Interactive effects of age and lower-limb muscle strength asymmetry on spatiotemporal gait parameters

Interactive effects of age and lower-limb muscle strength asymmetry on spatiotemporal gait parameters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Interactive effects of age and lower-limb muscle strength asymmetry on spatiotemporal gait parameters Gayeong Hyeon, Changhun Kim, Chul-Min Chung, Kyuseung Kim, Yungon Lee, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8350365/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Ageing is associated with gait deterioration, yet the influence of lower-limb muscle asymmetry on age-related gait changes remains unclear. This cross-sectional study investigated the interaction effects of age and lower-limb muscle strength asymmetry on spatiotemporal gait parameters in 345 community-dwelling adults aged 19–85 years. Lower-limb muscle strength was assessed using a hand-held dynamometer, and gait parameters were measured using an inertial measurement unit. Spearman correlation and multiple regression analyses examined associations between strength asymmetry and gait metrics, with interaction terms testing age-dependent effects. Age was associated with reduced stride length and some variability measures. Muscle asymmetry effects varied by joint: knee asymmetry correlated with spatial measures (stride length), while ankle asymmetry linked to temporal measures (stance, swing, and double support phases). Critically, knee extension asymmetry shortened stride length only in elderly participants, and ankle plantarflexion asymmetry worsened temporal symmetry exclusively in older adults, with no effects observed in younger groups. These findings demonstrate that lower-limb strength asymmetry, combined with ageing, selectively exacerbates gait deterioration. Assessment of bilateral strength balance, beyond absolute strength levels, is crucial for predicting gait stability and fall risk in older adults, supporting targeted intervention strategies for fall prevention. Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Ageing Lower-limb muscle strength asymmetry Gait asymmetry Gait variability Spatiotemporal gait parameters Interaction effects Figures Figure 1 Figure 2 Introduction Gait is achieved through the intricate coordination of the musculoskeletal and nervous systems, which serves as a key indicator of an individual’s independent daily living and health status 1 , 2 . However, this coordination weakens with age, resulting in gait abnormalities and, ultimately, loss of balance and falls. Approximately 60% of individuals aged ≥ 80 years experience gait abnormalities, with many experiencing recurrent falls 3 . Gait deterioration among the elderly is not only a cause of falls but also leads to functional dependence and reduced quality of life, consequently significantly increasing societal costs 4 – 6 . Therefore, from a public health perspective, early identification of gait characteristics and vulnerability factors in older people is critical for fall prevention and maintenance of functional independence. Clinical and research settings have primarily relied on single indicators, such as walking speed. However, even at the same speed, some individuals maintain a steady rhythm, whereas others exhibit irregular stride lengths, increasing their risk of losing balance with minor stimuli. Spatiotemporal gait metrics must be employed for precise assessment of gait characteristics. Stride length, walking speed, double support time and stance/swing ratio reflect performance level (mean) 7 , whereas coefficient of variation (CV) indicates stability and consistency 8 . Asymmetry assesses coordination between the left and right lower limbs 9 , 10 . CV and asymmetry are closely associated with fall risk 11 – 13 and are considered key measures that sensitively reflect gait quality more than mean indicators. Ageing is accompanied by neuromuscular decline and reduced physical fitness, which directly induces changes in the spatiotemporal characteristics of gait. Previous studies have indicated that in older adults, walking speed and stride length decrease, whereas double support time and gait variability increase 14 , 15 . Furthermore, gait asymmetry becomes more pronounced after middle age 16 . However, ageing alone does not induce a marked increase in asymmetry 9 , 17 , 18 , suggesting that the effects of ageing may be more pronounced when combined with other factors. Lower-limb muscle strength crucially contributes to walking speed and maintaining balance 19 ; however, muscle strength asymmetry—an imbalance between the left and right lower limbs—is an independent risk factor different from simple muscle weakness. Leg power asymmetry of approximately 15% has been found to be associated with impaired walking speed and balance 20 , and older women with a knee extensor strength difference of > 20% exhibit significantly worsened gait variability and symmetry 13 . A knee strength difference of > 25% can predict functional decline and falls with high accuracy 21 . Muscle asymmetry can arise from ageing, musculoskeletal injuries, neurological conditions, and lifestyle factors 22 – 24 . Such asymmetry leads to instability in daily activities, increasing the risk of falls 21 , 25 . In modern society, physical inactivity and sedentary lifestyles hasten overall lower-limb strength decline. When combined with lifestyle imbalances, muscle asymmetry can become further exacerbated 26 . Therefore, lower-limb muscle asymmetry should be considered a health indicator for predicting and preventing falls and functional decline in clinical patients and the general adult population. Recent studies indicate that the impact of muscle strength asymmetry can be amplified under specific conditions. Patients with stroke exhibit significantly greater knee muscle asymmetry than healthy controls, which influences gait variability and dual-support time 27 . However, existing studies have treated age and muscle strength asymmetry only as separate main effects, and muscle strength asymmetry is limited to the knee, leading to a lack of a comprehensive approach that encompasses the entire lower limb, including the hip and ankle. To the best of our knowledge, the impact of the interaction between these two factors on gait stability has not yet been systematically elucidated. Therefore, the present study aimed to investigate the effects of the interaction between age and lower-limb muscle asymmetry on spatiotemporal gait indicators (mean, variability and symmetry). Specifically, we hypothesised that even the same level of muscle asymmetry would be reflected more significantly as impaired gait function in older adults. By offering a novel perspective explaining gait stability in older adults, this study contributes to the development of fall prevention strategies and the maintenance of functional independence. Results Participant characteristics The mean age of the participants included in the final analysis was 51.17 ± 17.66 (range, 19–85) years, comprising 104 males (30.1%) and 241 females (69.9%). The age distribution was as follows: 98, 119 and 128 participants in the young (19–39 years), middle-aged (40–59 years) and elderly groups (≥ 60 years), respectively. Comparisons of baseline physical characteristics between different age groups revealed significant differences in height, weight, ASMI and PBF (p < 0.05). Height, weight and ASMI tended to decrease with increasing age, whereas PBF tended to increase. Detailed participant characteristics are provided in Table 1 . Table 1 Participant demographics by age group Variables Total ( n = 345) Young ( n = 98) Middle ( n = 119) Elderly ( n = 128) p -value Age (years) 51.17 ± 17.66 27.49 ± 5.44 51.03 ± 4.79 69.42 ± 5.69 < 0.001 Sex (M/F) ( n ) 104/241 40/58 31/88 33/95 0.025 Height (cm) 162.67 ± 8.56 167.10 ± 9.34 163.42 ± 7.41 158.57 ± 6.95 < 0.001 Weight (kg) 63.50 ± 11.51 67.31 ± 14.18 62.78 ± 11.13 61.25 ± 8.55 0.009 ASMI (kg/m²) 6.82 ± 1.03 7.09 ± 1.21 6.80 ± 1.01 6.64 ± 0.85 0.041 PBF (%) 30.39 ± 7.02 28.85 ± 7.85 29.47 ± 6.77 32.41 ± 6.09 < 0.001 Note. Values represent mean ± SD or n. p -values from the analysis of variance or Kruskal–Wallis tests for continuous variables and chi-square tests for categorical variables. ASMI, appendicular skeletal muscle mass index; PBF, per cent body fat. Intergroup differences Table 2 summarises the differences in lower-limb muscle strength asymmetry and gait indicators by age group. A significant intergroup difference was observed only in ankle plantarflexion (p = 0.025), with the middle-aged group exhibiting greater asymmetry than the young group. The elderly group did not show significantly different asymmetry compared to the other two groups, and no significant differences were observed for the other movements (hip flexion, extension, abduction; knee flexion, extension and ankle dorsiflexion). Table 2 Age-related comparison of lower-limb strength asymmetry and gait variables Variable Young ( n = 98) Middle ( n = 119) Elderly ( n = 128) p -value Strength asymmetry (%) Hip flexion 8.87 ± 6.35 a 9.92 ± 8.13 a 8.87 ± 6.87 a 0.763 Hip extension 13.00 ± 10.20 a 13.69 ± 10.10 a 12.15 ± 9.30 a 0.547 Hip abduction 9.12 ± 6.73 a 8.29 ± 7.49 a 9.67 ± 7.43 a 0.190 Knee flexion 11.71 ± 7.11 a 11.02 ± 8.50 a 11.68 ± 8.97 a 0.426 Knee extension 9.82 ± 8.40 a 11.89 ± 9.50 a 11.79 ± 8.34 a 0.106 Ankle plantarflexion 9.67 ± 7.52 a 13.29 ± 9.89 b 12.41 ± 9.36 ab 0.025 Ankle dorsiflexion 12.82 ± 9.60 a 14.12 ± 11.20 a 13.47 ± 10.73 a 0.816 Gait Performance Gait speed (m/s) 1.27 ± 0.15 a 1.40 ± 0.13 b 1.28 ± 0.17 a < 0.001 Stride time (s) 1.06 ± 0.08 a 0.99 ± 0.06 b 1.01 ± 0.07 b < 0.001 Stance phase (%) 61.22 ± 2.01 a 60.31 ± 1.80 b 61.42 ± 2.17 a < 0.001 Swing phase (%) 38.78 ± 2.01 a 39.69 ± 1.80 b 38.58 ± 2.17 a < 0.001 Double support (%) 22.17 ± 3.97 a 20.40 ± 3.37 b 22.58 ± 3.62 a < 0.001 Stride length (m) 1.33 ± 0.11 a 1.37 ± 0.09 b 1.27 ± 0.14 c < 0.001 Gait Variability ( CV , %) Stride time CV 2.65 ± 1.01 a 2.25 ± 0.65 b 2.30 ± 0.71 b 0.002 Double support CV 10.61 ± 5.39 a 9.66 ± 5.27 a 8.18 ± 4.57 b < 0.001 Stride length CV 4.82 ± 1.25 a 4.63 ± 2.03 a 4.68 ± 1.32 a 0.687 Gait asymmetry (%) Stride time asymmetry 0.08 ± 0.08 a 0.09 ± 0.08 a 0.08 ± 0.08 a 0.259 Stance phase asymmetry 1.63 ± 1.18 a 1.62 ± 1.32 a 1.96 ± 2.15 a 0.850 Swing phase asymmetry 2.53 ± 1.78 a 2.43 ± 1.94 a 3.06 ± 3.24 a 0.671 Stride length asymmetry 2.80 ± 2.75 ab 3.57 ± 2.42 a 2.34 ± 2.25 b 0.001 Note. Values represent mean ± SD. Different superscripts (a, b, c) indicate significant differences between age groups ( p < 0.05); groups sharing the same superscript are not significantly different. Analysis of variance variables used Tukey’s Honest Significant Difference; Kruskal–Wallis variables used Bonferroni-adjusted Mann–Whitney U. Among the mean gait metrics, intergroup differences were observed for all variables (p < 0.001). The young group exhibited longer stride time than the other two groups, whereas the elderly group exhibited slower walking speed and shorter stride length than the middle-aged group. Furthermore, in the elderly group, the stance and double support phases increased, and the swing phase decreased. Regarding gait variability (CV), the stride time CV and double support CV differed significantly with age (p < 0.01). The young and elderly groups exhibited the highest and lowest variabilities, respectively. Conversely, no significant difference in stride length CV was found among the groups (p = 0.687). Regarding gait asymmetry indicators, only stride length asymmetry differed significantly among the groups (p = 0.001), with the middle-aged group exhibiting greater asymmetry than the elderly group. Correlations Spearman’s correlation analysis revealed that age showed a weak positive correlation (rho = 0.117, p < 0.05) with KEA. Furthermore, age exhibited negative correlations with stride time (rho = − 0.268) and stride length (rho = − 0.269), stride time CV (rho = − 0.133), double support variability (double support CV, rho = − 0.245), and stride length asymmetry (rho = − 0.162). Regarding the relationship between lower-limb strength asymmetry and gait indicators, ADA showed the strongest positive correlation with stride length CV (rho = 0.205) and stride length asymmetry (rho = 0.241), whereas APA showed a weak association with stride length asymmetry (rho = 0.133). Analysis by age group revealed that in the young group, hip and knee asymmetries correlated with some gait mean and asymmetry indices (rho = 0.205–0.247). In the middle-aged group, ankle and knee extension asymmetries correlated with stride length asymmetry (rho = 0.207–0.328) and stride length variability (rho = − 0.256), respectively. Conversely, in the elderly group, knee extension and ankle asymmetries correlated with multiple indicators across gait mean values, variability and asymmetry (rho = − 0.174–0.249). Meanwhile, among lower-limb strength asymmetries, HAA did not show a correlation with any gait indicator. Regression models Table 3 presents the effects of age, lower-limb muscle asymmetry and the interaction term (age × asymmetry) on gait indicators, after adjusting for covariates of sex, ASMI and PBF. The significant main effects of age and strength asymmetry on gait parameters are illustrated in Fig. 1 . Table 3 Multiple regression analysis of predictors of gait outcomes Dependent variable \(\:{\varvec{Ｒ}}^{2}\) (%) Predictors β p-value Gait performance Gait speed 6.5 Sex 0.260 0.008 PBF –0.289 < 0.001 Stride time 16.9 Age –0.282 < 0.001 Sex –0.350 < 0.001 PBF 0.128 0.036 Stance phase 5.6 APA –0.145 0.008 PBF 0.205 0.002 Swing phase 5.6 APA 0.145 0.008 PBF -0.205 0.002 Double support phase 7.0 APA –0.141 0.010 PBF 0.222 < 0.001 Stride length 17.4 Age –0.166 0.001 Age × KEA –0.181 < 0.001 PBF –0.286 < 0.001 Gait variability (CV) Stride time CV 3.8 Age –0.158 0.005 Double support phase CV 5.7 Age –0.162 0.003 Stride length CV 9.9 ADA 0.267 < 0.001 Gait asymmetry Stride time asymmetry ns Stance phase asymmetry 4.9 Age × APA 0.139 0.012 Swing phase asymmetry 5.3 Age × APA 0.138 0.012 PBF 0.134 0.042 Stride length asymmetry 13.3 Age –0.113 0.038 KFA 0.125 0.018 APA 0.121 0.023 ADA 0.184 < 0.001 Note. Values represent standardized β coefficients. Predictors included age, sex, ASMI, PBF and lower-limb strength indices. Interaction terms are denoted as ‘age × asymmetry index’. Only significant predictors are shown. Significance was set at p < 0.05. PBF, per cent body fat; APA, ankle plantarflexion asymmetry; KEA, knee extension asymmetry; ADA, ankle dorsiflexion asymmetry; KFA, knee flexion asymmetry; CV, coefficient of variation. Among the mean gait indicators, stride time was significantly predicted by age (β = −0.282, p < 0.001), whereas stride length was predicted by age (β = −0.166, p = 0.001) and the interaction effect of age × KEA (β = −0.181, p < 0.001). Furthermore, stance (β = −0.145, p = 0.008), swing (β = 0.145, p = 0.008) and double support phases (β = −0.141, p = 0.010) were all significantly associated with APA. Conversely, no significant main effects or interaction effects were identified for walking speed. Regarding gait variability indices (CV), stride time CV (β = −0.158, p = 0.005) and double support CV (β = −0.162, p = 0.003) were significantly explained by age, whereas stride length CV was primarily predicted by ADA (β = 0.267, p < 0.001). Regarding gait asymmetry indicators, stride length asymmetry was significantly related to age (β = −0.113, p = 0.038), KFA (β = 0.125, p = 0.018), APA (β = 0.121, p = 0.023) and ADA (β = 0.184, p < 0.001). Stance and swing phase asymmetries were predicted by the age × APA interaction (β = 0.138–0.139, p = 0.012). Meanwhile, among the covariates, sex and PBF also showed significant associations with some gait mean and asymmetry indices. Detailed data are presented in Table 3 . Interaction effects Figure 2 . Interaction effects of age and strength asymmetry on gait outcomes. Regression lines indicate significant interactions (p < 0.05) exclusively in the older adults group. (a) KEA vs. stride length; (b) APA vs. stance phase asymmetry; (c) APA vs. swing phase asymmetry. For mean gait metrics, an increase in KEA correlated with a decrease in stride length only in the elderly group (R² = 0.201, β = −0.215, p = 0.009, f² = 0.056). For the gait asymmetry metric, increased APA was associated with a significant increase in stance phase and swing phase asymmetry exclusively in the elderly group (R² = 0.101–0.106, β = 0.233–0.242, p < 0.01, f² = 0.060–0.065). Lower-limb strength asymmetry did not emerge as a significant predictor of gait indicators in the young and middle-aged groups. Discussion This study systematically evaluated the effects of the interaction between age and lower-limb muscle asymmetry on gait function in a broad adult sample aged 19–85 years. The results revealed that KEA only exacerbated stride length reduction in the elderly, whereas APA only amplified temporal asymmetry deterioration in the elderly. These findings demonstrated that it is not merely an age effect but that muscle strength asymmetry, combined with ageing, accelerates gait vulnerability. This study confirmed that the impact of muscle strength imbalance varies with age. KEA did not significantly affect stride length in young and middle-aged adults but led to a marked reduction in stride length in the elderly. This result was consistent with the findings of Marques et al. 21 , who reported that stride length significantly decreased when KEA exceeded 25% in the elderly. The present study extended this finding by demonstrating a conditional effect, proving that this effect is pronounced only in the elderly. Although ADA did not show an age-related difference at the mean level, it disrupted temporal symmetry exclusively in the elderly. LaRoche et al. 13 reported that KEA is closely related to increased support phase asymmetry. A distinguishing feature of their study was that this sensitive indicator manifested at the ankle level, demonstrating that the temporal structure indicator was the gait element that most sensitively reflected the effects of muscle strength asymmetry, particularly in the ageing ankle. As shown in Fig. 2 , the interaction effects are visually evident. Although the young and middle-aged groups had nearly horizontal regression lines, indicating an unclear relationship, the elderly group showed a distinct slope where KEA, stride length, APA and temporal symmetry significantly deteriorated. These visual characteristics aligned with the statistical results, intuitively demonstrating the differential effects of muscle strength asymmetry across age groups. The reserve capacity model proposed by Romero-Ortuño et al. 33 can explain this age-specific pattern. Although young and middle-aged individuals can maintain performance through compensatory strategies even with the same asymmetry level, older adults may experience immediate stride shortening and loss of temporal symmetry even with minor imbalances caused by reduced muscle mass and neuromuscular function 34 . Si et al. 35 indicated that lower-limb strength asymmetry can reduce static postural control, which is more pronounced under conditions requiring high sensorimotor integration. Thus, strength asymmetry particularly leads to shortened stride length and disrupted temporal structure in older adults, which might be attributed to the loss of reserve capacity and limited compensatory strategies. The main effect of age was consistently linked to a short step length 14 , 36 ; however, some CV metrics did not show the commonly reported pattern of increased variability. This might be attributed to the study participants being relatively healthy community-dwelling adults, the dependence of the CV metric on mean values 15 , and the 6-min self-selected pace walking protocol employed in this study, which may have induced more stable walking patterns compared to short-distance walking in typical laboratory settings. The main effect of muscle asymmetry varied across joints, with the knee being more closely linked to spatial indicators, such as stride length, and the ankle more to temporal indicators, such as stance, swing and double support phases. This observation holds significant importance, as it quantitatively elucidates the differences in functional contributions across joints. Previous studies have reported that during ageing, walking strategy shifts from distal to proximal joints alongside a weakening of ankle push-off 37 , 38 . The present study demonstrated that ankle asymmetry selectively influences temporal structure indicators, whereas knee asymmetry selectively affects spatial indicators, reflecting the differences in the functional roles of each joint and indicating that muscle strength asymmetry can differentially affect gait indicators. Mechanistically, the ankle plantarflexors are critical for the 'push-off' phase and swing initiation, which dictates the temporal timing of the gait cycle 37 . In contrast, the knee extensors are essential for weight acceptance and limb stability during the stance phase, directly influencing the spatial reach of the stride 13 , 21 . In summary, the detrimental effects of lower-limb muscle asymmetry only became pronounced when combined with ageing, indicating selective vulnerability particularly in core indicators, such as stride length and temporal symmetry. This finding demonstrates that, beyond merely having a muscle strength difference, the combined effects of reduced reserve capacity caused by ageing and weakened ankle strategy can simultaneously impair gait stability and efficiency. Furthermore, muscle asymmetry might exert greater negative repercussions in the elderly via mechanisms such as increased metabolic cost 39 , reduced gait efficiency 40 and musculoskeletal burden 41 . Our findings had clinically significant implications. First, it confirmed that not only the absolute level of lower-limb strength but also the imbalance between the left and right sides can directly reduce walking performance. Thus, when assessing muscle strength, bilateral balance must be considered alongside absolute values. Second, the differential contribution of knee and ankle asymmetry to spatial and temporal walking indicators supports the need for tailored rehabilitation strategies considering joint characteristics. Third, age-specific interactions underscore the need for tailored intervention strategies by age group. Early detection and preventive management are crucial for young and middle-aged adults, whereas enhancing ankle stability and preserving knee function are key for the elderly. Fourth, muscle asymmetry suggests potential utility in rehabilitation and as a screening indicator for fall risk and in community-based gait assessment. The strengths of this study were as follows: (1) a large sample size exceeding 300 participants; (2) detailed asymmetry analysis segmented by hip, knee and ankle; and (3) direct validation of age × asymmetry interactions encompassing young, middle-aged and elderly populations. However, limitations were also evident. First, the cross-sectional design precludes causal inference. Second, the use of only isometric strength measurements limits the reflection of dynamic strength required during actual walking. Although isometric measurements do not fully capture the dynamic muscle action during gait, previous studies have demonstrated a strong correlation between isometric strength and functional gait performance, supporting its validity as a practical clinical screening tool 14 , 36 . Third, the exclusion of electromyography (EMG) or muscle quality indicators restricts mechanistic interpretation. Fourth, the sample centred on community-dwelling adults with a high proportion of women, which limits the generalisability of the results. Future research should adopt a longitudinal design to elucidate the long-term association between changes in muscle asymmetry and the decline in gait function. Furthermore, an integrated assessment of dynamic muscle strength, joint moments, EMG and muscle quality indicators would increase understanding of the mechanisms underlying the impact of muscle asymmetry on gait stability. Finally, it is necessary to verify whether intervention studies aimed at correcting knee and ankle asymmetries actually lead to improvements in gait symmetry and stability and consequently to fall prevention. In conclusion, this study analysed the effects of age and lower-limb muscle asymmetry on gait performance, variability and symmetry in adults aged 19–85 years. The results showed that KEA shortened the stride length in older adults, whereas APA exacerbated temporal asymmetry deterioration in this group. These interactions demonstrated that gait deterioration in older adults is not merely an effect of ageing but a combination of ageing and muscle imbalance, amplifying vulnerability. These findings hold clinically significant implications, showing that not only the absolute level of lower-limb muscle strength but also the left–right muscle imbalance itself can impair gait stability and efficiency. This parameter could serve as a key indicator for the early detection of fall risk and the design of individualised rehabilitation strategies. Particularly in the elderly, an integrated approach considering the restoration of ankle and knee function balance is necessary. From a preventive perspective, managing muscle imbalance from middle age onwards can contribute to the maintenance of walking independence in later life. Methods Study design and participants This cross-sectional study openly recruited participants through Yeungnam University and local community noticeboards, targeting adults residing in the community. Participants were required to be able to walk independently and have no musculoskeletal or neurological disorders within 6 months prior to the study. Individuals with cardiovascular disease, respiratory disease, artificial joints or metallic implants were excluded. A total of 360 individuals were recruited, and after excluding participants with measurement errors or missing values for key variables, 345 were ultimately included in the analysis. All participants received explanations regarding the study’s purpose, procedures, potential risks and their right to withdraw at any time. They voluntarily provided written informed consent. This study was approved by the Institutional Review Board of Yeungnam University (IRB-7002016-A-2023-015). All procedures were conducted in accordance with the Declaration of Helsinki. Lower-limb strength assessment Lower-limb muscle strength was measured using a hand-held dynamometer (microFET3®, Hoggan Health Industries, West Jordan, UT, USA), with data collected and recorded using TBS version 11.0.1 (Hoggan Health Industries). This device was reported to have high reliability in isometric strength measurements 28 . Lower-limb strength was assessed based on seven movements, including representative motions of the hip, knee and ankle. Each movement was performed according to standardized measurement procedures. All measurements were repeated thrice on each lower limb, with each attempt requiring the exertion of maximum force for 3 s. To minimize muscle fatigue, participants were allowed rest periods of 10 s between attempts and 30 s when changing measurement sites. The force values obtained for each movement were recorded as peak isometric force (lb), and the maximum value from the three measurements was used for further analysis. The measured values were converted into a lower-limb strength asymmetry index using the following formula 25 , 29 : $$\:Strength\:asymmetry\:\left(\%\right)=\frac{|Weak\:side-Strong\:side|}{Strong\:side}\times\:100$$ Accordingly, seven lower-limb strength asymmetry indices were calculated: hip flexion asymmetry (HFA), hip extension asymmetry (HEA), hip abduction asymmetry (HAA), knee flexion asymmetry (KFA), knee extension asymmetry (KEA), ankle plantarflexion asymmetry (APA) and ankle dorsiflexion asymmetry (ADA). Gait assessment Gait was measured using a 7D inertial measurement unit-based sensor (Physilog5®, GaitUp™, Lausanne, Switzerland) attached to the instep of the left and right shoes. This equipment has been shown to exhibit high validity and reliability in gait analysis 30 . Participants walked back and forth at their own chosen speed for 6 min between cones placed at either end of a 30-m straight section in an indoor gymnasium 31 . The collected sensor data were processed using the manufacturer-provided analysis software (GaitUp Lab, GaitUp™, Lausanne, Switzerland) and extracted in a spreadsheet format. Walking speed (m/s), stride length (m), gait cycle time (s), stance phase (%), swing phase (%) and double support phase (%) were analysed. The mean value, CV and asymmetry index were calculated for each variable. Gait variability and asymmetry were calculated using the following formulas 27 : $$\:Gait\:variability,\:CV\:\left(\%\right)=\frac{Standard\:deviation\:\left(SD\right)}{Mean}\times\:100$$ $$\:Gait\:asymmetry\:\left(\%\right)=\frac{|Weak\:side-Strong\:side|}{Strong\:side}\times\:100$$ Body composition assessment Body composition was measured using a bioelectrical impedance analyser (BIA) (InBody 370S, InBody Co., Ltd., Seoul, Korea). The BIA method has been reported to be a non-invasive and reliable tool for body composition assessment 32 . Per cent body fat (PBF) and appendicular skeletal muscle mass index (ASMI) were calculated from the measurement results. ASM was calculated by adding the fat-free mass of both upper and lower limbs and then dividing the sum by height squared (m²) to convert it into ASMI (kg/m²). PBF and ASMI were subsequently used as covariates. Statistical analysis All statistical analyses were conducted using IBM SPSS Statistics version 29.0 (IBM Corp., Armonk, NY, USA). Data normality was assessed using the Shapiro–Wilk test, and the homogeneity of variance was assessed using Levene’s test. To compare physical characteristics, lower-limb strength asymmetry and gait variables between age groups, one-way analysis of variance was used when normality was satisfied, whereas the Kruskal–Wallis test was used when normality was not satisfied. The relationship among age, lower-limb strength asymmetry index and gait indicators was assessed by Spearman’s correlation analysis. Subsequent multiple regression analysis included only lower-limb strength asymmetry variables showing significant correlations as independent variables. All regression models incorporated sex, ASMI and PBF as covariates, with all variables standardized using z-scores for analysis. Regression models including an age × strength asymmetry interaction term were established to verify the differential effects of lower-limb strength asymmetry by age. If the interaction term was significant, individual regression analyses were conducted for each age group, including the same covariates, and results were visualized. The sample size for the regression analysis was calculated using G*Power 3.1.9.4 (Heinrich Heine University Düsseldorf, Düsseldorf, Germany) based on α of 0.05, power (1 − β) of 0.90 and effect size f² of 0.35, yielding a required minimum sample size of 72 participants. The effect size of the regression model was calculated using Cohen’s \(\:{ｆ}^{2}\) . Values of 0.02–0.14, 0.15–0.34 and ≥ 0.35 were interpreted as small, medium and large effects, respectively. The significance level was set at p < 0.05 for all statistical analyses. Multicollinearity was assessed using variance inflation factors (VIF), and all values were confirmed to be within acceptable limits (VIF < 10), indicating no serious multicollinearity issues among the independent variables. Declarations Funding This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (Grant No. RS-2021-NR060125). Acknowledgements This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (RS-2021-NR060125) funded by the Ministry of Education (2025). Author contributions G.H. and S.S. conceived and designed the study. G.H., C.K., C.-M.C., K.K., Y.L., G.B.K. and S.S. collected the data. G.H. and S.S. analysed the data and wrote the main manuscript text. C.K., C.-M.C., K.K., Y.L., G.B.K. and S.S. reviewed and edited the manuscript. S.S. supervised the project and acquired funding. All authors read and approved the final manuscript. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. References Studenski, S. Gait speed reveals clues to lifelong health. JAMA Netw. Open. 2 , e1913112 (2019). Pirker, W. & Katzenschlager, R. Gait disorders in adults and the elderly: a clinical guide. Wien Klin. Wochenschr . 129 , 81–95 (2017). Mahlknecht, P. et al. Prevalence and burden of gait disorders in elderly men and women aged 60–97 years: a population-based study. PLoS ONE . 8 , e69627 (2013). Freiberger, E., Sieber, C. C. & Kob, R. Mobility in older community-dwelling persons: a narrative review. Front. Physiol. 11 , 881 (2020). 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Spatiotemporal gait asymmetry across the lifespan: a cross-sectional study in adults. RGUHS J. Physiother 3 (3) (2023). Zadik, S. et al. Age related changes in gait variability, asymmetry, and bilateral coordination—when does deterioration starts? Gait Posture . 96 , 87–92 (2022). Hirono, T. et al. Age-related changes in gait speeds and asymmetry during circular gait and straight-line gait in older individuals aged 60–79 years. Geriatr. Gerontol. Int. 21 , 404–410 (2021). Chung, C. M., Shin, S., Lee, Y. & Lee, D. Y. Determination of the predictors with the greatest influence on walking in the elderly. Medicina 58 , 1640 (2022). Portegijs, E. et al. Leg extension power asymmetry and mobility limitation in healthy older women. Arch. Phys. Med. Rehabil . 86 , 1838–1842 (2005). Marques, N. R., Brando, N. D. & dos Santos, G. V. Does quadriceps strength asymmetry is able to predict functional decline, gait abnormalities and falls in community-dwelling older adults? Preprint at (2023). https://doi.org/10.21203/rs.3.rs-2488885/v1 Heap-Eldridge, K. L., Thompson, B. J., Fisher, C., Louder, T. J. & Carey, J. A comprehensive examination of age-related lower limb muscle function asymmetries across a variety of muscle action types. Geriatrics 9 , 79 (2024). Zeng, Z. et al. Asymmetries and relationships between muscle strength, proprioception, biomechanics, and postural stability in patients with unilateral knee osteoarthritis. Front. Bioeng. Biotechnol. 10 , 922832 (2022). Hunnicutt, J. L. & Gregory, C. M. Skeletal muscle changes following stroke: a systematic review and comparison to healthy individuals. Top. Stroke Rehabil . 24 , 463–471 (2017). Skelton, D. A., Kennedy, J. & Rutherford, O. M. Explosive power and asymmetry in leg muscle function in frequent fallers and non-fallers aged over 65. Age Ageing . 31 , 119–125 (2002). Salsali, M. et al. Association between physical activity and body posture: a systematic review and meta-analysis. BMC Public. Health . 23 , 1670 (2023). Lee, Y., Kim, G. B. & Shin, S. Association between lower limb strength asymmetry and gait asymmetry: implications for gait variability in stroke survivors. J. Clin. Med. 14 , 380 (2025). Clarke, M. et al. Intra-tester and inter-tester reliability of the MicroFET 3 hand-held dynamometer. Physiother Pract. Res. 32 , 13–18 (2011). Carabello, R. J. et al. Lower extremity strength and power asymmetry assessment in healthy and mobility-limited populations: reliability and association with physical functioning. Aging Clin. Exp. Res. 22 , 324–329 (2010). Carroll, K. et al. Validation of shoe-worn Gait Up Physilog® 5 wearable inertial sensors in adolescents. Gait Posture . 91 , 19–25 (2022). Harada, N. D., Chiu, V. & Stewart, A. L. Mobility-related function in older adults: assessment with a 6-minute walk test. Arch. Phys. Med. Rehabil . 80 , 837–841 (1999). Roubenoff, R., Dallal, G. E. & Wilson, P. Predicting body fatness: the body mass index vs estimation by bioelectrical impedance. Am. J. Public. Health . 85 , 726–728 (1995). Romero-Ortuño, R. et al. Network physiology in aging and frailty: the grand challenge of physiological reserve in older adults. Front. Netw. Physiol. 1 , 712430 (2021). Piasecki, M. et al. Age-related neuromuscular changes affecting human vastus lateralis. J. Physiol. 594 , 4525–4536 (2016). Si, B. et al. The mechanism of static postural control in the impact of lower limb muscle strength asymmetry on gait performance in the elderly. PeerJ 12 , e17626 (2024). Kim, H. K., Kim, S. W., Hong, J. Y. & Baek, M. S. Gait parameters in healthy older adults in Korea. J. Mov. Disord . 18 , 55–61 (2024). DeVita, P. & Hortobagyi, T. Age causes a redistribution of joint torques and powers during gait. J. Appl. Physiol. 88 , 1804–1811 (2000). Anderson, D. E. & Madigan, M. L. Healthy older adults have insufficient hip range of motion and plantar flexor strength to walk like healthy young adults. J. Biomech. 47 , 1104–1109 (2014). Johnson, R. T., Bianco, N. A. & Finley, J. M. Patterns of asymmetry and energy cost generated from predictive simulations of hemiparetic gait. PLoS Comput. Biol. 18 , e1010466 (2022). Sadeghi, H., Allard, P., Prince, F. & Labelle, H. Symmetry and limb dominance in able-bodied gait: a review. Gait Posture . 12 , 34–45 (2000). Neme, J. R. Balancing act: muscle imbalance effects on musculoskeletal injuries. Mo Med. 119 , 225–228 (2022). Additional Declarations No competing interests reported. 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14:47:32","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128495,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8350365/v1/653c0a2e8befe273e49070f3.html"},{"id":100600443,"identity":"3eb038dc-db18-4af8-8ae3-9e3f0dec1c24","added_by":"auto","created_at":"2026-01-19 14:48:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear regression analysis of age and lower-limb strength asymmetry on gait parameters.\u003c/strong\u003e Scatter plots with regression lines showing significant main effects (p \u0026lt; 0.05). (a) Age vs. stride length; (b) KFA vs. stride length asymmetry; (c) APA vs. double support phase.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8350365/v1/a784b8d3d1075b6dd05677a1.png"},{"id":100600038,"identity":"65ec71cf-df9d-4c4f-b0f2-6c52b550e790","added_by":"auto","created_at":"2026-01-19 14:46:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":241109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction effects of age and strength asymmetry on gait outcomes.\u003c/strong\u003e Regression lines indicate significant interactions (p \u0026lt; 0.05) exclusively in the older adults group. (a) KEA vs. stride length; (b) APA vs. stance phase asymmetry; (c) APA vs. swing phase asymmetry.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8350365/v1/f35d4752608eceaeff9034c8.png"},{"id":100602727,"identity":"0a4a7508-3b47-4053-836c-2f41a05ceebd","added_by":"auto","created_at":"2026-01-19 15:19:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1419752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8350365/v1/993e84d7-01a7-40ed-8e01-c5b99f1b4a3d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interactive effects of age and lower-limb muscle strength asymmetry on spatiotemporal gait parameters","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGait is achieved through the intricate coordination of the musculoskeletal and nervous systems, which serves as a key indicator of an individual\u0026rsquo;s independent daily living and health status\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, this coordination weakens with age, resulting in gait abnormalities and, ultimately, loss of balance and falls. Approximately 60% of individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years experience gait abnormalities, with many experiencing recurrent falls\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Gait deterioration among the elderly is not only a cause of falls but also leads to functional dependence and reduced quality of life, consequently significantly increasing societal costs\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, from a public health perspective, early identification of gait characteristics and vulnerability factors in older people is critical for fall prevention and maintenance of functional independence.\u003c/p\u003e \u003cp\u003eClinical and research settings have primarily relied on single indicators, such as walking speed. However, even at the same speed, some individuals maintain a steady rhythm, whereas others exhibit irregular stride lengths, increasing their risk of losing balance with minor stimuli. Spatiotemporal gait metrics must be employed for precise assessment of gait characteristics. Stride length, walking speed, double support time and stance/swing ratio reflect performance level (mean)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, whereas coefficient of variation (CV) indicates stability and consistency\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Asymmetry assesses coordination between the left and right lower limbs\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. CV and asymmetry are closely associated with fall risk\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and are considered key measures that sensitively reflect gait quality more than mean indicators.\u003c/p\u003e \u003cp\u003eAgeing is accompanied by neuromuscular decline and reduced physical fitness, which directly induces changes in the spatiotemporal characteristics of gait. Previous studies have indicated that in older adults, walking speed and stride length decrease, whereas double support time and gait variability increase\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Furthermore, gait asymmetry becomes more pronounced after middle age\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, ageing alone does not induce a marked increase in asymmetry\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, suggesting that the effects of ageing may be more pronounced when combined with other factors.\u003c/p\u003e \u003cp\u003eLower-limb muscle strength crucially contributes to walking speed and maintaining balance\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e; however, muscle strength asymmetry\u0026mdash;an imbalance between the left and right lower limbs\u0026mdash;is an independent risk factor different from simple muscle weakness. Leg power asymmetry of approximately 15% has been found to be associated with impaired walking speed and balance\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and older women with a knee extensor strength difference of \u0026gt;\u0026thinsp;20% exhibit significantly worsened gait variability and symmetry\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. A knee strength difference of \u0026gt;\u0026thinsp;25% can predict functional decline and falls with high accuracy\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Muscle asymmetry can arise from ageing, musculoskeletal injuries, neurological conditions, and lifestyle factors\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Such asymmetry leads to instability in daily activities, increasing the risk of falls\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In modern society, physical inactivity and sedentary lifestyles hasten overall lower-limb strength decline. When combined with lifestyle imbalances, muscle asymmetry can become further exacerbated\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Therefore, lower-limb muscle asymmetry should be considered a health indicator for predicting and preventing falls and functional decline in clinical patients and the general adult population.\u003c/p\u003e \u003cp\u003eRecent studies indicate that the impact of muscle strength asymmetry can be amplified under specific conditions. Patients with stroke exhibit significantly greater knee muscle asymmetry than healthy controls, which influences gait variability and dual-support time\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, existing studies have treated age and muscle strength asymmetry only as separate main effects, and muscle strength asymmetry is limited to the knee, leading to a lack of a comprehensive approach that encompasses the entire lower limb, including the hip and ankle. To the best of our knowledge, the impact of the interaction between these two factors on gait stability has not yet been systematically elucidated. Therefore, the present study aimed to investigate the effects of the interaction between age and lower-limb muscle asymmetry on spatiotemporal gait indicators (mean, variability and symmetry). Specifically, we hypothesised that even the same level of muscle asymmetry would be reflected more significantly as impaired gait function in older adults. By offering a novel perspective explaining gait stability in older adults, this study contributes to the development of fall prevention strategies and the maintenance of functional independence.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant characteristics\u003c/p\u003e \u003cp\u003eThe mean age of the participants included in the final analysis was 51.17\u0026thinsp;\u0026plusmn;\u0026thinsp;17.66 (range, 19\u0026ndash;85) years, comprising 104 males (30.1%) and 241 females (69.9%). The age distribution was as follows: 98, 119 and 128 participants in the young (19\u0026ndash;39 years), middle-aged (40\u0026ndash;59 years) and elderly groups (\u0026ge;\u0026thinsp;60 years), respectively. Comparisons of baseline physical characteristics between different age groups revealed significant differences in height, weight, ASMI and PBF (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Height, weight and ASMI tended to decrease with increasing age, whereas PBF tended to increase. Detailed participant characteristics are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant demographics by age group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;345)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYoung\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;119)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElderly\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.17\u0026thinsp;\u0026plusmn;\u0026thinsp;17.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.49\u0026thinsp;\u0026plusmn;\u0026thinsp;5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.03\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.42\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (M/F) (\u003cem\u003en\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104/241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40/58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31/88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33/95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167.10\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163.42\u0026thinsp;\u0026plusmn;\u0026thinsp;7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158.57\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.50\u0026thinsp;\u0026plusmn;\u0026thinsp;11.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.31\u0026thinsp;\u0026plusmn;\u0026thinsp;14.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.78\u0026thinsp;\u0026plusmn;\u0026thinsp;11.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.25\u0026thinsp;\u0026plusmn;\u0026thinsp;8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.39\u0026thinsp;\u0026plusmn;\u0026thinsp;7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.41\u0026thinsp;\u0026plusmn;\u0026thinsp;6.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote.\u003c/b\u003e Values represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or \u003cem\u003en. p\u003c/em\u003e-values from the analysis of variance or Kruskal\u0026ndash;Wallis tests for continuous variables and chi-square tests for categorical variables. ASMI, appendicular skeletal muscle mass index; PBF, per cent body fat.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIntergroup differences\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the differences in lower-limb muscle strength asymmetry and gait indicators by age group. A significant intergroup difference was observed only in ankle plantarflexion (p\u0026thinsp;=\u0026thinsp;0.025), with the middle-aged group exhibiting greater asymmetry than the young group. The elderly group did not show significantly different asymmetry compared to the other two groups, and no significant differences were observed for the other movements (hip flexion, extension, abduction; knee flexion, extension and ankle dorsiflexion).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge-related comparison of lower-limb strength asymmetry and gait variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYoung\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;119)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElderly\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrength asymmetry (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip flexion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.87\u0026thinsp;\u0026plusmn;\u0026thinsp;6.35\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.92\u0026thinsp;\u0026plusmn;\u0026thinsp;8.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.87\u0026thinsp;\u0026plusmn;\u0026thinsp;6.87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip extension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.69\u0026thinsp;\u0026plusmn;\u0026thinsp;10.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.15\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip abduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.12\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.49\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.43\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnee flexion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.71\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.02\u0026thinsp;\u0026plusmn;\u0026thinsp;8.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.97\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnee extension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.82\u0026thinsp;\u0026plusmn;\u0026thinsp;8.40\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.89\u0026thinsp;\u0026plusmn;\u0026thinsp;9.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.79\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnkle plantarflexion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.52\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.29\u0026thinsp;\u0026plusmn;\u0026thinsp;9.89\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.36\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnkle dorsiflexion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.60\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.12\u0026thinsp;\u0026plusmn;\u0026thinsp;11.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.47\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGait Performance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGait speed (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride time (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStance phase (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwing phase (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.40\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGait Variability (\u003c/b\u003e\u003cb\u003eCV\u003c/b\u003e, \u003cb\u003e%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride time \u003cem\u003eCV\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support \u003cem\u003eCV\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.61\u0026thinsp;\u0026plusmn;\u0026thinsp;5.39\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.27\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length \u003cem\u003eCV\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGait asymmetry (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride time asymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStance phase asymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwing phase asymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length asymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote.\u003c/b\u003e Values represent mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Different superscripts (a, b, c) indicate significant differences between age groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); groups sharing the same superscript are not significantly different. Analysis of variance variables used Tukey\u0026rsquo;s Honest Significant Difference; Kruskal\u0026ndash;Wallis variables used Bonferroni-adjusted Mann\u0026ndash;Whitney U.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the mean gait metrics, intergroup differences were observed for all variables (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The young group exhibited longer stride time than the other two groups, whereas the elderly group exhibited slower walking speed and shorter stride length than the middle-aged group. Furthermore, in the elderly group, the stance and double support phases increased, and the swing phase decreased.\u003c/p\u003e \u003cp\u003eRegarding gait variability (CV), the stride time CV and double support CV differed significantly with age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The young and elderly groups exhibited the highest and lowest variabilities, respectively. Conversely, no significant difference in stride length CV was found among the groups (p\u0026thinsp;=\u0026thinsp;0.687).\u003c/p\u003e \u003cp\u003eRegarding gait asymmetry indicators, only stride length asymmetry differed significantly among the groups (p\u0026thinsp;=\u0026thinsp;0.001), with the middle-aged group exhibiting greater asymmetry than the elderly group.\u003c/p\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation analysis revealed that age showed a weak positive correlation (rho\u0026thinsp;=\u0026thinsp;0.117, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with KEA. Furthermore, age exhibited negative correlations with stride time (rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.268) and stride length (rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.269), stride time CV (rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.133), double support variability (double support CV, rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.245), and stride length asymmetry (rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.162).\u003c/p\u003e \u003cp\u003eRegarding the relationship between lower-limb strength asymmetry and gait indicators, ADA showed the strongest positive correlation with stride length CV (rho\u0026thinsp;=\u0026thinsp;0.205) and stride length asymmetry (rho\u0026thinsp;=\u0026thinsp;0.241), whereas APA showed a weak association with stride length asymmetry (rho\u0026thinsp;=\u0026thinsp;0.133).\u003c/p\u003e \u003cp\u003eAnalysis by age group revealed that in the young group, hip and knee asymmetries correlated with some gait mean and asymmetry indices (rho\u0026thinsp;=\u0026thinsp;0.205\u0026ndash;0.247). In the middle-aged group, ankle and knee extension asymmetries correlated with stride length asymmetry (rho\u0026thinsp;=\u0026thinsp;0.207\u0026ndash;0.328) and stride length variability (rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.256), respectively. Conversely, in the elderly group, knee extension and ankle asymmetries correlated with multiple indicators across gait mean values, variability and asymmetry (rho\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.174\u0026ndash;0.249).\u003c/p\u003e \u003cp\u003eMeanwhile, among lower-limb strength asymmetries, HAA did not show a correlation with any gait indicator.\u003c/p\u003e \u003cp\u003eRegression models\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the effects of age, lower-limb muscle asymmetry and the interaction term (age \u0026times; asymmetry) on gait indicators, after adjusting for covariates of sex, ASMI and PBF. The significant main effects of age and strength asymmetry on gait parameters are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple regression analysis of predictors of gait outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Ｒ}}^{2}\\)\u003c/span\u003e\u003c/span\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGait performance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGait speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStance phase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwing phase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support phase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge \u0026times; KEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGait variability (CV)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride time CV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble support phase CV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length CV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGait asymmetry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride time asymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStance phase asymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge \u0026times; APA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSwing phase asymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge \u0026times; APA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStride length asymmetry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote.\u003c/b\u003e Values represent standardized β coefficients. Predictors included age, sex, ASMI, PBF and lower-limb strength indices. Interaction terms are denoted as \u0026lsquo;age \u0026times; asymmetry index\u0026rsquo;. Only significant predictors are shown. Significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. PBF, per cent body fat; APA, ankle plantarflexion asymmetry; KEA, knee extension asymmetry; ADA, ankle dorsiflexion asymmetry; KFA, knee flexion asymmetry; CV, coefficient of variation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the mean gait indicators, stride time was significantly predicted by age (β = \u0026minus;0.282, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas stride length was predicted by age (β = \u0026minus;0.166, p\u0026thinsp;=\u0026thinsp;0.001) and the interaction effect of age \u0026times; KEA (β = \u0026minus;0.181, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, stance (β = \u0026minus;0.145, p\u0026thinsp;=\u0026thinsp;0.008), swing (β\u0026thinsp;=\u0026thinsp;0.145, p\u0026thinsp;=\u0026thinsp;0.008) and double support phases (β = \u0026minus;0.141, p\u0026thinsp;=\u0026thinsp;0.010) were all significantly associated with APA. Conversely, no significant main effects or interaction effects were identified for walking speed.\u003c/p\u003e \u003cp\u003eRegarding gait variability indices (CV), stride time CV (β = \u0026minus;0.158, p\u0026thinsp;=\u0026thinsp;0.005) and double support CV (β = \u0026minus;0.162, p\u0026thinsp;=\u0026thinsp;0.003) were significantly explained by age, whereas stride length CV was primarily predicted by ADA (β\u0026thinsp;=\u0026thinsp;0.267, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eRegarding gait asymmetry indicators, stride length asymmetry was significantly related to age (β = \u0026minus;0.113, p\u0026thinsp;=\u0026thinsp;0.038), KFA (β\u0026thinsp;=\u0026thinsp;0.125, p\u0026thinsp;=\u0026thinsp;0.018), APA (β\u0026thinsp;=\u0026thinsp;0.121, p\u0026thinsp;=\u0026thinsp;0.023) and ADA (β\u0026thinsp;=\u0026thinsp;0.184, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Stance and swing phase asymmetries were predicted by the age \u0026times; APA interaction (β\u0026thinsp;=\u0026thinsp;0.138\u0026ndash;0.139, p\u0026thinsp;=\u0026thinsp;0.012). Meanwhile, among the covariates, sex and PBF also showed significant associations with some gait mean and asymmetry indices. Detailed data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eInteraction effects\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cb\u003eInteraction effects of age and strength asymmetry on gait outcomes.\u003c/b\u003e Regression lines indicate significant interactions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) exclusively in the older adults group. (a) KEA vs. stride length; (b) APA vs. stance phase asymmetry; (c) APA vs. swing phase asymmetry.\u003c/p\u003e \u003cp\u003eFor mean gait metrics, an increase in KEA correlated with a decrease in stride length only in the elderly group (R\u0026sup2; = 0.201, β = \u0026minus;0.215, p\u0026thinsp;=\u0026thinsp;0.009, f\u0026sup2; = 0.056). For the gait asymmetry metric, increased APA was associated with a significant increase in stance phase and swing phase asymmetry exclusively in the elderly group (R\u0026sup2; = 0.101\u0026ndash;0.106, β\u0026thinsp;=\u0026thinsp;0.233\u0026ndash;0.242, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, f\u0026sup2; = 0.060\u0026ndash;0.065). Lower-limb strength asymmetry did not emerge as a significant predictor of gait indicators in the young and middle-aged groups.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically evaluated the effects of the interaction between age and lower-limb muscle asymmetry on gait function in a broad adult sample aged 19\u0026ndash;85 years. The results revealed that KEA only exacerbated stride length reduction in the elderly, whereas APA only amplified temporal asymmetry deterioration in the elderly. These findings demonstrated that it is not merely an age effect but that muscle strength asymmetry, combined with ageing, accelerates gait vulnerability.\u003c/p\u003e \u003cp\u003eThis study confirmed that the impact of muscle strength imbalance varies with age. KEA did not significantly affect stride length in young and middle-aged adults but led to a marked reduction in stride length in the elderly. This result was consistent with the findings of Marques et al.\u003csup\u003e21\u003c/sup\u003e, who reported that stride length significantly decreased when KEA exceeded 25% in the elderly. The present study extended this finding by demonstrating a conditional effect, proving that this effect is pronounced only in the elderly.\u003c/p\u003e \u003cp\u003eAlthough ADA did not show an age-related difference at the mean level, it disrupted temporal symmetry exclusively in the elderly. LaRoche et al.\u003csup\u003e13\u003c/sup\u003e reported that KEA is closely related to increased support phase asymmetry. A distinguishing feature of their study was that this sensitive indicator manifested at the ankle level, demonstrating that the temporal structure indicator was the gait element that most sensitively reflected the effects of muscle strength asymmetry, particularly in the ageing ankle.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the interaction effects are visually evident. Although the young and middle-aged groups had nearly horizontal regression lines, indicating an unclear relationship, the elderly group showed a distinct slope where KEA, stride length, APA and temporal symmetry significantly deteriorated. These visual characteristics aligned with the statistical results, intuitively demonstrating the differential effects of muscle strength asymmetry across age groups.\u003c/p\u003e \u003cp\u003eThe reserve capacity model proposed by Romero-Ortu\u0026ntilde;o et al.\u003csup\u003e33\u003c/sup\u003e can explain this age-specific pattern. Although young and middle-aged individuals can maintain performance through compensatory strategies even with the same asymmetry level, older adults may experience immediate stride shortening and loss of temporal symmetry even with minor imbalances caused by reduced muscle mass and neuromuscular function\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Si et al.\u003csup\u003e35\u003c/sup\u003e indicated that lower-limb strength asymmetry can reduce static postural control, which is more pronounced under conditions requiring high sensorimotor integration. Thus, strength asymmetry particularly leads to shortened stride length and disrupted temporal structure in older adults, which might be attributed to the loss of reserve capacity and limited compensatory strategies.\u003c/p\u003e \u003cp\u003eThe main effect of age was consistently linked to a short step length\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e; however, some CV metrics did not show the commonly reported pattern of increased variability. This might be attributed to the study participants being relatively healthy community-dwelling adults, the dependence of the CV metric on mean values\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and the 6-min self-selected pace walking protocol employed in this study, which may have induced more stable walking patterns compared to short-distance walking in typical laboratory settings.\u003c/p\u003e \u003cp\u003eThe main effect of muscle asymmetry varied across joints, with the knee being more closely linked to spatial indicators, such as stride length, and the ankle more to temporal indicators, such as stance, swing and double support phases. This observation holds significant importance, as it quantitatively elucidates the differences in functional contributions across joints. Previous studies have reported that during ageing, walking strategy shifts from distal to proximal joints alongside a weakening of ankle push-off\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The present study demonstrated that ankle asymmetry selectively influences temporal structure indicators, whereas knee asymmetry selectively affects spatial indicators, reflecting the differences in the functional roles of each joint and indicating that muscle strength asymmetry can differentially affect gait indicators. Mechanistically, the ankle plantarflexors are critical for the 'push-off' phase and swing initiation, which dictates the temporal timing of the gait cycle\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In contrast, the knee extensors are essential for weight acceptance and limb stability during the stance phase, directly influencing the spatial reach of the stride\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, the detrimental effects of lower-limb muscle asymmetry only became pronounced when combined with ageing, indicating selective vulnerability particularly in core indicators, such as stride length and temporal symmetry. This finding demonstrates that, beyond merely having a muscle strength difference, the combined effects of reduced reserve capacity caused by ageing and weakened ankle strategy can simultaneously impair gait stability and efficiency. Furthermore, muscle asymmetry might exert greater negative repercussions in the elderly via mechanisms such as increased metabolic cost\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, reduced gait efficiency\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and musculoskeletal burden\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur findings had clinically significant implications. First, it confirmed that not only the absolute level of lower-limb strength but also the imbalance between the left and right sides can directly reduce walking performance. Thus, when assessing muscle strength, bilateral balance must be considered alongside absolute values. Second, the differential contribution of knee and ankle asymmetry to spatial and temporal walking indicators supports the need for tailored rehabilitation strategies considering joint characteristics. Third, age-specific interactions underscore the need for tailored intervention strategies by age group. Early detection and preventive management are crucial for young and middle-aged adults, whereas enhancing ankle stability and preserving knee function are key for the elderly. Fourth, muscle asymmetry suggests potential utility in rehabilitation and as a screening indicator for fall risk and in community-based gait assessment.\u003c/p\u003e \u003cp\u003eThe strengths of this study were as follows: (1) a large sample size exceeding 300 participants; (2) detailed asymmetry analysis segmented by hip, knee and ankle; and (3) direct validation of age \u0026times; asymmetry interactions encompassing young, middle-aged and elderly populations.\u003c/p\u003e \u003cp\u003eHowever, limitations were also evident. First, the cross-sectional design precludes causal inference. Second, the use of only isometric strength measurements limits the reflection of dynamic strength required during actual walking. Although isometric measurements do not fully capture the dynamic muscle action during gait, previous studies have demonstrated a strong correlation between isometric strength and functional gait performance, supporting its validity as a practical clinical screening tool\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Third, the exclusion of electromyography (EMG) or muscle quality indicators restricts mechanistic interpretation. Fourth, the sample centred on community-dwelling adults with a high proportion of women, which limits the generalisability of the results.\u003c/p\u003e \u003cp\u003eFuture research should adopt a longitudinal design to elucidate the long-term association between changes in muscle asymmetry and the decline in gait function. Furthermore, an integrated assessment of dynamic muscle strength, joint moments, EMG and muscle quality indicators would increase understanding of the mechanisms underlying the impact of muscle asymmetry on gait stability. Finally, it is necessary to verify whether intervention studies aimed at correcting knee and ankle asymmetries actually lead to improvements in gait symmetry and stability and consequently to fall prevention.\u003c/p\u003e \u003cp\u003eIn conclusion, this study analysed the effects of age and lower-limb muscle asymmetry on gait performance, variability and symmetry in adults aged 19\u0026ndash;85 years. The results showed that KEA shortened the stride length in older adults, whereas APA exacerbated temporal asymmetry deterioration in this group. These interactions demonstrated that gait deterioration in older adults is not merely an effect of ageing but a combination of ageing and muscle imbalance, amplifying vulnerability. These findings hold clinically significant implications, showing that not only the absolute level of lower-limb muscle strength but also the left\u0026ndash;right muscle imbalance itself can impair gait stability and efficiency. This parameter could serve as a key indicator for the early detection of fall risk and the design of individualised rehabilitation strategies. Particularly in the elderly, an integrated approach considering the restoration of ankle and knee function balance is necessary. From a preventive perspective, managing muscle imbalance from middle age onwards can contribute to the maintenance of walking independence in later life.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and participants\u003c/p\u003e \u003cp\u003eThis cross-sectional study openly recruited participants through Yeungnam University and local community noticeboards, targeting adults residing in the community. Participants were required to be able to walk independently and have no musculoskeletal or neurological disorders within 6 months prior to the study. Individuals with cardiovascular disease, respiratory disease, artificial joints or metallic implants were excluded. A total of 360 individuals were recruited, and after excluding participants with measurement errors or missing values for key variables, 345 were ultimately included in the analysis. All participants received explanations regarding the study\u0026rsquo;s purpose, procedures, potential risks and their right to withdraw at any time. They voluntarily provided written informed consent. This study was approved by the Institutional Review Board of Yeungnam University (IRB-7002016-A-2023-015). All procedures were conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eLower-limb strength assessment\u003c/p\u003e \u003cp\u003eLower-limb muscle strength was measured using a hand-held dynamometer (microFET3\u0026reg;, Hoggan Health Industries, West Jordan, UT, USA), with data collected and recorded using TBS version 11.0.1 (Hoggan Health Industries). This device was reported to have high reliability in isometric strength measurements\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Lower-limb strength was assessed based on seven movements, including representative motions of the hip, knee and ankle. Each movement was performed according to standardized measurement procedures.\u003c/p\u003e \u003cp\u003eAll measurements were repeated thrice on each lower limb, with each attempt requiring the exertion of maximum force for 3 s. To minimize muscle fatigue, participants were allowed rest periods of 10 s between attempts and 30 s when changing measurement sites. The force values obtained for each movement were recorded as peak isometric force (lb), and the maximum value from the three measurements was used for further analysis.\u003c/p\u003e \u003cp\u003eThe measured values were converted into a lower-limb strength asymmetry index using the following formula\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Strength\\:asymmetry\\:\\left(\\%\\right)=\\frac{|Weak\\:side-Strong\\:side|}{Strong\\:side}\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAccordingly, seven lower-limb strength asymmetry indices were calculated: hip flexion asymmetry (HFA), hip extension asymmetry (HEA), hip abduction asymmetry (HAA), knee flexion asymmetry (KFA), knee extension asymmetry (KEA), ankle plantarflexion asymmetry (APA) and ankle dorsiflexion asymmetry (ADA).\u003c/p\u003e \u003cp\u003eGait assessment\u003c/p\u003e \u003cp\u003eGait was measured using a 7D inertial measurement unit-based sensor (Physilog5\u0026reg;, GaitUp\u0026trade;, Lausanne, Switzerland) attached to the instep of the left and right shoes. This equipment has been shown to exhibit high validity and reliability in gait analysis\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Participants walked back and forth at their own chosen speed for 6 min between cones placed at either end of a 30-m straight section in an indoor gymnasium\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe collected sensor data were processed using the manufacturer-provided analysis software (GaitUp Lab, GaitUp\u0026trade;, Lausanne, Switzerland) and extracted in a spreadsheet format. Walking speed (m/s), stride length (m), gait cycle time (s), stance phase (%), swing phase (%) and double support phase (%) were analysed. The mean value, CV and asymmetry index were calculated for each variable. Gait variability and asymmetry were calculated using the following formulas\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Gait\\:variability,\\:CV\\:\\left(\\%\\right)=\\frac{Standard\\:deviation\\:\\left(SD\\right)}{Mean}\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:Gait\\:asymmetry\\:\\left(\\%\\right)=\\frac{|Weak\\:side-Strong\\:side|}{Strong\\:side}\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBody composition assessment\u003c/p\u003e \u003cp\u003eBody composition was measured using a bioelectrical impedance analyser (BIA) (InBody 370S, InBody Co., Ltd., Seoul, Korea). The BIA method has been reported to be a non-invasive and reliable tool for body composition assessment\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Per cent body fat (PBF) and appendicular skeletal muscle mass index (ASMI) were calculated from the measurement results. ASM was calculated by adding the fat-free mass of both upper and lower limbs and then dividing the sum by height squared (m\u0026sup2;) to convert it into ASMI (kg/m\u0026sup2;). PBF and ASMI were subsequently used as covariates.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics version 29.0 (IBM Corp., Armonk, NY, USA). Data normality was assessed using the Shapiro\u0026ndash;Wilk test, and the homogeneity of variance was assessed using Levene\u0026rsquo;s test. To compare physical characteristics, lower-limb strength asymmetry and gait variables between age groups, one-way analysis of variance was used when normality was satisfied, whereas the Kruskal\u0026ndash;Wallis test was used when normality was not satisfied. The relationship among age, lower-limb strength asymmetry index and gait indicators was assessed by Spearman\u0026rsquo;s correlation analysis. Subsequent multiple regression analysis included only lower-limb strength asymmetry variables showing significant correlations as independent variables. All regression models incorporated sex, ASMI and PBF as covariates, with all variables standardized using z-scores for analysis. Regression models including an age \u0026times; strength asymmetry interaction term were established to verify the differential effects of lower-limb strength asymmetry by age. If the interaction term was significant, individual regression analyses were conducted for each age group, including the same covariates, and results were visualized.\u003c/p\u003e \u003cp\u003eThe sample size for the regression analysis was calculated using G*Power 3.1.9.4 (Heinrich Heine University D\u0026uuml;sseldorf, D\u0026uuml;sseldorf, Germany) based on α of 0.05, power (1\u0026thinsp;\u0026minus;\u0026thinsp;β) of 0.90 and effect size f\u0026sup2; of 0.35, yielding a required minimum sample size of 72 participants. The effect size of the regression model was calculated using Cohen\u0026rsquo;s\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ｆ}^{2}\\)\u003c/span\u003e\u003c/span\u003e. Values of 0.02\u0026ndash;0.14, 0.15\u0026ndash;0.34 and \u0026ge;\u0026thinsp;0.35 were interpreted as small, medium and large effects, respectively. The significance level was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all statistical analyses. Multicollinearity was assessed using variance inflation factors (VIF), and all values were confirmed to be within acceptable limits (VIF\u0026thinsp;\u0026lt;\u0026thinsp;10), indicating no serious multicollinearity issues among the independent variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (Grant No. RS-2021-NR060125).\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Basic Science Research Program through the National Research Foundation of Korea (RS-2021-NR060125) funded by the Ministry of Education (2025).\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eG.H. and S.S. conceived and designed the study. G.H., C.K., C.-M.C., K.K., Y.L., G.B.K. and S.S. collected the data. G.H. and S.S. analysed the data and wrote the main manuscript text. C.K., C.-M.C., K.K., Y.L., G.B.K. and S.S. reviewed and edited the manuscript. S.S. supervised the project and acquired funding. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStudenski, S. Gait speed reveals clues to lifelong health. \u003cem\u003eJAMA Netw. Open.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, e1913112 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePirker, W. \u0026amp; Katzenschlager, R. 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Balancing act: muscle imbalance effects on musculoskeletal injuries. \u003cem\u003eMo Med.\u003c/em\u003e \u003cb\u003e119\u003c/b\u003e, 225\u0026ndash;228 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ageing, Lower-limb muscle strength asymmetry, Gait asymmetry, Gait variability, Spatiotemporal gait parameters, Interaction effects","lastPublishedDoi":"10.21203/rs.3.rs-8350365/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8350365/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgeing is associated with gait deterioration, yet the influence of lower-limb muscle asymmetry on age-related gait changes remains unclear. This cross-sectional study investigated the interaction effects of age and lower-limb muscle strength asymmetry on spatiotemporal gait parameters in 345 community-dwelling adults aged 19\u0026ndash;85 years. Lower-limb muscle strength was assessed using a hand-held dynamometer, and gait parameters were measured using an inertial measurement unit. Spearman correlation and multiple regression analyses examined associations between strength asymmetry and gait metrics, with interaction terms testing age-dependent effects. Age was associated with reduced stride length and some variability measures. Muscle asymmetry effects varied by joint: knee asymmetry correlated with spatial measures (stride length), while ankle asymmetry linked to temporal measures (stance, swing, and double support phases). Critically, knee extension asymmetry shortened stride length only in elderly participants, and ankle plantarflexion asymmetry worsened temporal symmetry exclusively in older adults, with no effects observed in younger groups. These findings demonstrate that lower-limb strength asymmetry, combined with ageing, selectively exacerbates gait deterioration. Assessment of bilateral strength balance, beyond absolute strength levels, is crucial for predicting gait stability and fall risk in older adults, supporting targeted intervention strategies for fall prevention.\u003c/p\u003e","manuscriptTitle":"Interactive effects of age and lower-limb muscle strength asymmetry on spatiotemporal gait parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 13:43:15","doi":"10.21203/rs.3.rs-8350365/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-14T14:25:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T16:16:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T19:33:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107120552625279359883652598182466784740","date":"2026-03-01T15:11:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326442662157450342141417774119085075069","date":"2026-02-05T15:48:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-15T08:22:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-18T17:22:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-17T08:17:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-17T08:16:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-13T05:39:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"97c0df6a-511b-42dd-bec2-4bac578fb034","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61260673,"name":"Health sciences/Health care"},{"id":61260674,"name":"Health sciences/Medical research"},{"id":61260675,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-07T08:54:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 13:43:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8350365","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8350365","identity":"rs-8350365","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}