Lateral Asymmetries and Their Predictive Ability for Maximal Incremental Cycle Ergometer Performance in Road Cyclists

d to (1) determine and compare the magnitude and direction of asymmetry in lower limbs neuromuscular properties, range of motion, strength and muscle electrical activity (EMG) in well-trained male road cyclist across categories (elite, under-23 and junior); (2) establish test- and age-specific asymmetry thresholds for these variables to enable individualized classification; and (3) examine the relationship between these lateral asymmetries and performance in a maximal incremental cycle ergometer test. Fifty-five well-trained road cyclists were assessed through tensiomyography (TMG), active knee extension test (AKE), leg press and EMG of vastus lateralis (VL-EMG) during a maximal incremental cycling test. Junior cyclists showed lower asymmetry in VM than elite cyclists, but greater asymmetry in AKE. No significant differences were found in strength or VL- EMG during the maximal incremental cycle ergometer test. The magnitude and

cyclists were assessed through tensiomyography (TMG), active knee extension test (AKE), leg press and EMG of vastus lateralis (VL-EMG) during a maximal incremental cycling test. Junior cyclists showed lower asymmetry in VM than elite cyclists, but greater asymmetry in AKE. No significant differences were found in strength or VL- EMG during the maximal incremental cycle ergometer test. The magnitude and direction of lateral asymmetry differs between tests (TMG: 11.3–21.3%; AKE: 2.3%; leg-press: 9.8–31.9%; VL-EMG: 20.8–22.7%). Multiple linear regression revealed a significant predictive model for maximal incremental cycling ergometer performance based on lateral asymmetry in AKE, leg press and VL and rectus femoris contraction time (R 2 a = 0.23). These reference data can support trainers in monitoring and managing lateral asymmetry throughout the cyclists’ season. Keywords:tensiomyography; electromyography; range of motion; strength; professional cycling; performance prediction; endurance sports 1. Introduction Road cycling performance is highly related to physical [1], psychological [2] and tactical factors [3]. Physical performance-related factors in road cycling have traditionally been linked to the athlete’s physiological profile. Professional cyclists typically display very high aerobic capacity at both maximal and submaximal levels, as measured through maximal oxygen consumption (VO2max) [1]. However, evidence suggests that good cycling economy and efficiency can compensate for a relatively low VO2max [4]. In fact, Symmetry2025,17, 1060 https://doi.org/10.3390/sym17071060

Symmetry2025,17, 1060 2 of 19 cycling economy and lactate threshold may be more critical determinants of endurance performance than VO2max itself [5]. Furthermore, heart rate (HR) monitoring has long been used to relate exercise intensity during competition to maximum and submaximal laboratory reference values [1]. More recently, with the advent of portable outdoor power measurement devices, power output and its relationship with laboratory parameters have become key variables to consider in both training and competitive performance [6]. Beyond dispute, the endurance profile of cyclists—measured through physiological or mechanical variables—plays a fundamental role in cycling performance [1]. However, research in this area seems to have overlooked other highly trainable capacities, such as flexibility and strength. Although the literature on this topic is limited, evidence suggests that replacing a portion of endurance training with resistance training can be beneficial for improving time trial performance and maximum power output [7]. Lateral preference refers to the dominance of one side of the body in performing motor actions [8]. Limb dominance in sport has been examined in various ways depending on the motor characteristics of each discipline. In acyclic sports, lateral dominance is typically identified by determining which limb achieves higher values in sport-specific tasks [9], with lateral asymmetries often arising from sport-specific adaptations [10–12]. Although cyclical sports are generally assumed to follow a bilaterally symmetrical pattern, asymmetry has been observed in several of these sports regardless of age, gender, or competitive level [10]. Specifically, in cycling, the dominant leg appears to generate greater force in athletes who exhibit pedaling asymmetries [13,14]. However, to our knowledge, it remains unclear whether the leg that dominates in force application during pedaling also demonstrates dominance across other performance-related variables. There is no clear consensus regarding the relationship between bilateral symmetry and athletic performance, nor to what extent asymmetry influences injury risk [8,10,15–17]. However, scientific literature increasingly supports the idea that lateral asymmetry is both individual and sport-specific, and should therefore be addressed using task- and sport- specific thresholds [10,17,18]. Since the 1990s, these asymmetry thresholds have generally been treated as fixed values, typically ranging between 10% and 15%,

bilateral symmetry and athletic performance, nor to what extent asymmetry influences injury risk [8,10,15–17]. However, scientific literature increasingly supports the idea that lateral asymmetry is both individual and sport-specific, and should therefore be addressed using task- and sport- specific thresholds [10,17,18]. Since the 1990s, these asymmetry thresholds have generally been treated as fixed values, typically ranging between 10% and 15%, regardless of the type of test, the population assessed, or the sporting discipline. This generic approach overlooks the influence of task specificity and individual characteristics on asymmetry. In response to these limitations, more recent proposals, such as that of Dos’Santos et al. [19], have emphasized the need to establish asymmetry thresholds that are specific to the task, the variable measured, and the characteristics of the athlete population under study. This perspective offers greater ecological validity and may enhance the relevance of asymmetry assessments in applied sports contexts; however, asymmetry thresholds have yet to be specifically explored in the context of cycling. Conversely, research into asymmetries in cycling dates back over half acentury [20,21] , with early studies primarily examining their impact on performance through kinetic anal- yses of force application on the crank and pedals during pedaling [22]. Yet, no clear consensus has been reached in the scientific community. Some studies have associated more pronounced asymmetries in effective force with improved performance in a 4 km time trial [23], while others reported that pedaling asymmetries tend to decrease as exercise intensity increases [13]. This ongoing uncertainty highlights the need for further, more comprehensive exploration of this topic. Pedaling asymmetries have also been studied using kinematic analysis [24,25] and muscle activity measured by electromyography (EMG) [26,27]. Carpes et al. [27] reported that cyclists vastus lateralis (VL) muscle activity increases significantly as the exercise inten- sity rises, without significant inter-limb differences. Furthermore, although asymmetries

Symmetry2025,17, 1060 3 of 19 were not the focus of this study, Bini et al. [26] concluded that the VL was the only muscle among those analyzed that showed selective activation during a 40 km laboratory time trial. Their findings also reveal a strong relationship between VL and rectus femoris (RF) activation and the power output during the trial. These results suggest that EMG may serve as a valuable tool for detecting functional pedaling asymmetries. While most research has focused on pedaling analysis, other relevant components of cycling performance remain underexplored. In this regard, Yanci and Los Arcos [28] examined inter-limb asymmetries in relation to vertical jump performance, reporting that cyclists exhibited both lower jumping capacity and greater asymmetries compared to runners. These findings likely reflect sport-specific demands, as cycling does not require reactive strength. Similarly, Pimentel et al. [29] observed greater asymmetries in bone mineral density and lean mass among cyclists compared to non-cyclists. However, it remains unclear whether such asymmetries are also evident in other performance-related tests more directly associated with cycling. Nevertheless, to the best of our knowledge, asymmetries in competitive cyclists and their impact on performance have scarcely been addressed from a multifactorial perspective that included key components of physical preparation, such as muscle strength, range of motion (ROM), or resting muscle contractile capacity. In contrast, this multifaceted approach has been addressed in other sports such as volleyball [30], soccer [31], or ca- noeing [32] where different sport-specific capacities—such as concentric strength, ROM, muscle contractile properties assessed via tensiomyography (TMG), or muscle electrical activity during exercise—have been considered to identify athletes’ asymmetries. Based on the existing evidence and gaps in the literature, we hypothesized that (1) the magnitude of lateral asymmetry would not differ significantly between categories (junior, under-23 and elite cyclists) and (2) that the percentage of lateral asymmetry observed in neuromuscular, ROM, strength and muscle electrical activity variables would demonstrate moderate explanatory power for cyclists’ performance during a maximal incremental cycle ergometer test. Considering the paucity of research in this field among cyclists, the aim of this study is to (1) determine the magnitude

categories (junior, under-23 and elite cyclists) and (2) that the percentage of lateral asymmetry observed in neuromuscular, ROM, strength and muscle electrical activity variables would demonstrate moderate explanatory power for cyclists’ performance during a maximal incremental cycle ergometer test. Considering the paucity of research in this field among cyclists, the aim of this study is to (1) determine the magnitude and direction of potential asymmetries in neuromuscular properties, ROM, strength and muscle electrical activity asymmetries of the main muscles involved in pedaling in well-trained male road cyclists across categories (junior, under-23 and elite); (2) establish test- and category-specific asymmetry thresholds for neuromuscular properties, ROM, strength and muscle electrical activity, enabling individualized classifi- cation; and (3) examine the relationship between cyclists’ asymmetries in neuromuscular properties, ROM, strength and muscular electrical activity test with and performance in maximal incremental cycle ergometer test. By addressing these aims, this study seeks to provide novel and relevant contributions to scientific literature, offering a comprehensive and multifactorial approach to the analysis of asymmetries in cyclists. It proposes specific thresholds for their classification and develops a predictive model of cycling performance that can serve as a practical tool for coaches and sports performance professionals. 2. Materials and Methods 2.1. Study Design An exploratory cross-sectional comparative study design was conducted to determine the magnitude and direction of lateral asymmetry in cyclists based on their competitive category (elite, under-23, junior). In addition, a predictive research design was employed to examine the influence of lower limbs lateral asymmetry in contractile properties, ROM, strength and muscular electrical activity on performance in maximal incremental cycle

Symmetry2025,17, 1060 4 of 19 ergometer test (see Figure). All assessments were performed at the beginning of the competition season, following a day of active recovery. Figure 1.Study design framework [19,33]. 2.2. Participants An a priori sample size analysis was carried out using G*Power v.3.1. for Windows (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). The analysis considered the number of measurements and the grouping of cyclists, with parameters set at an alpha level of 0.05, statistical power of 0.95 and an effect size of 0.50. The result indicated that a minimum of 54 cyclists was required. The sample was composed of fifty-five well-trained road cyclists from elite (18), under-23 (27)and junior (10) categories, all actively competing in national and international amateur and professional cycling events and classified as tier 3 or 4 according to Mckay et al.’s [34] classification based on training volume and performance metrics (see Table). Inclusion criteria required cyclists to be healthy, injury-free at the time of data collection and to have successfully passed a medical screening compromising a submaximal exercise test and an ultrasound assessment to exclude any abnormalities in adaptation to exercise. Only cyclists with a minimum sporting level of tier 3 were eligible for inclusion [34]. Those not meeting these criteria were excluded from the study. Table 1.Descriptive statistics of road cyclists’ characteristics. Age (years) Height (cm) Weight (kg) Body Fat (%) Muscle Mass (kg) BMI Water (%) VO2max (mL/kg/min) Junior (n= 10) 16.33±0.50 170.63±4.1562.22±5.44 8.87±4.58 53.68±2.67 21.41±2.1166.00±4.03 65.58±6.12 Under-23 (n= 27)19.47±1.22 175.49±7.2165.64±7.27 7.04±3.01 57.74±5.04 20.81±2.3966.03±5.32 69.11±6.51 Elite (n= 18) 23.50±2.22 179.69±8.1469.75±7.75 7.81±3.87 61.35±7.59 21.57±2.1065.65±3.14 66.91±6.01 Total (n= 55) 20.31±2.90 175.97±7.2466.13±6.83 7.56±3.53 58.32±6.29 21.17±2.2465.90±4.40 68.0±6.61 Body composition was analyzed through bioelectrical impedance Tanita MC-780MA (Tanita Corporation, Tokyo, Japan). VO2max was obtained through Gas Exchange (Ergostik, Geratherm Respiratory GmbH, Bad Kissingen, Germany) during a maximal incremental cycling test. BMI (body mass index). All cyclists included in the sample have signed an informed consent informing about the assessment protocol, risk and benefits, which followed the principles of the Declaration of Helsinki (DoH)—Ethical Principles for Medical Research Involving Human Participants (1964) and its latest amendments from the 75th General Assembly

GmbH, Bad Kissingen, Germany) during a maximal incremental cycling test. BMI (body mass index). All cyclists included in the sample have signed an informed consent informing about the assessment protocol, risk and benefits, which followed the principles of the Declaration of Helsinki (DoH)—Ethical Principles for Medical Research Involving Human Participants (1964) and its latest amendments from the 75th General Assembly of the World Medical Association (WMA) in Finland, on 19 October 2024. This investigation was approved by the Institutional Review Board from the local University (01-170123) and the cycling club.

Symmetry2025,17, 1060 5 of 19 2.3. Procedure All tests were conducted during a recovery session, following the same protocol order for all participants: TMG, ROM, strength and EMG during a maximal incremental cycling test. To determine inter-limb asymmetries, the formula proposed by Bishop et al. [33] was applied Asymmetry(%)= DL−NDL DL×100 ×IF(DL<NDL, 1,−1) where DL is dominant leg, and NDL non-dominant leg, self-reported for each cyclist. This formula implements the ExcelIFfunction that enables to monitor both the magnitude and direction of lateral asymmetry, while avoiding issues related to the absolute magnitude of variation. The resulting values are expressed as percentage (%). 2.3.1. Neuromuscular Properties Assessment TMG was used to assess contractile properties of biceps femoris (BF), RF, VL and vastus medialis (VM) in both legs. Radial muscle belly displacement (Dm) was measured under isometric conditions using electrical stimulation, following the protocol described by García-García et al. [35,36]. A digital displacement transducer (GK 30, Panoptik d.o.o., Ljubljana, Slovenija) was positioned perpendicular to the thickest part of the muscle belly, in accordance with anatomical guidelines from Perotto et al. [37] (BF: at the midpoint of a line between the fibula head and the ischial tuberosity; RF: on the anterior aspect of the thigh, midway between the superior border of the patella and the anterior superior iliac spine; VL: over the lateral aspect of the thigh, one handbreadth above the patella; VM: four fingerbreadths proximal to the superior-medial angle of the patella.). Two self- adhesive electrodes (5×5 cm, Lessa®, AB Medica Group SA, Barcelona, Spain) were placed symmetrically at 5 cm from the digital transducer. Progressive electrical stimulation was applied in 10 mA increments up to a maximal output of 110 mA (EMF-FURLAN & Co. d.o.o., Ljubljana, Slovenia). The following parameters were recorded: Dm (in mm); contraction time (Tc, in ms), defined as the time from 10% to 90% of Dm and radial displacement velocity (Vrd, in mm·s −1 ), calculated as Dm80/Tc, where Dm80 represents the displacement during Tc. The curve with the highest Dm was selected for analysis. All measurements were performed by two experienced evaluators with extensive expertise in the

Dm (in mm); contraction time (Tc, in ms), defined as the time from 10% to 90% of Dm and radial displacement velocity (Vrd, in mm·s −1 ), calculated as Dm80/Tc, where Dm80 represents the displacement during Tc. The curve with the highest Dm was selected for analysis. All measurements were performed by two experienced evaluators with extensive expertise in the field. 2.3.2. ROM Assessment After a protocolized 20 min warm-up consisting of five general mobility exercises (4 sets of 10 repetition each), four isometric strength exercises (4 sets of 20 s each) and five minutes of continuous running at an intensity of 4–6 on the modified Borg’s Rate of Perceived Exertion (RPE) scale [38], cyclists performed the active knee extension (AKE) test to assess hip-knee ROM. The AKE was conducted according to the protocol described by Gajdosik and Lusin [39], with participants lying supine on a stretcher and starting at a 90 ◦ hip and knee flexion. A digital goniometer (Baseline Absolute Axis 360 ◦ , Fabrication Enterprises, Inc., White Plains, New York, USA) was used to measure knee extension ROM. Three attempts were performed on each limb, supervised by two expert evaluators—one responsible for recording the knee extension angle and the other for preventing compen- satory movements during the measurement. The best attempt for each cyclist was retained for further analysis. 2.3.3. Strength Assessment After a specific warm-up focused on position adjustment, cyclists performed a maxi- mal repetition semi-squat using a horizontal leg press machine (RS-1403 Leg Press ROC-IT

Symmetry2025,17, 1060 6 of 19 line; HOIST, Poway, CA, USA). The test was performed unilaterally, using one leg and the cyclist’s own body weight. Athletes were instructed to execute the concentric phase of the movement as explosive as possible while maintaining control during the eccentric phase. The test ended when the cyclist could no longer complete repetitions or when a deterioration in technical execution was observed by consensus among evaluators (i.e., knee valgus, hyperlordosis, oscillatory movements). A Linear Encoder (Chronojump Boscosys- tem, Barcelona, Spain) paired with Chronojump software (version 1.7.0 for Windows; Chronojump Boscosystem) was used to measure average speed (Vavg), average power (Pavg) and the number of repetitions (reps) performed. Previous studies have confirmed the validity and reliability of this method for measuring movement speed and estimating power, reporting intraclass correlation coefficient (ICC) between 0.95 and 0.988 [40]. 2.3.4. Muscular Electrical Activity Assessment During the maximal incremental cycling test on a cycle ergometer, surface EMG signals were recorded using the FREEEMG 1000 system (BTS Bioengineering, Garbagnate Milanese, MI, Italy). Self-adhesive hydrogel Ag/AgCl electrodes (40 mm, Meditrace- Kendall, Covidien IIc, Mansfield, MA, USA) were applied to shaved and alcohol-cleaned skin. Electrodes were placed on the vastus lateralis of both legs, aligned with muscle fibers in accordance with the SENIAM recommendations. The system operates in differential mode with a high input impedance of 100 MΩand a Common Mode Rejection Ratio (CMRR) greater than 110 dB at 50–60 Hz. The EMG signal was acquired at a sampling rate of 1000 H, with the amplifier gain was set to a 3.0 mV range, which is recommended for typical clinical and sports applications. Wireless EMG sensors transmitted the signal via Bluetooth to the receiving unit. Muscle activity was quantified using the Root Mean Square (RMS) of the EMG signal, calculated in moving windows of 100 ms, as follows RMS= v u u t 1 N N ∑ i=1 x N i wherex irepresents the EMG signal value at sampleiwithin the window ofNsamples [41]. Electrodes were positioned according to the anatomical guidelines described by Perotto et al. [37], with an interelectrode distance of 1 cm.

(RMS) of the EMG signal, calculated in moving windows of 100 ms, as follows RMS= v u u t 1 N N ∑ i=1 x N i wherex irepresents the EMG signal value at sampleiwithin the window ofNsamples [41]. Electrodes were positioned according to the anatomical guidelines described by Perotto et al. [37], with an interelectrode distance of 1 cm. After the test, electrode placement was visually inspected to confirm that no displacement had occurred during the protocol. 2.3.5. Maximal Incremental Cycle Ergometer Test Prior to starting the maximal incremental test, cyclists completed a standardized 20 min specific warm-up, pedaling on their own bicycle mounted on rollers, with intensity progressively increasing up to their individual functional threshold power. The maximal incremental cycling test was carried out on an electromagnetic brake cycle ergometer (Cardgirus W3+, Sabadell, Barcelona, Spain). Each cyclist individually adjusted the cycle ergometer setup to replicate their own bicycle position. The test followed a stepwise protocol starting at 100 W and increasing by 5 W every 15 s, while maintaining a constant pedaling cadence above 60 revolutions per minute (rev·min −1 ). The test concluded when the cyclist reached volitional exhaustion, was unable to maintain cadence or if any medical issues arose, monitored by the cardiologist F-R, D. Gas Exchange was recorded using an Ergostik CardioPart analyzer (Ergostik, Geratherm Respiratory GmbH, Bad Kissingen, Germany) to determine VO2max as well as ventilatory thresholds 1 and 2 (VT1 and VT2, respectively). The gas analyzer was calibrated two minutes prior to each test. Blood lactate concentration ([La]) was measured via cap- illary earlobe puncture at VT1, VT2 and three minutes post-test, using electro-enzymatic reactive strips (Lactate Scout 4, EKF diagnostic, SensLab GmbH, Leipzig, Germany). The

The study explores lateral asymmetries in road cyclists and their impact on performance.