Contribution of lower limb muscle strength to walking,postural sway and functional performance in elderly women

Abstract

BACKGROUND:

Aging-related deterioration of the lower limb muscle strength could highly influence the functional performance of elderly individuals.

OBJECTIVE:

To investigate how advancing age impacts the lower limb muscle strength and consequently affects the balance and walking performance.

METHODS:

Twenty-seven community-dwelling elderly females underwent isokinetic ankle dorsi/plantar flexion (ADF/APF), inversion/eversion (AIN/AEV), knee flexion/extension (KFL/KEX), hip flexion/extension (HFL/HEX), and abduction/adduction (HAB/HAD) tests, the six-minute walk test, open-eyed biped balance test on foam rubber and the performance-oriented mobility assessment (POMA).

RESULTS:

The Pearson’s product-moment correlation coefficients demonstrated that advancing age negatively influenced the relative work and moment produced in all the muscle groups, the POMA score ( $r=-$ 0.51), walking speed ( $r=-$ 0.62), and the vertical ( $r=$ 0.55) and anteroposterior ( $r=$ 0.54) postural sway velocities. The peak moment and work values of AINs and APFs; KFLs and KEXs; and HABs, HFLs, and HEXs showed a significantly positive correlation with the walking speed ( $\alpha\leqslant$ 0.05).

CONCLUSION:

The strength of HFLs, HEXs and HABs, as the important contributors to the walking performance, underwent attenuation as the age increased, consequently resulting in impairments of stepping profiles of elderly females. Elderly females are needed to be trained to reach the optimum levels of lower limb muscular strength to overcome premature incapacitation and have control over their independence in daily activities.

Keywords

6-m walk test Bipedal stance Isokinetic testing POMA score lower limb muscles’ moment

1. Introduction

Performing routine tasks and overcoming environmental barriers are crucial challenges among the ageing populations. Due to the high prevalence of different impairments/health restrictions and physical limitations, the demands for a safe and efficient execution of these tasks, such as normal walking and stable stance, seem to have increased among the senile populations [1]. Moreover, there is evidence linking the limitations in performing these activities to the increased risk of falls in elderly adults with advancing age [2]. Falls are a major cause of morbidity and mortality in this group of individuals, especially among those with a previous history of falls. More than 30% of age-related falls are recorded in senile population aged 65 years and above, thus highlighting the necessity of preventing falls in this community [3]. A plethora of intrinsic and extrinsic factors could result in falling. Although extrinsic factors, such as the ground surface, shoes, or the type of activity could play a role [4], the intrinsic or person-related parameters, such as age-related quantitative and qualitative alterations to the neuromuscular system and diseases, may also be the determinants of postural stability [4, 5, 6, 7]. Thus, the assumption that an accumulation of several extrinsic and intrinsic factors increases the risk of fall seems rational [4]. Tinetti et al. [8] reported that the falling risk varies from 8% to 78% between individuals with no risk factors and those with over four risk factors.

The central nervous system (CNS), which serves as the main coordinator of intrinsic variables, combines the ocular, muscular, joint, and vestibular information to keep the center of mass (CoM; vertical) line optimally close to the joint center of rotation and maintain the center of pressure (CoP) within the base of support. Consequently, the body posture remains upright [9, 10]. Former hypotheses have stated that with increasing age, the CNS sensitivity becomes feeble and the muscle strength decreases, due to which, individuals have lesser control over their postural stability [9, 10, 11]. Although muscular strength and balance capabilities are believed to have equally significant contributions to falls in the elderly [12], it must be noted that muscular weakness is the main reason for balance dysfunction, where eventually, the result is falling. Balance impairment and the permanent risk of falling are the major concerns of elderly health [13], given that they negatively influence functional independence [4]. Although balance has a broad role in most daily activities, including walking and reaching, any deficits in balance may result in morbidity or mortality in the elderly community [4].

While the mechanisms behind feeble equilibrium and mobility are fully understood, the decrease in lower limbs muscle strength is globally identified to detrimentally impact the balance and walking abilities [3, 14, 15, 16]. However, the effect of individual muscle groups on human locomotion and balance capabilities is still to be elucidated in a higher resolution.

Based on the literature discussed above, this study aimed at investigating the relationships of advancing age and lower limb muscle strength with balance and walking capabilities of elderly women.

2. Methods

2.1 Participants

A total of 27 community-dwelling elderly women voluntarily participated in this study. Their demographic and anthropometrical characteristics were recorded (Table 1). All of them were physically active, participating in organized physical activities and sports for at least two 60 min sessions per week and walking for more than two hours per day. Their physical activity level was screened by interview; this, however, was not an inclusion criterion. The exclusion criteria for this study were: (a) a history of falls, (b) musculoskeletal, neurological, orthopedic, or cardiopulmonary disorder, (c) usage of any walking aids, and (d) total joint replacement. Falling was defined to include any fall where the participant could precisely distinguish the time, venue, and the mechanism of falling [17]. Prior to participation, all participants signed an informed consent form and the study protocol was approved by the ethical standards committee of the Faculty of Physical Culture, Palacky University (no. 24/2017)- depends on journal. The procedures were in compliance with the 1964 Helsinki Declaration and its later amendments.

Table 1
Demographic and anthropometrical characteristics of participants ( $n=$ 27)

Parameter	Mean $\pm$ SD
Age (years)	69.1	$\pm$ 5.7
Height (cm)	163.6	$\pm$ 7.7
Mass (kg)	69.9	$\pm$ 18.4
Body mass index (kg/m ${}^{2}$ )	26.4	$\pm$ 4.3
Leg length (average of both – cm)	89.1	$\pm$ 5.4

2.2 Instrumentation and procedure

Prior to the study, the following three tests were performed to screen lower limb dominance: the Obstacle Crossing Test, the Ball Kick Test, and the Balance Recovery Test [18, 19]. The limb that was predominantly utilized to perform the above-mentioned tasks was determined as functionally dominant.

The Tinetti performance oriented mobility assessment (POMA) was used to assess a set of functional balance and gait tasks [20, 21]. The POMA total scale (perfect score $=$ 28), balance scale (perfect score $=$ 16), and gait scale (perfect score $=$ 12) were individually used in further statistical analysis. The six-minute walk (SMW) test, which is a rectification of the 12-minute walk-run test, was used to measure the maximum distance covered by an individual participant in six minutes [21, 22]. The participants were instructed to walk in their preferred walking speed, keep the speed stable, walk straight, and keep their heads in the “straight-ahead” position, without talking during the whole procedure. They were made to walk up and down a well-lit 20-meter long indoor corridor, and after turning at the corridor’s end, they were requested to continue at the same preferred speed as before.

In order to remove the effect of shoes, which is one of the extrinsic parameters impacting balance and could vary between individuals, the participants stood barefoot in a comfortable upright posture on a foam rubber (Airex Balance Pad, Airex AG, Sins, Switzerland; height $=$ 6 cm, width $=$ 50 cm, depth $=$ 41 cm) with their eyes opened and their arms alongside their bodies, on an AMTI force platform (Model OR6-5, Newton, Watertown, MA, USA; sampling frequency 200 Hz) [23]. Given that the main problem in elderly individuals is balance maintenance on unstable surfaces, the aim of this study was to observe their balance on the foam rubber to simulate real-life conditions. The participants were instructed to minimize postural sway by standing as still as possible for the duration of 30 seconds. The force plate recording began immediately after the participants had stabilized their posture.

For assessing the lower limb muscle quality, the subjects were made to participate in two identical measurement days, separated by a week. The first session was to familiarize the patient with the measurement procedure for the right leg, while the second session was for the assessment of both legs. Both sessions were carried out at the same time of the day ( $\pm$ 30 minutes), and the evaluation was performed by the same researcher, in order to reduce the effects of investigator inconstancy and diurnal impacts [24]. Prior to the assessment, participants performed a 5-minute general warm-up on a stationary Kettler ergometer (Heinz Kettler GmbH and Co. KG, Ense-Parsit, Germany), with an exercise workload of 1.5 W/kgBW at a constant 70 rpm pedal rate [24, 25]. This was followed by several stretching exercises of the lower extremity muscles [26].

Immediately after the warm-up, participants were seated on the Iso-Med 2000 isokinetic dynamometer (D&RFerstl, Hemnau, Germany) with the hip joint at 75 ${}^{\circ}$ (0 ${}^{\circ}$ $=$ full extension, level 1 seat height) to perform the knee extension/flexion test [24]. The hip, shoulder, chest, and femur were fixed by adjustable pads and straps for the most isolated position possible [25]. Participants were asked to keep their hands on their chest, to eliminate the effects of the hands’ force on the moment produced. Using the mechanical axis of the dynamometer’s head, the lateral femoral epicondyle was detected as the joint center of rotation, and the dynamometer adaptor was set approximately 3 cm above the lateral malleolus. Mechanical stops were also fixed at two maximum flexion and extension ends to prevent any unwanted movement of the dynamometer adaptor and keep the test as safe as possible. The participants, as a warm-up to the test, were then asked to execute the test three times with 50% of the maximal force, 75% of the maximal force, and the maximum possible force, respectively, with a 15-s rest after each repetition. Following the warm-up and prior to the test, gravity compensation for the weight of the tested leg was carried out. The maximal range of movement (ROM) was defined between 0 ${}^{\circ}$ to 75 ${}^{\circ}$ at an angular velocity of 60 ${}^{\circ}$ /s [27]. The maximum strength of knee extensors and flexors was measured using four concentric contractions, separated by 15-s periods of rest. Furthermore, the ankle was ensured to be in the neutral position, in order to avert unwanted calf muscle cramps.

For the ankle inversion/eversion test, the participants were seated on a 60 ${}^{\circ}$ back-inclined chair with a hip joint angle of 80 ${}^{\circ}$ and made to flex their knee (to approximately 110 ${}^{\circ}$ ) and place their feet on the adaptor. After three submaximal trials, the participants performed four concentric efforts initiated from 25 ${}^{\circ}$ of ankle eversion to 20 ${}^{\circ}$ of ankle inversion at an angular velocity of 30 ${}^{\circ}$ /s [28, 29]. In the ankle dorsi/plantar flexion test, the participants laid supine on the dynamometer’s table with the hip and knee in full extension. Two Velcro straps firmly fixed the feet to the adaptor. The lateral malleolus was detected as the ankle center of rotation by the mechanical axis, and after gravity compensation and training trials, four concentric contractions were executed from 10 ${}^{\circ}$ of dorsiflexion to 35 ${}^{\circ}$ of plantar flexion at an angular velocity of 30 ${}^{\circ}$ /s (0 ${}^{\circ}$ $=$ neutral position of talocrural joint) [28, 29]. A 15-second rest was given to the participants between each contraction.

In the hip flexion/extension test, the participants laid supine and the dynamometer adaptor was placed approximately 3 cm above the lateral femoral epicondyle. The shoulders were fixed in the ventral-to-dorsal and cranial-to-caudal directions by shoulder straps and pads. After the training endeavor and gravity compensation, the participants executed four maximal concentric contractions from 10 ${}^{\circ}$ to 100 ${}^{\circ}$ at an angular velocity of 60 ${}^{\circ}$ /s (0 ${}^{\circ}$ $=$ full hip extension) [30, 31]. For the hip abduction/adduction test, the participants were laid on their sides, and similarly, the dynamometer adaptor was placed approximately 3 cm above the lateral femoral epicondyle. Four maximal concentric contractions from 0 ${}^{\circ}$ to 50 ${}^{\circ}$ at a rotational velocity of 60 ${}^{\circ}$ /s were performed after positioning, training efforts, and gravity compensation (0 ${}^{\circ}$ $=$ neutral position of hip joint) [30, 31]. Subsequently, the second limb was tested using the same procedure, during which the individual settings were automatically activated, rechecked, and adjusted if necessary. During the entire testing procedure, the participants were asked to maintain the maximum force throughout the entire ROM and visual feedbacks were given to them.

2.3 Data analysis

The IsoMed Analyze V.1.0.5 (D. & R. Ferstl GmbH, Hemau, Germany) was employed for data recording and reduction. Peak moment, work, and peak power were the isokinetic variables extracted from the following individual tests: ankle invertors (AIN) and evertors (AEV), plantar flexors (APF), and dorsi flexor (ADF) tests; knee flexors (KFL) and extensors (KEX) tests; and hip abductors (HAB), adductors (HAD), flexors (HFL), and extensors (HEX) tests. All variables were then divided by the body mass, in order to calculate relative measures. Thereafter, the average values of relative peak torque of both, the dominant and non-dominant legs, were calculated as the general lower limb strength, and used for further statistical analysis.

Regarding the balance performance, force platform signals were filtered using the fourth-order low-pass Butterworth filter with a cut-off frequency of 10 Hz. Subsequently, the mean CoP velocity in three vertical, anteroposterior, and mediolateral directions was computed. Data analysis was performed using the MATLAB software (v. 2018a, MathWorks, Inc., Natick, MA, USA). The standard deviation of CoP sway and the mean CoP velocity of two trials in each direction was used for the analysis.

2.4 Statistical analysis

The normality of data distribution was checked using the Kolmogorov-Smirnov test. The correlation between age and leg muscles’ concentric strength variables with walking performance variables, functional mobility tests and balance performance was investigated using the Pearson’s product-moment correlation coefficient (Significance set as $p<$ 0.05). The magnitude of the correlations was determined using the modified scale by Hopkins et al. [32]: $r<$ 0.1 trivial; $>$ 0.1–0.3 small; $>$ 0.3–0.5 moderate; $>$ 0.5–0.7 large; $>$ 0.7–0.9 very large; $>$ 0.9 nearly perfect; and 1 perfect. For further multiple regression analysis, the variables that shown a significant correlation with muscle groups strengths, were included, and the coefficient of determination (R ${}^{2}$ ) was adopted for interpretation of relationships between these variables [33]. The SPSS 23.0 statistical analysis package (IBM SPSS, Armonk, NY, USA) was used for all statistical analyses.

Table 2
Descriptive measures of POMA, average walking speed, balance performance and muscle strength and their correlations with age

Variable	Mean $\pm$ SD		Correlation
			$r$		$p$
POMA	27.76	$\pm$ 0.38	$-$ 0.	51 ${}^{*}$	0.01
Average walking speed (m.s ${}^{-1}$ )	1.29	$\pm$ 0.21	$-$ 0.	62 ${}^{**}$	0.00
Sway velocity (mm.s ${}^{-1}$ )
AP	26.71	$\pm$ 6.12	0.	55 ${}^{**}$	0.00
ML	12.86	$\pm$ 5.47	0.	18	0.59
V	31.29	$\pm$ 8.41	0.	54 ${}^{**}$	0.00
AIN (Nm.kg ${}^{-1}$ )	0.20	$\pm$ 0.06	$-$ 0.	49 ${}^{*}$	0.01
AEV (Nm.kg ${}^{-1}$ )	0.19	$\pm$ 0.03	$-$ 0.	43	0.04
APF (Nm.kg ${}^{-1}$ )	0.91	$\pm$ 0.38	$-$ 0.	65 ${}^{**}$	0.00
ADF (Nm.kg ${}^{-1}$ )	0.33	$\pm$ 0.07	$-$ 0.	13	0.69
KFL (Nm.kg ${}^{-1}$ )	0.71	$\pm$ 0.23	$-$ 0.	50 ${}^{*}$	0.01
KEX (Nm.kg ${}^{-1}$ )	1.32	$\pm$ 0.27	$-$ 0.	47 ${}^{*}$	0.03
HAB (Nm.kg ${}^{-1}$ )	0.90	$\pm$ 0.34	$-$ 0.	62 ${}^{**}$	0.00
HAD (Nm.kg ${}^{-1}$ )	0.83	$\pm$ 0.24	$-$ 0.	37	0.10
HFL (Nm.kg ${}^{-1}$ )	1.03	$\pm$ 0.21	$-$ 0.	53 ${}^{**}$	0.00
HEX (Nm.kg ${}^{-1}$ )	1.46	$\pm$ 0.46	$-$ 0.	57 ${}^{**}$	0.00

POMA $=$ Tinetti Performance Oriented Mobility Assessment. AP $=$ Anteroposterior, ML $=$ Mediolateral, V $=$ Vertical, AIN $=$ Ankle invertors, AEV $=$ Ankle evertors, APF $=$ Ankle plantar flexors, ADF $=$ Ankle dorsi flexors, KFL $=$ Knee flexors, KEX $=$ Knee extensors, HAB $=$ Hip abductors, HAD $=$ Hip adductors, HFL $=$ Hip flexors, HEX $=$ Hip extensors. ${}^{*}$ Represents Moderate Correlation ( $p<$ 0.05). ${}^{**}$ Represents Strong Correlation ( $p<$ 0.01).

Table 3

Correlation between lower limbs muscle relative peak moment and work with POMA, average walking speed and postural sway

Variable	POMA	Average walking speed	Postural sway velocity
			AP	ML	V
AIN (Nm.kg ${}^{-1}$ )	0.31	0.63 ${}^{**}$	$-$ 0.04	0.14	$-$ 0.24
AEV (Nm.kg ${}^{-1}$ )	0.14	0.33	0.18	$-$ 0.08	0.19
APF (Nm.kg ${}^{-1}$ )	0.21	0.57 ${}^{**}$	0.22	$-$ 0.08	0.09
ADF (Nm.kg ${}^{-1}$ )	0.15	0.29	$-$ 0.31	0.33	0.14
KFL (Nm.kg ${}^{-1}$ )	0.35	0.59 ${}^{**}$	0.21	$-$ 0.24	0.28
KEX (Nm.kg ${}^{-1}$ )	0.25	0.52 ${}^{**}$	0.15	$-$ 0.35	0.38
HAB (Nm.kg ${}^{-1}$ )	0.39	0.77 ${}^{**}$	$-$ 0.04	0.22	$-$ 0.09
HAD (Nm.kg ${}^{-1}$ )	0.22	0.33	0.18	0.28	0.15
HFL (Nm.kg ${}^{-1}$ )	0.28	0.79 ${}^{**}$	0.27	0.16	0.36
HEX (Nm.kg ${}^{-1}$ )	$-$ 0.25	0.77 ${}^{**}$	$-$ 0.38	0.13	$-$ 0.31

${}^{*}$ Represents Moderate Correlation ( $p<$ 0.05). ${}^{**}$ Represents Strong Correlation ( $p<$ 0.01).

Table 4

Multiple regression analysis between ankle, knee and hip muscles strength and average walking speed

Independent variables	Average walking speed
	Standardized $\beta$ coefficient	$\beta$ significance	R ${}^{2}$	Adjusted R ${}^{2}$	Model significance
Model 1			0.523	0.495	$<$ 0.001
AIN	0.437	0.038
APF	0.591	0.009
Model 2			0.578	0.541	$<$ 0.001
KFL	0.623	0.005
KEX	0.398	0.046
Model 3			0.849	0.802	$<$ 0.001
HFL	0.663	0.002
HEX	0.532	0.014
HAB	0.714	0.001

R ${}^{2}$ $=$ coefficient of determination.

3. Results

Table 2 depicts the descriptive measurements of POMA, average walking speed, balance-related variables, and relative muscle strengths and their correlation with age. A strong consistency was seen in the POMA scores (CV $\approx$ 1%) among the participants, indicating their high functioning status. Advancing age was shown to have a significantly negative correlation with the moment produced, and consequently, the work and power measures of the entire lower limb muscles, except for the ankle evertors and hip adductors. The measures of CoPV ${}_{\text{AP}}$ and CoPV ${}_{\text{V}}$ also showed a significant increase as a result of increasing age. Additionally, it was found that functional status (POMA) and average walking speed significantly decreased with aging.

From the perspective of muscle quality, concentric strength of AINs, APFs, KFLs, KEXs, HFLs, HEXs, and HABs showed a strong positive correlation with the average walking speed (Table 3). Remarkably, the strength measures of mentioned muscle groups had no significant correlation with postural sway velocities in all the three directions. The regression analysis revealed that hip joints muscles (model 3 Table 4), including HFL, HEX and HAB, had the highest contribution (with almost 85% of the variance) to the identification of average walking speed. Ankle ( $\approx$ 52%) and knee ( $\approx$ 58%) joints muscle groups (models 1 and 2, Table 4) were of a lesser contribution to the determination average walking speed in this cohort.

4. Discussion

This study was designed to investigate the impacts of ageing on lower limb muscle qualities and the subsequent effects on walking and balance status of the elderly females. One of the main findings of this study was that with increasing age, the muscle strength of lower limbs decrease (except of ankle dorsiflexors, ankle evertors and hip abductors). Advancing age also negatively impacted the sway velocity during the quiet stance test on the foam rubber, the average walking speed, and the Tinetti POMA scale. On the other hand, ankle muscles including AINs and APFs, knee muscles, including KFXs, and KEFs, and hip muscles, including HFLs, HEXs and HABs, seemed to have an overwhelming contribution to the walking speed among elderly women.

The ability to perform daily tasks is vital in the senile population. Any decline in the functional capacity (which alludes to the activities that people perform to fulfill their fundamental needs) results in a sharp decrease in the quality of life [1]. From the simplest to the most sophisticated tasks, the entire motor performance demands locomotion and body balance. Among the intrinsic parameters impacting walking and balance in elderly individuals, neuromuscular components are the key determinants but they deteriorate with advancing age [9, 10, 11]. Considering that the muscle groups included in this study are effective determinants of postural stability [34, 35, 36], and that postural sway velocities of CoM ${}_{\text{AP}}$ and CoM ${}_{\text{V}}$ significantly increased with the an increase in the age, significant correlations were expected to exist between these muscles’ strength status and balance-related variables. The absence of correlation between the ankle and hip muscles’ concentric strengths and the balance performance may result from two main reasons. First, regarding the momentum-based balance control theory, the eccentric contractions of the APFs, KEXs, HEXs, and HADs uninterruptedly maintain the CoM line at the shortest distance from the knee and ankle joints’ centers of rotation, in order to minimize the moment produced in these joints. In addition, given that the body structure has a tendency to lose balance, the role of these eccentric contractions of the APFs, HEXs, and HADs in preventing the body from falling becomes more evident, and no relationship between the concentric strength and balance variables seems logical.

Second, the speed of response to balance loss seems to be another key element in maintaining the balance, and it is reported that ageing impacts this speed [37]. From a neuromuscular point of view, any decline in the cortical and spinal volatilities, along with slower and noisier neural signals, results in the decrease of motor unit quantity and quality which in turn deters the force development characteristics of the muscles and causes delays in neural signal transmission, respectively [1, 38, 39, 40]. Since the values for CoPV ${}_{\text{AP}}$ and CoPV ${}_{\text{V}}$ , which are the main predictors of CoM acceleration, underwent a significant increase, it was assumed that the response speed of the nervous system to the ankle, knee, and hip muscles decreased due to an increase in age. In lay terms, the sensory motors of ankle and hip muscles encountered a salient delay in the transmission of the spatiotemporal status of the CoM to the nervous system; consequently, a lag in the muscle contractions was observed.

An effective walking performance, especially the walking speed, for an acceptable duration adds to a better quality of life in the senile populations. Previous studies proved that the walking speed decreases by almost 10% from the age of 20 to 60 years, i.e., from $-$ 0.2 m.s ${}^{-1}$ to $-$ 0.6 m.s ${}^{-1}$ , and further shows a 4% annual decrease after retirement [38, 41, 42]. In line with formerly stated investigations, the strong negative correlation observed between the age and average walking speed also confirmed this notion. Nevertheless, since aging is a physiological process that affects the locomotor apparatus from the nervous system to the muscles and bones [43] this physiological aging could be different among individuals, according to their lifestyle habits [1]. To this end, it is necessary to look for physiological changes that negatively affect the walking performance. Muscles and the nervous system are by far the most influenced somatic organs that become weaker and slower during physiological aging [38, 39, 44, 45]. It was observed that the entire muscle groups that contributed to walking speed underwent a significant reduction in strength. On the other hand, walking performance, which is the backbone of daily locomotion, demands sufficient lower limb strength. Given that the senile population is often unaware of the importance of these muscles in improving the quality of life, the community needs to be educated on this.

Among the muscles groups that contribute to the walking performance, the HEX, HFL, and HAB had a very strong correlation with average walking speed ( $r>$ 0.7). Furthermore, as model summary 3 demonstrate, these 3 muscles had almost 85% contribution to the average walking speed. However due to their weakening the preferred walking speed slowed down in this cohort. Since these muscles, especially the HAB, play a two-fold role in balance maintenance and stepping during walking [46, 47], enhancement of their quality could increase the momentum produced and play a substantial role in the daily performance. Given that any weakness in these muscle groups directly targets the balance performance, the single leg support phase would significantly decrease, resulting in a reduction in the walking speed [48, 49]. Additionally, based on previous studies, the moment produced in the HABs plays a key role in the lateral stability of the single leg support phase, by counterbalancing the moment produced by the CoM weight [47, 50]. The strong negative correlation between HABs strength and advancing age ( $r=-$ 0.62) and its positive correlation with the average walking speed ( $r=$ 0.77) observed in this study also confirmed that the walking performance deteriorates as a consequence of HAB weakness.

The role of knee and ankle muscle groups is abundantly highlighted in former investigations [51, 52]. Mediolateral movements of ankle joint during gait counterbalances the reaction forces applied to the structure by the start of heel contact phase and delivers the lower limbs muscles (particularly ankle plantar flexors) forces during push off phase [51, 53]. Ankle plantar flexors, prior to the push off phase, eccentrically contract and then, using the stretch shortening cycle characteristic of the muscle, immediately transfer the elastic energy to the concentric contraction for an efficient push off [54, 55]. Ankle invertors, in addition, maintain mediolateral balance of ankle-foot complex during stance phase of gait cycle [53]. Interestingly, the outcomes portrayed that both AINs and APFs, with 52% of the variance, had a significant contribution to the average walking speed and both encountered a significant strength loss by advancing age. These outcomes emphasize the fact that not only the AINs and APFs strength decrement adversely impacts the produced force by APFs during push off but also negatively influence the ankle-foot balance control during stance phase.

Similarly, the strength measures of KFLs and KEXs, which had almost 58% contribution in identification of average walking speed, declined by age increase. Knee extensors, which have the shock absorber fuction during the first half of stance phase, contribute to prevention of excessive vertical CoM movement and help in producing extension momentum after mid-stance [52]. On the other hand, a quick and short activation of knee flexors during swing phase of the gait cycle, reduces the inertial momentum of hanged leg and increases the stride velocity and frequency [56]. Additionally, this muscle group co-contracts with the knee extensors during the knee extension prior to the heel contact [52]. The age-related weakness of these muscle groups leads to impairment in the stance and swing phases of the gait cycle and decreases the walking speed in elderly women.

That said, it should be emphasized that there was no indication that the ankle evertors, ankle dorsiflexors and hip adductors had neither undergone weakening with age nor did they have a significant contribution to walking performance in this cohort.

5. Conclusion

The study outcomes reinforce the relationship between age associated dynapenia, walking and balance capabilities in elderly women. Ankle plantar flexors, knee flexors/extensors, and hip flexors/extensors and abductors, which had significant contribution to walking performance encountered a considerable weakness that decreased the preferred walking speed among elderly women. Among the entire lower limbs muscle groups, the hip joint muscles have contributed heavily to identification of the average walking speed. This further underlines the importance of maintaining sufficient levels of strength measures in the abovementioned muscles.

Author contributions

CONCEPTION: Javad Sarvestan and Zuzana Kovacikova.

PERFORMANCE OF WORK: Javad Sarvestan, Zuzana Kovacikova, Petr Linduska and Zuzana Gonosova.

INTERPRETATION OR ANALYSIS OF DATA: Javad Sarvestan, Zuzana Kovacikova and Zdenek Svoboda.

PREPARATION OF THE MANUSCRIPT: Javad Sarvestan.

REVISION FOR IMPORTANT INTELLECTUAL CONTENT: Zuzana Kovacikova and Zdenek Svoboda.

SUPERVISION: Javad Sarvestan, Zuzana Kovacikova and Zdenek Svoboda.

Ethical considerations

Prior to participation, all participants signed an informed consent form and the study protocol was approved by the ethical standards committee of the Faculty of Physical Culture, Palacky University (no. 24/2017). The procedures were in compliance with the 1964 Helsinki Declaration and its later amendments.

Funding

The funding and support for this study was provided by the scientific grant of the Czech Science Foundation [GACR, No. 18-16107Y].

Footnotes

Acknowledgments

The authors have no acknowledgments.

Conflict of interest

The authors declare that there are no conflicts of interest.

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