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. 2026 Oct 13;12(6):e71254. doi: 10.1002/vms3.71254

Self‐Efficacy, Healthy and Sustainable Lifestyle, and Life Satisfaction: An Integrated Model Accounting for Self‐Reported Recreational Physical Activity Participation Status in Veterinary Students

Sevim Güllü 1, Alp Emre Yıldız 2, Maciej Witkowski 3, Hasan Hüseyin Arı 4,✉, Ozan Gündemir 5
PMCID: PMC13660717  PMID: 42867486

ABSTRACT

Background

Veterinary education is characterized by intensive curricula and demanding practical training, which may constrain students’ ability to maintain health‐promoting routines and sustainable lifestyle behaviors. In such contexts, general self‐efficacy—a psychological resource related to persistence and coping—may be associated with both lifestyle patterns and overall life satisfaction.

Objectives

This study examined the associations among general self‐efficacy, healthy and sustainable lifestyle behaviors, and life satisfaction in veterinary students; evaluated whether lifestyle statistically accounted for part of the self‐efficacy–life satisfaction association; and adjusted for self‐reported physical activity participation status derived from a single preference item.

Methods

A cross‐sectional sample of 554 veterinary students (378 females, 176 males) was recruited using non‐probability convenience sampling. Participants completed the General Self‐Efficacy Scale (GSES), the Healthy and Sustainable Lifestyle Scale (HSLS), and the Life Satisfaction Scale (LSS). Multiple regression and a nonparametric bootstrap indirect‐effect analysis (5,000 resamples) were performed with class level entered categorically; a numeric class‐level sensitivity analysis was also conducted.

Results

Higher self‐efficacy was associated with a healthier and more sustainable lifestyle, and both self‐efficacy and lifestyle were independently associated with life satisfaction. In the primary model using categorical class‐level coding, the indirect estimate through HSLS was 0.0765 (95% CI [0.0479, 0.1082]), representing 29.2% of the total association. A sensitivity analysis using numeric class‐level coding produced a similar indirect estimate (0.0778, 95% CI [0.0495, 0.1092]). Students who selected the no‐participation option on the physical activity preference item reported lower GSES, HSLS, and LSS scores than those who selected an activity option.

Conclusions

The findings identify a cross‐sectional pattern of association among self‐efficacy, lifestyle, and life satisfaction but do not establish temporal or causal relationships. Veterinary faculties may consider developing and formally evaluating student‐support initiatives that strengthen efficacy beliefs, facilitate healthy and sustainable routines, and provide accessible opportunities for recreational activity. Longitudinal and intervention studies using validated physical activity measures are needed before causal or program‐effectiveness conclusions are drawn.

Clinical Trial Registration

Not applicable

Keywords: academic stress, health promotion, higher education, indirect‐effect analysis, self‐reported recreational physical activity participation, subjective well‐being, sustainability

Among 554 veterinary students, greater general self‐efficacy was associated with healthier and more sustainable lifestyles and higher life satisfaction. A healthy and sustainable lifestyle statistically accounted for 29.2% of the self‐efficacy–life satisfaction association after adjustment for self‐reported recreational physical activity participation status.

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1. Introduction

Self‐efficacy, defined within the framework of Social Cognitive Theory, refers to an individual's belief in their capability to initiate and sustain a particular course of action to achieve desired outcomes (Bandura and Adams 1977; Bandura 1994, 1997). It reflects not merely possessing skills but the confidence to organize and employ them effectively under varying conditions (Bandura 1989, 1994). Self‐efficacy shapes motivational processes such as planning, strategy selection, persistence under difficulty, and appraisal of expected gains. Individuals with high self‐efficacy typically show greater effort, persistence, and adaptive coping, whereas those with low self‐efficacy may underuse existing capacities (Bandura 1997). It is strengthened through mastery experiences, vicarious learning, social persuasion, and constructive interpretation of physiological/affective states (Bandura 1994; Yıldırım and İlhan 2010). In health psychology, these mechanisms are especially salient, as evidence indicates that self‐efficacy influences how people initiate and modify health‐related behaviors (Rapley and Fruin 1999; Bandura 2005; Yıldırım and İlhan 2010). Accordingly, self‐efficacy is a core psychological determinant of adopting and sustaining healthy and sustainable lifestyle behaviors.

Building on this foundation, a healthy and sustainable lifestyle involves behaviors that protect and improve health while also supporting environmental responsibility. By internalizing health‐promoting habits and sustainability principles, individuals contribute to both personal well‐being and ecological preservation (Gökkaya 2024). In this context, recreational physical activity can enhance health and environmental awareness, supporting more sustainable lifestyle patterns (Niu et al. 2024). Outdoor recreation is associated with improved mental health, pro‐environmental attitudes, and quality of life (Winter et al. 2020), and physical activity may also foster more positive attitudes toward green/sustainable products via increased health and environmental consciousness, particularly post‐pandemic (Niu et al. 2024). These perspectives converge with broader work suggesting that personal happiness, societal welfare, and environmental sustainability reinforce one another within “sustainable well‐being” (Di Fabio 2017; Ronen and Kerre 2020).

Recreation, as a context for many lifestyle practices, refers to meaningful and enriching leisure engagement that supports satisfaction, self‐worth, and a positive self‐image and may facilitate “flow” through achievement, excitement, acceptance, and enhanced personal value (Karaküçük 2014). Recreational physical activity is central to health and well‐being: it supports general health, muscle and bone strength, weight management, mobility, and chronic disease prevention (Aydın et al. 2023). Beyond physiological benefits, leisure‐time physical activity can reduce distress, promote emotional balance, and increase subjective well‐being (Harvey et al. 2018; Hogan et al. 2015). Consistently, participants in sport and recreation tend to report higher happiness, vitality, life satisfaction, and psychosocial functioning than non‐participants (Balish et al. 2016; Demir and Duman 2019). Restorative nature experiences further indicate that recreational environments promote psychological renewal and emotional stability (Yoon et al. 2023), while emotional and cognitive leisure engagement supports stress reduction and sustainable well‐being (Nong et al. 2022). Overall, recreational engagement—organized exercise, outdoor activities, or informal movement—functions as a behavioral pathway supporting both physical health and broader well‐being.

In parallel, contemporary lifestyle patterns increasingly include sustainability‐oriented practices such as environmental awareness, responsible consumption, and health–ecology routines. Leisure‐time physical activity has been shown to strengthen sustainable lifestyle orientations through greater environmental sensitivity and health awareness (Niu et al. 2024). Likewise, positive lifestyle behaviors—regular activity, healthy habits, and pro‐environmental choices—are linked to higher subjective well‐being, happiness, and life satisfaction (Trudel‐Fitzgerald et al. 2019). These findings are consistent with sustainability psychology, which emphasizes reciprocal reinforcement between well‐being and sustainable lifestyles.

Life satisfaction, a key component of subjective well‐being, refers to individuals’ cognitive evaluation of life quality (Proctor et al. 2018). It reflects the degree of alignment between life circumstances, goals, and values and is more stable than momentary affect (Vittersø 2025). Higher life satisfaction is associated with better mental health, stronger social functioning, and more adaptive coping (Dou et al. 2023). Among university students, it is particularly important given academic pressure, identity development, and future planning. Higher life satisfaction is linked to fewer psychological symptoms, better academic outcomes, and more positive social engagement (Varghese and Mathew 2023), whereas lower life satisfaction relates to increased distress and reduced academic commitment (Franzen et al. 2021).

From a broader theoretical standpoint, Bandura's Self‐Efficacy Theory positions efficacy beliefs as a central mechanism of agency within reciprocal interactions among personal, behavioral, and environmental determinants (Almohammadi 2023). Efficacy beliefs develop through mastery, vicarious learning, social persuasion, and physiological/affective interpretation, shaping judgments about organizing and executing actions under demands (Almohammadi 2023; Feltz and Mugno 1983; Schunk 1996; Busse and Walter 2013). Evidence across domains shows that efficacy beliefs predict effort, persistence, emotion regulation, and performance (Klassen and Usher 2010; Weinberg et al. 1979), and health‐behavior interventions suggest that strengthening efficacy supports adoption/maintenance of adaptive behaviors and resilience against relapse (Almohammadi 2023; Scott et al. 2024). Consistent with this view, studies in university samples indicate that self‐efficacy relates to healthier behaviors and greater life satisfaction (Deng et al. 2023), and planning‐based behavior‐change research highlights that stronger self‐efficacy supports initiation and maintenance of routines that promote sustainable behavior and subjective well‐being (Koring et al. 2012; Wilhelm et al. 2025). Together, this literature underpins the GSES → HSLS → LSS pathway, positioning self‐efficacy as a key determinant through which lifestyle practices translate into greater life satisfaction.

Additional support comes from the systematic review and meta‐analysis by Ashford et al. (2010), synthesizing 27 physical activity interventions. Although efficacy gains were modest, increases in self‐efficacy consistently supported initiation and maintenance of lifestyle behaviors. In university contexts, this suggests that higher self‐efficacy facilitates organizing, sustaining, and regulating health‐promoting practices, aligning with the expectation that stronger efficacy beliefs promote sustainable behaviors and, ultimately, life satisfaction.

Within this framework, examining how general self‐efficacy is related to healthy and sustainable lifestyle patterns and life satisfaction is particularly relevant among veterinary students, who face intensive curricula, long practical training hours, high performance expectations, and the emotional demands of clinical and applied learning. These conditions may coincide with difficulty maintaining healthy routines and with lower well‐being. The present study therefore examined the associations among GSES, HSLS, and LSS while accounting for a preference‐derived self‐reported physical activity participation status. We hypothesized that higher GSES would be associated with higher HSLS (H1), higher HSLS with higher LSS (H2), and higher GSES with higher LSS (H3). We further examined whether HSLS statistically accounted for part of the GSES–LSS association (H4) and whether the core associations remained after adjustment for recreational physical activity participation status, gender, and class level (H5). Because all variables were measured concurrently, these hypotheses concern statistical associations and an indirect pattern, not temporal or causal pathways.

2. Materials and Methods

2.1. Study Design

This cross‐sectional study employed a quantitative relational survey design. The design was intended to estimate associations among measured variables at one time point; it cannot establish temporal sequence or causality.

Data were collected between September and November 2025 at Istanbul University‐Cerrahpasa, Faculty of Veterinary Medicine. An online questionnaire was administered through Google Forms to standardize data collection and facilitate access. The survey was distributed in classroom settings and through faculty‐based student groups. Participation was voluntary and anonymous, and all respondents provided electronic informed consent before completing the questionnaire.

2.2. Participants and Sampling

A total of 554 undergraduate and postgraduate veterinary students (378 females, 176 males) participated. Participants were recruited from the accessible student population using a non‐probability convenience sampling method. Recruitment was conducted at a single faculty; therefore, the sample was not probability‐based and should not be considered representative of all veterinary students.

2.3. Data Collection Tools

Participants reported their gender and class level and answered the single item, “Which of the following physical activities do you prefer most?” by selecting either a no‐participation option or one of nine activity preference options. This item was used only to derive a binary self‐reported recreational physical activity participation status. It did not assess the frequency, duration, intensity, or objectively verified level of physical activity. Three standardized scales were also administered.

  • −

    General Self‐Efficacy Scale‐GSES (Sherer et al. 1982; Turkish adaptation: Yıldırım and İlhan 2010) measures individuals’ perceived competence in initiating, persisting, and completing actions and includes three subdimensions: Initiation, Persistence, and Effort and Resistance to Discouragement. The scale uses a 5‐point Likert format (“How well does this describe you?” ranging from 1 = not at all to 5 = very well). Items 2, 4, 5, 6, 7, 10, 11, 12, 14, 16, and 17 are reverse‐scored. In the present study, only the total score was analyzed, and subscales were not used.

  • −

    Healthy and Sustainable Lifestyle Scale‐HSLS (Choi and Feinberg 2021; Turkish adaptation: Gökkaya 2024) consists of 24 items across six subdimensions—physical fitness, mental health, emotional health, spiritual health, environmentalism, and social awareness—rated on a 5‐point Likert scale (1 = strongly disagree, 5 = strongly agree). Higher scores indicate a healthier and more sustainable lifestyle. In the present study, only the total score was analyzed, and subscales were not used.

  • −

    Life Satisfaction Scale‐LSS (Diener et al. 1985; Turkish adaptation: Köker 1991) includes 5 items rated on a 7‐point Likert scale (1 = not at all appropriate, 7 = very appropriate), assessing individuals’ overall cognitive evaluations of their life satisfaction as a major indicator of subjective well‐being.

2.4. Statistical Analysis

All statistical analyses were performed in RStudio (version 2024.09.1). Data were imported from the original Excel dataset and screened before analysis. Scale items were oriented so that higher values indicated higher levels of the respective construct. For the General Self‐Efficacy Scale, negatively worded items (Items 2, 4, 5, 6, 7, 10, 11, 12, 14, 16, and 17) were coded in the high‐score direction before total scores were calculated. Total scores were obtained by summing item responses: HSLS_total (24 items; range 24–120), GSES_total (17 items; range 17–85), and LSS_total (5 items; range 5–35). The physical activity covariate was defined as a self‐reported physical activity participation status derived from the preference item. Selection of “I do not participate in physical activities” was coded as 1 (no‐participation option), whereas selection of any activity option was coded as 0 (activity option). This variable is not a measure of physical activity level or dose.

Internal consistency reliability of the total scores was evaluated using Cronbach's alpha (α) for HSLS, GSES, and LSS. Descriptive statistics were computed for all study variables. Bivariate associations among the three core constructs (GSES_total, HSLS_total, and LSS_total) were examined using Pearson product–moment correlations, and 95% confidence intervals were obtained for each correlation coefficient.

Two ordinary least‐squares multiple regression models were estimated. Gender was coded as 1 = female and 2 = male, with female as the reference category. Class level was coded as 1 = first year, 2 = second year, 3 = third year, 4 = fourth year, 5 = fifth year, 6 = master's, and 7 = doctoral. In all primary analyses, class level was treated categorically, with first year as the reference category, resulting in six indicator variables. Model 1 regressed HSLS_total on GSES_total, the preference‐derived activity status, gender, and the six class indicators. Accordingly, the model had nine predictor degrees of freedom and 544 residual degrees of freedom. Model 2 regressed LSS_total on HSLS_total, GSES_total, and the same covariates, yielding ten predictor degrees of freedom and 543 residual degrees of freedom. Unstandardized coefficients, standard errors, t statistics, p values, and model fit indices are reported.

To examine H4, an indirect‐effect (mediation) analysis was conducted with HSLS_total as the statistical mediator, GSES_total as the predictor, and LSS_total as the outcome. The mediator and outcome models used the same categorical covariate structure as the regression analyses. The indirect estimate (a × b), direct association estimate, total association, and proportion statistically accounted for were calculated with percentile confidence intervals from 5,000 nonparametric case‐resampling bootstrap samples. A sensitivity analysis repeated the procedure with class‐level coded numerically from 1 to 7 solely to evaluate coding sensitivity; this numeric coding was not used for substantive interpretation. Because the study was cross‐sectional and all measures were self‐reported at the same time, the indirect estimate represents a statistical decomposition of association and does not demonstrate temporal or causal mediation.

As a supplementary analysis, participants selecting an activity option (coded 0) and those selecting the no‐participation option (coded 1) were compared on HSLS_total, GSES_total, and LSS_total using Welch's independent‐samples t tests. Cohen's d and 95% confidence intervals were reported. The group labels are analytic shorthand for responses to the single preference item and should not be interpreted as validated physical activity levels. All tests were two‐tailed, with α = 0.05.

2.5. Ethical Approval

This research was approved by …¨ University Social and Human Sciences Ethics Committee (Approval No: ……/…¨).

3. Results

A total of 554 students participated, including 378 females (68.2%) and 176 males (31.8%). The class‐level distribution was 51 first‐year students (9.2%), 121 second‐year students (21.8%), 181 third‐year students (32.7%), 106 fourth‐year students (19.1%), 92 fifth‐year students (16.6%), one master's student (0.2%), and two doctoral students (0.4%).

For the single physical activity preference item, 118 students (21.3%) selected the no‐participation option, whereas 436 (78.7%) selected an activity option. Walking/running was the most frequently selected preference (n = 175, 31.6% of the full sample). The remaining responses were distributed across other activity options. These frequencies describe preferred response options and do not quantify actual participation frequency, duration, intensity, or physical activity level.

3.1. Reliability and Sample Characteristics

All analyses included 554 participants. Internal consistency was high for the three total scores (HSLS: Cronbach's α = 0.889; GSES: α = 0.903; LSS: α = 0.881). For the preference‐derived physical activity status, 78.7% selected an activity option and 21.3% selected the no‐participation option. Figure 1 presents this distribution, and Figure 2 shows the corresponding scale‐score distributions.

FIGURE 1.

FIGURE 1

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Self‐reported physical activity participation status derived from the single preference item. “Activity option” denotes selection of any activity preference; “no‐participation option” denotes selection of “I do not participate in physical activities.”

FIGURE 2.

FIGURE 2

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Scale total scores by preference‐derived self‐reported physical activity participation status (GSES, HSLS, and LSS).

3.2. Bivariate Associations Among the Core Variables

Pearson correlation analyses revealed statistically significant, positive associations among all core constructs. Self‐efficacy (GSES) was moderately correlated with healthy and sustainable lifestyle (HSLS), r = 0.448, 95% CI [0.379, 0.512], t(552) = 11.77, p < 0.001. Self‐efficacy was also positively associated with life satisfaction (LSS), r = 0.397, 95% CI [0.324, 0.465], t(552) = 10.16, p < 0.001. Finally, HSLS was positively related to LSS, r = 0.421, 95% CI [0.350, 0.487], t(552) = 10.91, p < 0.001. The corresponding scatterplots with fitted regression lines are displayed in Figure 3, 4, 5, illustrating clear linear trends consistent with the hypothesized directions.

FIGURE 3.

FIGURE 3

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Association between self‐efficacy (GSES) and healthy and sustainable lifestyle (HSLS).

FIGURE 4.

FIGURE 4

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Association between healthy and sustainable lifestyle (HSLS) and life satisfaction (LSS).

FIGURE 5.

FIGURE 5

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Association between self‐efficacy (GSES) and life satisfaction (LSS).

3.3. Multiple Regression Models

Model 1 examined the association between GSES and HSLS while adjusting for the preference‐derived activity status, gender, and categorical class level. The model was significant, F(9, 544) = 18.35, p < 0.001, R 2 = 0.2329 (adjusted R 2 = 0.2202). Higher GSES was associated with higher HSLS (B = 0.536, SE = 0.048, t = 11.26, p < 0.001). Selection of the no‐participation option was associated with lower HSLS (B = −3.088, SE = 1.225, t = −2.52, p = 0.012), and males had lower adjusted HSLS scores than females (B = −3.639, SE = 1.062, t = −3.43, p < 0.001). None of the class‐level indicators was statistically significant. Complete coefficients are shown in Table 1.

TABLE 1.

Complete multiple linear regression results.

Model 1: Outcome = HSLS total; F(9, 544) = 18.35, p < 0.001, R 2 = 0.2329, adjusted R 2 = 0.2202
Predictor B SE t p
Intercept 53.942 3.303 16.33 < .001
GSES total 0.536 0.048 11.26 < .001
No‐participation option (ref: activity option) −3.088 1.225 −2.52 .012
Male (ref: female) −3.639 1.062 −3.43 < .001
Second year (ref: first year) −2.637 1.929 −1.37 .172
Third year (ref: first year) −1.237 1.813 −0.68 .495
Fourth year (ref: first year) −2.385 1.964 −1.21 .225
Fifth year (ref: first year) −3.208 2.011 −1.60 .111
Master's (ref: first year) 3.583 11.595 0.31 .757
Doctoral (ref: first year) −1.947 8.342 −0.23 .816
Model 2: Outcome = LSS total; F(10, 543) = 19.41, p < 0.001, R2 = 0.2633, adjusted R2 = 0.2497
Predictor B SE t p
Intercept −0.440 2.085 −0.21 .833
HSLS total 0.143 0.022 6.44 < .001
GSES total 0.185 0.027 6.79 < .001
No‐participation option (ref: activity option) −0.831 0.637 −1.30 .193
Male (ref: female) −1.700 0.555 −3.06 .002
Second year (ref: first year) −0.276 0.999 −0.28 .783
Third year (ref: first year) 0.365 0.938 0.39 .697
Fourth year (ref: first year) −0.502 1.017 −0.49 .622
Fifth year (ref: first year) −1.856 1.042 −1.78 .075
Master's (ref: first year) 3.987 5.995 0.66 .506
Doctoral (ref: first year) 7.021 4.313 1.63 .104
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Note: Female, first‐year, and activity‐option groups are the reference categories.

Abbreviations: B, unstandardized coefficient; SE, standard error.

Model 2 examined LSS while adjusting for the same covariates and including both HSLS and GSES. The model was significant, F(10, 543) = 19.41, p < 0.001, R 2 = 0.2633 (adjusted R 2 = 0.2497). Higher HSLS (B = 0.143, SE = 0.022, t = 6.44, p < 0.001) and higher GSES (B = 0.185, SE = 0.027, t = 6.79, p < 0.001) were independently associated with higher LSS. The no‐participation option was not independently associated with LSS after adjustment (B = −0.831, SE = 0.637, t = −1.30, p = 0.193). Males had lower adjusted LSS scores than females (B = −1.700, SE = 0.555, t = −3.06, p = 0.002), and none of the class indicators was statistically significant. Complete coefficients are shown in Table 1.

3.4. Indirect‐Effect Analysis (HSLS as a Statistical Mediator of the GSES–LSS Association)

With class level entered categorically, the bootstrap indirect estimate linking GSES to LSS through HSLS was 0.0765 (95% CI [0.0479, 0.1082], p < 0.001). The direct association estimate was 0.1852 (95% CI [0.1275, 0.2453], p < 0.001), and the total association was 0.2616 (95% CI [0.2077, 0.3154], p < 0.001). The indirect component represented 29.2% of the total association (95% CI [17.8%, 42.8%], p < 0.001). The sensitivity analysis using numeric class coding yielded a similar indirect estimate of 0.0778 (95% CI [0.0495, 0.1092]) and a proportion of 30.9% (95% CI [19.0%, 45.3%]); thus, the substantive conclusion regarding the indirect pattern remained unchanged across the two coding approaches (Table 2). These estimates describe a cross‐sectional indirect pattern and do not establish temporal or causal mediation.

TABLE 2.

Sensitivity of indirect‐effect estimates to class‐level coding.

Class‐level coding Indirect estimate (95% CI) Direct association (95% CI) Total association (95% CI) Proportion (95% CI)
Categorical (primary) 0.0765 [0.0479, 0.1082] 0.1852 [0.1275, 0.2453] 0.2616 [0.2077, 0.3154] 0.292 [0.178, 0.428]
Numeric 1–7 (sensitivity) 0.0778 [0.0495, 0.1092] 0.1743 [0.1171, 0.2345] 0.2521 [0.1994, 0.3048] 0.309 [0.190, 0.453]
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Note: Percentile confidence intervals are based on 5,000 nonparametric case‐resampling bootstrap samples; all displayed indirect, direct, total, and proportion estimates had p < 0.001. Numeric coding was used only as a sensitivity analysis.

3.5. Group Differences by Preference‐Derived Physical Activity Participation Status (Supplementary Analysis)

Students who selected the no‐participation option were compared with those who selected an activity option. The no‐participation group had lower HSLS scores (M_activity option = 82.85, M_no‐participation = 76.72), Welch's t(175.78) = 4.47, p < 0.001, Cohen's d = 0.48, 95% CI [0.28, 0.69]; lower GSES scores (M_activity option = 59.71, M_no‐participation = 54.55), Welch's t(173.12) = 4.51, p < 0.001, Cohen's d = 0.49, 95% CI [0.29, 0.70]; and lower LSS scores (M_activity option = 21.62, M_no‐participation = 18.82), Welch's t(175.16) = 3.85, p < 0.001, Cohen's d = 0.42, 95% CI [0.21, 0.62]. These are group associations based on a single preference item and should not be interpreted as effects of physical activity.

4. Discussion

The present study examined an integrated set of cross‐sectional associations among GSES, HSLS, and LSS in veterinary students while adjusting for a preference‐derived physical activity participation status and demographic factors. Overall, the hypothesized associations were observed, as summarized in Table 3. The results should be interpreted as contemporaneous relationships among self‐reported variables rather than as evidence of temporal sequence or causal mechanisms.

TABLE 3.

Evaluation of hypotheses in light of the revised analyses.

Hypothesis Statement Findings Conclusion
H1 Higher GSES is associated with higher HSLS Adjusted association: B = 0.536, t = 11.26, p < 0.001; model R2 = 0.2329. Supported (associational)
H2 Higher HSLS is associated with higher LSS Adjusted association: B = 0.143, t = 6.44, p < 0.001. Supported (associational)
H3 Higher GSES is associated with higher LSS Adjusted association: B = 0.185, t = 6.79, p < 0.001. Supported (associational)
H4 HSLS statistically accounts for part of the GSES–LSS association Indirect estimate = 0.0765, 95% CI [0.0479, 0.1082]; approximately 29.2% of the total association. Supported as a cross‐sectional indirect association
H5 Core associations remain after adjustment for activity status and demographics GSES and HSLS remained significant in adjusted models; coding sensitivity did not change the indirect‐pattern conclusion. Supported
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Consistent with H1 (Figure 3), higher self‐efficacy was associated with a healthier and more sustainable lifestyle at both the bivariate and adjusted levels. This pattern is compatible with Social Cognitive Theory and with previous findings among university students (Amiri et al. 2019; Ryu and Park 2017). Nevertheless, the cross‐sectional design cannot determine whether efficacy beliefs preceded lifestyle practices, whether healthier routines reinforced efficacy beliefs, or whether both were related to unmeasured individual or contextual factors.

H2 was also supported (Figure 4): higher HSLS scores were associated with higher life satisfaction. This finding is consistent with reports linking health‐promoting and sustainability‐oriented behaviors with subjective well‐being (Khadra et al. 2025). The present result indicates covariation between lifestyle and life satisfaction within this sample; it does not demonstrate that lifestyle behaviors increased life satisfaction.

In support of H3 (Figure 5), GSES remained independently associated with LSS after adjustment for HSLS, the preference‐derived activity status, gender, and class level. Similar associations have been reported in university samples (Çakar 2012; O'Sullivan 2011; Van Zyl and Dhurup 2018). The simultaneous associations of GSES and HSLS with LSS suggest that psychological resources and lifestyle patterns each contain information related to students’ life evaluations, but the direction of these relationships cannot be determined from the present data.

The primary indirect‐effect analysis supported H4 as a statistical decomposition of association (Figure 6). With class level entered categorically, 29.2% of the total GSES–LSS association was represented by the indirect component through HSLS. The sensitivity analysis using numeric class coding produced a closely similar estimate of 30.9%, indicating that the main result was not dependent on the coding approach. However, this pattern does not establish that self‐efficacy temporally precedes lifestyle or that lifestyle subsequently changes life satisfaction. Reverse directionality, reciprocal relationships, and shared unmeasured determinants remain plausible.

FIGURE 6.

FIGURE 6

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Cross‐sectional indirect‐effect model (GSES–HSLS–LSS) using categorical class‐level coding. The diagram summarizes statistical associations and does not imply temporal or causal ordering.

The observed indirect pattern is nevertheless compatible with prior cross‐sectional work in health sciences students showing that healthy behaviors statistically accounted for part of the association between a psychological resource and life satisfaction (Rogowska et al. 2021). It is also consistent with broader literature linking personal resources, health consciousness, sustainable practices, and well‐being (Niu et al. 2024; Sarol 2024). These comparisons should be treated as hypothesis‐generating; repeated‐measures longitudinal studies are required to evaluate temporal ordering, and intervention studies are required to test causality.

H5 was supported in the sense that the principal GSES–HSLS–LSS associations remained after adjustment for the preference‐derived activity status and demographic variables. Students selecting an activity option also reported more favorable mean scores than those selecting the no‐participation option. Because the item asked which activity was preferred rather than how often, how long, or how intensely students were active, these differences cannot be interpreted as evidence of a health effect or a dose–response relationship.

In the adjusted models, selection of the no‐participation option remained associated with lower HSLS but was not independently associated with LSS after GSES and HSLS were included. This finding indicates that the binary preference‐derived status shared unique variance with HSLS in this sample. It does not demonstrate that physical activity influenced life satisfaction through lifestyle regulation.

4.1. Limitations

Several limitations should be considered. First, the cross‐sectional design and simultaneous self‐report measurement preclude conclusions about temporal ordering or causality; the mediation analysis is therefore an indirect‐effect decomposition rather than evidence of a causal mechanism. Second, non‐probability convenience sampling from a single veterinary faculty limits generalizability and may introduce selection bias. Third, the physical activity variable was derived from one preference item and did not assess actual frequency, duration, intensity, or objectively verified participation, creating potential misclassification. Fourth, common‐method variance, social‐desirability bias, and residual confounding may have influenced the observed associations. Finally, the master's and doctoral subgroups were very small (n = 1 and n = 2), so their class‐specific coefficients are imprecise and should not be interpreted substantively, although the categorical‐versus‐numeric sensitivity analysis showed that the core indirect estimate was stable.

5. Conclusion

This study identifies cross‐sectional associations among self‐efficacy, healthy and sustainable lifestyle, and life satisfaction in veterinary students. The indirect pattern through HSLS was statistically significant and remained similar under categorical and numeric class‐level coding, but it should not be interpreted as a temporal or causal mechanism. The findings can inform the design of future student‐support initiatives, provided that such initiatives are prospectively evaluated. Longitudinal studies, intervention designs, probability‐based multisite samples, and validated multidimensional physical activity measures are needed to determine directionality, generalizability, and practical effectiveness.

5.1. Practical Implications

The findings provide directions for program development and evaluation rather than evidence that any specific intervention will be effective.

Veterinary faculties could pilot student‐support initiatives that address efficacy beliefs, manageable health‐supportive routines, and sustainable daily practices. Examples include structured goal‐setting and coping workshops, access to supportive outdoor or recreational spaces, and campus practices that make healthy and environmentally responsible choices easier. Their effectiveness should be assessed prospectively rather than inferred from the present cross‐sectional associations.

Faculties may also provide accessible opportunities for recreational activity. However, the present single preference item cannot identify actual participation, frequency, duration, or intensity; therefore, it cannot indicate which activity type or dose should be recommended. Future program evaluations should include validated activity questionnaires or objective monitoring.

A coordinated approach combining psychological support, healthy and sustainable lifestyle resources, and accessible recreational opportunities may be a reasonable basis for pilot programs. Longitudinal follow‐up or controlled intervention studies should be used to determine whether such programs improve student well‐being.

Author Contributions

Sevim GÜLLÜ: conceptualization, methodology, resources, writing – original draft, project administration. Ozan Gündemir: conceptualization, methodology, formal analysis, resources, data curation, writing – original draft, writing – review and editing. Hasan Hüseyin Arı: methodology, writing – review and editing. Maciej Witkowski: formal analysis, writing – original draft. Alp Emre Yıldız: resources, data curation, writing – original draft, writing – review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

The authors have nothing to report.

Ethics Statement

This research was approved by the Istanbul University‐Cerrahpaşa Social and Human Sciences Ethics Committee (Approval No: 2025/436). The authors confirm that the ethical policies of the journal, as noted on the journal's author guidelines page, have been adhered to and the appropriate ethical review committee approval has been received. The US National Research Council's guidelines for the Care and Use of Laboratory Animals were followed.

Consent

Written informed consent for publication was obtained from all participants before completing the survey. The study did not include any identifiable personal data, images, or clinical information requiring additional publication consent.

Conflicts of Interest

The authors declare that they have no competing interests related to the content of this article.

Data Availability Statement

The datasets generated and/or analyzed during the present study are available from the corresponding author upon reasonable request. Due to confidentiality agreements and ethical restrictions, the data are not publicly accessible.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets generated and/or analyzed during the present study are available from the corresponding author upon reasonable request. Due to confidentiality agreements and ethical restrictions, the data are not publicly accessible.

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