Effect of Change of Direction (COD) Movement on Plantar Pressure and Foot Balance in Bilateral Limbs

Summary

This protocol details a standardized approach to quantify plantar pressure distribution, foot balance, and foot axis angle during 45° change-of-direction tasks using a Footscan pressure plate, enabling reproducible assessment of bilateral limb loading patterns at a controlled approach speed.

Abstract

This protocol establishes a standardized approach to test whether a 45° change-of-direction (COD) task elicits asymmetrical plantar loading between dominant and non-dominant limbs. The workflow includes pressure-plate calibration, barefoot participant preparation, and approach-speed control at 3.5 m·s⁻¹ (±5%) using timing gates to improve trial consistency. Plantar loading is quantified using ten-region foot segmentation, and stance is time-normalized to 101 points. Force trajectories are normalized to the average vertical force (Zavg), enabling between-limb comparison of time-continuous signals. Limb differences are evaluated using one-dimensional Statistical Parametric Mapping (SPM1d) with random field theory-based inference, complemented by discrete summary metrics (e.g., regional peaks and foot balance indices) as needed. In a cohort of 15 healthy adults performing standardized 45° COD trials, time-continuous analyses did not reveal curve-level statistical significance between limbs, indicating a high degree of functional symmetry under the present conditions. Although small directional tendencies were observed in some plantar regions, these should be interpreted as descriptive rather than definitive limb-specific strategies. Overall, the protocol provides a transparent and reproducible framework for bilateral plantar mechanics research, supporting applications in sports performance testing, footwear evaluation, rehabilitation monitoring, and clinical gait assessment.

Introduction

Change of direction (COD) movements are key skills in many sports, particularly in high-intensity activities such as football, basketball, and rugby, where speed and precision directly affect athletic performance and competitiveness^1,2. Research has shown that COD movements not only require athletes to possess high levels of lower limb strength and explosiveness but also demand good coordination and fine gait control^2,3,4. To gain a comprehensive understanding of the biomechanical mechanisms underlying COD movements, particularly the impact on plantar pressure, numerous studies have focused on exploring the kinematic and kinetic related parameters, especially changes under different movement speeds and gait conditions.

Plantar pressure distribution reflects the dynamic changes in foot structure, function, and overall body posture control^5,6,7. By analyzing plantar pressure, the physiological and pathological biomechanical parameters and functional characteristics of the human body under different postures and movement conditions could be understood^8,9. In recent years, the analysis of plantar pressure distribution has become an important tool in biomedical sciences, footwear design, and sports performance, thus helping to elucidate the complex biomechanical interactions between the foot and the ground. Previous work demonstrated that plantar pressure analysis can reveal performance-related adaptations; for example, professional soccer players show higher forefoot loading compared with recreational athletes, reflecting sport-specific demands on propulsion and stability. Meanwhile, dynamic activities, such as walking and landing, generated markedly greater plantar loads than static standing, emphasizing the biomechanical importance of foot pressure redistribution during movement¹⁰. Additionally, Pataky et al. contrasted traditional discrete sub-sampling with curve-based plantar-pressure SPM (pSPM) and showed that walking speed systematically affects peak plantar-pressure estimates, with findings highlighting that field-wise inference reduces dependence on arbitrary sampling windows, supporting our use of SPM for time-continuous analyses¹¹.

Sports and athletic activities impose unique biomechanical demands on the feet, particularly during rapid cutting and other high-intensity maneuvers. Amaro et al. (2020) reported that basketball-specific movements, such as running, cutting, and rebounding, produced movement-dependent variations in peak plantar pressure across foot regions¹². Serrano et al.¹³ further demonstrated that youth futsal players maintained consistent plantar pressure patterns during COD maneuvers on different surfaces, highlighting adaptive foot biomechanics in dynamic contexts. Kong et al.¹⁴ found significantly increased forefoot loads during sprinting, 45° cutting, and layups compared with running, emphasizing the biomechanical importance of plantar pressure redistribution during sport-specific tasks.

Despite the wide application of plantar pressure analysis in sports and footwear research, few studies have systematically examined plantar pressure distribution and foot-balance behavior during standardized COD tasks. The present study addresses this gap by proposing a reproducible protocol for bilateral plantar pressure and foot-balance assessment during a 45° COD maneuver under speed-controlled conditions. The authors selected a 45° cutting angle because it represents a commonly used moderate COD angle in biomechanical research, balancing sport relevance with laboratory feasibility and participant safety. Moreover, COD mechanics are known to vary with cutting angle and approach velocity; therefore, the present findings should be interpreted as specific to a standardized 45° task performed at a controlled approach speed. We hypothesized that the dominant limb would exhibit greater loading in propulsion-related forefoot regions, while the non-dominant limb would show increased mediolateral variability in foot-balance indices, reflecting its stabilization role. The selected speed and gait combinations are based on existing literature and open-source datasets combined with current biomechanical research on COD movements, to ensure the scientific and representative nature of the experimental conditions^15,16.

This study aims to reveal the distribution patterns of plantar pressure and foot balance during COD movements under standardized speed and gait conditions. By analyzing the biomechanical responses in this specific movement context, the findings are expected to provide theoretical insights for sports science, rehabilitation strategies, and sports footwear design, offering practical guidance for optimizing athlete training and movement techniques.

Protocol

The study has obtained approval from the Ethics Committee of the Research Institute at Ningbo University (Approval Number: ty2022020). All participants provided written consent, having been informed about the purpose, requirements, and procedures of the experiment. The equipment and software used are listed in the Table of Materials.

1. Laboratory preparation

A Footscan 2-m pressure plate (2.0 m × 0.40 m × 0.02 m, 16,384 sensors) was used and sampled at 480 Hz. The plate was embedded flush with the surrounding runway using protective mats to eliminate step-height artifacts. The system was zeroed and weight-calibrated according to the manufacturer's instructions, and the connected plate's serial number and calibration file were verified before testing.

Approach speed over the final 2 m prior to foot contact was monitored with dual infrared timing gates, spaced 2.0 m apart at a beam height of 80 cm and aligned with the runway. Gates were positioned on both sides of the runway so as not to interfere with the COD maneuver. Participants were required to maintain 3.5 m·s⁻¹ (±5%) along the approach path before contacting the pressure plate. Although timing gates quantify mean speed over the final 2 m, participants approached along the blue-mat runway and executed a 45° change-of-direction (COD) maneuver such that the plant (turning) foot contacted the active area of the pressure plate. To minimize foot-placement targeting, no visual markers were provided, and several practice trials were allowed. A trial was deemed valid if the plant foot achieved full contact within the active area without obvious stride adjustment; trials with partial contact or evident targeting were repeated.

2. Participant preparation

All participants provided written informed consent after receiving a clear explanation of the study's objectives and procedures, with opportunities to ask questions. To minimize inter-limb variability, only right-leg dominant individuals were recruited. Leg dominance was determined by the preferred kicking-leg test (≥2 confirmations across three trials); all participants were right-leg dominant. The final sample included fifteen healthy Chinese adults (9 males, 6 females; age: 22. years; weight: 65.52 kg; height: 170.3 cm), all of whom had no history of lower limb injuries or foot deformities in the past six months. The specific inclusion criterion was designed to reduce the confounding effects of age and sex, thereby improving the reproducibility and statistical validity of the experimental outcomes.

Participants were instructed to wear tight-fitting athletic pants and stand barefoot on the Footscan plate with both feet positioned together. Anthropometric data were then collected and recorded, including height, weight, foot length, shoe size, and arch type. The medial longitudinal arch height was assessed using the Arch Height Index (AHI), calculated as the ratio of dorsum height (measured at 50% of total foot length) to total foot length. AHI values between 0.310 and 0.356 were considered indicative of a normal arch structure¹⁷.

Before commencing the main trials, participants were first shown a demonstration of the required movement tasks. A 10-min slow jog was then performed by each participant as a standardized warm-up to ensure adequate physiological readiness for both static and dynamic recordings.

3. Sample size justification

A sensitivity analysis for a two-tailed paired t test (α=0.05, 1−β=0.80, n=15) indicated the study was powered to detect within-subject effects of approximately Cohen's dz ≈ 0.74 for the primary discrete outcome (FBI).

4. Plantar region segmentation and abbreviations

The plantar surface of the foot was divided into 10 anatomical regions by the Footscan software: hallux (T1), lesser toes considered as a single region (T2-T5), first to fifth metatarsals (M1-M5), medial heel (MH), lateral heel (LH), and midfoot (MF). For the Foot Balance Index (FBI), medial loading was defined as the summed normalized forces of M1, M2, and MF, whereas lateral loading comprised M3, M4, M5, and LH. This classification follows the default Footscan segmentation rules based on metatarsal heads, calcaneal split, and midfoot boundaries (30%-60% foot length). A full abbreviation key is provided in Figure 1A.

5. Static measurement

1. Posture and setup
   Participants stood barefoot on the flush-mounted pressure plate in a comfortable bipedal stance with feet approximately shoulder-width apart, knees extended but not locked, arms relaxed alongside the body, and gaze fixed on a wall mark at eye level ~3 m ahead. Feet were aligned with the plate's AP (anterior-posterior) axis; no visual targets were placed underfoot to avoid positional bias.
2. Stabilization and recording
   After a 3-5 s quiet-standing stabilization, a single static snapshot of the plantar pressure distribution was recorded. Two valid repetitions were collected per participant with ≥30 s seated rest between trials to minimize postural drift.
3. Validity and repetition criteria
   A static trial was considered valid if (1) full plantar contact of both feet was visible within the active area, (2) no obvious sway or step adjustments occurred during the stabilization window, and (3) there was no overlap or truncation of footprints at the plate margins. Trials not meeting these criteria were repeated.
4. Derived variables (static)
   From the static snapshot, we computed regional pressure/force across the ten anatomical regions (abbreviation key in Figure 1A) and descriptive indices used for protocol quality control (e.g., side-to-side total force ratio). Static data were used only to document baseline distribution and plate segmentation quality; all hypothesis testing was performed on dynamic COD trials.

6. Dynamic measurement

Participants performed barefoot 45° side-step COD trials. For the rightward-cut condition, the left (turning/plant) foot landed fully on the pressure plate. To ensure consistent foot placement and full plantar contact, an individual fixed start mark was set for each participant during familiarization so that the approach produced a right-then-left step sequence with the left turning step contacting the plate. Before formal testing, participants completed multiple familiarization runs to establish a natural, repeatable gait without visual targets on the plate. At least three valid trials were recorded per condition; trials with incomplete contact or visible targeting were discarded and repeated. For the leftward-cut condition, the start mark and step sequence were mirrored so that the right turning foot contacted the pressure plate under identical criteria.

The laboratory runway measured 10 m; the pressure plate was recessed flush and located 6 m from the start line, leaving ~6 m for approach and ~2 m for post-contact deceleration. Approach speed was monitored using two single-beam timing gates (beam height 80 cm) placed 2.0 m apart along the approach path, with the downstream gate aligned to the proximal edge of the pressure plate's active area, thereby measuring the final 2 m before foot contact. A trial was accepted as valid only when the measured approach speed was 3.5 m·s⁻¹ (±5%).

All trials were closely observed by trained researchers to ensure compliance with the instructed speed range and proper execution of the task. Kinetic data were automatically collected by the Footscan system and securely stored for subsequent analysis. Foot-strike pattern was not explicitly controlled; participants performed the task using their self-selected running styles.

7. Data processing

After the experiment, all collected data are manually processed using the Footscan software.

Each static and dynamic trial was carefully examined to identify any missing data or disrupted foot contact patterns to ensure data validity. The identification of left and right feet was determined based on dynamic plantar pressure images captured at foot contact, which provided a visual representation of peak pressure distribution and the location of the center of pressure during the stance phase.

The plantar surface was initially divided into ten anatomical regions using the system's automated segmentation function, which classified the foot into forefoot, midfoot, and rearfoot zones. Manual adjustments were then applied as necessary to ensure accurate regional delineation. Based on the final segmentation, pressure values from each region were extracted and used to generate regional pressure-time curves for further analysis.

Plantar pressure data were exported separately for both static and dynamic conditions. The exported parameters included foot angles, foot axes, foot balance, and zone-specific pressure values (Figure 1), which were extracted from the predefined foot regions.

NOTE: Despite strict adherence to the experimental protocol, some participants' data were incomplete due to the disappearance of marked trajectories within the Footscan software.

8. Plantar pressure and force

To examine plantar load distribution during stance, time-series plantar force and pressure data were extracted from ten plantar regions. Force/pressure-time curves were time-normalized to 101 points to ensure temporal comparability across trials. To minimize inter-subject variability (e.g., due to body mass), regional forces were normalized to the average vertical force over stance (Zavg), defined as,

  (1)

Where Fz (t) is the vertical ground-reaction force at frame t, and N is the number of stance frames. Normalized forces are reported as dimensionless (×1). In contrast, regional plantar pressures were analyzed in physical units (kPa) without normalization, as they are already expressed relative to contact area. This normalization scheme for forces follows previous studies¹⁸.

9. Foot balance

The foot balance index (FBI), shown in Equation 2, refers to the foot pronation or foot supination during the stance phase (Figure 1)¹⁹. Favg used for the calculation of the FBI is the average force of the entire plantar¹⁹. FBI was calculated as,

 (2)

where F_medial and F_lateral represent the summed plantar forces from medial (M1, M2, MH) and lateral (M3-M5, LH) regions, respectively. Positive FBI values indicate greater medial loading (pronation), whereas negative values indicate greater lateral loading (supination).

Note that MF was intentionally excluded from the FBI grouping (medial = M1, M2, MH; lateral = M3-M5, LH) to avoid arch-related variability and to focus on forefoot/heel balance .

10. Foot axis angle

The foot axis angle reflects the degree of internal and external rotation of the foot during gait. The angle is composed of a connected line between the midpoint of the heel and the midpoint of the gap between the second and third metatarsal. The line of direction of movement is defined as the foot axis angle¹⁹(Figure 1). The internal and external rotation of the foot in the horizontal plane was represented by negative and positive angles, respectively.

11. Statistical analysis

Discrete outcome measures (e.g., Foot Balance Index, plantar pressure in various foot regions, and foot axis angle) were analyzed using SPSS v19.0. For each of the 15 participants, data from the dominant and non-dominant foot were treated as paired samples. Therefore, paired t-tests were conducted for normally distributed data (with normality verified based on the distribution of the within-subject differences), while Wilcoxon signed-rank tests were applied for non-parametric comparisons. For each condition, trial-level curves were averaged within subject before between-limb comparisons; discrete metrics used the within-subject mean of valid trials.

For continuous plantar pressure time-series, values were Zavg-normalized (see Data Processing) and time-normalized to 101 points using linear interpolation. Subsequently, one-dimensional statistical parametric mapping (SPM1d) with paired t-tests was used to compare pressure curves across the full stance phase. Random field theory (RFT) was employed to control for field-wise error and correct for multiple comparisons across time²⁰. All time-series analyses were performed in MATLAB R2018a, and the significance threshold was set at 0.05.

To control for family-wise error across multiple discrete comparisons, p-values for discrete regional/metric outcomes were adjusted using the Holm-Bonferroni procedure (α = 0.05). Both unadjusted and adjusted significance interpretations are reported where relevant.

Results

Force and pressure time-varying changes in the ten regions

During change-of-direction movements, the dominant foot exhibited higher time-series force and pressure values than the non-dominant foot across most plantar regions (e.g., M1, M3, M4, MF, and SUM), particularly during the mid-to-late stance phase. However, statistical parametric mapping (SPM) analysis revealed that these differences did not reach the threshold for statistical significance (α = 0.05). For instance...

Discussion

This study explored bilateral differences in plantar pressure distribution and foot balance during a standardized 45° COD task. Overall, the time-continuous SPM1d analyses did not identify any statistically significant between-limb differences in regional force/pressure trajectories across stance. Although the dominant limb showed slightly higher magnitudes in some regions and the non-dominant limb showed the opposite tendency in others, these patterns should be interpreted as descriptive, non-significant tendencies...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
A Foustcan 2-m pressure plate	Tekscan Inc., USA	5101
Data acquisition software	Tekscan Inc., USA	www.tekscan.com/products/software
Infrared timing gates	Lafayette Instrument Company, USA	5016A
MATLAB	MathWorks, USA	www.mathworks.com/products/matlab
SPM1d toolbox	Open-source (Statistical Parametric Mapping)	www.spm1d.org