Abstract
a decisive action in soccer that is considerably taxing from a neuromuscular and energetic perspective. This study compared different calculation methods for the metabolic power (MP) and energy cost (EC) of sprinting using global positioning system (GPS) metrics and electromyography (EMG), with the aim of identifying potential differences in performance markers. Sixteen elite U17 male soccer players (age: 16.4±0.5 years; body mass: 64.6±4.4 kg; and height: 177.4±4.3 cm) participated in the study and completed four different submaximal constant running efforts followed by sprinting actions while using portable GPS-IMU units and surface EMG. GPS- derived MP was determined based on GPS velocity, and the EMG-MP and EC were calculated based on individual profiles plotting the MP of the GPS and all EMG signals acquired. The goodness of fit of the linear regressions was
and completed four different submaximal constant running efforts followed by sprinting actions while using portable GPS-IMU units and surface EMG. GPS- derived MP was determined based on GPS velocity, and the EMG-MP and EC were calculated based on individual profiles plotting the MP of the GPS and all EMG signals acquired. The goodness of fit of the linear regressions was assessed by the coefficient of determination (R 2 ), and a repeated measures ANOVA was used to detect changes. A linear trend was found in EMG activity during submaximal speed runs (R 2 = 1), but when the sprint effort was considered, the trend became exponential (R 2 = 0.89). The EMG/force ratio displayed two different trends: linear up to a 30 m sprint (R 2 = 0.99) and polynomial up to a 50 m sprint (R 2 = 0.96). Statistically significant differences between the GPS and EMG were observed for MP splits at 0–5 m, 5–10 m, 25–30 m, 30–35 m, and 35–40 m and for EC splits at 5–10 m, 25–30 m, 30–35 m, and 35–40 m (p≤0.05). Therefore, the determination of the MP and EC based on GPS technology underestimated the neuromuscular and metabolic engagement during the sprinting efforts. Thus, the EMG-derived method seems to be more accurate for calculating the MP and EC in this type of action. Keywords:football; maximum velocity; maximal running; GPS; EMG/force ratio 1. Introduction The energy cost (EC) and kinematics of various forms of locomotion (e.g., running) have been analyzed in numerous investigations [1–7] with the aim of elucidating the main mechanisms of different movements. These studies have practical applications and allow for evaluating the metabolic energy expenditure or predicting the “ideal” per- formance[8–14] based on the relationship between mechanics and energetics [7,15–19], which is one of the most crucial and extensively researched domains of human move- ment [3,4,16,20–27]. For example, di Prampero et al. [22] estimated the EC of the first 30 m of a sprint running from a standing position to overcome the challenges of directly measuring effort Sensors2024,24, 2577.
[7,15–19], which is one of the most crucial and extensively researched domains of human move- ment [3,4,16,20–27]. For example, di Prampero et al. [22] estimated the EC of the first 30 m of a sprint running from a standing position to overcome the challenges of directly measuring effort Sensors2024,24, 2577.
Sensors2024,24, 2577 2 of 12 during dynamic actions. In brief, the method relied on the equivalence between acceler- ation on flat ground and ascent at constant speed, with an equivalent slope defined by forward acceleration. Since the EC of constant speed on a varied range of slopes is well known [1–3,5,13,28], estimating the EC of the run is possible when the equivalence between forward acceleration and the slope is known. Therefore, di Prampero et al.’s [22] model has been suggested to redefine the concept of “high intensity”. Nevertheless, despite the new possibilities that arise from this approach [22] in terms of workload quantification and physical performance evaluation during training and competition [18,29–34], more evidence is still needed to determine the feasibility of EC estimation in applied scenarios. Some researchers tried to evaluate neuromuscular and metabolic engagement during training and match events by using portable technology to understand muscle activation thresholds. A first attempt to characterize the profile of neuromuscular activation during a soccer match was proposed by Montini et al. [35], with the intention to integrate, in competition, more traditional laboratory-based approaches (e.g., electromyography [EMG]) to help better understand the physiological demands of competitive soccer. The authors analyzed different intensity zones to create a relative performance model and suggested that this approach could be used to improve the understanding of the physiological require- ments of competitive soccer [35]. However, the EC and metabolic power (MP) calculated by EMG were not determined; thus, additional research is still necessary to consolidate measurements of economy and neuromuscular activation during performance activities that involve high-intensity running. This type of methodological approach is important for practitioners since, by using portable technologies, it is possible to collect data on more ecologically valid conditions than in laboratory settings. The literature has explored the behavior of EMG during sprints and submaximal runs since Mero & Komi [36]; however, to the authors’ knowledge, it has not been utilized for the calculation of the MP and EC until Colli’s work (unpublished data retrieved from laltrametodologia.com). Thus, this remains a topic that needs further investigation to better understand the main mechanistic–energetic
settings. The literature has explored the behavior of EMG during sprints and submaximal runs since Mero & Komi [36]; however, to the authors’ knowledge, it has not been utilized for the calculation of the MP and EC until Colli’s work (unpublished data retrieved from laltrametodologia.com). Thus, this remains a topic that needs further investigation to better understand the main mechanistic–energetic needs and, consequently, make meaningful methodological choices. Currently, there are numerous existing studies evaluating MP and energy expenditure, utilizing global positioning systems (GPS) and inertial measurement units (IMU) [24,32,33,37–43], but there is a complete absence of studies calculating these parameters from EMG technology. Analyzing submaximal and maximal sprint behavior with the aim of determining the MP and EC calculated by EMG and the EMG and force relationship could help clarify actual metabolic and neuromuscular engagement during linear running actions. The comparison of two distinct technologies (i.e., EMG and GPS- IMU) has the potential to provide precise estimates of relative effort for actions such as sprints, yielding hypothetical benefits. Therefore, the aims of this study were to (1) analyze submaximal running efforts at various constant speeds to investigate possible differing mechanical–energetic demands when compared to sprinting; (2) examine the behavior of the EMG activity-to-force ratio (EMG/F) in linear sprints over 30 m and 50 m and their corresponding 5 m sections; and (3) determine the EC and MP of sprinting assessed by GPS-IMU and EMG by creating an ad hoc neuromuscular profile utilizing muscle activation patterns. The present study may have significant implications for the establishment and structuring of training objectives. 2. Materials and Methods 2.1. Study Design A cross-sectional study design was used (Figure). Data were collected during the 2020/2021 competitive season, during the months of September through November, with players from the under-17 (U17) age category of a professional soccer club academy. To avoid a potential source of bias, de-identified data were analyzed by a researcher not directly involved in data collection. After a careful theoretical explanation accompanied by a practical demonstration, players completed four different submaximal constant running efforts followed by sprinting actions while using portable GPS-IMU units
players from the under-17 (U17) age category of a professional soccer club academy. To avoid a potential source of bias, de-identified data were analyzed by a researcher not directly involved in data collection. After a careful theoretical explanation accompanied by a practical demonstration, players completed four different submaximal constant running efforts followed by sprinting actions while using portable GPS-IMU units and surface
Sensors2024,24, 2577 3 of 12 EMG. All athlete measures were taken in a single testing session for each player during the pre-season period. The warm-up included mobility and running-based exercises for a duration of ~15 min. All warm-up exercises had been previously used by all the players, as they were applied in daily training.Sensors 2024, 24, 2577 3 of 14 directly involved in data collection. After a careful theoretical explanation accompanied by a practical demonstration, players completed four different submaximal constant run- ning efforts followed by sprinting actions while using portable GPS-IMU units and surface EMG. All athlete measures were taken in a single testing session for each player during the pre-season period. The warm-up included mobility and running-based exercises for a duration of ~15 min. All warm-up exercises had been previously used by all the players, as they were applied in daily training. Figure 1. Overview of the study design. 2.2. Participants A convenience sample of sixteen U17 football players (age: 16.4 ± 0.5 years; body mass: 64.6 ± 4.4 kg; height: 177.4 ± 4.3 cm; and BMI: 20.5 ± 1.3) of the “Elite Italian Cham- pionship” volunteered to participate in this study. A normal team practice and competi- tion schedule, consisting of at least four training sessions and one match per week, was maintained during the investigation period. Only players who were free from recent inju- ries or medical conditions that could limit their maximum effort were included in the study. Detailed information regarding all testing and training procedures was provided to the subjects and their legal guardians before the latter signed a written informed con- sent. The Local Human Subjects Ethics Committee approved the study in compliance with the Declaration of Helsinki. 2.3. Procedures 2.3.1. Constant Running and Sprint Testing Four incremental constant (C 1,2,3,4) running speeds (over 50 m, at theoretical required times of ~22.5, ~15, ~11.3, and ~9 s in “C 1”, “C2”, “C3”, and “C4”, respectively) and a sprint effort (where only the split of the maximum speed phase was taken) were used for the construction of an individual profile (detailed below; coded with
and Sprint Testing Four incremental constant (C 1,2,3,4) running speeds (over 50 m, at theoretical required times of ~22.5, ~15, ~11.3, and ~9 s in “C 1”, “C2”, “C3”, and “C4”, respectively) and a sprint effort (where only the split of the maximum speed phase was taken) were used for the construction of an individual profile (detailed below; coded with “S 5”). Timing adherence was manually controlled using stopwatches during the constant runs in the trials. All tests were conducted on the training and match field, and each player was given the appropri- ate technical clothing to maintain their running characteristics (ecological field test). As mentioned, the players started by performing the constant runs with the objective of hav- ing an approximate constant difference between runs rather than a set datum (impossible Figure 1.Overview of the study design. 2.2. Participants A convenience sample of sixteen U17 football players (age: 16.4±0.5 years; body mass: 64.6±4.4 kg; height: 177.4±4.3 cm; and BMI: 20.5±1.3) of the “Elite Italian Championship” volunteered to participate in this study. A normal team practice and competition schedule, consisting of at least four training sessions and one match per week, was maintained during the investigation period. Only players who were free from recent injuries or medical conditions that could limit their maximum effort were included in the study. Detailed information regarding all testing and training procedures was provided to the subjects and their legal guardians before the latter signed a written informed consent. The Local Human Subjects Ethics Committee approved the study in compliance with the Declaration of Helsinki. 2.3. Procedures 2.3.1. Constant Running and Sprint Testing Four incremental constant (C1,2,3,4) running speeds (over 50 m, at theoretical required times of ~22.5, ~15, ~11.3, and ~9 s in “C1”, “C2”, “C3”, and “C4”, respectively) and a sprint effort (where only the split of the maximum speed phase was taken) were used for the construction of an individual profile (detailed below; coded with “S5”). Timing adherence was manually controlled using stopwatches during the constant runs in the trials. All tests were conducted on the training and match field, and each
“C2”, “C3”, and “C4”, respectively) and a sprint effort (where only the split of the maximum speed phase was taken) were used for the construction of an individual profile (detailed below; coded with “S5”). Timing adherence was manually controlled using stopwatches during the constant runs in the trials. All tests were conducted on the training and match field, and each player was given the appropriate technical clothing to maintain their running characteristics (ecological field test). As mentioned, the players started by performing the constant runs with the objective of having an approximate constant difference between runs rather than a set datum (impossible for a field test that does not take place on an ergometer); thus, they were asked to maintain the same running characteristics during each trial. After the constant runs and a rest period (2 min), the players performed a total of three all-out sprints over 50 m. To establish the zone of maximum sprinting speed, a plateau with a delta of no more
Sensors2024,24, 2577 4 of 12 than 3 km·h −1 in the GPS data was selected to objectively determine when athletes reached their peak speed. A 5 min passive rest period was provided between trials to minimize fatigue effects on performance. Participants were encouraged to perform each sprint trial as fast as possible. 2.3.2. Electromyography Recording and Analysis During the trials, EMG shorts equipped with textile electrodes (Myonear Pro, Myontec, Kuopio, Finland) were used to collect muscle activation data (Figure). The conductive electrodes and the associated wires were integrated into the fabric. These electrodes cov- ered three main muscle groups, bilaterally, with 6 differential EMG biosignal channels: quadriceps, hamstrings, and glutes. Two sizes of shorts were available (medium and large), and the best fit was chosen for each participant. The proper size of the shorts is essential to establish necessary contact between electrodes and skin and to minimize or avoid any movement artifacts during dynamic activities [44]. Additionally, a small amount of water was applied to the electrodes before the participant put on the shorts to ensure adequate signal conduction, as previously recommended [45]. EMG signals were transmitted to a laptop and analyzed and collected at 1000 Hz with the Myontec ‘’Muscle Monitor” software version 3.1.0.4 (Myontec Ltd., Kuopio, Finland). Textile electrodes embedded in shorts appeared to provide comparable lower limb muscle activation data to traditional surface EMG [44]. Each trial was firstly filtered with a second-order Butterworth band pass (a bandwidth of 40–200 Hz, derived through an exploration of the frequency domain with a signal voltage and−3 dB cutoff frequency) filter, before being rectified and averaged over 100 Hz. In accordance with Kyröläinen et al. [46], who criticized the use of voluntary maximum isometric contractions (MVICs) for evaluating neuromuscular activation during running, the EMG data were normalized using the peak EMG activity (EMGpeak) detected during the sprint, thus allowing for greater repeatability of the measurements. In addition, the EMG signals during the runs were segmented into subphases to enable a detailed analysis not only of the overall trend (total EMG recording [EMGTOT_ecf], comprehensive of ground contact, eccentric and concentric, and
during running, the EMG data were normalized using the peak EMG activity (EMGpeak) detected during the sprint, thus allowing for greater repeatability of the measurements. In addition, the EMG signals during the runs were segmented into subphases to enable a detailed analysis not only of the overall trend (total EMG recording [EMGTOT_ecf], comprehensive of ground contact, eccentric and concentric, and of the flight phases) but also of the charac- teristics of each phase (i.e., eccentric [EMGe], concentric [EMGc], and flight phase) utilizing the LagalaColli software (version 1.0.2.218, Spinitalia S.R.L., Rome, Italy). The EMG/F ratio was determined with an arbitrary unit, consisting of the ratio of the normalized EMG signal with peak values and expressing it as a percentage and the resulting force (in N·kg −1 ), which was calculated by integrating the accelerations from the three axes (x, y, and z) using IMU technology.Sensors 2024, 24, 2577 5 of 14 Figure 2. (a) Back view of the sensor placement; (b) GPS unit; (c) EMG shorts equipped with textile electrodes with 6 differential EMG biosignal channels. 2.3.3. Ad Hoc Profiling and Metabolic Power Calculation Prior to the sprint analysis, an individual linear profile (including slope and inter- cept) was constructed for each athlete by plotting the MP of the GPS and muscle load (ML) from all the EMG signals acquired. The profile was individualized and made it possible to recalculate the MP from the EMG by a simple method that consisted of multiplying by the slope and then adding intercept (Equation (1)). Then, the EC was calculated by divid- ing the obtained value of MP by the speed achieved (Equation (2)). <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> <math><mrow><msub><mrow><mtext>MP</mtext></mrow><mrow><mtex t>EMG</mtext></mrow></msub><mtext>=(</mtext><msub><mrow><mtext >ML</mtext></mrow><mrow><mtext>EMG</mtext></mrow></msub><mte xt>·</mtext><mtext>SLOPE</mtext><mtext>)+I</mtext><mtext>NTERCEP T</mtext></mrow></math> <!-- MathType@End@5@5@ --> (1) <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> <math><mrow><msub><mrow><mtext>EC</mtext></mrow><mrow><mtex t>EMG</mtext></mrow></msub><mtext>=</mtext><mfrac><mrow><msub ><mrow><mtext>MP</mtext></mrow><mrow><mtext>EMG</mtext></mro w></msub></mrow><mrow><msub><mrow><mtext>SPEED</mtext></mro w><mrow><mtext>IMU</mtext></mrow></msub></mrow></mfrac></mro w></math> <!-- MathType@End@5@5@ --> (2) The EMG data were integrated with GPS-IMU signals to permit us to temporally and kinematically differentiate phases. Sprint analyses were conducted utilizing personalized Figure 2.(a) Back view of the sensor placement; (b) GPS unit; (c) EMG shorts equipped with textile electrodes with 6 differential EMG
namespace).tdl@MathML 2.0 (no namespace)@ --> <math><mrow><msub><mrow><mtext>EC</mtext></mrow><mrow><mtex t>EMG</mtext></mrow></msub><mtext>=</mtext><mfrac><mrow><msub ><mrow><mtext>MP</mtext></mrow><mrow><mtext>EMG</mtext></mro w></msub></mrow><mrow><msub><mrow><mtext>SPEED</mtext></mro w><mrow><mtext>IMU</mtext></mrow></msub></mrow></mfrac></mro w></math> <!-- MathType@End@5@5@ --> (2) The EMG data were integrated with GPS-IMU signals to permit us to temporally and kinematically differentiate phases. Sprint analyses were conducted utilizing personalized Figure 2.(a) Back view of the sensor placement; (b) GPS unit; (c) EMG shorts equipped with textile electrodes with 6 differential EMG biosignal channels.
Description
The research analyzes metabolic power and energy cost during running efforts in elite youth soccer players.