GRAPHICAL ABSTRACT

INTRODUCTION

Gait is the most basic and primitive means of human locomotion and the most frequent physical activity that humans perform daily [1,2]. Various factors influence gait, including musculoskeletal factors [3] and the peripheral and central nervous systems [4-6]. Even in healthy individuals, gait function decreases with age [7]. Gait disturbances increase the risk of falls in older adults and narrow their social range of activities, which can be a major axis of quality of life [8]. In neurological disorders, gait disturbance is an important motor symptom that shows characteristic patterns. In Parkinson’s disease, a distinctive parkinsonian gait is observed, and distinguishable patterns are also observed in disorders such as cerebellar ataxia, progressive supranuclear palsy, vascular parkinsonism, and normal pressure hydrocephalus [9-12]. Even in individuals with disorders that cause cognitive impairment, such as Alzheimer’s disease, characteristic gait patterns may be observed [13,14]. Analyzing gait disturbances can aid in the differential diagnosis of these neurological disorders and the monitoring of disease progression, highlighting the importance of gait patterns in healthy older adults.

Previous studies examining the gait patterns of healthy older adults using various devices and experimental protocols have concluded that gait patterns change with age, affecting spatiotemporal, temporal, spatial, and variability parameters [2,15-18]. However, few studies have properly accounted for surgical or medical histories that can significantly impact gait patterns (e.g., spinal or hip/knee joint surgery, active arthritis, cerebral infarction, or hemorrhage). Moreover, normal cognitive status needs to be screened for in studies on healthy older adults, since cognition is a crucial factor affecting gait patterns, as decreased attention and executive function can lead to gait deterioration [19,20].

Among the well-known factors that influence gait, race, and ethnicity are prominent [21,22]. Obtaining normative data for each race or ethnicity enables a more precise analysis for the diagnosis and research of movement disorders. Recently, many efforts have been made to apply quantitative gait analysis in movement disorder clinics, but unfortunately, normative data for Asians, especially Koreans, are currently extremely rare.

The relationship between gait parameters is key to understanding gait dynamics, providing insights into compensatory mechanisms from individual physical characteristics or age-related changes in gait [23,24]. Additionally, it helps reveal pathological gait alterations stemming from biomechanical disruptions in neurological disorders [25,26], which may not be fully captured by the analysis of single parameter changes alone.

In this study, we aimed to present data on physically and cognitively unimpaired healthy older adults to analyze the characteristics of gait, the interactions between various gait parameters, and the effects of demographics on gait parameters, establishing essential data for future research in South Korea.

MATERIALS & METHODS

Participants

We analyzed data from older adult participants who visited a single tertiary hospital between January 2022 and April 2024. Eligible participants were aged 50 years or older, showed no focal neurological symptoms, and exhibited no signs of parkinsonism during neurologic examinations. The exclusion criteria included individuals with a medical history of cerebral infarction, cerebral hemorrhage, dementia, idiopathic rapid eye movement sleep behavior disorder, psychosis, major depressive disorder, peripheral neuropathy, or recent knee, hip joint, or spine surgeries [27-34]. Additionally, patients who presented with symptoms including dizziness, tremor, bradykinesia, gait disturbance, postural instability, or acute pain [35] affecting the lumbar vertebrae, pelvis, or lower extremities while walking were excluded. Participants scoring below the 10th percentile on the Mini-Mental State Examination (MMSE), adjusted for age and education level [36], were also excluded from the study.

Quantitative gait analysis and gait parameters

All participants completed more than four trials (average walking distance, 22.0 m) of walking at their usual speed on the GAITRite Electronic Walkway (CIR Systems Inc., Peekskill, NY, USA), a 6-m-long and 0.6-m-wide walkway equipped with pressure-activated sensors for quantifying temporal and spatial gait parameters. Among the 184 participants, 74 wore shoes during the recording period, and 110 walked barefoot. Height and leg length were measured via plastic tape, and leg length was defined as the distance from the medial ankle to the anterior inferior iliac spine. The GAITRite system collected data on 20 gait parameters categorized into five subclasses: spatiotemporal (e.g., velocity in cm/s, cadence in steps/min), spatial (e.g., step length, stride length, base width in cm), temporal (e.g., step time, swing time, stance time, single- and double-support time in seconds), phase (e.g., percentage of gait cycle for swing time, stance time, single support time, and double support time), and variability (e.g., coefficient of variance [CV] in % for step length, stride length, base width, swing time, stance time, and double support time). The CV was calculated as follows:

Coefficient of variance (%) = Standard deviation of gait parameter Mean of gait parameter ×100.

Spatiotemporal, spatial, temporal, and phase gait parameters for both the left and right sides were averaged, and these average values were subsequently utilized to determine the gait variability parameters (Supplementary Figure 1 in the onlineonly Data Supplement).

Statistical analysis

All the statistical analyses were performed via R software, v.4.3.1 (R Foundation for Statistical Computing, Vienna, Austria; https://www.r-project.org), and MATLAB R2023b (https://kr.mathworks.com). Student’s t-tests or analysis of variance were conducted to compare continuous demographic variables such as age, MMSE score, and physical characteristics, followed by Levene’s test for equality of variances; chi-square tests were used to assess the categorical demographic variable sex. Permutation-based analysis of covariance (ANCOVA) with 10,000 resamplings was used to compare gait parameters, adjusting for age, height, and footwear between sexes, and for height and footwear across the four age strata. ANCOVA was used to compare gait parameters, adjusting for age and height between sexes, and for height alone across the four age strata. Pearson’s correlation was used to analyze the associations between gait parameters and age, MMSE score, and physical characteristics, followed by Bonferroni correction for each gait parameter. Additionally, multiple regression analyses were used to explore the influence of age, MMSE score, and physical characteristics on gait parameters. Cutoff values for normative gait parameters were defined as the 10th and 90th percentiles of each parameter, conditioned on age and height. These values were calculated via quantile regression, with age and height as independent variables, providing robust conditional quantile estimates. The final model parameters were determined through bootstrap aggregating with 1,000 bootstrap resamples to reduce variance and improve the reliability and precision of the predictive model.

Standard protocol approvals, registrations, and patient consent

This study was approved by the International Review Board of Wonju Severance Christian Hospital (Ref# CR322029), and the research protocol was aligned with the principles of the Declaration of Helsinki and its subsequent revisions. Written informed consent was obtained from all participants.

RESULTS

Characteristics of participants and gait parameters

Among the 184 participants, 90 were men and 94 were women. The average age was 72.2 years; the MMSE score was 26.7; and the average education level was 10.0 years. Compared with women, men were slightly older, had higher education levels; and had greater height, weight, and leg length; however, the MMSE scores did not differ between the sexes. The quantified gait parameters are detailed in Table 1, with differences across footwear conditions shown in Supplementary Table 1 (in the online-only Data Supplement). After adjusting for age, height, and footwear condition, women had a narrower base width and a greater CV base width (Table 1). Similar results were obtained after adjusting for age and height alone, as presented in Supplementary Table 2 (in the online-only Data Supplement).

Gait parameters across age groups

The numbers of participants in the 50s, 60s, 70s, and 80s age groups were 11, 70, 66, and 37, respectively. MMSE scores decreased with age. The physical characteristics, such as height, weight, and leg length, did not differ significantly across the age groups.

After adjusting for height and footwear conditions, gait velocity, step length, and stride length decreased with increasing age, whereas cadence and base width did not differ significantly across age groups. In terms of temporal parameters, the double support time increased with age; however, the step, swing, and single support times did not differ across the age groups. For the phase parameters, the swing/single support time percentage of the gait cycle decreased with increasing age, whereas the stance and double support time percentages increased with increasing age. Among the variability parameters, the CVs of the spatial and temporal parameters tended to increase, except for the CV of the base width (Table 2). After adjusting for age and height only, the results were similar, as shown in Supplementary Table 3 (in the online-only Data Supplement).

Interparameter correlations and correlations between demographic characteristics and gait parameters

Multiple parameter comparisons revealed that gait velocity was positively correlated with cadence, step and stride length, and swing/single support time percentage and was negatively associated with the temporal parameters of step, stance, and double support time and the phase parameters of stance time percentage and double support time percentage (Figure 1A). Cadence and step/stride length showed similar interparameter correlation patterns with velocity, except that they were not associated with step or stride length and additionally showed a negative correlation with swing/single support time. A wider base was associated with a decreased swing/single support time percentage and increased double support time, stance time percentage, and double support time percentage (Figure 1A); notably, it was positively correlated with the CV of step/stride length and the CV of swing time, whereas it was negatively correlated with the CV of base width. Among the temporal parameters, the stance and double support times were associated with the corresponding phase parameters, stance time percentage, and double support time percentage. However, the swing/single support time was not associated with the corresponding phase parameters (Figure 1A). Notably, the swing/single support time percentage was correlated with the stance and double support times but not with the swing/single support time. The CVs of step/stride length and base width were negatively correlated with their corresponding spatial parameters, the CV of stance time was correlated with their corresponding temporal and phase parameters, and the CV of swing time was correlated with its phase parameter but not with its temporal parameter. The CV of double support was positively correlated with other CV parameters (Figure 1A).

Age was associated with decreased velocity, step and stride lengths, and swing/single support times. It was positively correlated with the CVs of step/stride length, swing time, and double support time. The MMSE score was associated with velocity, step length, and stride length. Although height was associated with step/stride length, leg length was associated with a lower cadence and the temporal parameters of step and stance time (Figure 1B).

Effects of age, physical characteristics, and cognition on gait parameters

After adjusting for MMSE score, height, and leg length in the multiple linear regression model, age was significantly associated with decreased velocity, step/stride length, swing/single support time percentage and increased double support time, double support time percentage, and variability parameters, except for the CV of base width. Cadence, base width, swing and stance time were not significantly associated with age (Figure 2A and Supplementary Table 4 in the online-only Data Supplement). Height was positively correlated with velocity, step/stride length, and base width (Figure 2B and Supplementary Table 4 in the online-only Data Supplement). Leg length was negatively associated with cadence and positively associated with temporal parameters (Figure 2C and Supplementary Table 4 in the online-only Data Supplement). The MMSE score was not independently associated with gait parameters after adjustment (Figure 2D and Supplementary Table 4 in the online-only Data Supplement). The representative plots are shown in Figure 2.

Cutoff values for normative gait parameters

The formulae for calculating the 10th and 90th percentile values of normative gait parameters are provided in Table 3. Notably, these formulae include terms for age and height and are applicable within the ranges of 53 to 90 years for age and 137.1– 182.0 cm for height.

DISCUSSION

This single-center retrospective cohort study examined agestratified gait parameters in healthy older adults. Gait parameters were associated with age and physical characteristics, and interparameter associations were also observed.

With aging, gait velocity and step/stride length decrease. The swing/single support time remained unchanged, whereas the stance time increased owing to the increased double support time. The increased double support time may be due to the decrease in lower limb muscle strength and the resulting cautious gait in older adults [37-39]. Decreased swing/single support time has been reported in gait in individuals with neurological disorders such as multiple sclerosis [40,41], and Parkinson’s disease [42,43]. However, in healthy older adults, the absolute values of swing/single support time were unchanged across the different age groups in this study (Table 2). Compared with those of Western studies, our results revealed a slower gait velocity and shorter step length, whereas the temporal and phase parameters did not significantly differ [6,7,15,44,45]. Most studies including healthy older adults reported a velocity of more than 100 cm/s, but in our results, the velocity did not exceed 100 cm/s in the ≥60 s age group. The difference in gait parameters may be attributed to the differences in the physical characteristics of the Asian population (shorter height and leg length) compared with those of the Western population. In addition, ethnic differences in joint angular kinematics or anthropometric parameters such as the ratio of tibia length to femur length [46], the quadriceps angle [47], and the waist‒hip ratio [48] may have contributed to the differences in the results.

Among the 20 gait parameters, stance time, the CV of step and stride lengths, and the CV of swing time presented the greatest number of significant interparameter associations (16 out of 19 parameters) (Figure 1), followed by velocity, double support time, swing/single time percentage, and stance time percentage, each with significant associations (15 of the 19 parameters) (Figure 1). These interparameter associations may reflect the constant mechanical factors inherent in the physical mechanisms of gait [49]. Interestingly, a “worse” or shorter step length parameter is associated with increased variability (e.g., decreased step length correlates with increased CV of step length), whereas a “worse” or wider base is correlated with a lower CV of base width (e.g., wider base correlates with decreased CV of base width). It can be hypothesized that a shorter step length, which may result from weakened hip extensors and ankle plantar flexors in older adults, increases step length variability, and a wider base may be a strategic compensation mechanism to reduce variability in the base width [50,51]. The correlation between leg length and cadence may reflect a compensatory mechanism aimed at achieving maximum energy efficiency to maintain gait velocity. For individuals with longer legs, lower cadence can be more energy-efficient, as taking longer steps minimizes the number of muscular contractions required over a given distance. In contrast, individuals with shorter legs might adopt greater cadence at the same speed to prevent overstriding, which can increase energy expenditure. Additionally, the leg length influences the joint angle biomechanics, force distribution, and center-of-mass trajectory [52,53].

Age independently affected gait parameters in healthy older adults, influencing velocity, step/stride length, double support time, phase parameters, and most variability parameters. Agerelated changes in spatial and temporal variability may be caused by reduced lower extremity strength and range of motion, greater variability in muscle activation, and shifts in balance [4,54]. These differences in balance may result from a decline in central motor control, deterioration of the automatic stepping mechanism, or a general insufficiency in postural stability associated with aging [5,55]. Interestingly, height and leg length, as independent physical characteristics, showed different patterns of association with gait parameters. Height was associated primarily with velocity and spatial parameters, whereas leg length was linked to cadence and temporal and phase parameters. Faster gait speed in taller participants is likely influenced by mechanical factors such as longer steps and stride lengths [56,57]. Leg length is known to correlate with height [58], yet evidence for its independent effects on gait parameters is lacking. Our results suggest that leg length is associated with temporal gait parameters and potentially affects cadence. Therefore, height and leg length should be considered independent factors in quantitative gait analysis.

Wearing shoes influenced gait performance, resulting in significant differences in several gait parameters compared with walking barefoot (Supplementary Table 1 in the online-only Data Supplement). Specifically, walking with shoes led to a significant decrease in cadence and increases in step and stride length, which is consistent with the findings of previous studies [59,60]. These changes have been linked to improved gait performance, possibly due to ankle and hip adaptations from wearing shoes since childhood. We also reported that wearing shoes significantly affected the amount of time and percentage of time spent in double-limb support (that is, step time, stance time, and double support time), as previously reported [60,61]. This implies that walking with shoes may demand a more dynamically stable gait than walking barefoot, extending the double-limb support phase and reducing the single-limb support phase. However, adding footwear condition as a covariate in the statistical analysis did not significantly affect the results. Nonetheless, cautious attention will be required during gait assessments, as parameters can vary by up to 3% in spatiotemporal parameters, 7% in spatial parameters, and 10% in temporal, phase, and variability parameters in this study (Supplementary Table 1 in the online-only Data Supplement).

This study has several limitations. First, not all participants underwent brain imaging studies, which could mean that asymptomatic vascular burden may have influenced gait parameters. Second, while we presented cutoff values for normative gait parameters, determining these values solely within healthy individuals poses limitations, as external validation with patients suffering from movement or neurological disorders is lacking. In future studies, we aim to include such patient groups to validate and refine these values through methods such as receiver operating characteristic analysis or discriminant analysis. Finally, despite efforts to enroll medically healthy older adults, certain comorbidities—such as depressive mood, orthopedic conditions without acute pain, and metabolic factors that may influence gait patterns—were not fully controlled. Future studies should incorporate assessments of neuropsychological factors, orthopedic examinations and quantitative tests reflecting peripheral nerve conduction to achieve more accurate gait analysis.

In conclusion, gait is a crucial lifelong activity that significantly affects quality of life. Understanding normative gait parameters and their relationships with age and physical characteristics, as well as interparameter correlations, provides a valuable reference for further quantitative gait studies of neurological disorders and potential intervention studies on gait training. On the basis of these normative data, we anticipate that future research could characterize not only movement disorders but also other neurodegenerative diseases in South Korea.

Supplementary Materials

Notes

Conflicts of Interest

The authors have no financial conflicts of interest.

Funding Statement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government Ministry of Science and ICT (2022R1C1C1012535, and RS-2024-00358576), the Ministry of Education (RS-2023-00247986), and the Technology Innovation Program (20018182) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea).

Author Contributions

Conceptualization: Han-Kyeol Kim, Jin Yong Hong, Min Seok Baek. Data curation: Han-Kyeol Kim, Jin Yong Hong, Min Seok Baek. Formal analysis: all authors. Funding acquisition: Han-Kyeol Kim, Sung-Woo Kim, Min Seok Baek. Investigation: all authors. Supervision: Min Seok Baek. Visualization: Sung-Woo Kim. Writing—original draft: all authors. Writing—review & editing: all authors.

Acknowledgments

We gratefully thank Hyun Ji Lee, Ji Young Lee, and Seo Hee Kim at Wonju Severance Christian Hospital for their technical assistance in the participants’ enrollment and gait analysis.

Figure 1.

Interparameter associations among gait parameters and associations between demographic factors and gait parameters. In this heatmap, positive correlations are represented in red, and negative correlations are represented in blue. A: Interparameter associations among all gait parameters. B: Correlations between gait parameters and age, MMSE score, height, and leg length. CV, coefficient of variance; MMSE, Mini-Mental State Examination.

Figure 2.

Partial dependence plot for each independent variable in multiple linear regression models with various gait parameters as dependent variables. We examined the associations between age (A), height (B), leg length (C), MMSE (D), and representative gait parameters from spatiotemporal, spatial, temporal, phase, and variability categories via a multiple linear regression model. MMSE, Mini-Mental State Examination.

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