Abstract
Background
Bruxism is a parafunctional behavior frequently associated with psychosocial factors, particularly stress. University students, who are exposed to persistent academic and social demands, may be especially vulnerable to elevated stress levels alongside bruxism-related behaviors. Despite this, limited research has examined how perceived stress interacts with demographic and lifestyle variables among students reporting bruxism-related assessments.
Methods
This cross-sectional analytical study included 101 university students in Turkey who self-reported a previous bruxism-related assessment by a healthcare professional. Data were collected using an online structured questionnaire comprising demographic and lifestyle characteristics and the 10-item Perceived Stress Scale (PSS-10). Independent-samples t-tests, one-way ANOVA, and Pearson’s correlation analyses were conducted to evaluate associations between perceived stress scores and variables including gender, age, academic year, grade point average (GPA), caffeine consumption, physical activity, and the type of healthcare professional first consulted for bruxism-related complaints.
Results
The mean perceived stress score was 19.05 (SD = 5.72). Female students reported significantly higher stress levels than male students (p = 0.033, Cohen’s d = 0.58). Perceived stress levels also differed significantly according to the type of healthcare professional consulted, with higher stress scores observed among students who reported consultations with psychiatrists or neurologists for stress- or bruxism-related complaints, compared to those who consulted dentists or general practitioners (p = 0.013, partial η2 = 0.09). No significant associations were identified between perceived stress and age, academic year, GPA, caffeine consumption, or physical activity. The most frequently reported stress-related experiences included feelings of anger, loss of control, and being overwhelmed.
Conclusions
Perceived stress among university students reporting bruxism-related behaviors appears to be influenced primarily by gender and patterns of healthcare consultation. These findings support a biopsychosocial perspective in understanding stress-related aspects of bruxism-related behaviors in university populations and highlight the importance of considering gender differences and interdisciplinary care pathways when addressing student well-being.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12903-026-08365-9.
Keywords: Bruxism-related behaviors, Perceived Stress, University Students, Quantitative Analysis, Psychosocial Factors
Introduction
Bruxism is a parafunctional behavior characterized by repetitive or sustained jaw-muscle activity, including clenching or grinding of the teeth and/or bracing or thrusting of the mandible. According to the most recent international consensus, bruxism is conceptualized as a behavior rather than a disorder and is described according to its circadian manifestation as either sleep bruxism or awake bruxism. Importantly, current consensus emphasizes that bruxism cannot be “diagnosed” in a traditional clinical sense but can only be assessed at different levels of certainty based on self-report, clinical observation, or instrumental methods [1]. Within this framework, psychosocial factors—particularly perceived stress—have been consistently highlighted as key correlates of bruxism-related behaviors [3].
University students represent a population that is especially exposed to sustained psychosocial stressors, including academic demands, performance pressure, financial concerns, and uncertainty regarding future career prospects. Such stressors may contribute to heightened emotional strain and maladaptive behavioral responses. Contextual factors, including large-scale disruptions such as the COVID-19 pandemic, have further intensified stress levels in student populations worldwide [4]. Previous research indicates that bruxism-related behaviors are frequently reported among university students and are often associated with higher levels of perceived stress compared to the general population [5]. In dental student populations, questionnaire-based research has also shown that bruxism awareness and self-assessment can be substantial, while knowledge gaps remain regarding the multifactorial nature and treatment options [8]. Student populations have shown measurable frequencies of awake bruxism, which correlates with psychosocial factors including perceived stress [6]. However, the extent to which perceived stress varies according to demographic, academic, and lifestyle characteristics among students reporting bruxism-related assessments remains insufficiently explored.
Existing literature suggests that perceived stress is not uniformly distributed across student populations. Female students, for example, tend to report higher stress levels than male students, a pattern that has been attributed to a complex interplay of biological, psychological, and sociocultural factors [13–15]. In contrast, evidence regarding the relationship between academic performance and stress is mixed, with some studies reporting inverse associations and others finding weak or inconsistent links [16, 17]. Lifestyle-related factors such as physical activity and caffeine consumption have also been investigated as potential modifiers of stress, although their effects appear to be modest and context-dependent [18]. Together, these findings underscore the multifactorial nature of perceived stress and highlight the need for quantitative analyses that simultaneously consider multiple domains.
In addition to individual characteristics, patterns of healthcare consultation may provide indirect insights into psychosocial burden. Students experiencing higher levels of stress may be more likely to seek care from or be referred to different types of healthcare professionals, reflecting variations in care pathways rather than differences in diagnostic validity. Examining perceived stress in relation to such consultation patterns may therefore offer a useful behavioral and psychosocial perspective.
The present study aims to quantitatively examine perceived stress levels among university students reporting bruxism-related assessments and to analyze their associations with demographic, academic, and lifestyle variables. Additionally, the study explores whether perceived stress differs according to the type of healthcare professional first consulted for bruxism-related complaints. By focusing on behavioral self-reports and statistical associations, this study seeks to contribute to a clearer understanding of stress-related patterns in student populations within a biopsychosocial and analytical framework.
Methods
Study design and participants
This study employed a cross-sectional analytical design. The target population comprised university students from multiple higher education institutions across Turkey who self-reported a previous bruxism-related assessment or consultation with a healthcare professional for bruxism-related complaints. In line with current international consensus, bruxism was approached as a self-reported behavior rather than a clinical diagnosis.
Participant recruitment was conducted via student associations and official university mailing lists. A total of 140 students were invited to participate in the online survey, of whom 115 responded. After excluding 14 incomplete or duplicate responses, a final sample of 101 participants met the inclusion criteria and were retained for analysis. Missing data were handled using listwise deletion. The final response rate was approximately 72%. The participant recruitment and selection process is presented in Fig. 1 in accordance with the STROBE guidelines for cross-sectional studies.
Fig. 1.
STROBE flow diagram illustrating the participant recruitment and inclusion process. Of the 140 university students initially invited via student associations and university mailing lists, 115 responded to the online survey. After exclusion of incomplete or duplicate responses, 101 participants were included in the final analysis
Data collection tools
Data were collected using a structured online questionnaire consisting of two sections. The first section included items assessing demographic characteristics and lifestyle-related variables, such as gender, age, academic characteristics, caffeine consumption, physical activity, and patterns of healthcare consultation related to bruxism-related complaints. The second section comprised the 10-item Perceived Stress Scale (PSS-10), which was used to measure participants’ perceived stress levels. The Perceived Stress Scale was originally developed as a global measure of perceived stress [19].
The Turkish version of the PSS-10, previously validated by Eskin et al. [20], was employed in this study. Each item was rated on a 5-point Likert scale ranging from 0 (“never”) to 4 (“very often”), with four positively worded items reverse-coded prior to scoring. Total scores were calculated by summing item responses, with higher scores indicating greater levels of perceived stress. In the present sample, the internal consistency of the PSS-10 was high, with a Cronbach’s alpha coefficient of 0.86, indicating good reliability.
Variables
The primary dependent variable in this study was the total score obtained from the 10-item Perceived Stress Scale (PSS-10), treated as a continuous variable representing overall perceived stress.
Independent variables included demographic, academic, lifestyle-related, and healthcare consultation characteristics. Demographic variables comprised gender and age. Academic variables included academic year, academic department, and grade point average (GPA). Lifestyle-related variables consisted of self-reported caffeine consumption (regular vs. none) and physical activity status (regular vs. none).
In addition, patterns of healthcare consultation related to bruxism-related complaints were included as an independent variable. This variable was categorized according to the type of healthcare professional first consulted, namely dentist, general practitioner, psychiatrist, or neurologist. This classification was used to reflect differences in care pathways rather than diagnostic validity.
Sample size calculation
An a priori power analysis was conducted using G*Power 3.1 to determine the minimum required sample size for detecting a medium effect size in a multiple linear regression model [23]. Assuming a medium effect size (Cohen’s f2 = 0.15), an alpha level of 0.05, statistical power of 0.80, and seven predictors included in the final regression model (gender, age, academic year, GPA, physical activity, caffeine consumption, and type of healthcare professional consulted), the required minimum sample size was estimated to be 103 participants. The final analyzed sample consisted of 101 participants, which was close to the estimated requirement for detecting medium-sized effects; however, the study may have been slightly underpowered to detect small effect sizes.
Statistical analysis
Descriptive statistics, including means, standard deviations (SD), medians, interquartile ranges (IQR), and frequency distributions, were calculated for all study variables. Prior to parametric analyses, statistical assumptions were evaluated using the Shapiro–Wilk test for normality and Levene’s test for homogeneity of variances. When these assumptions were not met, non-parametric alternatives (Mann–Whitney U test and Kruskal–Wallis test) were additionally performed as sensitivity analyses.
Bivariate analyses were conducted using independent-samples t-tests, one-way analysis of variance (ANOVA) with Tukey post hoc comparisons, and Pearson’s correlation coefficients, as appropriate. To account for the multifactorial nature of perceived stress, multivariable linear regression models were constructed with the total PSS-10 score as the dependent variable. Independent variables included gender, age, academic year, grade point average (GPA), physical activity status, caffeine consumption, and type of healthcare professional first consulted for bruxism-related complaints. Standard model diagnostics were performed, including assessments of multicollinearity and residual distributions.
Regression results were reported using unstandardized beta coefficients with corresponding 95% confidence intervals (CIs), along with measures of effect size (Cohen’s d and partial η2, where applicable) and model explanatory power (R2). To reduce the risk of type I error due to multiple comparisons, the Benjamini–Hochberg false discovery rate (FDR) correction was applied.
All statistical analyses were conducted using Python version 3.11, employing the pandas (v2.1), statsmodels (v0.14), scipy (v1.11), and scikit-learn (v1.3) libraries. A two-tailed p-value of < 0.05 was considered statistically significant.
Results
Group comparisons
Female students reported significantly higher perceived stress scores (M = 21.4, SD = 6.1) compared to male students (M = 17.9, SD = 5.8), t(99) = 2.17, p = 0.033, indicating a medium effect size (Cohen’s d = 0.58, 95% CI [0.05, 1.11]; Fig. 2).
Fig. 2.
Boxplot of PSS-10 perceived stress scores by gender. Red dots indicate group means; red error bars represent 95% confidence intervals
Table 1 presents group comparisons of perceived stress scores across key variables.
Table 1.
Group comparisons of perceived stress scores
| Comparison | Mean (SD) | Test Statistic | Effect Size |
|---|---|---|---|
| Female vs. Male | 21.4 (6.1) vs. 17.9 (5.8) | t(99) = 2.17, p = 0.033 | Cohen’s d = 0.58 [95% CI: 0.05–1.11] |
| Psychiatrist/Neurologist vs. Dentist/GP | 23.0 (5.9) vs. 18.7 (6.2) | F(2,98) = 4.57, p = 0.013 | partial η2 = 0.09 [95% CI: 0.01–0.17] |
Perceived stress levels differed significantly according to the type of healthcare professional first consulted for bruxism-related complaints. Students who reported consultations with psychiatrists or neurologists exhibited higher stress scores compared to those who consulted dentists or general practitioners, F(2,98) = 4.57, p = 0.013, with a moderate effect size (partial η2 = 0.09, 95% CI [0.01, 0.17]; Fig. 3).
Fig. 3.
Boxplots of PSS-10 perceived stress scores by type of healthcare professional first consulted for bruxism-related complaints. Red dots indicate group means; red error bars represent 95% confidence intervals
Across academic departments, students enrolled in Dentistry, Law, and Medicine demonstrated the highest mean perceived stress scores; however, these differences did not reach statistical significance (Fig. 4A). Similarly, perceived stress levels did not differ significantly by year of study, although a slight increase was observed among graduate students (Fig. 4B).
Fig. 4.
A Mean PSS-10 perceived stress scores across the three academic departments with the highest sample representation (Law, Dentistry, and Medicine). Red error bars represent 95% confidence intervals. B Mean PSS-10 perceived stress scores by year of study (1st–4th year and graduate level). Red error bars represent 95% confidence intervals
Academic and lifestyle variables
No statistically significant associations were identified between perceived stress scores and academic variables, including grade point average (GPA) and academic year. Observed effect sizes were small (Cohen’s d < 0.20; partial η2 < 0.02), with wide confidence intervals, suggesting negligible relationships.
Lifestyle-related variables also showed no significant associations with perceived stress. Students who reported regular physical activity exhibited marginally lower stress scores (M = 19.8, SD = 6.0) compared to those who were physically inactive (M = 21.0, SD = 6.3); however, this difference was not statistically significant and was characterized by a small effect size (Cohen’s d = 0.18, 95% CI [–0.35, 0.70]). Similarly, perceived stress scores did not differ significantly between students who reported caffeine consumption and those who did not (Fig. 5A). Stress levels were also slightly lower among physically active students, although this difference did not reach statistical significance (Fig. 5B).
Fig. 5.
A Boxplot of PSS-10 perceived stress scores by caffeine consumption (yes/no). Gray dots indicate individual observations; boxplots show the median and IQR. B Boxplot of PSS-10 perceived stress scores by physical activity (yes/no). Gray dots indicate individual observations; boxplots show the median and IQR
Table 2 summarizes the associations between academic and lifestyle variables and perceived stress.
Table 2.
Academic and lifestyle variables associated with perceived stress
| Variable | Result | Effect Size |
|---|---|---|
| GPA (High vs. Low) | ns (p > 0.05) | d < 0.20 |
| Academic Year | ns (p > 0.05) | partial η2 < 0.02 |
| Caffeine (Yes vs. No) | ns (p > 0.05) | d = 0.10 [95% CI: –0.43, 0.62] |
| Physical Activity (Yes vs. No) | ns (p > 0.05) | d = 0.18 [95% CI: –0.35, 0.70] |
Multivariable regression analysis
The multivariable linear regression model explained 21% of the variance in perceived stress scores (R2 = 0.21). Female gender emerged as a significant predictor of higher stress levels (β = 2.85, 95% CI [0.32, 5.38], p = 0.027). In addition, the type of healthcare professional first consulted for bruxism-related complaints was significantly associated with perceived stress, with higher scores observed among students reporting consultations with mental health professionals (β = 3.41, 95% CI [0.89, 5.93], p = 0.009).
Other variables, including age, GPA, caffeine consumption, and physical activity, were not significantly associated with perceived stress in the multivariable model (all p > 0.10).
Table 3 presents the full regression results.
Table 3.
Multivariable regression results predicting perceived stress scores
| Predictor | β (95% CI) | p-value |
|---|---|---|
| Female gender | 2.85 (0.32–5.38) | 0.027 |
| Type of healthcare professional consulted (mental health vs. others) | 3.41 (0.89–5.93) | 0.009 |
| Age | 0.07 (–0.12–0.26) | 0.46 |
| GPA | –0.65 (–1.98–0.68) | 0.32 |
| Caffeine consumption | 0.48 (–1.23–2.19) | 0.58 |
| Physical activity | –0.92 (–2.45–0.61) | 0.24 |
Figure 6 illustrates the regression coefficients and corresponding 95% confidence intervals for all predictors included in the model.
Fig. 6.
Forest plot of multivariable regression coefficients (β) with 95% confidence intervals predicting PSS-10 perceived stress scores. Female gender and reporting consultation with a mental health professional (psychiatrist or neurologist) were significant predictors (p < 0.05)
Discussion
This study examined perceived stress levels among university students reporting bruxism-related behaviors and explored their associations with demographic, academic, and lifestyle variables. Several key findings emerged from the analysis. Most notably, female students reported significantly higher levels of perceived stress compared to male students. This finding is consistent with a substantial body of literature indicating that women tend to experience and report higher stress levels, which has been attributed to a combination of biological, psychological, and sociocultural factors [13, 14]. Greater emotional reactivity and a higher prevalence of internalizing symptoms, such as anxiety and depressive tendencies, have been proposed as potential mechanisms underlying these gender differences [15].
Another important finding was the association between perceived stress levels and patterns of healthcare consultation related to bruxism-related complaints. Students who reported consulting mental health professionals, such as psychiatrists or neurologists, exhibited higher stress scores compared to those who consulted dentists or general practitioners. Importantly, this pattern should not be interpreted as reflecting differences in diagnostic validity. Rather, it likely represents referral or selection effects, whereby individuals experiencing higher psychological distress are more inclined to seek care from, or be referred to, mental health professionals. Within this context, the type of healthcare professional consulted may be viewed as an indicator of psychosocial burden or care pathway characteristics rather than a determinant of bruxism-related behavior itself. This interpretation is consistent with previous research highlighting strong associations between bruxism-related behaviors and psychological factors such as anxiety, emotional dysregulation, and sleep-related complaints [3, 7, 21]. Evidence from adolescent samples also indicates that distress, anxiety, and somatization are associated with a higher likelihood of waking-state oral behaviors and awake bruxism [9]. In adult dental patient samples, awake and sleep bruxism have been associated with temporomandibular disorder signs and symptoms, while stress, depressive symptoms, and sleep quality are also linked with pain-related outcomes [10].
In contrast, academic variables—including grade point average, year of study, and academic discipline—were not significantly associated with perceived stress levels in the present sample. Although students enrolled in academically demanding fields such as Dentistry, Law, and Medicine demonstrated higher mean stress scores, these differences did not reach statistical significance. This finding aligns with mixed evidence in the literature, where some studies report inverse associations between academic performance and stress, while others suggest that academic achievement offers limited protection against stress in highly competitive educational environments [16, 17]. These results suggest that subjective psychological strain may play a more prominent role in stress perception than objective indicators of academic performance.
Lifestyle-related variables, including physical activity and caffeine consumption, were also not significantly associated with perceived stress. Nevertheless, students who reported engaging in regular physical activity exhibited slightly lower stress scores, consistent with prior research suggesting a modest stress-buffering effect of exercise [18]. Although these effects were small in magnitude, they point to the potential relevance of lifestyle factors as contextual modifiers of stress rather than primary determinants.
Item-level analysis of the Perceived Stress Scale further indicated that the most prominent stress-related experiences among participants included feelings of nervousness, perceived lack of control, and being overwhelmed by accumulating demands. These patterns are consistent with theoretical models linking bruxism-related behaviors to maladaptive coping strategies, heightened arousal, and perceived helplessness in response to stress [2, 22]. Such findings reinforce the notion that stress-related processes operate at the behavioral and psychosocial level rather than as discrete clinical entities. A dose–response relationship between non-functional waking-state oral behaviours (including awake bruxism activities) and psychological distress (depression, anxiety, and stress) has been reported [11].
Taken together, the findings support the relevance of a biopsychosocial perspective when examining stress-related aspects of bruxism-related behaviors in university students. Perceived stress appears to intersect with demographic characteristics and patterns of healthcare consultation, while academic and lifestyle factors show weaker or more context-dependent associations. These results underscore the value of interdisciplinary perspectives that integrate behavioral, psychological, and health-related considerations when studying stress and bruxism-related behaviors in student populations.
Limitations
This study has several limitations that should be carefully considered when interpreting the findings.
First, bruxism was not clinically diagnosed within the scope of this study but was assessed based on participants’ self-reports of prior bruxism-related assessments or consultations with healthcare professionals. In line with current international consensus, bruxism was conceptualized as a behavior rather than a disorder. Nevertheless, reliance on self-reported information may have introduced misclassification bias, particularly in the absence of objective confirmation through instrumental methods such as polysomnography or electromyography.
Second, the cross-sectional design precludes causal inference. The observed associations between perceived stress and demographic, academic, lifestyle, and healthcare consultation variables should therefore be interpreted as correlational rather than indicative of temporal or causal relationships.
Third, the association between perceived stress levels and the type of healthcare professional consulted may reflect referral pathways or selection effects rather than differences related to diagnostic validity. Students experiencing greater psychological distress may have been more likely to seek care from, or be referred to, mental health professionals. As such, healthcare consultation patterns should be interpreted as potential indicators of psychosocial burden rather than determinants of bruxism-related behaviors.
Fourth, although an a priori power analysis indicated that a minimum sample size of 103 participants was required to detect medium-sized effects in the planned regression model, the final analyzed sample consisted of 101 participants. While this number was close to the estimated threshold, the study may have been slightly underpowered, particularly for detecting small effect sizes. Consequently, non-significant findings—especially those related to academic performance and lifestyle variables—should be interpreted with caution, as small but potentially meaningful associations may not have reached statistical significance due to limited statistical power.
Fifth, the relatively modest sample size and the use of convenience sampling may limit the generalizability of the findings to broader university populations. Participants were recruited voluntarily through student networks and mailing lists, which may introduce sampling bias and restrict external validity.
Finally, the study did not include additional psychosocial or sleep-related variables, such as anxiety symptoms, depressive symptoms, or sleep quality, which are known to be relevant to both perceived stress and bruxism-related behaviors [12]. The absence of these variables limits the ability to fully capture the multidimensional psychosocial context underlying stress-related patterns.
Future research should address these limitations by employing longitudinal designs, incorporating objective or instrument-based assessments of bruxism-related behaviors, recruiting larger and more diverse samples, and including a broader range of psychosocial and behavioral variables to provide a more comprehensive understanding of stress-related mechanisms in student populations.
Conclusion
This study examined perceived stress among university students reporting bruxism-related behaviors and analyzed its associations with demographic, academic, and lifestyle variables. The findings indicate that perceived stress levels were higher among female students and among those who reported consulting mental health professionals for bruxism-related complaints. In contrast, age, academic performance, and lifestyle-related factors such as caffeine consumption and physical activity were not significantly associated with perceived stress in this sample.
These findings highlight the relevance of considering psychosocial and behavioral factors when examining stress in student populations reporting bruxism-related behaviors. The observed association between patterns of healthcare consultation and higher stress levels is likely reflective of care pathways and referral dynamics rather than differences in diagnostic validity. This underscores the importance of adopting a biopsychosocial perspective that acknowledges the interaction between individual characteristics, stress perception, and help-seeking behaviors.
Future research should prioritize longitudinal study designs to clarify temporal and potentially causal relationships between perceived stress and bruxism-related behaviors. Additionally, incorporating a broader range of psychosocial variables, such as anxiety, depressive symptoms, and sleep-related factors, may provide a more comprehensive understanding of stress-related patterns in student populations. Such approaches may contribute to more informed analytical frameworks for identifying risk indicators and understanding stress-related behaviors in higher education settings.
Supplementary Information
Acknowledgements
We thank all university students who voluntarily participated in this study.
Authors’ contributions
HS conceptualized the study and performed data collection and curation. SSD designed and supervised the study and conducted the formal analysis and interpretation. HS drafted the manuscript and performed the literature review. SSD critically revised and edited the manuscript. All authors read and approved the final manuscript.
Funding
This research was supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK) under the 2209-A University Students Research Projects Support Program. The funder had no role in the study design, data collection, analysis, interpretation, or manuscript preparation.
Data availability
The datasets generated and/or analysed during the current study are not publicly available due to participant confidentiality and ethical restrictions, but are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Van Yüzüncü Yıl University, Faculty of Social and Human Sciences (Approval No: 2025/08–21; Date: 17 April 2025). Electronic informed consent was obtained from all participants prior to participation.
Consent for publication
Not applicable. No identifying images or personal clinical details of participants are included in this manuscript; therefore, individual consent for publication was not required.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analysed during the current study are not publicly available due to participant confidentiality and ethical restrictions, but are available from the corresponding author on reasonable request.