Ankle Muscle Activations during Different Foot-Strike Patterns in Running

study analysed the landing performance and muscle activity of athletes in forefoot strike (FFS) and rearfoot strike (RFS) patterns. Ten male college participants were asked to perform two foot strikes patterns, each at a running speed of 6 km/h. Three inertial sensors and ve EMG sensors as well as one 24 G accelerometer were synchronised to acquire joint kinematics parameters as well as muscle activation, respectively. In both the FFS and RFS patterns, according to the intraclass correlation coef cient, excellent reliability was found for landing performance and muscle activation. Paired t tests indicated signi cantly higher ankle plantar exion in the FFS pattern. Moreover, biceps femoris (BF) and gastrocnemius medialis (GM) activation increased in the pre-stance phase of the FFS compared with that of RFS. The FFS pattern had signi cantly decreased tibialis anterior (TA) muscle activity compared with the RFS pattern during the pre-stance phase. The results demonstrated that the ankle strategy focused on controlling the foot strike pattern. The in uence of the FFS pattern on muscle activity likely indicates that an athlete can increase both BF and GM muscles activity.

of RFS. The FFS pattern had signi cantly decreased tibialis anterior (TA) muscle activity compared with the RFS pattern during the pre-stance phase. The results demonstrated that the ankle strategy focused on controlling the foot strike pattern. The in uence of the FFS pattern on muscle activity likely indicates that an athlete can increase both BF and GM muscles activity. Altered landing strategy in cases of FFS pattern may contribute both to the running ef ciency and muscle activation of the lower extremity. Therefore, neuromuscular training and education are required to enable activation in dynamic running tasks. Keywords:joint motion; landing pattern; biarticular muscle; neuromuscular training 1. Introduction Striking one's feet rapidly and repetitively against the ground is a fundamental running motion in sports. In particular, exceptional running techniques are associated with factors such as forward trunk lean angles [1], stride frequencies and lengths [2], and most importantly, strike patterns [3]. Due to poor running economy between forefoot strike (FFS) and rearfoot strike (RFS), it also affects the appearance of muscle fatigue, which in turn causes lower extremity injuries [4]. Strike patterns can be divided into forefoot strike, midfoot strike (MFS), and rearfoot strike according to the foot strike position [3,5]. Studies have shown that FFS is characterized by a high strike frequency and short stride length [6]. Compared with RFS, FFS allows for shorter contact time [6–8], and effective for obtaining a higher sprint speed and step frequency through the greater hip extension and knee exion velocities during the running phases [3]. The optimal choice between both depends on the sport. For example, sprinters generally choose FFS to increase their starting speed and maintain their maximum speed. However, to achieve these goals, runners must keep their centre of mass at a desirable forward position, which consumes more energy. Therefore, long distance runners typically choose RFS to achieve greater running ef ciency. This is because RFS increases running distance instead of running speed and helps runners conserve energy [9,10]. Previous studies have indicated that different foot-strike strategies can affect running economy and the biomechanics of lower extremity joints [11], and Sensors2021,21,

a desirable forward position, which consumes more energy. Therefore, long distance runners typically choose RFS to achieve greater running ef ciency. This is because RFS increases running distance instead of running speed and helps runners conserve energy [9,10]. Previous studies have indicated that different foot-strike strategies can affect running economy and the biomechanics of lower extremity joints [11], and Sensors2021,21, 3422.

Sensors2021,21, 3422 2 of 11 that muscle activation is a crucial factor affecting running economy [12,13]. According to these ndings, during runs, the lower extremity muscles must have the appropriate joint position, stability, and stiffness to ef ciently push the trunk forward. Speci cally, muscle activation changes occur to help the human body adapt itself to the dynamic load on its lower extremities during different movements. For example, the muscle activation changes when walking differ from those when running [14,15]. Muscle activation through repetitive tasks originates from foot- oor interaction; such interaction occurs during the landing phase to mitigate the impact of external forces on the foot when landing [15]. Dolenec et al. discovered that when lower extremities strike different ground materials (e.g., asphalt, gravel, and grass), tibialis anterior activation occurs to adjust joint stiffness and maintain a stable foot strike posture [16]. Moreover, in FFS, activation of the gastrocnemius muscle and soleus muscle happens earlier than that in RFS; The gastrocnemius and soleus muscles also play a major role in plantar exion during the early activated phase. Like the gastrocnemius, it is one of the calf muscles in the back of the leg. It connects to the Achilles tendon at the heel. You need this muscle to push your foot away from the ground, so the plantar exion muscle to provide FFS with higher levels of muscle hypertrophy [17] and form an isometric or lengthening contraction [18,19]. The aforementioned studies have highlighted the consequential effect of lower ex- tremity neuromuscular activation on the running economy. For example, previous studies reported that different foot strike strategies can affect the running economy and the biome- chanics of lower extremity joints [11], and that muscle activation is a crucial factor affecting the running economy [12,13]. Running economy can be quanti ed as the steady-state oxygen consumption during steady-state rate [20]. There is evidence that running economy is worse when running between FFS and RFS patterns [11]. According to these ndings, during runs, the lower-extremity muscles must have the appropriate joint position, stability, and stiffness to ef ciently push the trunk forward. However,

running economy [12,13]. Running economy can be quanti ed as the steady-state oxygen consumption during steady-state rate [20]. There is evidence that running economy is worse when running between FFS and RFS patterns [11]. According to these ndings, during runs, the lower-extremity muscles must have the appropriate joint position, stability, and stiffness to ef ciently push the trunk forward. However, the effects of the characteris- tics of neuromuscular activation in different foot strike patterns on the bene ts of running techniques merit further exploration. Accordingly, the present study compared the joint kinematics and muscle activation in the pre-landing and stance phases between different strike movements. Next, this study identi ed how lower-extremity activation contributed to running technique development. 2. Materials and Methods 2.1. Participants Ten division I male physical education students volunteered for the study (age: 21.7 1.9 years ; height: 1.70 0.05 m; mass: 65.1. 5.5 kg). All participants had com- pleted all experimental running conditions, and were free of any previous lower extremity injury at the time of testing. All participants were required to run at least 10 km per week and all were comfortable running on the treadmill for this study. The participant inclusion criteria included (1) no complaints of back, hip, knee, or ankle pain, (2) resumption of all pre-injury activities without limitation for at least 12 months before testing, and (3) no musculoskeletal trauma or neurosurgery within the previous six months. We excluded all participants that had sustained injury to any of their lower extremities within the previous year. In this study, the research was explained to every participant, and written consent was provided before participating in the study. The study was approved by the Ethics Committee of Antai Medical Care Cooperation Hospital (18-104-B). 2.2. Instrumentation Three IMU sensors (200 Hz; Myomotion, Noraxon Inc., Scottsdale, AZ, USA) were used for measurement of lower extremity joint kinematics in the sagittal plane. IMU data were recorded using a wireless inertial measurement system consisting of miniature IMU sensors securely mounted unilaterally to the shank (dominant leg), thigh (dominant leg), and pelvis. A Noraxon system with a 24G wireless

Instrumentation Three IMU sensors (200 Hz; Myomotion, Noraxon Inc., Scottsdale, AZ, USA) were used for measurement of lower extremity joint kinematics in the sagittal plane. IMU data were recorded using a wireless inertial measurement system consisting of miniature IMU sensors securely mounted unilaterally to the shank (dominant leg), thigh (dominant leg), and pelvis. A Noraxon system with a 24G wireless accelerometer (1500 Hz) and ve wireless electromyography sensors (1500 Hz) were used to de ne the initial foot

Sensors2021,21, 3422 3 of 11 contact and to collect muscle activity (Figure). According to SENIAM recommendations, double differentiated surface electrodes were placed in the direction of muscle bres on shaved cleaned skin: (1) rectus femoris (RF), (2) biceps femoris (BF), (3) tibialis anterior (TA), (4) gastrocnemius medialis (GM), (5) soleus (SO) [21]. The maximum voluntary isometric contraction (MVIC) values were obtained to normalise some of the studied variables [22]. The ankle and knee muscle groups's MVICs were performed using the BIODEX dynamometer (Biodex Systems 4 Pro, Biodex Inc., Shirley, New York, NY, USA). Each set of three MVICs was followed by at least 3 min rest. To minimize the effects of fatigue, at least 5 min was allowed between each MVIC group [23].Sensors 2021, 21, x FOR PEER REVIEW 3 of 10 sensors securely mounted unilaterally to the shank (dominant leg), thigh (dominant leg), and pelvis. A Noraxon system with a 24G wireless accelerometer (1500 Hz) and five wire- less electromyography sensors (1500 Hz) were used to define the initial foot contact and to collect muscle activity (Figure 1). According to SENIAM recommendations, double dif- ferentiated surface electrodes were placed in the direction of muscle fibres on shaved cleaned skin: (1) rectus femoris (RF), (2) biceps femoris (BF), (3) tibialis anterior (TA), (4) gastrocnemius medialis (GM), (5) soleus (SO) [21]. The maximum voluntary isometric contraction (MVIC) values were obtained to normalise some of the studied variables [22]. The ankle and knee muscle groups’s MVICs were performed using the BIODEX dyna- mometer (Biodex Systems 4 Pro, Biodex Inc., Shirley, New York, NY, USA). Each set of three MVICs was followed by at least 3 min rest. To minimize the effects of fatigue, at least 5 min was allowed between each MVIC group [23]. Figure 1. Full set-up position of IMU and EMG sensors. 2.3. Data Collection This protocol ensured that stable kinematics and muscle activity data could be col- lected after the 1–20 cycles necessary for the neuromuscular system to adapt to drastically altered task mechanics. Participants were asked to run on a treadmill at 6 km/h and per- form two

group [23]. Figure 1. Full set-up position of IMU and EMG sensors. 2.3. Data Collection This protocol ensured that stable kinematics and muscle activity data could be col- lected after the 1–20 cycles necessary for the neuromuscular system to adapt to drastically altered task mechanics. Participants were asked to run on a treadmill at 6 km/h and per- form two foot strike patterns (i.e., FFS and RFS patterns) in a random order. They per- formed a 5-min barefoot warm-up with a treadmill (Horizon Paragon 4, Johnson Inc., Tai- chung, TW), run at a free speed to familiarise themselves with running without shoes. A wireless accelerometer was fixed to the foot instep of the dominant leg prior to the running task. Every trial for the foot strike pattern lasted >30 s. The consecutive trials with a rest period of >1 min [13], and all the foot strike patterns were confirmed visually. Participants were allowed to make additional attempts until each strike pattern was completed with three successful trials. There were three consecutive FFS and then three RFS conducted randomly between FFA and RFS patterns. Each participant’s dominant leg was defined as the limb that would be used to kick a ball [24]. 2.4. Data Analysis The phase definition of this study is based on the running gait cycle. It is hoped that the simplest research equipment will be used for the defined phase, and the innovative staging method will be used to define the pre- and post-landing phase of this study simply and quickly, so as to formulate a new experimental procedure. The running gait cycle was further divided into two phases: pre-stance and stance (Figure 2). The pre-stance phase was defined as the swing phase, with the maximum angle of knee flexion occurring prior to initial contact. The stance phase was defined as the initial contact with the force plate prior to maximum velocity of knee extension. Initial contact was defined as the peak value Figure 1.Full set-up position of IMU and EMG sensors. 2.3. Data Collection This protocol ensured that stable kinematics and muscle activity data could be col-

knee flexion occurring prior to initial contact. The stance phase was defined as the initial contact with the force plate prior to maximum velocity of knee extension. Initial contact was defined as the peak value Figure 1.Full set-up position of IMU and EMG sensors. 2.3. Data Collection This protocol ensured that stable kinematics and muscle activity data could be col- lected after the 1–20 cycles necessary for the neuromuscular system to adapt to drastically altered task mechanics. Participants were asked to run on a treadmill at 6 km/h and perform two foot strike patterns (i.e., FFS and RFS patterns) in a random order. They performed a 5-min barefoot warm-up with a treadmill (Horizon Paragon 4, Johnson Inc., Taichung, TW), run at a free speed to familiarise themselves with running without shoes. A wireless accelerometer was xed to the foot instep of the dominant leg prior to the running task. Every trial for the foot strike pattern lasted >30 s. The consecutive trials with a rest period of >1 min [13], and all the foot strike patterns were con rmed visually. Participants were allowed to make additional attempts until each strike pattern was completed with three successful trials. There were three consecutive FFS and then three RFS conducted randomly between FFA and RFS patterns. Each participant's dominant leg was de ned as the limb that would be used to kick a ball [24]. 2.4. Data Analysis The phase de nition of this study is based on the running gait cycle. It is hoped that the simplest research equipment will be used for the de ned phase, and the innovative staging method will be used to de ne the pre- and post-landing phase of this study simply and quickly, so as to formulate a new experimental procedure. The running gait cycle was further divided into two phases: pre-stance and stance (Figure). The pre-stance phase was de ned as the swing phase, with the maximum angle of knee exion occurring

formulate a new experimental procedure. The running gait cycle was further divided into two phases: pre-stance and stance (Figure). The pre-stance phase was de ned as the swing phase, with the maximum angle of knee exion occurring

Sensors2021,21, 3422 4 of 11 prior to initial contact. The stance phase was de ned as the initial contact with the force plate prior to maximum velocity of knee extension. Initial contact was de ned as the peak value during the landing phase in accelerometer signals of the vertical axis. Maximum push-off velocity was de ned from initial contact to maximum velocity of knee extension. Kinematics and EMG data were processed using Noraxon 3.8.6 software. The IMU data were ltered using a cut-off frequency of 6 Hz with a fourth-order zero-lag Butterworth digital lter. The EMG process used the 10 to 500 Hz band-pass lter. The root mean square (RMS) algorithm was calculated using a 20-sample moving average. [25].Sensors 2021, 21, x FOR PEER REVIEW 4 of 10 during the landing phase in accelerometer signals of the vertical axis. Maximum push-off velocity was defined from initial contact to maximum velocity of knee extension. Kine- matics and EMG data were processed using Noraxon 3.8.6 software. The IMU data were filtered using a cut-off frequency of 6 Hz with a fourth-order zero-lag Butterworth digital filter. The EMG process used the 10 to 500 Hz band-pass filter. The root mean square (RMS) algorithm was calculated using a 20-sample moving average. [25]. Figure 2. The phases of the running gait cycle. Note: MKF indicates maximum angle of knee flex- ion; IC indicates initial contact; TO indicates take-off; Pre-stance phase is defined as MKF to IC; Stance phase is defined as IC to maximum velocity of knee extension. 2.5. Statistical Analysis Statistical analysis (SPSS 18, Inc., Chicago, IL, USA) was performed, and descriptive statistics (mean and standard deviation) were used to determine the characteristics of the participants. Paired sample t-test was used to compare initial contact of ankle angle, the maximum push-off velocity of knee extension, and muscle activation in the FFS and RFS strike patterns. The intraclass correlation coefficients (ICCs) were calculated. To assess the consistency and test–retest reliability of the measurement of joint pattern and muscle ac- tivation in five strikes of each pattern, the effect size (ES) was evaluated according to the

contact of ankle angle, the maximum push-off velocity of knee extension, and muscle activation in the FFS and RFS strike patterns. The intraclass correlation coefficients (ICCs) were calculated. To assess the consistency and test–retest reliability of the measurement of joint pattern and muscle ac- tivation in five strikes of each pattern, the effect size (ES) was evaluated according to the method of Cohen [26]. The level of significance was set at p < 0.05. 3. Results 3.1. The Intraclass Correlation Coefficient of Participants The reliability of the landing performance and muscle activity was generally good. Within an FFS pattern, initial contact angle, maximum push-off velocity, and muscle acti- vation all showed good reliability (Table 1). Specifically, the RFS strike pattern also had excellent reliability according to ICC data, mostly greater than 0.900 (Table 1). Table 1. The intraclass correlation coefficient of joint kinematics and muscle activation during stance phase. Parameters FFS RFS Pre-Stance Stance Pre-Stance Stance Initial contact angle Hip 0.989 0.989 Knee 0.975 0.969 Ankle 0.983 0.981 Maximum push-off velocity Hip 0.906 0.970 Knee 0.922 0.928 Ankle 0.988 0.967 Muscle activation RF 0.976 0.995 0.990 0.978 TA 0.815 0.968 0.978 0.915 BF 0.986 0.989 0.964 0.993 GM 0.929 0.986 0.942 0.978 SO 0.996 0.970 0.984 0.948 Figure 2. The phases of the running gait cycle. Note: MKF indicates maximum angle of knee exion; IC indicates initial contact; TO indicates take-off; Pre-stance phase is de ned as MKF to IC; Stance phase is de ned as IC to maximum velocity of knee extension. 2.5. Statistical Analysis Statistical analysis (SPSS 18, Inc., Chicago, IL, USA) was performed, and descriptive statistics (mean and standard deviation) were used to determine the characteristics of the participants. Paired samplet-test was used to compare initial contact of ankle angle, the maximum push-off velocity of knee extension, and muscle activation in the FFS and RFS strike patterns. The intraclass correlation coef cients (ICCs) were calculated. To assess the consistency and test–retest reliability of the measurement of joint pattern and muscle activation in ve strikes of each pattern, the effect size (ES) was evaluated according to the method

The study compares muscle activation in different foot-strike patterns during running.