Analysis about the Continuous Participation Behavior of  Dance for All Participants during the COVID-19 Pandemic by applying the Extended Health Belief Model

DOI: https://doi.org/10.21203/rs.3.rs-2498659/v1

Abstract

Background: Based on EHBM theory, this study added the concepts of DFAP’s attitudes toward COVID-19 and perceived behavior control to the main components perceived susceptibility, perceived benefit, and perceived barriers. In addition, these factors intended to verify the continuous behavioral intention of physical activity through DFA by empirically analyzing the influence relationship of the EHBM by CA into media information exposure and personal information exposure for COVID-19.

Methods:A survey was conducted on DFAP’s 665, who joined public sports facilities, academies, and clubs in Seoul and Gyeonggi Province in South Korea from August 2020 to February 2022. Data analysis was performed on Windows PC/SPSS 26.0 and AMOS 24.0 ver. frequency analysis, correlation analysis, confirmatory factor analysis and structural equation modeling were used to analyze the survey results.

Results:First, PB a sub-factor of the EHBM of have a statistically significant (p<0.05) effect on CPB. Second, the sub-factors of EHBM, CA and AT, have a statistically significant (p<0.001) effect on CPB. Third, PBC have a statistically significant (p<0.001) effect on CPB.

Conclusions:The results derived in the process of achieving this purpose are academically meaningful in that they present the direction of DFA activation in the 'New Normal Era' provide practical implications for related workers.

1. Introduction

COVID-19 first broke out in Wuhan, Hubei Province, China in December 2019 and spread rapidly around the world. Accordingly, on March 11, 2020, the World Health Organization declared Pandemic, the highest level of warning for the COVID-19 incident, out of concern about the seriousness of the spread of the infection. Such public health emergencies can affect the mental health of individuals, such as anxiety and emotional isolation, as well as community safety and welfare, such as economic losses, workplace and school closures, and lack of public health facilities [1].

The present, even though the outbreak of COVID-19 has been three years, the number of infections around the world continues to increase due to the expression of the mutant virus of COVID-19. Even if the COVID-19 virus ends in the future, the current situation is highly likely to be repeated due to the outbreak of a new infectious disease, and under the current situation, it seems impossible to return to life before COVID-19. Therefore, it means that it is inevitable to choose coexistence with COVID-19 through continuous preventive activities and improve awareness of COVID-19, and through this, you can enjoy various physical activities such as return to daily life, economic activities, leisure, and hobby activities [2]. Accordingly, academia in various fields, including medical staff around the world, is devoted to research on COVID-19.

In fact, in South Korea because of studying the psychological effects of COVID-19, 29.7% of all study participants experienced depression and 48.8% experienced anxiety, and 49.3% currently evaluated their quality of life negatively [34]. In addition, 8 countries except Korea reported that the public showed relatively high levels of anxiety (6.33–50.9%), depression (14.6–48.3%), post-traumatic stress (PTSD) symptoms (7–53.8%), and stress (8.1–81.9%) [57]. In this context, the UN presented 17 Sustainable Development Goals (SDGs) in 2016 with the goal of 2030, the third of which is ‘ensure healthy lives and promote well-being for all at ages’ which categorizes health and well-being-related goals in a total of nine details. Thus, Health and well-being, which is being promoted in various ways, can be linked to various fields, but it aims to pursue pleasure, which can be closely related to sports that can participate voluntarily [8]. The reason is that it can be confirmed through the results that sports have a positive effect on mental and physical health and contribute to improving the quality of life [9]. In particular, DFA is a physical activity that can purify negative emotions about depression, stress, and anger caused by COVID-19 by expressing inner emotions with various music and can be easily participated and enjoyed by anyone regardless of all ages. Therefore, this study aims to analyze the factors on the behavioral intention of how continuously human intend to participate in physical activities through DFA at the time of the prolonged COVID-19.

In this context, the EHBM is a theoretical conceptual structure most used to explain and predict human health behaviors and consists of perceived susceptibility, perceived benefit, and perceived barriers factors [1012]. These constituent factors have an important effect on modifying or maintaining health behavior in a desirable direction in certain risk situations such as human diseases [13]. In other words, the EHBM can provide a meaningful explanatory power to predict the continuous physical activity intention of DFAP’s by epidemic such as COVID-19. However, as previous studies related to this remain mainly on risk perception due to COVID-19, studies analyzing the continuous behavioral intention of physical activity on EHBM are very insufficient. In addition, the analysis of the causal relationship between factors and various factors in the EHBM has been limited as each factor of EHBM focuses only on the direct main effect on behavior [14].

Therefore, this study expanded the concept of DFAP's PS, PB, PD, CA, AT, and PBC to major components based on EHBM theory and applied it to a special era situation called the current Covid-19 Pandemic. In other words, the purpose of this is to empirically analyze the participation behavior of DFAPs in continuous physical activities through the application of EHBM. The results derived in the process of achieving this purpose are academically meaningful in that they present the direction of DFA activation in the 'New Normal Era' provide practical implications for related workers.

2. Theoretical Background And Hypothesis Setting

2.1. The relationship between PS(perceived susceptibility) and CPB(continuous participation behavior)

PS is the perception of how much one is exposed to a particular disease and refers to the degree of sensitivity an individual feels about the likelihood of developing the disease. If PS to a particular disease is high, highly likely to choose preventive activities or behavior [15]. Also, A study verifying the influence of PS on health behavior revealed that PS has a positive effect on preventive activities or behavior [16].

2.2. The relationship between PB(perceived benefits) and CPB

(continuous participation behavior)

PB is defined as the degree to which PB is perceived to be effective [17]. If an individual is concerned about infection of a disease and recognizes that taking PB is not effective even if the person is seriously aware of the disease, it is highly likely that activitiesor behavior will not continue [15].

2.3. The relationship between PD(perceived disability) and CPB

(continuous participation behavior)

PD refer to latent negative factor that are expected to experience when performing health behavior [15]. PD can act as a hindrance to performing continuous preventive activities or behavior [18].

2.4. The relationship between CA(Cues to Action) and CPB

(continuous participation behavior)

CA is an internal and external stimulus necessary to prevent or conduct disease, and internal clues consist of self-awareness of an individual's health status or disease, and external clues refer to interpersonal communication such as doctor's recommendatio, experience, recommendation, persuasion, and health information or campaigns [10].

In general, it was found that the higher the CA, the more the higher the CA, the more

preventive activities or behavior against diseases. [19]. According to previous studies related to the EHBM, when it is judged that it is helpful to perform a specific preventive a behavior by comprehensively considering these four factors (PS; PB; PD; CA), humans continue to perform the behavior [18].

2.5. The relationship between Attitude and CPB(continuous

participation behavior)

AT is a personal characteristic that appears by individual beliefs and past experiences and is a motivation for individual behavior [20]. Many previous studies for predicting CPB have shown strong predictive power between attitude and CPB [2123].

2.6. The relationship between PBC(perceived behavior control) and CPB(continuous participation behavior)

PBC is a belief that one has the right to make decisions or influence over a particular behavior [24]. According to related studies, individual control has a direct effect on continuously inducing actual behavioral intentions [21, 22, 23, 25].

2.7. Hypothesis setting and research model

Based on the results of previous studies, the following hypotheses and models were established [Figs. 1].

H1: The PS, a sub-factor of EHBM will have a significant effect CPB.

H2: The PB, a sub-factor of EHBM will have a significant effect CPB.

H3: The PD, a sub-factor of EHBM will have a significant effect CPB.

H4: The CA, a sub-factor of EHBM will have a significant effect CPB.

H5: The AT of DFAP’s will have a significant effect on their CPB.

H6: The PBC of DFAP’s will have a significant effect on their CPB.

3. Methods

3.1. Participants

In 2021, the Ministry of Culture, Sports and Tourism of South Korea, Seoul published the National Sports for all Participation Status which stated that Seoul and the Gyeonggi-do region had the highest number of sports club members out of 17 cities and provinces nationwide [26]. Based on this, 665 DFAP’s who had joined public sports facilities, private academies, and clubs in the Seoul and the Gyeonggi-do region were judged to reflect the various personal characteristics of the research participants and were selected as the sample group.

To identify participants, the convenience sampling method used among non-probability sampling (random sample), and the participants used the self-administration method where they completed the questionnaire and then handed it directly back to the researcher. For example, you can use a random sample that selects respondents entirely by chance from a large population as one sampling method (random sample). The sample size is the number of completion responses that you receive in the survey, which is called a sample because it represents only a part of a group of people, and you want to know their opinions or behavior [27]. To increase the understanding and reliability of the survey, researchers visited the site directly and conducted a face-to-face survey in which the questionnaire was is tributed and retrieved. All participants provided informed consent and this research was approved by the Institutional Ethics Review Committee of Gangneung-Wonju National University and complied with research ethics. Questionnaires was distributed to a total of 665 participants, and 630 questionnaires were selected and analyzed as final valid samples, excluding 35 that provided incomplete answers such as missing entries, double entries, and biased entries [Table 1].

Table 1

Demographic characteristic of participants

Variables

Classification

Frequency (n)

Percentage (%)

Gender

Male

Female

306

324

48.6

51.4

Age

10s

20s

30s

40s

Over 50s

175

144

122

106

83

27.8

22.8

19.4

16.8

13.2

Participation experience

less than 1yrs

1yrs less than 2yrs

2yrs – less than 3yrs

More than 3yrs

158

168

194

110

25.1

26.6

30.8

17.5

Total

 

630

100

3.2. Measurement tool

The questions for measuring the PS, PB, PD, and CA of EHBM were modified and supplemented to match the purpose and subject of this study based on the scale developed by Hochbaum (1958) [28] and extended by Rosenstock (1974) [19]. The detailed measurement questions consisted of three items about PS, three items about PB, three items about PD, and three items about CA. Also, questions for measuring the AT, PBC, and CPB of DFAP’s were modified and supplemented to match the purpose and subject of this study based on the scale developed by Ajzen (1991) [24] and reused by Choi (2020) [29]. All questions, except for the participants general characteristics, were measured using a 5-point Likert scale (1 = not at all, 5 = strongly agree).

3.3. Validity and Reliability of Measuring

To secure the intensive validity of the measurement tools(questionnaires) used in this study, convergent validity was verified with a total of five expert groups consisting of a professor in DFA professor, Sport Pedagogics Ph.D, and DFA Ph.D. In addition, confirmatory factor analysis was performed to discriminant validity [Table 2].

Table 2

Confirmatory factor analysis

Variables

Factors

SC

SE

t

C.R

AVE

Cronbach’s α

Perceived

Susceptibility

(PS)

Anyone can get epidemic such as COVID-19.

.843

-

-

.565

.791

.821

I can also get epidemic such as COVID-19 at any time.

.897

.050

21.21

People around me who participate in physical activi-ties such as DFA can get epidemic such as COVID-19.

.605

.044

15.59

Perceived

Benefits

(PB)

COVID-19 and preventive behavior compliance will

protect me and the people around me who participatein physical activities such as DFA.

.812

-

-

.653

.849

.843

When participating in physical activities such as DFA, I think top priority to comply with the preventive actions of COVID-19.

.891

.052

21.89

When participating in physical activities such as DFA, compliance with preventive behavior of COVID-19 is considered effective in preventing.

.712

.046

18.51

Perceived

Disability

(PD)

In physical activities such as DFA, compliance with COVID-19 and preventive behavior is mentally

burdensome.

.854

-

-

.760

.853

.872

In physical activities such as DFA, compliance with

COVID-19 and preventive behavior is and

preventive behavior is burdensome in economically.

.810

.045

22.83

In physical activities such as DFA, compliance with

COVID-19 and  preventive behavior is burdensome

in time.

.845

.040

23.81

Cues to

Action

(CA)

I often see news or articles related to epidemic such

as COVID-19 through the media.

.837

-

-

.705

.878

.888

I often talk about COVID-19 with people around

me who participate in physical activities such as

DFA.

.877

.044

25.30

People around me who participate in physical

activities such as DFAP’s are encouraged to

comply with COVID-19 prevention behavior.

.842

.041

24.35

Attitude

(AT)

I think that individual physical activity through DFA

is a desirable behavior when an epidemic such as

COVID-19.

.825

-

-

.727

.914

.910

I think that individual physical activity through DFA

is a valuable behavior when epidemic such as 

COVID-19.

.852

.043

25.10

I think that individual physical activity through DFA

is a beneficial behavior when epidemic such as 

COVID-19.

.858

.043

25.34

I think that individual physical activity through DFA

is a necessary behavior when epidemic such as 

COVID-19.

.855

.042

25.21

Perceived

Behavior

Control

(PBC)

If the epidemic such as COVID-19 doesn't end, restrictions

on physical activity through DFA.

.856

-

-

.712

.881

.870

If the epidemic such as COVID-19 doesn't end, 

the choice of physical activity through DFA is entirely up to me.

.779

.040

21.56

Even the epidemic such as COVID-19 doesn't end, 

I can do physical activities through DFA whenever I want.

.862

.040

23.41

Continuous

Participation

Behavior

(CPB)

Regardless of the epidemic such as COVID-19, 

I am willing to continue physical activities through DFA.

.785

-

-

.758

.904

.878

I have a plan to continue physical activities through DFA regardless of the epidemic such as COVID-19.

.876

.051

22.45

Regardless of epidemic such as COVID-19, I will 

recommend physical activities such as DFA to people around me.

.861

.050

22.28

χ²=268.821(df = 188, p = .000), CFI = .963, NFI = .966, TLI = .987, RMR = .024, RMSEA = .026

Which satisfies the acceptance level suggested by Bagozzi and Dholakia indicating that it is a relatively good model. Also, the construct reliability of all variables was .565 ~ .760, and the AVE was .791 ~ .914, indicating that the fit criteria suggested were (eigen value > .5, CR > .7, AVE > .5). Each variable was found to have concentrated validity by satisfying the values. Kim [30] explained that there was no problem with reliability if the alpha coefficient is .5 or more when the reliability test was carried out for all questions. As a result of using the internal consistency reliability analysis method with Cronbach's α value for reliability verification, it was found that Cronbach's α value was .821–.910 with relatively high reliability.

According to Bagozzi and Dholakia [31], the best model was evaluated when CFI, NFI, and TLI were .8 ~ .9 or more, and RMR and RMSEA were .05 or .08 or less. As a result of conducting confirmatory factor analysis based on this rationale, the model fit of this study was χ²=268.821, df = 188, CFI = .963, NFI = .966, TLI = .987, RMR = .024, and RMSEA = .026 which satisfies the acceptance level suggested by Bagozzi and Dholakia indicating that it is a relatively good model. Also, the construct reliability of all variables was .565 ~ .760, and the AVE was .791 ~ .914, indicating that the fit criteria suggested were (eigen value > .5, CR > .7, AVE > .5). Each variable was found to have concentrated validity by satisfying the values. Kim [30] explained that there was no problem with reliability if the alpha coefficient is .5 or more when the reliability test was carried out for all questions. As a result of using the internal consistency reliability analysis method with Cronbach's α value for reliability verification, it was found that Cronbach's α value was .821–.910 with relatively high reliability.

3.4. Data analysis process

The questionnaire used for the final analysis was the result of a data analysis using Windows PC/SPSS 26.0 ver. and AMOS 24.0 ver. after coding and error reviews. First, the general characteristics of the study participants were analyzed using a frequency analysis. Second, confirmatory factor analysis was performed to verify all factors, and reliability was verified by calculating the Cronbach’s α coefficient to ensure internal consistency reliability. Third, correlation analysis was performed to analyze the relationship between variables, and structural equation modeling (SEM) was performed to derive a structural model.

4. Results

4.1. Correlation analysis

The correlation was analyzed to confirm the correlation between each variable. As a result of the correlation analysis between each variable, it was found that there was no multicollinearity problem because no variable showed a correlation of .8 or higher in the range of the correlation coefficient value [32] of .308 to .572 [Table 3].

Table 3

Correlation analysis

Variables

1

2

3

4

5

6

7

PS ¹

1

           

PB²

0.330**

1

         

PD³

0.346**

0.387**

1

       

CA⁴

0.308**

0.342**

0.399**

1

     

AT⁵

0.565**

0.350**

0.360**

0.399**

1

   

PBC⁶

0.344**

0.564**

0.386**

0.353**

0.364**

1

 

CPB7

0.365**

0.397**

0.572**

0.395**

0.378**

0.393**

1

**p < 0.01

Also, Fornel & Larcker (1981) suggested that discriminant validity can be secured if the AVE value is larger than the squared value of the correlation coefficient the largest square value of the correlation coefficient was .572 (= .327), and the smallest value of AVE was .791, ensuring discriminant validity [33].

4.2. Model verification

The results of the analysis verified the suitability of the structural model estab-

lished in this study, χ²=253,570, df = 192, CFI = .965, NFI = .968, TLI = .990, RMR = .034, RMSEA = .023 [Table 4].

Table 4

Fit index of research model

A construct

χ²

df

p

CFI

NFI

TLI

RMR

RMSEA

 

Acceptance

level

253.570

192

.000

.965

.968

.990

.034

.023

 

According to Netemeyer, Boles, McKee and McMurrian (1997), [34] when the indicators of CFI, NFI, and TLI, which generally evaluate the overall fit of a model, are above .8 to .9, RMR and RMSEA are evaluated as a good model when they are less .8. Therefore, it was confirmed that this research model explains the research hypothesis and empirical dataset as a suitable model for adoption relatively well.

4.3. Hypothesis testing

The structural equation model was analyzed to confirm the causal relationship between the research hypothesis and the variables in the research model. [Table 5], [Figs. 2].

Based on the results of the testing analysis, First, the path coefficient of H1 was .021(t = 0.535, p < 0.05), and the hypothesis of ‘PS, a sub-factor of EHBM will have a significant effect CPB’ was rejected. Second, the path coefficient of H2 was .120(t = 2.167, p < 0.05), and the hypothesis of ‘PB, a sub-factor of EHBM will have a significant effect CPB’ was accepted. Third, the path coefficient of H3 was .082(t = 1.748, p < 0.05), and the hypothesis of ‘PD, a sub-factor of EHBM will have a significant effect CPB’ was rejected. Fourth, the path coefficient of H4 was .388(t = 7.925, p < 0.001), and the hypothesis of ‘CA, a sub-factor of EHBM will have a significant effect CPB’ was accepted. Fifth, the path coefficient of H5 was .472(t = 12.312, p < 0.001), and the hypothesis of ‘AT, a sub-factor of EHBM will have a significant effect CPB’ was accepted. Sixth, the path coefficient of H6 was .222(t = 4.595, p < 0.001), and the hypothesis of ‘PBC, a sub-factor of EHBM will have a significant effect CPB’ was accepted.

Table 5

Hypothesis testing result

H

Path

SE

CR

p

Accept/Reject

H1    PS → CPB

0.021

0.036

0.535

0.593

Reject

H2    PB → CPB

0.120

0.044

2.167

0.030*

Accept

H3    PD → CPB

0.082

0.040

1.748

0.081

Reject

H4    CA → CPB

0.388

0.037

7.925

0.000***

Accept

H5    AT → CPB

0.472

0.037

12.312

0.000***

Accept

H6    PBC → CPB

0.222

0.039

4.595

0.000***

Accept

*p < 0.05, ***p < 0.001

5. Discussion

Relationship between the DFAP’s continuous participation in physical activity based on the theory of EHBM during the COVID-19 pandemic.

First, PS, a sub-factor of EHBM have a significant effect CPB, H1 was rejected. These results are contrary to the study of Alhalaseh, et al., (2020) that reported that higher PS had a direct and powerful effect on specific behavioral intentions [35]. However, PS was found to be like the results of a study by Lee and Eu (2021), which reported that the influence on specific behavioral intentions was weaker than that of PB and CA [36]. This means that the higher the PS about certain epidemic such as COVID-19 the more difficult it is to induce continuous participation in physical activities such as DFA.

Second, PB, a sub-factor of EHBM have a significant effect CPB, H2 was accepted. It is judged that the higher the perception that the benefits that can be gained through COVID-19 prevention rules when participating in physical activities such as DFA, the higher the continuous participation behavior of DFA. These results are the same as the determinants of behavioral intention reported in the studies of Lau et al., (2020) and Hagg et al., (2016) [3738]. In other words, a strategy that emphasizes PB to prevent COVID-19 infection is most important for DFA participants to continuously participate in physical activities.

Third, PD, a sub-factor of EHBM have a significant effect CPB, H3 was rejected. These results contradict the results of Zhang et al., (2020) on overseas epidemic, but are consistent with the results of Kim (2020), who revealed that PD didn't significantly affect the intention of preventive behavior for restaurant consumers [3940]. As a result, it suggests that participation in physical activities is more important than loss or disability due to COVID-19 preventive measures in the process of participating in physical activities such as DFA.

Fourth, CA, a sub-factor of EHBM have a significant effect CPB, H4 was accepted. These results are consistent with a study by Reimann (2004) and Unson et al., (2005) who revealed that CA was formed through the course or symptoms of disease in the past, or through interpersonal communication or media, directly or indirectly affecting specific behaviors [4142]. Currently, COVID-19 is issue around the world, and the risk of infection and the spread of local communities are continuously reported. In addition, as preventive behaviors to prevent COVID-19 are continuously required, CA can be evaluated as an important determinant of promoting the continuous physical activity of DFAP’s.

Fifth, AT, a sub-factor of EHBM have a significant effect CPB, H5 was accepted. In this regard, Han et al., (2020) reported that tourists' AT toward COVID-19 have a significant effect on overseas travel selection behavior, and Bae & Chang (2020) said that AT and PBC control related to COVID-19 have a significant effect on untact tourism participation behavior [4344]. This means that even in the prolonged COVID-19 situation, DFAP’s positive AT acts as an important factor in continuing to participate in physical activities such as DFA by recognizing them as beneficial and valuable behavior rather than feeling dangerous during physical activities such as DFA.

Sixth, PBC, a sub-factor of EHBM have a significant effect CPB, H6 was accepted. In this regard, Noh and Kim (2013) reported that college students' PBC had a positive effect on their behavioral intentions through a study on the group travel decision process of college students, and Kang et al., (2013) reported that sports tourism participants This study also found that PBC had a positive effect on behavioral intention, which is consistent with the results of this study [4546]. As a result, factors such as time, cost, and margin of PBC are important factors in determining DFAP's continuous participation in physical activity.

6. Conclusions

The purpose of this study is to empirically analyze the participation behavior of DFAP’s in continuous physical activities by applying an EHBM to the special era situation of COVID-19 Pandemic. Therefore, the contents derived based on the results and discussions of this study are as follows.

First, H1, that PS, a sub-factor of EHBM will have a significant effect CPB, H1 was rejected. Second, H2, that PS, a sub-factor of EHBM will have a significant effect CPB, H2 was accepted. Third, H3, that PD, a sub-factor of EHBM will have a significant effect CPB, H3 was rejected. Fourth, H4, that CA, a sub-factor of EHBM will have a significant effect CPB, H4 was accepted. Fifth, H5, that AT, a sub-factor of EHBM will have a significant effect CPB, H5 was accepted. Sixth, H6, that PBC, a sub-factor of EHBM will have a significant effect CPB, H6 was accepted. Taken together, it means that the higher the PB, CA, AT, and PBC of DFAP’s, the higher the continuous participation behavior of physical activities such as DFA. Therefore, even if the epidemic crisis such as COVID-19 repeats again in the future, it is necessary to prepare a self-prevention plan to set a minimum number of people per day by promoting safety in physical activity spaces for DFAP’s to continue participating. Physical activities such as DFA are composed of groups, so they can be vulnerable to epidemic such as COVID-19. In other words, DFA operators should prepare and thoroughly manage their own preventive measures to prevent them from recognizing the risks such as COVID-19 infection caused by participation in DFA.

Also, to improve the positive attitudes of DFAP’s such as happiness, pleasure, and

satisfaction required to develop a program that allows participants to feel satisfaction in the process of achieving the goal by presenting various goals to the participants according to a certain cycle. In addition, stimulating the emotional aspects of participants using interior decorations, props, and lighting of space when participating in physical activities such as daily dance is also judged to be a way to continue participation in physical activities.

These study results are of academic significance in that they can serve as the basis

for follow-up study on continuous physical activity participation behavior in special era situations such as COVID-19. However, the following limitations exist in the process of conducting the study.

First, in this study, CA, AT, and PBC were set as additional variables in the HBM

according to the special era situation of COVID-19, but there will be various variables that induce continuous participation in physical activities such as DFA. Therefore, in subsequent studies, in-depth studies on continuous physical activity participation behavior should be conducted through the application of complex variables suitable for the situation of the times.

Second, this study limited DFAP’s to research subjects, but in the following study,

it is possible to provide more specific data than this study if the study reflects the individual characteristics of the subjects or the characteristics of each participating event.

Abbreviations

DFAP: Dance for all; DFAP’s: Dance for all participants; EHBM: Extended health Belief Model; PS: Perceived Susceptibility; PB: Perceived Benefits; PD: Perceived Disability; CA: Cues to Action; AT: Attitude, PBC: Perceived Behavior Control; CPB: Continuous Participation Behavior. 

Declarations

Acknowledgements

 We wish to thank all cooperating   public sports facilities, private academies, and clubs in the  Seoul and the Gyeonggi-do region  and participants for allowing us to use their data and contributing to this important research area.

Funding

Not applicable.

Availability of data and materials

 The datasets used and/or analyzed during the his study may not be used for any purpose other than this study.

Ethics approval and consent to participate

 This research was approved by the Investigator’s Center for the Institutional Review Board (approval number: GWNU IRB 2021-34).

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Authors’ contributions

Conceptualization, J.-H.A. and J.-S.L.; methodology, S.-Y.L.; software, S.-Y.L.; validation, S.-Y.L. and J.-S.L.;  formal analysis,  S.-Y.L.; investigation, S.-Y.L.; resources, J.-H.A. and J.-S.L.; data curation, S.-Y.L.; writing—original draft preparation, J.-H.A. and J.-S.L.; writing-review and editing, J.-H.A. and J.-S.L.; visualization, S.-Y.L.

Author details

   1Department of dance education, Kyunghee University, 21, Kyungheedae-ro 4-gil, Dongdaemun-gu, Seoul, Republic of Korea;  [email protected]. 2*Department of Physical Education, Gangneung-Wonju National University, Gangneung 25457, Republic of Korea;   [email protected]. 2*Department of dance education, Kyunghee University, 21, Kyungheedae-ro 4-gil, Dongdaemun-gu, Seoul, Republic of 

 Korea; [email protected]

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