third functional task – limb advancement [21]. Due to various functional tasks, different COP behaviour in individual subphases can be expected; however, this hypothesis has not been assessed in scientific literature yet. The aim of this study was to investigate age-related differences in the variability of COP movement during gait in several stance phase subphases in female groups. However, there is much evidence for greater variability of kinematic parameters during gait caused by aging; however, changes in the COP movement variability as indicators of aging have not yet been sufficiently discussed. We hypothesised that COP movement variability will increase with increasing age and is different among the specific subphases of the stance phase. 2. Material and methods 2.1. Subjects and experimental set-up Two groups of healthy women participated in this study. All participants were without any musculoskeletal or neurological problems at the time of measurement and during the one year time interval prior to the measurement. The younger group included 25 subjects, age 22.16 (SD 1.80) years, height 1.68 (SD 0.06) m, weight 61.24 (SD 6.99) kg and BMI 21.74 (SD 1.61). All participants from the younger group consisted of students at a local university at the time of the measurements. Subjects were in moderate to high physical condition, and no professional athletes were included. The elder group included 25 subjects, age 56.63 (SD 4.85) years, height 1.62 (SD 0.06) m, weight 70.53 (SD 13.92) kg and BMI 26.71 (SD 5.02). The elder participants consisted of individuals with various sedentary jobs who were in moderate physical condition. At a significance level of 0.05, the groups were different in all physical characteristics; weight (p = 0.004), height (p = 0.004) and BMI (p < 0.001). The subjects performed 8–10 trials of barefoot walking along an 8-m walkway at a self-selected speed. The mean walking speed was similar in the groups, 1.22 (SD 0.11) m s1 in the elder group and 1.23 (SD 0.06) m s1 in the younger group; thus, any potential effects of speed were not considered. The ground reaction force and movement of COP were recorded using two force plates Kistler 9286 AA (Kistler Instrumente AG, Winterthur, Switzerland) placed in a series in the middle of the walkway with a sampling rate of 200 Hz. The first three trials were excluded from further analyses to enable the subjects to become familiar with the experimental environment. The effects of the initiation and termination of gait were eliminated using the middle gait cycles. This study was approved by the Institutional Research Ethics Committee, and the participants provided written informed consent. 2.2. Data analysis The lower limit of the vGRF was determined as 5% of the subject’s body weight. The data were filtered using Matlab software (MATLAB R2010b, Mathworks, Inc., Natick, MA, USA). The third order lowpass Butterworth filter with a cut–off frequency of 6 Hz was used.

Fig. 1. Subphases of a stance phase: LR – loading response, MSt – mid-stance, TSt – terminal stance, PS – preswing, vGRF – vertical ground reaction force.

According to the behaviour of the vGRF, each stance phase was divided into four subphases: LR, MSt, TSt and PS. The vGRF during the stance phase has been previously described in detail by Ayyappa [20]. The subphases were identified as the following: LR – time interval between the heel strike and the first peak of the vGRF, MSt – time interval from the first peak of the vGRF to the minimum of the vGRF in the middle of the stance phase, TSt – time interval between the minimum of the vGRF in the middle of the stance phase and the second peak of the vGRF and PS – time interval between the second peak of the vGRF and toe-off. For further details, please refer to Fig. 1. Instantaneous displacement in the medial–lateral (Dxi) and anterior–posterior (Dyi) directions and the total displacement (Di) of COP movement were computed for each subphase. The characterisations and methods of calculating the variables are summarised in Table 1. The SDs of these variables were determined within each subphase and were considered as variables describing COP movement variability. All of the variables were computed separately for both limbs and then averaged within five trials. All of the calculations were performed using custom-written Matlab programs (MATLAB R2010b, Mathworks, Inc., Natick, MA, USA). 2.3. Statistical analysis The means of five trials from each subject were used for further analyses. Statistical analysis was performed using OriginPro 9.0 (OriginLab Corporation, Northampton, MA, USA) software. The Kolmogorov–Smirnov test was used to confirm normality. Oneway ANOVA and the Bonferroni post-hoc test were used to determine the difference between the subphases of the stance phase in both groups and the effect of age. The significance level was established at p = 0.05. The same process was used to determine differences in physical characteristics of the groups. Table 1 Description of observed variables. Variable

Description

Dxi

COP displacement in the medial–lateral direction within the time interval Dt

Dyi

COP displacement in the anterior–posterior direction within the time interval Dt

Di

Total COP displacement within the time interval Dt

Computation Dxi ¼ jXi  Xi1 j Dyi ¼ jY i  Y i1 j Di ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Dx2i þ Dy2i

Xi – medial–lateral coordinate of i-th COP position, Yi – anterior–posterior coordinate of i-th COP position, Dt – time interval (5  103 s) between consecutive recorded COP positions.

L. Bizovska et al. / Gait & Posture 40 (2014) 399–402

3. Results The mean and standard deviation values of the observed variables are shown in Table 2. The Kolmogorov–Smirnov test confirmed a normal distribution for all variables within all subphases. A comparison of the groups revealed significant differences in the LR subphase in the medial–lateral direction of COP movement (p < 0.001 for SD Dx) and in the total COP displacement (p = 0.037). A significant difference between groups in the MSt subphase was found only in the medial–lateral direction (p = 0.034 for SD Dx); no differences were found in the TSt subphase. Disparities in all of the observed variables were present in PS (p < 0.001 for SD Dx, SD D, p = 0.001 for SD Dy). The mean values of the observed variables were higher in the Elder group in all cases, which may indicate greater variability of COP movement caused by aging. To determine the differences between subphases, the variables were compared independently within groups. No differences were found when comparing the MSt  TSt subphases within the younger or elder group, thus it can be assumed that the variability of COP movement is similar in both subphases. Significant differences were found between all parameters in the LR  MSt, LR  TSt, MSt  PS and TSt  PS subphases in both groups (p < 0.001 for all cases). These results varied when comparing LR  PS. Although the COP movement in both directions and the total COP displacement showed significant differences between the subphases in the elder group (p = 0.004 for SD Dx; p < 0.001 for SD Dy, SD D); however, no differences were found in the younger group. 4. Discussion For gait variability assessments, kinematic gait parameters are most often used. However, to capture and analyse movement in 3D space, expensive equipment is required. Because a 3D motion capture system is not considered to be basic laboratory equipment, another evaluation of gait variability would be helpful. In this study, gait variability is assessed considering COP movement in different subphases of the stance phase recorded using force plates. These results showed that the COP movement variability increased in the elder group in the subphases LR, MSt and PS when compared with the younger group. This result was consistent with the findings of other studies, which investigated the kinematic parameters or conventional spatio-temporal gait variables; the gait variability was higher with increasing age [6,22,23]. The relationship of COP movement variability during gait and falls risk is still unclear. The association of COP variability (postural sways) to fall history was found in static conditions, such as narrow base stand in medial–lateral directions [7] or in standing with eyes open on a compliant surface [8]. The COP variability was greater for fallers. The relationship between fall history and increased gait variability was shown mainly in temporal-spatial variables, such

401

as stride length [24,25], stride time [4,5], swing time [24] or minimum foot clearance [26]. Increased gait variability in fallers compared with non-fallers was also found for kinematic variables [3]. Although we did not find any study that assessed COP variability during gait between fallers and non-fallers, these findings suggest that it could be associated with fall risk. However, this hypothesis should be confirmed in future studies. Dividing the stance phase into subphases revealed significant differences in the COP movement variability between subphases. The MSt and TSt subphases were similar in the COP movement variability in the younger and elder groups; the LR and PS subphases were similar in the younger group, but they were significantly different in the elder group. All of the other combinations showed significant differences for both groups and all variables when comparing subphases. According to these results, the whole stance phase assessment for gait stability or variability may be incorrect because the COP movement is specific in each subphase. The LR and PS subphases showed significantly higher values of variability parameters in both groups. Because variability is considered to be an indirect gait stability assessment [6], it can be assumed that the LR and PS subphases are the two least stable subphases of the stance phase. This conclusion was consistent with the functional division of the stance phase. Although MSt and TSt are parts of a single limb support and are intended to maintain limb and trunk stability [27], the LR and PS are components of a double support, which is characterised by weight transference from one lower limb to the other. Differences between subphases are most likely also associated with COP progression and its velocity during gait. Greater mean velocity indicates a greater total displacement, which can be associated with a greater displacement variability. Scientific studies reported a greater mean COP velocity during the initial phases of the stance phase, which correspond to the loading response [28] or in the first 40% and last 10% of the stance phase [29]. Unfortunately, these studies did not distinguish between the anterior–posterior and medial–lateral directions of movement. The effect of the medial–lateral direction is an increase in phases with COP progression angle (deflection between the COP progression direction and long axis of the foot). A greater angle value was found in both younger and older subjects during the forefoot push-off phase and in older subjects in phases that correspond with the loading response [28]. These findings suggested that elderly subjects tend to exhibit a more pronated foot and displayed a significant medial COP curve compared to young adults [28]. 4.1. Limitations The participants significantly differed in BMI values. It is known that the BMI affects stability in both static and dynamic conditions; however, its role in different subphases of the stance phase during gait should be investigated more in future studies. As investigated by Schultz et al. [30], a higher rate of falls and fall-associated

Table 2 Observed gait parameters for the different subphases in both groups. SD (mm)

Dx Dy D

LR

MSt

TSt

PS

Younger

Elder

Younger

Elder

Younger

Elder

Younger

Elder

0.85
0.29 1.75
0.68 1.87
0.74

1.60
0.37* 1.85
0.74 2.33
0.75*

0.11
0.04 0.51
0.10 0.50
0.10

0.15
0.07* 0.58
0.19 0.57
0.19

0.10
0.03 0.49
0.17 0.48
0.16

0.11
0.03 0.59
0.23 0.57
0.23

0.74
0.25 1.92
0.69 2.13
0.66

1.34
0.37* 2.85
1.19* 3.28
1.14*

LR – loading response, MSt – mid-stance, TSt – terminal stance, PS – preswing, SD – standard deviation, Dx (Dy) – COP displacement in the medial–lateral (anterior– posterior) direction, D – total COP displacement. * p < 0.05.

402

L. Bizovska et al. / Gait & Posture 40 (2014) 399–402

injuries occur in females when compared to males; thus, only female groups were included in this study. However, this should not be interpreted as a limitation because as investigated by Chiu et al. [19], no gender-related differences were found in COP movement variables. The COP movement can also be affected by foot type and other morphological factors, which were not considered in this study. 5. Conclusions This study proposed the possibility of gait variability assessment considering COP movement in different subphases of a stance phase. Significant differences were found between the variability of the COP movement in different subphases and between different age groups. The COP movement variability increased with increasing age. In future studies, it would be appropriate to consider the relationship between COP variability during the gait and quiet stance because both are indicators of instability in different (dynamic/static) conditions.

Acknowledgements The authors acknowledge and thank the participants of this study. The study was supported by a research grant from the Czech Science Foundation (grant no. 13-32105S) “Analysis of healthrelated benefits of walking: assessment of walking intervention in sedentary adults”. Conflict of interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. References [1] Rubenstein LZ, Josephson KR. The epidemiology of falls and syncope. Clin Geriatr Med 2002;18:141–58. [2] Talbot LA, Musiol RJ, Witham EK, Metter EJ. Falls in young, middle-aged and older community dwelling adults: perceived cause, environmental factors and injury. BMC Public Health 2005;5:86. [3] Barak Y, Wagenaar RC, Holt KG. Gait characteristics of elderly people with a history of falls: a dynamic approach. Phys Ther 2006;86(11):1501–10. [4] Hausdorff JM, Edelberg HK, Mitchell SL, Goldberger AL, Wei JY. Increased gait unsteadiness in community-dwelling elderly fallers. Arch Phys Med Rehab 1997;78(3):278–83. [5] Miyoshi H, Kinugasa T, Urushihata T, Yuki S. Relationship between stride time variability of walking and fall experience in middle aged and elderly woman. Jpn J Phys Fit Sport 2011;60(1):121–32.

[6] Hamacher D, Singh NB, Van Dieën JH, Heller MO, Taylor WR. Kinematic measures for assessing gait stability in elderly individuals: a systematic review. J R Soc Interface 2011;8:1682–98. [7] Melzer I, Benjuya N, Kaplanski J. Postural stability in the elderly: a comparison between fallers and non-fallers. Age Ageing 2004;33(6):602–7. [8] Merlo A, Zemp D, Zanda E, Rocchi S, Meroni F, Tettamanti M, et al. Postural stability and history of falls in cognitively able older adults: the Canton Ticino study. Gait Posture 2012;36(4):662–6. [9] Horak FB, Wrisley DM, Frank J. The balance evaluation systems test (BESTest) to differentiate balance deficits. Phys Ther 2009;89:484–98. [10] Biswas A, Lemaire ED, Kofman J. Dynamic gait stability index based on plantar pressures and fuzzy logic. J Biomech 2008;41:1574–81. [11] Dingwell JB, Cusumano JP, Cavanagh PR, Sternad D. Local dynamic stability versus kinematic variability of continuous overground and treadmill walking. J Biomech Eng 2001;123:27–32. [12] England SA, Granata KP. The influence of gait speed on local dynamic stability of walking. Gait Posture 2007;25:172–8. [13] Bruijn SM, van Dieën JH, Meijer OG, Beek PJ. Is slow walking more stable? J Biomech 2009;42:1506–12. [14] Dingwell JB, Kang HG. Differences between local and orbital dynamic stability during human walking. J Biomech Eng 2007;129:586–93. [15] Hurmuzlu Y, Basdogan C. On the measurement of dynamic stability of human locomotion. J Biomech Eng 1994;116:30–6. [16] Hsue B-J, Miller F, Su F-C. The dynamic balance of the children with cerebral palsy and typical developing during gait. Part I: Spatial relationship between COM and COP trajectories. Gait Posture 2009;29:465–70. [17] Hsue B-J, Miller F, Su F-C. The dynamic balance of the children with cerebral palsy and typical developing during gait. Part II: Instantaneous velocity and acceleration of COM and COP and their relationship. Gait Posture 29 2009;471–6. [18] Schmid M, Beltrami G, Zambarbieri D, Verni G. Centre of pressure displacement in trans-femoral amputees during gait. Gait Posture 2005;21:255–62. [19] Chiu M-C, Wu H-C, Chang L-Y. Gait speed and gender effects on centre of pressure progression during normal walking. Gait Posture 2013;37:43–8. [20] Ayyappa E. Normal human locomotion, Part 2: Motion ground reaction force and muscle activity. J Prosthet Orthot 1997;9(2):42–57. [21] Ayyappa E. Normal human locomotion, Part 1: Basic concepts and terminology. J Prosthet Orthot 1997;9(1):10–7. [22] Callisaya ML, Blizzard L, Schmidt MD, McGinley JL, Srikanth VK. Ageing and gait variability – a population-based study of older people. Age Ageing 2010;39:191–7. [23] Kang HG, Dingwell JB. Separating the effects of age and walking speed on gait variability. Gait Posture 2008;27:572–7. [24] Taylor ME, Delbaere K, Mikolaizak AS, Lord SR, Close JC. Gait parameter risk factors for falls under simple and dual task conditions in cognitively impaired older people. Gait Posture 2013;37(1):126–30. [25] Reelick MF, Kessels RP, Faes MC, Weerdesteyn V, Esselink RA, Olde Rikkert MG. Increased intra-individual variability in stride length and reaction time in recurrent older fallers. Aging Clin Exp Res 2011;23(5–6):393–9. [26] Barrett RS, Mills PM, Begg RK. Systematic review of the effect of ageing and falls history on minimum foot clearance characteristics during level walking. Gait Posture 2010;32(4):429–35. [27] Perry J, Burnfield JM. Gait analysis: normal and pathological function. New Jersey: SLACK Incorporated; 2010. [28] Chiu MC, Wu HC, Chang LY, Wu MH. Center of pressure progression characteristics under the plantar region for elderly adults. Gait Posture 2013;37(3):408–12. [29] Cornwall MW, McPoil TG. Velocity of the center of pressure during walking. J Am Podiatr Med Assoc 2000;90(7):334–8. [30] Schultz AB, Ashton-Miller JA, Alexander NB. What leads to age and gender differences in balance maintenance and recovery? Muscle Nerve Suppl 1997;5:S60–S64a.