Bin Chen
Shushu Hua
Zaibin Tu
Chang Hu
Jing Yang- 1Physical Education College, Jiangxi Normal University, Nanchang, China
- 2College of Humanities and Social Sciences, Zhongkai University of Agriculture and Engineering, Guangzhou, China
- 3School of Educational Science, Hanshan Normal University, Chaozhou, China
- 4School of Psychology and Sociology, Mianyang Normal University, Mianyang, China
Background: Grit, defined as perseverance and passion for long-term goals, is a vital psychological trait that contributes to academic success and overall wellbeing. At the same time, regular PA supports physical and mental health, yet its engagement often declines among university students. Although grit has been linked to health-promoting behaviors, the mechanisms underlying this association remain unclear. This study investigated the mediating role of self-efficacy from a variable-centered perspective and identified student profiles based on grit and self-efficacy through a person-centered approach.
Methods: A cross-sectional survey of 3,752 Chinese university students was conducted. Structural equation modeling tested the mediating role of self-efficacy in the grit-PA relationship. Latent Profile Analysis identified subgroups with distinct combinations of grit and self-efficacy, with PA levels compared across profiles.
Results: The variable-centered analysis revealed that grit was positively associated with PA (β = 0.370, p < 0.001), with self-efficacy serving as a significant partial mediator, accounting for 21% of the total effect (indirect effect = 0.077, 95% CI [0.058, 0.097]). The person-centered analysis identified three distinct profiles: “Limited Self-Regulation” (16.12%), “Moderate Self-Regulation” (55.84%), and “Strong Self-Regulation” (28.04%). Students in the Strong Self-Regulation profile demonstrated significantly higher PA engagement (M = 49.03) compared to those in the Moderate (M = 31.56) and Limited (M = 17.84) profiles (F = 225.11, p < 0.001, η2 = 0.107).
Conclusion: Self-efficacy is consistent with a mediating role linking grit to PA among university students. The identification of three distinct profiles reveals meaningful heterogeneity in motivational configurations. Students with high grit and self-efficacy demonstrate optimal PA engagement, while those low in both represent a vulnerable subgroup requiring targeted intervention. Integrating variable- and person-centered approaches provides a comprehensive understanding of motivational processes underlying active lifestyles in higher education, emphasizing the need for tailored, profile-specific interventions.
1 Introduction
Physical inactivity is now recognized as a significant global public health challenge (Santos et al., 2023; van Sluijs et al., 2021; Dogra et al., 2022). According to the World Health Organisation, approximately 31% of adults fail to meet even the minimum recommended levels of physical activity (PA), a shortfall that directly contributes to the rising prevalence of chronic diseases (Guthold et al., 2020; Strain et al., 2024; Morouço et al., 2022; Chen et al., 2025). University students sit at a pivotal developmental juncture: they are transitioning into adulthood while simultaneously laying the foundations of their future professional and personal lives (Maggs, 2024). Their health behaviors, therefore, have consequences that reverberate far beyond the campus, shaping labor-force productivity, healthcare demand, and ultimately societal wellbeing (Sanci et al., 2022; Almoraie et al., 2025; Jong et al., 2025). However, accumulating evidence suggests that students’ physical activity levels decline sharply after they enter higher education (Hammer et al., 2025; Deforche et al., 2015). Confronted with heavy academic workloads, social pressures, and career preparation, many struggle to maintain regular exercise routines (Hilger-Kolb et al., 2020; Brown et al., 2024). The resulting sedentariness heightens their risk for cardiometabolic conditions and is linked to increased anxiety, depression, and stress (Booth et al., 2023; Biddle et al., 2023; Biddle et al., 2021).
Most investigations into student inactivity have focused on external barriers, such as crowded timetables, limited facilities, high membership fees, or a lack of social support (Griffiths et al., 2022; Moore et al., 2023; Silva et al., 2022). Although valuable, such work cannot fully explain why two students exposed to the same campus environment often display markedly different exercise patterns. This discrepancy suggests that intrapersonal factors—particularly enduring psychological attributes—may be decisive in determining who persists in PA when circumstances are unfavorable (Vella et al., 2023; Wilhite et al., 2023; Martín-Rodríguez et al., 2024). Accordingly, the present study shifts the analytic lens from external constraints to internal motivation, examining how positive psychological traits are associated with students’ exercise behavior.
Among the traits highlighted by contemporary positive psychology, grit—defined as sustained perseverance and passion for long-term goals (Duckworth et al., 2007)—has attracted substantial attention. Individuals high in grit show greater Resilience in the face of setbacks and a stronger willingness to invest effort over extended periods (Calo et al., 2024; Biggs et al., 2024; Sulla et al., 2022). Applied to health behavior, gritty students may endure the fatigue, time pressure, and monotony that often accompany training, viewing exercise as an indispensable pathway to long-term health (Hartzel et al., 2021; Bae et al., 2024; Estep et al., 2021). Despite the intuitive appeal of this link, the psychological mechanism translating grit into actual PA remains under-specified (Rutberg et al., 2020).
One plausible mediator is self-efficacy—the belief in one’s capability to organize and execute courses of action required to attain a goal (Bandura, 1997). Social Cognitive Theory posits that self-efficacy is a proximal determinant of both the initiation and maintenance of behavior (Schwarzer and Renner, 2000; Bandura, 1991). Students with higher grit are more likely to persist through repeated bouts of practice, thereby accumulating mastery experiences that fortify their confidence in exercising successfully (Daniels et al., 2021). Heightened exercise-specific self-efficacy, in turn, should enhance motivation, goal setting, and behavioral persistence (Blom et al., 2021; Martinez Kercher et al., 2024). Thus, self-efficacy may function as the cognitive “bridge” that channels grit into sustained PA (Dai et al., 2025).
However, traditional variable-centered analyses implicitly assume that all individuals follow the same psychological pathway, risking the oversight of meaningful heterogeneity (Liu et al., 2024; Stiller et al., 2023). In reality, the interplay between grit and self-efficacy may vary considerably. Some students may exhibit both persistence and confidence, while others may show perseverance without corresponding self-belief, or vice versa. To address these gaps, the present study integrates variable- and person-centered methodologies. We first test whether self-efficacy mediates the grit–PA relationship at the population level, and then employ latent profile analysis to uncover distinct psychological profiles and compare their physical activity engagement. The findings are expected to provide actionable insights for designing tailored, evidence-based interventions that promote active lifestyles in higher education settings.
2 Theoretical framework and hypotheses
2.1 Grit and physical activity
Grit is a key positive personality trait that reflects the ability to sustain effort and passion toward long-term, meaningful goals (Jiang et al., 2023). Duckworth et al. (2021) conceptualized grit as consisting of two dimensions: consistency of interest and perseverance of effort. In the context of PA, these dimensions jointly serve as an internal motivational foundation that supports continuous participation (Rosenkranz et al., 2023; Wang et al., 2021). Consistency of interest refers to maintaining long-term enthusiasm for health or fitness improvement rather than engaging in short-lived or impulsive exercise (Tross et al., 2024). Individuals with high consistency of interest can concentrate on their goals despite competing distractions or time pressures (Buabang et al., 2025). Perseverance of effort captures the ability to remain determined and psychologically resilient in the face of challenges such as muscle fatigue, time constraints, plateaus in progress, or minor injuries (Biggs et al., 2024).
Empirical evidence consistently supports a positive link between grit and PA (Cormier et al., 2024). Studies of college athletes have found that individuals with higher grit demonstrate greater training involvement and better performance outcomes (Moles et al., 2017). Research in general student populations also suggests that grit positively predicts both the frequency and duration of moderate-to-vigorous PA (Yu et al., 2024). This relationship may be explained through self-regulation theory, as gritty individuals exhibit stronger self-control and are more capable of postponing immediate gratification for long-term health benefits (Dorina et al., 2023). They tend to perceive discomfort or difficulty as a natural element of progress rather than as a reason to withdraw, which is linked to stronger adherence and persistence in exercise. The following hypothesis is thus proposed:
Hypothesis 1 (H1): Grit is positively associated with university students’ PA levels.
2.2 The mediating role of self-efficacy
Although grit provides the motivational foundation for persistence, its influence on behavior operates through cognitive mechanisms (Jiang et al., 2023). Self-efficacy, as proposed in Bandura’s Social Cognitive Theory, represents the belief that one can effectively perform actions to achieve specific goals (Pajares, 1996). It is a primary determinant of behavior initiation and maintenance (Phillips and More, 2022; Hennessy et al., 2020).
The present study posits that self-efficacy mediates the relationship between grit and PA, operating through two interrelated processes. First, grit facilitates the development of self-efficacy (De La Cruz et al., 2021; Alhadabi and Karpinski, 2020). Bandura identified mastery experiences as the most influential source of efficacy (Welch and West, 1995). Students with high grit persist in their training efforts despite difficulties, which enables them to accumulate small but meaningful successes, such as improving their endurance or learning a new movement (Cocić et al., 2024). Each successful experience reinforces their confidence in their abilities and gradually strengthens their exercise-related self-efficacy (Wang et al., 2022). Prior research in educational and occupational settings has confirmed that grit is positively associated with self-efficacy (Zhou and Hou, 2025).
Second, self-efficacy acts as a direct motivator of behavior (Shengyao et al., 2024). Individuals with strong self-efficacy are more likely to initiate and maintain PA (Xie et al., 2025). They tend to set clear and challenging fitness goals, interpret fatigue or scheduling pressures as manageable, and recover faster from failure or frustration (Hull and Vultaggio, 2019). Substantial evidence from health psychology supports self-efficacy as a robust predictor of PA engagement and persistence (Chu et al., 2021; Maio et al., 2020).
Together, these two pathways suggest that grit enhances self-efficacy through mastery experiences, and heightened self-efficacy, in turn, promotes sustained participation in exercise. The following hypothesis is thus proposed:
Hypothesis 2 (H2): Self-efficacy mediates the relationship between grit and PA.
2.3 A person-centered perspective on grit and self-efficacy
Traditional variable-centered approaches, such as regression or mediation analyses, typically assume structural homogeneity within the population (Woo et al., 2024). They focus on average relationships between variables, presuming that the exact underlying psychological mechanisms apply uniformly across individuals. However, this assumption often masks meaningful within-group variability and may overlook distinct subtypes of individuals who exhibit different psychological configurations (Huang et al., 2025b). To capture this underlying heterogeneity, a person-centered perspective provides a complementary analytic framework.
The LPA enables researchers to identify unobserved subgroups within a population based on shared patterns across multiple continuous variables (Beattie et al., 2022). In the context of this study, grit and self-efficacy are unlikely to have identical effects for all individuals. Instead, these traits may combine in diverse ways to form qualitatively distinct psychological profiles, each associated with unique patterns of exercise motivation and behavior. For example, some students may exhibit both high perseverance and strong self-belief, whereas others may be persistent but lack confidence, or confident but inconsistent in maintaining effort. These nuanced combinations cannot be adequately explained through variable-centered methods alone.
Theoretically, Self-Determination Theory (SDT) provides a valuable lens through which to understand this heterogeneity. SDT emphasizes that sustained behavioral engagement arises from the alignment of intrinsic motivation and perceived competence, a conceptually related concept to self-efficacy (Ryan and Deci, 2000). When individuals possess both a strong internal drive (reflecting grit) and a robust sense of competence (reflecting self-efficacy), they are more likely to internalize health-related goals, experience autonomous motivation, and maintain consistent PA (Dunston et al., 2020). Conversely, an imbalance between effort and confidence may be associated with motivational conflict and behavioral inconsistency.
Recent research employing person-centered approaches in positive psychology supports the value of this perspective. Studies have demonstrated that distinct psychological profiles based on constellations of positive traits, such as Resilience, optimism, and self-efficacy, are differentially associated with outcomes including academic achievement, emotional well-being, and adaptive coping (Sabouripour et al., 2021; Egan et al., 2021; Popa-Velea et al., 2021; Ye et al., 2024). Applying this framework to the present study, latent profile analysis is expected to reveal subgroups of university students characterized by distinct configurations of grit and self-efficacy. By examining how these profiles differ in their engagement in PA, the study aims to uncover the nuanced interplay between personal dispositions and health behavior, thereby extending current understanding beyond average effects. The following hypothesis is thus proposed:
Hypothesis 3 (H3): Distinct latent profiles of university students can be identified based on their levels of grit and self-efficacy.
Hypothesis 4 (H4): These latent profiles differ significantly in PA levels.
2.4 Aims of the study
The present study aims to (Santos et al., 2023) examine the mediating role of self-efficacy in the relationship between grit and PA among university students, and (van Sluijs et al., 2021) identify distinct psychological profiles combining grit and self-efficacy to explore group differences in PA engagement. These dual approaches aim to advance theoretical understanding and provide practical insights for designing targeted health promotion interventions in higher education contexts.
3 Materials and methods
3.1 Participants and procedure
This study employed a cross-sectional design using convenience sampling to recruit participants from multiple universities across China (see Figure 1). Data were collected online through Wenjuanxing1, a reliable and secure Chinese web-based survey platform. Survey invitations containing the participation link were distributed via university counselors, student organizations, and official online groups. Students could voluntarily access the questionnaire using either computers or mobile devices.
Figure 1. Data collection flowchart.
Prior to data collection, a priori sample-size estimation was conducted using G*Power 3.1. The calculation was based on a multiple regression model with two predictors, assuming a small-to-medium effect size (f2 = 0.02), a significance level of α = 0.05, and a statistical power of 0.95. The analysis indicated a minimum requirement of 776 participants to detect significant effects. Considering the study’s potential for attrition due to missing or invalid data, the target sample size was set at more than 3,000 participants.
Data were collected between March 20 and April 20, 2025, from 17 universities located in Jiangxi, Sichuan, Hunan, Guizhou, and Guangdong. Participation was voluntary and uncompensated. Students were eligible for inclusion if they met the following criteria: (1) Full-time undergraduate or graduate enrollment at a Chinese university; (2) Age of 18 years or older; (3) Ability to understand and complete the questionnaire in Chinese; (4) Willingness to provide informed consent and participate voluntarily. Exclusion criteria were applied as follows: (1) Duplicate submissions identified through an identical IP address or device information; (2) Incomplete or blank responses (missing more than 10% of items); (3) Abnormally short completion time (<180 s) or uniform responses indicating inattentive participation; (4) Self-reported history of diagnosed psychological disorders (e.g., major depression, generalized anxiety) that might substantially influence motivational or behavioral responses; (5) Presence of a known physical illness, injury, or medical condition that restricted the ability to engage in regular PA.
A total of 3,968 questionnaires were submitted. After data screening and exclusion of invalid cases based on the criteria above, 3,752 valid responses were retained for analysis, yielding an effective response rate of 94.6%. The final sample size exceeded the calculated requirement, providing sufficient statistical power for both variable-centered and person-centered analyses.
The data-collection process was standardized across all participating universities. Students accessed the survey via an online link distributed through institutional channels. Upon entering the survey webpage, they first read a detailed information statement describing the study objectives, procedures, estimated participation time, and confidentiality policies. Participation required explicit electronic consent before proceeding. The online questionnaire took approximately 10–15 min to complete. It included demographic items (age, gender, grade level, and geographic region), followed by validated instruments measuring grit, self-efficacy, and PA. To ensure response quality, attention-check items were embedded to detect careless answering. Each IP address and device could submit only once, and the platform automatically prevented multiple entries. The dataset was subsequently downloaded in encrypted form and stored on a password-protected institutional server accessible only to the research team.
All participants completed an informed consent form electronically prior to beginning the survey. The consent form emphasized that participation was entirely voluntary, data would be kept confidential, and participants could withdraw at any time without consequence. Sensitive questions about physical or psychological conditions were included only for screening purposes and were optional. The study protocol was reviewed and approved by the Institutional Ethics Committee of the corresponding author’s university (IRB-JXNU-PEC-20240402), and all procedures complied with the ethical standards of the Declaration of Helsinki. No personally identifying information (such as name, student ID, or contact details) was collected.
3.2 Measures
3.2.1 Grit
The present investigation utilized the Grit-S instrument for measuring grit. Initially constructed by Duckworth and Quinn (2009), this tool underwent subsequent modification by Hu to enhance its applicability for assessing persistence in athletic contexts within Chinese populations (Hu et al., 2025). The instrument features a two-dimensional structure comprising eight items, each evaluated using a five-point response format, ranging from 1 (Does not describe me at all) to 5 (Describes me perfectly). Example item: I get excited about new exercise challenges and work hard to overcome them. Within our sample, the measure exhibited robust internal consistency, yielding a Cronbach’s alpha value of 0.821, thereby confirming its psychometric adequacy.
3.2.2 Physical activity
In this investigation, PA was measured using the PARS instrument, which underwent cultural adaptation by Liang (1994). This measurement tool examines three core components of PA: exercise intensity, time spent in activity, and occurrence frequency, employing a 5-point rating system for each component. Prior validation studies conducted by Yuan have confirmed the psychometric properties of this tool among Chinese participants (Yuqing et al., 2025). Physical activity was quantified by multiplying exercise intensity by adjusted duration (Duration – 1) and frequency, yielding a composite PA score. Higher values reflect elevated levels of PA participation. The instrument exhibited satisfactory internal reliability in our sample, yielding a Cronbach’s alpha coefficient of 0.827.
3.2.3 Self-efficacy
The self-efficacy scale employed in this research was initially developed by Jerusalem in 1995 (Jerusalem and Schwarzer, 1995) and subsequently adapted by Hu et al. (2025) for use with Chinese populations, demonstrating satisfactory reliability and validity. The instrument comprises 10 items forming a unidimensional construct, with a sample item being “I am convinced that I can handle unexpected challenges during physical exercise.” A 4-point Likert scale is utilized, ranging from 1 (completely disagree) to 4 (completely agree), where elevated scores represent higher levels of self-efficacy. The scale demonstrated good internal consistency in the present study, with a Cronbach’s alpha of 0.826.
3.3 Data analysis
3.3.1 Variable-centered approach
All variable-centered procedures were executed using SPSS version 26.0. The analytic sequence comprised several stages designed to verify measurement quality, explore preliminary associations, and test the hypothesized mediation model. First, descriptive statistics (mean, standard deviation, skewness, and kurtosis) were computed for grit, self-efficacy, and PA to evaluate data distribution characteristics and the suitability for parametric testing. No substantial deviations from normality were detected. Next, the internal consistency of each instrument was examined using Cronbach’s α. Reliability levels met established criteria, confirming that items measured their respective constructs consistently and could be used for hypothesis testing.
To identify potential covariates, group difference tests were conducted on participants’ demographic variables (e.g., gender, age, education, and place of birth). Demographic variables were compared using one-way ANOVA or independent-samples t-tests. Those variables demonstrating statistically significant differences in PA levels (p < 0.05) were subsequently included as control variables in the mediation analysis to account for their potential confounding influence.
Following this preliminary stage, Pearson correlation coefficients were calculated to examine the pairwise associations among grit, self-efficacy, and PA. These analyses provided an empirical basis for subsequent regression modeling.
Prior to mediation testing, potential multicollinearity among predictors was assessed. Variance Inflation Factor (VIF) and tolerance statistics confirmed acceptable independence among variables, ensuring model stability. The hypothesized mediational pathway was then tested using Hayes’s PROCESS macro (Model 4) (Yue et al., 2025). This approach relies on ordinary least-squares regression and provides both direct and indirect effect estimates. A bootstrapping procedure with 5,000 resamples was employed to generate bias-corrected 95% confidence intervals for the indirect effect. Control variables identified through variance or t-test analyses were entered to partial out their confounding impacts. This analytic strategy rigorously evaluated whether self-efficacy functioned as a link between grit and physical activity engagement within the student sample.
3.3.2 Person-centered approach
To complement the variable-centered findings and capture individual-level heterogeneity, a person-centered analysis was conducted using Mplus version 8.3. The analysis applied LPA to identify distinct student subgroups characterized by unique constellations of grit and self-efficacy levels.
All LPA models were estimated via maximum-likelihood with robust standard errors (MLR). To maintain a parsimonious and interpretable structure, indicator means were freely estimated across profiles, their variances were constrained to equality, and inter-indicator covariances were fixed to zero. To ensure reproducible solutions and avoid local maxima, the estimation algorithm utilized 500 random initial starts and 100 final-stage optimizations.
Model adequacy was evaluated using multiple criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and sample-size–adjusted BIC (aBIC), with lower values indicating better relative fit (Huang et al., 2025a). Entropy scores were inspected to gauge classification accuracy, and the Lo–Mendell–Rubin adjusted likelihood-ratio test (LMR) together with the Bootstrap Likelihood-Ratio Test (BLRT) were applied to test whether adding the profile significantly improved the model (Wan et al., 2025). Selection of the final model balanced statistical fit, classification precision, and theoretical interpretability.
After determining the optimal number of profiles, students’ physical activity levels were compared across latent groups using a one-way ANOVA conducted in SPSS 26.0. When omnibus effects were significant, Bonferroni-adjusted post hoc tests were used to identify specific pairwise group differences.
4 Results
4.1 Common method bias analysis
Given that data for all study variables (Grit, Self-efficacy, and PA) were collected via self-report questionnaires at a single time point, we assessed the potential influence of common method variance using Harman’s single-factor test. An unrotated exploratory factor analysis was conducted on all items. The results revealed that the first factor accounted for 30.695% of the total variance, falling well below the critical threshold of 40% (Yue et al., 2025). This finding suggests that the observed common method bias in this study is within the acceptable range.
4.2 Demographic information
The final sample comprised 3,752 university students (mean age = 19). As shown in Table 1, participants included 1,715 males (45.71%) and 2,037 females (54.29%). Most participants were undergraduates (n = 3,184, 84.86%), followed by smaller proportions of master’s students (n = 501, 13.35%) and doctoral students (n = 67, 1.79%). Urban and rural origins were relatively balanced, with 1,675 (44.64%) participants from urban areas and 2,077 (55.36%) from rural areas, respectively.
Table 1. Demographic characteristics and group differences among participants.
Gender exhibited significant associations with grit (t = 18.848, p < 0.001), PA (t = 17.249, p < 0.001), and self-efficacy (t = 9.212, p < 0.001). Educational level was significantly related to grit (F = 3.120, p < 0.05), while place of birth showed a significant association only with self-efficacy (t = −2.331, p < 0.05).
4.3 Correlation analysis
As illustrated in Table 2, the descriptive statistics and bivariate correlations among the primary study variables are presented. Mean scores were 3.189 (SD = 0.924) for grit, 2.602 (SD = 0.672) for self-efficacy, and 34.240 (SD = 31.855) for PA. Skewness and kurtosis values for all variables fell within acceptable ranges, indicating approximately normal distributions suitable for parametric analyses. Correlation analyses revealed significant positive associations among all variables. Grit demonstrated positive correlation with self-efficacy (r = 0.542, p < 0.01) and positive correlation with PA (r = 0.419, p < 0.01). Self-efficacy was also positively correlated with PA (r = 0.326, p < 0.01). These preliminary findings support the hypothesized relationships and provide an empirical foundation for subsequent mediation and latent profile analyses.
Table 2. Variable statistics and relationships in grit, physical activity, and self-efficacy.
4.4 The mediation analyses
Before testing the mediation model, multicollinearity diagnostics were conducted. The variance inflation factor (VIF = 1.415) was well below the threshold of 5, indicating acceptable independence among predictor variables and confirming model stability. Table 3 presents the regression coefficients for each pathway in the mediation model.
Table 3. Mediation effect regression results.
As shown in Model 1, grit was significantly associated with self-efficacy (β = 0.545, t = 37.956, p < 0.001). Model 2 demonstrated that both grit (β = 0.293, t = 16.414, p < 0.001) and self-efficacy (β = 0.142, t = 8.216, p < 0.001) were independently associated with PA. In Model 3, when self-efficacy was excluded, grit alone was associated with PA (β = 0.370, t = 24.195, p < 0.001).
Table 4 summarizes the decomposition of total, direct, and indirect effects. The total effect of grit on PA was significant (Effect = 0.370, 95% CI [0.340, 0.400]). After controlling for self-efficacy, the direct effect remained significant (Effect = 0.293, 95% CI [0.258, 0.328]), accounting for 79% of the total effect. Critically, the indirect effect through self-efficacy was also significant (Effect = 0.077, 95% CI [0.058, 0.097]), representing 21% of the total effect. The bootstrap confidence interval excluded zero, confirming partial mediation. These findings indicate that self-efficacy serves as a significant mediator in the relationship between grit and PA, supporting Hypothesis 1 and 2. The mediation pathway is illustrated in Figure 2.
Table 4. Mediating effects of self-efficacy between grit and physical activity.
Figure 2. Mediation effect diagram. ***p < 0.001.
4.5 Latent profile analysis
LPA was performed to identify student subgroups based on their configurations of grit and self-efficacy. Models with one to five profiles were systematically compared using Mplus 8.3. Table 5 displays the fit indices.
Table 5. Latent profile analysis models.
While AIC, BIC, and aBIC decreased continuously across models, the three-profile solution demonstrated optimal entropy (0.924), indicating superior classification precision. Both LMR and BLRT tests were found to be significant for all models. Although four- and five-profile solutions showed a favorable statistical fit, they produced extremely small subgroups (4.69 and 5.68%), which limited their interpretability. Considering statistical adequacy, classification accuracy, and theoretical clarity, the three-profile model was retained as the most suitable.
Figure 3 presents three distinct profiles with pronounced characteristics: Profile 1 (n = 605, 16.12%) exhibited low grit and self-efficacy, labeled “Limited Self-Regulation”; Profile 2 (n = 2,095, 55.84%) showed moderate levels on both dimensions, termed “Moderate Self-Regulation”; Profile 3 (n = 1,052, 28.04%) demonstrated high grit and self-efficacy, designated “Strong Self-Regulation.” These results confirm Hypothesis 3, revealing heterogeneous psychological configurations among university students.
Figure 3. Latent profiles of grit with self-efficacy.
4.6 Effect of latent profile classification
One-way ANOVA examined PA differences across the three profiles (see Table 6). Significant differences emerged (F = 225.108, p < 0.001, η2 = 0.107), with profile membership accounting for 10.7% of variance in PA. As shown in Table 6, the Strong Self-Regulation profile reported the highest activity levels (M = 49.031, SD = 29.587), followed by Moderate Self-Regulation (M = 31.555, SD = 31.459), and Limited Self-Regulation (M = 17.835, SD = 25.922).
Table 6. Group differences across latent profiles.
Bonferroni post hoc tests revealed all pairwise differences were significant (p < 0.001). The pattern showed A3 > A2 > A1, confirming that distinct grit–self-efficacy configurations correspond to differential PA engagement (see Table 7). These findings support Hypothesis 4, demonstrating that distinct configurations of grit and self-efficacy are systematically associated with differential patterns of PA engagement among university students.
Table 7. Multiple comparisons.
5 Discussion
5.1 The mediating role of self-efficacy
This study found that grit was positively associated with PA and that self-efficacy significantly mediated this relationship. In other words, students higher in grit tended to be more active, mainly because they felt more capable of initiating and sustaining exercise.
These findings align with prior research, which shows that grit is related to health-promoting behaviors and that self-efficacy robustly associates with exercise initiation and adherence (Hartzel et al., 2021; Liu et al., 2023). Similar mediation patterns have been reported between other positive traits (e.g., Resilience, optimism) and PA via self-efficacy, suggesting a consistent pathway across motivational constructs (Neumann et al., 2021; Jiang et al., 2025; Peng et al., 2025). Several mechanisms may explain the mediation. First, gritty students are more likely to persist through early challenges, accumulating mastery experiences that directly strengthen efficacy beliefs—Bandura’s most potent source of self-efficacy (Alhadabi and Karpinski, 2020; Kleppang et al., 2023). Second, higher efficacy reduces perceived barriers (such as time, fatigue, and setbacks), making effort more efficient and goal pursuit more autonomous (Bandura, 1982). Third, within Self-Determination Theory, the combination of sustained effort (grit) and perceived competence (self-efficacy) promotes the internalization of health goals, thereby stabilizing exercise behavior over time (Deci and Ryan, 2008).
Theoretical implications are twofold: grit provides motivational stamina, but behavior change crystallizes through efficacy-based self-regulation; thus, perseverance without perceived capability may not translate into action. Practically, interventions should pair grit-building (long-term goal framing, reflective persistence training) with efficacy-enhancing strategies (graded mastery tasks, credible peer modeling, specific feedback, and reappraisal of physiological cues). Targeting both dispositions can convert determination into durable activity habits, offering a scalable route for university health programs to increase PA participation.
5.2 The different latent profiles
A significant contribution of the present study is the application of a person-centered methodology, which extends beyond the population-level averages typically found in variable-centered models. In confirming Hypothesis 3, our LPA identified three statistically distinct and theoretically coherent profiles based on the interplay of grit and self-efficacy: “Strong Self-Regulation,” “Moderate Self-Regulation,” and “Limited Self-Regulation.” This finding substantiates the premise that individuals possess heterogeneous psychological configurations rather than adhering to a single motivational pathway.
The “Strong Self-Regulation” profile (Profile 3, 28.04% of the sample) represents an optimal psychological constellation for behavioral pursuit. Individuals in this group possess both high perseverance of effort (grit) and robust confidence in their capabilities (self-efficacy). This synergistic combination aligns with motivational frameworks, such as Self-Determination Theory, which posits that sustained engagement is fostered by the alignment of internal drive and perceived competence (Rigby and Ryan, 2018). High grit provides the Resilience to persist through the inherent discomforts and logistical barriers of exercise (e.g., fatigue, time constraints), while high self-efficacy provides the cognitive assurance needed to initiate action, set challenging goals, and manage setbacks effectively (Yu et al., 2024).
Conversely, the “Limited Self-Regulation” profile (Profile 1, 16.12%) characterizes a psychologically vulnerable subgroup. These students are encumbered by a dual deficit: low perseverance for long-term goals and low confidence in their ability to execute health-related actions. This combination likely creates a pernicious cycle of motivational failure (De La Cruz et al., 2021). Low self-efficacy inhibits behavioral initiation, while low grit ensures that any attempts are quickly abandoned upon facing difficulty, thus preventing the accumulation of mastery experiences necessary to build self-efficacy in the first place. This profile signifies a state of amotivation or motivational conflict that variable-centered approaches would fail to isolate.
Finally, the “Moderate Self-Regulation” profile (Profile 2) was the largest subgroup, encompassing over half of the university students sampled (55.84%). This finding is critical, as it suggests that the “average” student possesses a functional, yet suboptimal, motivational architecture. While not entirely lacking in grit or confidence, their moderate levels may render them inconsistent in their health pursuits, as they tend to succeed under favorable conditions but struggle to maintain activity when faced with significant academic or social pressures (Li et al., 2024). The identification of this large, intermediate group is paramount for public health, as they represent a primary target for broad-based interventions aimed at shifting motivational levels from adequate to optimal.
5.3 Effect of latent profile
Our analysis provided robust support for Hypothesis 4, demonstrating that these distinct psychological profiles are associated with significant, real-world differences in health behavior. The findings revealed a clear, hierarchical pattern in PA engagement, with the “Strong Self-Regulation” group reporting the highest levels, followed by the “Moderate” group, and finally the “Limited” group (A3 > A2 > A1).
The behavioral outcomes of the “Strong Self-Regulation” group (M = 49.031) align with theoretical expectations. When individuals concurrently possess both the motivational “engine” (grit) and the cognitive “steering” (self-efficacy), they appear maximally equipped to navigate the path from intention to action (Rodrigues et al., 2023). This finding empirically highlights that it is the combination of high perseverance and high confidence, rather than either trait in isolation, that is associated with the most adaptive behavioral outcomes in this sample (Datu et al., 2022).
The starkly low PA levels of the “Limited Self-Regulation” group (M = 17.835) identify them as a high-risk population in terms of behavioral deficits. Their minimal engagement in PA is the logical behavioral correlate of their psychological profile, which lacks both the requisite drive and perceived capability (Barbosa et al., 2024). This group exemplifies a potential failure point in health motivation, characterized by a dual deficit that corresponds to the lowest levels of health behavior.
The “Moderate Self-Regulation” profile, constituting the majority of the sample (55.84%), warrants critical theoretical attention. While this group exhibits significantly higher physical activity levels (M = 31.555) compared to the “Limited” profile, their engagement remains sub-optimal relative to the “Strong” profile. Theoretically, this configuration can be interpreted through Social Cognitive Theory as a state of context-dependent efficacy (Islam et al., 2023). These students likely possess sufficient self-efficacy to initiate physical tasks under normal conditions but lack the resilience, a core component of grit, required to maintain these behaviors when facing academic stressors or fatigue (Jiang et al., 2025). Unlike the “Strong” profile, their efficacy beliefs are not robust enough to serve as a buffer against external barriers (Ntoumanis et al., 2020), leading to a “competent but inconsistent” behavioral pattern (Antunes et al., 2021). This finding suggests that the primary challenge in university health promotion is not merely initiating behavior among the disengaged, but facilitating the transition of this “competent but inconsistent” majority from external regulation to autonomous persistence.
In sum, the integration of person-centered analysis provides a critical layer of granularity. While our mediation model established that self-efficacy is a key pathway linking grit to action, the LPA results reveal who within this population is most and least equipped for this pathway at this point. This dual-method approach offers a more comprehensive and granular understanding of the motivational architecture underlying PA, moving beyond a single, monolithic model to reveal distinct subgroups whose psychological configurations are differentially associated with health behavior.
5.4 Practical implications
The results suggest clear actions for university health initiatives. First, the finding that grit’s link to PA is channeled through self-efficacy is a key insight. It implies that programs should focus less on promoting abstract persistence and more on building tangible exercise confidence. This can be achieved through practical strategies, such as setting achievable goals or providing structured mastery experiences. Second, our profiles reveal that a uniform intervention strategy is likely to be ineffective, as students possess very different motivational makeups. Those in the “Limited Regulation” profile require foundational support for both their low confidence and persistence. The large “Moderate Regulation” group, representing the “average” student, is a prime target for broad initiatives aimed at making activity more accessible and habitual. Finally, students in the “Strong Regulation” group are not an intervention target but a potential resource; they could be engaged as peer leaders. Recognizing and responding to this student heterogeneity, rather than assuming a single pathway, is essential for improving campus-wide PA. These recommendations warrant testing in longitudinal and randomized trials.
5.5 Strengths and limitations
This study possesses several methodological strengths that contribute to the literature. A primary strength is the innovative adoption of a dual-method approach. By integrating a variable-centered (SEM) mediation analysis with a person-centered (LPA) approach, we were able to not only elucidate the pathway (i.e., the mediating role of self-efficacy) but also to identify the distinct profiles of individuals for whom these dynamics operate differently. This mixed-method design provides a more granular and ecologically valid understanding than either approach in isolation, moving beyond population-level averages. Furthermore, the large sample size (N = 3,752) lends considerable statistical power to our analyses, thereby enhancing the reliability of both the mediation model and the latent profile solution. Finally, by applying this dual framework to the under-investigated triad of grit, self-efficacy, and PA, this study offers novel insights into the heterogeneous motivational architectures within the critical population of university students.
Nevertheless, the findings must be interpreted in light of several limitations. The most significant constraint is the cross-sectional nature of the data. This design, while helpful in identifying associations, fundamentally precludes any inferences of causality or directionality. Although our mediation model is theoretically grounded (from grit to PA through self-efficacy), we cannot empirically rule out alternative or reciprocal relationships (e.g., PA engagement building self-efficacy, which in turn fosters grit).
Second, our reliance on self-report questionnaires for all variables introduces potential measurement biases. Specifically, PA data is susceptible to social desirability and Recall bias, with individuals often overestimating the duration and intensity of their activity. Future research would benefit from incorporating objective measures, such as accelerometers, to validate the PA outcomes.
Third, the generalizability of the findings is constrained. Our sample was drawn exclusively from university students in China. While this provides valuable insights into a large and important demographic, the specific motivational profiles and their prevalence may not be transferable to other populations, such as adolescents, working adults, or students in different cultural contexts. The characteristics of the identified profiles are inherently sample-dependent.
Finally, while our LPA included the core constructs of grit and self-efficacy, it is plausible that other psychological or social variables (e.g., perceived social support, barriers to exercise, or autonomous motivation) could contribute to more nuanced or different profile solutions. Future person-centered studies could incorporate a wider array of variables to capture a more comprehensive picture of students’ motivational constellations.
6 Conclusion
This study, adopting a dual-method approach that integrated variable-centered and person-centered analyses, offers novel insights into the relationship between grit and PA among university students. From a variable-centered perspective, our findings confirmed the critical mediating role of self-efficacy in the pathway linking grit to PA. More importantly, the person-centered Latent Profile Analysis demonstrated that students are not motivationally homogenous. Instead, they can be classified into three qualitatively distinct profiles—“Strong Self-Regulation,” “Moderate Self-Regulation,” and “Limited Self-Regulation”—which exhibited significant hierarchical differences in their PA engagement.
Taken together, our findings underscore that promoting health behaviors requires attention not only to this Grit-Self-efficacy-PA psychological pathway but also to the people (the heterogeneity of the student population). The results indicate that a combination of high grit and high self-efficacy is associated with the most adaptive health outcomes, providing an empirical basis to move beyond one-size-fits-all interventions toward more precise, personalized strategies tailored to students’ distinct motivational configurations. Crucially, the identification of the dominant “Moderate Self-Regulation” profile reveals that the decline in university physical activity is largely driven by a failure to sustain motivation among the majority, rather than a total lack of capability. Therefore, interventions should prioritize converting this “potential” group into active participants by strengthening their grit, rather than focusing solely on the most disengaged populations.
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 authors.
Ethics statement
The studies involving humans were approved by Jiangxi Normal University of Ethics Committee (IRB-JXNU-PEC-20240402). 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
BC: Software, Funding acquisition, Resources, Writing – original draft, Project administration, Visualization, Methodology, Investigation, Writing – review & editing, Data curation, Conceptualization, Validation. SH: Writing – review & editing, Investigation, Software, Writing – original draft, Funding acquisition, Conceptualization, Supervision, Formal analysis, Data curation. ZT: Writing – review & editing, Funding acquisition, Writing – original draft, Supervision, Project administration, Software, Visualization, Formal analysis. CH: Visualization, Data curation, Project administration, Resources, Validation, Software, Investigation, Writing – review & editing, Funding acquisition, Conceptualization, Writing – original draft. JY: Funding acquisition, Resources, Validation, Project administration, Investigation, Supervision, Writing – original draft, Data curation, Conceptualization, Writing – review & editing, Methodology.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by Mianyang Normal University (Grant nos. CXTD2023PY07 and QD2024A04).
Conflict of interest
The author(s) declared that this work 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 author(s) declared that Generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Footnotes
References
Alhadabi, A., and Karpinski, A. C. (2020). Grit, self-efficacy, achievement orientation goals, and academic performance in university students. Int. J. Adolesc. Youth. 25, 519–535. doi: 10.1080/02673843.2019.1679202
Almoraie, N. M., Alothmani, N. M., Alomari, W. D., and Al-amoudi, A. H. (2025). Addressing nutritional issues and eating behaviours among university students: a narrative review. Nutr. Res. Rev. 38, 53–68. doi: 10.1017/S0954422424000088,
Antunes, R., Rebelo-Gonçalves, R., Amaro, N., Salvador, R., Matos, R., Morouço, P., et al. (2021). Higher physical activity levels may help buffer the negative psychological consequences of coronavirus disease 2019 Pandemic. Front. Psychol. 12:672811. doi: 10.3389/fpsyg.2021.672811,
Bae, M.-H., Zhang, X., and Lee, J.-S. (2024). Exercise, grit, and life satisfaction among Korean adolescents: a latent growth modeling analysis. BMC Public Health 24:1392. doi: 10.1186/s12889-024-18899-8,
Bandura, A. (1991). Social cognitive theory of self-regulation. Organ. Behav. Hum. Decis. Process. 50, 248–287. doi: 10.1016/0749-5978(91)90022-L
Bandura, A. (1997). Self-efficacy: The exercise of control. W H Freeman/Times Books/Henry Holt and Co.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist 37, 122–147. doi: 10.1037/0003-066x.37.2.122
Barbosa, R de O, Santos, GCdos, Silva, JMda, Silva, TM de S, Dias, PHG, Correa, RC, et al. (2024). Does autonomous motivation and self-efficacy mediate associations between environmental factors and physical activity in adolescents? BMC Psychol.. 12:548. doi: 10.1186/s40359-024-02055-3
Beattie, M., Konttinen, H., Volanen, S., and Hankonen, N. (2022). Latent profile analysis as a method for process evaluations: discovering response subgroups in a mindfulness intervention. Soc. Sci. Med. 296:114748. doi: 10.1016/j.socscimed.2022.114748,
Biddle, S. J. H., Gorely, T., Faulkner, G., and Mutrie, N. (2023). Psychology of physical activity: a 30-year reflection on correlates, barriers, and theory. Int. J. Sport Exerc. Psychol. 21:7261. doi: 10.1080/1612197X.2022.2147261
Biddle, S. J. H., Henson, J., Davies, M. J., Khunti, K., Sutton, S., Yates, T., et al. (2021). Device-assessed total and prolonged sitting time: associations with anxiety, depression, and health-related quality of life in adults. J. Affect. Disord. 287, 107–114. doi: 10.1016/j.jad.2021.03.037,
Biggs, A. T., Seech, T. R., Johnston, S. L., and Russell, D. W. (2024). Psychological endurance: how grit, resilience, and related factors contribute to sustained effort despite adversity. J. Gen. Psychol. 151, 271–313. doi: 10.1080/00221309.2023.2253955,
Blom, V., Drake, E., Kallings, L. V., Ekblom, M. M., and Nooijen, C. F. J. (2021). The effects on self-efficacy, motivation and perceived barriers of an intervention targeting physical activity and sedentary behaviours in office workers: a cluster randomized control trial. BMC Public Health 21:1048. doi: 10.1186/s12889-021-11083-2,
Booth, J. N., Ness, A. R., Joinson, C., Tomporowski, P. D., Boyle, J. M. E., Leary, S. D., et al. (2023). Associations between physical activity and mental health and behaviour in early adolescence. Ment. Health Phys. Act. 24:100497. doi: 10.1016/j.mhpa.2022.100497
Brown, C. E. B., Richardson, K. E., Halil-Pizzirani, B., Atkins, L., Yücel, M., and Segrave, R. A. (2024). Key influences on university students’ physical activity: a systematic review using the theoretical domains framework and the COM-B model of human behaviour. BMC Public Health 24:418. doi: 10.1186/s12889-023-17621-4,
Buabang, E. K., Donegan, K. R., Rafei, P., and Gillan, C. M. (2025). Leveraging cognitive neuroscience for making and breaking real-world habits. Trends Cogn. Sci. 29, 41–59. doi: 10.1016/j.tics.2024.10.006,
Calo, M., Judd, B., and Peiris, C. (2024). Grit, resilience and growth-mindset interventions in health professional students: a systematic review and meta-analysis. Med. Educ. 58, 902–919. doi: 10.1111/medu.15391
Chen, B., Yang, J., Huang, W., Zhang, W., Yang, C., and Hu, C. (2025). The latent profile structure of alexithymia in the elderly and its relationship to eating behaviors: the mediating role of physical activity. Front. Psychol. 16:1701168. doi: 10.3389/fpsyg.2025.1701168
Chu, I., Chen, Y., Wu, P., Wu, W.-L., and Guo, L.-Y. (2021). The associations between self-determined motivation, multidimensional self-efficacy, and device-measured physical activity. Int. J. Environ. Res. Public Health 18:8002. doi: 10.3390/ijerph18158002,
Cocić, D., Larkin, P., Hendry, D. T., Williams, A. M., O’Connor, D., and Bilalić, M. (2024). How small differences grow over time – the snowball effect of grit on practice in sport. Int. J. Sport Exerc. Psychol. 23, 830–852. doi: 10.1080/1612197x.2024.2359476
Cormier, D. L., Ferguson, L. J., Gyurcsik, N. C., Briere, J. L., Dunn, J. G. H., and Kowalski, K. C. (2024). Grit in sport: a scoping review. Int. Rev. Sport Exerc. Psychol. 17, 1–38. doi: 10.1080/1750984X.2021.1934887
Dai, L., Su, B., and Liu, Q. (2025). Influence of grit on adolescents’ exercise adherence: the mediating role of exercise self-efficacy and the moderating role of self-control. Acta Psychol. 255:104952. doi: 10.1016/j.actpsy.2025.104952,
Daniels, B. T., Human, A., Gallagher, K., and Howie, E. (2021). Relationships between grit, physical activity, and academic success in university students: domains of physical activity matter. J. Am. Coll. Heal. 71, 1897–1905. doi: 10.1080/07448481.2021.1950163,
Datu, J. A., Yuen, M., Fung, E., Zhang, J., Chan, S., and Wu, F. K. Y. (2022). The satisfied lives of gifted and gritty adolescents: linking grit to career self-efficacy and life satisfaction. J. Early Adolesc. 42, 1052–1072. doi: 10.1177/02724316221096082
De La Cruz, M., Zarate, A., Zamarripa, J., Castillo, I., Borbon, A., Duarte, H., et al. (2021). Grit, self-efficacy, motivation and the readiness to change index toward exercise in the adult population. Front. Psychol. 12:732325. doi: 10.3389/fpsyg.2021.732325,
Deci, E., and Ryan, R. M. (2008). Self-determination theory: a macrotheory of human motivation, development, and health. Can. Psychol./Psychol. Can. 49, 182–185. doi: 10.1037/a0012801
Deforche, B., Van Dyck, D., Deliens, T., and De Bourdeaudhuij, I. (2015). Changes in weight, physical activity, sedentary behaviour and dietary intake during the transition to higher education: a prospective study. Int. J. Behav. Nutr. Phys. Act. 12:16. doi: 10.1186/s12966-015-0173-9,
Dogra, S., Dunstan, D. W., Sugiyama, T., Stathi, A., Gardiner, P. A., and Owen, N. (2022). Active aging and public health: evidence, implications, and opportunities. Annu. Rev. Public Health 43:439. doi: 10.1146/annurev-publhealth-052620-091107,
Dorina, I., Mullan, B., Boyes, M., and Liddelow, C. (2023). Utility of temporal self-regulation theory in health and social behaviours: a meta-analysis. Br. J. Health Psychol. 28, 397–438. doi: 10.1111/bjhp.12631,
Duckworth, A. L., Peterson, C., Matthews, M. D., and Kelly, D. R. (2007). Grit: perseverance and passion for long-term goals. J. Pers. Soc. Psychol. 92, 1087–1101. doi: 10.1037/0022-3514.92.6.1087,
Duckworth, A. L., and Quinn, P. D. (2009). Development and validation of the short grit scale (grit-S). J. Pers. Assess. 91, 166–174. doi: 10.1080/00223890802634290
Duckworth, A. L., Quinn, P. D., and Tsukayama, E. (2021). Revisiting the factor structure of grit: a commentary on Duckworth and Quinn (2009). J. Pers. Assess. 103, 573–575. doi: 10.1080/00223891.2021.1942022,
Dunston, E., Messina, E. S., Coelho, A., Chriest, S., Waldrip, M., Vahk, A., et al. (2020). Physical activity is associated with grit and resilience in college students: is intensity the key to success? J. Am. Coll. Heal. 70, 216–222. doi: 10.1080/07448481.2020.1740229,
Egan, H., O’hara, M., Cook, A., and Mantzios, M. (2021). Mindfulness, self-compassion, resiliency and wellbeing in higher education: a recipe to increase academic performance. J. Furth. High. Educ. 46, 301–311. doi: 10.1080/0309877x.2021.1912306
Estep, A., Martin, J., Toczko, M., and Boolani, A. (2021). Influence of grit and lifestyle on mental health in college students during the COVID-19 Pandemic. Med. Sci. Sports Exer. 53:303. doi: 10.1249/01.mss.0000762668.70317.9c
Griffiths, K., Moore, R., and Brunton, J. (2022). Sport and physical activity habits, behaviours and barriers to participation in university students: an exploration by socio-economic group. Sport Educ. Soc. 27:7766. doi: 10.1080/13573322.2020.1837766
Guthold, R., Stevens, G. A., Riley, L. M., and Bull, F. C. (2020). Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adoles. Health. 4, 23–35. doi: 10.1016/S2352-4642(19)30323-2,
Hammer, T. M., Johansson, J., Emaus, N., Furberg, A.-S., Gracia-Marco, L., Morseth, B., et al. (2025). Changes in accelerometer-measured physical activity and self-reported leisure time physical activity from adolescence to young adulthood: a longitudinal cohort study from the fit futures study. Int. J. Behav. Nutr. Phys. Act. 22:99. doi: 10.1186/s12966-025-01799-4,
Hartzel, B., Martin, J., Estep, A., Toczko, M., and Boolani, A. (2021). Relationships between grit and healthy lifestyle behaviors in undergraduate college students during the COVID-19 PANDEMIC. Med. Sci. Sports Exerc. 53:228. doi: 10.1249/01.mss.0000761684.11540.d0
Hennessy, E., Johnson, B. T., Acabchuk, R. L., McCloskey, K., and Stewart-James, J. (2020). Self-regulation mechanisms in health behavior change: a systematic meta-review of meta-analyses, 2006–2017. Health Psychol. Rev. 14, 6–42. doi: 10.1080/17437199.2019.1679654,
Hilger-Kolb, J., Loerbroks, A., and Diehl, K. (2020). ‘When I have time pressure, sport is the first thing that is cancelled’: a mixed-methods study on barriers to physical activity among university students in Germany. J. Sports Sci. 38, 2479–2488. doi: 10.1080/02640414.2020.1792159,
Hu, C., Zhang, W., Huang, W., and Jin, C. (2025). How grit enhances physical exercise in college students: mediating roles of personal growth initiative and self-efficacy. Front. Psychol. 16:1652984. doi: 10.3389/fpsyg.2025.1652984,
Huang, W., Chen, B., and Hu, C. (2025a). 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,
Huang, W., Chen, B., and Hu, C. (2025b). 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 13:1663474. doi: 10.3389/fpubh.2025.1663474,
Hull, E. E., and Vultaggio, J. (2019). Grit, fitness, and goal setting. Med. Sci. Sports Exerc. 51:391. doi: 10.1249/01.mss.0000561672.05204.7a
Islam, K. F., Awal, A., Mazumder, H., Munni, U. R., Majumder, K., Afroz, K., et al. (2023). Social cognitive theory-based health promotion in primary care practice: a scoping review. Heliyon. 9:e14889. doi: 10.1016/j.heliyon.2023.e14889,
Jerusalem, M., and Schwarzer, R. (1995). General self-efficacy scale–revised–English version. Measures in Health Psychology: A User’s Portfolio. Causal and control beliefs Windsor.
Jiang, Y., Fu, Y., and Dong, X. (2025). Effects of physical exercise on college students’ academic self-efficacy: the chain mediating role of future orientation and mental toughness. Front. Psychol. 16:1604725. doi: 10.3389/fpsyg.2025.1604725,
Jiang, W., Tang, X., Ye, J., and Jiang, J. (2023). What drives daily perseverance and passion? Grit, conscientiousness, and goal pursuit experiences. Personal. Soc. Psychol. Bull. 49, 727–743. doi: 10.1177/01461672221076970,
Jiang, C., Wang, K., and Qin, H. (2025). Physical exercise and children’s resilience: mediating roles of self-efficacy and emotional intelligence. Front. Psychol. 16:1491262. doi: 10.3389/fpsyg.2025.1491262,
Jiang, L., Zhang, S., Li, X., and Luo, F. (2023). How grit influences high school students’ academic performance and the mediation effect of academic self-efficacy and cognitive learning strategies. Curr. Psychol. 42, 94–103. doi: 10.1007/s12144-020-01306-x
Jong, M. D., Plüg, S., and Collins, A. (2025). The healthy hard worker: a critical analysis of young adult south Africans’ discursive constructions of health. Int. J. Cult. Stud. 28, 797–813. doi: 10.1177/13678779251322905,
Kleppang, A. L., Steigen, A. M., and Finbråten, H. (2023). Explaining variance in self-efficacy among adolescents: the association between mastery experiences, social support, and self-efficacy. BMC Public Health 23:1665. doi: 10.1186/s12889-023-16603-w,
Li, K., Wang, H., Siu, O., and Yu, H. (2024). How and when resilience can boost student academic performance: a weekly diary study on the roles of self-regulation behaviors, grit, and social support. J. Happiness Stud. 25:36. doi: 10.1007/s10902-024-00749-4
Liang, D. (1994). Stress levels among college students and their relationship with physical exercise. Chin. Ment. Health J. 1, 5–6.
Liu, G., Chen, Q., Yuan, X., Qian, M., and Du, S. (2024). The association between intergroup contact and psychological capital among adolescents from Chinese ethnic minority areas: a latent profile analysis. Int. J. Intercult. Relat. 102:102053. doi: 10.1016/j.ijintrel.2024.102053
Liu, Y.-S., Lu, C.-W., Chung, H.-T., Wang, J.-K., Su, W.-J., and Chen, C.-W. (2023). Health-promoting lifestyle and life satisfaction in full-time employed adults with congenital heart disease: grit as a mediator. Eur. J. Cardiovasc. Nurs. 23, 348–357. doi: 10.1093/eurjcn/zvad104,
Maggs, J. L. (2024). John Schulenberg as a developmental scholar and mentor: personal reflections. J. Res. Adolesc. 34, 1332–1340. doi: 10.1111/jora.13024,
Maio, S. D., Keller, J., Hohl, D. H., Schwarzer, R., and Knoll, N. (2020). Habits and self-efficacy moderate the effects of intentions and planning on physical activity. Br. J. Health Psychol. 26, 50–66. doi: 10.1111/bjhp.12452,
Martinez Kercher, V. M., Watkins, J. M., Goss, J. M., Phillips, L. A., Roy, B. A., Blades, K., et al. (2024). Psychological needs, self-efficacy, motivation, and resistance training outcomes in a 16-week barbell training program for adults. Front. Psychol. 15:15. doi: 10.3389/fpsyg.2024.1439431,
Martín-Rodríguez, A., Gostian-Ropotin, L. A., Beltrán-Velasco, A. I., Belando-Pedreño, N., Simón, J. A., López-Mora, C., et al. (2024). Sporting mind: the interplay of physical activity and psychological health. Sports. 12:37. doi: 10.3390/sports12010037,
Moles, T. A., Auerbach, A. D., and Petrie, T. A. (2017). Grit happens: moderating effects on motivational feedback and sport performance. J. Appl. Sport Psychol. 29, 418–433. doi: 10.1080/10413200.2017.1306729
Moore, R., Edmondson, L., Gregory, M., Griffiths, K., and Freeman, E. (2023). Barriers and facilitators to physical activity and further digital exercise intervention among inactive British adolescents in secondary schools: a qualitative study with physical education teachers. Front. Public Health 11:1193669. doi: 10.3389/fpubh.2023.1193669,
Morouço, P., Carreira, B., and Pinto, R. (2022). P06-13Movida.Cronos – eHealth app at primary care to fight sedentary behavior. Eur. J. Pub. Health 32:ckac095.098. doi: 10.1093/eurpub/ckac095.098
Neumann, R., Ahrens, K. F., Kollmann, B., Goldbach, N., Chmitorz, A., Weichert, D., et al. (2021). The impact of physical fitness on resilience to modern life stress and the mediating role of general self-efficacy. Eur. Arch. Psychiatry Clin. Neurosci. 272, 679–692. doi: 10.1007/s00406-021-01338-9,
Ntoumanis, N., Ng, J., Prestwich, A., Quested, E., Hancox, J. E., Thøgersen-Ntoumani, C., et al. (2020). A meta-analysis of self-determination theory-informed intervention studies in the health domain: effects on motivation, health behavior, physical, and psychological health. Health Psychol. Rev. 15, 214–244. doi: 10.1080/17437199.2020.1718529,
Pajares, F. (1996). Self-efficacy beliefs in academic settings. Rev. Educ. Res. 66, 543–578. doi: 10.3102/00346543066004543
Peng, B., Chen, W., Wang, H., and Yu, T. (2025). How does physical exercise influence self-efficacy in adolescents? A study based on the mediating role of psychological resilience. BMC Psychol. 13:285. doi: 10.1186/s40359-025-02529-y,
Phillips, L. A., and More, K. (2022). Evaluating behavior change factors over time for a simple vs. complex health behavior. Front. Psychol. 13:962150. doi: 10.3389/fpsyg.2022.962150,
Popa-Velea, O., Pîrvan, I., and Diaconescu, L. (2021). The impact of self-efficacy, optimism, resilience and perceived stress on academic performance and its subjective evaluation: a cross-sectional study. Int. J. Environ. Res. Public Health 18:8911. doi: 10.3390/ijerph18178911,
Rigby, C., and Ryan, R. M. (2018). Self-determination theory in human resource development: new directions and practical considerations. Adv. Dev. Hum. Resour. 20, 133–147. doi: 10.1177/1523422318756954
Rodrigues, F., Figueiredo, N., Jacinto, M., Monteiro, D., and Morouço, P. (2023). Social-cognitive theories to explain physical activity. Educ. Sci. 13:122. doi: 10.3390/educsci13020122
Rosenkranz, R. R., Ridley, K., Guagliano, J. M., and Rosenkranz, S. K. (2023). Physical activity capability, opportunity, motivation and behavior in youth settings: theoretical framework to guide physical activity leader interventions. Int. Rev. Sport Exerc. Psychol. 16, 529–553. doi: 10.1080/1750984X.2021.1904434
Rutberg, S., Nyberg, L., Castelli, D., and Lindqvist, A.-K. (2020). Grit as perseverance in physical activity participation. Int. J. Environ. Res. Public Health 17:17. doi: 10.3390/ijerph17030807,
Ryan, R. M., and Deci, E. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55, 68–78. doi: 10.1037/0003-066x.55.1.68,
Sabouripour, F., Roslan, S., Ghiami, Z., and Memon, M. (2021). Mediating role of self-efficacy in the relationship between optimism, psychological well-being, and resilience among Iranian students. Front. Psychol. 12:675645. doi: 10.3389/fpsyg.2021.675645,
Sanci, L., Williams, I., Russell, M., Chondros, P., Duncan, A.-M., Tarzia, L., et al. (2022). Towards a health promoting university: descriptive findings on health, wellbeing and academic performance amongst university students in Australia. BMC Public Health 22:2430. doi: 10.1186/s12889-022-14690-9,
Santos, A. C., Willumsen, J., Meheus, F., Ilbawi, A., and Bull, F. C. (2023). The cost of inaction on physical inactivity to public health-care systems: a population-attributable fraction analysis. Lancet Glob. Health 11, e32–e39. doi: 10.1016/S2214-109X(22)00464-8,
Schwarzer, R., and Renner, B. (2000). Social-cognitive predictors of health behavior: action self-efficacy and coping self-efficacy. Health Psychol. 19, 487–495. doi: 10.1037/0278-6133.19.5.487,
Shengyao, Y., Salarzadeh Jenatabadi, H., Mengshi, Y., Minqin, C., Xuefen, L., and Mustafa, Z. (2024). Academic resilience, self-efficacy, and motivation: the role of parenting style. Sci. Rep. 14:5571. doi: 10.1038/s41598-024-55530-7,
Silva, R. M. F., Mendonça, C. R., Azevedo, V. D., Memon, A. R., Noll, P. R. E. S., and Noll, M. (2022). Barriers to high school and university students’ physical activity: a systematic review. PLoS One 17:e0265913. doi: 10.1371/journal.pone.0265913,
Stiller, M., Ebener, M., and Hasselhorn, H. M. (2023). Job quality continuity and change in later working life and the mediating role of mental and physical health on employment participation. J. Labour Mark. Res. 57:12. doi: 10.1186/s12651-023-00339-6
Strain, T., Flaxman, S., Guthold, R., Semenova, E., Cowan, M., Riley, L. M., et al. (2024). National, regional, and global trends in insufficient physical activity among adults from 2000 to 2022: a pooled analysis of 507 population-based surveys with 5·7 million participants. Lancet Glob. Health 12, e1232–e1243. doi: 10.1016/S2214-109X(24)00150-5,
Sulla, F., Aquino, A., and Rollo, D. (2022). University students’ online learning during COVID-19: the role of grit in academic performance. Front. Psychol. 13:13. doi: 10.3389/fpsyg.2022.825047,
Tross, L. F. S., Magalhães Dias, H., and Callegari Zanetti, M. (2024). Maintaining exercise in fitness Centre settings: insights from the physical activity maintenance theory. Int. J. Qual. Stud. Health Well-being 19:2409832. doi: 10.1080/17482631.2024.2409832,
Sluijs, EMFvan, Ekelund, U, Crochemore-Silva, I, Guthold, R, Ha, A, Lubans, D, et al. 2021; 398:429–442. doi: 10.1016/S0140-6736(21)01259-9 Physical activity behaviours in adolescence: current evidence and opportunities for intervention. Lancet.
Vella, S. A., Aidman, E., Teychenne, M., Smith, J. J., Swann, C., and Rosenbaum, S. (2023). Optimising the effects of physical activity on mental health and wellbeing: a joint consensus statement from sports medicine Australia and the Australian Psychological Society. J. Sci. Med. Sport 26, 132–139. doi: 10.1016/j.jsams.2023.01.001,
Wan, H., Huang, W., Zhang, W., and Hu, C. (2025). Exploring adolescents’ social anxiety, physical activity, and core self-evaluation: a latent profile and mediation approach. IJMHP 27, 1611–1626. doi: 10.32604/ijmhp.2025.070457
Wang, K., Li, Y., Zhang, T., and Luo, J. (2022). The relationship among college students’ physical exercise, self-efficacy, emotional intelligence, and subjective well-being. Int. J. Environ. Res. Public Health 19:19. doi: 10.3390/ijerph191811596,
Wang, R., Shirvan, M. E., and Taherian, T. (2021). Perseverance of effort and consistency of interest: a longitudinal perspective. Front. Psychol. 12:12. doi: 10.3389/fpsyg.2021.743414,
Welch, D. C., and West, R. (1995). Self-efficacy and mastery: its application to issues of environmental control, cognition, and aging. Dev. Rev. 15, 150–171. doi: 10.1006/drev.1995.1007
Wilhite, K., Booker, B., Huang, B.-H., Antczak, D., Corbett, L., Parker, P., et al. (2023). Combinations of physical activity, sedentary behavior, and sleep duration and their associations with physical, psychological, and educational outcomes in children and adolescents: a systematic review. Am. J. Epidemiol. 192, 665–679. doi: 10.1093/aje/kwac212,
Woo, S. E., Hofmans, J., Wille, B., and Tay, L. (2024). Person-centered modeling: techniques for studying associations between people rather than variables. Annu. Rev. Organ. Psychol. Organ. Behav. 11, 453–480. doi: 10.1146/annurev-orgpsych-110721-045646
Xie, L., Ma, W., Du, K., Huang, Y., Li, A., Wang, H., et al. (2025). Association between exercise self-efficacy and physical activity in elderly individuals: a systematic review and meta-analysis. Front. Psychol. 16:1525277. doi: 10.3389/fpsyg.2025.1525277,
Ye, S., Lin, X., Jenatabadi, H. S., Samsudin, N., Ke, C., and Ishak, Z. (2024). Emotional intelligence impact on academic achievement and psychological well-being among university students: the mediating role of positive psychological characteristics. BMC Psychol. 12:389. doi: 10.1186/s40359-024-01886-4
Yu, H., Zhu, T., Tian, J., Zhang, G., Wang, P., Chen, J., et al. (2024). Physical activity and self-efficacy in college students: the mediating role of grit and the moderating role of gender. PeerJ 12:e17422. doi: 10.7717/peerj.17422,
Yue, X., Wang, X., Lu, L., and Hu, C. (2025). Associations between negative emotions and eating behaviors in older adults: a network analysis and the mediating role of physical activity. Front. Public Health 13:1677170. doi: 10.3389/fpubh.2025.1677170,
Yuqing, Y., Huang, W., Hu, C., Zhang, W., and Chen, B. (2025). Relationships among anxiety, psychological resilience, and physical activity in university students: variable-centered and person-centred perspectives. Front. Psychol. 16:1694344. doi: 10.3389/fpsyg.2025.1694344
Keywords: grit, latent profile analysis, mediation analysis, physical activity, self-efficacy, university students
Citation: Chen B, Hua S, Tu Z, Hu C and Yang J (2026) An investigation of the relationship between grit, physical activity, and self-efficacy: a variable-centered and person-centered approach. Front. Psychol. 16:1742211. doi: 10.3389/fpsyg.2025.1742211
Edited by:
Pedro Morouço, Clínica Espregueira - FIFA Medical Centre of Excellence, PortugalCopyright © 2026 Chen, Hua, Tu, Hu and Yang. 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: Chang Hu, aHVjaGFuZ0BqeG51LmVkdS5jbg==; Jing Yang, Y2hlcnJ5amV1bmVAZ21haWwuY29t
†These authors have contributed equally to this work and share first authorship