Differences in Lower Limb Muscle Activation and Variability Across Walking Speeds by Age and Fall Risk

doi:10.21203/rs.3.rs-4981443/v1

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Differences in Lower Limb Muscle Activation and Variability Across Walking Speeds by Age and Fall Risk

https://doi.org/10.21203/rs.3.rs-4981443/v1

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This study aimed to determine the differences in lower limb muscle activation and variability at preferred, slow, and fast walking speeds according to age and fall risk. We divided 301 participants into groups based on age (young older: 70–79 years vs. old older: 80–90 years) and fall risk (fall risk vs. non-fall risk). We measured muscle activation and its coefficients of variation (CV) for the rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and medial gastrocnemius muscle (GCM) at speeds 20% slower, 20% faster, and 40% faster than the preferred speed (PS). When compared by age, older adults exhibited greater changes in RF and GCM activities, versus young older adults; however, the CV was not significantly different. Fall risk older adults had significantly lower GCM activity and higher CVs of RF, BF, TA, and GCM in PS. With changes in gait speed, older adults at risk of falling had significantly increased CVs of RF, BF, and GCM. Our findings provide new evidence that variability rather than muscle activity increases with walking speed in older adults at risk of falls, highlighting the importance of decreasing muscle activity variability in preventing fall risk.

Health sciences/Health care

Health sciences/Risk factors

Fall risk

Muscle activation

Older adults

Safe Walking

Variability

In an aging society, reducing the risk of falls, which can diminish physical function, independence in daily living, and quality of life in older adults, has emerged as a major issue. The ability to walk safely is an essential physical ability required to minimize the risk of falls and maintain physical independence ¹. Gait and balance abilities are closely related and are crucial factors to predict quality of life and mortality rates ². Age-related loss of muscle mass can alter muscle activity patterns and lead to decreased gait and balance abilities ³. In particular, muscle strength and activation in the lower limbs can affect walking ⁴, balance ability ^5,6, and fall risk ⁷.

Promsri et al. ⁸ reported that lower scores on the Short Physical Performance Battery (SPPB), which measures fall risk, are associated with increased gait instability and, consequently, a higher risk of falls. Gait instability refers to a deterioration in the consistency of gait and is represented by variability; higher gait variability indicates worse walking consistency ⁹, making variability a key indicator of gait consistency strongly associated with fall risk ⁹. Among older adults, gait variability is more pronounced in those with greater muscle activation variability, and can be closely related to walking speed ¹⁰.

Recent studies have emphasized the importance of identifying changes in lower limb muscle activity to predict and prevent falls in older adults ¹¹. Furthermore, older adults exhibit greater muscle activation variability than younger adults do when performing rapid repetitive movements ¹². As people age, not only can lower limb muscle strength and activation decrease but also the variability in lower limb muscle activation during walking can increase; this is closely related to fall risk ¹³. Therefore, age-related decreases in lower limb muscle activation may lead to increased muscle activation variability as walking speed increases, and this increased variability in response to changes in walking speed could be closely related to fall risk.

Previous studies have identified the muscles involved in the response to changes in speed in both young and older adults ¹⁴, demonstrated a correlation between balance ability and gait variability ¹⁵, and identified specific lower limb muscles related to fall risk ¹⁶. However, there is a relative lack of research analyzing the activation and variability of specific lower limb muscles in response to age-related changes in walking speed with increase in age in older adults. It is particularly important to investigate changes in muscle activation and variability with changes in walking speed in older adults at risk of falls.

We hypothesized that older adults (80–90 years) and older adults at risk of falls (fall-risk older adults) will experience a decrease in lower limb muscle activation and greater variability as walking speed increases, compared with young older adults (70–79 years) and older adults who are not at risk of falls (non-fall-risk older adults). We, therefore, aimed to identify differences in lower limb muscle activation and variability at preferred, slow, and fast walking speeds among community-dwelling older adults based on age group and fall risk. Additionally, we sought to identify the patterns of muscle activation in response to changes in walking speed among older adults at risk of falls.

Design and ethical considerations

To determine the variability of muscle activity at different walking speeds, all older adults were randomly assigned to walk at different walking speeds. This study recruited 301 older adults between August 2023 and January 2024. Before commencing the experimental process, detailed information about the study procedure and safety was provided to the participants, who subsequently signed a written informed consent form. This study was approved by the Institutional Review Board of Gachon University (Clinical trial registration number: KCT0009118) and conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Participants and procedures

The participants were 301 community-dwelling older adults (aged 70–90 years), who were recruited through posters in community centers and based on telephone interviews, according to the following criteria. We included older adults 1) aged ≥ 70 years who were able to perform activities of daily living with or without assistive devices; 2) not currently experiencing orthopedic problems in the lower extremities or neurological disorders, such as cerebral infarction; and 3) who had not undergone surgery within 6 months due to musculoskeletal diseases of the lower limb. Individuals who had a Mini-Mental State Examination score < 24 or difficulty in walking on a treadmill and those who did not perform the measurement procedures were excluded. At the beginning of the study, 368 older adults were recruited. However, 11 older adults did not meet the inclusion criteria, and 56 were unable to perform the measurement procedures because of difficulty in walking on a treadmill.

All participants were asked to complete a questionnaire about their health status and experience of falling, and their lower-extremity strength and balance ability were measured. Subsequently, lower limb muscle activity and variability were measured using electromyography while the participants walked on a treadmill. Before performing treadmill walking, all participants walked 14 m to measure their preferred walking speed. To minimize errors in measuring the average preferred walking speed caused by acceleration and deceleration, the time taken to walk the middle 10 m was used, after excluding the time taken to walk initial and final 2 m. The participant then adapted to treadmill walking at the preferred walking speed obtained from the ground walking measurements. If any issues arose due to speed differences during treadmill adaptation, the preferred walking speed was adjusted by ± 0.1 km/h. After determining the preferred speed (PS), the participants took a 2-min rest before walking on a treadmill at their PS. They then walked at speeds 20% slower, 20% faster, and 40% faster than their PS for 90 s each, with a rest period of approximately 1 min between trials. Muscle activity was measured for approximately 60 s, starting 20 s after the participant began walking on the treadmill.

All participants had their lower limb muscle activity measured at their PS and were then randomly assigned to one of the three speed sequences (20% slower, 20% faster, 40% faster; 20–40% faster, 20% slower; and 40% faster, 20% slower, 20% faster) to have their lower limb muscle activity measured again. The order of assignment was determined using a randomization website (http://www.randomization.com). To ensure fairness and objectivity, the randomization process was conducted by a researcher who was not directly involved with the study.

All data were collected from a university laboratory and a community center. The researchers were aware of the purpose of the study but the outcome assessors were not. G*Power 3.1.9 software was used to calculate the required sample size for the study, and power = 0.8, 𝜶 = 0.05, and effect size = 0.5 were determined based on a two-tailed test. The calculated sample size was 128.

Data collection

Fall risk

The SPPB score is a fall-risk assessment tool for older adults that can distinguish fallers from non-fallers ¹⁷. SPPB (0–12 points) was used to measure physical performance, and consisted of balance, 4-m walk, and five timed chair stand tests. In the balance test, the participants were asked to maintain their feet in an aligned position, then in a semi-aligned position, and finally, in a side-by-side position, for 10 s each. The 4-m walk involved walking at a usual pace. For the timed chair stand test, a pre-test of five trials was conducted. The participants were asked to stand up from a chair with their arms crossed over their chests, and the time from the first sitting position to the last standing position was measured in seconds during the fifth stand. The SPPB score was calculated according to a previous study ¹⁸. Because an SPPB score ≤ 9 is related to fall rate ¹⁹, we considered this score to be indicative of risk of falls.

Balance ability

The timed up-and-go (TUG) test and functional reach test (FRT), which are valid tools for screening balance deficits that increase the risk of falls in older adults, were used ^20,21. The participants stood with their feet shoulder-width apart, made fists, raised their arms until they were parallel to the floor, and the position of the third metacarpal bone was measured. Subsequently, while maintaining fixed support in the standing position, the participant was asked to stretch forward as much as possible. Differences in distance from the third metacarpal bone were measured ²². The TUG test required patients to stand up from a chair, walk 3 m, turn around, walk back to the chair, and sit down, and the time taken to complete this was measured ²³. The TUG test and FRT were conducted three times and measurements were taken, and the average values were used.

Lower limb muscle activity and variability

Surface electromyography (sEMG) equipment (Ultium ESP, Noraxon, USA) was used to measure the activation of the lower limb muscles during walking on a treadmill. The settings for the EMG signal collection were as follows: sampling rate of 2,000 Hz, band-pass filter of 10–500 Hz, and notch filter of 60 Hz. Before electrode placement, the participant’s skin was shaved and cleaned with alcohol. Disposable dipole electrodes (Ag/AgCl) were attached to the skin of the participants at the midline of the muscle belly, and electrode placement was performed according to sEMG (surface EMG for non-invasive assessment of muscles) guidelines ²⁴. The sEMG data were collected from four lower-extremity muscles: the rectus femoris (RF), tibialis anterior (TA), biceps femoris (BF), and gastrocnemius muscle (GCM) ²⁵. Using the root mean square (RMS) to measure muscle activation in older adults is effective, and the correlation between RMS values and muscle strength makes this test reliable to assess age-related muscle changes ^26,27. Therefore, in this study, the collected data was processed using the RMS method, and the unit is µV. The variability in the measured muscle activity was calculated as the coefficient of variation (CV), which was expressed as the ratio of the standard deviation to the mean (standard deviation/mean × 100) of each muscle activity, and presented as a percentage ²⁸.

Statistical analysis

SPSS 26.0 software (IBM, Armonk, NY) was used for statistical analyses. Frequency and descriptive statistics were analyzed to assess the general characteristics of the participants.

Participants were stratified according to age (70–79 years, n = 159; 80–90 years, n = 140) and fall risk (fall risk, n = 141; non-fall risk, n = 160). A comparison of lower limb muscle activity at the preferred walking speed based on age and fall risk was conducted using an independent t-test. A one-way repeated-measures analysis of variance (ANOVA) was used to evaluate the differences in lower limb muscle activity at the PS and at the three different speeds within each group. To examine changes between groups according to age and fall risk, a two-way repeated-measures ANOVA was used. The effect sizes of the interaction effect were calculated using eta-squared (η²), with effect sizes defined as up to 0.02 for small changes, 0.13 for moderate changes, and 0.26 for large changes.

All data were expressed as mean ± standard deviation. The level of significance was set at α < 0.05. In within-group comparisons, a p-value < 0.013 was considered statistically significant, established by applying the Bonferroni correction in post-hoc tests (α = 0.05/4 = 0.013).

A total of 301 participants (135 men, 166 women) were included in the study. In the age classification, there were 159 young older adults aged 70–79 years and 142 older adults aged 80–90 years. According to fall-risk classification, there were 141 older adults in the fall-risk group and 160 in the non-fall-risk group. In older adults, the SPPB scores were significantly lower and balance ability was reduced, compared with young older adults. Similarly, in non-fall-risk older adults, balance ability was reduced, compared with fall-risk older adults (Table 1).

Table 1

General characteristics of subjects according to age and fall risk level.
	Young older (n = 159)	Old older (n = 142)	t	p	Fall risk (n = 141)	Non-fall risk (n = 160)	t	p
Age (years)	75.12 ± 2.59	84.17 ± 3.57	-25.438	< .001	79.97 ± 5.69	78.81 ± 5.13	1.857	.064
Weight (Kg)	61.17 ± 8.85	59.36 ± 10.82	1.599	.111	59.27 ± 9.51	61.23 ± 10.08	-1.726	.085
Height (cm)	158.53 ± 7.72	156.54 ± 8.80	2.079	.038	155.62 ± 8.05	159.33 ± 8.14	-3.956	< .001
SPPB (score)	10.32 ± 1.62	9.05 ± 2.76	4.924	< .001	7.74 ± 1.87	11.47 ± 0.70	-23.326	< .001
TUG (sec)	9.88 ± 2.22	10 .86 ± 4.42	-7.487	< .001	13.21 ± 4.46	9.59 ± 1.66	.528	< .001
FRT (cm)	24.23 ± 9.18	19.01 ± 6.56	5.604	< .001	18.66 ± 7.01	24.50 ± 8.68	-6.369	< .001
SPPB: Short physical performance battery, TUG: timed up-and-go test, FRT: functional reach test

Comparing the PS of the two groups, the CV for RF and TA activities were higher and GCM activity was significantly lower in older adults than in young older adults. The changes in muscle activity and the CV according to walking speed showed significant differences in the young older adults for RF (p < .001, η² = .176), BF (p < .001, η² = .227), TA (p < .001, η² = .202), and GCM (p < .001, η² = .176) activities. Compared with the PS, muscle activity significantly decreased when the speed was 20% slower, and significantly increased when the speed was 40% faster. The CV significantly increased only for TA activity when the speed was 20% slower. Older adults exhibited significantly lower RF (p < .001, η² = .269), BF (p < .001, η² = .215), TA (p < .001, η² = .165), and GCM (p < .001, η² = .282) activities, compared with young older adults. All muscle activity significantly decreased when the speed was 20% slower than the PS, and significantly increased when the speed was 40% faster. However, there were no differences in CV across all muscles for various speeds. Comparisons between the two groups showed that older adults exhibited more significant changes in RF (p = .002, η² = .016) and GCM (p = .010, η² = .013) activities, compared with young older adults; however, the two groups demonstrated no significant difference in the all muscle CV (Tables 2 and 3, respectively).

Table 2

Comparison of muscle activity and variability between young old and old older adults.
	Young older adults				Old older adults
	PS	-20% velocity	20% velocity	40% velocity	PS	-20% velocity	20% velocity	40% velocity
RF activity (µV)	32.59 ± 15.03	27.50 ± 13.72^b	33.76 ± 17.06	36.29 ± 17.88^b	30.93 ± 16.98	26.88 ± 15.42^b	33.73 ± 17.41^b	39.55 ± 23.78^b
RF CV (%)	13.04 ± 6.51a	12.59 ± 5.69	13.45 ± 7.74	12.92 ± 6.73	16.07 ± 10.85	13.89 ± 7.54^b	16.23 ± 9.02	15.36 ± 9.06
BF activity (µV)	46.45 ± 20.55	39.29 ± 19.00^b	46.39 ± 21.49	49.84 ± 23.04^b	47.00 ± 22.59	40.05 ± 20.38^b	48.48 ± 24.38	53.11 ± 25.39^b
BFCV (%)	13.11 ± 8.95	12.39 ± 5.72	11.78 ± 5.02	12.45 ± 7.51	13.26 ± 5.96	13.85 ± 6.11	14.13 ± 7.61	14.58 ± 9.52
TA activity (µV)	60.29 ± 25.09	52.52 ± 22.24^b	58.37 ± 23.65^*	61.39 ± 23.64	54.98 ± 23.40	49.40 ± 22.30^b	54.20 ± 23.83	57.52 ± 23.51^b
TA CV (%)	12.39 ± 4.59a	13.85 ± 6.63^b	12.13 ± 5.03	12.98 ± 5.54	15.61 ± 8.80	15.41 ± 7.09	14.72 ± 7.10	14.98 ± 7.01
GCM activity (µV)	61.67 ± 24.62^a	54.31 ± 25.71^b	59.47 ± 23.45^*	65.85 ± 26.94^b	53.45 ± 23.76	46.38 ± 22.37^b	55.50 ± 25.29^b	61.90 ± 28.53^b
GCM CV (%)	12.5 ± 4.93	12.83 ± 5.04	12.20 ± 5.10	12.99 ± 6.31	13.80 ± 5.87	14.93 ± 6.58^b	14.45 ± 5.73	15.27 ± 7.08^b
RF: Rectus femoris, BF: Biceps femoris, TA: Tibialis anterior, GCM: Gastrocnemius medialis, PS: Preferred speed, CV: coefficient of variation
^a p < .05, a significant difference between young and old older groups in PV using independent t-test
^bp < .05, a significant difference compared PV using one-way repeated ANOVA

Table 3

Statistical analysis of muscle activity and CV within and between groups in young old and old older adults.
	Young older adults^b				Old older adults^b				Between group^c
	F	p	η²	adj sig^a	F	p	η²	adj siga	F	p	η²
RF activity (µV)	33.955	< .001	.176	Yes	51.765	< .001	.269	Yes	4.834	.002	.016
RF CV (%)	0.795	.497	.005	No	3.009	.030	.021	No	1.112	.343	.004
BF activity (µV)	46.821	< .001	.227	Yes	38.699	< .001	.215	Yes	1.349	.257	.004
BF CV (%)	1.604	.188	.010	No	1.593	.190	.011	No	2.540	.055	.008
TA activity (µV)	40.232	< .001	.202	Yes	27.827	< .001	.165	Yes	0.894	.444	.003
TA CV (%)	8.876	.001	.036	Yes	0.721	.540	.005	No	1.526	.206	.005
GCM activity (µV)	34.014	< .001	.176	Yes	55.338	< .001	.282	Yes	3.808	.010	.013
GCM CV (%)	1.071	.361	.007	No	2.278	.079	.016	No	0.903	.439	.003
RF: Rectus femoris, BF: Biceps femoris, TA: Tibialis anterior, GCM: Gastrocnemius medialis, CV: coefficient of variation
^a Adjusted significance (significance alteration p-value to .013)
^b Statistical analysis was performed using one-way repeated-measures ANOVA. ^c Statistical analysis was performed using the two-way repeated-measures ANOVA

Comparing fall-risk and non-fall-risk older adults walking at the PS, we found that fall-risk older adults had significantly lower GCM activity and significantly higher CV for RF, BF, TA, and GCM activities than did non-fall-risk older adults. Changes in muscle activity and the CV with gait speed showed significant differences in RF (p < .001, η² = .206), BF (p < .001, η² = .204), TA (p < .001, η² = .155), and GCM (p < .001, η² = .157) activities in fall-risk older adults compared with non-fall-risk older adults. Compared with the PS, muscle activity was significantly reduced at a 20% slower speed and significantly increased at a 40% faster speed. The CV for RF (p = .002, η² = .033) and GCM activities (p < .003, η² = .033) were also significantly reduced at 20% slower speed and significantly increased at 40% faster speed. In non-fall-risk older adults, there were significant differences in RF (p < .001, η² = .232), BF (p < .001, η² = .239), TA (p < .001, η² = .209), and GCM (p < .001, η² = .298) activities, with a significant decrease in muscle activity at 20% slower speed and a significant increase in muscle activity at 40% faster speed, compared with fall-risk older adults. There was also a significant difference in the CV for TA (p < .001, η² = .044) and GCM (p = .004, η² = .027) activities, with a significant decrease in the CV as speed increased. The CV for RF activity was also significantly reduced at 40% faster speed; however, it was not significantly different in non-fall risk older adults. Comparisons between the two groups showed that fall-risk older adults had significantly greater increases in the CV for RF (p = .002, η² = .016), BF (p = .047, η² = .009, and GCM (p < .001, η² = .022) activities at 40% faster speed, compared with non-fall-risk older adults (Tables 4 and 5).

Table 4

Comparison of muscle activity and CV between fall risk older and non-fall risk older adults
	Fall risk older				Non-fall risk older
	PS	-20% velocity	20% velocity	40% velocity	PS	-20% velocity	20% velocity	40% velocity
RF activity (µV)	32.69 ± 16.13	28.40 ± 15.18^b	35.93 ± 19.72^b	39.45 ± 23.71^b	31.02 ± 15.82	26.16 ± 13.88^b	31.81 ± 16.05	36.38 ± 17.93^b
RF CV (%)	15.88 ± 10.79^a	14.05 ± 7.37^b	16.29 ± 9.40	16.80 ± 9.51	13.22 ± 6.66	12.45 ± 5.85	12.84 ± 7.02	11.64 ± 5.29^b
BF activity (µV)	46.15 ± 21.97	39.93 ± 19.94^b	48.30 ± 24.16^b	51.78 ± 24.25^b	47.20 ± 21.14	39.40 ± 19.42^b	46.55 ± 21.72	51.02 ± 23.76^b
BF CV (%)	14.12 ± 6.18^a	14.24 ± 7.14	14.64 ± 8.00	15.62 ± 10.06^b	13.34 ± 8.72	11.80 ± 4.30	11.30 ± 4.16	11.53 ± 6.42
TA activity (µV)	55.27 ± 23.96	49.37 ± 22.57^b	54.34 ± 24.52	56.57 ± 23.29	60.00 ± 24.67	52.54 ± 22.00^b	58.24 ± 23.04^*	62.24 ± 23.66^b
TA CV (%)	15.31 ± 8.88^a	15.64 ± 7.69	14.77 ± 7.32	15.77 ± 7.53	12.68 ± 4.79	13.66 ± 5.94^b	12.09 ± 4.71	11.78 ± 4.41^b
GCM activity (µV)	50.36 ± 20.96^a	44.10 ± 20.87^b	51.32 ± 20.76	56.21 ± 25.61^b	64.34 ± 25.61	56.33 ± 26.04^b	63.18 ± 26.00	70.90 ± 27.77^b
GCM CV (%)	14.69 ± 6.30^a	14.99 ± 6.44	15.18 ± 5.99	16.94 ± 7.74^b	11.78 ± 4.07	12.78 ± 5.18^b	11.56 ± 4.42	11.50 ± 4.43
RF: Rectus femoris, BF: Biceps femoris, TA: Tibialis anterior, GCM: Gastrocnemius medialis, PS: Preferred speed, CV: coefficient of variation
^a p < .05, statistical analysis for PV comparison between two groups used independent t-test
^bp < .05, a significant difference compared PV using one-way repeated ANOVA

Table 5

Statistical analysis of muscle activity and variability within and between groups in fall risk and non-fall risk older adults
	Fall risk older^b				Non-fall risk older^b				Between group^c
	F	p	η²	adj sig^a	F	p	η²	adj sig^†	F	p	η²
RF (µV)	36.504	< .001	.206	Yes	48.029	< .001	.232	Yes	1.108	.345	.004
RF CV (%)	4.867	.002	.033	Yes	2.712	.044	.017	No	4.876	.002	.016
BF (µV)	36.026	< .001	.204	Yes	49.823	< .001	.239	Yes	1.218	.302	.004
BF CV (%)	2.137	.095	.015	No	1.086	.355	.007	No	2.664	.051	.009
TA (µV)	25.850	< .001	.155	Yes	41.947	< .001	.209	Yes	1.605	.187	.005
TA CV (%)	0.866	.459	.006	No	7.335	< .001	.044	Yes	2.321	.074	.008
GCM (µV)	26.356	< .001	.157	Yes	67.642	< .001	.298	Yes	1.334	.262	.004
GCM CV (%)	4.849	.003	.033	Yes	4.438	.004	.027	Yes	6.624	< 0.001	.022
RF: Rectus femoris, BF: Biceps femoris, TA: Tibialis anterior, GCM: Gastrocnemius medialis, CV: coefficient of variation
^aAdjusted significance (significance alteration p-value to .013)
^b Statistical analysis was performed using one-way repeated-measures ANOVA. ^c Statistical analysis was performed using the two-way repeated-measures ANOVA

We compared lower limb muscle activity and variability according to walking speed between young older adults and older adults, as well as between fall-risk and non-fall-risk groups, to identify muscle activation with varying speed changes and to quantitatively analyze the specific effects of increased walking speed on muscle activity variability. Older adults had lower GCM activation at their PS than did young older adults, with increased variability in RF and TA activities. As walking speed increased, RF muscle activation increased; however, GCM activation significantly decreased. Additionally, fall-risk older adults had lower GCM activation at their PS than did non-fall-risk older adults, with increased CV for RF, BF, TA, and GCM activities. Furthermore, as walking speed increased, the CV in non-fall-risk older adults decreased, whereas it increased in fall-risk older adults, with significant differences, particularly in the CV for RF and GCM activities.

As people age, muscle strength decreases, and physical function deteriorates ²⁹. When walking speed increases beyond the preferred pace, ankle plantar flexion and knee and hip flexion also increase ³⁰, indicating that activation of the ankle and hip muscles is essential for walking stability ³¹. However, as walking speed increases, people tend to rely more on proximal muscles, such as the hip muscles, to compensate for weakened plantar flexors as walking speed increases ³². Our results showed that the activities of the RF, BF, and GCM significantly increased at a speed 40% faster than the PS in both young older adults and older adults. Notably, in older adults, GCM activity was significantly lower at the PS and RF and GCM activities increased more prominently at a 40% faster speed, compared with young older adults. These findings are consistent with those of previous studies, suggesting that maintaining RF and GCM activation is crucial for walking stability as people age.

Additionally, in fall-risk older adults, the activation of the RF, BF, and GCM increased significantly at a speed 40% faster than the PS, similar to that in non-fall-risk older adults. However, the TA was not significantly activated in fall-risk older adults. Lim et al. (Lim et al., 2023) found that stroke patients with reduced balance showed greater cortical input to the RF than to the TA on the paretic side during walking, which increased gait variability. This indicates that inadequate activation of the TA with an increase in walking speed can lead to decreased balance or an increased risk of falls.

Moreover, older adults at risk of falls had decreased GCM activation as walking speed increased, compared with older adults who are not at risk of falls. These results suggest that the increased RF muscle activation with walking speed represents a compensatory mechanism for weakened GCM muscles during walking, whereas the reduced GCM muscle activation is associated with a higher fall risk ^33,34. Indeed, older adults and fall-risk older adults had decreased balance ability, as determined by the TUG test and FRT, compared with young older adults and non-fall-risk older adults, respectively. Thus, older adults with decreased balance ability may have an inefficient pattern of lower-body muscle activation to maintain walking stability as walking speed increases, which may affect their ability to walk, particularly at high speeds ¹⁰.

Walking variability is a sensitive indicator of abnormal walking ³⁵. Aging affects muscle strength and force control, increases force output variability, and decreases motor performance in older adults ³⁶. Older adults with higher variability in lower-extremity muscle activation have greater gait variability at different walking speeds, which increases the risk of falls ¹⁰. Our results showed that older adults had significantly higher CVs for RF and TA activities at their PS than did the young older adults; however, there was no significant difference when walking speed decreased or increased. In contrast, fall-risk older adults had higher CVs for RF, BF, TA, and GCM activities at their PS than did non-fall-risk older adults. In addition, in non-fall-risk adults, the CV decreased when walking speed increased, whereas fall-risk older adults had significantly higher CVs for RF and GCM activities.

Recently study ³⁷ reported a significant correlation between muscle activation variability measured by EMG and task performance, and variability in RF activity increases during walking with age ³⁸. This suggests that increased muscle activation variability during repetitive movements may lead to difficulty in task performance and that increased variability in RF activity in older adults may lead to walking difficulties. Lim et al. ³⁹ also found that, in patients with impaired balance ability, the RF had greater activation than did the TA, which led to increased gait variability. Therefore, variability in RF activity is more important than TA activation in maintaining a stable gait.

Moreover, our results showed that, in older adults and fall-risk older adults, variability increased when walking speed was 20% slower and 40% faster than the PS, compared with young older adults and non-fall-risk older adults, respectively. This result is similar to that of previous studies that showed that muscle activity variability increases with decreasing walking speed ⁴⁰. Therefore, additional attention is needed when training at slow walking speeds ⁴¹. Furthermore, when walking speed increased by 40% of the PS, non-fall-risk older adults showed decreased CV, whereas fall-risk older adults showed increased CV, with significant differences, particularly for RF and GCM activities. Reducing variability can help achieve stable walking and reduce fall risk ⁴². These results suggest that as walking speed increases, muscle activation stability decreases, indicating that muscle control mechanisms in older adults operate differently depending on fall-risk status. This implies that abnormal gait and fall risk can be identified not only through spatiotemporal variability but also through RF and GCM muscle activation variability. Therefore, identifying changes in variability at a speed 40% faster than the PS may be a factor in identifying reduced balance and fall risks in older adults.

This study has some limitations. First, only older adults who were able to walk on a treadmill in a laboratory setting were included. This may not fully reflect walking in daily life and may have potential differences from real walking. Second, some older adults were unable to perform the study procedures because of fear of walking on a treadmill, and muscle activation may be affected by psychological factors such as fear ^43,44. Additionally, this study did not consider that a 40% increase in walking speed might be excessive for some older adults.

Despite these limitations, this is the first study to systematically analyze muscle activity and variability according to changes in walking speed based on age and fall risk. We identified the characteristics of muscle activation during walking in older adults at risk of falls and provide foundational data for fall prevention. By further understanding the mechanism of the muscle response according to changes in walking speed, it will be possible to improve walking safety in older adults. Future research should include analysis of walking in real-life environments and verification of the effects of specific rehabilitation programs aimed at improving lower limb muscle activation and variability.

This study identified lower limb muscle activation and variability during walking according to age and fall risk, and found that in older adults at risk for falls, variability in muscle activation increased with changes in walking speed. In particular, in older adults at high risk for falls, variability rather than muscle activation during walking increased with increasing walking speed. These findings suggest that muscle activation variability, rather than muscle activation itself, is more closely related to fall risk. Therefore, reducing muscle activation variability may be an important strategy for fall prevention in at-risk older adults.

Acknowledgements

This research is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00246536). The author wishes to thank Park WH, Kim UB, Lim JT, Park JS and Lee HI who technically assisted at the Geriatric Health Care and Physical Activity Laboratory at Gachon University.

Author contributions

Park and Bae conceived and designed research, and conducted experiments, analyzed data. Park and Bae wrote the manuscript, and the authors have read and agreed to the published version of the manuscript.

Data availability

The datasets analysed in this study are available from the corresponding author upon reasonable request.

Competing Interests Statement

The authors declare no conflict of interest.

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No competing interests reported.

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Differences in Lower Limb Muscle Activation and Variability Across Walking Speeds by Age and Fall Risk

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