Sport Specific Differences in Upper Limb Median Frequency Responses During Isometric Push and Pull Tasks in Volleyball and Fitness Athletes

Sport Specific Differences in Upper Limb Median Frequency Responses During Isometric Push and Pull Tasks in Volleyball and Fitness Athletes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sport Specific Differences in Upper Limb Median Frequency Responses During Isometric Push and Pull Tasks in Volleyball and Fitness Athletes Özgür Dinçer, Hasan Sözen, Serhat Öztürk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8827731/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Apr, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 12 You are reading this latest preprint version Abstract Purpose: To compare sport-specific differences in upper-limb muscle fatigue characteristics, assessed via surface EMG median frequency (MF), during brief isometric push and pull tasks performed in seated and standing postures. Methods: Thirty-six athletes (volleyball, n=18; fitness, n=18) performed 20-s isometric contractions in four conditions (seated upright row, seated shoulder press, standing upright row, standing shoulder press) using both dominant and non-dominant limbs. Surface EMG was recorded bilaterally from the biceps brachii, triceps brachii, deltoid, and trapezius. MF was calculated using Welch’s method from a steady-state window (5-15 s of the 20-s contraction) to avoid onset and end-of-trial transients and expressed in Hz. Linear mixed-effects models tested the effects of group, posture, action, and side. Results: MF was higher during the upright row than the shoulder press for the biceps, deltoid, and trapezius (p<0.001; within-subject dz=0.59–1.02). For the triceps, significant effects of group (p=0.019), posture (p=0.026), and a group×posture interaction (p=0.019) were observed, with fitness athletes showing higher MF than volleyball athletes particularly in the seated condition. No robust main effects or interactions involving side were detected. Conclusion: In these brief isometric tasks, MF responses were predominantly task-dependent (pull>push) for biceps, deltoid, and trapezius, whereas triceps MF showed sport- and posture-dependent modulation. These findings support the use of task-specific EMG spectral outcomes to probe fatigue-related neuromuscular characteristics across athletic populations. surface electromyography median frequency muscle fatigue resistance training push-pull tasks Figures Figure 1 KEY POINTS MF was consistently higher in the upright row than the shoulder press for the biceps, deltoid, and trapezius, indicating a robust task (pull vs push) effect on EMG spectral behaviour. Triceps MF exhibited group- and posture-dependent modulation, including a significant group×posture interaction, suggesting that sport background can influence posture-related spectral responses. Dominant–non-dominant side effects were minimal, implying largely symmetrical MF responses during brief standardized isometric contractions. INTRODUCTION Sport-specific training history may influence neuromuscular coordination, the pattern of muscle activation, and the response to fatigue during standardised strength tasks. Surface electromyography (sEMG) is widely used for the non-invasive assessment of these neuromuscular responses; it provides important information, particularly through frequency domain indicators that reflect fatigue-related spectral changes. Among these indicators, median frequency (MF) stands out as one of the most frequently reported indices in the literature, due to the tendency of spectral power to shift towards lower frequencies during sustained or repetitive contractions [ 1 – 4 ]. Although MF is a practical and relatively easy-to-interpret criterion, the direction and magnitude of change in MF are not solely the result of physiological processes; methodological factors such as signal stability, the windowing approach used, noise level, and spectrum shape can also significantly influence the results. Recent methodological studies have demonstrated the sensitivity of MF/mean frequency estimation to spectral structure and noise; they have emphasised that standardising data collection and signal processing steps is critical in studies where MF is reported as the primary output [ 5 , 6 ]. Therefore, studies comparing MF across tasks and athlete groups should clearly ground their findings within both a physiological and methodological framework. Upper extremity strength exercises exhibit distinct differences in terms of joint kinematics, postural/stabilisation requirements, and the relative contribution of agonist and synergist muscles. Therefore, even if the contraction duration is kept the same, whether the task is predominantly ‘pushing’ (e.g. shoulder press) or ‘pulling’ (e.g. upright row) in character may lead to differences in fatigue-related indicators, because the primary force generators and stabilisation strategies change depending on the purpose of the movement. Furthermore, it is known that not only the type of task but also the posture can influence neuromuscular demands: standing exercises typically increase the need for balance/stabilisation throughout the body and can alter upper extremity muscle activation compared to seated exercises, even if the external task is similar [ 7 ]. Similarly, findings related to overhead sports indicate that measurable decreases in MF may be observed in shoulder and upper extremity muscles following demanding protocols; this supports the notion that MF may be a sensitive indicator for capturing upper extremity fatigue responses in athletic populations [ 3 ]. Volleyball players, due to their constant exposure to repetitive overhead and high-speed upper extremity movements, may develop different neuromuscular strategies compared to athletes whose training relies more on general resistance exercises. In athletes involved in overhead sports, the timing and distribution of shoulder and arm muscle activation during functional tasks can differ significantly; this suggests that training history may have a meaningful effect on muscle load sharing and the response to fatigue [ 8 , 9 ]. However, it remains unclear whether MF responses differ between volleyball and fitness athletes during standardised isometric push and pull tasks; furthermore, it is not yet clear whether posture (sitting/standing) or limb dominance additionally modifies these MF patterns under controlled conditions. The purpose of this study was to compare MF responses of the biceps brachii, triceps brachii, deltoid, and trapezius between volleyball and fitness athletes during 20-s isometric shoulder press (push) and upright row (pull) tasks performed in seated and standing postures, using dominant and non-dominant limbs. We hypothesized that MF would differ between push and pull tasks and that triceps given its functional relevance to pushing would show group- and posture-dependent patterns. METHODS Participants A total of 36 male athletes participated (volleyball, n = 18: age 21.2 ± 2.9 years, height 181.1 ± 6.3 cm, weight 78.7 ± 10.3 kg; fitness athletes, n = 18: age 22.1 ± 2.5 years, height 179.8 ± 4.9 cm, weight 80.4 ± 10.4 kg). Participant characteristics are reported in Table 1 , including age, height, body mass, sex distribution, and training age. Training age was defined as the number of years of continuous, structured training in the participant’s current discipline and was recorded via self-report. Inclusion criteria were: (i) male athletes aged 18–25 years, (ii) at least 3 years of continuous training experience in volleyball or fitness/resistance training, (iii) currently training at least 3 sessions per week, and (iv) absence of pain during upper-limb exercise testing. Exclusion criteria were: (i) history of upper-extremity surgery, (ii) current or recent (past 6 months) shoulder, elbow, or wrist injury, (iii) any neurological, cardiovascular, or musculoskeletal condition that could affect performance or EMG recordings, and (iv) use of analgesic/anti-inflammatory medication within 48 hours prior to testing. Dominant arm was defined as the self-reported preferred arm used for throwing and sport-specific skilled tasks (e.g., serving/spiking in volleyball); arm dominance was recorded via self-report. Ethical approval was obtained from the Ordu University Clinical Research Ethics Committee (decision no: 2020/276; application no: KAEK 219). Written informed consent to participate was obtained from all participants prior to their inclusion in the study. The study conformed to the latest revision of the Declaration of Helsinki. Experimental design and tasks Participants completed a standard data collection session consisting of four 20 s isometric tasks: upright rowing and shoulder press performed in seated and standing positions using a cable machine. Tasks were performed bilaterally (dominant and non-dominant sides) with a consistent grip width and verbal encouragement to maintain the required joint position throughout each contraction. Upper-limb joint angles were standardized and monitored using a wearable inertial motion system (MyoMotion; Noraxon USA Inc., Scottsdale, AZ, USA). For all conditions, the target position was defined as approximately 90° shoulder abduction and 90° elbow flexion (90/90 position) (Fig. 1 ), which has been commonly used to standardize isometric shoulder and upper-limb testing postures [ 10 – 13 ]. To minimize onset/offset transients, participants were instructed to reach the target position smoothly and to avoid extraneous trunk motion. A rest period of at least 60 s was provided between trials. Joint positions were verified via the MyoMotion angle output during each trial, following a similar standardization approach to our previous work [ 14 ]. Surface EMG acquisition Surface EMG was recorded bilaterally from the biceps brachii, triceps brachii, deltoid, and trapezius using a wireless EMG system (myoMUSCLE, Noraxon USA Inc., Scottsdale, AZ, USA) at a sampling frequency of 2000 Hz with bipolar pre-gelled Ag/AgCl surface electrodes. Prior to electrode placement, the skin was prepared (shaving if required, gentle abrasion, and alcohol cleaning) to reduce impedance. Electrode locations were selected according to SENIAM recommendations, aligned parallel to the presumed muscle fiber direction, and placed over the muscle belly with an inter-electrode distance of 20 mm (center to center). A reference (ground) electrode was positioned over an electrically neutral bony site (olecranon process of the ulna). Signals were acquired with the manufacturer’s default analog front-end settings, including online band-pass filtering (10–500 Hz) and a 50-Hz notch filter to reduce power-line interference. Given the online 10–500 Hz band-pass (plus 50 Hz notch) filtering during acquisition, offline filtering was restricted to an additional 20 Hz high-pass step to mitigate residual low-frequency drift/movement artefacts; no additional low-pass or notch filtering was applied offline. Signal processing and median frequency Offline EMG processing followed our previously published framework. In brief, raw EMG signals were high pass filtered at 20 Hz using a Butterworth filter to attenuate movement artefacts and baseline drift [ 15 ]. MF was then calculated from the power spectrum estimated with Welch’s method. To ensure comparable steady-state estimates across trials, MF was computed from 5–15 s of each 20-s contraction (i.e., after initial stabilization and before end-of-trial anticipatory relaxation/position readjustments), thereby excluding onset and offset transients, as applied previously [ 14 ]. MF was defined as the frequency that divides the total spectral power into two equal areas; a downward shift in MF during sustained isometric contraction is commonly interpreted as an indicator of fatigue-related spectral compression [ 16 ]. MF was expressed in Hz (i.e., absolute median frequency) without normalization to MVC or a reference contraction. Welch’s method was implemented using a Hamming window (1-s window length, 50% overlap) with a fast Fourier transform FFT length of 2048 points. Frequency domain features were obtained by computing the power spectrum using the FFT, from which MF was derived. Statistical analysis Sample size was justified for the primary within-subject contrast (upright row vs shoulder press). A two-tailed paired t-test power analysis (alpha = 0.05, power = 0.80) assuming a conservative medium effect (Cohen’s dz = 0.60) indicated a minimum of 24 participants. We recruited 36 participants (18 per group) to ensure adequate power for the primary action effect and to support mixed-effects modelling and potential attrition. Under the smallest observed action effect in this dataset (dz = 0.59), the achieved power with n = 36 was approximately 0.93. Power analyses were computed in Python using stats models. Continuous variables are presented as mean ± SD. Between-group differences in participant characteristics were assessed with independent-samples t-tests. For each muscle, linear mixed-effects models (random intercept for participant) tested fixed effects of group (volleyball vs fitness), posture (seated vs standing), action (row vs press), side (dominant vs non-dominant), and their interactions. Wald χ² tests were used for inference with α = 0.05. For significant effects, follow-up comparisons were summarized using mean differences and standardized effect sizes (Cohen’s dz for within-subject contrasts and Cohen’s d for between-group contrasts). All analyses were performed in IBM SPSS Statistics (version 29.0.2; IBM Corp., Armonk, NY, USA). RESULTS Table 1 Participant characteristics (mean ± SD). Characteristic Volleyball (n = 18) Fitness (n = 18) p value Age (years) 21.2 ± 2.9 22.1 ± 2.5 0.365 Height (cm) 181.1 ± 6.3 179.8 ± 4.9 0.503 Body mass (kg) 78.7 ± 10.3 80.4 ± 10.4 0.637 Training age (years) 7.6 ± 2.7 7.4 ± 2.6 0.82 Table 1 summarizes the participant characteristics for the volleyball and fitness groups. There were no significant differences in age, height, body mass, or training age (all p > 0.05), indicating that the groups were comparable at baseline with respect to key anthropometric and training-related variables. Table 2 a. Linear mixed-effects model Wald χ² tests for fixed effects (Biceps). Muscle Effect χ² df p Biceps group 0.00 1 0.945 Biceps posture 0.59 1 0.442 Biceps action 15.26 1 < 0.001 Biceps side 0.52 1 0.473 Biceps group×posture 0.49 1 0.484 Biceps group×action 1.01 1 0.315 Biceps posture×action 0.00 1 0.989 Biceps group×side 0.30 1 0.586 Biceps posture×side 0.84 1 0.358 Biceps action×side 0.09 1 0.765 Biceps group×posture×action 0.14 1 0.707 Biceps group×posture×side 0.20 1 0.656 Biceps group×action×side 0.02 1 0.890 Biceps posture×action×side 0.41 1 0.521 Biceps group×posture×action×side 0.21 1 0.649 Table 2 a presents the Wald χ² tests from the linear mixed-effects model for the biceps median frequency. A significant main effect of action was observed (χ²=15.26, df = 1, p 0.05), and none of the interaction terms reached significance (all p > 0.05), suggesting that the action-related difference was consistent across groups, postures, and arms. Table 2 b. Linear mixed-effects model Wald χ² tests for fixed effects (Deltoid). Muscle Effect χ² df p Deltoid group 2.05 1 0.152 Deltoid posture 0.09 1 0.770 Deltoid action 18.46 1 < 0.001 Deltoid side 0.44 1 0.506 Deltoid group×posture 0.18 1 0.675 Deltoid group×action 0.29 1 0.588 Deltoid posture×action 0.62 1 0.429 Deltoid group×side 0.88 1 0.349 Deltoid posture×side 0.27 1 0.604 Deltoid action×side 0.50 1 0.480 Deltoid group×posture×action 0.05 1 0.815 Deltoid group×posture×side 0.21 1 0.644 Deltoid group×action×side 0.89 1 0.344 Deltoid posture×action×side 0.22 1 0.639 Deltoid group×posture×action×side 0.03 1 0.856 Table 2 b summarizes the Wald χ² tests from the linear mixed-effects model for the deltoid median frequency. A significant main effect of action was found (χ²=18.46, df = 1, p 0.05). In addition, none of the two-, three- or four-way interactions reached significance (all p > 0.05), suggesting that the action effect was consistent across athlete groups, postures, and arms. Table 2 c. Linear mixed-effects model Wald χ² tests for fixed effects (Trapezius). Muscle Effect χ² df p Trapezius group 2.21 1 0.137 Trapezius posture 0.17 1 0.682 Trapezius action 12.12 1 < 0.001 Trapezius side 0.00 1 0.973 Trapezius group×posture 1.25 1 0.264 Trapezius group×action 0.46 1 0.498 Trapezius posture×action 0.11 1 0.739 Trapezius group×side 0.05 1 0.824 Trapezius posture×side 0.01 1 0.903 Trapezius action×side 0.17 1 0.680 Trapezius group×posture×action 0.40 1 0.528 Trapezius group×posture×side 0.09 1 0.764 Trapezius group×action×side 0.10 1 0.755 Trapezius posture×action×side 0.03 1 0.853 Trapezius group×posture×action×side 0.01 1 0.931 Table 2 c presents the Wald χ² tests from the linear mixed-effects model for the trapezius median frequency. A significant main effect of action was detected (χ²=12.12, df = 1, p 0.05). Furthermore, none of the interaction terms were significant (all p > 0.05), suggesting that the action-related difference in trapezius median frequency was consistent across athlete groups, postures, and arms. Table 2 d. Linear mixed-effects model Wald χ² tests for fixed effects (Triceps). Muscle Effect χ² df p Triceps group 5.47 1 0.019 Triceps posture 4.99 1 0.026 Triceps action 0.70 1 0.403 Triceps side 0.00 1 0.968 Triceps group×posture 5.51 1 0.019 Triceps group×action 1.18 1 0.277 Triceps posture×action 1.50 1 0.221 Triceps group×side 0.09 1 0.770 Triceps posture×side 0.39 1 0.531 Triceps action×side 0.01 1 0.905 Triceps group×posture×action 1.44 1 0.231 Triceps group×posture×side 0.31 1 0.580 Triceps group×action×side 0.04 1 0.844 Triceps posture×action×side 0.03 1 0.853 Triceps group×posture×action×side 0.08 1 0.775 Table 2 d reports the Wald χ² tests from the linear mixed-effects model for the triceps median frequency. Significant main effects were observed for group (χ²=5.47, df = 1, p = 0.019) and posture (χ²=4.99, df = 1, p = 0.026), indicating that triceps median frequency differed between athlete groups and between postural conditions. Importantly, a significant group×posture interaction was also detected (χ²=5.51, df = 1, p = 0.019), suggesting that the effect of posture on triceps median frequency was not uniform across groups. In contrast, there was no main effect of action (p = 0.403) or side (p = 0.968), and no additional interaction terms reached significance (all p > 0.05). Table 3 Median frequency (MF, Hz) by action pooled across posture, side and group (mean ± SD). Muscle Press (push) MF (Hz) Row (pull) MF (Hz) Biceps 57.47 ± 10.66 65.56 ± 10.50 Triceps 63.72 ± 11.78 69.50 ± 9.74 Deltoid 61.12 ± 12.40 72.31 ± 9.72 Trapezius 63.79 ± 10.30 71.52 ± 8.85 Table 3 presents the pooled MF values by action, averaged across posture, side, and group. Across muscles, MF was higher during the row (pull) than the press (push). The largest action-related differences were observed in the deltoid (press: 61.12 ± 12.40; row: 72.31 ± 9.72) and trapezius (press: 63.79 ± 10.30; row: 71.52 ± 8.85), with a similar pattern in the biceps (press: 57.47 ± 10.66; row: 65.56 ± 10.50). For the triceps, the pooled values were also higher in row than press (press: 63.72 ± 11.78; row: 69.50 ± 9.74), but this difference should be interpreted descriptively because the mixed-effects model did not show a significant main effect of action for triceps (p = 0.403). Table 4 Triceps MF (Hz) by group and posture pooled across action and side (mean ± SD). Group Seated MF (Hz) Standing MF (Hz) Volleyball 63.74 ± 12.79 66.03 ± 10.49 Fitness 69.89 ± 9.12 66.77 ± 11.32 Table 4 summarizes triceps MF values stratified by group and posture, pooled across action and side. In the volleyball group, triceps MF was slightly higher in the standing posture compared with seated (66.03 ± 10.49 vs 63.74 ± 12.79). In contrast, the fitness group showed the opposite pattern, with higher MF in the seated posture than in standing (69.89 ± 9.12 vs 66.77 ± 11.32). This crossover pattern is consistent with the significant group×posture interaction observed in the mixed-effects model (Table 2 d). DISCUSSION This study compared EMG MF responses during brief isometric push (shoulder press) and pull (upright row) tasks performed by volleyball and fitness athletes across seated and standing postures and dominant/non-dominant limbs. The most consistent finding was a robust action effect across all recorded muscles: MF was higher during the upright row than during the shoulder press. In the conventional interpretation of EMG spectral behaviour, a reduction in MF (or a more pronounced shift to lower frequencies over time) is often associated with fatigue-related changes such as decreased muscle fibre conduction velocity and altered motor unit behaviour [ 2 ]. Therefore, the lower MF values detected under press conditions may indicate that the spectral shift associated with fatigue is relatively more pronounced under these task constraints. However, it should be emphasised that MF is not the sole indicator representing “fatigue”; factors such as force level, motor unit recruitment strategy, and synchronisation can influence MF, and these factors can alter spectral distribution even during relatively brief isometric contractions [ 2 , 17 ]. A reasonable mechanical explanation for the lower MF observed during the press is that the isometric shoulder press position requires relatively higher stabilisation and strength. In this position, holding the arm in a specific posture can increase the load on the stabilising muscles around the shoulder girdle, particularly the elbow extensors. Furthermore, it has been demonstrated that external task constraints such as posture, joint angle, and contraction duration can selectively alter the distribution of fatigue among shoulder-related muscles; this supports the notion that even isometric tasks that appear similar in duration can produce different spectral responses [ 18 ]. In contrast, the upright row task may relatively reduce fatigue-related spectral compression in the muscles recorded within the same time window by distributing the mechanical load more towards the scapular elevators and elbow flexors and may therefore result in higher MF values. Our findings are consistent with studies showing that fatigue protocols targeting the shoulder region can lead to a decrease in MF in the shoulder and scapular muscles in individuals engaged in overhead sports, which supports the physiological rationale that MF may be a sensitive indicator of fatigue related changes [ 3 ]. Although this study used 20-second short isometric contractions rather than long-term endurance/until-exhaustion protocols, when contraction requirements and recruitment strategies differ significantly between conditions, even short-term contractions can produce measurable spectral differences [ 2 , 18 ]. A more nuanced picture emerged for the triceps: MF demonstrated a pattern sensitive to both athletic background and posture, alongside the main effects of group and posture, as well as the group×posture interaction. This may reflect the divergence in training specificity. Fitness athletes, who are generally more exposed to resistance training and seated press variants with similar postures, may experience more efficient muscle recruitment during press tasks and a shift in the balance between peripheral fatigue-related changes and neural control strategies. On the other hand, postural regulation may alter the activation patterns of upper extremity muscles, including the biceps, triceps, deltoids, and trapezius; this provides a plausible mechanism whereby performance constraints such as sitting versus standing may interact with athletic background to shape MF responses [ 7 ]. From an application perspective, these findings indicate that when comparing spectral fatigue indicators across athlete groups, posture should not be considered a “secondary” contextual variable, particularly in task-specific muscles such as the triceps during pushing. However, the effects observed between the dominant and non-dominant limbs were quite limited; this indicates that spectral responses in trained athletes were largely symmetrical under short-term isometric conditions. This symmetry may reflect high bilateral exposure to sports practice and strength training and/or the fact that 20 s is insufficient for subtle neuromuscular differences originating from the side to translate into distinct and consistent spectral separations. From an application perspective, it is thought that MF-based comparisons in short, standardised isometric tasks may be more sensitive to task mechanics (push-pull) and posture-related constraints than to limb dominance. Methodologically, when evaluating MF differences, it is necessary to consider the known sensitivity of the MF estimation to signal processing steps and spectral estimation preferences. Simulation-based studies show that mean/median frequency calculations are affected by factors such as the shape of the spectrum, noise level, and the method used to estimate power spectral density, which can alter both the absolute values and the detectability of differences between conditions [ 5 , 6 ]. These findings highlight the critical importance of executing the signal processing pipeline in a transparent, consistent, and standardised manner in studies reporting MF as the primary output. Furthermore, studies from sports settings support that MF may be a valid and relatively stable spectral indicator for monitoring local fatigue; however, they emphasise that sensitivity may vary depending on contextual variables such as task type, intensity, and measurement approach [ 4 ]. Overall, the present results highlight that task direction (push vs pull) is a dominant determinant of MF responses across upper-limb muscles, while sport background and posture can produce muscle-specific modulation most notably in the triceps underscoring the need to interpret EMG spectral outcomes within a biomechanics- and training-context framework. Limitations A few limitations must be accepted. First, the protocol used a brief fixed duration isometric contraction, which strengthens standardization but may not capture time-dependent fatigue dynamics (e.g., MF slopes) that become clearer during longer trials. Second, MF was evaluated as an absolute spectral estimate (Hz) without normalization to MVC or a reference contraction; therefore, between group comparisons may partly reflect baseline spectral differences and task-specific recruitment strategies. Third, the sample included only male athletes, limiting generalizability to female cohorts. Future work could extend these findings by combining MF with complementary indices (e.g., amplitude features, MF slope, or conduction-velocity-oriented metrics where feasible), and by explicitly quantifying external load and joint angles to better map mechanical constraints to muscle-specific spectral behaviour. CONCLUSION In male volleyball and fitness athletes, EMG MF during brief 20-s isometric tasks was primarily influenced by task action. MF was consistently higher during the upright row (pull) than the shoulder press (push), with a significant action effect observed for the biceps, deltoid, and trapezius. In contrast, triceps MF showed group- and posture-dependent behaviour, including a significant group×posture interaction, while side (dominant vs non-dominant) effects were minimal across muscles. Collectively, these findings indicate that push–pull task mechanics are a dominant determinant of upper-limb MF responses, whereas sport background and posture can selectively modulate triceps spectral behaviour, supporting the use of task-specific EMG spectral metrics to compare neuromuscular characteristics across athletic populations. Abbreviations cm centimetre(s) EMG Electromyography FFT Fast Fourier Transform Hz Hertz kg kilogram(s) MF Median Frequency mm millimetre(s) MVC Maximum Voluntary Contraction s second(s) SENIAM Surface Electromyography for the Non-Invasive Assessment of Muscles Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Ordu University Clinical Research Ethics Committee, Ordu University (decision no: 2020/276; application no: KAEK 219). All participants were informed about the study procedures and provided written informed consent prior to participation. Consent for publication Not applicable. Competing Interests The authors declare that they have no competing interests. Funding This research received no external funding. Author Contribution Conceptualization: ÖD, HS, SÖ; Methodology: ÖD, HS, SÖ; Data collection: SÖ, ÖD; Formal analysis: HS, ÖD; Investigation: HS, SÖ, ÖD; Writing-original draft: ÖD, SÖ; Writing-review & editing: HS, ÖD, SÖ; Supervision: ÖD. Acknowledgement The authors thank all the participants involved in the study for their patience and commitment to their involvement. Data Availability The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. References Sozen H, Erdogan E, Ince A, Soylu AR. Determination of electromyography-based coordinated fatigue levels in agonist and antagonist muscles of the thigh during squat press exercise. Ann Appl Sport Sci. 2019;7(3):21–30. Sun J, Liu G, Sun Y, Lin K, Zhou Z, Cai J. Application of surface electromyography in exercise fatigue: a review. Front Syst Neurosci. 2022;16:893275. Klich S, Kawczyński A, Pietraszewski B, Zago M, Chen A, Smoter M, et al. Electromyographic evaluation of the shoulder muscle after a fatiguing isokinetic protocol in recreational overhead athletes. Int J Environ Res Public Health. 2021;18(5):2516. Puce L, Pallecchi I, Marinelli L, Mori L, Bove M, Diotti D, et al. Surface electromyography spectral parameters for the study of muscle fatigue in swimming. Front Sports Act Living. 2021;3:644765. Corvini G, Conforto S. A simulation study to assess the factors of influence on mean and median frequency of semg signals during muscle fatigue. Sensors. 2022;22(17):6360. Corvini G, D’Anna C, Conforto S. Estimation of mean and median frequency from synthetic sEMG signals: Effects of different spectral shapes and noise on estimation methods. Biomed Signal Process Control. 2022;73:103420. Choi J, Lin Y, Loh PY. The Effects of Standing Working Posture on Operation Force and Upper Limb Muscle Activation When Using Different Pointing Devices. Int J Environ Res Public Health. 2022;19(16):10217. Yang F, Lu C, Yun X, Qian C. Effects of neuromuscular training on stability in volleyball athletes: a systematic review and meta-analysis. Front Sports Act Living. 2025;7:1724934. Owens LP, Khaiyat O, Coyles G. Muscle activations of the upper extremity and core during elevation and rotational movements in overhead throwing athletes. Int J Sports Phys Ther. 2024;19(4):466. Hwang Ujae, Kim Jhee, Gwak G, tae, Kim Mhwan. Comparison of Elbow Extensor Muscle Strength and EMG Activity in Supine and Prone Positions in Healthy Subjects. J Musculoskelet Sci Technol. 2018;2(1):16–9. Roman-Liu D, Tokarski T. Upper limb strength in relation to upper limb posture. Int J Ind Ergon. 2005;35(1):19–31. Ashworth B, Hogben P, Singh N, Tulloch L, Cohen DD. The Athletic Shoulder (ASH) test: reliability of a novel upper body isometric strength test in elite rugby players. BMJ Open Sport Exerc Med. 2018;4(1):e000365. Saeterbakken AH, Fimland MS. Effects of body position and loading modality on muscle activity and strength in shoulder presses. J Strength Cond Res. 2013;27(7):1824–31. Akyildiz C, Sözen H. The effects of lower extremity static muscle fatigue on balance components. Sport Sci Health. 2023;19(3):897–908. De Luca CJ, Gilmore LD, Kuznetsov M, Roy SH. Filtering the surface EMG signal: Movement artifact and baseline noise contamination. J Biomech. 2010;43(8):1573–9. De Luca CJ. Myoelectrical manifestations of localized muscular fatigue in humans. Crit Rev Biomed Eng. 1984;11(4):251–79. Park JS, Jung MC, Kim JY, Mo SM. Developing Synthetic Parameters Using Frequency Band Ratios for Muscle Fatigue Analysis During Isometric Contractions by Using Shoulder Muscles. Sensors. 2025;25(7):2191. Kim JY, Park JS, Kim DJ, Im S. Evaluation of fatigue patterns in individual shoulder muscles under various external conditions. Appl Ergon. 2021;91:103280. Additional Declarations No competing interests reported. Supplementary Files ethicscommitteeapproval.pdf Cite Share Download PDF Status: Published Journal Publication published 17 Apr, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Revision requested 16 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 23 Feb, 2026 Editor assigned by journal 21 Feb, 2026 Editor invited by journal 17 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 16 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8827731","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596934303,"identity":"af69ffa1-a70c-44f7-8556-fa9be46ec1d7","order_by":0,"name":"Özgür Dinçer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYNACNgkQyfiAgQ1EJxCvhdkAruUAYS0wjcRo4ec/fk26oswij7+991nFh7LDDPzsOQbMH/fg1iLZcKZM8sw5iWKJM8fNbs44d5hBsueNAcOBZ7i1GBzsSZNsbJNI3CCRxnabt+0wg8GNHKAWPC4zOMwD1SL/jK0YpMWeoJZj7MegtrCxMYNtkSCgRbKHh9my4ZxE4owzacySM86l80iceVZw4AweLcAQe3izoawusb/9GOOHD2XWcvztyRsfVODRwsDAY4DKBRF4NTAwsD/ALz8KRsEoGAWjAACEi1BR9nRPIgAAAABJRU5ErkJggg==","orcid":"","institution":"Ordu University","correspondingAuthor":true,"prefix":"","firstName":"Özgür","middleName":"","lastName":"Dinçer","suffix":""},{"id":596934304,"identity":"4626e09e-ecf9-47b7-a9f8-d92c180999e4","order_by":1,"name":"Hasan Sözen","email":"","orcid":"","institution":"Ordu University","correspondingAuthor":false,"prefix":"","firstName":"Hasan","middleName":"","lastName":"Sözen","suffix":""},{"id":596934305,"identity":"cdaa90d0-369c-4d91-b64f-646b92957963","order_by":2,"name":"Serhat Öztürk","email":"","orcid":"","institution":"Ordu University","correspondingAuthor":false,"prefix":"","firstName":"Serhat","middleName":"","lastName":"Öztürk","suffix":""}],"badges":[],"createdAt":"2026-02-09 08:23:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8827731/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8827731/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13102-026-01700-1","type":"published","date":"2026-04-17T15:58:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103539798,"identity":"1e56b826-a296-4602-bf0a-d9db0f2bb793","added_by":"auto","created_at":"2026-02-26 19:41:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":555536,"visible":true,"origin":"","legend":"\u003cp\u003eUpright rowing and shoulder press performed in seated (A) and standing (B) positions using a cable machine under isometric contraction conditions. For all conditions, the target position was defined as approximately 90° shoulder abduction and 90° elbow flexion (90/90 position), which has been commonly used to standardize isometric shoulder and upper-limb testing postures.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8827731/v1/f6c7c5d140a1c6014e863de2.png"},{"id":107350904,"identity":"700b35ba-2384-420c-9b4e-bed1a23bfae5","added_by":"auto","created_at":"2026-04-20 16:06:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":935163,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8827731/v1/8572af29-f0d3-49be-bc9a-6d327472216d.pdf"},{"id":104779163,"identity":"e4d22846-7ba3-40f9-919d-5f2898e0d575","added_by":"auto","created_at":"2026-03-17 07:36:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":276406,"visible":true,"origin":"","legend":"","description":"","filename":"ethicscommitteeapproval.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8827731/v1/ab93d97f3473117539c2095c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sport Specific Differences in Upper Limb Median Frequency Responses During Isometric Push and Pull Tasks in Volleyball and Fitness Athletes","fulltext":[{"header":"KEY POINTS","content":"\u003cp\u003eMF was consistently higher in the upright row than the shoulder press for the biceps, deltoid, and trapezius, indicating a robust task (pull vs push) effect on EMG spectral behaviour.\u003c/p\u003e\u003cp\u003eTriceps MF exhibited group- and posture-dependent modulation, including a significant group\u0026times;posture interaction, suggesting that sport background can influence posture-related spectral responses.\u003c/p\u003e\u003cp\u003eDominant\u0026ndash;non-dominant side effects were minimal, implying largely symmetrical MF responses during brief standardized isometric contractions.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eSport-specific training history may influence neuromuscular coordination, the pattern of muscle activation, and the response to fatigue during standardised strength tasks. Surface electromyography (sEMG) is widely used for the non-invasive assessment of these neuromuscular responses; it provides important information, particularly through frequency domain indicators that reflect fatigue-related spectral changes. Among these indicators, median frequency (MF) stands out as one of the most frequently reported indices in the literature, due to the tendency of spectral power to shift towards lower frequencies during sustained or repetitive contractions [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although MF is a practical and relatively easy-to-interpret criterion, the direction and magnitude of change in MF are not solely the result of physiological processes; methodological factors such as signal stability, the windowing approach used, noise level, and spectrum shape can also significantly influence the results. Recent methodological studies have demonstrated the sensitivity of MF/mean frequency estimation to spectral structure and noise; they have emphasised that standardising data collection and signal processing steps is critical in studies where MF is reported as the primary output [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, studies comparing MF across tasks and athlete groups should clearly ground their findings within both a physiological and methodological framework.\u003c/p\u003e \u003cp\u003eUpper extremity strength exercises exhibit distinct differences in terms of joint kinematics, postural/stabilisation requirements, and the relative contribution of agonist and synergist muscles. Therefore, even if the contraction duration is kept the same, whether the task is predominantly \u0026lsquo;pushing\u0026rsquo; (e.g. shoulder press) or \u0026lsquo;pulling\u0026rsquo; (e.g. upright row) in character may lead to differences in fatigue-related indicators, because the primary force generators and stabilisation strategies change depending on the purpose of the movement. Furthermore, it is known that not only the type of task but also the posture can influence neuromuscular demands: standing exercises typically increase the need for balance/stabilisation throughout the body and can alter upper extremity muscle activation compared to seated exercises, even if the external task is similar [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, findings related to overhead sports indicate that measurable decreases in MF may be observed in shoulder and upper extremity muscles following demanding protocols; this supports the notion that MF may be a sensitive indicator for capturing upper extremity fatigue responses in athletic populations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVolleyball players, due to their constant exposure to repetitive overhead and high-speed upper extremity movements, may develop different neuromuscular strategies compared to athletes whose training relies more on general resistance exercises. In athletes involved in overhead sports, the timing and distribution of shoulder and arm muscle activation during functional tasks can differ significantly; this suggests that training history may have a meaningful effect on muscle load sharing and the response to fatigue [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, it remains unclear whether MF responses differ between volleyball and fitness athletes during standardised isometric push and pull tasks; furthermore, it is not yet clear whether posture (sitting/standing) or limb dominance additionally modifies these MF patterns under controlled conditions.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to compare MF responses of the biceps brachii, triceps brachii, deltoid, and trapezius between volleyball and fitness athletes during 20-s isometric shoulder press (push) and upright row (pull) tasks performed in seated and standing postures, using dominant and non-dominant limbs. We hypothesized that MF would differ between push and pull tasks and that triceps given its functional relevance to pushing would show group- and posture-dependent patterns.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 36 male athletes participated (volleyball, n\u0026thinsp;=\u0026thinsp;18: age 21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 years, height 181.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3 cm, weight 78.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3 kg; fitness athletes, n\u0026thinsp;=\u0026thinsp;18: age 22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 years, height 179.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 cm, weight 80.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4 kg). Participant characteristics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, including age, height, body mass, sex distribution, and training age. Training age was defined as the number of years of continuous, structured training in the participant\u0026rsquo;s current discipline and was recorded via self-report. Inclusion criteria were: (i) male athletes aged 18\u0026ndash;25 years, (ii) at least 3 years of continuous training experience in volleyball or fitness/resistance training, (iii) currently training at least 3 sessions per week, and (iv) absence of pain during upper-limb exercise testing. Exclusion criteria were: (i) history of upper-extremity surgery, (ii) current or recent (past 6 months) shoulder, elbow, or wrist injury, (iii) any neurological, cardiovascular, or musculoskeletal condition that could affect performance or EMG recordings, and (iv) use of analgesic/anti-inflammatory medication within 48 hours prior to testing. Dominant arm was defined as the self-reported preferred arm used for throwing and sport-specific skilled tasks (e.g., serving/spiking in volleyball); arm dominance was recorded via self-report. Ethical approval was obtained from the Ordu University Clinical Research Ethics Committee (decision no: 2020/276; application no: KAEK 219). Written informed consent to participate was obtained from all participants prior to their inclusion in the study. The study conformed to the latest revision of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental design and tasks\u003c/h3\u003e\n\u003cp\u003eParticipants completed a standard data collection session consisting of four 20 s isometric tasks: upright rowing and shoulder press performed in seated and standing positions using a cable machine. Tasks were performed bilaterally (dominant and non-dominant sides) with a consistent grip width and verbal encouragement to maintain the required joint position throughout each contraction. Upper-limb joint angles were standardized and monitored using a wearable inertial motion system (MyoMotion; Noraxon USA Inc., Scottsdale, AZ, USA). For all conditions, the target position was defined as approximately 90\u0026deg; shoulder abduction and 90\u0026deg; elbow flexion (90/90 position) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which has been commonly used to standardize isometric shoulder and upper-limb testing postures [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To minimize onset/offset transients, participants were instructed to reach the target position smoothly and to avoid extraneous trunk motion. A rest period of at least 60 s was provided between trials. Joint positions were verified via the MyoMotion angle output during each trial, following a similar standardization approach to our previous work [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSurface EMG acquisition\u003c/h3\u003e\n\u003cp\u003eSurface EMG was recorded bilaterally from the biceps brachii, triceps brachii, deltoid, and trapezius using a wireless EMG system (myoMUSCLE, Noraxon USA Inc., Scottsdale, AZ, USA) at a sampling frequency of 2000 Hz with bipolar pre-gelled Ag/AgCl surface electrodes. Prior to electrode placement, the skin was prepared (shaving if required, gentle abrasion, and alcohol cleaning) to reduce impedance. Electrode locations were selected according to SENIAM recommendations, aligned parallel to the presumed muscle fiber direction, and placed over the muscle belly with an inter-electrode distance of 20 mm (center to center). A reference (ground) electrode was positioned over an electrically neutral bony site (olecranon process of the ulna). Signals were acquired with the manufacturer\u0026rsquo;s default analog front-end settings, including online band-pass filtering (10\u0026ndash;500 Hz) and a 50-Hz notch filter to reduce power-line interference. Given the online 10\u0026ndash;500 Hz band-pass (plus 50 Hz notch) filtering during acquisition, offline filtering was restricted to an additional 20 Hz high-pass step to mitigate residual low-frequency drift/movement artefacts; no additional low-pass or notch filtering was applied offline.\u003c/p\u003e\n\u003ch3\u003eSignal processing and median frequency\u003c/h3\u003e\n\u003cp\u003eOffline EMG processing followed our previously published framework. In brief, raw EMG signals were high pass filtered at 20 Hz using a Butterworth filter to attenuate movement artefacts and baseline drift [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. MF was then calculated from the power spectrum estimated with Welch\u0026rsquo;s method. To ensure comparable steady-state estimates across trials, MF was computed from 5\u0026ndash;15 s of each 20-s contraction (i.e., after initial stabilization and before end-of-trial anticipatory relaxation/position readjustments), thereby excluding onset and offset transients, as applied previously [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. MF was defined as the frequency that divides the total spectral power into two equal areas; a downward shift in MF during sustained isometric contraction is commonly interpreted as an indicator of fatigue-related spectral compression [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. MF was expressed in Hz (i.e., absolute median frequency) without normalization to MVC or a reference contraction. Welch\u0026rsquo;s method was implemented using a Hamming window (1-s window length, 50% overlap) with a fast Fourier transform FFT length of 2048 points. Frequency domain features were obtained by computing the power spectrum using the FFT, from which MF was derived.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSample size was justified for the primary within-subject contrast (upright row vs shoulder press). A two-tailed paired t-test power analysis (alpha\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;0.80) assuming a conservative medium effect (Cohen\u0026rsquo;s dz\u0026thinsp;=\u0026thinsp;0.60) indicated a minimum of 24 participants. We recruited 36 participants (18 per group) to ensure adequate power for the primary action effect and to support mixed-effects modelling and potential attrition. Under the smallest observed action effect in this dataset (dz\u0026thinsp;=\u0026thinsp;0.59), the achieved power with n\u0026thinsp;=\u0026thinsp;36 was approximately 0.93. Power analyses were computed in Python using stats models. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Between-group differences in participant characteristics were assessed with independent-samples t-tests. For each muscle, linear mixed-effects models (random intercept for participant) tested fixed effects of group (volleyball vs fitness), posture (seated vs standing), action (row vs press), side (dominant vs non-dominant), and their interactions. Wald χ\u0026sup2; tests were used for inference with α\u0026thinsp;=\u0026thinsp;0.05. For significant effects, follow-up comparisons were summarized using mean differences and standardized effect sizes (Cohen\u0026rsquo;s dz for within-subject contrasts and Cohen\u0026rsquo;s d for between-group contrasts). All analyses were performed in IBM SPSS Statistics (version 29.0.2; IBM Corp., Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVolleyball (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFitness (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e181.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e179.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e78.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e80.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the participant characteristics for the volleyball and fitness groups. There were no significant differences in age, height, body mass, or training age (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the groups were comparable at baseline with respect to key anthropometric and training-related variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea. Linear mixed-effects model Wald χ\u0026sup2; tests for fixed effects (Biceps).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaction\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003ea presents the Wald χ\u0026sup2; tests from the linear mixed-effects model for the biceps median frequency. A significant main effect of action was observed (χ\u0026sup2;=15.26, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that median frequency differed between the shoulder press and upright row conditions. No significant main effects were detected for group, posture, or side (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and none of the interaction terms reached significance (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the action-related difference was consistent across groups, postures, and arms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb. Linear mixed-effects model Wald χ\u0026sup2; tests for fixed effects (Deltoid).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaction\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003eb summarizes the Wald χ\u0026sup2; tests from the linear mixed-effects model for the deltoid median frequency. A significant main effect of action was found (χ\u0026sup2;=18.46, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that median frequency differed between the shoulder press and upright row conditions. No significant main effects were observed for group, posture, or side (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In addition, none of the two-, three- or four-way interactions reached significance (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the action effect was consistent across athlete groups, postures, and arms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ec. Linear mixed-effects model Wald χ\u0026sup2; tests for fixed effects (Trapezius).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaction\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003ec presents the Wald χ\u0026sup2; tests from the linear mixed-effects model for the trapezius median frequency. A significant main effect of action was detected (χ\u0026sup2;=12.12, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that median frequency differed between the shoulder press and upright row conditions. No significant main effects were found for group, posture, or side (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Furthermore, none of the interaction terms were significant (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that the action-related difference in trapezius median frequency was consistent across athlete groups, postures, and arms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ed. Linear mixed-effects model Wald χ\u0026sup2; tests for fixed effects (Triceps).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaction\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;action\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eposture\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egroup\u0026times;posture\u0026times;action\u0026times;side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003ed reports the Wald χ\u0026sup2; tests from the linear mixed-effects model for the triceps median frequency. Significant main effects were observed for group (χ\u0026sup2;=5.47, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.019) and posture (χ\u0026sup2;=4.99, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.026), indicating that triceps median frequency differed between athlete groups and between postural conditions. Importantly, a significant group\u0026times;posture interaction was also detected (χ\u0026sup2;=5.51, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;=\u0026thinsp;0.019), suggesting that the effect of posture on triceps median frequency was not uniform across groups. In contrast, there was no main effect of action (p\u0026thinsp;=\u0026thinsp;0.403) or side (p\u0026thinsp;=\u0026thinsp;0.968), and no additional interaction terms reached significance (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMedian frequency (MF, Hz) by action pooled across posture, side and group (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePress (push) MF (Hz)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRow (pull) MF (Hz)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e57.47\u0026thinsp;\u0026plusmn;\u0026thinsp;10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e65.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriceps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e63.72\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e69.50\u0026thinsp;\u0026plusmn;\u0026thinsp;9.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeltoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e61.12\u0026thinsp;\u0026plusmn;\u0026thinsp;12.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e72.31\u0026thinsp;\u0026plusmn;\u0026thinsp;9.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrapezius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e63.79\u0026thinsp;\u0026plusmn;\u0026thinsp;10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e71.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the pooled MF values by action, averaged across posture, side, and group. Across muscles, MF was higher during the row (pull) than the press (push). The largest action-related differences were observed in the deltoid (press: 61.12\u0026thinsp;\u0026plusmn;\u0026thinsp;12.40; row: 72.31\u0026thinsp;\u0026plusmn;\u0026thinsp;9.72) and trapezius (press: 63.79\u0026thinsp;\u0026plusmn;\u0026thinsp;10.30; row: 71.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.85), with a similar pattern in the biceps (press: 57.47\u0026thinsp;\u0026plusmn;\u0026thinsp;10.66; row: 65.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.50). For the triceps, the pooled values were also higher in row than press (press: 63.72\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78; row: 69.50\u0026thinsp;\u0026plusmn;\u0026thinsp;9.74), but this difference should be interpreted descriptively because the mixed-effects model did not show a significant main effect of action for triceps (p\u0026thinsp;=\u0026thinsp;0.403).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTriceps MF (Hz) by group and posture pooled across action and side (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeated MF (Hz)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStanding MF (Hz)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolleyball\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e63.74\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e66.03\u0026thinsp;\u0026plusmn;\u0026thinsp;10.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e69.89\u0026thinsp;\u0026plusmn;\u0026thinsp;9.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e66.77\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes triceps MF values stratified by group and posture, pooled across action and side. In the volleyball group, triceps MF was slightly higher in the standing posture compared with seated (66.03\u0026thinsp;\u0026plusmn;\u0026thinsp;10.49 vs 63.74\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79). In contrast, the fitness group showed the opposite pattern, with higher MF in the seated posture than in standing (69.89\u0026thinsp;\u0026plusmn;\u0026thinsp;9.12 vs 66.77\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32). This crossover pattern is consistent with the significant group\u0026times;posture interaction observed in the mixed-effects model (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study compared EMG MF responses during brief isometric push (shoulder press) and pull (upright row) tasks performed by volleyball and fitness athletes across seated and standing postures and dominant/non-dominant limbs. The most consistent finding was a robust action effect across all recorded muscles: MF was higher during the upright row than during the shoulder press. In the conventional interpretation of EMG spectral behaviour, a reduction in MF (or a more pronounced shift to lower frequencies over time) is often associated with fatigue-related changes such as decreased muscle fibre conduction velocity and altered motor unit behaviour [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, the lower MF values detected under press conditions may indicate that the spectral shift associated with fatigue is relatively more pronounced under these task constraints. However, it should be emphasised that MF is not the sole indicator representing \u0026ldquo;fatigue\u0026rdquo;; factors such as force level, motor unit recruitment strategy, and synchronisation can influence MF, and these factors can alter spectral distribution even during relatively brief isometric contractions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A reasonable mechanical explanation for the lower MF observed during the press is that the isometric shoulder press position requires relatively higher stabilisation and strength. In this position, holding the arm in a specific posture can increase the load on the stabilising muscles around the shoulder girdle, particularly the elbow extensors. Furthermore, it has been demonstrated that external task constraints such as posture, joint angle, and contraction duration can selectively alter the distribution of fatigue among shoulder-related muscles; this supports the notion that even isometric tasks that appear similar in duration can produce different spectral responses [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In contrast, the upright row task may relatively reduce fatigue-related spectral compression in the muscles recorded within the same time window by distributing the mechanical load more towards the scapular elevators and elbow flexors and may therefore result in higher MF values.\u003c/p\u003e \u003cp\u003eOur findings are consistent with studies showing that fatigue protocols targeting the shoulder region can lead to a decrease in MF in the shoulder and scapular muscles in individuals engaged in overhead sports, which supports the physiological rationale that MF may be a sensitive indicator of fatigue related changes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although this study used 20-second short isometric contractions rather than long-term endurance/until-exhaustion protocols, when contraction requirements and recruitment strategies differ significantly between conditions, even short-term contractions can produce measurable spectral differences [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A more nuanced picture emerged for the triceps: MF demonstrated a pattern sensitive to both athletic background and posture, alongside the main effects of group and posture, as well as the group\u0026times;posture interaction. This may reflect the divergence in training specificity. Fitness athletes, who are generally more exposed to resistance training and seated press variants with similar postures, may experience more efficient muscle recruitment during press tasks and a shift in the balance between peripheral fatigue-related changes and neural control strategies. On the other hand, postural regulation may alter the activation patterns of upper extremity muscles, including the biceps, triceps, deltoids, and trapezius; this provides a plausible mechanism whereby performance constraints such as sitting versus standing may interact with athletic background to shape MF responses [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. From an application perspective, these findings indicate that when comparing spectral fatigue indicators across athlete groups, posture should not be considered a \u0026ldquo;secondary\u0026rdquo; contextual variable, particularly in task-specific muscles such as the triceps during pushing.\u003c/p\u003e \u003cp\u003eHowever, the effects observed between the dominant and non-dominant limbs were quite limited; this indicates that spectral responses in trained athletes were largely symmetrical under short-term isometric conditions. This symmetry may reflect high bilateral exposure to sports practice and strength training and/or the fact that 20 s is insufficient for subtle neuromuscular differences originating from the side to translate into distinct and consistent spectral separations. From an application perspective, it is thought that MF-based comparisons in short, standardised isometric tasks may be more sensitive to task mechanics (push-pull) and posture-related constraints than to limb dominance.\u003c/p\u003e \u003cp\u003eMethodologically, when evaluating MF differences, it is necessary to consider the known sensitivity of the MF estimation to signal processing steps and spectral estimation preferences. Simulation-based studies show that mean/median frequency calculations are affected by factors such as the shape of the spectrum, noise level, and the method used to estimate power spectral density, which can alter both the absolute values and the detectability of differences between conditions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These findings highlight the critical importance of executing the signal processing pipeline in a transparent, consistent, and standardised manner in studies reporting MF as the primary output. Furthermore, studies from sports settings support that MF may be a valid and relatively stable spectral indicator for monitoring local fatigue; however, they emphasise that sensitivity may vary depending on contextual variables such as task type, intensity, and measurement approach [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, the present results highlight that task direction (push vs pull) is a dominant determinant of MF responses across upper-limb muscles, while sport background and posture can produce muscle-specific modulation most notably in the triceps underscoring the need to interpret EMG spectral outcomes within a biomechanics- and training-context framework.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eA few limitations must be accepted. First, the protocol used a brief fixed duration isometric contraction, which strengthens standardization but may not capture time-dependent fatigue dynamics (e.g., MF slopes) that become clearer during longer trials. Second, MF was evaluated as an absolute spectral estimate (Hz) without normalization to MVC or a reference contraction; therefore, between group comparisons may partly reflect baseline spectral differences and task-specific recruitment strategies. Third, the sample included only male athletes, limiting generalizability to female cohorts. Future work could extend these findings by combining MF with complementary indices (e.g., amplitude features, MF slope, or conduction-velocity-oriented metrics where feasible), and by explicitly quantifying external load and joint angles to better map mechanical constraints to muscle-specific spectral behaviour.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn male volleyball and fitness athletes, EMG MF during brief 20-s isometric tasks was primarily influenced by task action. MF was consistently higher during the upright row (pull) than the shoulder press (push), with a significant action effect observed for the biceps, deltoid, and trapezius. In contrast, triceps MF showed group- and posture-dependent behaviour, including a significant group\u0026times;posture interaction, while side (dominant vs non-dominant) effects were minimal across muscles. Collectively, these findings indicate that push\u0026ndash;pull task mechanics are a dominant determinant of upper-limb MF responses, whereas sport background and posture can selectively modulate triceps spectral behaviour, supporting the use of task-specific EMG spectral metrics to compare neuromuscular characteristics across athletic populations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ecm\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecentimetre(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEMG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectromyography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFFT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFast Fourier Transform\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHz\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHertz\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ekg\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ekilogram(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedian Frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003emm\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emillimetre(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMVC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMaximum Voluntary Contraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003es\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esecond(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSENIAM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurface Electromyography for the Non-Invasive Assessment of Muscles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e Ethical approval for this study was obtained from the Ordu University Clinical Research Ethics Committee, Ordu University (decision no: 2020/276; application no: KAEK 219). All participants were informed about the study procedures and provided written informed consent prior to participation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: \u0026Ouml;D, HS, S\u0026Ouml;; Methodology: \u0026Ouml;D, HS, S\u0026Ouml;; Data collection: S\u0026Ouml;, \u0026Ouml;D; Formal analysis: HS, \u0026Ouml;D; Investigation: HS, S\u0026Ouml;, \u0026Ouml;D; Writing-original draft: \u0026Ouml;D, S\u0026Ouml;; Writing-review \u0026amp; editing: HS, \u0026Ouml;D, S\u0026Ouml;; Supervision: \u0026Ouml;D.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank all the participants involved in the study for their patience and commitment to their involvement.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSozen H, Erdogan E, Ince A, Soylu AR. Determination of electromyography-based coordinated fatigue levels in agonist and antagonist muscles of the thigh during squat press exercise. Ann Appl Sport Sci. 2019;7(3):21\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun J, Liu G, Sun Y, Lin K, Zhou Z, Cai J. Application of surface electromyography in exercise fatigue: a review. Front Syst Neurosci. 2022;16:893275.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlich S, Kawczyński A, Pietraszewski B, Zago M, Chen A, Smoter M, et al. Electromyographic evaluation of the shoulder muscle after a fatiguing isokinetic protocol in recreational overhead athletes. Int J Environ Res Public Health. 2021;18(5):2516.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuce L, Pallecchi I, Marinelli L, Mori L, Bove M, Diotti D, et al. Surface electromyography spectral parameters for the study of muscle fatigue in swimming. Front Sports Act Living. 2021;3:644765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorvini G, Conforto S. A simulation study to assess the factors of influence on mean and median frequency of semg signals during muscle fatigue. Sensors. 2022;22(17):6360.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorvini G, D\u0026rsquo;Anna C, Conforto S. Estimation of mean and median frequency from synthetic sEMG signals: Effects of different spectral shapes and noise on estimation methods. Biomed Signal Process Control. 2022;73:103420.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi J, Lin Y, Loh PY. The Effects of Standing Working Posture on Operation Force and Upper Limb Muscle Activation When Using Different Pointing Devices. Int J Environ Res Public Health. 2022;19(16):10217.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang F, Lu C, Yun X, Qian C. Effects of neuromuscular training on stability in volleyball athletes: a systematic review and meta-analysis. Front Sports Act Living. 2025;7:1724934.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwens LP, Khaiyat O, Coyles G. Muscle activations of the upper extremity and core during elevation and rotational movements in overhead throwing athletes. Int J Sports Phys Ther. 2024;19(4):466.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang Ujae, Kim Jhee, Gwak G, tae, Kim Mhwan. Comparison of Elbow Extensor Muscle Strength and EMG Activity in Supine and Prone Positions in Healthy Subjects. J Musculoskelet Sci Technol. 2018;2(1):16\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoman-Liu D, Tokarski T. Upper limb strength in relation to upper limb posture. Int J Ind Ergon. 2005;35(1):19\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshworth B, Hogben P, Singh N, Tulloch L, Cohen DD. The Athletic Shoulder (ASH) test: reliability of a novel upper body isometric strength test in elite rugby players. BMJ Open Sport Exerc Med. 2018;4(1):e000365.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeterbakken AH, Fimland MS. Effects of body position and loading modality on muscle activity and strength in shoulder presses. J Strength Cond Res. 2013;27(7):1824\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkyildiz C, S\u0026ouml;zen H. The effects of lower extremity static muscle fatigue on balance components. Sport Sci Health. 2023;19(3):897\u0026ndash;908.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Luca CJ, Gilmore LD, Kuznetsov M, Roy SH. Filtering the surface EMG signal: Movement artifact and baseline noise contamination. J Biomech. 2010;43(8):1573\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Luca CJ. Myoelectrical manifestations of localized muscular fatigue in humans. Crit Rev Biomed Eng. 1984;11(4):251\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark JS, Jung MC, Kim JY, Mo SM. Developing Synthetic Parameters Using Frequency Band Ratios for Muscle Fatigue Analysis During Isometric Contractions by Using Shoulder Muscles. Sensors. 2025;25(7):2191.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JY, Park JS, Kim DJ, Im S. Evaluation of fatigue patterns in individual shoulder muscles under various external conditions. Appl Ergon. 2021;91:103280.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"surface electromyography, median frequency, muscle fatigue, resistance training, push-pull tasks","lastPublishedDoi":"10.21203/rs.3.rs-8827731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8827731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose: To compare sport-specific differences in upper-limb muscle fatigue characteristics, assessed via surface EMG median frequency (MF), during brief isometric push and pull tasks performed in seated and standing postures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Thirty-six athletes (volleyball, n=18; fitness, n=18) performed 20-s isometric contractions in four conditions (seated upright row, seated shoulder press, standing upright row, standing shoulder press) using both dominant and non-dominant limbs. Surface EMG was recorded bilaterally from the biceps brachii, triceps brachii, deltoid, and trapezius. MF was calculated using Welch’s method from a steady-state window (5-15 s of the 20-s contraction) to avoid onset and end-of-trial transients and expressed in Hz. Linear mixed-effects models tested the effects of group, posture, action, and side.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: MF was higher during the upright row than the shoulder press for the biceps, deltoid, and trapezius (p\u0026lt;0.001; within-subject dz=0.59–1.02). For the triceps, significant effects of group (p=0.019), posture (p=0.026), and a group×posture interaction (p=0.019) were observed, with fitness athletes showing higher MF than volleyball athletes particularly in the seated condition. No robust main effects or interactions involving side were detected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: In these brief isometric tasks, MF responses were predominantly task-dependent (pull\u0026gt;push) for biceps, deltoid, and trapezius, whereas triceps MF showed sport- and posture-dependent modulation. These findings support the use of task-specific EMG spectral outcomes to probe fatigue-related neuromuscular characteristics across athletic populations.\u003c/p\u003e","manuscriptTitle":"Sport Specific Differences in Upper Limb Median Frequency Responses During Isometric Push and Pull Tasks in Volleyball and Fitness Athletes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 19:41:44","doi":"10.21203/rs.3.rs-8827731/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T09:16:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T14:01:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-07T09:48:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20149441025407636618804645444890995740","date":"2026-03-02T21:19:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T12:38:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154128816826487863774662636830010058356","date":"2026-02-24T05:07:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246179893743898208667917223444807800937","date":"2026-02-23T21:29:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-23T21:26:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-21T06:44:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-17T06:04:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T09:56:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2026-02-16T09:34:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7e09aef2-5423-4c32-98d5-11ad3a34464d","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:04:11+00:00","versionOfRecord":{"articleIdentity":"rs-8827731","link":"https://doi.org/10.1186/s13102-026-01700-1","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2026-04-17 15:58:02","publishedOnDateReadable":"April 17th, 2026"},"versionCreatedAt":"2026-02-26 19:41:44","video":"","vorDoi":"10.1186/s13102-026-01700-1","vorDoiUrl":"https://doi.org/10.1186/s13102-026-01700-1","workflowStages":[]},"version":"v1","identity":"rs-8827731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8827731","identity":"rs-8827731","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}