Trial design and hypothesis
This was a controlled laboratorial, within subject, repeated measures study. It was hypothesized that both orthoses would lower the ATL when compared with the orthotic intervention. It was also hypothesized that CASO would have a larger ATL reduction than that of HL for flatfeet runners.
Participants
12 recreational runners of age greater than 18 years who trained regularly for running at least once per week and with running experience for 1 year or more(Cheung et al., 2007), with excessive foot pronation and with hind-foot strike landing pattern were recruited.
Foot Posture Index (FPI) was used as evaluating pronated foot posture. FPI is a non-invasive method of assessing the degree of standing foot posture with the scores reflecting highly supinated (−5 to −12), supinated (−1 to −4), neutral (0 to +5), pronated (+6 to +9) or highly pronated (+10 to +12). This is a validated instrument, which is adequately reliable as a screening tool standing foot posture (Evans et al., 2018; Keenan et al., 2018). Subjects with FPI scores of 6-12 were recruited in the current study.
All participants should be free of Achilles tendinopathy and triceps surae injury for 6 months with no previous surgery as well as not previously attempted any foot orthoses intervention before this study. All subjects were clinically assessed by the same Prosthetist and Orthotist for the range of motion, strength and flexibility of the lower extremities. Subjects exhibiting leg-length discrepancy, rigid forefoot varus deformity, gastrocnemius equinus, structural hallux limitus, or rigidus were excluded. Any musculoskeletal or neurological disorders, which would affect normal running gait, were also excluded.
Sample size
The sample size was based on previous similar studies (Donoghue et al., 2008, Kelly et al., 2011; Sinclair et al., 2014). Also, according to a priori power analysis, 12 participants were considered to be sufficient for minimal statistical power ≥ 0.8 and α=0.05.
Foot orthoses
Customized arch support orthoses (CASO)
The CASO (fig 1-3) was made from the traditional hand-made method, all subjects were asked to lie prone with both feet as a non-weight bearing position and the foot plantar surface was casted by using the Plaster of Paris bandages bilaterally in a subtalar neutral position manipulated by the same Prosthetist and Orthotist. The negative casts were placed in a calcaneal vertical position and a positive mold was created from the negative cast by filling Plaster. Custom-molded orthoses were then fabricated from the positive mold with 3-mm polypropylene (Polystone ® P copolymer, from Röchling) using a vacuum press method with an extrinsic ethyl vinyl acetate (EVA) standard rearfoot posting, cut at 50% of the length of the heel cup. A 3mm multiform cover was added at last according to the shape of shoes.
Orthotic heel lift (HL)
The HL was made by high-density ethyl vinyl acetate (EVA), it was wedge-shaped which tapered over its 8.2 cm length from a height of 18 mm posteriorly to finish flush anteriorly. It was added underneath to the insole of the rearfoot of each shoe by double-sided adhesive tape in order to prevent any sliding movement during running. (fig 4)
Data collection
Kinematic data
Kinematic data was captured by an 8-camera motion capturing system (Vicon 370, Oxford, UK). Kinematic analysis and a calculation of the position of the center of mass were performed using the lower body Plug-in-Gait model. 16 Retroreflective markers were positioned onto the posterior superior iliac spine, anterior superior iliac spine, lateral thigh, lateral femoral epicondyle, lateral shank, lateral malleolus, calcaneus and second metatarsal head (on shoes) bilaterally following anatomical landmark for defining anatomical frames of the right foot and shank. (fig 5) The foot segment was tracked using the metatarsal and calcaneus markers. Before data collection, static calibration trials were obtained with subjects in the anatomical position allowing the anatomical markers are in reference with the technical marker positions as shown in Figure 5.
Kinetic data
Piezoelectric force platform (AMTI force plates) was used for capturing kinetic data once subjects striking with their right (dominant) foot. The stance phase of the running cycle was identified at the time over which ≥20 N of vertical force was applied to the force platform (Sinclair et al., 2011).
Achilles tendon loading (ATL)
Ankle joint kinetics were computed using the Newton–Euler inverse-dynamics. Net external ankle joint moments were then calculated which has been shown to give a relevant approximation for internal joint loading (Zhao et al. 2007).
An algorithmic model was used as a predictive technique to determine ATL. This technique has been shown to be sufficiently sensitive to resolve differences in ATL during running with different footwear (Kulmala et al., 2013; Sinclair et al., 2014).
Achilles tendon load (ATL) is determined by dividing the plantarflexion moment (MPF) by the estimated Achilles tendon moment arm (MA):
ATL = MPF/MA
The moment arm was quantified as a function of the ankle sagittal plane angle (SAK) using the procedure described by Self and Paine (Self and Paine, 2001), the equation is calculated by Rugg et al. using magnetic resonance imaging (MRI) (Rugg et al., 1990).
MA = −0.5910 + 0.08297 SAK–0.0002606 SAK²
ATL was normalized to body weight (B.W.) and Achilles tendon loading rate (ATLR) (B.W. sˉ¹) was also calculated as a function of the change in ATL from initial contact to peak ATL divided by the time to peak ATL.
Study settings
There were totally two face-to-face appointments for each subject. In the first appointment, initial screening and assessment based on the inclusion and exclusion criteria by Prosthetists and Orthotists were done. For the assessment for flatfeet by FPI, subjects were required to stand in a relaxed position; the rearfoot was first assessed through palpation of the head of the talus, observation of the curves above and below the lateral malleoli and the degree of the inversion/eversion of the calcaneus. Scores would be summed according to the six parameters. Subjects with FPI scores of 6-12 were recruited Then for the forefoot, the bulge in the region of the talonavicular joint, the extent of abduction/adduction of the forefoot on the rearfoot and the congruence of the medial longitudinal arch. Subjects fulfilling research requirement were casted for the CASO by the method mentioned above and demographic data were collected. Data collection was done in the second appointment. The second appointment was arranged once the CASO and HL were ready (which was ~2-3 weeks). (fig 6)
The experimental procedure was conducted in a 10m long gait lab. Before testing commenced, there was 15 minutes acclimatization period for each subject to walk or run on runway. During this period, the subject walked or ran with orthoses at their comfortable speed and gait pattern to adjust to the surroundings and to make sure the orthoses were comfortable and the running gait was consistent. After this period, subjects were asked if they needed more time. If they asked for more time, additional familiarization was given until they feel accustomed to the condition. There were 3 minutes rest before testing to avoid muscle fatigue (Garcia-Perez et al., 2014).
Interventions
For the testing condition, subjects were asked to run on the runway at a self-selected speed in threes conditions: (1) run without orthoses, (2) run with HL and (3) run with CASO. The test sequence was randomized. Recording began once subjects ran with their comfortable running pace. The eight-camera system and the force platform were used to record the kinematic and kinetic data synchronously at 250 and 1000 Hz respectively. Subjects were required to complete five acceptable trials (completely contacted the force plate with the right leg with acceptable speed and without targeting) running at a self-selected speed at each condition. To minimize the effect of speed on biomechanical parameters, all actual trials were required to be within ±5% of the determined average self-selected speed (Queen et al., 2006), while the running speed was monitored by mobile phone positioned 1.5 m from the force plates using a video radar application (SpeedClock, Sten Kaiser, version 3.1) (Sobhani et al., 2015).
Data processing
VICON Nexus v2.6, Oxford Metrics software (Plug-in-Gait model) was used to compute joint kinematics and kinetics. Data were exported to MatLab software (R2019a) for further processing. Kinematics and kinetics data were time normalized (0-100%) of the stance phase and averaged across five trials to get individual mean curves. Joint angles and internal joint moments (N.m.kgˉ¹) during the stance phase of running were determined across five successful force plate contacts of the right leg. Marker trajectories and kinetic data were low-pass filtered using a fourth-order Butterworth filter with cutoff frequencies of 12 and 50Hz respectively. Kinetic variables were all normalized for body mass.
Statistical Analysis
Normality distribution analysis was carried out by the Shapiro Wilks test, considering a normal distribution when P > 0.05. Demographic characteristics such as age, height and weight were included. Mean and standard deviation (SD) were applied to the data set to describe quantitative data. For the normality of the variables, One-way repeated measures ANOVA was conducted in order to examine the differences in primary outcomes: peak Achilles tendon load (B.W.) and Achilles tendon loading rate (B.W. sˉ¹) ,as well as secondary outcomes include peak plantarflexion moment, time to peak Achilles tendon force and peak dorsiflexion angle among the three conditions. Post-hoc comparisons with Bonferroni correction was used as a follow-up analysis.
All statistical tests were conducted by means of the IBM SPSS Statistics software (SPSS, v22, Inc, Chicago, Illinois). Statistically significant differences were considered at P < 0.05 with a 95% confidence interval (CI). Effect sizes in terms of Cohen’s d were calculated in order to quantify the differences between the three conditions (https://www.socscistatistics.com/effectsize/). The effect is regarded as small, medium and large when Cohen’s d is 0.2, 0.5 and 0.8 respectively (Laknes., 2013).