Wenying Huang
Bin Chen
Chang Hu- Physical Education College, Jiangxi Normal University, Nanchang, China
Background: Physical inactivity is a significant public health issue among female college students. This study aimed to explore the complex relationship between self-rated health (SRH), social anxiety (SA), and physical activity (PA) in a sample of female Chinese university students, employing both variable-centered and person-centered approaches.
Methods: A cross-sectional survey was conducted with a sample of 1,452 female Chinese university students who completed the Self-Perceived Health Scale (to measure SRH), the Social Interaction Anxiety Scale–6 and the Social Phobia Scale–6 (to measure SA), and a validated single-item measure of PA (to measure PA). First, a mediation model was used to test the indirect effect of SRH on PA through SA. Second, Latent Profile Analysis was conducted to identify distinct subgroups based on individuals’ SRH and SA levels. Finally, ANOVA was used to examine PA differences across the identified profiles.
Results: The mediation analysis revealed that SA partially mediated the relationship between SRH and PA, accounting for 12% of the total effect (CI [0.011, 0.090]). The Latent Profile Analysis identified three distinct profiles: “Healthy-Resilient” (21.62%), characterized by high SRH and low SA; “Moderate-Adapting” (70.39%), with average SRH and SA; and “Unhealthy-Anxious” (7.99%), with low SRH and high SA. The “Unhealthy-Anxious” group reported significantly lower levels of PA than the other two groups.
Conclusion: Our findings suggest that SA is a significant psychological mechanism linking SRH to PA among female Chinese university students. From a public health perspective, these findings highlight the importance of addressing mental health factors, such as SA, in interventions designed to promote PA in this population. The identification of distinct subgroups underscores the need for tailored interventions over one-size-fits-all approaches.
1 Introduction
Physical activity (PA) is any bodily movement produced by skeletal muscles that results in energy expenditure (1–4). It is widely recognized that regular PA is a cornerstone of maintaining physical and mental health and preventing chronic diseases (5–7). The university period is a critical life stage for the formation of personal habits, and PA patterns established during this time have a decisive impact on an individual’s long-term health trajectory (8, 9). Despite its benefits, insufficient PA among female university students remains a global health concern (10–12). The World Health Organization has consistently highlighted a gender disparity in activity levels, noting that women are generally less active than men (13, 14). This phenomenon is especially evident among adolescents and young adults (5, 15). For example, a large-scale study involving female university students found that nearly 35% were sedentary, with another 34% engaging in PA only sporadically (16). Similarly, other research indicates that a substantial portion of female students fail to meet the recommended guidelines of 150 min of moderate-to-vigorous PA per week (17). Female university students in China may be a particularly vulnerable population for physical inactivity (18, 19). They often face a unique confluence of stressors, including intense academic competition and societal expectations related to traditional gender roles, which may prioritize academic achievement over physical wellbeing (20, 21). This, combined with the newfound independence and lifestyle changes that accompany university life, can make it challenging for young women to establish and maintain healthy physical activity habits (22, 23). Therefore, identifying and understanding the psychological factors that influence PA participation in this specific population is crucial for developing effective interventions.
In addition, among the numerous potential factors, Self-Rated Health (SRH), an individual’s subjective perception and overall evaluation of their own health status, has garnered considerable academic attention (24–26). While objective health indicators are valuable, SRH provides a more holistic and personally meaningful assessment of an individual’s overall health state (27). It captures a person’s subjective experience of their health, which is a powerful predictor of health behaviors, morbidity, and mortality, even after controlling for objective health status (28). In the context of our study, SRH is particularly relevant as we are interested in how an individual’s perception of their health influences their engagement in health-promoting behaviors like physical activity.
2 Literature review and hypotheses development
2.1 Self-rated health and physical activity
SRH is not an objective physiological measure, yet it has been proven to be a robust predictor of future health outcomes and mortality across diverse populations (29, 30). Recent research continues to reinforce this connection, demonstrating a consistent positive association between higher SRH and greater engagement in health-promoting behaviors, including PA (31–34). For instance, a large cross-sectional survey of adolescents found a strong, positive dose–response relationship, where more frequent vigorous PA was linked to better SRH (35). Similarly, a 2025 study underscored that SRH remains a key moderating factor, where the protective effects of PA on mental health outcomes were significantly more pronounced in individuals who rated their health as good (36). This growing body of evidence suggests that an individual’s subjective health belief is a critical cognitive antecedent to action (37). Nevertheless, this association’s psychological pathway remains under-investigated, particularly among young adults. In other words, how an individual’s “feeling” about their health translates into “actual” PA behavior is a process that warrants deeper investigation.
2.2 The mediating role of social anxiety
This study posits that Social Anxiety (SA) may play a critical mediating role in the relationship between SRH and PA. SA is defined as a marked fear or anxiety in social or performance situations where one might be scrutinized by others (38, 39). Given that many forms of PA (e.g., gym workouts, team sports) take place in public or semi-public settings, they inherently constitute potential social performance contexts (40). According to self-presentation theory, individuals with a lower evaluation of their own state (such as health or physical appearance) are more likely to anticipate negative evaluation in social settings, thereby triggering anxiety (41–43). The influence of SA on physical activity may be particularly pronounced in collectivist cultures, such as China (44). In these cultural contexts, where there is a strong emphasis on social harmony and avoiding loss of face, the fear of being negatively evaluated by others can be a powerful deterrent to participating in public activities, including sports and exercise. This may be especially true for women, who may face greater scrutiny regarding their physical appearance and competence in physical activities. It follows logically that lower SRH could lead to avoidance of PA by heightening SA, particularly anxiety related to one’s physical appearance, as a strategy to evade potential social discomfort (45, 46). Thus, SA is a likely psychological bridge connecting poor health perception to lower activity levels, and verifying this mediation pathway is the primary objective of this study.
2.3 The variable-centered and person-centered approaches
Previous research on these variables has predominantly relied on “variable-centered” approaches (e.g., structural equation modeling), which aim to estimate the average relationships among variables for an entire sample (47, 48). A fundamental limitation of this approach is its “population-average” assumption, which presumes that all individuals follow the same psychological model (49). This assumption may obscure significant heterogeneity within the population. For instance, not all individuals with low SRH will experience the same degree of SA, and the resulting impact on their PA may also differ. Overlooking such potential heterogeneity limits a comprehensive understanding of the phenomenon and may lead to inadequately targeted intervention strategies.
To address this methodological gap, this study adopts a complementary “person-centered” strategy, namely Latent Profile Analysis (LPA) (50, 51). Unlike variable-centered methods that examine relationships between variables, LPA aims to identify data-driven, qualitatively distinct latent subgroups based on individuals’ response patterns across a set of observed variables (52). This approach allows us to investigate a more profound question: Do specific subgroups, defined by unique combinations of SRH and SA levels, exist among female university students? If so, do their PA patterns also exhibit significant differences? By identifying these potential “health-anxiety” profiles, we can move beyond a focus on “average effects” and toward an analysis of the specific risk patterns of different profiles of individuals.
2.4 Aims and objectives of the study
In summary, this study employs a dual-analytic approach, integrating both variable-centered and person-centered methods, to comprehensively investigate the relationships among SRH, SA, and PA in female university students. The research will first test the mediating effect of SA using structural equation modeling to uncover the average causal pathway. Subsequently, it will utilize LPA to identify subgroups with distinct “health-anxiety” characteristics and examine the differences in PA levels across these groups. By integrating the findings from both methods, this study aims to provide new insights into the complex psychological mechanisms influencing PA among female university students and to offer empirical support for the future development of personalized intervention programs based on the characteristics of different subpopulations. Based on the theoretical framework and literature review presented above, this study proposes the following hypotheses: H1 (SA will mediate the relationship between SRH and PA); H2 (There will be distinct and meaningful latent profiles of female college students based on their levels of SRH and SA); H3 (These profiles will differ significantly in their levels of PA).
3 Materials and methods
3.1 Participants and procedure
This study adopted a cross-sectional survey design, conducted through an online questionnaire platform. To ensure sufficient statistical power for our analyses, an a priori power calculation was performed using G*Power 3.1 software. For a standard regression-based mediation model (53), with a desired power (1 − β) of 0.95 and a significance level (α) set at 0.05, a minimum of 119 participants would be required to detect a medium effect size (f2 = 0.15). The final sample size obtained in this study substantially surpasses this calculated threshold, providing robust statistical power for the subsequent analyses.
This study utilized a convenience sampling strategy for recruitment from six universities across Jiangxi and Yunnan provinces in China. Data collection over a one-month period beginning March 17, 2025. To be included in the study, individuals had to meet the following criteria: (a) Participants must be biologically female; (b) be currently enrolled as a university student; and (c) provide informed consent to participate. Exclusion criteria were established to ensure data quality and sample homogeneity, and included: (a) a self-reported history of any formally diagnosed mental health disorder; (b) failure to complete the entire survey; or (c) responses flagged as invalid due to excessively short completion times, which would suggest inattentive participation. In addition, to ensure data reliability, missing data and outliers were excluded from the study. An initial pool of 1,538 individuals accessed and responded to the survey. Following the application of the pre-defined inclusion and exclusion criteria, 86 responses were removed from the dataset. This screening process resulted in a final valid sample of 1,452 participants. The age of the participants in the final sample ranged from 18 to 30 years.
Informed consent was obtained from all participants before they began the survey. The consent form clearly outlined the study’s objectives, the voluntary nature of participation, data confidentiality measures, and the participant’s right to withdraw at any point without penalty. As a token of appreciation for their time and effort, each individual who submitted a valid and complete survey received compensation of 3 CNY (US$0.42). All study procedures received ethical approval from our institution (IRB-JXNU-PEC-2025019).
3.2 Measures
3.2.1 Self-rated health
Participants’ SRH was evaluated using the Self-Perceived Health Scale (SPHS-12) developed by Tinajero-Chávez et al. (54). This instrument comprises 12 items distributed across three sub-dimensions: physical health, psychological health, and healthy lifestyle. Respondents indicated their level of agreement with each statement (e.g., “I can keep my balance, for example, by standing on one foot”) on a 6-point Likert scale, anchored at 1 (Disagree) and 6 (Absolutely agree). The SPHS-12 yields a raw total score ranging from 12 to 72. To facilitate interpretation, we divided the total score by the number of items (12) to obtain a mean score ranging from 1 to 6. The mean score of 3.666 reported in the results therefore reflects the average item score rather than the raw total score. Higher mean values indicate better self-rated health. In the current study, the Cronbach’s α of 0.890 indicates good internal consistency.
3.2.2 Physical activity
A single-item measure with established validity in large-scale research was employed to assess PA. Participants were asked to respond to the question: “Over the past 7 days, how many days did you engage in at least 20 min of physical exercise or other activity that made you sweat or breathe heavily?.” Responses, ranging from 0 to 7 days, were treated as a continuous variable for statistical analysis. Although this method cannot fully represent the participants’ level of PA, it has shown good effectiveness in several large-scale studies conducted previously (55–57).
3.2.3 Social anxiety
SA was assessed using two widely recognized and complementary short-form scales: the Social Interaction Anxiety Scale (SIAS-6) and the Social Phobia Scale (SPS-6) (58). Each scale contains six items. Participants rated how characteristic a statement was of them using a 5-point scale (0 = “Not at all characteristic of me” to 4 = “Extremely characteristic of me”). Higher mean scores on each scale indicate more severe symptoms. Both instruments have demonstrated strong psychometric properties and have been extensively validated for use with Chinese university students (59–61). For the current sample, good internal consistency was confirmed, with a Cronbach’s α of 0.829 for the SIAS-6 and 0.799 for the SPS-6.
3.3 Data analysis
The data were processed and analyzed using SPSS 26.0 and Mplus 8.3, with the threshold for statistical significance established at p < 0.05 for all tests (62). Initially, preliminary analyses were conducted, including the calculation of descriptive statistics and Pearson correlation coefficients to ascertain the basic characteristics and interrelationships of the key study variables. Subsequently, the variable-centered analysis proceeded by testing the proposed mediation pathway. Specifically, the PROCESS macro (Model 4) for SPSS was employed to investigate the indirect effect of SRH on PA via the mediating variable of SA. To rigorously assess the significance of this indirect effect, a bootstrapping procedure involving 5,000 resamples was executed to generate 95% bias-corrected confidence intervals, with an effect considered significant if its Confidence interval did not encompass zero.
To complement the variable-centered findings, a person-centered approach was implemented using LPA within Mplus 8.3. A systematic process of model comparison was undertaken, beginning with a one-profile solution and incrementally adding profiles. The selection of the optimal model was a multifaceted decision, guided by a comprehensive evaluation of statistical fit indices, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the sample-size adjusted BIC (aBIC) (63). Further consideration was given to the Lo–Mendell–Rubin and Bootstrapped Likelihood Ratio Tests (LMR-LRT and BLRT) to determine if a model with more profiles offered a statistically significant improvement in fit. The clarity of classification, as indicated by the Entropy value, along with the theoretical coherence and parsimony of the resulting profiles, were also critical factors in the final determination. Once the optimal profile solution was identified, a one-way ANOVA, followed by Bonferroni post-hoc tests for pairwise comparisons, was conducted to determine if profile membership was associated with significant mean differences in PA.
4 Results
4.1 Demographic information on female college students
Table 1 summarizes the demographic characteristics of the sample, including education level, place of birth, ethnicity, and BMI categories. A one-way ANOVA revealed a highly significant effect of education on SA scores (F = 5.674, p < 0.01) and also showed significant effects of BMI on both SRH (F = 4.168, p < 0.01) and SA (F = 3.501, p < 0.05). The independent samples t-test indicated that there were significant differences between ethnic groups for PA (t = 2.025, p < 0.05) and SA (t = −2.619, p < 0.01). In contrast, no significant differences were found based on place of birth for any of the three outcome variables.
Table 1. Basic demographic characteristics of participants.
4.2 Correlation analysis
The descriptive statistics and Pearson correlation coefficients for the primary study variables indicated that SRH was significantly and positively correlated with PA, suggesting that students who perceived their health more favorably reported higher levels of PA. Conversely, SRH was strongly and negatively correlated with SA. Furthermore, a significant negative correlation was observed between PA and SA, indicating that higher SA was associated with less engagement in PA (Table 2).
Table 2. Correlation coefficients of self-rated healthy, social anxiety, and physical activity in female college students.
4.3 The mediation analyses
To test Hypothesis 1, we examined the mediating role of SA in the relationship between SRH and PA using the PROCESS macro (Model 4). First, a collinearity diagnosis was conducted. The results showed that the Variance Inflation Factor for the variables was 1.636, well below the common threshold of 10, indicating that multicollinearity was not a concern. For the mediation analysis, we controlled for demographic variables as potential confounding variables (Table 3). These covariates were selected based on preliminary analyses and their established association with health and physical activity in the literature. The effect of SRH on the mediator, SA, was significant (β = −0.625, p < 0.001). The effect of the mediator, SA, on PA was also significant (β = −0.080, p < 0.01). The direct effect of SRH on PA remained significant (β = 0.360, p < 0.001) (Figure 1). Crucially, the indirect effect of SRH on PA through SA was also statistically significant (β = 0.050, Boot se = 0.020), with a 95% bias-corrected confidence interval that did not contain zero [0.011, 0.090] (Table 4). This result supports H1, indicating that SA partially mediates the relationship between SRH and PA. The mediating effect accounted for 12% of the total effect.
Table 3. Mediation effect regression results.
Figure 1. Mediation effect diagram. The effect of SRH on the mediator, SA, was significant (β = −0.625, p < 0.001). The effect of the mediator, SA, on PA was also significant (β = −0.080, p < 0.01). The direct effect of SRH on PA remained significant (β = 0.360, p < 0.001).
Table 4. Mediating effects of social anxiety between self-rated healthy and physical activity.
4.4 Latent profile analyses
In line with Hypothesis 2, we conducted an LPA to identify distinct subgroups of female college students based on their levels of SRH and SA. We compared models with one to five latent profiles. As shown in the LPA table, the fit indices (AIC, BIC, and aBIC) continued to decrease as more profiles were added (Table 5). However, considering the principle of parsimony, theoretical interpretability, and the significant LMR and BLRT tests (p < 0.001), the three-profile solution was selected as the optimal model. This solution also demonstrated excellent classification accuracy, with a high entropy value of 0.971. The three identified profiles were named based on their distinct patterns of SRH and SA: Unhealthy-Anxious (A1, n = 116, 7.99%). This group, the smallest of the three, was characterized by the lowest SRH and the highest levels of SA (Figure 2). Healthy-Resilient (A2, n = 314, 21.62%): This group was characterized by the highest levels of SRH and the lowest levels of SA. Moderate-Adapting (A3, n = 1,022, 70.39%): This was the largest group, representing the majority of students, who reported average levels of both SRH and SA.
Table 5. Comparison of fit indices for latent profile analysis models.
Figure 2. Latent profiles of self-rated health and social anxiety.
4.5 Effect of latent profile classification
To test Hypothesis 3, a one-way ANOVA was conducted to examine whether physical activity levels differed significantly across the three latent profiles. The analysis revealed a significant main effect of profile membership on PA, F (2, 1,449) = 167.276, p < 0.001, η2 = 0.118 (Table 6).
Table 6. Group differences in PA across latent profiles.
Subsequent post-hoc comparisons employing the Bonferroni test revealed significant disparities among all three groups. Specifically, the “Healthy-Resilient” group (M = 5.59, SD = 1.09) reported significantly more physical activity than both the “Moderate-Adapting” group (M = 3.83, SD = 2.00) and the Low “Unhealthy-Anxious” group (M = 2.43, SD = 1.67). Furthermore, the “Moderate-Adapting” group engaged in significantly more physical activity than the “Unhealthy-Anxious” group. These findings fully support H3, demonstrating that the identified health-anxiety profiles are associated with distinct patterns of physical activity behavior (A2 > A3 > A1) (Table 7).
Table 7. Multiple comparisons result.
5 Discussion
The present study provides novel insights into the interplay of self-perceived health, social anxiety, and physical activity among female Chinese university students. It is important to interpret these findings within this specific demographic and cultural context, as the observed relationships and profiles may differ in other populations. The variable-centered mediation analysis revealed how SRH relates to PA, identifying SA as a significant psychological mechanism. Poorer self-perceived health is associated with higher SA, which in turn is linked to lower physical activity. The person-centered analysis complements this by identifying who is most affected. The “Unhealthy-Anxious” profile represents a subgroup for whom this mediating pathway is likely most pronounced. This group, characterized by the combination of low SRH and high SA, exhibited the lowest levels of PA.
5.1 The mediating effect of social anxiety
A core finding of this study is that SA plays a significant partial mediating role in the relationship between SRH and PA. This result not only statistically validates our hypothesis but, more importantly, reveals a specific psychological pathway. Specifically, when a female student perceives herself as “unhealthy,” this negative self-cognition can become a psychological vulnerability (64), making her more likely to anticipate scrutiny and negative evaluation in settings that require physical “display,” which in turn triggers or exacerbates her SA (65–67). This finding is highly consistent with the tenets of self-presentation theory and resonates with a large body of existing research (68). For example, prior studies have confirmed that lower SRH is associated with greater negative emotional experiences such as depression and anxiety (69–72); our study further links this psychological vulnerability to SA in specific contexts. Concurrently, the role of SA as a potent inhibitor of PA participation has been well-documented (73). The distinctive contribution of our research lies in the integration of these three variables into a unified model, thereby elucidating the chain of effects from “health perception” to “SA” to “activity behavior.” This pathway also can be understood through a Cognitive-Behavioral theory (74). A low self-rating of health acts as a negative core belief about one’s physical self. According to Cognitive-Behavioral theory, when an individual with this belief enters a social-evaluative context, such as a gym or group sport, this cognition is activated (75). This triggers a cascade of anxious thoughts (e.g., “Others will notice how unfit I am”) and a hypervigilance to physiological sensations (e.g., sweating, heavy breathing), which are catastrophically misinterpreted as signs of incompetence or physical failure. The resulting social anxiety then motivates avoidance behaviors—in this case, avoiding physical activity—to prevent the feared social judgment. Thus, SA is not merely correlated with SRH and PA; it serves as the cognitive and emotional bridge that translates a negative health perception into a behavioral pattern of inactivity. The practical implication of this finding is that it shifts the focus of intervention from a relatively vague and difficult-to-change variable (SRH) to a more specific and actionable psychological target (SA). These findings are particularly resonant within the Chinese cultural context. Factors such as the emphasis on academic performance over physical pursuits and Confucian-heritage values that emphasize group harmony and “saving face” can heighten social-evaluative fears (76), potentially making SA a more potent barrier to PA than in Western cultures (77, 78). This may be especially salient for female students, who often face distinct societal pressures regarding physical appearance and competence in traditionally male-dominated domains like sports (79). The fear of failing to meet these aesthetic and performance standards can compound the anxiety, making public physical activity a high-stakes event (80). Furthermore, a cultural reluctance to openly discuss mental health may lead students to somaticize their anxiety, focusing on perceived physical health deficits (low SRH) rather than acknowledging the underlying social fear (71, 81, 82). Therefore, to effectively promote PA among female students with low SRH, providing health education or emphasizing the benefits of exercise is insufficient; it is imperative to concurrently address the underlying SA.
5.2 Latent profile of self-rated health and social anxiety
Consistent with Hypothesis 2, this study successfully employed a person-centered approach to identify three distinct latent profiles among female college students based on their SRH and SA. This finding is crucial as it moves beyond a variable-centered understanding to reveal the significant heterogeneity within this population. The three profiles were named “Healthy-Resilient,” “Moderate-Adapting,” and “Unhealthy-Anxious.”
The “Moderate-Adapting” group was the largest, encompassing over 70% of the students. This profile represents the normative experience for female college students, who are typically navigating a complex environment characterized by moderate academic and social pressures (83, 84). Their average levels of SRH and SA likely reflect a general adaptation to the daily stressors of university life, representing a functional equilibrium (85). While this group may not be at immediate risk, they could be susceptible to shifting toward a more maladaptive profile if stressors increase without adequate coping resources.
In stark contrast, the “Healthy-Resilient” group (21.62%) represents a highly adaptive group. These students exhibit a synergy of positive psychological attributes. Their high SRH likely reflects not only good physical health but also a robust sense of self-efficacy and a positive self-concept (86). This psychological fortitude acts as a buffer against social stressors (87). Consequently, their low SA is a logical correlate; with strong self-confidence and positive social experiences, they are less likely to fear negative evaluation from others. This profile aligns with literature on positive psychology and resilience, embodying the ideal state that health promotion initiatives often aim to cultivate (88–90).
Most critically, the analysis identified an “Unhealthy-Anxious” group. Although the smallest group (7.99%), its existence is of significant clinical and practical importance. This profile represents a maladaptive state where poor health perception and intense social fear appear to be mutually reinforcing. The low SRH could stem from objective health problems, but it is equally likely to be magnified by a negative cognitive bias, where individuals are prone to catastrophize minor physical symptoms (91, 92). Simultaneously, high SA, particularly the fear of negative evaluation, can lead to hypervigilance toward one’s own body and its functions, causing any perceived flaw or physiological response to be interpreted as a sign of poor health (93–95). This creates a debilitating vicious cycle: feeling unhealthy heightens social self-consciousness, and fearing social judgment amplifies the negative perception of one’s health. This group is likely to experience significant impairments in their social and academic functioning, extending far beyond the context of physical activity.
While person-centered studies combining SRH and SA are nascent, the “Unhealthy-Anxious” profile is conceptually consistent with high-risk typologies identified in variable-centered research among Western youth, which often link poor health perceptions with internalizing symptoms like anxiety and depression (96, 97). However, the cultural emphasis on social harmony and avoidance of “losing face” in China may make this specific combination of poor health perception and social fear a particularly potent driver of inactivity compared to individualistic cultures (82). Future cross-cultural research is needed to verify if these exact profiles emerge in other populations.
5.3 Effect of latent profile classification on physical activity
Supporting Hypothesis 3, the one-way ANOVA results confirmed that the three identified profiles correspond to significantly different levels of physical activity, following a clear pattern: the “Healthy-Resilient” group was most active, followed by the “Moderate-Adapting” group, with the “Unhealthy-Anxious” group being the least active. This finding powerfully validates the person-centered approach by linking distinct psychological typologies to tangible behavioral outcomes. The high activity level of the “Healthy-Resilient” group is well explained by Self-Determination Theory, a motivational framework that posits human behavior is driven by the basic psychological needs for autonomy, competence, and relatedness (98). According to this theory, behaviors are sustained when these needs are satisfied (99, 100). This group likely engages in physical activity out of intrinsic motivation, finding it enjoyable and personally rewarding (101). Their high SRH reflects a strong sense of competence, while their low SA allows them to feel relatedness in group settings without fear, thus creating a self-reinforcing, positive cycle of engagement.
The “Moderate-Adapting” group, representing the majority, displayed an intermediate level of physical activity. Their behavioral patterns can be understood through the lens of the Transtheoretical Model of Health Behavior Change: a stage-based model describing how individuals progress through precontemplation, contemplation, preparation, action, and maintenance in adopting health behaviors (102). This group is likely not in a stable state of maintenance but rather in the “Contemplation” or “Preparation” stages, weighing the pros and cons of regular exercise (103). Their engagement is therefore more variable and highly susceptible to situational factors. Lacking the robust intrinsic motivation of the resilient group and the significant psychological barriers of the anxious group, their decision to be active may depend heavily on external influences (104). Positive factors like social support from peers, accessible and appealing campus facilities, or a manageable academic workload can serve as catalysts, moving them toward the “Action” stage (105). Conversely, negative stressors like exam periods or social conflicts can easily tip the decisional balance, leading to relapse or sustained inactivity. This makes them a crucial target for broad-based, low-threshold university wellness programs designed to provide the necessary “nudge” to foster consistency.
Conversely, the profound inactivity of the “Unhealthy-Anxious” group is best explained by Cognitive-Behavioral Models of SA, such as the one proposed by Wells (106), a framework where maladaptive cognitions trigger anxiety and avoidance behaviors that reinforce dysfunctional beliefs. For these individuals, exercise settings are not neutral environments but are laden with perceived social threat. Upon entering such a setting, their negative core beliefs (“I am incompetent,” “I am physically inadequate”) are activated. This triggers a shift to intense self-focused attention and monitoring of their own physiological sensations (e.g., heart rate, sweating), which they catastrophically misinterpret as signs of imminent health failure or social humiliation. To prevent this feared outcome, they engage in “safety behaviors,” with the most definitive being complete avoidance of the activity. This avoidance, while reducing short-term anxiety, prevents any opportunity to disconfirm their negative beliefs, thus trapping them in a powerful, self-perpetuating cycle of inactivity and psychological distress.
5.4 Practical implications
Our findings offer several actionable implications for university administrators, public health planners, and campus wellness professionals. First and foremost, the identification of three distinct profiles underscores the need to tailor interventions. Rather than universal campaigns, universities should develop differentiated programs. For the large “Moderate-Adapting” group, low-barrier initiatives that build a supportive campus culture may be sufficient to “nudge” them toward more consistent activity. For the “Unhealthy-Anxious” group, however, more intensive support is needed, such as small, women-only fitness classes in private settings or workshops based on Cognitive Behavioral Therapy (CBT) to directly address social-evaluative fears related to exercise.
Second, these results advocate for the integration of mental and physical health services on campus. Our study shows that SA is a significant barrier to the health-promoting behavior of physical activity. Therefore, university wellness initiatives should embed SA screening into routine health assessments to identify at-risk students. This would facilitate early intervention and collaboration between counseling services and physical education departments to provide holistic support.
Third, institutions can leverage digital platforms to reach students who may be hesitant to seek face-to-face help. For students in the “Unhealthy-Anxious” profile, telehealth counseling, mental health apps focused on managing anxiety, or virtual fitness classes can serve as a less intimidating first step to engagement. These tools can help build self-efficacy in a private setting before transitioning to in-person activities.
Finally, our findings should inform policy and practice at an institutional level. The results provide a clear evidence base for allocating resources toward a multi-tiered system of support that addresses the specific psychological needs of different student profiles. By investing in targeted, evidence-based programs, universities can more effectively support student wellbeing, improve long-term health trajectories, and foster a campus environment where all students have the opportunity to thrive.
5.5 Strengths and limitations
This study has several strengths. First and foremost is its dual-analytic approach, which integrates both variable-centered (mediation) and person-centered (LPA) methods. This methodological synergy provides a more comprehensive understanding than either approach could alone. The mediation analysis illuminates how SRH relates to PA on average—identifying SA as a key mechanism—while the person-centered analysis reveals who is most affected by identifying distinct subgroups with different risk patterns. This moves beyond a ‘one-size-fits-all’ model and allows for the identification of a high-risk ‘Unhealthy-Anxious’ group that requires targeted intervention. Second, the study addresses a significant public health concern—physical inactivity—within a large and understudied population of female Chinese university students, providing valuable data to inform culturally specific health promotion efforts.
Despite the valuable insights provided, this study has several limitations that should be acknowledged.
First, the study employed a cross-sectional design, which captures data at a single point in time. Although our mediation model is based on self-presentation theory, the relationships between SRH, SA, and PA are likely to be complex and potentially bidirectional. For example, it is plausible that low levels of physical activity could lead to a decline in self-rated health and an increase in SA over time. Future research should utilize longitudinal or experimental designs to disentangle the causal pathways and temporal precedence among these variables.
Second, all primary variables were measured using self-report questionnaires. This method is susceptible to potential biases, such as social desirability bias (e.g., participants might over-report their physical activity levels or under-report their SA) and recall bias. Although self-report measures are common and practical for large-scale surveys, incorporating objective measures in future studies, such as using accelerometers to track physical activity or conducting clinical interviews to assess SA, would enhance the validity and reliability of the findings.
Third, the sample was limited to female college students from some universities in China. This specificity limits the generalizability of our findings to male students, students from other universities or cultural backgrounds, and young adults who are not in higher education. The unique academic and social pressures faced by this demographic may influence the observed relationships, and future research is needed to test whether these profiles and associations hold true in more diverse populations.
Fourth, and relatedly, physical activity was assessed with a single-item measure. While this approach is efficient for large-scale screening, a single item cannot capture the multifaceted nature of physical activity, including its various dimensions like frequency, duration, intensity, and type (e.g., aerobic, strength training, leisure-time). This may lead to an oversimplification of individuals’ activity levels and a less precise estimation of the relationship between physical activity and other variables. Future research should consider employing more comprehensive, validated multi-item questionnaires, such as the International Physical Activity Questionnaire (IPAQ), to obtain a more nuanced assessment.
Finally, while we controlled for several demographic variables, there may be other unmeasured confounding variables that could influence the results. Factors such as socioeconomic status, depression, academic major, personality traits (e.g., neuroticism, extraversion), and access to recreational facilities could also play a role in shaping students’ health perceptions, anxiety levels, and activity patterns. Future studies could benefit from including a broader range of potential covariates to provide a more comprehensive understanding of these complex relationships.
Collectively, these limitations mean our findings should be interpreted with caution. They provide a valuable snapshot and a strong theoretical model, but the magnitude of the effects may be biased, and the causal direction remains unconfirmed. The results are best understood as a foundational step highlighting a high-risk group and a key psychological mechanism that now require more robust, longitudinal, and multi-measure investigation.
6 Conclusion
This study adopted a dual-analytic approach to investigate the links between SRH, SA, and PA in female college students. The mediation analysis demonstrated that SA partially mediates the SRH–PA relationship, while the person-centered analysis revealed three distinct profiles with significantly different PA levels. Together, these findings highlight SA as a key psychological mechanism and underscore the heterogeneity within female student populations.
These results call for moving beyond one-size-fits-all strategies. Tailored interventions should be designed for each subgroup: sustaining intrinsic motivation in the “Healthy-Resilient,” providing supportive nudges for the “Moderate-Adapting,” and delivering CBT-informed, low-stigma programs for the “Unhealthy-Anxious.” Future research should employ longitudinal designs to establish causality, incorporate qualitative methods to understand subgroup experiences, and replicate findings across diverse cultural contexts.
At a policy level, these findings provide a foundation for evidence-based programming in university health systems. Embedding SA screening into routine campus health assessments, integrating mental health services with physical education programs, and leveraging digital platforms such as mobile apps and online counseling could create accessible, targeted solutions. Investing in such integrated approaches is not ancillary but essential to supporting student wellbeing and long-term public health.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Physical Education College, Jiangxi Normal University (IRB-JXNU-PEC-2025019). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
WH: Conceptualization, Methodology, Project administration, Software, Writing – original draft, Writing – review & editing. BC: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. CH: Conceptualization, Formal analysis, Investigation, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The authors declare that no Gen AI was used in the creation of this manuscript.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2025.1681504/full#supplementary-material
References
1. Allen, KD. Cost-effectiveness of physical activity and exercise therapy programs for knee osteoarthritis: making the case for health plan coverage. Osteoarthr Cartil. (2020) 28:719–20. doi: 10.1016/j.joca.2020.02.833
2. Giustino, V, Parroco, A, Gennaro, A, Musumeci, G, Palma, A, and Battaglia, G. Physical activity levels and related energy expenditure during COVID-19 quarantine among the Sicilian active population: a cross-sectional online survey study. Sustainability. (2020) 12:4356. doi: 10.3390/su12114356
3. Oppert, J-M, Bellicha, A, and Ciangura, C. Physical activity in management of persons with obesity. Eur J Intern Med. (2021) 93:8–12. doi: 10.1016/j.ejim.2021.04.028
4. Veronese, N, Soysal, P, Demurtas, J, Solmi, M, Bruyère, O, Christodoulou, N, et al. Physical activity and exercise for the prevention and management of mild cognitive impairment and dementia: a collaborative international guideline. Eur Geriatr Med. (2023) 14:925–52. doi: 10.1007/s41999-023-00858-y
5. Popovic, S, and Masanovic, B. Effects of physical activity and social activity on physical health and social inclusion of elderly people. Iran J Public Health. (2019) 48:1922–1923. doi: 10.18502/ijph.v48i10.3503
6. Huang, Y, and Lu, Z. A cross-sectional study of physical activity and chronic diseases among middle-aged and elderly in China. Sci Rep. (2024) 14:30701. doi: 10.1038/s41598-024-78360-z
7. Stöcker, N, Gaser, D, Oberhoffer-Fritz, R, and Sitzberger, C. Kids TUMove—A holistic program for children with chronic diseases, increasing physical activity and mental health. J Clin Med. (2024) 13:3791. doi: 10.3390/jcm13133791
8. Klaiber, P, and Chen, FS. Long-term health implications of students’ friendship formation during the transition to university. Appl Psychol Health Well-Being. (2018) 10:290–308. doi: 10.1111/aphw.12131
9. Vanhelst, J, Béghin, L, Drumez, É, Labreuche, J, Polito, A, Ruyter, TD, et al. Changes in physical activity patterns from adolescence to young adulthood: the BELINDA study. Eur J Pediatr. (2023) 182:2891–902. doi: 10.1007/s00431-023-04948-8
10. Grasdalsmoen, M, Eriksen, HR, Lønning, KJ, and Sivertsen, B. Physical exercise, mental health problems, and suicide attempts in university students. BMC Psychiatry. (2020) 20:175. doi: 10.1186/s12888-020-02583-3
11. Guthold, R, Stevens, GA, Riley, LM, and Bull, FC. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolescent Health. (2020) 4:23–35. doi: 10.1016/S2352-4642(19)30323-2
12. Sheng, J, Ariffin, IAB, and Tham, J. The influence of exercise self-efficacy and gender on the relationship between exercise motivation and physical activity in college students. Sci Rep. (2025) 15:11888. doi: 10.1038/s41598-025-95704-5
13. Althoff, T, Sosič, R, Hicks, JL, King, AC, Delp, SL, and Leskovec, J. Large-scale physical activity data reveal worldwide activity inequality. Nature. (2017) 547:336–9. doi: 10.1038/nature23018
14. Sfm, C, Van Cauwenberg, J, Maenhout, L, Cardon, G, Lambert, EV, and Van Dyck, D. Inequality in physical activity, global trends by income inequality and gender in adults. Int J Behav Nutr Phys Act. (2020) 17:142. doi: 10.1186/s12966-020-01039-x
15. Mayo, X, Luque-Casado, A, Jimenez, A, and del Villar, F. Physical activity levels for girls and young adult women versus boys and young adult men in Spain: a gender gap analysis. Sustainability. (2020) 12:6265. doi: 10.3390/su12156265
16. Radu, L-E, Făgăraş, S-P, and Vanvu, G. Physical activity index of female university students. Procedia Soc Behav Sci. (2015) 191:1763–6. doi: 10.1016/j.sbspro.2015.04.375
17. Aljehani, NM, Razee, H, Ritchie, J, Valenzuela, T, Bunde-Birouste, A, and Alkhaldi, G. Exploring female university students’ participation in physical activity in Saudi Arabia: a mixed-methods study. Front Public Health. (2022) 10:10. doi: 10.3389/fpubh.2022.829296
18. Chen, P, Wang, D, Shen, H, Yu, L, Gao, Q, Mao, L, et al. Physical activity and health in Chinese children and adolescents: expert consensus statement (2020). Br J Sports Med. (2020) 54:1321–31. doi: 10.1136/bjsports-2020-102261
19. Hao, M, Fang, Y, Yan, W, Gu, J, Hao, Y, and Wu, C. Relationship between body dissatisfaction, insufficient physical activity, and disordered eating behaviors among university students in southern China. BMC Public Health. (2022) 22:2054. doi: 10.1186/s12889-022-14515-9
20. Deng, G, Wen, Y, Cheng, J, Huang, B, and Liu, N. Analysis of the associations between moderate-to-vigorous physical activity and screen time on psychological symptoms among university students: a cross-sectional survey based on six geographic regions in China. BMC Psychiatry. (2024) 24:504. doi: 10.1186/s12888-024-05945-3
21. Guo, M, Wang, XZ, and Koh, K. Association between physical activity, sedentary time, and physical fitness of female college students in China. BMC Womens Health. (2022) 22:502. doi: 10.1186/s12905-022-02108-y
22. Oftedal, S, Fenton, S, Hansen, V, Whatnall, MC, Ashton, L, Haslam, RL, et al. Changes in physical activity, diet, sleep, and mental well-being when starting university: a qualitative exploration of Australian student experiences. J Am Coll Heal. (2023) 72:3715–24. doi: 10.1080/07448481.2023.2194426
23. Guerriero, M, Dipace, A, Monda, A, Maria, AD, Polito, R, Messina, G, et al. Relationship between sedentary lifestyle, physical activity and stress in university students and their life habits: a scoping review with PRISMA checklist (PRISMA-ScR). Brain Sci. (2025) 15:78. doi: 10.3390/brainsci15010078
24. Hamplová, D, Klusáček, J, and Mráček, T. Assessment of self-rated health: the relative importance of physiological, mental, and socioeconomic factors. PLoS One. (2022) 17:e0267115. doi: 10.1371/journal.pone.0267115
25. Hautekiet, P, Saenen, ND, Demarest, S, Keune, H, Pelgrims, I, Van der Heyden, J, et al. Air pollution in association with mental and self-rated health and the mediating effect of physical activity. Environ Health. (2022) 21:29. doi: 10.1186/s12940-022-00839-x
26. Martin, SD, Urban, RW, Johnson, AH, Magner, D, Wilson, JE, and Zhang, Y. Health-related behaviors, self-rated health, and predictors of stress and well-being in nursing students. J Prof Nurs. (2022) 38:45–53. doi: 10.1016/j.profnurs.2021.11.008
27. Fylkesnes, K, Jakobsen, MD, and Henriksen, NO. The value of general health perception in health equity research: a community-based cohort study of long-term mortality risk (Finnmark cohort study 1987–2017). SSM Popul Health. (2021) 15:100848. doi: 10.1016/j.ssmph.2021.100848
28. Lorem, G, Cook, S, Leon, DA, Emaus, N, and Schirmer, H. Self-reported health as a predictor of mortality: a cohort study of its relation to other health measurements and observation time. Sci Rep. (2020) 10:4886. doi: 10.1038/s41598-020-61603-0
29. Fan, Y, and He, D. Self-rated health, socioeconomic status and all-cause mortality in Chinese middle-aged and elderly adults. Sci Rep. (2022) 12:9309. doi: 10.1038/s41598-022-13502-9
30. Haugg, F, Lee, G, He, J, Johnson, J, Zapaishchykova, A, Bitterman, DS, et al. Imaging biomarkers of ageing: a review of artificial intelligence-based approaches for age estimation. Lancet Healthy Longev. (2025) 6:100728. doi: 10.1016/j.lanhl.2025.100728
31. Marques, A, Peralta, M, Santos, T, Martins, J, and Matos, MGD. Self-rated health and health-related quality of life are related with adolescents’ healthy lifestyle. Public Health. (2019) 170:89–94. doi: 10.1016/j.puhe.2019.02.022
32. Bandeira, A d S, Duca, GFD, Delevatti, RS, Manta, SW, Silveira, PM, Leonel, L d S, et al. Association between simultaneity of health-risk behaviours and self-rated health in Brazilian adolescents. PLoS One. (2022) 17:e0271503. doi: 10.1371/journal.pone.0271503
33. Kim, A, and Woo, K. Gender differences in the relationship between informal caregiving and subjective health: the mediating role of health promoting behaviors. BMC Public Health. (2022) 22:311. doi: 10.1186/s12889-022-12612-3
34. Bagherinia, M, Rezaeian, S, Shakiba, E, Maleki, R, Mohammad Karimi Mazhin, A, Darvishigilan, H, et al. Analysis of the mediating role of life style in the relationship between health literacy and self-rated health employing structural equation modeling. Sci Rep. (2025) 15:283. doi: 10.1038/s41598-024-79835-9
35. Wang, Y, She, W, Chi, G, and Wang, J. Vigorous physical activity and self-rated health during adolescence: a cross-sectional survey. Front Public Health. (2022) 10:10. doi: 10.3389/fpubh.2022.961361
36. Lee, D-J, and Park, K-S. Association between physical activity and suicidal ideation: the moderating effect of self-rated health status. Healthcare. (2025) 13:1506. doi: 10.3390/healthcare13131506
37. Sloan, FA. Subjective beliefs, health, and health behaviors. J Risk Uncertain. (2024) 69:105–44. doi: 10.1007/s11166-024-09435-5
38. McBride, NL, Bates, GW, Elphinstone, B, and Whitehead, R. Self-compassion and social anxiety: the mediating effect of emotion regulation strategies and the influence of depressed mood. Psychol Psychother Theory Res Pract. (2022) 95:1036–55. doi: 10.1111/papt.12417
39. Prieto-Fidalgo, Á, and Calvete, E. The role of interpretation biases and safety behaviours in social anxiety: an intensive longitudinal study. Behav Cogn Psychother. (2024) 52:49–64. doi: 10.1017/S1352465823000358
40. Cohn-Schwartz, E. Pathways from social activities to cognitive functioning: the role of physical activity and mental health. Innov Aging. (2020) 4:igaa015. doi: 10.1093/geroni/igaa015
41. Schlenker, BR, and Leary, M. Social anxiety and self-presentation: a conceptualization and model. Psychol Bull. (1982) 92:641–69. doi: 10.1037/0033-2909.92.3.641
42. Gök, BG, and Yalçınkaya-Alkar, Ö. Clarifying the association of social anxiety with cognitive variables: the role of self-esteem, self-perception, fears of positive and negative evaluation, and post-event processing. Scand J Psychol. (2022) 64:278–87. doi: 10.1111/sjop.12886
43. Dismuke, AN, and Pond, RS. Increasing efficacy to reduce state anxiety: an application of the self-presentation theory of social anxiety. J Soc Clin Psychol. (2024) 43:445–72. doi: 10.1521/jscp.2024.43.5.445
44. Guo, S, Izydorczyk, B, Lipowska, M, Kamionka, A, Lizińczyk, S, Sajewicz-Radtke, U, et al. Socio-cultural attitudes toward the body as a predictor of motivation for physical activity in young people brought up in Asian and European culture—Chinese-Polish comparison. BMC Sports Sci Med Rehabil. (2023) 15:52. doi: 10.1186/s13102-023-00662-y
45. Shi, C, Yan, J, Wang, L, and Shen, H. Exploring the self-reported physical fitness and self-rated health, mental health disorders, and body satisfaction among Chinese adolescents: a cross-sectional study. Front Psychol. (2022) 13:13. doi: 10.3389/fpsyg.2022.1003231
46. Östberg, D, and Nordin, S. Three-year prediction of depression and anxiety with a single self-rated health item. J Ment Health. (2022) 31:402–9. doi: 10.1080/09638237.2021.2022610
47. Lutfi, A, Alrawad, M, Alsyouf, A, Almaiah, MA, Al-Khasawneh, A, Al-Khasawneh, AL, et al. Drivers and impact of big data analytic adoption in the retail industry: a quantitative investigation applying structural equation modeling. J Retail Consum Serv. (2023) 70:103129. doi: 10.1016/j.jretconser.2022.103129
48. Cheung, GW, Cooper-Thomas, HD, Lau, RS, and Wang, LC. Reporting reliability, convergent and discriminant validity with structural equation modeling: a review and best-practice recommendations. Asia Pac J Manag. (2024) 41:745–83. doi: 10.1007/s10490-023-09871-y
49. Moreau, D, and Corballis, M. When averaging goes wrong: the case for mixture model estimation in psychological science. J Exp Psychol Gen. (2018) 148:9. doi: 10.1037/xge0000504
50. Hornstra, L, Mathijssen, ACS, Denissen, JJA, and Bakx, A. Academic motivation of intellectually gifted students and their classmates in regular primary school classes: a multidimensional, longitudinal, person- and variable-centered approach. Learn Individ Differ. (2023) 107:102345. doi: 10.1016/j.lindif.2023.102345
51. Lin, S, Mastrokoukou, S, and Longobardi, C. Social relationships and social media addiction among adolescents: variable-centered and person-centered approaches. Comput Hum Behav. (2023) 147:107840. doi: 10.1016/j.chb.2023.107840
52. Zhao, M, Wang, R, Zhao, Z, Li, L, Luo, H, and Wu, L. The relationship between boredom proneness, the behavioral inhibition system, and anxiety in college students: variable-centered and person-centered analytic approaches. Front Psychol. (2024) 15:15. doi: 10.3389/fpsyg.2024.1414736
53. Kang, H. Sample size determination and power analysis using the G*power software. J Educ Eval Health Prof. (2021) 18:17. doi: 10.3352/jeehp.2021.18.17
54. Tinajero-Chávez, LI, Mora-Romo, JF, Bravo-Doddoli, A, Cruz-Narciso, BV, Calleja, N, and Toledano-Toledano, F. Design, development, and validation of the self-perceived health scale (SPHS). Healthcare. (2023) 11:2007. doi: 10.3390/healthcare11142007
55. Waasdorp, TE, Mehari, KR, Milam, AJ, and Bradshaw, CP. Health-related risks for involvement in bullying among middle and high school youth. J Child Fam Stud. (2018) 28:2606–17. doi: 10.1007/s10826-018-1260-8
56. Narmandakh, A, Roest, AM, de Jonge, P, and Oldehinkel, AJ. Psychosocial and biological risk factors of anxiety disorders in adolescents: a TRAILS report. Eur Child Adolesc Psychiatry. (2021) 30:1969–82. doi: 10.1007/s00787-020-01669-3
57. Huang, W, Chen, B, and Hu, C. The latent profile structure of negative emotion in female college students and its impact on eating behavior: the mediating role of physical exercise. Front Public Health. (2025) 13:1663474. doi: 10.3389/fpubh.2025.1663474
58. Peters, L, Sunderland, M, Andrews, G, Rapee, RM, and Mattick, RP. Development of a short form social interaction anxiety (SIAS) and social phobia scale (SPS) using nonparametric item response theory: the SIAS-6 and the SPS-6. Psychol Assess. (2012) 24:66–76. doi: 10.1037/a0024544
59. Ouyang, X, Cai, Y, and Tu, D. Psychometric properties of the short forms of the social interaction anxiety scale and the social phobia scale in a Chinese college sample. Front Psychol. (2020) 11:2214. doi: 10.3389/fpsyg.2020.02214
60. Ganotice, FA, Shen, X, Yuen, JKY, Chow, YMA, Wong, AMY, Chan, KMK, et al. Students’ interaction anxiety and social phobia in interprofessional education in Hong Kong: mapping a new research direction. Ann Med. (2023) 55:2210842. doi: 10.1080/07853890.2023.2210842
61. Li, X, Liu, Y, Rong, F, Wang, R, Li, L, Wei, R, et al. Physical activity and social anxiety symptoms among Chinese college students: a serial mediation model of psychological resilience and sleep problems. BMC Psychol. (2024) 12:440. doi: 10.1186/s40359-024-01937-w
62. Fernández, RS, Crivelli, L, Guimet, NM, Allegri, RF, and Pedreira, ME. Psychological distress associated with COVID-19 quarantine: latent profile analysis, outcome prediction and mediation analysis. J Affect Disord. (2020) 277:75–84. doi: 10.1016/j.jad.2020.07.133
63. Spurk, D, Hirschi, A, Wang, M, Valero, D, and Kauffeld, S. Latent profile analysis: a review and “how to” guide of its application within vocational behavior research. J Vocat Behav. (2020) 120:103445. doi: 10.1016/j.jvb.2020.103445
64. Weisenburger, RL, Dainer-Best, J, Zisser, M, McNamara, ME, and Beevers, CG. Negative self-referent cognition predicts future depression symptom change: an intensive sampling approach. Cogn Emot. (2024) 39:829–43. doi: 10.1080/02699931.2024.2400298
65. Liao, J, Xia, T, Xu, X, and Pan, L. The effect of appearance anxiety on social anxiety among college students: sequential mediating effects of self-efficacy and self-esteem. Behav Sci. (2023) 13:692. doi: 10.3390/bs13080692
66. Wan, Z, Li, S, and Fang, S. The effect of negative physical self on social anxiety in college students: the bidirectional chain mediation roles of fear of negative evaluation and regulatory emotional self-efficacy. Psychol Res Behav Manag. (2024) 17:2055–66. doi: 10.2147/PRBM.S457405
67. Tsartsapakis, I, and Zafeiroudi, A. Social physique anxiety in college students: the role of study field, physical activity, physical self-perception, and self-esteem. Sports. (2025) 13:13. doi: 10.3390/sports13040119
68. Leary, M, and Allen, A. Self-presentational persona: simultaneous management of multiple impressions. J Pers Soc Psychol. (2011) 101:1033–49. doi: 10.1037/a0023884
69. Kent, BV, Stroope, S, Kanaya, A, Zhang, Y, Kandula, N, and Shields, AE. Private religion/spirituality, self-rated health, and mental health among US South Asians. Qual Life Res. (2019) 29:495–504. doi: 10.1007/s11136-019-02321-7
70. Mu, F, Liu, J, Lou, H, Zhu, W, Wang, Z, and Li, B. Influence of physical exercise on negative emotions in college students: chain mediating role of sleep quality and self-rated health. Front Public Health. (2024) 12:1402801. doi: 10.3389/fpubh.2024.1402801
71. Okafor, CN, Jean-Berluche, D, Paltzer, J, Kwon, E, Grigoropoulou, N, Vander Weele, TJ, et al. A cross-national analysis of demographic variation in self-rated mental health across 22 countries. Commun Med. (2025) 5:320. doi: 10.1038/s43856-025-01038-8
72. Myklebust, LHR, Eide, D, Wüsthoff, LEC, Melis, F, Oldervoll, AL, De Pirro, S, et al. Changes in self-reported mental health, anxiety and depression among patients in heroin-assisted treatment: findings from a Norwegian trial project. Eur Addict Res. (2025):1–11. doi: 10.1159/000547375
73. Wang, Q, Zhang, J, and Xiao, M. Association between social media use, physical activity level, and depression and anxiety among college students: a cross-cultural comparative study. Sci Rep. (2025) 15:6596. doi: 10.1038/s41598-025-88140-y
74. Kendall, PC. Cognitive-behavioral therapies with youth: guiding theory, current status, and emerging developments. J Consult Clin Psychol. (1993) 61:235–47. doi: 10.1037/0022-006X.61.2.235
75. Deng, Y, and Wang, X. The impact of physical activity on social anxiety among college students: the chain mediating effect of social support and psychological capital. Front Psychol. (2024) 15:15. doi: 10.3389/fpsyg.2024.1406452
76. Hu, C, Zhang, W, Huang, Y, and Jin, C. How grit enhances physical exercise in college students: mediating roles of personal growth initiative and self-efficacy. Front Psychol. (2025) 16:1652984. doi: 10.3389/fpsyg.2025.1652984
77. Krieg, A, and Xu, Y. Ethnic differences in social anxiety between individuals of Asian heritage and European heritage: a meta-analytic review. Asian Am J Psychol. (2015) 6:66–80. doi: 10.1037/a0036993
78. Feng, H, Xuan, C, and Yi, S. The more popular, the happier? Exploring curvilinear relationships between popularity and subjective well-being in the social networking context of China. Asian J Soc Psychol. (2025) 28:e70047. doi: 10.1111/ajsp.70047
79. Pankowiak, A, Casey, M, Eime, R, and Westerbeek, H. A socio-ecological approach to understanding the experiences of women and girls playing male-dominated sports. Sport Soc. (2024) 1–20. doi: 10.1080/17430437.2024.2390989
80. Yang, J, Bartle, G, Kirk, D, and Landi, D. Chinese students’ experiences of ‘high-stakes’ assessment: the role of fitness testing. Phys Educ Sport Pedagog. (2025) 1–14. doi: 10.1080/17408989.2025.2449664
81. White, GM. The role of cultural explanations in ‘somatization’ and ‘psychologization’. Soc Sci Med. (1982) 16:1519–30. doi: 10.1016/0277-9536(82)90067-3
82. Berkson, M. A confucian defense of shame: morality, self-cultivation, and the dangers of shamelessness. Religion. (2021) 12:32. doi: 10.3390/rel12010032
83. Schönfeld, P, Brailovskaia, J, Zhang, XC, and Margraf, J. Self-efficacy as a mechanism linking daily stress to mental health in students: a three-wave cross-lagged study. Psychol Rep. (2018) 122:2074–95. doi: 10.1177/0033294118787496
84. Gao, W, Ping, S, and Liu, X. Gender differences in depression, anxiety, and stress among college students: a longitudinal study from China. J Affect Disord. (2019) 263:292–300. doi: 10.1016/j.jad.2019.11.121
85. Ong, AD, and Leger, KA. Advancing the study of resilience to daily stressors. Perspect Psychol Sci. (2022) 17:1591–603. doi: 10.1177/17456916211071092
86. Huang, Q, Wang, X, Ge, Y, and Cai, D. Relationship between self-efficacy, social rhythm, and mental health among college students: a 3-year longitudinal study. Curr Psychol. (2021) 42:9053–62. doi: 10.1007/s12144-021-02160-1
87. Erving, CL, Williams, TR, Holt, AJ, and Taylor, A. Anticipatory race-related stress and depressive symptoms among U.S. black women attending a historically black university: are psychosocial resources stress buffers? Sociol Inq. (2025) 95:470–91. doi: 10.1111/soin.12626
88. Kobau, R, Seligman, M, Peterson, C, Diener, E, Zack, M, Chapman, D, et al. Mental health promotion in public health: perspectives and strategies from positive psychology. Am J Public Health. (2011) 101:1–9. doi: 10.2105/AJPH.2010.300083
89. Wu, Y, Yu, W, Wu, X, Wan, H, Wang, Y, and Lu, G. Psychological resilience and positive coping styles among Chinese undergraduate students: a cross-sectional study. BMC Psychol. (2020) 8:79. doi: 10.1186/s40359-020-00444-y
90. Verdolini, N, and Vieta, E. Resilience, prevention and positive psychiatry. Acta Psychiatr Scand. (2021) 143:281–3. doi: 10.1111/acps.13288
91. Acosta-González, HN, and Marcenaro-Gutierrez, Ó. The relationship between subjective well-being and self-reported health: evidence from Ecuador. Appl Res Qual Life. (2020) 16:1961–81. doi: 10.1007/s11482-020-09852-z
92. Parsons, S, Songco, A, Booth, C, and Fox, E. Emotional information-processing correlates of positive mental health in adolescence: a network analysis approach. Cognit Emot. (2021) 35:956–69. doi: 10.1080/02699931.2021.1915752
93. Zhang, Y, Li, Y, and Mai, X. Fear of negative evaluation modulates the processing of social evaluative feedback with different valence and contexts. Cereb Cortex. (2022) 33:4927–38. doi: 10.1093/cercor/bhac390
94. Finn, D, Cardini, F, Aspell, JE, Swami, V, and Todd, J. The impact of body image on social cognition: fear of negative evaluation mediates the relationship between body surveillance and interpersonal distance in women. Body Image. (2024) 51:101777. doi: 10.1016/j.bodyim.2024.101777
95. Morrison, A, Goldin, P, and Gross, JJ. Fear of negative and positive evaluation as mediators and moderators of treatment outcome in social anxiety disorder. J Anxiety Disord. (2024) 104:102874. doi: 10.1016/j.janxdis.2024.102874
96. Stone, LB, and Veksler, AE. Stop talking about it already! Co-ruminating and social media focused on COVID-19 was associated with heightened state anxiety, depressive symptoms, and perceived changes in health anxiety during spring 2020. BMC Psychol. (2022) 10:22. doi: 10.1186/s40359-022-00734-7
97. Wright, LJ, Veldhuijzen van Zanten, JJCS, and Williams, SE. Examining the associations between physical activity, self-esteem, perceived stress, and internalizing symptoms among older adolescents. J Adolesc. (2023) 95:1274–87. doi: 10.1002/jad.12201
98. Ryan, RM, Deci, EL, Vansteenkiste, M, and Soenens, B. Building a science of motivated persons: self-determination theory’s empirical approach to human experience and the regulation of behavior. Motiv Sci. (2021) 7:97–110. doi: 10.1037/mot0000194
99. Teixeira, P, Carraça, E, Markland, D, Silva, M, and Ryan, RM. Exercise, physical activity, and self-determination theory: a systematic review. Int J Behav Nutr Phys Act. (2012) 9:78–8. doi: 10.1186/1479-5868-9-78
100. Corella, C, Abarca-Sos, A, Gallardo, LO, Martín-Albo, J, and Zaragoza, J. Longitudinal study of physical activity in college students: testing self-determination theory based on stages of change. Curr Psychol. (2021) 41:9053–62. doi: 10.1007/s12144-020-01269-z
101. Hagger, M, and Chatzisarantis, N. Self-determination theory and the psychology of exercise. Int Rev Sport Exerc Psychol. (2008) 1:103–79. doi: 10.1080/17509840701827437
102. Prochaska, JO, and Velicer, WF. The transtheoretical model of health behavior change. Am J Health Promot. (1997) 12:38–48. doi: 10.4278/0890-1171-12.1.38
103. Horiuchi, S, Tsuda, A, Kobayashi, H, Fallon, E, and Sakano, Y. Self-efficacy, pros, and cons as variables associated with adjacent stages of change for regular exercise in Japanese college students. J Health Psychol. (2017) 22:1003–993. doi: 10.1177/1359105315621779
104. Oliveira, D, and Angela, C. When risky decisions generate externalities. J Risk Uncertain. (2021) 63:59–79. doi: 10.1007/s11166-021-09357-6
105. Li, J, Xiao, M, Huang, H, and Liu, H. A latent profile analysis of subjective exercise experiences among physically vulnerable college students and psychiatric symptoms correlates during three phases of the COVID-19 pandemic in Wuhan, China. Front Psychol. (2023) 14:14. doi: 10.3389/fpsyg.2023.1118489
Keywords: self-rated health, physical activity, social anxiety, female college students, mediation analysis, latent profile analysis
Citation: Huang W, Chen B and Hu C (2025) Exploring self-rated health, physical activity, and social anxiety among female Chinese university students: a variable- and person-centered analysis. Front. Public Health. 13:1681504. doi: 10.3389/fpubh.2025.1681504
Edited by:
Bojan Masanovic, University of Montenegro, MontenegroReviewed by:
Monica Ewomazino Akokuwebe, University of the Witwatersrand, South AfricaShazia Tahira, Bahria University, Pakistan
Copyright © 2025 Huang, Chen and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence:Bin Chen, Y2hlbjAxMzA2NkBnbWFpbC5jb20=; Chang Hu, aHVjaGFuZ0BqeG51LmVkdS5jbg==
†These authors have contributed equally to this work and share first authorship