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Article

Spatiotemporal Gait Parameters in Community-Dwelling Old-Old Koreans: Impact of Muscle Mass, Physical Performance, and Sarcopenia

by
Youngyun Jin
1,
Taewan Kim
1,
Donghyun Kim
2,
Young-Min Park
3,4,*,† and
Jinkyung Cho
1,*,†
1
College of Sport Science, Sungkyunkwan University, Suwon 16419, Republic of Korea
2
Department of Sports and Health Science, Hanbat National University, Daejeon 34158, Republic of Korea
3
Division of Health and Kinesiology, Incheon National University, Incheon 22012, Republic of Korea
4
Sport Science Institute & Health Promotion Center, Incheon National University, Incheon 22012, Republic of Korea
*
Authors to whom correspondence should be addressed.
†
These authors were equally contributed as corresponding authors.
Appl. Sci. 2025, 15(8), 4379; https://doi.org/10.3390/app15084379
Submission received: 24 February 2025 / Revised: 7 April 2025 / Accepted: 9 April 2025 / Published: 16 April 2025
(This article belongs to the Special Issue Sports Medicine, Exercise, and Health: Latest Advances and Prospects)
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Abstract

:
Muscle mass and physical function are key risk factors for sarcopenia, with emerging evidence suggesting links to gait variability in older adults. This study investigated the relationships between muscle mass decline, poor physical performance, sarcopenia, and spatiotemporal gait parameters in 242 Korean older adults (mean age: 79.1 ± 4.2 years). Muscle mass (MM) was measured via dual-energy X-ray absorptiometry (DXA), physical performance (PP) via the Short Physical Performance Battery (SPPB), and gait parameters (gait speed, stride length, double-limb stance) via the Optogait® system. Stride length significantly influenced low MM, while double-limb stance was linked to increased risks of poor PP and sarcopenia. The area under the curve (AUC) for double-limb stance was 0.698 (95% CI: 0.633–0.763, p < 0.001) for poor PP and 0.647 (95% CI: 0.568–0.726, p = 0.001) for sarcopenia. A novel model combining gait speed and double-limb stance achieved an AUC of 0.702 (95% CI: 0.622–0.781, p < 0.001) with 78.9% sensitivity and 76.3% specificity. These findings highlight spatiotemporal gait analysis as a promising tool for early sarcopenia detection and management in older adults.

1. Introduction

Sarcopenia, a progressive loss of skeletal muscle mass and function, significantly contributes to mobility impairments, physical frailty, and increased risks of falls, hospitalization, cardiovascular diseases, respiratory diseases, cancer mortality, and all-cause mortality in older adults [1,2]. However, alterations in gait patterns and balance among the elderly are multifactorial, encompassing not only sarcopenia but also neurological disorders (e.g., Parkinson’s disease, stroke), sensory impairments (e.g., vestibular dysfunction, peripheral neuropathy), musculoskeletal conditions (e.g., osteoarthritis), and polypharmacy effects [3,4]. While these factors collectively influence mobility, sarcopenia remains a critical and modifiable risk factor, as defined by the Asian Working Group for Sarcopenia (AWGS) through criteria such as low muscle mass, reduced muscle strength, and poor physical performance [5]. Given its substantial impact on physical function and quality of life, the early detection of sarcopenia is critical for implementing timely interventions, yet its specific contribution to gait variability amidst these other causes warrants further exploration.
Among older adults, those aged 75 years and above face a particularly heightened risk of sarcopenia-related mobility impairment. Evidence suggests that muscle mass and function decline more rapidly in this age group, resulting in greater susceptibility to gait disturbances, increased fall risk, and diminished independence [3]. Despite the well-documented importance of mobility assessments, traditional screening methods for sarcopenia—such as gait speed, grip strength, and muscle mass measurements—may not fully capture subtle neuromuscular changes that contribute to mobility decline [4,6]. In this context, spatiotemporal gait parameters, including stride length, step variability, and double-limb stance duration, have gained attention as potential indicators of sarcopenia-related impairments [7]. These gait characteristics reflect underlying deficits in muscle coordination, strength, and balance control, offering a more comprehensive understanding of mobility limitations in aging populations [8].
Despite emerging evidence linking gait parameters to sarcopenia, the correlation between specific spatiotemporal gait characteristics and declines in muscle mass and physical performance—defined as reduced functional capacity in gait speed, balance, or lower extremity strength, as measured by the short physical performance battery (SPPB)—remains underexplored in older adults [9]. Additionally, while wearable sensor-based gait analysis technologies offer valuable insights into mobility impairments, their integration into routine clinical assessments remains limited due to variability in protocols and measurement standardization [10].
This study aims to investigate the associations between muscle mass decline, poor physical performance, and spatiotemporal gait parameters (e.g., stride length, double-limb stance, gait speed) in community-dwelling Korean older adults aged 75 years and above. We seek to enhance early detection strategies and contribute to the development of targeted interventions for preserving mobility and independence in aging populations, particularly within the context of sarcopenia as a modifiable risk factor among multiple contributors to gait alterations.

2. Materials and Methods

2.1. Study Participants

This cross-sectional study recruited 300 community-dwelling older adults aged 75 years and above from welfare centers in Suwon, Republic of Korea, between September 2022 and October 2023. After applying exclusion criteria, 242 participants were included in the final analysis, comprising 47 men (19.4%) and 195 women (80.6%), with a mean age of 79.1 ± 4.2 years (range: 75–92 years). Inclusion criteria were as follows: (1) age ≥ 75 years, (2) the ability to ambulate independently without assistive devices, (3) the willingness and ability to provide written informed consent, and (4) the capacity to complete the required physical assessments (e.g., gait analysis and DXA scanning). Participants were excluded if they had a diagnosed case of dementia or severe cognitive impairment that significantly impaired their ability to communicate, severe cardiovascular diseases (e.g., heart failure, history of myocardial infarction), musculoskeletal disorders affecting gait (e.g., severe osteoarthritis, previous lower limb fractures), or were unable to comply with DXA measurement protocols or gait assessments (Figure 1). All participants provided written informed consent before enrollment, and the study received ethical approval from the Institutional Review Board of Sungkyunkwan University (SKKU 2022-08-039).

2.2. Measurement of Body Composition

Whole-body composition, including height, weight, muscle mass, and body fat, was assessed using DXA (QDR 4500A, Hologic, Waltham, MA, USA) following a previously validated protocol [11]. Waist circumference (WC) was measured at the midpoint between the bottom of the rib cage and the top of the iliac crest. The body mass index (BMI) was calculated as body weight (kg) divided by height (m2).

2.3. Measurement of Muscle Mass, Physical Performance, and Sarcopenia

Sarcopenia was defined according to the Asian Working Group for Sarcopenia (AWGS) consensus report. The AWGS 2019 defines sarcopenia as an age-related decline in skeletal muscle mass accompanied by a loss of muscle strength and/or reduced physical performance [5]. Appendicular skeletal muscle mass (ASM) was calculated as the sum of DXA-based muscle masses in the arms and legs. The appendicular skeletal muscle mass index (AMMI) was then calculated as the total ASM (kg) divided by height (m2) in accordance with the 2019 AWGS consensus updates on sarcopenia diagnosis and treatment. The cutoff value for low AMMI was set at <7.0 kg/m2 for males and <5.4 kg/m2 for females.
The short physical performance battery (SPPB) assesses physical function through (1) the gait speed test, (2) the 5 × Sit-to-Stand test (5 × STS), and (3) the standing balance test [12]. SPPB scores range from 0 to 12 points, with scores ≤ 9 indicating poor physical performance (PP) as per the AWGS 2019 guidelines [5]. Participants were classified into four groups based on their AMMI and SPPB scores, as detailed in Table 1.

2.4. Measurement of Spatiotemporal Gait Parameters

Spatiotemporal gait parameters were assessed using the Optogait® optical system (Microgate Corporation, Bolzano, Italy) and its accompanying software (version 1.9.7.0, Microgate Corporation, Bolzano, Italy). The Optogait® optical system consists of transmitting and receiving bars positioned parallel to each other at a distance of 1 m. As participants moved between the transmitting and receiving bars, the system detected interruptions in the optical signal and automatically calculated spatiotemporal gait parameters based on the presence of a foot within the recording area. Previous studies have confirmed the high validity of the Optogait® optical system for assessing spatiotemporal gait parameters in older adults [13]. To minimize the impact of training, all participants wore comfortable shoes during the gait ability test, which was repeated three times in random order. The following gait parameters were measured: gait speed, gait speed variability, stride length, stride length variability, double-limb stance, and double-limb stance variability.

2.5. Fall Efficacy and Balance Confidence

Fall efficacy was assessed using the Korean adaptation of the Falls Efficacy Scale (FES), originally developed by Tinetti et al. [14] and later standardized by Jang et al. [15]. This scale measures participants’ confidence in performing daily activities without experiencing balance issues or falls. Each of the 10 activities was rated on a scale from 1 to 10, with higher scores indicating greater confidence and lower scores indicating lower confidence. Participants’ fall efficacy was calculated by summing the scores across all activities, resulting in a total score ranging from 10 to 100 points, with higher totals indicating better fall prevention efficacy [16]. Balance confidence was evaluated using the Korean adaptation of the Activities-Specific Balance Confidence Scale (ABC) developed by Powell et al. [17] and standardized by Jang et al. [15]. This scale assesses confidence in balance across various activities and settings, comprising 16 items. Participants rated their confidence in performing these activities without falling or losing balance on a scale of 0% to 100%, with higher scores indicating higher confidence levels. The average score across all 16 items represented the total balance confidence score.

2.6. Other Variables

Age, education level, smoking status, alcohol consumption, and comorbidities were assessed through face-to-face interviews employing a questionnaire administered by trained interviewers. Participants self-reported the number of comorbidities, such as hypertension, diabetes, cardiovascular disease, osteoarthritis, or cancer. Additionally, participants disclosed their smoking status (non-smoker/past smoker vs. current smoker) and alcohol consumption frequency (no consumption, ≤1 time per month, and ≥2 times per month).

2.7. Statistical Analysis

All data were analyzed using SPSS v.23.0 for Windows (IBM Corp., Armonk, NY, USA). Prior to analysis, normality was assessed using the Kolmogorov–Smirnov test, and homogeneity was assessed using Levene’s test. Descriptive statistics were calculated using analysis of variance for continuous variables and the chi-square test for categorical variables. Continuous variables are presented as the mean ± standard deviation (SD), while categorical variables are presented as a number (n) or percentage (%). To test the research hypothesis, study participants were categorized into four subgroups based on their AMMI and SPPB scores, as mentioned earlier (i.e., normal, low MM, poor PP, and sarcopenia). One-way analysis of variance (ANOVA) was used to compare the means of gait parameters among the four groups. Following the one-way ANOVA, post hoc analysis was conducted using the Bonferroni method for group comparisons. Stepwise multivariate linear regression analysis was used to explore the association between gait parameters and the prevalence of low muscle mass, poor physical function, and sarcopenia. Covariates included age, sex, education, BMI, WC, and comorbidities. Receiver operating characteristic (ROC) curves were constructed to determine the best association between gait parameters and low muscle mass, poor physical performance, and sarcopenia. The significance level was set at p < 0.05.

3. Results

Table 2 presents the descriptive statistics of the 242 participants included in the study. Among them, 21.9% were assessed as normal, while 28.5%, 26.4%, and 23.1% were classified with low MM, poor PP, and sarcopenia, respectively. Significant differences were observed in age (p = 0.002), educational level (p = 0.005), BMI (p < 0.001), WC (p = 0.016), AMMI (p < 0.001), and SPPB scores (p < 0.001). The sarcopenia group was significantly older than those in the normal group (p = 0.049) and the low MM group (p = 0.025). Individuals in the normal or poor PP groups had a higher BMI (p < 0.001) compared to those in the low MM and sarcopenia groups. Additionally, the low MM and sarcopenia groups had significantly lower WC (p = 0.023, p = 0.041, respectively) than the normal group. Furthermore, individuals in the normal and low MM groups exhibited significantly lower scores on the FES (p < 0.001) and ABC (p < 0.001) compared to those in the poor PP and sarcopenia groups.
Table 3 presents the results of covariance analysis for gait parameters categorized by sarcopenia status. Concerning gait parameters, the duration of the double-limb stance was significantly longer in the poor PP and sarcopenia groups compared to both the normal MM (p < 0.001) and low MM (p < 0.001) groups. Stride length was lower in the poor PP (p = 0.001) and sarcopenia groups (p < 0.001) compared to the normal group. Furthermore, the variability of stride length in the normal group was significantly lower than in the low MM (p = 0.009), poor PP (p = 0.013), and sarcopenia (p = 0.001) groups.
Table 4 presents the results of the stepwise multiple linear regression analysis, where AMMI, SPPB score, and sarcopenia were considered dependent variables, while gait parameters served as independent variables. The analysis revealed significant associations: AMMI with double-limb stance (β = –0.155, p = 0.037), SPPB with gait speed (β = –0.205, p = 0.001), and double-limb stance (β = –0.287, p < 0.001), and sarcopenia with gait speed (β = –0.120, p = 0.043), stride length (β = 0.283, p < 0.001), and double-limb stance (β = –0.244, p = 0.001).
ROC curve analysis was performed to assess the predictive utility of gait parameters according to low muscle mass, poor physical performance, and sarcopenia (Figure 2 and Table 5). The ROC curve for poor PP and sarcopenia prediction with a double-limb stance yielded AUCs of 0.698 (95% CI = 0.633–0.763, p < 0.001) and 0.647 (95% CI = 0.568–0.726, p = 0.001), respectively. The corresponding cutoff points were 25.0% (sensitivity = 0.658, 1-specificity = 0.663) and 24.1% (sensitivity = 0.691, 1-specificity = 0.594), respectively.
We combined these gait parameters and developed a new model comprising the gait speed and double-limb stance (Figure 3). The AUC was 0.702 (95% CI: 0.622–0.781, p < 0.001), with a sensitivity of 78.9% and a specificity of 76.3%, indicating improved predictive performance (Table 6).

4. Discussion

Age-related sarcopenia significantly compromises walking stability and increases the risk of falls, thereby elevating the incidence of accidental death among the elderly population. Consequently, this study aims to investigate the relationship between skeletal muscle characteristics and specific gait parameters in older adults aged 75 and over afflicted by conditions such as muscle mass decline, poor physical performance, and sarcopenia. Among the participants, 21.9% were classified as normal (mean age: 78.1 years), while 28.5% exhibited muscle mass decline (mean age: 78.1 years), 26.4% demonstrated poor physical performance (mean age: 80.1 years), and 23.1% were identified with sarcopenia (mean age: 80.2 years). In this study, individuals with decreased physical performance or sarcopenia showed lower levels of fall efficacy (FEC) and balance self-efficacy (ABC) along with distinct gait parameter characteristics among the low MM, poor PP, and sarcopenia groups. Specifically, individuals with poor PP and sarcopenia demonstrated shorter double-limb stances, shorter stride lengths, and greater variability in gait speed and stride length compared to the normal or low MM groups. Notably, the combined model of gait speed and double-limb stance showed higher accuracy in predicting sarcopenia.
In line with prior research, our study also highlights a significant correlation between age and sarcopenia, indicating that as individuals age, the risk of sarcopenia, coupled with muscle weakness, escalates [18,19].
A decline in muscle function (or strength) has been linked to compromised lower extremity function [20], mobility limitations [21], decreased independence in activities of daily living [22], and diminished health-related quality of life [23,24] among older adults. Thus, the early detection of muscle strength decline and sarcopenia in the elderly is crucial for effectively mitigating the occurrence of falls, disabilities, and mortality, necessitating the immediate implementation of preventive measures such as physical activity, nutrition, and medical interventions.
Age-related changes in gait parameters encompass reductions in gait speed, stride length, stride width, and double-limb stance rate. Previous studies consistently demonstrated a positive correlation between muscle strength and gait speed [25,26]. Slower gait speed has consistently been linked to a higher risk of falls, hospitalization, and mortality, particularly among older adults with low muscle mass, compromised physical performance, and sarcopenia [27,28,29]. Similar to earlier findings, this study did not confirm the relationship between muscle mass and gait speed. However, a significant correlation was observed between physical performance and sarcopenia [26]. This suggests that muscle function impacts gait speed, while muscle mass does not. A decline in muscle mass alongside a decrease in gait speed or mobility impairment is believed to be associated with aging [29]. Despite the continued influence of sarcopenia on gait speed, it is established that slow gait speed is linked to the occurrence of sarcopenia [30,31]. In addition to our findings, Doi et al. revealed in a longitudinal study of 4121 older Japanese participants that various gait parameters, including gait speed, stride length, cadence, and stride length variability, were predictive of the onset of disability [32]. They established specific cutoff values for each parameter (gait speed: 1.10 m/s; stride length: 1.15 m; stride length variability, 2.86%) and noted a higher risk of disability onset with an increasing number of parameters, indicating low gait function. Particularly in older adults with sarcopenia, characterized by low muscle mass and compromised physical performance, gait speed is slower and stride length is shorter, with higher stride length variability [33]. Consistent with prior studies, our research found that older adults with reduced muscle mass or sarcopenia exhibited impaired gait, including slower gait speed and shorter stride lengths. Stride length emerged as a significant predictor of sarcopenia, aligning with previous findings that shorter stride length reflects underlying deficits in muscle strength and coordination [34]. While we did not categorize stride length into ‘normal’ or ‘low’ thresholds, the observed reductions in the poor PP and sarcopenia groups underscore its potential as a marker of mobility impairment. This finding suggests that stride length, as a continuous measure, captures subtle changes in neuromuscular function that may precede or accompany sarcopenia, enhancing its utility in mobility assessments.
Moreover, our data unveiled a novel role for double-limb stance in gait, demonstrating its effectiveness both independently and in combination for predicting poor physical performance and sarcopenia. Notably, the combined model integrating gait speed and double-limb stance was developed, yielding an AUC of 0.702 (95% CI: 0.622–0.781, p < 0.001), with enhanced predictive capability demonstrated by a sensitivity of 78.9% and a specificity of 76.3% in predicting sarcopenia. This combined model represents an unexpected yet promising advancement over individual gait parameters, as prior studies have typically focused on single metrics like gait speed or stride length for sarcopenia screening [26,33]. For instance, Doi et al. [32] reported gait speed (cutoff: 1.10 m/s) and stride length (cutoff: 1.15 m) as predictors of disability onset, with AUCs generally lower than our combined model’s 0.702, suggesting that integrating double-limb stance enhances predictive power beyond what single parameters achieve. We interpret this improvement as a reflection of the complementary nature of gait speed, which captures overall mobility efficiency, and double-limb stance, which reflects balance and stability deficits linked to muscle weakness and sarcopenia [34,35]. This finding was not entirely anticipated, given the limited attention double-limb stance has received in prior sarcopenia research compared to gait speed. Its novelty may stem from its ability to detect subtle neuromuscular changes that single metrics overlook, a hypothesis supported by Callisaya et al. [36], who noted double-limb stance variability’s association with dynamic balance. Alternatively, enhanced accuracy could reflect population-specific gait patterns in our Korean cohort, raising questions about the model’s generality across diverse ethnic groups or settings (e.g., institutionalized vs. community-dwelling adults). Future longitudinal studies could test this model’s applicability and refine its thresholds, potentially establishing it as a broadly actionable biomarker for early sarcopenia detection.
Our findings underscore the significance of assessing gait parameters for identifying the risk of sarcopenia in older adults. Future research should prioritize longitudinal studies to enhance our comprehension of how these parameters forecast the development and progression of sarcopenia. Such studies can guide tailored interventions aimed at preventing falls, disabilities, and mortality among the elderly.
This study offers several notable strengths that enhance its contribution to the field. First, the use of the Optogait® optical system provided highly reliable and objective measurements of spatiotemporal gait parameters, validated in prior research for older adults [12]. Second, the inclusion of a well-characterized sample of 242 community-dwelling older Koreans aged 75 years and above, assessed with both DXA-based muscle mass measurements and the short physical performance battery (SPPB), allowed for the robust classification of sarcopenia according to the AWGS 2019 criteria. Third, the development of a novel predictive model combining gait speed and double-limb stance represents an innovative advancement, demonstrating improved sensitivity and specificity over individual parameters. These strengths underscore the study’s potential to inform clinical practice by providing a practical, non-invasive approach to sarcopenia screening. Despite the strengths of this study, several limitations should be acknowledged. First, its cross-sectional design limits the ability to determine causal relationships between gait parameters and sarcopenia-related impairments. Longitudinal studies are needed to assess whether gait changes predict sarcopenia progression. Second, the study sample consists only of community-dwelling older adults in Korea, which may limit the generalizability of the findings to institutionalized or more diverse populations. Additionally, the study does not explore the neuromuscular mechanisms underlying gait alterations in sarcopenia, which could be further investigated using electromyographic (EMG) or biomechanical assessments. Lastly, while gait parameters showed predictive value, further research is needed to assess their clinical applicability and establish threshold values for sarcopenia screening in routine assessments. Addressing these limitations will help improve the clinical relevance and broader applicability of gait analysis in sarcopenia detection.

5. Conclusions

The results of this study underscore the importance of gait analysis as a valuable tool for the early prediction and management of sarcopenia and decline in physical performance among older adults. Specifically, the significant associations between stride length, double-limb stance, and sarcopenia, along with the enhanced predictive accuracy of the combined gait speed and double-limb stance model (AUC = 0.702, sensitivity = 78.9%, specificity = 76.3%), highlight the potential of spatiotemporal gait parameters for identifying at-risk individuals. Incorporating these parameters into routine clinical assessments could improve sarcopenia risk stratification and facilitate timely interventions, such as physical activity or nutritional support, to enhance mobility and quality of life for older individuals.

Author Contributions

Conceptualization, J.C. and Y.J.; methodology, Y.J. and D.K.; validation, Y.-M.P.; formal analysis, T.K.; data curation, T.K.; writing—original draft preparation, Y.J. and Y.-M.P.; writing—review and editing, J.C. visualization, T.K.; supervision, J.C.; project administration, J.C.; funding acquisition, Y.-M.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A5A8043551).

Institutional Review Board Statement

The Institutional Review Board of Human Studies approved the study protocol (SKKU 2022-08-039, 30 August 2022). All methods were conducted in accordance with the Declaration of Helsinki guidelines and written informed consent was obtained from all participants.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the corresponding authors, upon reasonable request.

Acknowledgments

We sincerely thank all participants who actively contributed to this study.

Conflicts of Interest

The authors declare that they have no competing interests.

References

  1. Yoshiko, A.; Shiozawa, K.; Niwa, S.; Takahashi, H.; Koike, T.; Watanabe, K.; Katayama, K.; Akima, H. Association of Skeletal Muscle Oxidative Capacity with Muscle Function, Sarcopenia-Related Exercise Performance, and Intramuscular Adipose Tissue in Older Adults. Geroscience 2024, 46, 2715–2727. [Google Scholar] [CrossRef] [PubMed]
  2. Petermann-Rocha, F.; Gray, S.R.; Pell, J.P.; Ho, F.K.; Celis-Morales, C. The Joint Association of Sarcopenia and Frailty with Incidence and Mortality Health Outcomes: A Prospective Study. Clin. Nutr. 2021, 40, 2427–2434. [Google Scholar] [CrossRef]
  3. Beaudart, C.; Rabenda, V.; Simmons, M.; Geerinck, A.; De Carvalho, I.A.; Reginster, J.Y.; Thiyagarajan, J.A.; Bruyère, O. Effects of protein, essential amino acids, β-hydroxy β-methylbutyrate, creatine, dehydroepiandrosterone and fatty acid supplementation on muscle mass, muscle strength and physical performance in older people aged 60 years and over: A systematic review on the literature. J. Nutr. Health Aging 2018, 22, 117–130. [Google Scholar] [CrossRef] [PubMed]
  4. Longobucco, Y.; Krumpoch, S.; Lauretani, F.; Angileri, V.; Sieber, C.; Marzetti, E.; Calvani, R.; Cherubini, A.; Landi, R.B.; Freiberger, E.; et al. Gait Characteristics in Community-Dwelling Older Persons with Low Skeletal Muscle Mass and Low Physical Performance. Aging Clin. Exp. Res. 2022, 34, 1563–1571. [Google Scholar] [CrossRef] [PubMed]
  5. Chen, L.K.; Woo, J.; Assantachai, P.; Auyeung, T.W.; Chou, M.Y.; Iijima, K.; Jang, H.C.; Kang, L.; Kim, M.; Kim, S.; et al. Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J. Am. Med. Dir. Assoc. 2020, 21, 300–307. [Google Scholar] [CrossRef]
  6. Mori, K.; Murata, S.; Goda, A.; Kikuchi, Y.; Shiraiwa, K.; Horie, J.; Nakano, H. Gait Characteristics of Dynapenia, Sar-copenia, and Presarcopenia in Community-Dwelling Japanese Older Women: A Cross-Sectional Study. Healthcare 2022, 10, 1905. [Google Scholar] [CrossRef]
  7. Kim, S.; Park, S.; Lee, S.; Seo, S.H.; Kim, H.S.; Cha, Y.; Kim, J.T.; Kim, J.W.; Ha, Y.C.; Yoo, J.I. Assessing Physical Abilities of Sarcopenia Patients Using Gait Analysis and Smart Insole for Development of Digital Biomarker. Sci. Rep. 2023, 13, 10602. [Google Scholar] [CrossRef]
  8. Gonçalves, L.d.S.; da Silva, L.S.L.; Bengamim, C.J.R.; Junior, M.F.T.; Bohn, L.; Abud, G.F.; Ortiz, G.U.; de Freitas, E.C. The effects of different exercise training types on body composition and physical performance in older adults with sarcopenic obesity: A systematic review and meta-analysis. J. Nutr. Health Aging 2023, 27, 1076–1090. [Google Scholar] [CrossRef]
  9. Kim, B.; Youm, C.; Park, H.; Lee, M.; Choi, H. Association of Muscle Mass, Muscle Strength, and Muscle Function with Gait Ability Assessed Using Inertial Measurement Unit Sensors in Older Women. Int. J. Environ. Res. Public Health 2022, 19, 9901. [Google Scholar] [CrossRef]
  10. Perez-Lasierra, J.L.; Azpíroz-Puente, M.; Alfaro-Santafé, J.; Almernar-Arasanz, A.J.; Alfaro-Santafé, J.; Gómez-Bernal, A. Sarcopenia Screening Based on the Assessment of Gait with Inertial Measurement Units: A Systematic Review. BMC Geriatr. 2024, 24, 863. [Google Scholar] [CrossRef]
  11. Salamone, L.M.; Fuerst, T.; Visser, M.; Cauley, J.A.; Nevitt, M.; Tylavsky, F.; Lohman, T.G. Measurement of Fat Mass Using DEXA: A Validation Study in Elderly Adults. J. Appl. Physiol. 2000, 89, 345–352. [Google Scholar] [CrossRef] [PubMed]
  12. Guralnik, J.M.; Simonsick, E.M.; Ferrucci, L.; Glynn, R.J.; Berkman, L.F.; Blazer, D.G.; Scherr, P.A.; Wallace, R.B. A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission. J. Gerontol. 1994, 49, 85–94. [Google Scholar] [CrossRef]
  13. Lienhard, K.; Schneider, D.; Maffiuletti, N.A. Validity of the Optogait Photoelectric System for the Assessment of Spatio-temporal Gait Parameters. Med. Eng. Phys. 2013, 35, 500–504. [Google Scholar] [CrossRef]
  14. Tinetti, M.E.; Richman, D.; Powell, L. Falls Efficacy as a Measure of Fear of Falling. J. Gerontol. 1990, 45, P239–P243. [Google Scholar] [CrossRef]
  15. Jang, S.R.; Cho, S.I.; Oh, S.W.; Lee, E.S.; Baik, H.W. The Validity and Reliability of Korean Fall Efficacy (FES) and Activities-Specific Balance Confidence Scale (ABC). Ann. Geriatr. Med. Res. 2003, 7, 255–268. [Google Scholar]
  16. Huh, J.; Lim, S.; Lee, D. Development of the Korean Falls Efficacy Scale (FES-K) for the Elderly. Korean J. Phys. Educ. 2010, 49, 193–201. [Google Scholar]
  17. Powell, L.E.; Myers, A.M. The Activities-Specific Balance Confidence (ABC) Scale. J. Gerontol. A Biol. Sci. Med. Sci. 1995, 50, 28–34. [Google Scholar] [CrossRef]
  18. Larsson, L.; Degens, H.; Li, M.; Salviati, L.; Lee, Y.I.; Thompson, W.; Kirkland, J.L.; Sandri, M. Sarcopenia: Aging-Related Loss of Muscle Mass and Function. Physiol. Rev. 2019, 99, 427–511. [Google Scholar] [CrossRef]
  19. Wang, H.; Huang, W.Y.; Zhao, Y. Efficacy of Exercise on Muscle Function and Physical Performance in Older Adults with Sarcopenia: An Updated Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2022, 19, 8212. [Google Scholar] [CrossRef]
  20. Ramari, C.; Hvid, L.G.; de David, A.C.; Dalgas, U. The Importance of Lower-Extremity Muscle Strength for Lower-Limb Functional Capacity in Multiple Sclerosis: Systematic Review. Ann. Phys. Rehabil. Med. 2020, 63, 123–137. [Google Scholar] [CrossRef] [PubMed]
  21. Jung, H.; Tanaka, S.; Iwnmoto, Y.; Kawano, T.; Yamasaki, M.; Tanaka, R. Reductions in Muscle Strength and Range of Motion Cause Locomotion Disability via Locomotion-Related Functional Limitation in Japanese Older Adults: A Cross-Sectional Study. J. Aging Res. 2021, 2021, 6627767. [Google Scholar] [CrossRef]
  22. Liu, D.; Fan, Y.B.; Tao, X.H.; Pan, W.L.; Wu, Y.X.; Wang, X.H.; He, Y.Q.; Xiao, W.F.; Li, Y.S. Mitochondrial Quality Control in Sarcopenia: Updated Overview of Mechanisms and Interventions. Aging Dis. 2021, 12, 2016–2030. [Google Scholar] [CrossRef]
  23. Yang, S.; Li, T.; Yang, H.; Wang, J.; Liu, M.; Wang, S.; He, Y.; Jiang, B. Association between Muscle Strength and Health-Related Quality of Life in a Chinese Rural Elderly Population: A Cross-Sectional Study. BMJ Open 2020, 10, e026560. [Google Scholar] [CrossRef]
  24. Li, D.; Zhang, C.; Saini, J.; Singh, S.; Nathani, R.; Thangamuthu, K.; Suresh, M.; Atkinson, E.J.; Achenbach, S.J.; Gompel, J.V.; et al. Determinants of Muscle Function and Health-Related Quality of Life in Patients with En-Dogenous Hypercortisolism: A Cross-Sectional Study. Eur. J. Endocrinol. 2023, 188, 603–612. [Google Scholar] [CrossRef] [PubMed]
  25. Middleton, A.; Fulk, G.D.; Beets, M.W.; Herter, T.M.; Fritz, S.L. Self-Selected Walking Speed Is Predictive of Daily Am-Bulatory Activity in Older Adults. J. Aging Phys. Act. 2016, 24, 214–222. [Google Scholar] [CrossRef] [PubMed]
  26. Hayashida, I.; Tanimoto, Y.; Takahashi, Y.; Kusabiraki, T.; Tamaki, J. Correlation between muscle strength and muscle mass, and their association with walking speed. PLoS ONE 2014, 9, e111810. [Google Scholar] [CrossRef]
  27. Lindemann, U. Spatiotemporal Gait Analysis of Older Persons in Clinical Practice and Research. Z. Gerontol. Geriatr. 2020, 53, 171–178. [Google Scholar] [CrossRef]
  28. Doi, T.; Nakakubo, S.; Tsutsumimoto, K.; Kurita, S.; Ishii, H.; Shimada, H. Spatiotemporal gait characteristics and risk of mortality in community-dwelling older adults. Maturitas 2021, 151, 31–35. [Google Scholar] [CrossRef]
  29. Auyeung, T.W.; Lee, S.W.; Leung, J.; Kwok, T.; Woo, J. Age-associated decline of muscle mass, grip strength and gait speed: A 4-year longitudinal study of 3018 community-dwelling older Chinese. Geriatr. Gerontol. Int. 2014, 14, 76–84. [Google Scholar] [CrossRef]
  30. Liu, L.K.; Lee, W.J.; Liu, C.L.; Chen, L.Y.; Lin, M.H.; Peng, L.N.; Chen, L.K. Age-Related Skeletal Muscle Mass Loss and Physical Performance in Taiwan: Implications to Diagnostic Strategy of Sarcopenia in Asia. Geriatr. Gerontol. Int. 2013, 13, 964–971. [Google Scholar] [CrossRef]
  31. Tanimoto, Y.; Watanabe, M.; Sun, W.; Sugiura, Y.; Hayashida, I.; Kusabiraki, T.; Tamaki, J. Sarcopenia and Falls in Community-Dwelling Elderly Subjects in Japan: Defining Sarcopenia According to Criteria of the European Working Group on Sarcopenia in Older People. Arch. Gerontol. Geriatr. 2014, 59, 295–299. [Google Scholar] [CrossRef] [PubMed]
  32. Doi, T.; Nakakubo, S.; Tsutsumimoto, K.; Kim, M.J.; Kurita, S.; Ishii, H.; Shimada, H. Spatio-temporal gait variables pre-dicted incident disability. J. Neuroeng. Rehabil. 2020, 17, 11. [Google Scholar] [CrossRef] [PubMed]
  33. Zhou, Z.; Huang, Y.; Wang, J.; Su, H.; Tang, H.; Wang, Y. A Novel Digital Biomarker of Sarcopenia in Frail Elderly: New Combination of Gait Parameters under Dual-Task Walking. Front. Aging Neurosci. 2023, 15, 1087318. [Google Scholar] [CrossRef] [PubMed]
  34. Fan, Y.; Zhang, B.; Huang, G.; Zhang, G.; Ding, Z.; Li, Z.; Sinclar, J.; Fan, Y. Sarcopenia: Body composition and gait analysis. Front. Aging Neurosci. 2022, 14, 909551. [Google Scholar] [CrossRef]
  35. Fan, Y.; Li, Z.; Han, S.; Lv, C.; Zhang, B. The influence of gait speed on the stability of walking among the elderly. Gait Posture 2016, 47, 31–36. [Google Scholar] [CrossRef]
  36. Callisaya, M.L.; Blizzard, L.; McGinley, J.L.; Schmidt, M.D.; Srikanth, V.K. Sensorimotor factors affecting gait variability in older people—A population-based study. J. Gerontol. A Biol. Sci. Med. Sci. 2010, 65, 386–392. [Google Scholar] [CrossRef]
Applsci 15 04379 g001
Figure 1. Flowchart of the study participants.
Figure 1. Flowchart of the study participants.
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Figure 2. Receiver operating characteristic (ROC) curve for low MM (A), poor PP (B), and sarcopenia (C) status prediction with gait parameters. Low MM: low muscle mass; poor PP: poor physical performance.
Figure 2. Receiver operating characteristic (ROC) curve for low MM (A), poor PP (B), and sarcopenia (C) status prediction with gait parameters. Low MM: low muscle mass; poor PP: poor physical performance.
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Figure 3. The ROC curve for sarcopenia prediction: A new model (gait speed + double-limb stance rate) noticeably improved the AUC. ROC: receiver operating characteristic; AUC: area under the ROC curve.
Figure 3. The ROC curve for sarcopenia prediction: A new model (gait speed + double-limb stance rate) noticeably improved the AUC. ROC: receiver operating characteristic; AUC: area under the ROC curve.
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Table 1. Classification of participants into groups based on AMMI and SPPB scores.
Table 1. Classification of participants into groups based on AMMI and SPPB scores.
GroupAMMISPPBDescription
NormalMen: ≥7.0 kg/m2
Women: ≥5.4 kg/m2		>9 scoreAdequate muscle mass and physical performance
Low Muscle Mass
(Low MM)		Men: <7.0 kg/m2
Women: <5.4 kg/m2		>9 scoreReduced muscle mass, normal performance
Poor Physical Performance
(Poor PP)		Men: ≥7.0 kg/m2
Women: ≥5.4 kg/m2		≤9 scoreAdequate muscle mass, impaired performance
SarcopeniaMen: <7.0 kg/m2
Women: <5.4 kg/m2		≤9 scoreReduced muscle mass and impaired performance
AMMI, appendicular skeletal muscle mass index; SPPB: short physical performance battery.
Table 2. Characteristics of study participants.
Table 2. Characteristics of study participants.
VariableNormal
(n = 53)		Low MM
(n = 69)		Poor PP
(n = 64)		Sarcopenia
(n = 56)		p Value
Age (years)78.1 ± 3.5 d78.1 ± 4.0 c,d80.1 ± 3.7 b80.2 ± 5.2 a,b0.002
Sex, n (%)
Male8 (15.1)19 (27.5)9 (14.1)11 (19.6)0.194
Female45 (84.9)50 (72.5)55 (85.9)45 (80.4)
Education (years)5.7 ± 4.27.8 ± 4.5 c5.2 ± 4.6 b5.9 ± 4.70.005
BMI (kg/m2)26.8 ± 2.6 b,d23.3 ± 3.4 a,c25.9 ± 2.7 b,d23.5 ± 2.4 a,c<0.001
WC (cm)102.3 ± 12.1 b,d95.2 ± 14.3 a98.2 ± 13.595.4 ± 12.5 a0.016
SBP (mmHg)130.3 ± 15.9131.1 ± 12.7130.8 ± 14.2130.5 ± 14.90.991
DBP (mmHg)70.2 ± 9.171.3 ± 8.771.1 ± 9.370.8 ± 8.20.914
AMMI (kg/m2)6.4 ± 0.7 b,d5.6 ± 0.7 a,c6.3 ± 0.8 b,d5.4 ± 0.8 a,c<0.001
SPPB (score)10.7 ± 0.7 c,d10.7 ± 0.6 c,d8.3 ± 0.9 a,b8.1 ± 1.0 a,b<0.001
Smoking status, n (%)
Non/past smoking53 (100)67 (97.1)61 (95.3)54 (96.4)0.184
Current smoking 02 (2.9)3 (4.7)2 (3.6)
Alcohol consumption, n (%)
Non-drinker365446450.291
≤1 time/month6525
≥2 times/month1110166
Comorbidity, n (%)
02 (3.8)12 (17.4)11 (17.2)9 (16.1)0.276
123 (43.4)32 (46.4)26 (40.6)19 (33.9)
≥228 (42.8)25 (36.2)27 (42.2)28 (50.0)
FES (score)94.5 ± 6.2 c,d92.8 ± 10.6 c,d85.9 ± 14.1 a,b89.3 ± 11.7 a,b<0.001
ABC (score)87.8 ± 7.5 c,d85.3 ± 13.9 c,d76.7 ± 17.1 a,b75.5 ± 16.8 a,b<0.001
BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; AMMI, appendicular skeletal muscle mass index; SPPB, short physical performance battery; FES, fall efficacy scale; ABC, activity-specific balance confidence scale; MM, low muscle mass; poor PP, poor physical performance. Statistically significant differences were observed in the following comparisons: a difference vs. normal, b difference vs. low MM, c difference vs. poor PP, and d difference vs. sarcopenia. p < 0.05.
Table 3. Covariance analysis of gait parameters by sarcopenia status.
Table 3. Covariance analysis of gait parameters by sarcopenia status.
ParametersNormal
(n = 53)		Low MM
(n = 69)		Poor PP
(n = 64)		Sarcopenia
(n = 56)		p Value
Gait speed (m/s)1.25 ± 0.16 d1.26 ± 0.17 d1.31 ± 0.231.41 ± 0.29 a,b<0.001
Stride length (cm)115.7 ± 9.8 c,d114.8 ± 8.0 c,d108.5 ± 11.4 a,b,d103.2 ± 9.6 a,b,c<0.001
Double-limb stance (%)23.9 ± 2.7 c,d24.1 ± 2.6 c,d26.2 ± 3.6 a,b26.4 ± 3.4 a,b<0.001
Gait speed variability3.3 ± 1.8 c,d4.6 ± 2.94.8 ± 3.0 a5.6 ± 3.1 a<0.001
Stride length variability2.3 ± 1.3 b,c,d3.5 ± 3.4 a3.5 ± 2.4 a3.8 ± 2.0 a0.001
Double-limb stance variability5.1 ± 3.76.6 ± 3.95.9 ± 4.86.4 ± 3.40.180
Low MM: low muscle mass; poor PP: poor physical performance; significantly different compared to a difference vs. normal, b difference vs. low MM, c difference vs. poor PP, d difference vs. sarcopenia. p < 0.05. Comparison of gait parameters according to sarcopenia status.
Table 4. Comparison of independent predictors for gait parameters according to sarcopenia status.
Table 4. Comparison of independent predictors for gait parameters according to sarcopenia status.
Gait Parametersβ95% CIp Value
AMMI (kg/m2)Double-limb stance−0.155−0.080–−0.0020.037
SPPB (score)Gait speed−0.205−1.690–−0.0350.001
Double-limb stance−0.287−0.198–−0.016<0.001
Sarcopenia (score)Gait speed−0.120−1.519–−0.0230.043
Stride length0.2830.020–0.057<0.001
Double-limb stance−0.244−0.171–−0.0460.001
AMMI, appendicular skeletal muscle mass index; SPPB, short physical performance battery; 95% CI: 95% confidence interval. Adjusted for age, sex, BMI, WC, and comorbidity.
Table 5. Receiver operating characteristic (ROC) curve analysis for sarcopenia status.
Table 5. Receiver operating characteristic (ROC) curve analysis for sarcopenia status.
Gait ParametersAUC (95% CI)p ValueSensitivitySpecificity
Low MMDouble-limb stance0.502 (0.429–0.576)0.94912.0%87.2%
Stride length0.430 (0.357–0.502)0.05924.0%95.7%
Gait speed0.549 (0.477–0.622)0.18529.6%84.6%
Poor PPDouble-limb stance0.698 (0.633–0.763)<0.00165.8%66.3%
Stride length0.269 (0.329–0.417)<0.00142.4%58.2%
Gait speed0.585 (0.431–0.578)0.0220.20098.3%
SarcopeniaDouble-limb stance0.647 (0.568–0.726)0.00169.1%59.4%
Stride length0.236 (0.170–0.302)<0.00146.4%59.1%
Gait speed0.618 (0.530–0.707)0.00740.0%81.8%
Low MM, low muscle mass; poor PP, poor physical performance; AUC, area under the curve; 95% CI, 95% confidence interval.
Table 6. Receiver operating characteristic (ROC) curve for sarcopenia: the new model (gait speed + double-limb stance).
Table 6. Receiver operating characteristic (ROC) curve for sarcopenia: the new model (gait speed + double-limb stance).
AUC (95% CI)p ValueSensitivitySpecificity
Gait speed + double-limb stance0.702 (0.622–0.781)<0.00178.9%76.3%
AUC: area under the curve; 95% CI: 95% confidence interval.
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Jin, Y.; Kim, T.; Kim, D.; Park, Y.-M.; Cho, J. Spatiotemporal Gait Parameters in Community-Dwelling Old-Old Koreans: Impact of Muscle Mass, Physical Performance, and Sarcopenia. Appl. Sci. 2025, 15, 4379. https://doi.org/10.3390/app15084379

AMA Style

Jin Y, Kim T, Kim D, Park Y-M, Cho J. Spatiotemporal Gait Parameters in Community-Dwelling Old-Old Koreans: Impact of Muscle Mass, Physical Performance, and Sarcopenia. Applied Sciences. 2025; 15(8):4379. https://doi.org/10.3390/app15084379

Chicago/Turabian Style

Jin, Youngyun, Taewan Kim, Donghyun Kim, Young-Min Park, and Jinkyung Cho. 2025. "Spatiotemporal Gait Parameters in Community-Dwelling Old-Old Koreans: Impact of Muscle Mass, Physical Performance, and Sarcopenia" Applied Sciences 15, no. 8: 4379. https://doi.org/10.3390/app15084379

APA Style

Jin, Y., Kim, T., Kim, D., Park, Y.-M., & Cho, J. (2025). Spatiotemporal Gait Parameters in Community-Dwelling Old-Old Koreans: Impact of Muscle Mass, Physical Performance, and Sarcopenia. Applied Sciences, 15(8), 4379. https://doi.org/10.3390/app15084379

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