INTRODUCTION
The application of neuromuscular electrical stimulation (NMES), involves the application of intermittent electrical stimulation to superficial skeletal muscles, induces skeletal muscle contraction through the percutaneous activation of peripheral nerves. This activation can occur directly via the depolarization of motor neurons or indirectly through the depolarization of sensory afferents (Enoka et al., 2020). Because NMES training does not require voluntary muscle contraction, this training method is frequently used not only for patients with disabilities but also for healthy individuals.
Clinical studies have demonstrated the therapeutic potential of NMES to enhance or mitigate the negative effects associated with physical inactivity in patients with disabilities or chronic illnesses. Additionally, NMES has been widely utilized as a training tool in the field of sports to improve neuromuscular function (Akagi et al., 2023; Bezerra et al., 2011). However, manipulating NMES parameters, such as frequency (Hz), current intensity (mA), pulse width (μs), waveform (rectangular, triangular, sinusoidal), and stimulation cycle (duty cycle) during NMES application can yield diverse outcomes (Herzig et al., 2015; Maffiuletti, 2010; Mukherjee et al., 2023). For example, applying high frequencies of 60–100 Hz can simultaneously stimulate motor axons and sensory afferents, inducing strength gain and muscle hypertrophy (Maffiuletti, 2010; Veldman et al., 2016), whereas low frequencies of 20–50 Hz can selectively stimulate motor axons and promote strength gains (Maffiuletti, 2010; Sluka and Walsh, 2003). Furthermore, NMES recruits motor units (MUs) in a random order, in contrast to the ‘size principle’ of voluntary contraction, which describes the recruitment pattern of motor neurons based on their size; specifically, slow MUs, associated with small-diameter motor neuron axons, are activated before fast MU, linked to large-diameter axons (Gregory and Bickel, 2005). For example, a low frequency (20 Hz) improves strength based on the selective recruitment of type II muscle fibers (Hamada et al., 2004), whereas a high frequency (60 Hz) increases strength and diameter in both types I and II muscle fibers (Kern et al., 2014). Although the stimulated neural pathways differ for each frequency range, few studies have compared the effects of high- and low-frequency NMES on muscle characteristics.
Furthermore, NMES application not only affects neuromuscular function but also exerts positive effects on the quality of muscles (Akagi et al., 2023). Echo intensity (EI), obtained through B-mode ultrasound imaging, has gained considerable attention as a non-invasive and accessible method for assessing muscle quality (Jo and Kim, 2024; Stock and Thompson, 2021). Higher EI values are generally indicative of increased amounts of non-contractile elements within the muscle, such as intramuscular fat and fibrous connective tissue (Pillen et al., 2009). EI offers several advantages in both clinical and research settings. Moreover, because EI can be quantified from standard grayscale ultrasound images, it provides a practical means to monitor muscle adaptations over time in response to various interventions, including exercise, immobilization, or neuromuscular stimulation (Akagi et al., 2023; Matta et al., 2018; Ota et al., 2020; Stock and Thompson, 2021). To the best of our knowledge, the present study is the first to evaluate the frequency-dependent effects of NMES on muscle quality, whereas Akagi et al. (2023) investigated changes in EI following NMES application in a more general context. This study aims to provide deeper insight into how specific electrical stimulation parameters can be optimized to enhance muscle function by examining the distinct effects of different NMES frequencies on EI, thereby informing the effectiveness of rehabilitation and therapeutic protocols targeting muscle health.
Conversely, some studies have reported that NMES training may induce transient peripheral impairments. For instance, muscle injury (Mackey et al., 2008) and delayed training effects (Zory et al., 2010) have been observed following repeated NMES sessions. Additionally, the acute application of electrical stimuli to evoke isometric contractions has been associated with muscle fatigue, soreness, and damage (Theurel et al., 2007). These adverse effects are thought to result from delayed neural adaptations caused by low-frequency stimulation, which may exacerbate muscle damage. Hence, a comprehensive comparison of the onset and recovery time-course effects of NMES at varying frequencies is crucial to elucidate their distinct physiological impacts.
Thus, this study aimed to compare the effectiveness of high- and low-frequency NMES in enhancing strength and muscle quality and to investigate the duration of these effects, after the intervention.
MATERIALS AND METHODS
Participants
Before recruiting participants, a statistical power analysis was performed a priori estimation using G*Power (v 3.1.9.4, Kiel University) to determine the sample size. The input parameters were as follows: Statistical test=analysis of variance (ANOVA): Repeated measures (RMs), within-between interaction; type I error rate of α=0.05; power β=0.8; effect size f=0.3; number of groups=2 (low vs. high frequency); number of measurements=5; corr among rep measures=0.50; and nonsphericity correction ɛ=1. Therefore, the overall sample size was 16. A total of 20 healthy young males participated in the study. Additionally, we excluded participants who did not meet the following criteria: age between 18 and 29 years, no history of cardiovascular or orthopedic disease, and a sedentary lifestyle and no involvement in strength training for at least 6 months. Alcohol consumption was evaluated using the Alcohol Use Disorder Identification Test – Korean, with participants showing a mean score of 6.19±1.88, indicating normal drinking behavior. Before the study initiation, we explained the study requirements, benefits, potential risks, and discomfort to the participants. Written informed consent was obtained from all the participants. The study protocol was approved by the Institutional Review Board of Kyungpook National University (2023-0414). Ethical guidelines outlined in the Declaration of Helsinki (last modified in 2013) were strictly followed and study flow diagram is presented in Fig. 1.
Experimental protocol
The recruited participants were randomly assigned to either the high-frequency NMES (HFES) group or the low-frequency NMES (LFES) group (n=10 each); however, data from only 16 participants were used in the final analysis, excluding four who were unable to complete participation because of personal schedules during the experiment. The HFES group included 8 participants (age: 22.88±1.23 years; height: 174.73±2.35 cm; and weight: 69.14±3.67 kg) and the LFES group included 8 participants (age: 23.75±0.82 years; height: 173.79±2.58 cm; and weight: 70.73±5.29 kg). The participants visited the laboratory for a familiarization session for at least 2 days before the main experiment to assess baseline physical information. For familiarization, the participants were instructed to perform maximum voluntary isometric contractions (MVICs) and submaximal isometric contractions. On the day of the experiment, prior to MVIC, the ultrasound images were acquired from rectus femoris (RF) and vastus lateralis (VL). MVIC strength values were measured at 90° for the dominant leg. The dominance was approved using questionnaires (van Melick et al., 2017). The intervention was applied 3 times per week for 6 weeks, and the assessments were conducted at baseline, midintervention (week 3) and postintervention (weeks 6, 8 and 10) (Fig. 2).
NMES intervention
The intervention, which consisted of 30 contractions per session, 3 times per week for 6 weeks, was applied to the bilateral quadriceps in each group, with the participants seated in a knee extension machine with the hip and knee joints fixed at 90°. NMES was applied via four adhesive electrodes (5×5 cm) using a portable NMES device (Wireless Pro). Each electrode was attached to the innervated surfaces of the VL and vastus medialis and bilaterally to the innervated surface of the RF approximately 10 cm below the femoral triangle (Nishikawa et al., 2021). The parameters of the NMES were set to be identical except for frequency as follows: waveform (rectangular symmetrical pulses with a duration of 400 μsec), stimulation period (6.25:12=on:off), and intensity (50% of MVIC), with a frequency of 100 Hz for the HFES group and 50 Hz for the LFES group. During stimulation, the participants were constantly reminded to fully relax their quadriceps (Maffiuletti, 2010; Nishikawa et al., 2021).
Electromyography
To derive the characteristics of the neuromuscular system during MVIC, signals were recorded for the RF and VL using a decomposition electromyography (EMG) sensor (Galileo, Delsys). The sensor attachment positions were set according to recommendations described by Zaheer et al. (2012). The skin was shaved and cleansed with alcohol before electrode attachment. The Nuprep gel was used to minimize skin electrical resistance, and sensors were attached to the muscle belly of each muscle along the direction of the muscle bundle. Interelectrode impedance was measured using an impedance meter (EL-CHECK, BIOPAC Systems), kept below 2,000 Ω. To determine the neuromuscular properties during MVIC and submaximal isometric contractions, EMG signals derived from the RF and VL were analyzed using custom MATLAB code (R2023a, MathWorks). The EMG signals from MVIC sessions were analyzed using the maximum value of the root mean square with a 100-msec window length and 99% overlap after rectification.
Force
A load cell with a built-in strain gauge (MNC-200L, CAS) was used to measure the knee extension strength. The participants sat on a dynamometer (HumacNORM, CSMi) with a load cell attached and secured to the dynamometer using a belt to keep their upper body upright and limit pelvic movement. A strap was placed on the lateral malleolus of the ankle and connected to the load cell to record the muscle strength at a 90° knee angle. Analog signals were collected and digitized at 200 Hz with 4th order Butterworth digital filtering using an oscilloscope (Infinii Vision 100 X; Keysight), and force values were analyzed using a custom-made software. To measure MVIC strength, three maximal force efforts for 5-sec were performed with a 60-sec rest between MVIC contractions. If any of the three MVIC strength values differed by >5% between two similar values, one additional measurement was obtained, and the highest of the MVIC strength values with a difference of ≤5% were selected for analysis.
Ultrasound images
B-mode images of the femur were acquired using a 50-mm linear probe with 9.1–13 MHz mounted on an ultrasound machine (S12, SonoScape Co.). To acquire ultrasound images, the participants rested for 10 min in the supine position before measurement. Transverse ultrasound images acquired at the same locations as the surface EMG electrode attachments. For consistent image extraction, the measurement site was marked. All images were acquired by a single examiner, and the brightness, contrast, and focus of the ultrasound system were kept the same throughout the measurement. Three images of each muscle extracted at rest were stored on a computer, and the two images with the most similar EI were selected for analysis. All images were digitized and analyzed using ImageJ software (ver. 1.52a, National Institutes of Health). EI was calculated within a 1×1-cm region of interest for each recorded image. The region of interest was defined as the muscle region without the bone and septum that displayed the best reflection. The mean value of the grayscale histogram distribution (0–255= black to white) was considered the EI. On the longitudinal images, the vertical distance of the muscle excluding the fascia was measured and adopted as the muscle thickness (Jo and Kim, 2024).
Statistical analysis
In F-test, effect size F typically ranges from 0 to 1.0. Small effect sizes are generally considered to be around 0.1, medium around 0.25, and large around 0.40. In the present study, F=0.4 was established as a pragmatic level, contemplating both the feasibility of the study and resource constraints. All data are presented as means±standard errors and analyzed using IBM SPSS Statistics ver. 25.0 (IBM Co.). Normal distributions for all parameters were analyzed using the Shapiro–Wilk test. A one-way analysis of variance (ANOVA) was performed to compare the results after the intervention with those at baseline. A two-way (time by group) RMs ANOVA was used to compare the effects of NMES between the groups. When significant main effects were present, Bonferroni’s post hoc test was used to assess the differences between groups. Significance was set at P<0.05.
RESULTS
Force
A two-way RM ANOVA revealed that the assumption of sphericity had not been violated in Mauchly’s test (W=0.524, χ2 [9]= 8.021, P>0.05). A significant interaction was observed between time and group (F [4, 56]=3.532, P<0.05, ηp2=0.201), with a significant difference between the HFES (9.74%±2.38%) and LFES groups (−2.91%±1.98%) at week 3 (P<0.01). Furthermore, MVIC showed a significant main effect in the HFES group (P< 0.05), although no significant difference was observed in the LFES group (P>0.05). The HFES group demonstrated a significant difference from baseline values at week 6 (5.58%±1.58%, P<0.01) (Fig. 3).
Muscle activation
A two-way RM ANOVA was performed to determine the interaction effect of time and group on muscle activity, and muscle activation showed no significant interaction effect for RF and VL (P>0.05). Furthermore, there are no significant main effects of HFES and LFES in RF and VL (P>0.05) (Table 1).
Muscle structure and quality
A two-way RM ANOVA determined that there were no significant interactions in muscle thickness. However, there were significant main effects of HFES and LFES in RF and VL (P<0.05). Specifically, HFES had significant differences in week 6 (RF: 2.74± 0.12 cm, P<0.01; VL: 2.82±0.18 cm, P<0.05). Moreover, LFES had significant differences in week 3 (RF: 2.51±0.12 cm, P<0.05), week 6 (RF: 2.67±0.10 cm, P<0.01; VL: 2.82±0.18 cm, P<0.01), week 8 (VL: 2.38±0.11 cm, P<0.05) and week 10 (VL: 2.34± 0.15 cm, P<0.05) (Table 1).
As a result of the two-way RM ANOVA for the EI of the RF, the Mauchly test indicated that the assumption of sphericity was violated (W=0.224, χ2 [9]=18.584, P<0.05), a Greenhouse-Geisser correction (ɛ=0.643) was applied to examine the results. There was a significant interaction effect between time and group (F [4, 36.008]=3.099, P<0.05, η2=0.181), with significant differences at week 8 (P<0.01) and week 10 (P<0.05) between the HFES (week 8: −0.67±0.62 a.u.; week 10: −0.71±0.72 a.u.) and LFES (week 8: −7.64±1.66 a.u.; week 10: −8.78±2.11 a.u.). Furthermore, the EI of the LFES in the RF and VL showed significant main effects. Specifically, there were significant differences with pre at week 8 (VL: −4.57±1.16 a.u., P<0.05) and week 10 (VL: −7.29±1.52 a.u., P<0.01) (Fig. 4).
DISCUSSION
This study aimed to compare the changes in neuromuscular function of healthy young men following HFES or LFES application. The main findings are as follows: First, MVIC increased significantly in the HFES group during the intervention period, showing a significant interaction effect between the groups. Second, EI decreased in the LFES group after cessation of the intervention, showing a significant interaction effect between the groups in RF.
Many previous studies have reported an increase in MVIC after NMES training (Akagi et al., 2023; Colson et al., 2009). In the present study, HFES significantly increased, whereas LFES did not demonstrate significant changes during the intervention period. Neural adaptations, generally expressed as strength gains, have also been observed during NMES training. Although an increase in strength after typical resistance exercise training involves spinal (the motoneuron soma and distal to it) or supraspinal (structures proximal to the motoneuron soma) adaptations owing to internal stimulation, NMES training triggers neural adaptations by activating motor axon branches because of external stimulation (Hortobagyi and Maffiuletti, 2011). Despite these differences, NMES training induces neural adaptations that lead to an increase in strength, as confirmed by the results of the present study. However, after cessation of the intervention, both groups showed an increasing trend of increased MVIC compared to preintervention values, although the difference was not statistically significant. Zory et al. (2010) reported no significant difference in MVIC immediately after 4 weeks of NMES intervention but a significant increase 4 weeks after intervention cessation, which they attributed to the NMES intervention causing muscle damage and delayed neural adaptation (Zory et al., 2010). Muscle damage caused by NMES was hypothesized to be caused by the impairment of the excitation–contraction coupling mechanism due to fatigue induced by low-frequency stimulation. This was explained based on a study by Westerblad and Allen in 2002, who showed that repeated low-frequency stimulation delayed the recovery of muscle strength (delayed low-frequency recovery), and the study reported that, unlike low-frequency stimulation, repeated high-frequency stimulation resulted in the recovery of muscle strength immediately after cessation of the intervention (Westerblad and Allen, 2002).
Although NMES was applied only for 6 weeks, significant reductions in EI were observed in the LFES group at week 8 and 10, suggesting a delayed adaptation in muscle quality. This finding aligns with previous work by Zory et al. (2010), who reported that functional impairments or neural adaptations following NMES may emerge after the end of the stimulation period (Zory et al., 2010). In contrast, the HFES group showed earlier improvements in muscle strength (MVIC), but minimal changes in EI, indicating that the two frequencies may target different physiological mechanisms. The sustained decrease in EI after LFES cessation may reflect ongoing metabolic or structural remodeling, such as reductions in intramuscular fat or fibrotic tissue (Gondin et al., 2011). This underlines the necessity of assessing muscle quality beyond the immediate postintervention period. Moreover, the dissociation between MVIC and EI responses suggests that stimulation frequency should be tailored based on whether the goal is to improve neuromuscular performance or muscle composition. These findings are further supported by Akagi et al. (2023), who found that NMES-induced strength gains were significantly correlated with muscle hypertrophy but not with changes in EI, indicating that improvements in muscle quality and strength may occur through distinct physiological pathways (Akagi et al., 2023).
This study has certain limitations. As it was conducted with young adult men, the findings may not be generalizable to the broader population. In particular, previous studies have reported significant differences in the MVIC and EI between older populations and younger adults (Astephen Wilson et al., 2015; Ota et al., 2020; Yamaguchi et al., 2023). Furthermore, sex differences should be considered in EI analyses (Ota et al., 2020; Stock and Thompson, 2021). Therefore, only young adult men were recruited in this study to minimize the variance in individual characteristics that could affect the results. Furthermore, the properties of the vastus medialis were not assessed among individual muscles; therefore, detailed trends in the individual quadriceps muscles could not be determined. However, changes in the muscle characteristics of the RF and VL, which are the largest contributors to knee extension (Saito and Akima, 2013), can be used to describe the quadriceps to a limited extent. Therefore, future studies should examine the effects of each type of NMES on different populations to determine the influence of population characteristics on NMES parameter settings.
In conclusion, this study examined the differential effects of high- and low-frequency NMES on neuromuscular function and muscle quality. HF NMES demonstrated immediate enhancements in neuromuscular function throughout the intervention period, indicating its efficacy inducing rapid adaptations. In contrast, LF NMES did not produce significant improvements during the intervention but exhibited delayed benefits following the cessation of training. These results suggest that the frequency of NMES modulates neuromuscular adaptations via different mechanisms. Although NMES is known to have metabolic effects on components that can affect the morphology and quality of muscle, it appears to be preferentially involved at certain frequencies due to differences in frequency settings. Overall, the present study contributes valuable evidence to the efficacy of frequency-specific NMES and underscores its practical utility in both clinical rehabilitation and athletic performance programs through individualized training strategies. Further research is needed to elucidate the underlying mechanisms linking NMES-induced changes in muscle fiber type characteristics and contributions to neuromuscular adaptation. Understanding these mechanisms will provide deeper insights into the optimization of NMES protocols.
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