The Relationship Between Foot Anthropometrics, Lower-Extremity Kinematics, and Ground Reaction Force in Elite Female Basketball Players: An Exploratory Study Investigating Arch Height Index and Navicular Drop

Abstract

Static and dynamic foot function can be evaluated using easy-to-implement, low-cost measurements like arch height index (AHI) and navicular drop (ND). Connections between AHI/ND and lower-extremity kinematics/kinetics have largely focused on gait. Some studies exist evaluating basketball players; however, these predominantly focus on men. To our knowledge, few studies evaluate female athletes, and none have investigated connections between AHI/ND and lower-extremity biomechanics in elite female basketball players. Thus, we conducted an IRB-approved observational investigation of 10 female, National Collegiate Athletic Association (NCAA) Division 1 basketball players, evaluating connections between AHI/ND and lower-extremity biomechanics during basketball activities. Participants completed one visit wherein bilateral AHI/ND measurements and kinematics/kinetics were captured via optical motion capture and force-instrumented treadmill during basketball activities (walking, running, vertical/horizontal jumping, side shuffles, 45° cuts). No connections existed between the AHI and any variable during any task. Contrastingly, ND was statistically significantly correlated with medial/lateral force maximum and range during left cutting. This implies that individuals with stiffer feet produced more side-to-side force than those with more foot mobility during cutting. This is the first report connecting ND to lower-extremity biomechanics in elite, female basketball players. This could inform novel interventions and technologies to improve frontal kinematics/kinetics.

1. Introduction

Proper biomechanics is critical for successful athletic performance. In many athletic movements, multiple lower-extremity segments/joints (i.e., the pelvis, hips, knees, ankles) are utilized, with the feet frequently functioning as the initial contact point with practice/competition surfaces. As the lower extremity’s foundation, the foot’s complicated structure is partially responsible for successfully achieving effective biomechanics during many tasks [1]. This complexity manifests as an arched structure, partly controlling posture and torque [2].

Given the important role the foot plays during sports like basketball, evaluating the relationship between static/dynamic foot function and lower-extremity biomechanics is critical for athletic populations. One structure, the medial longitudinal arch, plays a functional role during both shock absorption and propulsion. Current static/dynamic functional measurements of this structure are somewhat limited. Radiographic techniques quantify high-resolution structure/function, but expose participants to radiation. In contrast, visual/physical measurements provide relatively accurate quantification of the medial longitudinal arch without radiation. Two low-cost and easy-to-implement measurements are arch height index (AHI) and navicular drop (ND). The AHI is a static, ratio measurement of foot height relative to foot length, comparing the distance from the floor to the foot dorsum at 50% foot length versus the distance from the calcaneus to the metatarsal heads. In contrast, ND is a dynamic measurement capturing distance change from the standing surface to the navicular tuberosity, considering an unweighted neutral posture versus a standing relaxed posture.

Prior work using AHI and ND has connected these measures to lower-extremity kinematics/kinetics, including center of pressure, force production, external ankle rotation, changes during fatigue, and connection to injury risk [3,4,5,6,7,8,9,10,11,12,13]. However, a significant limitation of these efforts is that they solely focused on runners during gait, a population training for a predominantly cyclic activity performed in the anterior–posterior direction. Far fewer investigations have assessed connections between AHI/ND and lower-extremity biomechanics in other populations (e.g., basketball players), whose sport-specific tasks involve more challenging non-linear motions (e.g., lateral cutting, side shuffles).

Specifically, little work exists evaluating connections between AHI/ND and lower-extremity biomechanics in basketball players during these challenging sport-specific motions. Some studies have undertaken this task in relation to basketball; however, they predominantly focused on male athletes [14,15,16,17]. A gap is notable given that biomechanics sex differences exist during challenging athletic tasks, like those performed in basketball [18,19,20,21,22]. Moreover, investigating female athletes is necessary given the well-established sex differences regarding injury risk (e.g., ACL rupture) during sports that require lateral motions [23,24,25]. To our knowledge, one study exists evaluating the AHI in female basketball players [26]. However, this study focused on amateur athletes, enrolling individuals who were mid-adolescence and still going through stages of puberty. This is significant given that those athletes were still experiencing musculoskeletal maturation, including bone density, body composition, and muscle/adipose tissue alterations [27,28]. Perhaps most critical for this investigation, puberty has also been found to have a significant influence on biomechanics during more challenging movements like cutting and jumping [29,30].

Thus, there is a gap in the literature on the connection between AHI/ND and lower-extremity biomechanics during sport-specific, challenging movements in elite, post-pubescent, female basketball players. As such, the primary objective of this study was exploring relationships between AHI/ND and lower-extremity biomechanics during sport-specific, challenging movements in female, National Collegiate Athletic Association (NCAA) Division 1 (D1) basketball players. In completing this study, we hoped to better understand (1) the connection between foot structure (i.e., AHI, ND) and function (i.e., lower-extremity biomechanics), as well as (2) the utility of two non-invasive, low-cost, radiation-free methods (i.e., AHI, ND) for predicting that function. Given ND is a measurement that occurs in the frontal plane, we hypothesized that a significant correlation would exist between ND and peak frontal plane ankle angle and peak medial–lateral GRF during lateral cutting motions.

2. Materials and Methods

2.1. Enrollment and Participants

Following institutional review board (IRB) approval, this observational cohort study included a convenience sample of NCAA D1 female basketball players from one institution (exclusion criteria: age < 18 or >25, not a D1 basketball player, musculoskeletal/neuromuscular disability impacting movement). An a priori power analysis was performed using previous investigations to establish sample size estimates from correlation coefficients across multiple kinematic variables (G*Power 3.1.9.7; Keil University, Kiel, Germany; p < 0.05, power > 0.80) [9]. The resultant sample sizes ranged from 4 to 11. Accordingly, 10 D1 female basketball players were enrolled from one institution (age = 20.7 ± 2.1 years, height = 183.0 ± 6.5 cm, mass = 76.2 ± 10.1 kg). After eligibility screening and once informed consent was given, participants visited our biomechanics laboratory for ~2 h.

2.2. Data Collection

2.2.1. Surveys

After informed consent was obtained, participants completed the Edinburgh Handedness and Waterloo Footedness surveys, which evaluate hand/foot dominance for correlations.

2.2.2. Anthropometrics

Participant demographics and anthropometrics were captured, including date of birth, height (Seca 213 Stadiometer; Seca GmbH & Co. KG., Hamburg, Germany), and weight (Seca 700 Mechanical Scale; Seca GmbH & Co. KG., Hamburg, Germany). Bilateral femoral (greater trochanter to lateral femoral epicondyle) and tibial (lateral tibial condyle superior surface to lateral malleolus) lengths were quantified via a cloth tape measure.

Bilateral foot length/width were recorded via an Xsens MVN Tape Measure (Xsens Technologies B.V., Enschede, The Netherlands). Foot length was measured from posterior calcaneus to the most anterior aspect of the longest phalanx and subsequently used in AHI and ND data processing (see below). Foot width was measured from the most lateral to the most medial portion of the fifth and first metatarsals, respectively. Finally, bilateral passive goniometric three-dimensional (3D) hip, two-dimensional (2D) knee (sagittal, frontal), and three-dimensional ankle range of motion (ROM) were collected by the biomechanics lab director. Herein, we report entire ROM (i.e., maximum to minimum ROM).

2.2.3. Foot Measurements

AHI

AHI is a static, non-weight-bearing foot ratio measurement calculated by comparing the distance between the floor and foot dorsum at 50% foot length to the distance between the heel and distal first metatarsal. Using a previously validated approach [31,32,33], bilateral medial images of each participant’s feet were captured via an 8th-generation iPad (Apple Inc., Cupertino, CA, USA) positioned ~10 cm above the floor and ~30 cm from the medial foot border (Figure 1A). Two images were captured: (1) seated, unweighted posture and (2) weight-bearing, relaxed standing. All photos were uploaded/processed through MATLAB (MATLAB R2021a; The MathWorks, Inc., Natick, MA, USA).

Unweighted images (.jpg) were uploaded using ‘imread’ and rotated, orienting plantar/dorsal foot surfaces “down” and “up”, respectively. Accounting for horizontal image alignment deviations, plantar heel/forefoot surfaces were manually selected/rotated to horizontal. Correcting for scaling, the distance between the posterior calcaneus and anterior great toe was selected (Figure 1B, green vertical lines), scaling pixels to physical foot length (see Anthropometrics). The same points were utilized to compute 50% foot length (Figure 1B, red vertical line). A horizontal line was created from where the calcaneus and distal forefoot contacted the ground (Figure 1B, horizontal green line). Finally, the most superior foot dorsum at 50% foot length was selected (Figure 1B, red vertical line). The AHI was calculated as the ratio of the vertical distance between this point and the horizontal floor line compared to the distance from the most posterior calcaneus aspect to the distal first metatarsal [34]. One investigator (biomechanics laboratory director) completed AHI measurements on the same image three times. The average of these values was utilized as each participant’s AHI.

ND

ND was calculated as described by several previous studies [35,36,37]. Specifically, ND was computed as the difference in the distance between the floor and the navicular bone comparing seated non-weight-bearing and standing weight-bearing images. Prior to image capture, the navicular tuberosity was manually palpated and marked in black ink. Following image upload/preprocessing (see AHI), the black ink mark was identified digitally and the distance between the mark and the floor was quantified in both images. ND was the difference between those two distances (Figure 1B, fuchsia vertical line). The same single investigator completed ND measurements on the same image three times. The average of these values was utilized as each participant’s ND.

2.2.4. Biomechanics Instrumentation

Participants were instrumented with optical motion capture (MOCAP) markers and completed all activities in self-selected game performance shoes at a self-selected pace on bilateral force plates.

MOCAP

Optical MOCAP calibration (8 M3 Miqus Cameras, Qualisys AB, Goteborg, Sweden) was completed per the manufacturer’s specifications via a standard ‘L’ bracket alignment grid and a 300 mm wand for 60 s (f_s = 100 Hz). A previously validated lower-extremity retroreflective marker set was utilized [38,39,40]. Specifically, markers were placed bilaterally on the 1st/5th metatarsal heads, posterior calcanei, and lateral/medial malleoli, with four-marker clusters on the lateral shanks/thighs, lateral/medial femoral epicondyles, and anterior/posterior superior iliac spines (ASISs/PSISs) (Figure 2). All MOCAP marker data were captured and stored in Qualisys Track Manager (QTM) at 100 Hz [39].

Force Plates

Three-dimensional ground reaction forces (GRFs) were captured using bilateral instrumented treadmill force plates (Bertec Corporation, Columbus, OH, USA) throughout all participants’ movements (f_s = 1000 Hz). Prior to each activity, force plates were zeroed under a no-force condition.

Data Capture Metrics

In QTM, 3D MOCAP position data and GRFs were temporally synchronized and recorded during all activities. Following data processing, QTM files were converted to .c3d and exported to Visual3D (V3D, C-Motion, Inc., Gaithersburg, MD, USA). V3D was used for final kinematic/kinetic analyses. In V3D, the outcome measures were 3D pelvis, bilateral hip/knee/ankle kinematics, and bilateral 3D GRFs during each activity.

2.2.5. Activities

Static and Dynamic Calibrations

Static and dynamic calibrations were captured [41], creating base skeletal models and bony segment coordinate systems defined using modified versions of the International Society of Biomechanics definitions for the pelvis, femora, tibiae, and feet [42,43]. Static calibration was captured while participants stood stationary on the treadmill with arms across their chest (~10 s). Dynamic calibration (5 repetitions; bilateral squats, single leg raises to 90°/90°, hip circumduction) was captured to assist QTM in automatically identifying markers (AIM).

Activity Trials

Participants then completed activities with MOCAP/GRF data simultaneously being recorded. Thirty-second self-selected-speed walking and running trials [44,45] were completed, respectively. Next, three repetitions were completed of vertical jump [46,47], horizontal standing broad jump [48,49], left/right side shuffle [50,51], and left/right 45-degree cuts [52,53] (Figure 3). All trials were processed in their entirety.

Participants performed countermovement vertical jumps [46,47], beginning from a neutral posture with one foot on each force plate, descending into a semi-squat, accelerating upward to achieve maximal vertical height, and landing on the same respective force plates (Figure 3A). Horizontal jump [48,49] was performed similarly, beginning in neutral posture with feet on force plates and performing a countermovement broad jump to maximize horizontal distance, landing off of the force plates (Figure 3B). Side shuffles [50,51] were performed in both directions at a self-selected speed with ~6 shuffle steps (two on force plates) (Figure 3C). Then, 45-degree lateral cuts [52,53] were performed at a self-selected speed in both directions. Participants began with two anterior running steps, a penultimate preparatory step recorded on force plates, an ultimate cutting step recorded on the contralateral force plate, and two termination steps (Figure 3D).

2.3. Data Processing

2.3.1. QTM Tracking

Reflective MOCAP markers were manually labeled in QTM to appropriate anatomical landmarks during static calibration. The static calibration file was used to create an AIM model within QTM, enabling automatic marker identification/labeling in subsequent trials. Dynamic calibration was labeled via the AIM model. Any remaining unlabeled markers were labeled manually/tracked for the entire trial. Tracked dynamic calibration was then added to the AIM model. This finalized AIM model was applied to all activity files to automatically identify and track all markers. Any remaining unlabeled markers were labeled/tracked manually. Following the completion of labeling/tracking, static and activity QTM files were converted to .c3d and exported to V3D.

2.3.2. V3D MOCAP Marker Processing

Static calibration .c3d files created the base skeleton for each participant in V3D. The pelvis was defined using the CODA model via bilateral ASIS/PSIS markers, creating a segment origin at the ASIS marker midpoint and an X-Y plane passing through ASIS/PSIS markers (sagittal axis: +X = from origin to right ASIS, anterior tilt = negative; transverse axis: +Z = from origin rostrally perpendicular to X-Y plane, right turn = negative; frontal axis: +Y = X × Z from origin anterior, left tilt = negative). Given that optical MOCAP markers could not be placed on the hip-joint rotation centers, right and left hip-joint centers (RHJC, LHJC, respectively) were computed as follows [54,55]:

$$\mathbf{R}\mathbf{H}\mathbf{J}\mathbf{C}\left(\mathbf{x},\mathbf{y},\mathbf{z}\right)=\left(\mathbf{0.36}·\mathbf{A}\mathbf{S}\mathbf{I}\mathbf{S} \mathbf{D}\mathbf{i}\mathbf{s}\mathbf{t}\mathbf{a}\mathbf{n}\mathbf{c}\mathbf{e},-\mathbf{0.19}·\mathbf{A}\mathbf{S}\mathbf{I}\mathbf{S} \mathbf{D}\mathbf{i}\mathbf{s}\mathbf{t}\mathbf{a}\mathbf{n}\mathbf{c}\mathbf{e},-\mathbf{0.3}·\mathbf{A}\mathbf{S}\mathbf{I}\mathbf{S} \mathbf{D}\mathbf{i}\mathbf{s}\mathbf{t}\mathbf{a}\mathbf{n}\mathbf{c}\mathbf{e}\right)$$

(1)

$$\mathbf{L}\mathbf{H}\mathbf{J}\mathbf{C}\left(\mathbf{x},\mathbf{y},\mathbf{z}\right)=\left(-\mathbf{0.36}·\mathbf{A}\mathbf{S}\mathbf{I}\mathbf{S} \mathbf{D}\mathbf{i}\mathbf{s}\mathbf{t}\mathbf{a}\mathbf{n}\mathbf{c}\mathbf{e},-\mathbf{0.19}·\mathbf{A}\mathbf{S}\mathbf{I}\mathbf{S} \mathbf{D}\mathbf{i}\mathbf{s}\mathbf{t}\mathbf{a}\mathbf{n}\mathbf{c}\mathbf{e},-\mathbf{0.3}·\mathbf{A}\mathbf{S}\mathbf{I}\mathbf{S} \mathbf{D}\mathbf{i}\mathbf{s}\mathbf{t}\mathbf{a}\mathbf{n}\mathbf{c}\mathbf{e}\right)$$

(2)

Bilateral femoral segments were defined from LHJC/RHJC proximally to the lateral/medial femoral epicondyle markers distally (knee joint). Femoral tracking during the activity trials occurred via thigh marker clusters with +X axes pointing to the right between the medial/lateral femoral epicondyle markers (sagittal axis: extension = negative), and +Z axes pointing from the femoral epicondyle midpoint to the RHJC/LHJC (transverse axes: external rotation = negative). +Y axes were the cross product of the +X and +Z axes pointing approximately anteriorly (frontal axes: abduction = negative).

Bilateral tibial segments were defined from the lateral/medial femoral epicondyle markers proximally (knee joint) to the lateral/medial malleolar markers distally (ankle joint). Tibial tracking occurred in the activity trials via shank marker clusters. +X axes were created between the medial/lateral malleolar markers pointing to the right (sagittal axes: extension = negative). +Z axes were defined from the malleolar marker midpoint pointing proximally toward the femoral epicondylar marker midpoint (transverse axes: external rotation = negative). Lastly, +Y axes were the cross product of the positive X and Z axes (frontal axes: valgus = negative).

Bilateral feet segments were defined proximally at malleolar markers and tracked via the posterior heel and 1st/5th metatarsal head markers rotating relative to the shin segments. Sagittal plane ankle rotation was defined as foot marker rotation about the shin +X axes (plantarflexion = negative). Frontal plane ankle rotation was defined as foot marker rotation about the shin +Y axes (eversion = negative). Transverse plane ankle rotation was defined as the foot markers rotating about the shin +Z axes (external rotation = negative).

Base static skeletal models were applied to all activity .c3d files. To remove high-frequency noise, 3D MOCAP marker positions were low-pass filtered forward/backward (4th-order Butterworth; f_cutoff = 6 Hz). Interpolation was performed on all marker gaps ≤ 10 frames (3rd-order polynomial least squares function).

2.3.3. V3D GRF Processing

Three-dimensional GRFs were low-pass filtered forward/backward (4th-order Butterworth; f_cutoff = 25 Hz) for all activities. One set of GRF data for each activity was processed without normalizing to body weight, and a second set of GRF data for each activity was subsequently normalized to body weight. During walking/running, vertical GRF data subdivided strides by computing each stride’s heel strike, defined when vertical GRF began below and subsequently exceeded 20 N. All kinematics/kinetics were computed from heel strike to subsequent heel strike and averaged across strides.

2.3.4. V3D Output Variables

Three-dimensional kinematics were computed using the supplied pipeline algorithms in V3D, rotating the distal segments relative to the proximal segments (e.g., femur about pelvis) using Cardan sequencing. Vertical jump height was calculated during vertical jump trials, subtracting the average starting PSIS marker position during quiescent neutral standing from the maximal PSIS marker height. Horizontal jump distance was calculated during horizontal jump trials, subtracting the initial posterior heel marker position from the landing posterior heel marker position.

The outcome variables, including peak vertical jump height, maximal horizontal jump distance, 3D segment/joint kinematics (pelvis, hip, knee, ankle), 3D GRFs without normalization to body weight (anterior/posterior, medial/lateral, inferior/superior), and 3D GRFs with normalization to body weight, were exported to .txt files and converted to .xlsx (Microsoft Corp., Redmond, WA, USA). In Excel, maximum and minimum kinematic/kinetic variables were computed for each activity. For walking/running, the absolute most positive (e.g., hip flexion) and negative (e.g., hip extension) values during each stride were computed and subsequently averaged across strides. For jumps, the absolute most positive and negative values for each variable were computed during each trial’s takeoff period. For shuffles and lateral cuts, the absolute most positive and negative values for each variable were calculated during force plate contact. This corresponded to force generation during the middle two shuffle strides during shuffles and the ultimate cutting step during cutting. Subsequently, the range for all variables was computed during each trial (maximum minus minimum). All maximum, minimum, and range values for each trial were averaged across trials for each activity and participant. The final outcome metrics included participants’ maximum, minimum, and range averages across activity trials for 3D kinematics and GRFs, as well as average maximum vertical jump height and average maximal horizontal jump distance.

2.3.5. Statistical Analysis

Statistical analyses were performed in SPSS. Data normality was evaluated for each variable via the Shapiro–Wilk test with α = 0.05. Statistical outliers (>3 standard deviations away from the mean) were removed before conducting further statistics. Following outlier removal, paired t-tests were used to compare the left and right values for tibial length, femoral length, foot length, foot width, AHI, ND, and goniometric ROM measures with a Bonferroni correction (ɑ = 0.05/14 ≈ 0.0036). Pearson correlations were then completed between the two independent variables (AHI, ND) and all dependent variables (3D kinematics/GRF maximum, minimum, and range; maximum vertical height; maximum horizontal distance). During walking, running, and vertical/horizontal jump trials, correlations were completed on respective, ipsilateral sides (i.e., left AHI/ND versus left outcome metrics, right AHI/ND versus right outcome metrics). For cuts, correlations were computed comparing the ultimate step AHI/ND to all outcome variables bilaterally (i.e., left foot for right cuts, right foot for left cuts). For shuffles, correlations were computed bilaterally on bilateral sides (i.e., left/right AHI/ND versus left/right outcome metrics). Statistical significance was set at ɑ = 0.05. However, a Bonferroni correction was completed for each activity, accounting for correlation quantity (ɑ = 0.05/30 ≈ 0.0017 for walking, running; ɑ = 0.05/48 ≈ 0.00104 for cuts; ɑ = 0.05/96 ≈ 0.00052 for shuffles; ɑ = 0.05/100 ≈ 0.0005 for horizontal jumps; ɑ = 0.05/38 ≈ 0.0013 for vertical jumps).

3. Results

3.1. Participant Characteristics

The participant’s characteristics (

Table 1. Participant characteristics, including age, height, mass, years of experience playing competitive basketball, foot dominance, and hand dominance.

Age (years)	20.7 ± 2.1
Height (cm)	183.0 ± 6.5
Mass (kg)	76.1 ± 10.0
Experience (years)	10.9 ± 4.5
Footedness	0.68 ± 0.29
Handedness	0.76 ± 0.25

, mean ± standard deviation) represented a typical breadth of age, class year, and position. This cohort included two true freshman, sophomores, red-shirt sophomores (third years), and graduate students (sixth years), as well as one true junior and one graduate student (fifth year). Competitive basketball experience ranged from 6 to 19 years. Five participants were guards, three were forwards, and two were centers. Most participants wore low-top shoes (n = 8), with two donning mid-tops. All participants were right-hand/foot-dominant, as determined by the Edinburgh Handedness and Waterloo Footedness surveys. Participant anthropometrics are shown in

Table 2. Participant anthropometrics, including mean values for bilateral femoral length (cm), tibial length (cm), foot length (cm), foot width (cm), AHI, and ND (cm).

Variable	Left	Right	t-Test Results
Femur Length (cm)	46.8 ± 2.7	46.9 ± 2.7	0.66
Tibia Length (cm)	45.4 ± 3.5	45.7 ± 3.5	0.05
Foot Length (cm)	26.8 ± 2.0	26.8 ± 1.8	0.74
Foot Width (cm)	10.1 ± 0.6	10.1 ± 0.6	0.68
Arch Height Index	0.34 ± 0.03	0.34 ± 0.02	0.60
Navicular Drop (cm)	0.35 ± 0.31	0.61 ± 0.50	0.20

. Bilateral femoral length, tibial length, foot length, and foot width were normally distributed (p > 0.12 for all variables). Paired t-tests showed no statistically significant asymmetries between left and right for all anthropometric variables.

Goniometric ROM (

Table 3. Bilateral goniometric ROM in 3D for the hip and ankle joints and 2D for the knee joint. Statistically significant results shown in bold, italicized font.

Variable	Left (°)	Right (°)	Shapiro–Wilk L/R	t-Test Results
Hip Sagittal	135.8 ± 17.2	133.1 ± 17.2	0.18/0.33	0.50
Hip Frontal	54.7 ± 7.1	60.9 ± 9.0	0.85/0.44	0.006
Hip Transverse	74.2 ± 11.3	69.0 ± 9.0	0.84/0.44	0.006
Knee Sagittal	136.6 ± 10.0	132.8 ± 10.5	0.56/0.04	0.21
Knee Frontal	0.9 ± 1.0	0.8 ± 1.0	0.03/0.01	0.80
Ankle Sagittal	52.1 ± 9.3	54.6 ± 12.8	0.53/0.95	0.42
Ankle Frontal	45.1 ± 11.3	38.3 ± 7.9	0.79/0.65	0.09
Ankle Transverse	36.1 ± 10.4	37.0 ± 9.2	0.85/0.02	0.78

) was normally distributed bilaterally for hip sagittal/frontal/transverse and ankle sagittal/frontal measures (p > 0.17 for all). Data were also normally distributed for left-knee sagittal and left-ankle transverse measures (p > 0.55). Original measures for right-knee sagittal, left/right-knee frontal, and right-ankle transverse were not normally distributed (p < 0.05 for all measures). Following the removal of statistical outliers, t-tests yielded no statistically significant asymmetries for any goniometric ROM measurement.

3.2. AHI and ND

AHI and ND (Table 2) were normally distributed. No statistically significantly asymmetries were noted for either metric (AHI p = 0.60, ND p = 0.20).

3.3. Correlations Between AHI, ND, and Biomechanics Variables

3.3.1. AHI

No statistically significant correlations were found comparing the AHI to the maximum, minimum, or range for all outcome variables of any activity.

3.3.2. ND

No statistically significant correlations were found comparing ND to the maximum, minimum, or range for all outcome variables of walking, running, right cutting, left/right shuffles, horizontal jump, or vertical jump. In contrast, two statistically significant correlations were found during left-cutting movements. Specifically, a statistically significant correlation was noted between ND and maximum medial–lateral force generation (Figure 4A, r = −0.91, p = 0.0003). For every 1 cm increase in ND (1 cm increase in medial arch drop), maximum medial–lateral force generation decreased by ~260 N during left cuts. However, when normalizing forces to body weight, this correlation only trended toward significance (Figure 4C, r = −0.7, p = 0.02). Despite lacking significance, the relationship was still considered a strong negative correlation, with 1 cm increases in ND resulting in ~1/3 less medial–lateral force generation.

A statistically significant correlation was also found during left cuts between ND and medial–lateral force production range (Figure 4B, r = −0.93, p = 0.0001). With 1 cm ND increases, ~312 N decreases in medial–lateral force production range were found. When normalizing to body weight, this correlation was still strong; however, it only trended toward statistical significance (Figure 4D, r = −0.77, p = 0.009). Despite lacking statistical significance, for every 1 cm increase in ND a reduction of 0.42 BW in medial–lateral force production range was noted. No other significant correlations were noted between ND and any other maximum, minimum, or range of kinematic or kinetic variables during any activity.

4. Discussion

4.1. Summary

The feet and associated arches are critical to proper biomechanics, including postural control and torque generation [1]. However, few studies exist evaluating static/dynamic foot function in relation to sport-specific biomechanics. To our knowledge, no studies exist assessing these metrics in elite, musculoskeletally mature female basketball players during challenging basketball movements. This is particularly important due to the well-known discrepancies between men and women during sports with significant lateral motions like basketball [23,24,25]. Accordingly, we evaluated static foot anthropometrics via the AHI and dynamic foot function via ND to assess any connections to biomechanics in ten D1 female basketball players during basketball-specific activities.

Specifically, we computed AHI and ND using a previously validated method, discovering both measures were normally distributed with no significant left versus right asymmetries. Moreover, both AHI and ND were equivalent to well-established norms in similar populations (AHI: 0.362 ± 0.046 vs. 0.34 ± 0.02; ND: 4.8 mm vs. 4.25 mm) [5,35,56]. Accordingly, although our participants were likely outliers for other variables like height (~22 cm > global averages) [57], these individuals were within normal AHI/ND ranges.

Interestingly, prior work is conflicted in terms of connecting AHI and ND to biomechanics variables in a number of different activities. With respect to ambulatory tasks like walking and running, some investigators have found significant connections between AHI/ND and lower-extremity kinematics/kinetics, while others have failed to do so [3,58,59]. Herein, we found no such connection during walking or running in this population.

Outside of investigations assessing AHI and ND during ambulatory tasks, few studies exist assessing AHI/ND in relation to biomechanics variables during sport-specific activities, like basketball. Even fewer studies have included female basketball players. One investigation by Greene et al. did compare male and female high school basketball players, noting anthropometric and performance differences [26]. However, they enrolled individuals who were still undergoing puberty, which is known to greatly influence a number of biomechanics factors [27,28]. While Gazbare et al. did investigate these connections, they only did so in recreational basketball players, combining female and male athletes together [60]. This is a significant limitation given there are likely kinematic/kinetic differences between amateur athletes and expert athletes. Accordingly, our analysis represents a novel evaluation of musculoskeletally mature, elite female basketball players and the impact AHI and ND have on biomechanics during challenging basketball movements.

In contrast to several previous studies in different populations [58,61], we did not find any connection between AHI and any kinematic variable during any activity. We did, however, discover two connections between ND and biomechanics, but only during left cutting (i.e., using the right foot to cut to the left). Specifically, we discovered that as ND increased (navicular bone dropped to a greater extent), maximum medial–lateral force and medial–lateral force range significantly reduced (by 258 N and 312 N, respectively). This finding confirms part of our initial hypothesis (greater ND would result in lower M-L force production) and implies that individuals with reduced frontal plane foot mobility (i.e., stiffer foot and lower ND) may have a more stable platform upon which they can push laterally into the ground during ‘side-to-side’ motions. This may have significant implications for injury risk across the foot mobility/force production spectrum. For example, prior work by Eslami et al. hypothesized that increased ND values (more mobile feet) could be connected with increased frontal plane ankle/knee torque [3], and thus increase the potential for injury to structures responsible for the medial–lateral stability of the knee/ankle. In contrast, individuals with lower ND values (stiffer feet) produced larger medial–lateral GRFs, which may increase the risk of injury of the foot if those forces exceed the mechanical capabilities of the hard/soft tissue therein. No other significant findings were noted with respect to ND, including all kinematic variables. These findings reject the other part of our initial hypothesis (greater ND would result in greater ankle inversion/eversion).

Interestingly, all participants were right-foot-dominant. This may imply cutting to the left is their “preferred” cut direction. Accordingly, these results might have particular importance for the directionality of motion during basketball. Critically, these results are well aligned with other research groups that have found a similar connection to foot stiffness and medial–lateral force production during several tasks [62,63]. Despite this capability in individuals with a stiffer arch herein, the implications on overall athletic performance and/or injury propensity remain unknown.

4.2. Limitations

While the statistically significant findings herein are critical, this study was not without potential flaws. First, we only evaluated a convenience sample of one D1 women’s basketball team (n = 10). Enrolling additional participants from other institutions/geographies would be preferable; however, we were inherently limited by using a convenience sample based on proximity. And while our sample does represent a typical presentation of this population for age, experience level, and position, extrapolation may not be advisable. Additionally, previous work has shown that ND is impacted by foot length [37]. While we saw no connections between foot length and ND herein, there are other possible correlations we did not explore. Future studies should investigate these connections.

There are additional limitations we must acknowledge surrounding our methods. First, our foot measurements were limited to AHI/ND. Other measurements (e.g., dorsal arch height difference) may better evaluate connections between foot function and lower-extremity biomechanics in basketball players [32]. Second, we did not control the pace that participants completed each activity. Rather, participants self-selected how rapidly they completed all tasks, facilitating the normalization of all tasks to self-selected speeds. Additionally, participants chose their own competition footwear to complete tasks. These shoes differed in ankle collar height, midsole material stiffness, upper material composition, etc. While this likely added variability to our results and may have influenced kinematics/kinetics, it also likely strengthens the findings herein given significance was found even with multiple footwear choices. One statistical limitation includes the potential for being overly conservative using Bonferroni corrections. Specifically, due to the quantity of comparisons herein, we may have statistically overcorrected and missed real effects as a result. Future studies should investigate other statistical methods for assessing these biomechanics phenomena. Lastly, a limitation imposed by the optical MOCAP system and biomechanics laboratory space available was our data capture frequency. Many previous studies have used 120–250 Hz to capture optical MOCAP data. Herein, we were capable of achieving 100 Hz capture speed, which may have limited the result’s fidelity.

5. Conclusions

These findings may have implications for several facets of sport-specific training/performance. For example, coaches, strength/conditioning staff, and athletic trainers can better pre-evaluate athlete foot stiffness to prepare appropriate training and injury prevention plans. This information could also be utilized to inform the development of novel interventions to improve dynamic foot function, like low-dye taping [64] or foot orthoses [16,17], which have shown promise in altering biomechanics and athletic performance.