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Psychometric properties of the Chinese version of the Exercise Self-Efficacy Scale for the Transtheoretical Model: a confirmatory analysis among Chinese children and adolescents

BMC Public Health volume 24, Article number: 151 (2024) Cite this article

Abstract

Background

Self-efficacy has been recognized as a critical component in people’s participation and maintenance of physical activity. This study aims to validate the Chinese version of the Exercise Self-Efficacy Scale (ESE) among Chinese children and adolescents using confirmatory factor analysis (CFA).

Methods

A cross-sectional study was conducted on two primary and two secondary schools in central China. The ESE scale was translated into Chinese (ESE-C) using the standard forward-backward translation method. Data were analyzed using Mplus 8 for the CFA.

Results

The final model showed a satisfactory level of goodness-of-fit (CFI = 0.918; TLI = 0.905; SRMR = 0.043; RMSEA = 0.066), indicating a good construct validity of the ESE-C for children and adolescents in mainland China. Furthermore, the final ESE-C model achieved composite reliability values of 0.963 and average variance extraction values of 0.597, indicating sufficient convergent and discriminant validity. Besides, the Cronbach’s alpha value was 0.964, demonstrating excellent internal consistency of the ESE-C scale.

Conclusion

The ESE-C scale is a valid instrument for assessing exercise self-efficacy among children and adolescents in mainland China.

Peer Review reports

Introduction

Insufficient physical activity (PA) and sedentary behavior (SB) have emerged as a global epidemic among children and adolescents, displaying a troubling upward trend [1, 2]. This concerning situation is caused by various factors and poses potential risks to children’s future health. Given the crucial role of PA in children’s growth, development, and mental well-being [3,4,5], there is an urgent need for effective interventions to enhance their PA levels.

China is no exception to this trend. After experiencing dramatic economic growth and improved living conditions, the levels of PA among children and adolescents have also raised concerns [6]. According to WHO guidelines, more than 87% of Chinese children and adolescents do not meet the recommended PA recommendations [2]. However, research on PA and SB in Chinese populations remains limited, with existing studies highlighting significant concerns across all age groups, especially among children and adolescents aged 8 to 12 [7,8,9,10].

To promote and sustain youth participation in PA, a promising approach is to implement the Transtheoretical Model (TTM), which is commonly used in mental health counseling to describe behavioral change [11–12]. TTM has found extensive application in various health-related domains, with a particular emphasis on promoting engagement in PA and ensuring adherence to exercise routines [13]. TTM perceives behavior change as a dynamic process, encompassing cognitive, affective, and behavioral strategies while delineating individuals’ readiness for change and progression through distinct stages [14]. Notably, a substantial body of research has utilized TTM to develop interventions aimed at increasing PA participation [13, 15,16,17]. The fundamental constructs within TTM include stages of change, change processes, decision balance, self-efficacy, and temptation [16]. Our study’s primary focus was on evaluating the Chinese version of the TTM exercise self-efficacy scale among Chinese children and adolescents.

Within the TTM framework, self-efficacy holds significant importance as a psychological construct. Self-efficacy (SE) refers to a person’s belief in their ability to overcome obstacles and successfully complete tasks that lead to positive outcomes [18]. It has been found that individuals with high levels of self-efficacy tend to experience more positive emotions and successfully accomplish their tasks [18]. Importantly, physical self-efficacy is considered a reliable predictor of an individual’s engagement and adherence to physical activity [19]. Additionally, SE is a major predictor of the type of PA one chooses, the level of effort invested in the chosen activity, and how one deals with obstacles during the activity [20].

To assess exercise self-efficacy, researchers have developed several scales, including the 18-item Exercise Self-Efficacy Scale (ESE) by Bandura [21], the 9-item Self-Efficacy for Exercise Scale (SEE) by Resnick and Jenkins [22], the 17-item Physical Activity Self-Efficacy Scale (PASES) by Saunders et al. [23], the 16-item Cardiac Exercise Self-Efficacy Scale (CESEI) by Hickey et al. [24], the 10-item SCI Exercise Self-Efficacy Scale (ESES) by Kroll et al. [25], and the 14-item Tai Chi Exercise Self-Efficacy Scale (TCSE) by Li et al. [26]. All six scales have undergone psychometric testing, but the last three are specifically targeted to particular populations. The CESEI consists of 16 items measuring an individual with cardiovascular risks’ confidence to engage in physical activity in various situations, each rated on a 5-point Likert scale from 1 = “very unconfident” to 5 = “quite a lot of confidence” [24]. The ESES was designed to assess the self-confidence of participants with spinal cord injuries in performing regular physical activities and exercise on a 4-point Likert scale (ranging from 1 = not at all true to 4 = always true) [25]. The TCSE was designed to assess participants’ perceived self-efficacy in performing Tai Chi, with responses to each item ranging from 0 (not at all confident) to 100 (very confident) [26].

Among the first three scales, the nine-item SEE developed by Resnick and Jenkins was designed to assess people’s confidence to continue exercising in the face of barriers to exercise, with items rated from 1 (not confident) to 10 (very confident). Its brevity makes it a time-efficient and practical choice for large-scale studies, especially in busy clinical settings or when used alongside multiple measures [22]. The Chinese version of the SEE scale has been validated among older adults in Taiwan and middle-aged patients with coronary heart disease in Hong Kong [27, 28]. The PASES, developed by Saunders et al., uses a 5-point Likert scale containing 17 indicators divided into 3 factors. However, some subsequent studies do not support this multidimensional scale. Therefore, some researchers generated a shortened 8-item unidimensional model (S-PASES), which has received more supportive research.

However, the Exercise Self-Efficacy Scale (ESE) developed by Bandura [21] currently stands as the most widely used and efficient tool for assessing an individual’s exercise self-efficacy. With 18 items, the ESE offers a more comprehensive evaluation, encompassing a broader range of exercise-related situations and challenges [29]. It has been extensively validated in various populations [19, 29,30,31,32,33,34,35,36], demonstrating reliability and validity across diverse cultural contexts. Additionally, the ESE is based on Bandura’s ‘Social Cognitive Theory,’ a widely accepted and influential psychological framework, further supporting its theoretical foundations.

Nevertheless, it is important to note that no validated Chinese version of the ESE scale is currently available [37]. As a result, researchers in mainland China resort to self-developed or differently translated versions of the ESE scales [38,39,40,41,42,43]. This discrepancy poses a challenge in effectively applying the Transtheoretical Model (TTM) to enhance PA levels across different groups in China. Therefore, our investigation focuses on assessing the validity and reliability of the Chinese version of the Exercise Self-Efficacy Scale (ESE-C) specifically among children and adolescents in China. The unique characteristics of this age group, including their differing comprehension skills compared to adults, warrant a thorough evaluation of the scale’s suitability in this context. As a result, our study serves as a crucial prerequisite, ensuring that the ESE-C is a valid and reliable tool for measuring exercise self-efficacy in children and adolescents, thus enabling more effective research in this area in the future.

Materials and methods

Participants

A total of 1,573 primary and secondary school students from Jiangxi, China, willingly participated in this study, comprising 894 boys and 679 girls, with a mean age of 12 years (SD = 1.68). Among them, 842 (53.5%) were primary school students, and 731 (46.5%) were secondary school students. The majority of participants identified as Han Chinese (99.2%). Prior to their involvement in the study, informed consent from parent or legal guardian were obtained from all participants.

Measures

The study included two self-reported scales. A demographic survey collected information on participants’ age, gender, grade, and ethnic background. The Chinese version of the ESE scale (ESE-C), based on Bandura’s original version [18], was used. It comprised 18 items measuring self-efficacy, represented by a single factor. Participants rated themselves on a 5-point Likert scale, ranging from 1 (Not at all confident) to 5 (Completely confident).

Ethics and procedures

Prior to data collection, ethical approval was obtained from both the USM Human Research Ethics Committee (USM/JEPeM/21,090,638) and the Jiangxi Medical College Human Research Ethics Committee (Approval No: (RH)2022-5). The translation process of the ESE scale into Chinese followed Brislin’s standard forward-backward translation procedure (1986) [44].

Two bilingual researchers first independently translated the scale, collaboratively forming a draft version through comparison and discussion. This draft underwent evaluation by five experts in sports science, sports psychology, and physical education, who provided valuable insights and refinements. For linguistic precision and cross-cultural adaptability, two additional bilingual researchers back-translated the improved Chinese version into English, iteratively modifying it until achieving consensus with the original scale. Furthermore, ten native Chinese speakers reviewed the draft scale, identifying and addressing any ambiguities or difficulties, playing a pivotal role in shaping the final version. Subsequently, a pilot study involved ten native Chinese students answering the scale, revealing no new concerns about content and confirming the scale’s finalization.

Following the translation process, a convenience sampling method was employed for participant selection, enrolling 1573 children and adolescents aged 9–15 years. The sampling involved the deliberate selection of four public or private primary and middle schools in Shangrao City, Jiangxi province. Shangrao City was stratified into urban and suburban areas, and two primary and two middle schools were conveniently recruited from each zone. To ensure a representative sample, five classes were then randomly chosen from each of the four schools, covering grades 4 through 9. Data collection took place on the school campuses. Prior to questionnaire administration, the researcher briefly introduced it to the students and distributed the informed consent form to students and their parents or legal guardians. Participants were requested to answer the questions honestly and to the best of their ability.

Statistical analysis

The validity of the hypothesized ESE-C measurement models was assessed using confirmatory factor analysis (CFA) with the statistical software Mplus 8. In this analysis, all 18 items were treated as observed variables, while the factor was considered the latent variable.

To ensure the reliability of our analysis, we checked the data for multivariate normality. The results indicated that the assumption of normality was not met, as evidenced by significant outcomes in the Mardia multivariate skew (p <.001) and kurtosis (p <.001) tests. As a result, to address the non-normality in subsequent CFAs, we opted for the robust maximum likelihood estimator (MLR), which is well-suited for handling such data [45, 46].

To establish the validity of the questionnaire, it is customary among researchers to present multiple fit indices [47]. Accordingly, we utilized several fit indices along with their respective threshold values: comparative fit index (CFI) and Tucker and Lewis index (TLI) with desired values above 0.90, root mean square error of approximation (RMSEA) with a desired value below 0.08, probability RMSEA with a desired value above 0.07, and standardized root mean square residual (SRMR) with a desired value below 0.08 [48].

Items with factor loadings below 0.4 were identified as problematic [49, 50] and underwent evaluation for potential removal after conducting a thorough theoretical review. During the process of model re-specification, we examined the CFA modification index (MI) to identify opportunities for refining the measurement models and achieving the best fit. The re-specification process involved introducing correlations between items’ residuals within the same factor, which was supported by sound theoretical reasoning [49].

To assess the reliability of the final ESE-C measurement model, we used composite reliability (CR) based on Raykov’s method in Mplus [51], CR is similar to Cronbach’s Alpha, indicating internal consistency, and we set a minimum acceptable threshold of 0.60 and above [52] for reliability. Convergent validity was evaluated using Average Variance Extracted (AVE), with values above 0.5 indicating strong convergent validity [53]. Additionally, McDonald’s Omega was calculated to provide a complementary perspective on reliability, addressing unidimensionality and measurement errors more robustly than Cronbach’s alpha [54]. Reporting both coefficients enhances the comprehensiveness of our reliability assessment, contributing to a nuanced understanding of the scale’s internal consistency and reinforcing the robustness of our findings. These evaluations rigorously confirm the reliability and convergent validity of the ESE-C measurement model [53].

To assess discriminant validity, inter-factor correlations were examined. Adequate discriminant validity is established when factor correlations remain less than or equal to 0.85 [55]. Test-retest reliability was evaluated using a sub-sample of 76 participants, involving the calculation of the Intraclass Correlation Coefficient (ICC). Reliability was considered satisfactory when ICC values exceeded 0.70 [56]. This approach confirms the distinctiveness of the factors and ensures the stability and consistency of measurements over time.

Results

Descriptive statistics

A total of 1573 students, aged between 9 and 15 years old (M = 12), participated in the study (as shown in Table 1). The participants comprised 842 primary school students and 731 secondary school students. Among them, 894 were boys, and 679 were girls. The majority of participants belonged to the Han Chinese ethnic group, consistent with the demographic composition of most regions in China.

Table 1 Descriptive statistics of demographic characteristics and study variables of participants (n = 1573)
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Confirmatory factor analysis

The tests of the initial hypothesis ESE-C model (model-1) exhibited poor overall fit indices except for SRMR (CFI = 0.869; TLI = 0.852; SRMR = 0.050; RMSEA (CI: 90%) = 0.082 (0.079, 0.086)). To improve the fit index of the model, we iteratively added several correlations between items’ residuals in the model, starting with the highest value of the modification index (MI) and gradually addressing lower MI values until the model adequately fits the data. The correlations were added on items’ residuals within the same factor. Through this re-specification process, we arrived at the final model (see Table 2, Final model, see Fig. 1). The final model demonstrated a satisfactory level of goodness-of-fit with fit indices: CFI = 0.918; TLI = 0.905; SRMR = 0.043; RMSEA (CI: 90%) = 0.066(0.062, 0.070), indicating a well-validated ESE-C for our study with children and adolescents in mainland China.

Table 2 Summary for ESE-C constructs model fit indices (Model-1)
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Fig. 1
figure 1

Modified ESE-C measurement model (Model-1, all samples)

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Furthermore, our study included children and adolescents from both primary and secondary schools. To determine the replicability of the Chinese version of the ESE scale among students at different academic levels, we divided the participants into a primary school group (Model-2) and a secondary school group (Model-3) based on the overall sample (Model-1) and tested the models’ fitness individually.

The model fit indices for Model-2 (primary school) are presented in Table 3. The initial hypothesis model (Model-2) also exhibited poor overall fit indices: CFI = 0.861, TLI = 0.843, SRMR = 0.055, RMSEA (CI: 90%) = 0.082 (0.077, 0.087). To improve the model’s fit, we added five correlations between items’ residuals to the initial ESE-C Model-2 (ESE7 with ESE6; ESE2 with ESE1; ESE8 with ESE4; ESE16 with ESE10; ESE17 with ESE16). All the correlations were added on items’ residuals within the same factor. The final model fit indices then satisfied the required threshold with CFI = 0.923, TLI = 0.909, SRMR = 0.047, RMSEA (CI: 90%) = 0.062 (0.057, 0.067) (see Table 3, Final Model-2, see Fig. 2).

Table 3 Summary for ESE-C constructs model fit indices (Model-2)
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Fig. 2
figure 2

Modified ESE-C measurement model (Model-2, primary school students)

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The initial model fit indices of Model-3 (Secondary school) also showed poor overall fit indices with CFI = 0.875; TLI = 0.858; SRMR = 0.047; RMSEA (CI: 90%) = 0.086(0.080, 0.091). To enhance the model’s fit, we added three correlations between items’ residuals to the initial ESE-C Model-3 (ESE7 with ESE6; ESE6 with ESE5; ESE8 with ESE4). Then, the final model fit indices met the threshold requirement with CFI = 0.917; TLI = 0.904; SRMR = 0.042; RMSEA (CI: 90%) = 0.071(0.065, 0.076). (see Table 4, Final Model-3, see Fig. 3).

Table 4 Summary for ESE-C constructs model fit indices (Model-3)
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Fig. 3
figure 3

Modified ESE-C measurement model (Model-3, secondary school students)

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Composite reliability (CR) and discriminant validity

As shown in Table 5, the final ESE-C model achieved CR values of 0.963 and AVE values of 0.597, indicating sufficient convergent and discriminant validity. Model-2 (Primary school) and Model-3 (Secondary school) also yielded acceptable CR and AVE values. Therefore, the model achieved adequate convergence and discriminant validity.

Table 5 Summary of CR, AVE value for each model
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Internal consistency

The Cronbach alpha values for Model-1, Model-2 (Primary school), and Model-3 (Secondary school) were 0.964, 0.961, and 0.968, respectively (see Table 6). Likewise, the McDonald’s Omega values for these models were identical, indicating excellent internal consistency across all three.

Table 6 Summary of Cronbach alpha and McDonald’s Omega value for each model
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Test-retest reliability

A test-retest reliability analysis spanning two weeks and involving 76 participants was conducted to assess the stability of responses. The mean score on the ESE-C scale exhibited a slight shift from 54.5 (Day 1, SD = 14.5) to 55.3 (Day 14, SD = 14.6). The ICC value was 0.870 (95% CI = 0.815, 0.921, p-value < 0.001), indicating adequate stability.

Discussion

This study aimed to assess the reliability and validity of the Chinese version of the Exercise Self-Efficacy Scale (ESE-C) using Confirmatory Factor Analysis (CFA) among Chinese children and adolescents. The findings revealed that no problematic items were identified in the ESE-C, leading to the retention of all 18 items in the final model, which demonstrated good reliability.

The significance of physical activity (PA) for individuals of all ages has gained widespread recognition [51]. To encourage and sustain youth involvement in PA, the adoption of the Transtheoretical Model (TTM) has emerged as a promising strategy in various contexts [14, 15, 17]. TTM incorporates behavioral and cognitive strategies, placing emphasis on exercise persistence and its interplay with psychological factors [12]. Self-efficacy, a fundamental construct of TTM, plays a central role in motivating individuals to initiate and maintain PA [21]. To assess individuals’ exercise self-efficacy, various measurement tools have been developed, with Bandura’s Exercise Self-Efficacy scale being widely acknowledged [21].

However, the application of the Transtheoretical Model (TTM) and the Exercise Self-Efficacy Scale (ESE) to promote PA participation among diverse populations in mainland China remains limited. Existing studies also lack comprehensive validation of the Chinese version of the ESE scale (ESE-C), leading to wide variations in the questionnaires used by scholars in mainland China when applying the TTM and the ESE scale [36,37,38, 42, 43, 58,59,60,61,61]. Some researchers utilized self-developed scales [58], while others employed different versions of the ESE scale, such as Saunders’ 17-item Physical Activity Self-Efficacy Scale (PASES) [26] or Resnick and Jenkins’ 9-item Exercise Self-Efficacy Scale (ESES) [22, 61]. Notably, there has been no validation study of Bandura’s original Exercise Self-Efficacy Scale (ESE) in Chinese yet. This lack of thorough validation poses a hindrance to further research in mainland China. Therefore, this study sought to validate the reliability and validity of the ESE-C among Chinese children and adolescents, confirming its suitability for this age group.

While this study makes a significant contribution to the existing literature by translating and validating the ESE scale in Chinese, supporting the validity of the ESE-C, it is crucial to acknowledge certain limitations. The study’s results are specific to Chinese children and adolescents, with the sampling area confined to one city in southern China. As a result, the generalizability of the findings to other age groups and educational levels remains uncertain. Future research should aim to replicate this study in diverse regions of China, encompassing various age groups and educational backgrounds, to further validate the applicability of the ESE-C.

Furthermore, despite its valuable contributions, this study lacks a cross-cultural comparison with other cultures. To address this limitation, we recommend that future studies undertake comparisons of the CFA model of ESE across different study populations or countries. Techniques such as multigroup analysis and invariance tests could be employed for a comprehensive cross-cultural understanding.

Additionally, this study employed a non-probability sampling method to recruit participants, which may limit its representativeness for the entire Chinese population. We highly recommend implementing improved sampling methods in future research to enhance the scope and representativeness of data collection. It would be advantageous to consider diverse socio-economic backgrounds, different age strata, various educational backgrounds, and both urban and rural areas when conducting studies. By doing so, the findings will be more applicable and generalizable to the entire population of Chinese children and adolescents. A broader and more inclusive approach to data collection will contribute to a more comprehensive understanding of exercise self-efficacy among young individuals in China and facilitate the development of effective interventions to promote physical activity and well-being in this population.

Conclusion

This study provides strong support for the validity and reliability of the Exercise Self-Efficacy Scale among Chinese children and adolescents. Consequently, the scale can serve as a valuable tool for measuring exercise self-efficacy in this specific age group.

Data availability

The dataset used during the current study is available on reasonable request from LY and GK.

References

  1. Bull FC, World Health. Organization 2020 guidelines on physical activity and sedentary Behaviour. Br J Sports Med. 2020;54(24):1451–62. https://doi.org/10.1136/bjsports-2020-102955.

    Article  PubMed  Google Scholar 

  2. World Health Organization. Global Status Report on Physical Activity Available from: https://www.who.int/teams/health-promotion/physical-activity/global-status-report-on-physical-activity-2022. [2022 October 19].

  3. Hajar MS, Rizal H, Kuan G. Effects of physical activity on sustained attention: a systematic review. Sci Med. 2019;29(2):32864. https://doi.org/10.15448/1980-6108.2019.2.32864.

    Article  Google Scholar 

  4. Rodriguez-Ayllon M, Cadenas-Sánchez C, Estévez-López F, Muñoz NE, Mora-Gonzalez J, Migueles JH, et al. Role of physical activity and sedentary behavior in the mental health of preschoolers, children and adolescents: a systematic review and meta-analysis. Sports Med. 2019;49(9):1383–410. https://doi.org/10.1007/s40279-019-01099-5.

    Article  PubMed  Google Scholar 

  5. Ortega FB, Ruiz JR, Castillo MJ, Sjöström M. Physical fitness in childhood and adolescence: a powerful marker of health. Int J Obes. 2008;32(1):1–11. https://doi.org/10.1038/sj.ijo.0803774.

    Article  CAS  Google Scholar 

  6. Ran B, Si-Tong C, Yanlei W, Jun X, Lei W, Liye Z, Yujun C. Sedentary Behavior Research in the Chinese Population: a systematic scoping review. Int J Environ Res Public Health. 2020;17(10):3576. https://doi.org/10.3390/ijerph17103576.

    Article  Google Scholar 

  7. Chen ST, Liu Y, Tremblay MS, et al. Meeting 24-h movement guidelines: prevalence, correlates, and the relationships with overweight and obesity among Chinese children and adolescents. J Sport Health Sci. 2021;10(3):349–59. https://doi.org/10.1016/j.jshs.2020.07.002.

    Article  PubMed  Google Scholar 

  8. Tian Y, Jiang C, Wang M, et al. BMI, leisure-time physical activity, and physical fitness in adults in China: results from a series of national surveys, 2000–14. Lancet Diabetes Endocrinol. 2016;4(6):487–97. https://doi.org/10.1016/S2213-8587(16)00081-4.

    Article  PubMed  Google Scholar 

  9. Shen H, Yan J, Hong JT, et al. Prevalence of physical activity and sedentary behavior among Chinese children and adolescents: variations, gaps, and recommendations. Int J Environ Res Public Health. 2020;17(9):3066. https://doi.org/10.3390/ijerph17093066.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhu Z, Tang Y, Zhuang J, et al. Physical activity, screen viewing time, and overweight/obesity among Chinese children and adolescents: an update from the 2017 physical activity and fitness in China—the youth study. BMC Public Health. 2019;19:197. https://doi.org/10.1186/s12889-019-6515-9.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Fava JL, Velicer WF, Prochaska JO. Applying the transtheoretical model to a representative sample of smokers. Addict Behav. 1995;20(2):189–203. https://doi.org/10.1016/0306-4603(94)00062-X.

    Article  CAS  PubMed  Google Scholar 

  12. Prochaska JO, DiClemente CC, Velicer WF, Rossi JS. Criticisms and concerns of the transtheoretical model in light of recent research. Br J Addict. 1992;87(6):825–8. https://doi.org/10.1111/j.1360-0443.1992.tb01973.x.

    Article  CAS  PubMed  Google Scholar 

  13. RJ S. Psychological models. Exercise Adherence-Its Impact on Public Health. 1988:125–153.

  14. Prochoska JO, Velicer WF. The transtheoretical model of health behaviour change. Am J Health Promot. 1997;12(1):38–48. https://doi.org/10.4278/0890-1171-12.1.38.

    Article  Google Scholar 

  15. Liu KT, Kueh YC, Arifin WN, Kim Y, Kuan G. Application of Transtheoretical Model on behavioral changes, and amount of physical activity among University’s students. Front Psychol. 2018;9:2402. https://doi.org/10.3389/fpsyg.2018.02402.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Plummer BA, Velicer WF, Redding CA, Prochaska JO, Rossi JS, Pallonen UE. Stage of change, decisional balance, and temptations for smoking measurement and validation in a large, school-based population of adolescents. Addict Behav. 2001. https://doi.org/10.1016/S0306-4603(00)00144-1.

    Article  PubMed  Google Scholar 

  17. Tuah NA, Amiel C, Qureshi S, Car J, Kaur B, Majeed A. Transtheoretical model for dietary and physical exercise modification in weight loss management for overweight and obese adults. Cochrane Database Syst Rev. 2011;(10). https://doi.org/10.1002/14651858.CD008066.pub2.

  18. Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Adv Behav Res Ther. 1978;1(4):139–61. https://doi.org/10.1016/0146-6402(78)90002-4.

    Article  Google Scholar 

  19. Zelle DM, Corpeleijn E, Klaassen G, et al. Fear of movement and low self-efficacy are important barriers in physical activity after renal transplantation. PLoS ONE. 2016;11(2):e0147609. https://doi.org/10.1371/journal.pone.0147609.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bandura A. Social foundations of thought and action. Englewood Cliffs NJ. 1986;1986:23–8.

    Google Scholar 

  21. Bandura A, Freeman WH, Lightsey R. Self-efficacy: the exercise of control. Springer. 1999. https://doi.org/10.1891/0889-8391.13.2.158.

    Article  Google Scholar 

  22. Resnick B, Jenkins LS. Testing the reliability and validity of the self-efficacy for exercise scale. Nurs Res. 2000;49(3):154–9.

    Article  CAS  PubMed  Google Scholar 

  23. Saunders RP, Pate RR, Felton G, et al. Development of questionnaires to measure psychosocial influences on children’s physical activity[J]. Prev Med. 1997;26(2):241–7. https://doi.org/10.1006/pmed.1996.0134.

    Article  CAS  PubMed  Google Scholar 

  24. Mickey ML, Owen SV, Froman RD. Instrument Development: Cardiac Diet and Exercise Self-Efficacy. Nurs Res. 1992;41(6):347–53.

    Article  Google Scholar 

  25. Kroll T, Kehn M, Ho PS, Groah S. The SCI Exercise Self-Efficacy Scale (ESES): development and psychometric properties. Int J Behav Nutr Phys Act. 2007;4(1):34. https://doi.org/10.1186/1479-5868-4-34.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Li F, McAuley E, Harmer P, Duncan TE, Chaumeton NR. Tai Chi enhances Self-Efficacy and Exercise Behavior in older adults. J Aging Phys Act. 2001;9(2):161–71. https://doi.org/10.1123/japa.9.2.161.

    Article  Google Scholar 

  27. Lee LL, Perng SJ, Ho CC, Hsu HM, Lau SC, Arthur A. A preliminary reliability and validity study of the Chinese version of the self-efficacy for exercise scale for older adults. Int J Nurs Stud. 2009;46(2):230–8. https://doi.org/10.1016/j.ijnurstu.2008.09.003.

    Article  PubMed  Google Scholar 

  28. Wong EML, Leung DYP, Sit JWH, Chan AWK, Chair SY. Prospective validation of the Chinese Version of the self-efficacy for Exercise Scale among Middle-aged patients with Coronary Heart Disease. Rehabil Nurs J. 2020;45(2):74. https://doi.org/10.1097/RNJ.0000000000000156.

    Article  Google Scholar 

  29. Sabo A, Kueh YC, Kuan G. Psychometric properties of the malay version of the self-efficacy for exercise scale. PLoS ONE. 2019;14(5):e0215698. https://doi.org/10.1371/journal.pone.0215698.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Rydwik E, Hovmöller F, Boström C. Aspects of reliability and validity of the Swedish version of the self-efficacy for Exercise Scale for older people. Physiother Theory Pract. 2014;30(2):131–7. https://doi.org/10.3109/09593985.2013.838614hh.

    Article  PubMed  Google Scholar 

  31. Rizal H, Hajar MS, Kueh YC, Muhamad AS, Kuan G. Confirmatory factor analysis of the malay-Language transtheoretical model of physical activity among Malaysian primary school children. Malays J Med Sci MJMS. 2019;26(2):99–113. https://doi.org/10.21315/mjms2019.26.2.11.

    Article  PubMed  Google Scholar 

  32. Kim K, Xue QL, Walton-Moss B, Nolan MT, Han HR. Decisional balance and self-efficacy mediate the association among provider advice, health literacy and Cervical cancer screening. Eur J Oncol Nurs. 2018;32:55–62. https://doi.org/10.1016/j.ejon.2017.12.001.

    Article  PubMed  Google Scholar 

  33. Wu SF, Courtney M, Edwards H, McDowell J, Shortridge-Baggett LM, Chang PJ. Development and validation of the Chinese version of the Diabetes management self-efficacy scale. Int J Nurs Stud. 2008;45(4):534–42. https://doi.org/10.1016/j.ijnurstu.2006.08.020.

    Article  Google Scholar 

  34. Hidrus A, Kueh YC, Norsa’adah B, Kuan G. Malay version of exercise self-efficacy: a confirmatory analysis among malaysians with type 2 Diabetes Mellitus. Int J Environ Res Public Health. 2020;17(3):922. https://doi.org/10.3390/ijerph17030922.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Taylor-Piliae RE, Froelicher ES. Measurement properties of Tai Chi exercise self-efficacy among ethnic Chinese with coronary Heart Disease risk factors: a pilot study. Eur J Cardiovasc Nurs. 2004;3(4):287–94. https://doi.org/10.1016/j.ejcnurse.2004.09.001.

    Article  PubMed  Google Scholar 

  36. Shin Y, Jang H, Pender NJ. Psychometric evaluation of the exercise self-efficacy scale among Korean adults with chronic Diseases. Res Nurs Health. 2001;24(1):68–76. https://doi.org/10.1002/1098-240X(200102)24:1<68::AID-NUR1008>3.0.CO;2-C.

    Article  CAS  PubMed  Google Scholar 

  37. Guo ZM. The connotation, Research Progress, Rethinking and Future Trend of Transtheoretical Model in Physical Exercise. J Xi’an Phys Educ Univ. 2019;36(1):057–64. https://doi.org/10.1016/j.ijnurstu.2008.09.003.

    Article  Google Scholar 

  38. Zheng X, Zhang CC, Jin ZS, Lu J, Hou LH. The study of college physical exercise behavior phase and process of change based on the transtheoretical model. Chin J Dis Control Prev. 2019;23(10):1186–90. https://doi.org/10.16462/j.cnki.zhjbkz.2019.10.006.

    Article  Google Scholar 

  39. Zhu XF, Cao YC. Effect of Exercise intervention based on the transtheoretical model for obese children. Chin J Social Med. 2021;38(3):316–9. https://doi.org/10.3969/j.issn.1673-5625.2021.03.021.

    Article  Google Scholar 

  40. Hu JB, Wang J. Research on vocational college students’ physical activity with the transtheoretical model. Zhejiang Sport Science. 2014;36(1):56–62.

    Google Scholar 

  41. Ma AG, Wang XQ, Wang NH, Zhang PH. Investigation and analysis of the change in physical exercising behaviors of civil servants by using the transtheoretical model. J Phys Educ. 2009;16(9):51–4.

    Google Scholar 

  42. Bao HH. Change of physical exercise behaviors of Mongolian university students using transtheoretical model. J Shengyang Sports Univ. 2012;31(2):71–4.

    Google Scholar 

  43. Wang DS, Ma YZ. An analysis of mediating effect about self-efficacy in behavior change process of physical exercise[J]. J Xi’an Phys Educ Univ. 2014;31:242–9. https://doi.org/10.16063/j.cnki.issn1001-747x.2014.02.023.

    Article  Google Scholar 

  44. Brislin RW. Back-translation for Cross-cultural Research. J Cross-Cult Psychol. 1970;1(3):185–216. https://doi.org/10.1177/135910457000100301.

    Article  Google Scholar 

  45. Kim YH. Application of the transtheoretical model to identify psychological constructs influencing exercise behavior: a questionnaire survey. Int J Nurs Stud. 2007;44(6):936–44. https://doi.org/10.1016/j.ijnurstu.2006.03.008.

    Article  PubMed  Google Scholar 

  46. Godin G, Shephard RJ. A simple method to assess exercise behavior in the community. Can J Appl Sport Sci. 1985;10(3):141–6.

    CAS  PubMed  Google Scholar 

  47. Hair JF. Multivariate data analysis. 2009.

  48. Nunnally JC. Psychometric theory—25 years ago and now. Educ Res. 1975;4(10):7–21. https://doi.org/10.3102/0013189X004010007.

    Article  Google Scholar 

  49. Hair JF Jr, Hult GTM, Ringle CM, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). Springer Nat. 2021. https://doi.org/10.1007/978-3-030-80519-7.

    Article  Google Scholar 

  50. Haas S, Nigg CR. Construct validation of the stages of change with strenuous, moderate, and mild physical activity and sedentary behaviour among children. J Sci Med Sport. 2009;12(5):586–91. https://doi.org/10.1016/j.jsams.2008.11.001.

    Article  PubMed  Google Scholar 

  51. Raykov T, Marcoulides GA. Scale reliability evaluation under multiple assumption violations. Struct Equ Modeling. 2016;23(2):302–13. https://doi.org/10.1080/10705511.2014.938597.

    Article  Google Scholar 

  52. Tseng W-T, Dörnyei Z, Schmitt N. A New Approach to assessing Strategic Learning: the case of self-regulation in Vocabulary Acquisition. Appl Linguist. 2006;27:78–102. https://doi.org/10.1093/applin/ami046.

    Article  Google Scholar 

  53. Fornell C, Larcker DF. Evaluating Structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18:39. https://doi.org/10.2307/3151312.

    Article  Google Scholar 

  54. Beland S, Cousineau D, Loye N. Using the Mcdonald’s Omega Coefficient instead of Cronbach’s Alpha. McGill J Educ. 2017;52(3):791–804. Retrieved from: //WOS:000437472500013.

  55. Browne M, Cudeck R. Confirmatory factor analysis for applied research: alternative ways of assessing model fit. New York NY: Guilford; 2006.

    Google Scholar 

  56. Hausenblas HA, Nigg CR, Downs DS, Fleming DS, Connaughton DP. Perceptions of exercise stages, barrier self-efficacy, and decisional balance for middle-level school students. J Early Adolesc. 2002;22(4):436–54. https://doi.org/10.1177/027243102237191.

    Article  Google Scholar 

  57. Cui SJ, Research on Physical Activities of Higher Vocational College Students with Transtheoretical Model. J Guangzhou Sport Univ. 2014;34(2):113–9. https://doi.org/10.13830/j.cnki.cn44-1129/g8.2014.02.032.

    Article  Google Scholar 

  58. Ji LC. A Study of Physical Activity Attitudes of College Students in Harbin City based on the Stage of Change Model [Master’s thesis]. Shenyang: Shenyang Sport University; 2019. https://doi.org/10.27329/d.cnki.gstyc.2019.000025.

  59. Zhuge SB. Family Support Based on Transtheoretical Model on the Relationship with Children’s Participation in Amateur Swing and Exercise Behavior [Master’s thesis]. Hangzhou: Hangzhou Normal University; 2019.

  60. Ying B. Research on Chinese University Students’s Physical Activity with Transtheoretical Model [dissertation]. Shanghai: East China Normal University; 2007.

  61. Chen H, Dai J, Gao Y. Measurement invariance and latent mean differences of the Chinese version physical activity self-efficacy scale across gender and education levels. J Sport Health Sci. 2019;8(1):46–54. https://doi.org/10.1016/j.jshs.2017.01.004.

    Article  PubMed  Google Scholar 

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Acknowledgements

We would like to express our sincere gratitude to all of the study participants and the schools and teachers who supported us in conducting the study.

Funding

This study was supported by the Social Science Foundation of Jiangxi Province (23TY11) and the National Social Science Foundation of China (Grant: 19BTY092). In addition, it was partially supported by the Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme (FRGS) with Project Code: FRGS/1/2020/SKK06/USM/03/13.

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Authors and Affiliations

  1. School of Physical Education, Shangrao Normal University, Shangrao, Jiangxi, 334000, China

    Liying Yao & Mingzhu Pan

  2. Exercise and Sports Science Programme, School of Health Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, 16150, Malaysia

    Liying Yao, Mingzhu Pan & Garry Kuan

  3. Sports Reform and Development Research Center of Henan University, School of Physical Education, Henan University, Kaifeng, China

    Ke Zhou & Yanli Zhou

  4. Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia

    Yee Cheng Kueh

  5. School of Physical Education & Sports Science, South China Normal University, Guangzhou, China

    Hongyou Liu & Zhongbiao Liu

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  1. Liying Yao
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  2. Ke Zhou
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  3. Yanli Zhou
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  4. Yee Cheng Kueh
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  8. Garry Kuan
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Contributions

YL, YCK, & GK made substantial contributions to the conception or design of the work and drafted the work. YL, YCK, & GK made substantial contributions to the conception or design of the work and revised the work critically for important intellectual content. All authors (LY, KZ, YZ, YCK, HL, ZL, MP, and GK) gave their final approval to the publication of this manuscript and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Ke Zhou or Garry Kuan.

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Ethics approval and consent to participate

The ethical approval was obtained from both the USM Human Research Ethics Committee (USM/JEPeM/21090638) and the Jiangxi Medical College Human Research Ethics Committee (Approval No: (RH)2022-5). It followed the Declaration of Helsinki, and written informed consent was obtained from the parents or legal guardian.

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The authors declare no competing interests.

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Yao, L., Zhou, K., Zhou, Y. et al. Psychometric properties of the Chinese version of the Exercise Self-Efficacy Scale for the Transtheoretical Model: a confirmatory analysis among Chinese children and adolescents. BMC Public Health 24, 151 (2024). https://doi.org/10.1186/s12889-023-17596-2

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BMC Public Health

ISSN: 1471-2458

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