Abstract
n running, step rate and step angle are important, but the relationship between the two parameters is not clear in the literature. This study aimed to investigate the effect of step rate manipulation on step angle in running. A group of twenty healthy recreational runners aged between 30 and 59 years who regularly run 15–90 km per week were recruited. Kinematic data were recorded using a motion capture system while running on a treadmill. Participants maintained a self-selected speed and then altered their step rate using a metronome and completed three thirty-second trials at the preferred step rate, 10% above the preferred step rate and 10% below the preferred step rate. The results showed that the step angle is not significantly correlated with the step rate and kept at roughly 37 degrees at the preferred step rate and 10% lower than the preferred step rate but increased to 42 deg when the step rate increased to 10% of the preferred step rate. The step angles were not significantly different between
rate. The results showed that the step angle is not significantly correlated with the step rate and kept at roughly 37 degrees at the preferred step rate and 10% lower than the preferred step rate but increased to 42 deg when the step rate increased to 10% of the preferred step rate. The step angles were not significantly different between the male and female or between sides. This finding provides an understanding of the association of step rate re-training on swing phase parameters. Keywords:step rate; step angle; step length; running; gender 1. Introduction Sport Ireland’s annual report 2017 [1] stated that running is the third most popular form of sports participation in Ireland. Additionally, worldwide participation levels in organized running races peaked in 2016 with a total of 9.1 million participants. According to Andersen [2] worldwide participation levels over the last 10 years has increased by 57.8%. Furthermore, running research has dramatically accelerated with a recent emphasis on developing wearable running technology to analyse running techniques. Time and distance gait parameters or spatiotemporal step characteristics are commonly measured with portable and laboratory devices, which include step rate (SR), contact time (CT), flight time (FT), stride length (SDL), step length (SL), step height (SH) and step angle (SA). SR is a temporal parameter that has been extensively examined in running biomechan- ics [3]. SR is defined as the number of ground contact events per amount of time. Typically, SR is retrained using a metronome to change the runner’s preferred SR. Schubert et al. [3] suggested that increasing running SR affects impact peak, kinematics, and kinetics, thus reducing injury rate risks. Biomechanical analysis of running has largely concentrated on stance phase vari- ables [4] and less so on swing phase biomechanics. A swing phase spatial parameter that has been examined and associated with running economy (RE) is SA [5–8]. SA is defined as “the angle of the parabola tangent derived from the theoretical arc traced by a foot Appl. Sci.2024,14, 1059.
swing phase spatial parameter that has been examined and associated with running economy (RE) is SA [5–8]. SA is defined as “the angle of the parabola tangent derived from the theoretical arc traced by a foot Appl. Sci.2024,14, 1059.
Appl. Sci.2024,14, 1059 2 of 14 during a step and the ground” (See Figure). SA is an indirect indicator of swing phase biomechanics related to the vertical component of ground contact forces. A higher SA has been related to reduced ground CT and improved RE [5–8]. Moreover, Morin et al. [9] identified SR as an indirect factor affecting leg stiffness because of its effect on minimising CT. Therefore, it seems significant to investigate the relationship between SR manipulation and SA due to their intimate association with CT.Appl. Sci. 2024, 14, x FOR PEER REVIEW 2 of 14 Biomechanical analysis of running has largely concentrated on stance phase variables [4] and less so on swing phase biomechanics. A swing phase spatial parameter that has been examined and associated with running economy (RE) is SA [5–8]. SA is defined as “the angle of the parabola tangent derived from the theoretical arc traced by a foot during a step and the ground” (See Figure 1). SA is an indirect indicator of swing phase biome- chanics related to the vertical component of ground contact forces. A higher SA has been related to reduced ground CT and improved RE [5–8]. Moreover, Morin et al. [9] identified SR as an indirect factor affecting leg stiffness because of its effect on minimising CT. There- fore, it seems significant to investigate the relationship between SR manipulation and SA due to their intimate association with CT. Figure 1. The definitions of step angle in running. Note: the curve is the trace of the foot, L is the step length, H is the max height reached by the foot, and α the step angle. While considerable research exists regarding spatiotemporal parameters during run- ning, there is a lack of research on the relationship between step rate and step angle, nor the report between sex differences when SR is manipulated. In comparison to men, women are nearly twice as likely to have a running injury [10]. It has been suggested that there are lower extremity differences between sex in running kinematics [11]. Only two studies have investigated the influence of sex
on the relationship between step rate and step angle, nor the report between sex differences when SR is manipulated. In comparison to men, women are nearly twice as likely to have a running injury [10]. It has been suggested that there are lower extremity differences between sex in running kinematics [11]. Only two studies have investigated the influence of sex differences on spatiotemporal step charac- teristics [12,13]. However, these studies examined spatiotemporal step characteristics at a constant speed and SR or incremental running test. Hence, the importance of comparing sex differences was considered in this novel study of SR manipulation on spatial step char- acteristics. Prior research evaluating asymmetry between lower limbs in gait has identified sig- nificant differences in kinetic and kinematic measures [14,15]. Zifchock et al. demon- strated that never-injured runners had normal levels of asymmetry ranging from 3.1% up to 49.8% for kinetic forces [16]. In this current original investigation into SA utilising mo- tion capture, we sought to identify any spatial asymmetries. The purpose of the present study was three-fold: (1) to investigate if SA has a predi- cable interaction with SR, (2) to establish if SA differs between genders with SR changes, and (3) to determine any spatial asymmetrical between the left and right sides. It was hy- pothesised that there would be a significant correlation between SA and SR, and signifi- cant SA differences were found between (a) sex and (b) lower limb symmetry. The reason for investigating step angle with varying step rates was to determine if this parameter is comparable at all step rate changes, not just increased step rates as seen with increased running speed, previously found in other studies. It is also important to measure and compare spatial parameters such as SL and SH, as these are components of SA and could provide a further understanding of the relationships between these param- eters. Gender differences were investigated to clarify if there are any changes since current research into spatiotemporal characteristics is inconclusive [12,13]. Since this study al- lowed participants to self-select their running speed, it was worthwhile to measure gait
as SL and SH, as these are components of SA and could provide a further understanding of the relationships between these param- eters. Gender differences were investigated to clarify if there are any changes since current research into spatiotemporal characteristics is inconclusive [12,13]. Since this study al- lowed participants to self-select their running speed, it was worthwhile to measure gait symmetry, as some studies have found that running speed and running experience affect Figure 1.The definitions of step angle in running. Note: the curve is the trace of the foot, L is the step length, H is the max height reached by the foot, andαthe step angle. While considerable research exists regarding spatiotemporal parameters during run- ning, there is a lack of research on the relationship between step rate and step angle, nor the report between sex differences when SR is manipulated. In comparison to men, women are nearly twice as likely to have a running injury [10]. It has been suggested that there are lower extremity differences between sex in running kinematics [11]. Only two studies have investigated the influence of sex differences on spatiotemporal step characteristics [12,13]. However, these studies examined spatiotemporal step characteristics at a constant speed and SR or incremental running test. Hence, the importance of comparing sex differences was considered in this novel study of SR manipulation on spatial step characteristics. Prior research evaluating asymmetry between lower limbs in gait has identified signif- icant differences in kinetic and kinematic measures [14,15]. Zifchock et al. demonstrated that never-injured runners had normal levels of asymmetry ranging from 3.1% up to 49.8% for kinetic forces [16]. In this current original investigation into SA utilising motion capture, we sought to identify any spatial asymmetries. The purpose of the present study was three-fold: (1) to investigate if SA has a predica- ble interaction with SR, (2) to establish if SA differs between genders with SR changes, and (3) to determine any spatial asymmetrical between the left and right sides. It was hypothe- sised that there would be a significant correlation between SA and SR, and significant SA differences were found
study was three-fold: (1) to investigate if SA has a predica- ble interaction with SR, (2) to establish if SA differs between genders with SR changes, and (3) to determine any spatial asymmetrical between the left and right sides. It was hypothe- sised that there would be a significant correlation between SA and SR, and significant SA differences were found between (a) sex and (b) lower limb symmetry. The reason for investigating step angle with varying step rates was to determine if this parameter is comparable at all step rate changes, not just increased step rates as seen with increased running speed, previously found in other studies. It is also important to measure and compare spatial parameters such as SL and SH, as these are components of SA and could provide a further understanding of the relationships between these parameters. Gender differences were investigated to clarify if there are any changes since current research into spatiotemporal characteristics is inconclusive [12,13]. Since this study allowed participants to self-select their running speed, it was worthwhile to measure gait symmetry, as some studies have found that running speed and running experience affect the level of symmetry. Asymmetry could be seen as important due to the metabolic cost of changing gait retraining and fatigue, which can increase asymmetry in participants. Lastly, if step rate and step angle are related, then it could be used as a further parameter to monitor when injured participants are being guided through gait retraining programs in clinics and rehabilitation.
Appl. Sci.2024,14, 1059 3 of 14 2. Materials and Methods 2.1. Participants Twenty healthy (9 females, 11 males) recreational runners, without any current injuries, who ran a minimum of 15 km a week (mean: 33.0±standard deviation 20.3 km, ages 43.0±10.2 years, heights 1.73 m±10.2 and body mass 71.8±12.1 kg) volunteered to participate in the study. The study was approved by the School of Medicine and School of Life Sciences Research Ethics Committee at the University of Dundee (SMED REC 19/38), and the participants provided written informed consent in accordance with institutional policies. 2.2. Protocol Before data collection, each participant’s preferred running speed and preferred step rate were determined while running on a treadmill (Body Power, Motorised treadmill, model no: Sprint T700, Sport-Tiedje GmbH, Schleswig, Germany) after a 5-min warm- up. Participants were instructed to adjust the speed as needed over this period until they identified a speed that was representative of their typical moderate-intensity run. This speed was then employed for all three SR trials. SR was visually calculated over a 30-speriod by counting the number of right foot strikes and multiplying by four. When calculating steps, we used a round number of steps, ignoring the half/part of the step. The process was repeated to guarantee accuracy with the average value used. Vicon ® motion capture system (Oxford, UK), including 6 MX T20 cameras and 10 Van- tage 5 cameras, was used to collect movement data. The lower limb body model (Plug-in- Gait) with 24 reflective markers was placed on each subject, and the kinematic data was recorded at 400 Hz during all running conditions, as shown in Figure.Appl. Sci. 2024, 14, x FOR PEER REVIEW 3 of 14 the level of symmetry. Asymmetry could be seen as important due to the metabolic cost of changing gait retraining and fatigue, which can increase asymmetry in participants. Lastly, if step rate and step angle are related, then it could be used as a further parameter to monitor when injured participants are being guided through gait retraining programs in clinics and rehabilitation. 2. Materials and Methods 2.1. Participants Twenty healthy (9 females,
to the metabolic cost of changing gait retraining and fatigue, which can increase asymmetry in participants. Lastly, if step rate and step angle are related, then it could be used as a further parameter to monitor when injured participants are being guided through gait retraining programs in clinics and rehabilitation. 2. Materials and Methods 2.1. Participants Twenty healthy (9 females, 11 males) recreational runners, without any current inju- ries, who ran a minimum of 15 km a week (mean: 33.0 ± standard deviation 20.3 km, ages 43.0 ± 10.2 years, heights 1.73 m ± 10.2 and body mass 71.8 ± 12.1 kg) volunteered to par- ticipate in the study. The study was approved by the School of Medicine and School of Life Sciences Research Ethics Committee at the University of Dundee (SMED REC 19/38), and the participants provided written informed consent in accordance with institutional policies. 2.2. Protocol Before data collection, each participant’s preferred running speed and preferred step rate were determined while running on a treadmill (Body Power, Motorised treadmill, model no: Sprint T700, Sport-Tiedje GmbH, Schleswig, Germany ) after a 5-min warm-up. Par- ticipants were instructed to adjust the speed as needed over this period until they identi- fied a speed that was representative of their typical moderate-intensity run. This speed was then employed for all three SR trials. SR was visually calculated over a 30-s period by counting the number of right foot strikes and multiplying by four. When calculating steps, we used a round number of steps, ignoring the half/part of the step. The process was re- peated to guarantee accuracy with the average value used. Vicon ® motion capture system (Oxford, UK), including 6 MX T20 cameras and 10 Vantage 5 cameras, was used to collect movement data. The lower limb body model (Plug- in-Gait) with 24 reflective markers was placed on each subject, and the kinematic data was recorded at 400 Hz during all running conditions, as shown in Figure 2. Figure 2. Marker placement in lower limb model. According to the requirement of the Vicon system and model, a “T-pose” calibration trial was
collect movement data. The lower limb body model (Plug- in-Gait) with 24 reflective markers was placed on each subject, and the kinematic data was recorded at 400 Hz during all running conditions, as shown in Figure 2. Figure 2. Marker placement in lower limb model. According to the requirement of the Vicon system and model, a “T-pose” calibration trial was performed to establish joint centres, body segment coordinate systems, segment lengths and the local positions of tracking markers with Vicon Nexus 2.8.1 capture soft- ware. Figure 2.Marker placement in lower limb model. According to the requirement of the Vicon system and model, a “T-pose” calibration trial was performed to establish joint centres, body segment coordinate systems, segment lengths and the local positions of tracking markers with Vicon Nexus 2.8.1 capture software. Participants were then asked to run at their preferred running speed under the three SR trials: preferred step rate (PSR) = 152–176 SPM (step per min or cadence), 10% below preferred step rate (10% below PSR) = 137–159 SPM, 10% above-preferred step rate (10% above PSR) = 167–194 SPM. The order of the SR trials was randomised for each participant, with 30 s of data collection for each condition and 30 s of rest between SR trials. Subjects ran in time with a digital audio metronome to facilitate the correct SR. Data collection did not begin until the participants could maintain the prescribed SR for a minimum of 1 min determined by visual inspection. There was 2 min of rest between different step rates.
Appl. Sci.2024,14, 1059 4 of 14 The iPad application “Pro Metronome” by EUM Lab for IOS (Figurea) was utilised in conjunction with a Voombox Portable Bluetooth speaker (model: Voombox-Travel, Divoom ® , Shenzhen, China) (Figureb) to help participants maintain their step rate throughout the study. Once the step rate was determined by the participant and researcher, the metronome would produce the sound with the step rate, and then the participant maintained the step rate according to the sounds. The 10% increase and decrease to pre-SR were adjusted according to this way.Appl. Sci. 2024, 14, x FOR PEER REVIEW 4 of 14 Participants were then asked to run at their preferred running speed under the three SR trials: preferred step rate (PSR) = 152–176 SPM (step per min or cadence), 10% below preferred step rate (10% below PSR) = 137–159 SPM, 10% above-preferred step rate (10% above PSR) = 167–194 SPM. The order of the SR trials was randomised for each participant, with 30 s of data collection for each condition and 30 s of rest between SR trials. Subjects ran in time with a digital audio metronome to facilitate the correct SR. Data collection did not begin until the participants could maintain the prescribed SR for a minimum of 1 min determined by visual inspection. There was 2 min of rest between different step rates. The iPad application “Pro Metronome” by EUM Lab for IOS (Figure 3a) was utilised in conjunction with a Voombox Portable Bluetooth speaker (model: Voombox-Travel, Divoom ®, Shenzhen, China) (Figure 3b) to help participants maintain their step rate throughout the study. Once the step rate was determined by the participant and re- searcher, the metronome would produce the sound with the step rate, and then the par- ticipant maintained the step rate according to the sounds. The 10% increase and decrease to pre-SR were adjusted according to this way. (a) (b) Figure 3. The metronome application—Pro Metronome (a) and Voombox portable Bluetooth speaker (b). 2.3. Data Processing After recording trial data, Vicon Nexus software (v 2.8.1) was used to format the data, including marker labelling, marker
par- ticipant maintained the step rate according to the sounds. The 10% increase and decrease to pre-SR were adjusted according to this way. (a) (b) Figure 3. The metronome application—Pro Metronome (a) and Voombox portable Bluetooth speaker (b). 2.3. Data Processing After recording trial data, Vicon Nexus software (v 2.8.1) was used to format the data, including marker labelling, marker gap filling, marker smoothing and lower limb pipe- lines. Gait events were manually identified for foot contact and terminal stance and then auto-correlated. Kinematic data were formatted with Woltring filtering and processed through a dynamic Plug-in-Gait model. During each of the three SR trials, spatiotemporal characteristics (described below) were measured for every step and averaged for each in- dividual trial. - Step length: (SL, in meters [m]): based on the length the treadmill belt moved from toe-off to initial contact in successive steps. - Step height (SH, in meters [m]): the maximum height the lateral malleolus marker (L/RANK) reaches during a step. - Step rate: (SR in steps per minute [SPM]) the number of ground contact events per minute. - Step angle: (SA in degrees [°]) the angle of a parabola tangent deriving from the SL and the SH during a step and calculated by the formulas below. Referring to Figure 1, given that the origin is the point of foot taking off, height h and distance x, there is a function as below, and k is a coefficient to be determined: Figure 3.The metronome application—Pro Metronome (a) and Voombox portable Bluetooth speaker (b). 2.3. Data Processing After recording trial data, Vicon Nexus software (v 2.8.1) was used to format the data, including marker labelling, marker gap filling, marker smoothing and lower limb pipelines. Gait events were manually identified for foot contact and terminal stance and then auto-correlated. Kinematic data were formatted with Woltring filtering and processed through a dynamic Plug-in-Gait model. During each of the three SR trials, spatiotemporal characteristics (described below) were measured for every step and averaged for each individual trial. - Step length: (SL, in meters [m]): based on the length the treadmill belt moved from
Description
The relationship between step rate and step angle in running was investigated.