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Classifications Based on Dynamic Navicular Drop during Gait and Characteristics of Flat Foot Muscle Morphology

Kengo Fukuda

^1,2,*

Kazunori Okamura

³,

Tomohiro Ikeda

⁴,

Kohei Egawa

¹ and

Shusaku Kanai

Graduate School of Comprehensive Scientific Research, Prefectural University of Hiroshima, 1-1 Gakuen-cho, Mihara-shi 723-0053, Hiroshima, Japan

Department of Rehabilitation, Innoshima Ishikai Hospital, 1962 Innoshima Nakanosyo-cho, Onomichi-shi 722-2211, Hiroshima, Japan

Department of Physical Therapy, Faculty of Health and Welfare, Prefectural University of Hiroshima, 1-1 Gakuen-cho, Mihara-shi 723-0053, Hiroshima, Japan

⁴

Department of Rehabilitation Medicine, Okayama University, 2-5-1 Shikatacho, Kita-ku, Okayama 700-8558, Okayama, Japan

Author to whom correspondence should be addressed.

Biomechanics 2024, 4(4), 633-641; https://doi.org/10.3390/biomechanics4040045

Submission received: 31 July 2024 / Revised: 6 September 2024 / Accepted: 9 October 2024 / Published: 16 October 2024

(This article belongs to the Special Issue Personalized Biomechanics and Orthopedics of the Lower Extremity)

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Abstract

This study investigated the collapse of the medial longitudinal arch (MLA) as a risk factor for medial tibial stress syndrome (MTSS), hypothesizing that overuse of extrinsic foot muscles to prevent MLA collapse can lead to disability. Twenty healthy adults (age: 20.8 ± 0.8, height: 162.2 ± 10.4, weight: 54.9 ± 9, BMI: 20.8 ± 1.7) (39 feet) with a foot posture index score below 6 and no recent lower extremity orthopedic history participated. Ultrasonography measured foot muscle cross-sectional areas, while three-dimensional motion analysis using VICON assessed foot kinematics during gait, focusing on navicular height at initial contact (ICNH) and dynamic navicular drop (DND) during the stance phase. Hierarchical cluster analysis based on ICNH and DND compared muscle cross-sectional areas between clusters using ANOVA or Kruskal–Wallis test. The analysis indicated that ICNH was lower in clusters 1 and 3 than in cluster 2, and DND was smaller in clusters 1 and 2 than in cluster 3. Although there was no significant difference in muscle cross-sectional area between the clusters, the flexor hallucis longus tended to be thicker in cluster 1 than in cluster 3 (p = 0.051). The findings suggest that flexor digitorum longus may help prevent MLA compression during loading, indicating that overuse of extrinsic foot muscles may contribute to MTSS development.

Keywords:

flat foot; foot kinematics; foot muscle morphology; medial tibial stress syndrome

1. Introduction

Medial longitudinal arch (MLA) collapse, commonly known as flat foot, is one of the most prevalent foot alignment disorders and a significant risk factor for overuse injuries such as medial tibial stress syndrome [1] and plantar fasciitis [2]. Recent research has increasingly focused on foot kinematics [3,4] and muscle function [5,6,7,8,9] in the context of overuse syndrome prevention.

Studies have shown that individuals with flat feet exhibit distinctive gait characteristics, including increased tibialis posterior muscle activity [5] and enhanced plantar flexion and external rotation of the ankle joint during the stance phase [3]. Angin et al. [10] investigated the relationship between the six-item foot posture index (FPI), which assesses flat foot severity, and the cross-sectional area (CSA) of foot muscles. They found negative correlations between FPI and intrinsic foot muscle CSA and positive correlations with extrinsic foot muscle CSA.

These findings suggest that flat-footed individuals may compensate for MLA collapse and intrinsic foot muscle dysfunction in static situations by increasing extrinsic muscle activity, which affects ankle joint dynamics. For instance, medial tibial stress syndrome is thought to result from tibial periosteum traction stress due to overactivity of extrinsic foot muscles like the tibialis posterior and flexor digitorum longus [1,11,12].

While these studies provide valuable insights, they primarily predict dynamic activity based on static foot alignment assessments. However, some reports indicate that static assessments poorly predict dynamic foot kinematics [3,13]. Consequently, there has been growing interest in the evaluation of changes in the MLA during movement [14,15].

Understanding the relationship between MLA collapse characteristics during gait and muscle morphology is crucial for accurately determining the etiology of overuse syndromes. Although ultrasonography cannot directly measure dynamic muscle activity, muscle morphology likely reflects repeated muscle activity and offers a non-invasive method to predict muscle function.

Therefore, this study aimed to investigate the characteristics of MLA collapse during gait and its relationship with foot muscle CSA. We hypothesize that individuals exhibiting less MLA collapse during gait will have hypertrophied extrinsic foot muscles, particularly those contributing to ankle joint rotation. Our study offers several key advantages over existing flatfoot research methods. First, unlike traditional static measures, our method captures the dynamic behavior of the MLA during gait, providing a more functional assessment of flatfoot. Second, we combine kinematic analysis with muscle morphology evaluation, offering a comprehensive understanding of the relationship between foot structure and function. This research could provide valuable insights into the compensatory mechanisms in flat feet and contribute to more effective prevention and treatment strategies for related overuse injuries.

2. Materials and Methods

2.1. Participants

This study was conducted with the approval of the Ethics Committee of the Prefectural University of Hiroshima, and all participants provided written informed consent. The research team approached 148 students at the Prefectural University of Hiroshima to provide details about the study and invite participation. Exclusion criteria were established, including any history of lower extremity injury within the six months preceding the study and a score exceeding 6 points on the six-item foot posture index. Twenty participants who met the eligibility criteria based on these standards were included in this study.

2.2. Measurement of Foot Kinematics during Gait

This study examined the kinematic characteristics of barefoot subjects’ feet during gait, focusing on two key measures: initial contact navicular height (ICNH) and dynamic navicular drop (DND). The DND was calculated as the difference between ICNH and the lowest point of the navicular during the stance phase [14,15] (Figure 1). This method of DND measurement is widely accepted and has demonstrated reliability [14,15].

We utilized a motion capture system (Vicon, Oxford Metrics, Oxford, UK) consisting of 12 MX-T20S cameras operating at 100 Hz. Data processing was performed using Vicon Nexus software and Visual3D (C-Motion Inc., Germantown, MD, USA).

A trained researcher (KF) placed 9.5-mm reflective markers on specific anatomical landmarks: the navicular tuberosity, the medial aspect of the first metatarsal head, the lateral aspect of the fifth metatarsal head, and the distal end of the calcaneus (Figure 2). Due to the constraints of our experimental setup, including the positioning of cameras and the limited capture volume, we were only able to measure one complete gait cycle per trial. While we acknowledge that this may not capture the full variability of gait, it was the maximum achievable with our current facility setup. For statistical analysis, the mean ICNH and DND values of the stance phase for five separate trials were used.

2.3. Measurement of Foot Muscle Morphology

Ultrasound imaging was employed to assess the CSA of select foot muscles: abductor hallucis, flexor hallucis brevis, flexor digitorum brevis, flexor hallucis longus, flexor digitorum longus, and peroneus longus. To capture these measurements, we utilized a SONIMAGE MX1 ultrasound system (Konica Minolta, Tokyo, Japan) with an 11-MHz linear array probe. The measurement protocol followed procedures detailed in previous studies [6,16], with all examinations conducted by a single experienced investigator (KO). Recognizing the significant relationship between lower limb muscle volume and body weight [17], we normalized each muscle’s CSA to the participant’s body weight (mm²/kg) to account for inter-individual differences. All participants were within the normal BMI range. In our statistical analysis, we used the mean of three measurements for each muscle.

2.4. Statistical Analysis

To categorize participants based on foot kinematics during gait, we employed hierarchical cluster analysis. The model incorporated ICNH and DND measurements. We selected Euclidean distance as the metric and applied the Ward linkage method. Silhouette scores were calculated for each potential number of clusters (ranging from 2 to 5) to determine the optimal cluster solution. After identifying clusters of participants with flat feet, we verified data normality using the Shapiro–Wilk test. Differences in cluster variables, demographic characteristics, and CSA of selected foot muscles were analyzed using either a one-way analysis of variance or the Kruskal–Wallis test, depending on data distribution. For post hoc comparisons, we used the Games–Howell and Steel–Dwass methods, as they do not require the assumption of equal variances between groups. We calculated effect sizes (d or r) for continuous variables based on their normality. The effect size d was interpreted as small (0.2–0.5), medium (0.5–0.8), or large (>0.8), while r was considered small (0.1–0.3), medium (0.3–0.5), or large (≥0.5). Statistical significance was set at p < 0.05. All analyses were conducted using SPSS 20.0 for Windows (SPSS Inc., Chicago, IL, USA).

3. Results

We conducted hierarchical cluster analysis using ICNAV and DND, resulting in three distinct clusters (Figure 3), which was confirmed as the optimal solution using silhouette analysis (silhouette score = 0.5247). The navicular height patterns during the gait stance phase for each cluster are illustrated in Figure 4. Table 1 presents the cluster variables and additional foot kinematics data. Significant differences in ICNH were observed between clusters 1 and 2 (mean difference [95% confidence interval] = −6.414 [−9.650 to −3.178] cm², p < 0.01, d = 3.07) and clusters 2 and 3 (mean difference [95% confidence interval] = 4.570 [−0.165–9.305], p < 0.01, d = 2.33). For DND, significant differences were found between clusters 1 and 3 (p < 0.01, r = 0.70) and clusters 2 and 3 (p < 0.01, r = 0.84). Table 2 details the demographic variables and FPI scores, which assess static foot alignment. While age, height, and weight showed no significant differences across clusters, body mass index differed significantly between clusters 1 and 2 (mean difference [95% confidence interval] = 1.306 [−0.103–2.715], p < 0.05, d = 1.24). No significant differences were observed in FPI scores or CSA of any muscle among the clusters. The p-values and effect sizes for CSA comparisons among clusters are provided in the Supplementary Materials.

4. Discussion

This study aimed to classify flat-footed individuals based on MLA kinematics during gait and explore relationships with foot muscle morphology. We identified three distinct clusters of flat feet and observed notable, though not statistically significant, differences in flexor digitorum longus (FDL) CSA between these clusters.

Participants with flat feet were classified into three clusters based on MLA kinematics during gait. Although not statistically significant, notable differences in FDL CSA, an extrinsic foot muscle, were observed between clusters.

Particularly, a noteworthy trend was observed in FDL CSA between cluster 1 (low ICNH, small DND) and cluster 3 (low ICNH, large DND). While not reaching statistical significance (p = 0.051), a moderate effect size (r = 0.43) was observed, suggesting that cluster 1 tended to have larger FDL compared with cluster 3. This finding partially aligns with previous studies reporting the crucial role of FDL in preventing MLA compression [6,18,19,20].

The FDL muscle plays a key role in maintaining the MLA during gait by actively flexing the toes and providing dynamic support to the arch structure. Its action helps to resist the flattening of the arch under load, particularly in individuals with flat feet who may rely more heavily on this muscle for arch stability.

A recent study further highlighted the importance of considering bilateral differences in foot morphology and gait patterns. Pan et al. found that even in healthy runners with similar foot morphology between left and right feet, there can be significant asymmetries in running kinematics [19]. This emphasizes the need to consider both limbs when assessing foot function and muscle activity, as asymmetries in FDL activation or size could potentially contribute to differences in MLA kinematics between feet.

The smaller DND observed in cluster 1 suggests that this group may utilize FDL more to prevent MLA collapse. Kobayashi et al. [21] reported that individuals with flat feet tend to use FDL to maintain the MLA. Our results support the possibility that overuse of FDL in cluster 1 participants may have contributed to the reduced MLA collapse observed in this group.

However, no statistically significant differences were found in the CSA of intrinsic muscles between clusters. This may relate to the findings of Okamura et al. [18], who reported an increase in MLA after fatiguing intrinsic muscles, suggesting that extrinsic muscles might compensate for intrinsic muscle dysfunction to prevent MLA collapse. Our results suggest that in flat feet, the degree of MLA collapse may differ depending on whether FDL compensates for intrinsic muscle dysfunction.

Our findings present an interesting contrast to those reported by Angin et al. [10]. While they found a negative correlation between FPI scores and the CSA of intrinsic muscles like abductor hallucis and flexor hallucis brevis, our study did not reveal significant differences in intrinsic muscle CSA across the clusters. This discrepancy might be attributed to several factors. Firstly, our study focused on participants with similar flat-footed characteristics (FPI < 6), whereas Angin et al. examined a broader range of foot types. This narrower range in our study might have limited the variability in intrinsic muscle size. Secondly, our classification was based on dynamic measures (ICNH and DND) rather than static measures like the FPI. This suggests that static measures of foot posture may not fully capture the complexity of foot function during gait. The lack of clear differences in intrinsic muscles in our study, despite variations in MLA kinematics, implies that the relationship between foot posture, muscle morphology, and dynamic function may be more complex than previously thought. These findings underscore the importance of considering both static and dynamic measures in clinical assessment and highlight the need for further research to elucidate the intricate relationships between foot structure, muscle morphology, and function in individuals with flat feet.

Furthermore, our previous study [15] changing the kinematic task from gait to running showed that participants with larger CSA of abductor hallucis and flexor hallucis brevis had larger DND, while those with smaller CSA of intrinsic foot muscles had smaller DND. This suggests the possibility that some participants use active intrinsic muscles to endure the load, while others shift the load position laterally due to the inability to endure the load with small intrinsic muscles.

These results suggest that during gait, intrinsic muscle dysfunction may be compensated by extrinsic muscles (particularly FDL), while during higher loads such as running, the foot kinetic strategy may change by shifting the load laterally in response to the amount of load.

The novelty of this study lies in investigating the relationship between MLA collapse during gait and muscle morphology, suggesting that flat foot may prevent MLA collapse by overusing the FDL. This could contribute to understanding the pathophysiology of medial tibial stress syndrome, where flat foot is a risk factor [1,11].

However, this study has several limitations that should be addressed in future research. The small sample size and uneven distribution of participants across clusters may affect the generalizability and statistical efficacy of the results, potentially contributing to the lack of statistical significance in some findings, particularly regarding FDL CSA differences between clusters (p = 0.051). Additionally, the use of ultrasound for muscle evaluation and the focus on only ICNH and DND for MLA kinematics clustering may oversimplify the complex nature of foot function.

Future studies should aim to recruit larger, more balanced samples, employ direct muscle activity evaluation methods such as electromyography, and incorporate additional MLA kinematic indicators and plantar pressure distribution measures for a more comprehensive assessment. Despite these limitations, our study provides valuable preliminary insights into the relationship between MLA kinematics during gait and foot muscle morphology in individuals with flat feet, laying the groundwork for future investigations in this area.

5. Conclusions

This study identified three distinct groups of flat-footed individuals based on their MLA kinematics during gait. While no significant differences were found in the CSA of intrinsic foot muscles, a trend towards larger FDL muscle CSA in individuals with less MLA collapse was observed. This finding, despite not reaching statistical significance, showed a moderate effect size. These results suggest a potential role of the FDL muscle in maintaining arch stability during gait in flat-footed individuals. Further research with larger sample sizes and electromyographic evaluation is needed to validate these findings and enhance our understanding of muscle function in flat feet.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomechanics4040045/s1.

Author Contributions

K.F.: Conceptualization, Methodology, Validation, Formal Analysis, Investigation, Resources, Data Curation, Writing—Original Draft, Project Administration. K.O.: Conceptualization, Methodology, Validation, Investigation, Data Curation, Writing—Review & Editing, Visualization, and Project Administration. T.I.: Conceptualization, Methodology, Validation, Investigation, Writing—Review & Editing, Visualization, and Project Administration. K.E.: Conceptualization, Methodology, Validation, Writing—Review & Editing, and Visualization. S.K.: Conceptualization, Methodology, Resources, Data Curation, Writing—Review & Editing, Supervision, Project Administration. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the JSPS KAKENHI [Grant Number JP19K20043].

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the Prefectural University of Hiroshima (protocol code 20MH00 and 10 September 2020.

Informed Consent Statement

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

Data Availability Statement

Data are available in a publicly accessible repository (https://zenodo.org/records/13935103, accessed on 5 October 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Navicular height during the stance phase of gait and the definition of dynamic navicular drop. Dynamic navicular drop was defined as the difference between navicular height at initial contact and minimum value during the stance phase of gait.

Figure 2. The marker attachment positions. DCA, distal end of the calcaneus; NAV, navicular tuberosity; 1 MH, medial aspect of the first metatarsal head; 5 MH, lateral aspect of the fifth metatarsal head.

Figure 3. Ward’s minimum variance linkage dendrogram of the hierarchical cluster analysis of medial longitudinal arch kinematics during gait representing the 3-cluster solution. Three groups are highlighted in red color for cluster 1, green for cluster 2, and blue for cluster 3.

Figure 4. Navicular height during the stance phase of gait in 3 clusters. Three groups are highlighted by red color for cluster 1, green for cluster 2, and blue for cluster 3.

Table 1. Cluster variables for each of the clusters. Values are mean ± standard deviation or median [25th/75th interquartile].* significant difference compared with cluster 1; ** significant difference compared with cluster 2; *** significant difference compared with cluster 3.

Variables	Cluster 1 n = 20 (51.3%)	Cluster 2 n = 10 (25.6%)	Cluster 3 n = 9 (23.1%)
Navicular height at initial contact (mm)	11.5 ± 2.7 **	19.5 ± 2.6 ^,**	14.1 ± 2.3 **
Dynamic navicular drop (mm)	3.7 [2.9/4.5] ***	4.5 [4.0/5.5] ***	8.7 [8.5/10.9] ^,*

Table 2. Demographic variables and external variables in each of the clusters. Values excluding sex are mean ± standard deviation or median [25th/75th interquartile]. * significant difference compared with cluster 1; ** significant difference compared with cluster 2; The CSA of each muscle was normalized to the participant’s body weight (mm²/kg).

Variables	Cluster 1 n = 20 (51.3%)	Cluster 2 n = 10 (25.6%)	Cluster 3 n = 9 (23.1%)
Demographic variables Sex (woman/man)	14/6	4/6	7/2
Age (years)	21.0 [20.0/21.0]	21.0 [20.8/21.0]	21.0 [21.0/22.0]
Height (cm)	160.9 ± 8.5	165.5 ± 9.6	161.4 ± 11.3
Weight (kg)	54.4 ± 7.2	54.1 ± 6.3	53.6 ± 5.8
Body mass index (kg/m²)	20.9 ± 1.1 **	19.7 ± 0.7 *	20.6 ± 1.4
Six item foot posture index (points)	9.1 ± 1.4	7.8 ± 1.0	8.4 ± 1.5
Muscle cross-sectional area
Abductor halluces (mm²/kg)	2.0 [1.8/2.3]	2.3 [1.8/2.8]	2.1 [1.6/2.3]
Flexor hallucis brevis (mm²/kg)	2.8 [2.5/3.6]	2.7 [2.6/3.2]	2.6 [2.5/2.8]
Flexor digitorum brevis (mm²/kg)	1.8 [1.5/2.2]	1.8 [1.6/2.4]	1.5 [1.4/1.8]
Flexor hallucis longus (mm²/kg)	2.2 ± 0.5	2.4 ± 0.6	2.0 ± 0.5
Flexor digitorum longus (mm²/kg)	1.7 [1.3/1.9]	1.3 [1.2/1.7]	1.2 [1.1/1.5]
Peroneus longus (mm²/kg)	2.9 [2.6/4.0]	3.2 [2.9/3.6]	2.9 [2.8/3.2]

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MDPI and ACS Style

Fukuda, K.; Okamura, K.; Ikeda, T.; Egawa, K.; Kanai, S. Classifications Based on Dynamic Navicular Drop during Gait and Characteristics of Flat Foot Muscle Morphology. Biomechanics 2024, 4, 633-641. https://doi.org/10.3390/biomechanics4040045

AMA Style

Fukuda K, Okamura K, Ikeda T, Egawa K, Kanai S. Classifications Based on Dynamic Navicular Drop during Gait and Characteristics of Flat Foot Muscle Morphology. Biomechanics. 2024; 4(4):633-641. https://doi.org/10.3390/biomechanics4040045

Chicago/Turabian Style

Fukuda, Kengo, Kazunori Okamura, Tomohiro Ikeda, Kohei Egawa, and Shusaku Kanai. 2024. "Classifications Based on Dynamic Navicular Drop during Gait and Characteristics of Flat Foot Muscle Morphology" Biomechanics 4, no. 4: 633-641. https://doi.org/10.3390/biomechanics4040045

APA Style

Fukuda, K., Okamura, K., Ikeda, T., Egawa, K., & Kanai, S. (2024). Classifications Based on Dynamic Navicular Drop during Gait and Characteristics of Flat Foot Muscle Morphology. Biomechanics, 4(4), 633-641. https://doi.org/10.3390/biomechanics4040045

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