Methods (these are modified from Jarvis’ PhD Thesis, 2013)

One hundred healthy subjects (71 female, 29 male) were originally recruited by a former PhD student at the University of Salford after ethical approval had been gained from the research and ethics panel at the University of Salford (ethical approval code – RGE C08/090) (Jarvis PhD thesis, 2013). Participants (demographics are depicted in table 4.5) were asymptomatic, and were aged between 18-45 years. This was to ensure physiological and skeletal maturity had been reached and to decrease the incidence of health conditions (including osteoarthritis) associated with individuals over the age of 45, which can lead to structural changes in the lower limbs and foot; such as reduced range of motion at the subtalar and ankle joint (Nigg et al., 1992, Jarvis PhD thesis, 2013). Therefore, data obtained by Jarvis can be considered as collected solely from healthy participants.

Screening of participants and data collection including the Foot Posture Index (FPI) was undertaken in the podiatry clinic at the University of Salford by a single experienced podiatrist; ensuring consistent data collection. Symptomatic participants not meeting the inclusion criteria were excluded from the study, and no further data was collected from them. These screening data were recorded on a Microsoft Excel spreadsheet and stored anonymously.

Gait instrumented analysis of the foot and leg was conducted in the gait laboratory at the University of Salford, where 3D foot and leg kinematic data was obtained using a 12 infra-red camera OQUS system (Qualisys system, Qualisys, Gothenburg, Sweden) with retro reflective markers. Walking trials were carried out where participants were requested to walk at a self- selected walking speed. Qualisys Track Manager (QTM) was used for collection and processing (digitisation) of data obtained (Jarvis PhD thesis, 2013).

4.5.1.1 Data Processing

Kinetic and kinematic data re-processing of the original data was carried out by the investigator of this thesis. In brief, data were checked by reviewing individual subject data using Visual3D software, where 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 model was rebuilt using Visual3D to the same specifications as the previous model used within Chapter 3 (section 3.3) in order to identify segments. A new results report pipeline was then applied using

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Visual3D. The data previously collected by Jarvis only used kinematic data (Jarvis PhD thesis, 2013), and therefore the results report pipeline enabled the investigator to determine kinetic data (the moment) for each subject. After data checking had been performed, the following data were excluded from processing: data with a low number of walking trials considered inadequate to obtain an accurate result, data which did not depict the moment in results, data which showed an inaccurate moment with noise interference, and data where the foot was not fully placed on at least one force plate, meaning kinetic data could not be achieved.

From the original 100 healthy subjects collected during the Jarvis trial (Jarvis PhD Thesis, 2013), the data concerning 90 subjects (26 males and 64 females) were used for this investigation, and 43 limbs were identified as being unsuitable for further exploration. Therefore, data concerning 137 limbs were identified as being suitable for further exploration by the investigator. Suitable data (with a minimum of 5 successful trials) were exported from Visual 3D to Microsoft Excel and prepared for analysis. 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 by V3D (Visual 3D, 2015). 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). The model segments were consistent with the model segments used within section 4.5.3 of this chapter.

Kinematic and kinetic data were filtered using a Butterworth 4th _{order bi-directional low pass}

filter with cut off frequencies of 6Hz for kinematics (Winter, 2009) and 25Hz for kinetics (Schneider and Chao, 1983) (Yu et al., 1999). Joints kinematics were calculated using an X- Y-Z Euler rotation sequence, where X (sagittal plane) represented flexion/extension, Y (coronal plane) adduction/abduction or eversion/inversion, and Z (transverse plane) internal/external rotation. Joint kinetic data were calculated using 3-D inverse dynamics and

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the joint moment data was normalised to body mass and presented as external moments referenced to proximal segment. Each gait parameter of interest for each of the studies was then exported from V3D to Microsoft Excel 2010 (Microsoft Washington, USA). The rearfoot was defined using cluster tracking markers (with four retroreflective markers fixed to each rigid cluster pad) between the shank segment and the foot segment.

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

Microsoft Excel software was used to collect the mean and standard deviation (SD) of each variable that was exported. 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).

4.5.2 Results

The data analysis was undertaken in order to further explore any possible link between static and dynamic foot posture and the magnitude of the EKAM using a larger data set to identify if any relationship existed between the FPI and rearfoot motion, and FPI and the magnitude of the EKAM, and rearfoot FPI classifications and rearfoot motion, and the magnitude of the EKAM.

Ninety healthy subjects participated in the study. The mean age for all participants was 30.2 (± 9.17) years, age range 18-45 years; mean height 1.67 (±0.08) m; height range 1.54-1.84 m; mean mass 71.7 kg (±14.0); mass range 47-107 kg; mean body mass index (BMI) 25.14 (±5.05) kg/m2 (table 4.5). The average self-selected walking speed of all participants was 1.292 (±0.146) m/s.

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Table 4.5: Demographic measurements and standard deviation for all study participants.

Gender Males (N=26) Females (N=64)

Age ± (SD) (years) 28.2 ± (8.9) 29.5 ± (10.4)

Height ± (SD) (m) 1.76 ± (0.07) 1.64 ± (0.05)

Mass ± (SD) (kg) 80.6 ± (12.6) 68.1 ± (13.1)

Results for all participants including; the mean EKAM, mean eversion and inversion dynamic rearfoot, mean ROM (Figure 4.3, 4.4, 4.5), mean subtalar joint eversion and inversion, mean FPI, and the FPI range are depicted in table 4.6.

Table 4.6: The mean and standard deviation (SD) for all measurements for 137 heathy limbs.

Peak EKAM: First peak maximum external knee adduction moment, Sagittal moment: Maximum first peak. FPI: Foot Posture Index, ROM: Range of Motion, STJ: Subtalar Joint, Ev: Eversion, Inv: Inversion, SD: standard deviation. Barefoot Measurements Mean SD (º) Peak EKAM (Nm/kg) 0.304 0.13 Sagittal Moment (Nm/kg) 0.243 0.23 Rearfoot Inv (º) 9.23 3.76 Rearfoot Ev (º) -4.18 3.17 ROM (º) 13.42 3.28 Ev STJ (º) -12 3.03 Inv STJ (º) 11 4.05 Rearfoot FPI 0 0.85 Total FPI 3 3.56

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Figure 4.3 – Mean knee flexion/extension moment in the sagittal plane for 137 limbs. Error bars indicate the ± 1

standard deviation.

Figure 4.4 – Mean external knee adduction moment (EKAM) in the frontal plane for 137 limbs Error bars indicate the ± 1 standard deviation.

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0 10 20 30 40 50 60 70 80 90 100 Fl e xi o n (- ) (N m /K g) Exte n si o n (+) Gait cycle % Sagittal moment -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0 10 20 30 40 50 60 70 80 90 100 E K A M A b d u ct ion (- ) ( Nm /K g) ad d u ct ion (+ ) Gait cycle % Frontal Plane

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Figure 4.5 – Mean dynamic rearfoot angle motion in the frontal plane for 137 limbs. Error bars indicate the ± 1

standard deviation.

The results indicate that no relationship existed between the FPI and the max EKAM, r=-0.118 (p=0.170) (table 4.7). Additionally, no association was identified between the FPI and rearfoot ROM, r=0.145 (p=0.095). Results concerning the FPI and dynamic inversion and eversion of the rearfoot indicate no relationship exists, r=0.116 (p=0.176) and ρ=0.040 (p=0.642) respectively. No relationship was identified between the FPI and subtalar joint eversion and inversion, r=-0.040 (p=0.643) and r=0.052 (p=0.546) respectively. Also, no association was found between eversion and inversion of the rearfoot in the FPI classification (static) and the EKAM and the ROM (dynamic) r=-0.045 (p=0.601) and ρ =0.089 (p=0.298) respectively.

Conversely, eversion and inversion of the rearfoot in the FPI classification data demonstrates a weak negative relationship to eversion dynamic rearfoot data, ρ=-0.183 (p=0.032) (table 4.7). Also, a weak negative relationship was identified between the EKAM and dynamic inversion and eversion of the rearfoot, r=-0.259 (p=0.002), ρ=-0.201 (p=0.019) respectively (table 4.7). However, the relationship was very weak and therefore, it can be considered that minimal correlation existed between FPI inversion, eversion and dynamic rearfoot, also between the EKAM and dynamic rearfoot eversion and inversion (figure 4.6, 4.7 and 4.8).

-10 -5 0 5 10 15 0 10 20 30 40 50 60 70 80 90 100 E ve rsi o n ( -) A n gl e (Deg ) In ve rsi o n ( + ) Stance phase % Rearfoot vs tibia frontal plane

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Figure 4.6 – Scatterplot graphs depicting the correlation between eversion and the EKAM.

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Table 4.7 - The relationship between FPI, rearfoot motion and the EKAM in 137 healthy limbs.

Rearfoot (Barefoot) Correlations P-Value

FPI vs EKAM r= -0.118 P= 0.170 FPI vs ROM r= 0.145 P= 0.095 FPI vs Inversion r= 0.116 P= 0.176 FPI vs Eversion ρ= -0.040 P= 0.642 FPI vs STJ Eversion r= -0.040 P= 0.643 FPI vs STJ Inversion r= 0.052 P= 0.546

Inv/Ev Calc FPI vs EKAM ρ= -0.045 P= 0.601

Inv/Ev Calc FPI vs ROM ρ= 0.089 P= 0.298

Inv/Ev Calc FPI vs inversion ρ= -0.075 P= 0.384 Inv/Ev Calc FPI vs Eversion ρ= -0.183 P= 0.032

EKAM vs ROM r= -0.082 P= 0.341

EKAM vs Inversion r= -0.259 P= 0.002

EKAM vs Eversion ρ= -0.201 P= 0.019

EKAM vs STJ Eversion r= 0.051 P= 0.552

EKAM vs STJ inversion r= 0.050 P= 0.562

FPI: Foot Posture Index, EKAM: External Knee Adduction Moment, ROM: Range of Motion, STJ: Subtalar Joint,

Calc: Calcaneus, r: Pearson Coefficient Correlation (parametric), ρ: Spearman Coefficient correlation (non-

parametric).

The results indicated that no association exists between the FPI, EKAM and rearfoot motion when not classifying individuals according to foot type which can be achieved with the FPI. However, some results showed very weak yet still significant association, which indicates that further investigation is required in order to determine if any relationship exists after dividing the limbs into groups related to the rearfoot FPI classification and to answer the research question, that if any relationships exist between static foot posture (assessed using the FPI), dynamic rearfoot motion and the magnitude of the EKAM.

4.5.3 Does classifying the data into foot type groups (inverted, everted and neutral)