The mechanism of mandatory exercise check-in on college students’ intention to use fitness apps: a complete mediation model

Abstract

Based on the Theory of Planned Behavior (TPB), this study investigates the underlying motivational mechanism linking subjective norms (SN) to the behavioral intention (BI) to use fitness applications among college students. A questionnaire survey was conducted on 387 university students in Shanghai to construct a sequential mediation model. The results reveal that while SN has a significant total effect on app usage intention, its direct effect is statistically insignificant. Crucially, the analysis confirms that the relationship between SN and BI is entirely driven by a complete serial mediation involving Self-determined Motivation (SDM) and Exercise Volume (EV). This mechanism reveals that external norms influence intention predominantly through the process of internalization mediated by SDM. The findings emphasize that the long-term efficacy of mandatory policies hinges on fostering intrinsic motivation. Therefore, higher education sports policies should shift from a purely task-driven framework to a motivation-driven approach to effectively enhance exercise quality and cultivate students’ autonomous and persistent exercise habits.

Introduction

In recent years, the rapid advancement of internet technology has provided new opportunities for promoting national fitness through the “Internet + Sport” model [1]. Within this context, sports and fitness apps have rapidly gained popularity on university campuses due to their innovative functionalities [2, 3]. Consequently, many Chinese universities have integrated these apps into their physical education curricula, requiring students to use designated applications for tracking their exercise [4]. The number of recorded workouts check-ins is often incorporated into students’ overall physical education grades [5].

While the integration of sports and fitness apps has, to some extent, increased the frequency of physical activity among university students during their leisure time and positively impacted their physical health, this mandatory approach often falls short of fully stimulating students’ intrinsic motivation for exercise [6]. Many students perceive these exercise check-ins merely as a school-assigned task, completing only the minimum required activity without genuine initiative or sustained engagement [7]. Research indicates that moderate-intensity exercise yields the most optimal benefits for physical health [8]. However, the volume of exercise accumulated through task-oriented check-ins is frequently insufficient to achieve these desired health outcomes [9]. Consequently, a critical challenge for public physical education in Chinese universities is how to enhance university students’ behavioral intention to use sports and fitness apps, foster their spontaneous participation in exercise, cultivate lasting exercise habits, and maintain higher levels of physical activity.

The Theory of Planned Behavior (TPB) is a predominant psychological theory in the study of behavioral intention (BI) [10]. Due to its high predictive validity and broad applicability, TPB, alongside other psychological theories, has increasingly become a significant theoretical framework for research on physical activity behavior in recent years [11–13]. TPB posits that BI and perceived behavioral control (PBC) are the most direct determinants of an individual’s behavior. Behavioral intention, in turn, is jointly determined by three social-cognitive variables: attitude (AT), subjective norms (SN), and PBC. Specifically, AT refers to an individual’s positive or negative evaluation of a particular behavior. SN represents the perceived social pressure an individual experiences, primarily stemming from the attitudes and views of important individuals or groups regarding the specific behavior. PBC, on the other hand, denotes an individual’s subjective perception of their control over voluntarily performing a particular behavior. In other words, the more positive an individual’s attitude towards a behavior, the greater the influence of external subjective norms, and the stronger their perceived behavioral control, the more intense their intention to perform that behavior will be, thereby increasing the likelihood of its execution [10]. The theoretical frame is illustrated in Fig. 1.

Fig. 1.

The frame of the theory of planned behavior

The selection of TPB as the foundational framework for this study is particularly appropriate given the unique nature of the ‘Internet + Sport’ policy in Chinese universities. Since the behavior in question is heavily influenced by institutional mandates, TPB provides a robust structure to categorize these external pressures (SN) and internal evaluations (AT and PBC), allowing for a nuanced analysis of how institutional requirements translate into individual psychological drivers.

Despite TPB’s demonstrated effectiveness in explaining individual behavior, it has limitations in fully revealing the intrinsic reasons behind an individual’s choice to perform a certain behavior, thereby restricting the ability to intervene based on predictive outcomes. A systematic review of relevant literature indicates that most studies confirm a significant positive predictive relationship among AT, SN, and PBC with BI. However, the influence of SN on BI is generally considered the weakest; some studies even report no direct effect of SN on BI. There is no correlation found between subjective norms and individuals’ entrepreneurial intentions, prompting their call for improved measurement in future research [14]. In the domain of sports, SN within the TPB did not significantly predict high school students’ physical activity BI [15]. Responding to this, TPB’s originator, through three studies on different topics, suggested that when PBC is low, individuals’ BI is more susceptible to the influence of SN [16]. Conversely, when PBC is high, the impact of SN on BI becomes non-significant. This finding helps to explain why the relationship between SN and BI is frequently observed to be weak in TPB research, and it further confirms that the predictive effect of SN on BI is susceptible to the influence of other variables.

While traditional TPB applications often treat SN as a direct ‘pressure-to-intention’ pathway, the mandatory context of campus exercise apps necessitates a more sophisticated interpretation. Existing literature remains divided on whether SN retains its predictive validity when behavior is externally regulated. By positioning this study at the intersection of TPB’s social-cognitive components and the motivational mechanisms of Self-Determination Theory (SDT), we aim to move beyond the static ‘predictive relationship’ and instead offer a more dynamic explanation of how normative pressure is processed internally before it manifests as behavioral intention.

Self-Determined Motivation (SDM) is considered highly correlated with multiple variables within the TPB framework [17]. It was found that SDM is a significant predictor of SN, exhibiting a substantial positive relationship [18]. In the field of sports, SDM significantly predicts the continued intention to use wearable fitness devices [19]. Furthermore, through a randomized controlled trial, SDM can enhance individuals’ willingness to adopt a Mediterranean diet by influencing cognitive attitude [20], thereby providing empirical support for the theoretical integration of TPB and SDT. Consequently, it is highly probable that SDM has a significant predictive effect on university students’ BI to use sports and fitness apps and also plays a mediating role in the influence of SN on BI.

Furthermore, Exercise Volume (EV) is also closely linked to the levels of SDM and BI. The proponents of SDT discovered that intrinsic motivation is crucial for achieving and sustaining long-term exercise adherence [21]. This perspective has been substantiated by both empirical and review studies. University students’ exercise motivation can effectively predict their exercise intensity [22] and serves as a key predictor for maintaining higher EV over time [23]. Therefore, it is highly probable that SDM has a significant predictive effect on EV and influences BI through a mediating effect.

Based on the review above, this study will systematically investigate the predictive effect of SN on university students’ BI to use sports and fitness apps. It will also explore the mediating roles of EV and SDM within this relationship, with SN as the independent variable, EV and SDM as mediating variables, and BI as the dependent variable. The hypotheses are listed below:

• H1: SN has a significant positive effect on university students' BI to use sports and fitness apps.

• H2: EV significantly mediates the relationship between SN and BI.

• H3: SDM significantly mediates the relationship between SN and Behavioral Intention BI.

• H4: SDM and EV play a serial mediating role in the relationship between SN and BI.

This study addresses a critical gap by exploring the key factors influencing university students’ behavioral intention to use sports and fitness apps. Its primary aim is to provide a theoretical basis for universities to develop more targeted exercise intervention strategies, ultimately maximizing the efficacy of these apps in improving student physical health. Concurrently, this research seeks to offer a novel perspective for expanding and refining the TPB model within the realm of physical activity.

Method

Research design and participants

This study employed a cross-sectional research design, recruiting a convenience sample of 392 university students in Shanghai. Data were collected through a battery of standardized psychometric scales administered via an online survey platform. After a rigorous data screening process, 5 responses were excluded due to incomplete answers or patterned responses (e.g., straight-lining). Consequently, 387 valid questionnaires were retained for final analysis, resulting in an effective recovery rate of 98.7%.

Measures

Physical activity rating scale (PARS-3)

EV among university students was measured using the PARS-3, revised by Liang [24]. This scale employs a 5-point Likert-type design and comprises three items. Scoring for the scale is as follows: exercise intensity and exercise frequency are scored from 1 to 5, while exercise duration is scored from 0 to 4. EV is calculated by multiplying exercise intensity, exercise duration, and exercise frequency. Based on the calculated scores, EV was categorized into three levels: scores less than or equal to 19 indicated low EV, scores between 20 and 42 indicated moderate EV, and scores greater than or equal to 43 indicated high EV. This standardized scoring and categorization method allows the PARS-3 to effectively reflect university students’ exercise behavior characteristics, serving as a reliable measurement tool for subsequent analyses.

Theory of planned behavior scale and self-determined motivation scale

The questionnaire incorporated the TPB and SDM scales to explore university students’ BI to use sports and fitness apps and its influencing factors. The measurement of SDM relied on a four-item subscale from the Treatment Self-Regulation Questionnaire (TSRQ), specifically focusing on autonomous motivation, developed by Williams et al. [25]. In parallel, AT, SN and PBC were measured by respective Questionnaires developed by Motl et al. [26]. A 5-point Likert scale was used to quantify students’ SN, SDM, and BI regarding their engagement in physical activity via these apps, with some items reverse-scored. Scores for each dimension were calculated by summing the item values and taking the average; higher scores indicate more positive performance by the sample in the corresponding dimension. The reliability and validity tests for the questionnaire yielded robust results: Cronbach’s alpha coefficients for all scale items were greater than 0.8, indicating high internal consistency. Furthermore, all KMO values exceeded 0.75, confirming the suitability of the data for factor analysis. These findings collectively ensure the study’s validity and the sound design of the scales, thereby providing a reliable foundation for subsequent analyses.

Statistical analysis

All gathered data will be processed using SPSS 26.0 and PROCESS v4.1 statistical software. Subsequent to data standardization, a range of statistical analyses, such as correlation analysis and regression analysis, will be conducted to achieve the research aims. Crucially, a mediating effect model will be systematically built, following established methodological steps, to provide a structural explanation of the intricate internal mechanisms between each variable.

Results

Descriptive statistics

This study analyzed 387 valid questionnaires, revealing that 86.0% (n = 333) of respondents were users of exercise and fitness apps (as shown in Table 1), while 14.0% (n = 54) were non-users. Further stratified analysis of the 333 app users showed that 182 individuals (54.7%) were long-term users, having continuously used such applications for over a year. Notably, among the various exercise and fitness apps, campus running apps emerged as the most popular choice, with 175 users (52.6%). This prevalence aligns with the widespread implementation of “Internet + Sports” policies in Chinese universities, indicating that most college students have begun to form exercise app usage habits under institutional guidance.

Table 1.

Descriptive statistical analysis

1. Use of fitness apps
		Number		Percent (%)
Fitness apps users		333		86.0
Non-users		54		14.0
2. Fitness app names
		Number		Percent (%)
Apps for Campus Running		175		52.6
Others		158		47.4
3. Exercise Volume among fitness apps user
		Number		Percent (%)
Low		255		76.6
Medium or High		78		23.4
4. Differences in BI and fitness level between fitness app users and non-users
	Use of fitness apps	Mean	Std. Deviation	t
BI	Fitness apps users	3.433	0.814	5.079***
	Non-users	2.838	0.697
Fitness Level	Fitness apps users	2.740	0.746	2.185*
	Non-users	2.500	0.666

***. p＜0.001 

*. p＜0.05

However, the assessment of exercise volume, based on the PARS-3 scale, presented a different reality: 255 users (76.6%) out of the 333 app users were classified as having low exercise volume. Despite over half of the students consistently using exercise and fitness apps for physical activity, their actual exercise load was significantly below scientific training standards.

To investigate whether behavioral intention and perceived behavioral control directly determine behavior, independent samples t-tests were conducted to measure differences in behavioral intention between college students who use exercise and fitness apps and those who do not. The results revealed a significant difference (p < 0.05) between the two groups. Specifically, college students who use apps demonstrated a significantly higher mean behavioral intention (M = 3.433, SD = 0.814) compared to non-users (M = 2.838, SD = 0.697). Meanwhile, app users demonstrated a significantly higher mean fitness level (M = 2.740, SD = 0.746) than non-users (M = 2.500, SD = 0.666). This finding indicates that app users possess a higher level of behavioral intention and they are healthier than non-users.

Correlation analyses

Correlations among subjective norm, exercise volume, self-determined motivation, and behavioral intention are presented in Table 2. Subjective norm is significantly related to self-determined motivation (r = 0.560, p < 0.001), exercise volume (r = 0.295, p < 0.001), and behavioral intention (r = 0.442, p < 0.001). Behavioral intention is positively correlated with self-determined motivation (r = 0.777, p < 0.001) and exercise volume (r = 0.396, p < 0.001). Also, self-determined motivation is significantly related to exercise volume (r = 0.329, p < 0.001).

Table 2.

The correlations among subjective norm, exercise volume, self-determined motivation, and behavioral intention

	Subjective Norm	Self-Determined Motivation	Exercise Volume	Behavioral Intention
Subjective Norm	1
Self-Determined Motivation	0.560***	1
Exercise Volume	0.295***	0.329***	1
Behavioral Intention	0.442***	0.777***	0.396***	1

***. p<0.001

Regression analysis

The results of the multiple linear regression analysis are shown in Table 3. The coefficient of subjective norm value is -0.016, which indicates that subjective norm and behavioral intention have a negative influence. However, such negative influence was insignificant (P > 0.05). Besides, both self-determined motivation (B = 0.715, P < 0.001) and exercise volume (B = 0.007, P < 0.001) have significantly positive influence on behavioral intention.

Table 3.

The multiple linear regression model

Model	B	Std. Error	Beta	t	P	VIF
Constant	0.837	0.121		6.892	0.000
Subjective Norm	-0.016	0.037	-0.017	-0.435	0.664	1.488
Self-Determined Motivation	0.715	0.038	0.733	19.010	0.000	1.523
Exercise Volume	0.007	0.001	0.160	4.782	0.000	1.144

R² = 0.626 F = 213.321^***

Dependent Variable: Behavioral Intention

To verify the premise of the mediation effect, two additional linear regressions were conducted, with self-determined motivation and exercise volume as the dependent variables, respectively. Subjective norm demonstrated a significant positive influence on both self-determined motivation (B = 0.560, P < 0.001) and exercise volume (B = 3.637, P < 0.001). Also, self-determined motivation demonstrated a significant positive influence on exercise volume (B = 5.334, P < 0.001). Thus, a mediation model featuring subjective norm as the independent variable, behavioral intention as the dependent variable, and exercise volume and self-determined motivation as mediators, was supported.

Mediation analyses

The detailed results of the mediation analyses are presented in Table 4. Confirming the initial hypothesis, SN exhibited a significant and positive total effect on BI (β = 0.442, P < 0.001). Crucially, the analysis revealed that this relationship is fully mediated by the introduction of SDM and EV as mediators.

Table 4.

Mediating effects of SDM and EV between SN and BI

	Item	Standardized Effect	SE	LLCI	ULCI
Total Effect	SN→BI	0.442***	0.045	0.343	0.518
Direct Effect	SN→BI	-0.017	0.037	-0.089	0.057
Total Indirect Effect	SN→BI	0.458***	0.040	0.381	0.538
Indirect Effect 1	SN→SDM→BI	0.411***	0.037	0.339	0.486
Indirect Effect 2	SN→EV→BI	0.026***	0.012	0.005	0.053
Indirect Effect 3	SN→SDM→EV→BI	0.021***	0.007	0.009	0.035

***. p<0.001

Specifically, the direct effect of SN on BI, after controlling for the mediators, was found to be non-significant (β=-0.017, P > 0.05), contrasting sharply with the total indirect effect, which was highly significant and robust (β = 0.458, P < 0.001). This evidence substantiates the complete mediation model.

Further dissection of the indirect effect reveals three distinct, significant pathways. The dominant pathway is the two-step indirect effect via SDM: SN→SDM→BI (β = 0.411, P < 0.001). This pathway accounts for the vast majority of the total indirect effect, underscoring the powerful capacity of SN to internally motivate individuals, which in turn drives their intention to act.

The remaining significant indirect effects highlight the dual roles of both mediators. The first simple mediation path runs through EV: SN→EV→BI (β = 0.026, P < 0.001). Furthermore, the three-step serial mediation pathway, SN→SDM→EV→BI, was also found to be significant (β = 0.021, P < 0.001). This serial effect clarifies that SN not only foster intrinsic motivation (SN →SDM, β = 0.560, P < 0.001) but also positively influence EV directly (SN→EV, β = 0.162, P < 0.001), thereby influencing subsequent behavioral intention through a complex web of motivational and structural mechanisms, as visually summarized in Fig. 2.

Fig. 2.

Schematic diagram of the final mediation path

Furthermore, to verify the robustness of the full mediation effect, we re-conducted the mediation analysis specifically for students with moderate and high EV levels. The results consistently showed that the total and indirect effects of SN on BI were significant, while the direct effect remained non-significant. These findings confirm the robustness of the complete serial mediation effect with SDM and EV as mediating variables.

Discussion

This study highlights the potential role of SN in its association with the use of exercise apps among university students. The high adoption rate observed (upwards of 86% of students utilized fitness apps to record daily exercise, with over half using school-mandated check-in applications) reflects the alignment of the “Internet + Sports” policy in domestic higher education [5]. This widespread use suggests a link between institutional policy and mandatory requirements and the shaping of campus sports culture [27], where exercise is integrated from a voluntary choice into a commonly accepted behavioral routine. This process indicates that external normative pressure is associated with individual behavior patterns [28], laying a foundation for future habit formation, and offers valuable experience for macro-level policy guidance in emerging sports technologies.

However, identifying the relationship of SN is insufficient; our research’s primary theoretical contribution lies in uncovering the potential underlying mechanism. The mediation analysis revealed that the influence of SN on BI is entirely indirect, demonstrating a complete mediation effect. Specifically, the direct path from SN to BI was non-significant (β=-0.017, P > 0.05). Conversely, the total indirect effect remains highly significant and robust (β = 0.458, P < 0.001). This result is consistent with some academic observations suggesting that SN’s direct predictive association on BI is relatively weaker compared to other TPB variables like AT and PBC [16, 29, 30]. Therefore, our empirical finding suggests a re-examination of SN’s function in the TPB model, particularly in digitally enforced health contexts. We propose that SN may act not as a direct behavioral pressure but as an antecedent that precedes the motivational process. It is associated with creating the necessary supportive or compulsory institutional environment that enables the internalization of exercise behavior, thereby facilitating subsequent, more powerful psychological processes. This offers a critical new perspective for refining the TPB, emphasizing the need to explore underlying transmission pathways when analyzing the impact of social norms on behavioral intention.

Furthermore, this research identifies SDM as a key factor related to university students’ exercise app usage intention [31, 32]. The regression analysis demonstrated that SDM has the most significant positive predictive relationship with BI (β = 0.733, P < 0.001), with its influence strength far exceeding that of EV. This indicates that the successful promotion of sustained app usage is less about simply increasing activity output and more about cultivating the student’s internal desire and interest [33]. The mediation model results emphasize the prominent position of SDM in the motivational hierarchy [34, 35]. The two-step indirect pathway, SN→SDM→BI (β = 0.411, P < 0.001), was found to be the dominant path, with an indirect effect dozens of times higher than other simpler paths. This finding suggests that the utility of external social pressure [36], such as mandatory school check-in requirements, is most evident through the process of internalization, where the external demand is transformed into an individual’s self-determined motivation [37]. When students report a transition from perceiving the external norm as a purely coercive task to aligning it with their personal volition, their intention to use the app tends to be higher [38]. This provides strong empirical validation for integrating the core principles of Self-Determination Theory into TPB for behavioral modeling. The model suggests a sequence where social pressure (SN) first transforms into internal drive (SDM), which then correlates with intermediate behavioral indicators (EV), ultimately predicting the intention (BI). Therefore, future higher education sports policies should fundamentally shift from compulsion to guidance, centering on the need to foster and cultivate students’ intrinsic motivation.

This study’s data also highlight the important mediating role of EV [39, 40]. The analysis revealed that the combined influence of SN and SDM successfully promotes increased EV among university students, which subsequently contributes to their intention to use the exercise app [41, 42]. This path provides a crucial insight into how external drivers eventually translate into concrete, measurable exercise behavior [43]. However, the data simultaneously reveal a critical paradox that warrants immediate policy adjustment. Despite the high app prevalence driven by external norms, a striking 76.6% of the student users were found to have a low level of EV that does not meet scientific health standards. This phenomenon suggests that reliance solely on external norm compulsion, while successful in ensuring consistent app use and task completion, fails to translate into exercise of sufficient scientific intensity, duration, or quality necessary for genuine health promotion [44]. This exposes a deep-seated issue in the current “Internet + Sports” policy: students are participating merely to fulfill a requirement, not to achieve effective, health-focused exercise [45].

Consequently, the college exercise promotion policy framework needs to transition from mandatory participation and quantitative metrics to fostering intrinsic motivation and behavioral guidance. After successfully completing the primary goal of embedding the exercise habit, higher education institutions should urgently transition their focus to enhancing the quality of student exercise. Given that SDM is the critical link, universities and app developers must design interventions that promote the satisfaction of the three basic psychological needs inherent in SDT. Specifically, efforts should focus on promoting the three basic psychological needs inherent in SDT: Autonomy, by offering diverse sport choices and flexible, personalized goals within the app to allow students to feel ownership over their routines [46]; Competence, by integrating refined app functions such as individualized training plans, real-time physiological feedback, and progress tracking to reinforce skill mastery and sense of accomplishment [47]; and Relatedness, by reframing social check-in features to build supportive, non-judgmental communities, ensuring that the app acts as a supportive tool rather than solely a compliance enforcer [48, 49].

In addition, the observation that SN exerts its influence solely through indirect pathways raises a broader theoretical question: is the traditional role of Subjective Norm in the TPB model being challenged, or even shaken, in the context of digital health interventions? Traditionally, SN was conceptualized as a direct determinant of intention; however, our findings, aligning with a growing body of empirical evidence, suggest that its predictive power may be diminishing or shifting in mandatory, digitally-monitored environments. This ‘de-coupling’ of the direct SN-BI link implies that social pressure alone may no longer suffice to generate genuine behavioral intention without the mediation of internal psychological processes. Future research should, therefore, move beyond the simplistic debate of whether SN ‘works’ and instead delve deeper into its multifaceted facilitation mechanisms. Specifically, scholars should explore the boundary conditions—such as different levels of institutional coercion, cultural orientations toward authority, or the presence of social support—under which SN might regain its direct influence or shift its role from a ‘compliance driver’ to a ‘meaning-making antecedent.’ Such explorations would be vital for evolving the TPB into a more dynamic framework capable of explaining complex behavior in the age of ‘Internet + Sports’.

The success of higher education sports reform hinges on transcending the purely compulsory framework and skillfully internalizing external norms into students’ self-determined motivation. This will ensure that every recorded activity contributes positively to the students’ physical health, thereby achieving the true educational objective.

Conclusion

This study explored the underlying motivational pathways linking subjective norms to behavioral intention in the context of mandatory fitness app usage. The key finding is the robust empirical evidence for the statistical mediation of the SN to BI pathway through the sequential involvement of SDM and EV. This mechanism suggests that SN functions primarily as an antecedent that is linked to the motivational internalization process, rather than as a direct predictor of intention.

Theoretically, this study challenges the traditional assumption of SN as a direct determinant of intention within the TPB framework, particularly in digitally enforced environments. Our findings reveal a ‘de-coupling’ of the direct association between SN and BI, suggesting that in mandatory ‘Internet + Sports’ contexts, social pressure functions as an antecedent to internal psychological processes rather than a direct driver of behavior. By identifying the critical mediating roles of SDM and EV, this research provides a more dynamic, process-oriented expansion of the TPB. It underscores the necessity for future models to shift from examining simple predictive relationships to exploring the boundary conditions, such as levels of institutional coercion, under which external mandates are successfully internalized into genuine behavioral intention.

Despite these contributions, the present study is subject to several methodological and contextual limitations. First, the sample was confined to university students in Shanghai, which limits the generalizability of our findings across broader regional and demographic contexts. Second, the reliance on a cross-sectional survey design restricts our ability to definitively establish the causal relationships between variables. Researchers should continue to expand the sample to include students from different regions and universities to enhance external validity and incorporate experimental intervention methods. By designing controlled experiments focused on enhancing self-determined motivation, the efficacy of the suggested practical policy shifts can be scientifically validated, offering robust evidence for highly practical and effective sports policies.

Authors’ contributions

YH has produced the study design under the supervision of LL. YH and LL have done data management and extraction. YH did the data analysis. YH and LL contributed to the manuscript writing, revising, and editing. All authors read and approved the final manuscript.

Funding

This study was funded by the Seventh Supervisor’s Academic Leadership Project of Shanghai International Studies University (Grant No. 2024DSYL014) and the Shanghai Education and Science Research Project (Grant No. C2025312).

Data availability

The datasets generated and analyzed during the current study are not publicly available due to compliance with institutional guidelines but they are available from the corresponding author (LL) on a reasonable request.

Declarations

Ethics approval and consent to participate

The research that led to these results was approved by the Ethics Committee of School of Education, SISU (IRB Number: SISUGJ2025021). Informed consent to participate was obtained from all of the participants in the study. This study did not process any personal data and the dataset does not contain any direct or indirect identifiers of participants. This study was performed in line with the principles of the Declaration of Helsinki. All methods were carried out following the institutional guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.