Data Collection Procedures

The following assessments were carried out on each participant in order to assess clinical static foot posture, and also to assess lower limb kinetics and kinematics;

4.3.2.1 The Foot Posture Index

Individual participant’s foot posture was assessed using the Foot Posture Index (FPI), a 6 criteria foot posture assessment (Lee et al., 2015). The subject stood in a relaxed bipedal position. The six criterion of the FPI include the following assessment items, which were carried out on both limbs of each subject within the study: talar head palpation curves above and below the lateral malleoli, calcaneal angle, talonavicular bulge, medial longitudinal arch, and forefoot to rearfoot alignment. Each item was scored on a 5-point scale (between -2 and +2), providing a sum of all items between -12 (highly supinated) and +12 (highly pronated), with a score of 2 to 12 indicating a pronated foot, a score of -2 to -12 indicating a supinated

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foot, and a score of +1 to -1 indicating a neutral foot (Redmond et al., 2006, Levinger et al., 2010, Barton et al., 2012, Lee et al., 2015).

The total FPI score, and also the rearfoot classification of the FPI score were then used later in the data analysis within this study to determine if the foot and the rearfoot of individual participants were everted, neutral, or inverted. The FPI scores were used to provide inferences to whether a relationship existed between static rearfoot posture, dynamic rearfoot posture and the magnitude of the EKAM in healthy subjects, and also to establish whether the total FPI score represents rearfoot motion, and if the rearfoot FPI classification can represent the rearfoot motion and the magnitude of the EKAM.

A similar study previously conducted by Buldt et al., (2015) assessed the total FPI score amongst other measures to statically measure the whole foot, and concluded that the FPI displayed the strongest association with kinematic variables compared with the other foot measurements. Therefore, this study provides novelty by specifically assessing both static rearfoot posture, and rearfoot motion.

The FPI was assessed by the same examiner, who was experienced at taking these measurements.

4.3.2.2 Gait analysis

Qualisys motion analysis systems with sixteen computerised infra-red OQUS cameras (Qualisys, AB, Gothenburg, Sweden) and two AMTI force platforms (AMTI BP400X600, AMTI, USA) were used to collect kinematic and kinetic data as per Chapter 3 (section 3.2). Marker data were captured by sixteen OQUS infrared cameras (Qualysis, Sweden) and Qualysis Track manager (Qualysis, Sweden), in order to capture the 3D positions of the retro reflective markers that were attached to each subject’s skin, over bony landmarks in both lower limbs.

Individual retroreflective markers were placed on the lower limbs as described within chapter 3 (section 3.2.6), on the foot (1st, 2nd, and 5th metatarsal head and calcaneal tubercle, styloid and navicular), ankle (medial and lateral malleolus), knee (lateral and medial femoral condyle, tibial tuberosity and fibular head), thigh (greater trochanter), and the pelvis (right and left anterior superior iliac spine, right and left posterior and superior iliac spine, and right and left iliac crest). Fixed cluster pads, each holding four retroreflective markers were attached to the

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shank, thigh, pelvis, and the forefoot. Rearfoot data was captured using a heel cup cluster tracking marker set with three retroreflective markers attached to it.

The methodology within this chapter utilised biomechanical data collection procedures in order to define the 3D motion data capture, force measurement and segment modelling and computation which are explained in detail within the methodology chapter of this thesis (chapter 3, section 3.2). Any deviation or additional materials or techniques used within this study are detailed below. Individual participants were requested to stand with both feet on a force platform for 10 seconds whilst a static 3-dimensional image was obtained. A successful trial was one when the foot was placed completely on the force platform during stance phase. Each individual completed five walking trials for each foot at a self-selected speed.

4.3.3 Data Processing

All collected joint kinetic and kinematic data for the 30 lower limbs (both left and right lower limbs of all 15 participants) were processed using Qualisys Track Manager software. Individual reflective markers were labelled and digitised, and any anomalies in movements in marker trajectories were corrected. The data were then exported directly from Qualisys Track Manager software to Visual3D software (version 4.91, C-Motion Inc, USA). The raw marker tracking data were filtered using a Butterworth 4th order bi-directional low pass filter with a cut-off frequency of 6Hz. The analogue data were filtered with a cut-off frequency of 25Hz. Dynamic skeletal graphics created in Visual3D, controlled by subject kinematics were used in the interpretation of results (Buczek et al., 2010). Kinematic and force plate data were filtered to prevent noise interference in the results, and gaps and breaks in the data were filled using polynomial interpolation algorithms within Visual3D software to prevent error within the results.

The lower limbs were treated as seven segments modelled as rigid bodies, which were; the pelvis, left and right thighs, shanks and feet. A right-handed local coordinate system of each segment was defined by landmarks placed on the anatomical points. The CODA pelvis model was used, which was defined using the anatomical locations of the ASIS (Anterior Superior Iliac Spine) and the PSIS (Posterior Superior Iliac Spine). The motion was tracked by four reflective markers on a rigid plastic plate fixed with an elastic belt to the back of the pelvis. Each segment was treated as a free rigid body with six degrees of freedom. The joint kinematics were calculated using an X–Y–Z Cardan sequence. The external joint moment data were calculated using three-dimensional inverse dynamics and normalised to body mass (Nm/kg).

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Automatic gait event definition was utilised in all trials, which captured data when the vertical GRF exceeded 20 Newtons (N) in value. The gait cycle was defined as the movement and events from heel strike of the foot on the force platform, to the subsequent heel strike of the same foot. Stance phase was defined as heel strike of the foot to the subsequent toe-off of the same foot.

The measurement of both the motion of the lower limbs and the forces acting upon them whilst each subject walked was conducted in order to collect kinematic and kinetic data of the lower limbs. Direct measurement of loading on the medial compartment of the knee is difficult, therefore the EKAM provides an indirect measure of the knee joint loading (Wang et al., 1990, Maly et al., 2002). Resulting alterations in the EKAM signify changes to the load distribution across the knee joint (Maly et al., 2002). The EKAM was extracted during the first peak of early stance phase (0-20% of the gait cycle, 0-33% of the stance phase), which has been shown to be the most directly related to medial compartment loading (Sharma et al., 1998, Baliunas et al., 2002, Miyazaki et al., 2002, Birmingham et al., 2007, Henriksen et al., 2010, Erhart et al., 2011).

The positions of reflective markers are translated into the pose (position and orientation) of the corresponding model (a collection of rigid segments, with each segment corresponding to a body segment and major bone structure), identified using motion tracking equipment within V3D. The body segments which are tracked are defined by proximal and distal endpoints located inside the subject’s body (Visual 3D, 2015). The model is referred to as a six degree of freedom (DOF) model due to having six variables that describe its position and orientation in 3-D space (3 variables describe segment translation in three orthogonal axes, and 3 variables describe the rotation about each axis). The anthropometric measurements of individual subjects (height and body mass) were entered into the software for usage in kinetic calculations. Pelvis, thigh, leg and foot segments were then modelled using anatomical landmarks or joint centres and the radius of the proximal and distal end of the segment and the tracking markers (Buczek et al., 2010). The Visual3D model segments and tracking markers are detailed in chapter three (table 3.2).

Cluster tracking markers (with four retroreflective markers fixed to each rigid cluster pad) between the shank segment and the foot segment were used to define the rearfoot. Peak EKAM and peak flexion/extension moment (sagittal moment) data were exported from Visual3D to Microsoft Excel software, with the peak representing early stance phase of the gait cycle.

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Additionally, range of motion (ROM) of the rearfoot (inversion and eversion) data in stance phase were exported for all trial participants, and analysed using SPSS software.

The mean and standard deviation (SD) of each variable that was exported was calculated using Microsoft Excel software. Data was then transferred to SPSS software, to apply normality testing and the correlation coefficient. Normality testing allows the identification of normal or abnormal distribution (parametric or non-parametric) of data. For parametric data, the Pearson test was applied, and for non-parametric data, the Spearman correlation coefficient test was applied. Statistical analysis, specifically normality testing was performed on the variables in order to identify the most suitable correlation coefficient test to apply. The Kolmogorov- Smirnov test and the Shapiro-Wilks test were utilised (section 4.5.3.1).

4.3.3.1 Statistical analysis

Statistical analysis was performed with SPSS (Statistical Package for the Social Sciences) software programs (IBM SPSS Statistics, IBM Corporation), specifically normality testing was performed on each variable in order to identify the most suitable correlation coefficient test to apply. The Kolmogorov-Smirnov test (ideal for use on sample sizes of 50 or more subjects or the Shapiro-Wilks test (ideal for use on sample sizes of less than 50 subjects) were utilised. A p<0.05, indicates non-normal distribution. For the 15 healthy subjects, the Shapiro-Wilks test was used.

The normality test allows the researcher to identify whether these data in each of the variables to be tested was different than that of normal distribution (parametric) or abnormally distributed (non-parametric). For parametric data, the Pearson correlation coefficient test is most suited and was applied, and for non-parametric data, the Spearman correlation coefficient test is most suitable, and was applied.

The parametric testing was applied when three assumptions were met, including; normalcy of data or normal distribution, independence of data (one group did not influence another) and homogeneity of data (where variances in each group are similar).

Within table 4.4 correlations will be noted with a ρor anr value, with ρ indicating data to be non-parametric, and r indicating data to be parametric. The ‘perfect’ correlation coefficient is always -1 or +1, +1 indicates perfect positive correlation, and -1 indicates perfect negative correlation. A perfect relationship would depict all points on a scatterplot to fall on a straight line. Correlation can be considered ‘strong’ when falling between -0.7 and -0.9 or +0.7 and

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+0.9, moderate when between -0.4 and -0.6 or +0.4 and +0.6, weak when between -0.1 and - 0.3 or +0.1 and +0.3, and no correlation or association is present when the correlation coefficient is 0 (Dancey and Reidy, 2011). The level of significance of the results data was p<0.05, indicating significant association between the variables. If p>0.05, non-significant association was present between the variables.