Stress‐, Anxiety‐, and Gender‐Related Modulation of Masseter Muscle Activity During Wakefulness

ABSTRACT

Background

Psychological stress and anxiety are known to influence muscle activity, particularly in the masticatory system. However, the interactive effects of stress, trait anxiety, and gender on objective masseter muscle activity remain poorly understood.

Objectives

This study investigates the association between psychological factors, stress and trait anxiety, as well as gender, and masseter muscle activity during various tasks designed to induce stress or relaxation.

Methods

Thirty‐four healthy participants (16 males, 18 females; mean age 28.1 ± 3.1 years) were recruited. Trait anxiety was assessed using the State–Trait Anxiety Inventory (STAI‐Y2), and stress levels were measured using a Visual Analog Scale (VAS). Participants completed four randomised tasks (reading, video‐watching, math‐solving, bead‐grabbing) over two experimental days while wearing a wireless EMG logger to record masseter muscle activity. Main outcomes included burst frequency (per hour), wave peak value (%MVC), and integral activity (%MVC·s). Linear mixed models assessed the effects of stress, trait anxiety, and gender. Self‐reported oral behaviours (partial Oral Behaviour Checklist; POBC) were also evaluated.

Results

Stress significantly reduced burst frequency (−3.5% per unit; p = 0.016) and wave peak value (−1.9% per unit; p < 0.001) in males but not females. Low‐anxiety individuals exhibited increased integral muscle activity with rising stress (+4.8% per unit; p = 0.002), while other groups showed no such trend. Trait anxiety positively correlated with POBC scores (r _s = 0.409, p = 0.016).

Conclusions

Masseter muscle activity is differentially modulated by stress, anxiety, and gender. These findings highlight the psychophysiological complexity underlying stress‐related oral parafunctional behaviours and support individualised assessment approaches.

Stress‐, anxiety‐, and gender‐related factors jointly affect masseter muscle activity during wakefulness.

1. Background

Stress and anxiety are psychological states that significantly influence physiological processes, including muscle activity [1, 2, 3]. Stress responses can affect muscle tension and motor control, particularly in the masticatory muscles [4, 5], which are critical for oral and facial functions. Prolonged stress exposure has been implicated in parafunctional behaviours such as clenching and grinding [6]. These behaviours highlight the importance of understanding the interplay between stress and physiological responses. Subjective stress levels are often assessed using the Visual Analog Scale (VAS), a 0–100 mm continuum widely recognised for its sensitivity in capturing changes in perceived stress [7, 8] before and after tasks that induce or relieve stress [9, 10].

Trait anxiety, an enduring personality characteristic, is closely associated with heightened reactivity to stress and distinct physiological patterns [11, 12]. The State–Trait Anxiety Inventory (STAI) is a well‐validated tool for assessing anxiety, consisting of separate components for state and trait anxiety. The STAI‐Y2 subscale, which evaluates trait anxiety through 20 items scored from 20 to 80, provides a robust measure of enduring anxiety levels [13]. Higher STAI‐Y2 scores often correlate with altered neuromuscular control and muscle activity patterns [14]. Despite its extensive use in research and clinical settings [15], studies examining the role of STAI‐Y2 in assessing the relationship between trait anxiety and masticatory muscle activity under stress‐inducing and relaxing conditions are limited [16].

Electromyography (EMG) is a reliable method for quantifying muscle activity [17, 18], offering valuable insights into physiological responses under controlled conditions. While prior research has investigated the effects of stress and anxiety on muscle function [19, 20, 21], there is limited integration of perceived stress levels measured by VAS, trait anxiety assessed through STAI‐Y2, and objective muscle activity. Addressing these psychological parameters concurrently provides a more comprehensive framework for understanding their combined impact on muscle activity.

Building on the measurement protocols established in our earlier research [22], this study extends the scope of the original dataset by incorporating additional psychological variables. By integrating these parameters with EMG‐based analyses, we aim to investigate the association between stress, trait anxiety, and masseter muscle activity during stress‐inducing and relaxing tasks, addressing research questions that were not the primary focus of the previous investigation. In addition, as a secondary objective, this study explores the moderating role of gender in these psychophysiological relationships. Based on prior studies indicating that psychological stress and anxiety modulate masticatory muscle activity [4, 5, 16], and that such modulation may differ between males and females [23, 24, 25], we formulated the following hypotheses: 1. Increased perceived stress, as measured by VAS, would be associated with changes in masseter muscle activity (burst frequency, peak value, and integral value). 2. Trait anxiety would interact with stress to influence muscle activity, with individuals showing different EMG responses depending on their anxiety level. 3. Gender would moderate these psychophysiological relationships, with males and females showing distinct stress‐related EMG patterns.

2. Materials and Methods

This study utilised data from a previously conducted experiment on masseter muscle activity during various tasks under controlled conditions. While the detailed methodology, including participant recruitment, task protocols, and EMG recording procedures, has been described in our prior publication [22], a concise summary is provided here to ensure clarity and completeness. A total of 34 healthy participants, recruited from the Institute of Science Tokyo (formerly Tokyo Medical and Dental University), completed two experimental sessions at least 1 week apart. Participants were screened using the DC/TMD pain screener, and those scoring ≥ 3 were excluded. Individuals with systemic or mental disorders, significant dental anomalies, or use of prostheses were also excluded. Each participant completed four 15‐minute tasks (reading, bead‐grabbing, video‐watching, and simple arithmetic), presented in a randomised order. Tasks were conducted in a quiet, temperature‐controlled room with the participant seated upright without a headrest. A 10‐minute break followed each task. During breaks, participants were allowed to rest, communicate, or drink water. Masseter muscle activity was recorded using a lightweight wireless EMG Logger (GC Co. Ltd., Japan) corresponding to the habitual masticatory side, which sampled at 1 kHz and stored data to a micro‐SD card. Prior to attachment, the skin was cleansed and electrodes were placed along the masseter muscle belly, following anatomical landmarks. Calibration was conducted before each session using two maximum voluntary clench (MVC) efforts in habitual occlusion, each lasting 3 seconds. The EMG logger remained undisturbed throughout recording. On Day 2, the procedure was repeated using the same EMG attachment site and conducted at the same time of day (AM or PM) as the first session in randomly different task order. Ethical approval was obtained from the Ethics Committee of the Institute of Science Tokyo (Approval No. D2023‐043), and all participants provided written informed consent.

For this study, only data from Day 1 and Day 2 were analysed, while data from Day 3 originally recorded as a long‐duration EMG assessment during daily activities were excluded. Additional psychological variables were incorporated into this study to extend the original dataset. Stress levels were assessed using a Visual Analog Scale (VAS) on a 0–100 mm scale, both before (as a baseline) and after each task. Trait anxiety was measured at the conclusion of the study using the State–Trait Anxiety Inventory (STAI) [13]. The STAI is a widely used psychological assessment tool designed to measure anxiety levels in individuals. It consists of two components: the State Anxiety Scale, which assesses current emotional states, and the Trait Anxiety Scale, which measures enduring anxiety traits across various situations. The STAI has undergone extensive validation and is used in diverse clinical and research contexts [15]. The questionnaire consists of 20 items measuring state anxiety (Y1) and another 20 items assessing trait anxiety (Y2). Responses are rated on a 4‐point scale, with scores ranging from 1 to 4. For this study, only the Trait Anxiety Scale (Y2) was used, with total scores ranging from 0 to 80.

2.1. Data Analysis

In this study, the possibility of awake bruxism (AB) in participants was evaluated using the partial Oral Behaviour Checklist (POBC), consistent with the methodology employed in our previous research. The POBC score was calculated by assessing six specific items (number 3, 4, 5, 6, 7, 11) from the OBC [26], which reflect behaviours associated with AB, such as grinding, clenching, and tensing the jaw muscles during waking hours and related to TMD [27]. Each of the six items was rated on a scale of 0 to 4. As no established cutoff values for the OBC exist, only items relevant to TMD were included in the POBC calculation.

EMG data from the first and second days were analysed using W‐EMG Viewer software (GC Co., Tokyo, Japan). The maximum peak amplitude recorded in the Maximum Voluntary Contraction (MVC) waveforms was used as the MVC value for each participant. Bursts were identified in the EMG recordings if their amplitude exceeded three times the baseline amplitude, had a minimum duration of 0.08 s, and were separated from neighbouring bursts by at least 0.08 s. The following metrics were subsequently calculated: burst number per hour, the ratio of burst peak value to MVC (%MVC), and the integral value (%MVC·s). The difference between baseline and post‐task VAS stress levels was used to quantify perceived stress levels for each task. Participants were categorised into three groups based on their trait anxiety scores (Y2): the bottom 20% (low anxiety), the middle 60% (medium anxiety), and the top 20% (high anxiety).

ANOVA and Kruskal–Wallis tests were applied to compare the mean age, POBC score, and STAI‐Y2 score between the different trait anxiety groups. A paired‐sample t‐test was conducted to compare the mean VAS scores between baseline and after each task. The Friedman test, followed by Wilcoxon signed‐rank tests with Bonferroni adjustment, was used to compare EMG data across tasks. Given the non‐independence of the EMG dataset, a linear mixed model analysis was conducted [28], incorporating three dependent variables (burst number/h, %MVC, and %MVC·s). The optimal models were identified using a backward stepwise approach. As gender was a secondary objective of this study, it was included in the model based on prior evidence suggesting its influence on masticatory muscle activity and bite force [23, 24, 29, 30]. The fixed effects considered were trait anxiety group, stress level, gender, and their interactions, while participant and stress level were treated as random effects. Since the specific tasks were not the primary focus of this study, the analysis primarily focused on VAS stress level changes, which were included in the models as a representative measure of task effects. The analysis began with the full model, which included all potential interactions, fixed effects, and random effects. Factors were sequentially removed based on comparisons between the full and reduced models, with the selection of the final model guided by the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Bayes factor, and p‐values [31]. This process was repeated until the most fit model was achieved. Some fixed effects were not retained in the final model because they did not significantly improve model fit or were not statistically significant predictors according to the chosen criteria. Spearman's correlation was used to evaluate the relationship between STAI‐Y2 scores and POBC.

Statistical analyses were performed using SPSS version 21.0 software (IBM Inc., Armonk, NY, USA), R (R Core Team, 2024), and the lme4 package (Bates, Maechler, Bolker, & Walker, 2015) [32]. The assumptions of the linear mixed model, including linearity, absence of multicollinearity, homoscedasticity, and normality of residuals, were thoroughly assessed. A p‐value of less than 0.05 was considered statistically significant. The significance of fixed effects was evaluated using p‐values, while t‐values were used to interpret the strength and direction of associations.

3. Results

3.1. Participant and Task Characteristics

The dataset analysis of 34 participants revealed that 16 (47.1%) were male and 18 (52.9%) were female, with a mean age of 28.06 years (±3.1 SD). The average trait anxiety score (STAI‐Y2) for the entire sample was 41.18 (±8.3 SD). Based on their scores, participants were categorised into three groups: low trait anxiety (percentile < 20, STAI‐Y2 score < 34, N = 6), medium trait anxiety (percentile 20–80, STAI‐Y2 score 34–48, N = 21), and high trait anxiety (percentile > 80, STAI‐Y2 score > 48, N = 7). Table 1 presents these characteristics. No significant differences were observed in age across the trait anxiety groups (p = 0.821). While there was a trend towards higher POBC scores in individuals with greater trait anxiety, the difference did not reach statistical significance (p = 0.089).

TABLE 1.

Characteristic of participants (mean ± SD).

	Low trait anxiety (n = 6)	Medium trait anxiety (n = 21)	High trait anxiety (n = 7)	Total (n = 34)	p
Age	27.33 ± 2.7	28.19 ± 3.4	28.29 ± 2.7	28.06 ± 3.1	0.821
POBC	0.50 ± 0.8	1.57 ± 1.6	2.86 ± 2.3	1.65 ± 1.8	0.089
STAI‐Y2	29.67 ± 4.1 a	40.43 ± 3.8 a	53.29 ± 3.3 a	41.18 ± 8.3	< 0.001

Abbreviations: POBC, partial oral behaviour checklist; STAI‐Y2, trait anxiety scores.

^a The mean difference is significant at the 0.05 level.

Among the four tasks, the reading task (VAS change: 5.81 ± 12.45, p = 0.010) and the video task (VAS change: 11.04 ± 12.50, p < 0.001) evoked significant increases in self‐reported stress levels, indicating successful induction of mental stress. In contrast, the bead‐grabbing task (VAS change: 1.00 ± 14.03, p = 0.680) and the mathematical task (VAS change: −0.22 ± 14.08, p = 0.928) did not significantly alter VAS scores. Although the primary aim of this study was not to compare the stress‐inducing potential of individual tasks, these findings confirm task‐related variation in subjective stress responses.

Correspondingly, EMG results showed significant task‐related changes across all three outcome measures. The number of bursts per hour significantly decreased during the bead‐grabbing task compared to the reading (p = 0.003) and video tasks (p < 0.001), with a marginal difference compared to the mathematical task (p = 0.051). For wave peak value (%MVC), both the bead‐grabbing and mathematical tasks showed significantly lower values than the reading (p = 0.016 for both) and video tasks (p = 0.001 for both). Similarly, the integral value (%MVC·sec) was significantly reduced during the bead‐grabbing and mathematical tasks compared to the reading and video tasks (p ≤ 0.006). No significant differences were observed between the bead‐grabbing and mathematical tasks or between the reading and video tasks across all EMG measures. Friedman tests confirmed significant overall differences across tasks for burst number (χ ² = 19.18, df = 3, p < 0.001), wave peak value (χ ² = 23.435, df = 3, p < 0.001), and integral value (χ ² = 27.282, df = 3, p < 0.001).

3.2. Mixed Model Analysis of Masticatory Muscle Activity

Initial inspection of the residual plots revealed the presence of heteroskedasticity, and the residuals' histogram indicated deviations from the normality assumption. Consequently, a log‐10 transformation was applied to the outcome EMG data. Upon re‐evaluating the model assumptions, the log‐transformed EMG variables using the base‐10 logarithm (log₁₀ of burst number/h, log₁₀ of %MVC, and log₁₀ of %MVC·s) were utilised in the subsequent analyses. This transformation was performed to correct for skewness and to meet the assumptions of normality required for linear mixed model analysis [33]. Residual analysis indicated no major violations of homoscedasticity or normality assumptions in any model after transformation. For ease of interpretation, the results are presented as percentages, with transformed estimates back‐transformed to the original scale using the exponential function, as shown in Table 2.

TABLE 2.

Mixed model analysis for the relationship between masticatory muscle activity, VAS stress, trait anxiety, and gender.

	Estimate	SE	df	t‐value	p
Burst number per hour
Intercept	1.766	0.120	31.218	14.773	< 0.001 b
VAS stress	−0.002	0.004	262.475	−0.632	0.528
Gender (male)	−0.006	0.177	33.042	−0.035	0.972
VAS stress × gender (Male)	−0.013	0.005	265.64	−2.414	0.016 a
Burst peak value (%MVC)
Intercept	0.918	0.047	31.903	19.632	< 0.001 b
VAS stress	0.001	0.002	267.988	0.724	0.470
Gender (male)	−0.141	0.070	34.279	−2.016	0.052
VAS stress × gender (Male)	−0.010	0.003	244.003	−3.659	< 0.001 b
Integral value (%MVC·s)
Intercept	1.450	0.135	33.069	10.771	< 0.001 b
Low trait anxiety	−0.324	0.194	30.941	−1.665	0.106
Moderate trait anxiety	−0.124	0.155	32.383	−0.805	0.427
VAS stress	−0.007	0.004	266.0	−1.902	0.058
Low trait anxiety × VAS stress	0.028	0.009	181.80	3.142	0.002 b
Moderate trait anxiety × VAS stress	0.001	0.005	261.10	0.303	0.762

^a Significant at the 0.05 level.

^b Significant at the 0.01 level.

3.2.1. Model 1: Burst Frequency (Burst Number Per Hour)

The mixed model for burst frequency incorporated VAS stress, gender, and their interaction as fixed effects, with participant ID as a random effect. The interaction between VAS stress and gender was significant (β = −0.013, SE = 0.005, t = −2.414, p = 0.016), indicating that the effect of stress on burst number varies by gender (Figure 1). Specifically, for males, each unit increase in VAS stress was associated with a 3.5% reduction in burst frequency when combining this interaction with the main effect of VAS stress, while for females, the reduction per unit increase in VAS stress was smaller and non‐significant at 0.52% (β = −0.002, SE = 0.004, t = −0.632, p = 0.528). The main effect of gender was not significant (β = −0.006, SE = 0.177, t = −0.035, p = 0.972).

FIGURE 1.

Multivariate relationship between gender, VAS stress, and the log10‐transformed burst number per hour. The figure represents the statistical model output, with each coloured line indicating the model fit for an individual participant. The bold lines represent the overall trend for each gender group.

3.2.2. Model 2: Wave Peak Value (%MVC)

The analysis of wave peak value revealed that the main effect of VAS stress was not statistically significant (β = 0.001, SE = 0.002, t = 0.724, p = 0.470), indicating that wave peak values do not significantly change with increasing VAS stress for females (the reference group). Specifically, each unit increase in VAS stress corresponds to an approximate 0.30% increase in wave peak value, although this effect is not statistically meaningful. The main effect of sex indicated that males have lower wave peak values than females (β = −0.141, SE = 0.070, t = −2.016, p = 0.0517). On average, males exhibited a 28.66% reduction in wave peak values compared to females when VAS stress is held constant. However, this difference did not reach statistical significance. Importantly, the interaction between VAS stress and sex was significant (β = −0.010, SE = 0.003, t = −3.659, p < 0.001), suggesting that the effect of VAS stress on wave peak values varies by gender (Figure 2). For males, each unit increase in VAS stress is associated with an additional 2.22% decrease in wave peak value compared to females. When combining this interaction with the main effect of VAS stress, the total reduction in wave peak value for males per unit increase in stress is approximately 1.93%.

FIGURE 2.

Multivariate relationship between gender, VAS stress, and the log10‐transformed wave peak value (%MVC). The figure represents the statistical model output, with each coloured line indicating the model fit for an individual participant. The bold lines represent the overall trend for each gender group.

3.2.3. Model 3: Integral Muscle Activity (%MVC·S)

For integral muscle activity (%MVC·s), the model included VAS stress, trait anxiety, and their interaction as fixed effects, with participant intercepts as random effects. Residual analysis showed no significant deviations from model assumptions. Participants with low trait anxiety exhibited lower integral values compared to those with high trait anxiety, but this effect was not statistically significant (β = −0.324, SE = 0.194, t = −1.665, p = 0.106), corresponding to a 52.57% reduction in MVC. Similarly, the difference between medium and high trait anxiety was non‐significant (β = −0.124, SE = 0.155, t = −0.805, p = 0.427), with an estimated reduction of 24.22%. VAS stress showed a marginally significant negative effect on integral MVC (β = −0.007, SE = 0.004, t = −1.902, p = 0.058), indicating that each unit increase in stress was associated with an approximate 1.71% reduction in %MVC·s. The interaction between low trait anxiety and VAS stress (Figure 3) was significant (β = 0.028, SE = 0.009, t = 3.142, p = 0.002), suggesting that for individuals with low trait anxiety, each unit increase in VAS stress resulted in a 4.83% increase in integral (%MVC·s) values when combined with the main effect of VAS stress. However, no significant interaction effect was found for the medium trait anxiety group (β = 0.001, SE = 0.005, t = 0.303, p = 0.762), indicating that stress had a similar impact on integral MVC values for those with medium and high trait anxiety.

FIGURE 3.

Multivariate relationship between trait anxiety, VAS stress, and the log10‐transformed Integral value (%MVC.s). The figure represents the statistical model output, with each coloured line indicating the model fit for an individual participant. The bold lines represent the overall trend for each trait anxiety group.

3.3. Correlation Between Trait Anxiety and POBC Scores

The correlation analysis revealed a significant positive relationship between trait anxiety (STAI‐Y2) scores and POBC scores (r _s = 0.409, p = 0.016), as shown in Table 3. This indicates that participants with higher STAI‐Y2 scores tended to report higher POBC scores.

TABLE 3.

Correlation analysis between STAI‐Y2 scores & POBC.

		POBC
STAI‐Y2	r s	0.409 a
	p	0.016

Abbreviations: POBC, partial oral behaviour checklist; STAI‐Y2, trait anxiety scores.

^a Correlation is significant at the 0.05 level (2‐tailed).

4. Discussion

This study examined the influence of stress and trait anxiety on masseter muscle activity during cognitive and motor tasks by integrating self‐reported psychological measures (VAS stress and STAI‐Y2) with objective EMG recordings. The findings reveal significant interactions between stress, anxiety, and gender in shaping masticatory muscle activity, contributing to the growing understanding of the interplay between psychological and physiological factors in stress‐related oral parafunctional behaviours.

4.1. Effects of Stress, Trait Anxiety, and Gender on Masticatory Muscle Activity

Our results demonstrated a significant interaction between stress and gender in both burst frequency per hour and wave peak value (%MVC). Specifically, increased VAS stress was associated with a reduction in burst frequency in males but not in females, suggesting that men exhibit greater sensitivity to stress‐induced reductions in repetitive muscle activation. This may reflect distinct neuromuscular adaptations to psychological stress. To our knowledge, this is the first study to investigate the combined effects of stress, trait anxiety, and gender on masseteric EMG activity in a controlled setting, making direct comparisons with prior research challenging. Previous studies have established associations between AB, anxiety, stress, and various psychological factors, though most relied on self‐reported questionnaires or clinical diagnoses, which may lack the reliability of EMG‐based assessments [34, 35, 36]. For instance, Jeffrey et al. [37] reported that clenching frequency was more strongly associated with perceived stress than trait anxiety, aligning with our findings. However, their study did not observe gender‐related differences in motor responses, possibly due to differences in stress induction protocols or reliance on self‐reported AB. There is substantial evidence that men and women exhibit distinct physiological responses to stress. Levartovsky et al. [25] found that male dental students with AB experienced heightened bruxism episodes under acute stress, whereas females demonstrated more stable responses, likely due to differences in stress‐coping mechanisms. Similarly, Soto‐Goñi et al. [34] highlighted that individual differences in coping strategies significantly influence AB manifestation. Our findings further support these observations, emphasising the need to consider sex‐specific neuromuscular responses when evaluating stress‐related changes in masticatory function.

A similar interaction between stress and gender was observed for wave peak value (%MVC), with males exhibiting a greater reduction in peak muscle activity under increasing stress levels. This suggests that stress‐related modulation of muscle force generation differs between genders, with males experiencing a more pronounced decline. Previous research has shown that, in the absence of stress, males and females exhibit comparable EMG activity during resting and centric occlusion positions [38] and during mastication of a standardised bolus [29]. However, males typically display higher EMG levels during maximal clenching tasks [23, 24]. The non‐significant main effect of gender in our study during non‐maximal bite force conditions aligns with these findings. Our results extend this knowledge by demonstrating that stress‐induced reductions in peak muscle activity are more pronounced in males, which may have implications for stress‐related dysfunctions such as nonfunctional masseter muscle activity during wakefulness and temporomandibular disorders (TMDs).

Trait anxiety emerged as a significant factor influencing integral muscle activity (%MVC·s). Specifically, individuals with low trait anxiety exhibited a paradoxical increase in integral muscle activity with rising stress levels, whereas no significant modulation was observed in those with moderate or high trait anxiety. This finding suggests that individuals with lower trait anxiety may compensate for stress by increasing masticatory muscle activation, potentially as a coping mechanism. One possible explanation for this association is the adoption of active stress coping strategies. Villada et al. [39] suggest that individuals with lower anxiety levels are more likely to engage in problem‐focused coping strategies and physical activities to regulate stress. This active response may manifest as increased muscle engagement, as these individuals attempt to manage stress rather than passively endure it. Additionally, the study demonstrated that awake bruxers frequently engage in adaptive coping strategies such as positive reappraisal [34], allowing them to reinterpret stressors as manageable challenges. This cognitive approach may reinforce sustained muscle activation under psychological strain. These findings align with our results, in which low‐anxiety individuals exhibited a significant positive interaction between stress and integral muscle activity. Conversely, the absence of significant modulation in the moderate and high trait anxiety groups suggests a blunted or dysregulated physiological response to stress. Higher trait anxiety has been associated with emotion‐focused coping and mental disengagement, which are often considered maladaptive stress regulation strategies. Villada et al. [39] noted that highly anxious individuals tend to exhibit less flexible motor responses to external stressors, potentially leading to a diminished ability to adjust muscle activity dynamically. Taken together, these results underscore the role of individual differences in anxiety in shaping stress‐related changes in muscle function. Understanding these variations may inform the development of targeted interventions for individuals with heightened psychological vulnerability, particularly in clinical contexts where stress‐related parafunctional behaviours contribute to orofacial pain and TMDs.

Although the current study did not aim to examine the nature of individual tasks, task‐induced changes in VAS stress levels were used as a unified indicator of psychological modulation. A detailed analysis and interpretation of task‐specific effects on masseter muscle activity have been reported in our previous publication [22]. While the specific components contributing to significant effects differed across the EMG parameters examined, our findings confirm that stress, trait anxiety, and gender play crucial roles in modulating masseter muscle activity. Each parameter, burst frequency, peak amplitude (%MVC), and integral value (%MVC·s), was influenced by at least one of these psychological or biological factors. Clinicians should consider these individual differences when evaluating patients presenting with oral parafunctional behaviours‐related symptoms and developing tailored management strategies.

4.2. Discrepancies Between Self‐Reported and Physiological Measures

The correlation analysis and EMG data present an intriguing contrast in the relationship between trait anxiety and oral parafunctional behaviours. Self‐reported data (POBC scores) demonstrated a significant positive correlation with trait anxiety (STAI‐Y2 scores), indicating that individuals with higher trait anxiety tend to report more frequent oral parafunctional behaviours. However, objective EMG measurements of muscle activity (%MVC·s) did not show the same association with trait anxiety. This discrepancy underscores the distinction between self‐reported behaviours and physiological measures, a phenomenon documented in previous research.

The significant correlation between trait anxiety and POBC scores (r _s = 0.409, p = 0.016) aligns with studies linking anxiety to self‐reported oral parafunctional behaviours, such as bruxism and clenching [21]. Self‐report measures are inherently influenced by an individual's perception, which may be shaped by psychological factors like anxiety. Individuals with higher trait anxiety might be more attuned to their oral behaviours or prone to overreporting due to heightened self‐monitoring [40]. Conversely, studies have frequently reported discrepancies between self‐reported bruxism and objective physiological measures. For example, Lavigne et al. [41] observed that self‐reported bruxism did not consistently correlate with EMG‐recorded muscle activity during sleep. Similarly, van der Meulen et al. [42] found that self‐reported clenching was weakly associated with EMG‐measured muscle activity in awake individuals. These findings suggest that self‐reported measures may primarily reflect perceived behaviours or psychological distress rather than actual physiological activity.

4.3. Methodological Considerations

The use of EMG to quantify masseter muscle activity provided objective insights into the physiological responses to stress and anxiety. The application of a linear mixed model allowed for the examination of both fixed and random effects, accounting for the non‐independence of repeated measures and individual variability. Additionally, the inclusion of gender and trait anxiety as moderators highlights the importance of considering these factors in studies on stress and muscle activity. However, several methodological limitations should be acknowledged. First, the relatively small sample size, determined based on previous research calculations, may limit the generalisability of the findings. While the study design ensured a controlled experimental setting, a larger and more diverse sample would enhance statistical power and external validity. Second, while trait anxiety was included in the mixed model analysis, it was treated as a categorical variable (low, medium, high) rather than as a continuous score. This approach was chosen to facilitate interpretability of interaction effects and to explore possible threshold patterns in stress response. However, modelling anxiety categorically may limit the ability to detect more subtle or linear associations. Future studies with larger samples may benefit from modelling trait anxiety as a continuous predictor. Moreover, the reliance on self‐reported measures for stress (VAS) and anxiety (STAI‐Y2) introduces the possibility of subjective bias. Future studies incorporating physiological stress markers, such as cortisol levels or heart rate variability, could provide a more comprehensive understanding of the relationship between psychological states and muscle activity. Additionally, while the current analysis focused on the effects of task‐induced stress and trait anxiety on muscle activity, we did not examine the direct relationship between trait anxiety and subjective stress perception. Future studies may benefit from exploring whether individuals with high anxiety are more sensitive to perceived stress during daily or task‐based challenges. Third, although the study employed a well‐controlled experimental design, the ecological validity of the stress‐inducing tasks should be considered. Laboratory‐based tasks may not fully replicate the complexity and variability of real‐world stressors, potentially influencing the observed physiological responses. Future research should explore ambulatory EMG monitoring in naturalistic settings to capture dynamic fluctuations in muscle activity under everyday conditions. Finally, the study focused on short‐term stress responses, leaving open the question of whether these effects persist over extended periods or contribute to the development of chronic orofacial conditions. Longitudinal studies investigating prolonged exposure to stress and its impact on masticatory muscle activity would be valuable for understanding potential long‐term consequences.

5. Conclusion

This study provides evidence that stress, trait anxiety, and gender interact to influence masseter muscle activity during wakefulness. Males exhibited greater stress‐related reductions in burst frequency and wave peak value, while individuals with low trait anxiety showed increased integral muscle activity under stress. These findings contribute to the understanding of psychophysiological mechanisms in masticatory muscle function and highlight the need for individualised approaches to stress‐related oral health interventions. Future research should explore the long‐term impact of these factors on nonfunctional masseter muscle activity during wakefulness and TMD, as well as targeted intervention strategies.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.