Abstract
udy investigated biomechanical assessments in trail running, comparing two wear- able devices—Stryd Power Meter and GARMIN RP. With the growing popularity of trail running and the complexities of varied terrains, there is a heightened interest in understanding metabolic pathways, biomechanics, and performance factors. The research aimed to assess the inter- and intra-device agreement for biomechanics under ecological conditions, focusing on power, speed, cadence, vertical oscillation, and contact time. The participants engaged in trail running sessions while wearing two Stryd and two Garmin devices. The intra-device reliability demonstrated high consistency for both GARMIN RPand Stryd TM , with strong correlations and minimal variability. How- ever, distinctions emerged in inter-device agreement, particularly in power and contact time uphill, and vertical oscillation downhill, suggesting potential variations between GARMIN RPand Stryd TM measurements for specific running metrics. The study underscores that caution should be taken in interpreting device data, highlighting the importance of measuring with the same device, considering contextual and individual factors, and acknowledging the limited research under real-world trail conditions. While the small sample size and participant variations were limitations, the strength of this study lies in conducting this
TM measurements for specific running metrics. The study underscores that caution should be taken in interpreting device data, highlighting the importance of measuring with the same device, considering contextual and individual factors, and acknowledging the limited research under real-world trail conditions. While the small sample size and participant variations were limitations, the strength of this study lies in conducting this investigation under ecological conditions, significantly contributing to the field of biomechanical measurements in trail running. Keywords:wearable technology; running metrics; outdoor testing; trail running analysis 1. Introduction Trail running, recently recognized by the International Association of Athletics Fed- erations (IAAF) as an emerged running discipline, has rapidly increased in popularity, which has led to a growing scientific interest in the field of sports science [1]. The inherent difficulty of trail running, often characterized by positive and negative elevation changes in the terrain, increases the energy and muscular demand during its practice and heightens the importance of the strategic combination of these metabolic pathways and biomechanical conditions for optimal performance [1]. The complexity of this sport involves numerous performance factors, including aerobic capacity, anaerobic threshold, muscular strength, and resistance to force due to both positive and negative elevation changes [2,3]. Addi- tionally, considerations such as thermoregulation, running economy, and biomechanical adaptations in uphill and downhill running play a crucial role [2,3]. These adaptations encompass changes in the foot strike pattern, joint kinematics, and energy cost [2,3]. This is why biomechanical variables such as power, speed, cadence, and contact time are worthy of evaluation due to their close relationship with performance. In athletic disciplines, power denotes the rate of performing work and is a determinant of explosive strength, which is essential for activities necessitating rapid force application. Speed is the scalar quantity representing the distance covered per unit of time, which is pivotal in disciplines Sensors2024,24, 3570.
Sensors2024,24, 3570 2 of 18 requiring swift transit. Cadence, the frequency of stride or pedal revolutions per minute, is a critical factor in endurance sports, influencing the metabolic cost and endurance ca- pacity [1,2]. Moreover, vertical oscillation and contact time are biomechanical parameters in gait analysis. Vertical oscillation quantifies the vertical displacement of the center of mass during locomotion, while contact time measures the duration of foot–ground interac- tions [4]. These parameters are indicative not only of performance, running economy, and biomechanical efficiency, but also of injury risk [5]. However, biomechanical measurements under ecological conditions according to the terrain typology in trail athletes are uncommon and limited to laboratory exercise tests [1,6]. The past decade witnessed significant technological advancements in wearable sports devices for assessing running biomechanics. These devices enable the measurement of training loads and the development of training protocols [7]. This biomechanical evalu- ation of running is usually performed in a laboratory using treadmills, force plates, and motion capture systems. Nevertheless, this approach is usually both inaccessible and too expensive for practitioners, emphasizing the need for more affordable methods suitable for outdoor use. The significance of monitoring biomechanical parameters measured under real-world conditions during trail running has increased owing to their association with performance. It is necessary to objectively assess biomechanical parameters under ecological conditions to quantify the training load and performance [6]. The availability and popularity of wearable sports technology for running has grown extensively in recent years [8]. In this context, recent advances in wearable technology have accelerated the development of less obtrusive and more precise and affordable devices designed to monitor a wide range of parameters in trail runners exercising in their normal environments [8]. Recently, there has been an effort to produce low-cost, portable gait and running analysis equipment. This has allowed researchers to remove participants from an artificial laboratory environment and measure participants in a more natural environment [9] Commercially available wearable technology, and the biofeedback provide by them, has been welcomed by coaches and runners, including those in the trail running community. The commercial availability of such devices in the market is
gait and running analysis equipment. This has allowed researchers to remove participants from an artificial laboratory environment and measure participants in a more natural environment [9] Commercially available wearable technology, and the biofeedback provide by them, has been welcomed by coaches and runners, including those in the trail running community. The commercial availability of such devices in the market is extensive, including popular brands such as GARMINRP, Stryd TM , RunScribe, Polar, and Suunto, among others. These devices, equipped with Global Positioning System (GPS), Inertial Measurement Unit (IMU) sensors, and more, provide insights into running metrics. These devices are being used to quantify the training load by reporting data such as cadence, stride length, power, contact time, or vertical oscillation [10]. However, more high-quality research is needed to deter- mine the accuracy and reliability of measurements obtained by this new technology, which is continuously developing and advancing [11], but is still in the exploratory phase [12]. In the market, there are two standout wearable devices designed for measuring run- ning biomechanics on the market. On one hand, there is the Stryd Power Meter, which is a foot-pod device that has been used recently to measure variables related to trail run- ning performance [13] and this device is capable of measuring power, contact time, flight time, step length, vertical oscillation, and cadence [6,7,9]. On the other hand, there are devices from the GARMINRPbrand. Apart from measuring heart rate and estimating energy expenditure, these devices provide data on distance, speed, and elevation [14], vertical oscillation [15], contact time [16], and cadence [8]. While these kinds of devices have been extensively studied in laboratory settings using treadmills for testing, there is a notable dearth of research in real-world scenarios, conducting tests under natural conditions. This gap in knowledge is particularly significant in the context of trail running, where the complexity arises from the diverse terrain characteristics [6]. The comparison between motorized treadmill running and overground running highlights variations in sagittal plane measures and spatiotemporal parameters, with conflicting findings across analyses of kinematics, kinetics, muscle activity, and muscle-tendon outcomes [17]. Notably, these comparisons do
conditions. This gap in knowledge is particularly significant in the context of trail running, where the complexity arises from the diverse terrain characteristics [6]. The comparison between motorized treadmill running and overground running highlights variations in sagittal plane measures and spatiotemporal parameters, with conflicting findings across analyses of kinematics, kinetics, muscle activity, and muscle-tendon outcomes [17]. Notably, these comparisons do not exclusively involve trail running, which presents unique char- acteristics in overground locomotion [17]. Thus, it is imperative to understand how these
Sensors2024,24, 3570 3 of 18 devices measure in real environmental and terrain conditions, specifically whether these devices demonstrate high intra-device reliability and exhibit agreement between device measurements, which is essential to determine the interchangeability of these devices in practical applications. Moreover, investigating these aspects on both uphill and downhill terrains provides insights into potential variations, ensuring a comprehensive evaluation of device performance across diverse slope conditions. This gap can be addressed by focusing on assessing how the Stryd TM and GARMINRPdevices perform in real-world conditions, specifically exploring their intra-device reliability and agreement between measurements. This study aimed to investigate and compare the biomechanical data reported by the two most popular wearable devices, GARMINRPand Stryd TM , which can provide measures of biomechanics during trail running under natural conditions. Specifically, our objectives include, on the one hand, assessing the consistency and agreement of biomechanical measurements within the GARMINRPor Stryd TM devices by comparing one device against the other. On the other hand, we sought to investigate the inter- device reliability and agreement by comparing the biomechanical parameters measured by a Stryd TM device against a GARMINRPdevice. By conducting these comparisons, we aimed to provide insights into the reliability and consistency of these wearable devices in measuring key biomechanical variables, offering valuable information for both researchers and practitioners in the field of trail running and sports science. 2. Materials and Methods 2.1. Participants The five participants (four males and one female) included in the study were healthy young adults with an average age of 33±4.4 years. The participants were required to have a minimum of one year of experience in trail running. The average body mass index (BMI) of the participants was 22.6±2.2 kg/m 2 . Before testing, the participants were informed about the procedure and study protocol, and signed informed consent was collected from each participant. This study was performed in accordance with the ethical guidelines of the Helsinki Declaration of 1964 (revised in Fortaleza, 2013) [18]. The study was reviewed and approved by the Ethics Committee of the University of San Jorge (code No. 005–19/20). 2.2. Study Design To assess
about the procedure and study protocol, and signed informed consent was collected from each participant. This study was performed in accordance with the ethical guidelines of the Helsinki Declaration of 1964 (revised in Fortaleza, 2013) [18]. The study was reviewed and approved by the Ethics Committee of the University of San Jorge (code No. 005–19/20). 2.2. Study Design To assess the inter-device reliability and intra-device reliability of the GARMINRP and Stryd TM wearable devices, we compared the recorded data from both devices on a field running track and the data collected from both sessions were pooled together. The variables analyzed were power, speed, cadence, vertical oscillation, and contact time for both devices. While there are other wearable devices, such as Runscribe, the decision to include Stryd was based on the necessity for the runner to wear all devices. To minimize potential interference, particularly related to device positioning, Stryd was chosen, as it demonstrated higher reliability in measuring power compared to Runscribe [19], and has the closest agreement with the theoretical power output [20]. Additionally, the findings from Kozinc et al. [21] revealed an unacceptable coefficient of variation for power, foot strike type, and horizontal ground reaction force rate. The participants completed two sessions of a trail running course, with a 2.5 km distance and a 195 m elevation gain, followed by a descent along the same route, as illustrated in Figure. The tests were conducted at the same time of day on both days. A one-week recovery period was implemented between sessions for the participants. 2.3. Equipment On the left wrist of the participants, two GarminRPFenix 7S Solar watches (Garmin Ltd., Southampton, UK) were placed. This device is capable of detecting running biomechanics, activity, and sleep using several sensors, including a triaxial accelerometer, a Global Naviga- tion Satellite System (GNSS) sensor, including GPS functionality, and a photodiode sensor for photoplethysmography measurements. This device measures running biomechanics
Sensors2024,24, 3570 4 of 18 variables such as power, speed, cadence, vertical oscillation, and ground contact time. The participants also wore a GARMINRPHRM-PRO heart rate monitor below the pectoral zone in a centered and vertically oriented position. Power was assessed by the GARMINRP HRM-PRO heart rate monitor and speed, cadence, vertical oscillation, and ground con- tact time were measured by the GARMINRPFenix 7S Solar watch. The measured power specifically refers to the external mechanical power, calculated from force (estimated via accelerations using accelerometers) and velocity. This includes the work exerted by runners during both the loading phase and subsequent push-off to counteract environmental factors such as ground reaction force, gravity, and surface friction.Sensors 2024, 24, x FOR PEER REVIEW 4 of 18 Figure 1. Trail course profile. 2.3. Equipment On the left wrist of the participants, two Garmin RP Fenix 7S Solar watches (Garmin Ltd., Southampton, UK) were placed. This device is capable of detecting running biome- chanics, activity, and sleep using several sensors, including a triaxial accelerometer, a Global Navigation Satellite System (GNSS) sensor, including GPS functionality, and a photodiode sensor for photoplethysmography measurements. This device measures run- ning biomechanics variables such as power, speed, cadence, vertical oscillation, and ground contact time. The participants also wore a GARMIN RP HRM-PRO heart rate mon- itor below the pectoral zone in a centered and vertically oriented position. Power was as- sessed by the GARMIN RP HRM-PRO heart rate monitor and speed, cadence, vertical os- cillation, and ground contact time were measured by the GARMIN RP Fenix 7S Solar watch. The measured power specifically refers to the external mechanical power, calculated from force (estimated via accelerations using accelerometers) and velocity. This includes the work exerted by runners during both the loading phase and subsequent push-off to coun- teract environmental factors such as ground reaction force, gravity, and surface friction. On the right foot of the participants, two Stryd TM foot pods (Stryd Powermeter; Stryd, Inc., Boulder, CO, USA) were attached. This device is a carbon fiber-reinforced foot pod based on a 6-axis inertial motion sensor (3-axis gyroscope and 3-axis accelerometer). The variables
and subsequent push-off to coun- teract environmental factors such as ground reaction force, gravity, and surface friction. On the right foot of the participants, two Stryd TM foot pods (Stryd Powermeter; Stryd, Inc., Boulder, CO, USA) were attached. This device is a carbon fiber-reinforced foot pod based on a 6-axis inertial motion sensor (3-axis gyroscope and 3-axis accelerometer). The variables measured with this Stryd TM technology include power, speed, cadence, vertical oscillation, and the duration of contact time. Figure 2 illustrates the equipment that the runners wore during the trial. The foot- wear during the test was chosen by each participant, according to their personal prefer- ences. The runners also carried two Android smartphones with the Stryd TM application in a waist pack to collect data from the devices. At the beginning of the test, the weight, height, and age of the participants were entered into both the watches and the Stryd mo- bile application. Two researchers simultaneously initiated the watches and mobile de- vices. Subjects were asked to stop both the watches and the mobile app upon completing the descent. Figure 1.Trail course profile. On the right foot of the participants, two Stryd TM foot pods (Stryd Powermeter; Stryd, Inc., Boulder, CO, USA) were attached. This device is a carbon fiber-reinforced foot pod based on a 6-axis inertial motion sensor (3-axis gyroscope and 3-axis accelerometer). The variables measured with this Stryd TM technology include power, speed, cadence, vertical oscillation, and the duration of contact time. Figure during the test was chosen by each participant, according to their personal preferences. The runners also carried two Android smartphones with the Stryd TM application in a waist pack to collect data from the devices. At the beginning of the test, the weight, height, and age of the participants were entered into both the watches and the Stryd mobile application. Two researchers simultaneously initiated the watches and mobile devices. Subjects were asked to stop both the watches and the mobile app upon completing the descent.Sensors 2024, 24, x FOR PEER REVIEW 5 of 18 Figure 2. Graphical description of runner instrumentation with wearable
height, and age of the participants were entered into both the watches and the Stryd mobile application. Two researchers simultaneously initiated the watches and mobile devices. Subjects were asked to stop both the watches and the mobile app upon completing the descent.Sensors 2024, 24, x FOR PEER REVIEW 5 of 18 Figure 2. Graphical description of runner instrumentation with wearable electronic devices for the test. 2.4. Data Extraction The data capture frequency was 1 Hz for both devices (Stryd TM and GARMIN RP), a sampling frequency similar to that used in similar studies on running biomechanics [7,8,16]. Following data recording, the data were exported for subsequent analyses. The data upload to the Garmin Connect software , version 5.1, o n a PC was conducted to en- sure synchronization with GPS time stamps, resulting in the acquisition of files in “.fit” format. Conversely, the Stryd TM device employs a distinct approach, utilizing the smartphone for data upload and relying on GPS time stamps for temporal synchroniza- tion. After data transmission to the Stryd TM platform, the files were downloaded in “.fit” format. Both sets of files were then processed through the free license for GoldenCheetah software, version 3.6, which facilitated the temporal synchronization of all files possessing timestamp marks (.fit) before collectively exporting them to Excel. After synchronization, both files had the same number of samples for analysis. 2.5. Statistical Analysis The Statistical Package for the Social Sciences (SPSS) version 23.0 (SPSS Inc., Chicago, IL, USA) was used to perform all the statistical analyses. Statistical significance was set at p < 0.05 for all tests. The data are presented as the mean ± standard deviation (SD). Measurements obtained from both sessions were pooled together. To determine the intra-device reliability, the measurements, including power, speed, cadence, vertical os- cillation, and contact time, obtained from one device (either GARMIN RP or Stryd TM ) were compared to those recorded by the second device of the same brand. Moreover, the meas- urements of the running biomechanics obtained from the Stryd TM devices were compared to those recorded by the GARMIN RP devices to determine
including power, speed, cadence, vertical os- cillation, and contact time, obtained from one device (either GARMIN RP or Stryd TM ) were compared to those recorded by the second device of the same brand. Moreover, the meas- urements of the running biomechanics obtained from the Stryd TM devices were compared to those recorded by the GARMIN RP devices to determine the inter-device reliability. To evaluate agreement in inter- and intra-class measurements, the Intraclass Corre- lation Coefficient (ICC) and its 95% confidence limits were utilized. Values less than 0.5 are indicative of poor reliability, values between 0.5 and 0.75 indicate moderate reliability, values between 0.75 and 0.9 indicate good reliability, and values greater than 0.90 indicate excellent reliability [22]. Additionally, the Coefficient of Variation (CV, %) was calculated to assess the relative variability of the measurements. For intra-device comparisons, meas- urements from one GARMIN RP device were compared to a second GARMIN RP device, both worn by the same individual on the same wrist. Similarly, measurements from one Stryd TM device were compared to a second Stryd TM device, both worn by the same indi- vidual on their foot. As stated before, the tests were conducted on different days following the comparison protocol. In order to assess the strength of the linear relationship between variables, the Pear- son correlation coefficient was employed as a measure in this study [23]. Furthermore, Figure 2.Graphical description of runner instrumentation with wearable electronic devices for the test.
Sensors2024,24, 3570 5 of 18 2.4. Data Extraction The data capture frequency was 1 Hz for both devices (Stryd TM and GARMINRP), a sampling frequency similar to that used in similar studies on running biomechanics [7,8,16]. Following data recording, the data were exported for subsequent analyses. The data upload to the Garmin Connect software, version 5.1, on a PC was conducted to ensure synchronization with GPS time stamps, resulting in the acquisition of files in “.fit” format. Conversely, the Stryd TM device employs a distinct approach, utilizing the smartphone for data upload and relying on GPS time stamps for temporal synchronization. After data transmission to the Stryd TM platform, the files were downloaded in “.fit” format. Both sets of files were then processed through the free license for GoldenCheetah software, version 3.6, which facilitated the temporal synchronization of all files possessing timestamp marks (.fit) before collectively exporting them to Excel (Version 2110). After synchronization, both files had the same number of samples for analysis. 2.5. Statistical Analysis The Statistical Package for the Social Sciences (SPSS) version 23.0 (SPSS Inc., Chicago, IL, USA) was used to perform all the statistical analyses. Statistical significance was set at p< 0.05 for all tests. The data are presented as the mean±standard deviation (SD). Measurements obtained from both sessions were pooled together. To determine the intra-device reliability, the measurements, including power, speed, cadence, vertical oscil- lation, and contact time, obtained from one device (either GARMINRPor Stryd TM ) were compared to those recorded by the second device of the same brand. Moreover, the mea- surements of the running biomechanics obtained from the Stryd TM devices were compared to those recorded by the GARMINRPdevices to determine the inter-device reliability. To evaluate agreement in inter- and intra-class measurements, the Intraclass Corre- lation Coefficient (ICC) and its 95% confidence limits were utilized. Values less than 0.5 are indicative of poor reliability, values between 0.5 and 0.75 indicate moderate reliability, values between 0.75 and 0.9 indicate good reliability, and values greater than 0.90 indicate excellent reliability [22]. Additionally, the Coefficient of Variation (CV, %) was calculated to assess the
Description
The research assesses the reliability of two wearable devices in measuring biomechanics during trail running.