Skip to main content
Download PDF

The Tendency to Avoid Physical Activity and Sport Scale (TAPAS): Rasch analysis with differential item functioning testing among a Chinese sample

BMC Psychology volume 11, Article number: 369 (2023) Cite this article

A Correction to this article was published on 30 November 2023

This article has been updated

Abstract

Background

The benefits of physical activity are well-known to prevent multiple long-term health conditions. Physical appearance and weight-related stigma may influence individuals’ decision to engage in physical activity and sport. Therefore, the present study examined the psychometric properties of a newly developed instrument, the Tendency to Avoid Physical Activity and Sport Scale (TAPAS), using modern test theory.

Methods

A total of 2319 university students were recruited from mainland China and they completed the TAPAS. Rasch analysis was used to examine the TAPAS’ rating scaling functioning, test unidimensionality, item hierarchy, ceiling and floor effects, and differential item functioning (DIF). Moreover, the concurrent validity of the TAPAS was examined using the Weight Self-Stigma Questionnaire (WSSQ), Weight Bias Internalization Scale (WBIS), and body mass index (BMI).

Results

Unidimensionality was confirmed except for one item. Items corresponding to attitude toward physical activity were more easily adopted compared to items corresponding to actual behavioral aspects. No ceiling and floor effects were found. No DIF existed in the TAPAS items. The TAPAS was strongly correlated with both the WSSQ and WBIS, but not BMI.

Conclusion

The study showed that overall, the TAPAS has robust psychometric properties. However, future research needs to address the misfit item and explore the feasibility of applying the TAPAS to other populations including wider ethnic groups, age ranges, and life stages.

Peer Review reports

Introduction

Insufficient physical activity has been found to be one of the leading risk factors for non-communicable, long-term health conditions, such as heart disease [1], cancer [2, 3], chronic respiratory disease [4, 5], and diabetes mellitus [6]. In addition, prior systematic reviews have shown that physical activity and sports have positive and significant effects on individuals’ wellbeing and quality of life [7, 8]. The World Health Organization (WHO) has provided clear suggestions and guidelines regarding the levels of physical activity for different age groups. For example, adults aged 18 to 64 years old should perform at least 150 min of moderate-intensity aerobic physical activity, or at least 75 min of vigorous-intensity aerobic physical activity (or an equivalent combination) per week to maintain good health [9]. Although the benefits of physical activity are well-known and have been promoted over the decades, studies still show that approximately one-third of adults worldwide do not reach the recommended levels of physical activity [10, 11].

Several studies have reported the pragmatic barriers that prohibit individuals in participating in physical activity, such as lack of childcare support, and lack of financial and community resources [12], as well as lack of social support and motivation [13]. A recent study proposed and confirmed that participants’ concerns about their own physical appearance and weight-related stigma may influence their enjoyment when participating in physical activity [14]. More specifically, the researchers assessed enjoyment in participation (using the Physical Activity Enjoyment Scale), weight stigma (using five-item Perception of Teasing Scale and Weight Bias Internalization Scale), and avoidance participation (using the TAPAS). They found that decreased enjoyment and stigmatized perceptions could potentially lead to individuals deciding to avoid engaging in physical activity [14]. Prior findings have also indicated that body appearance evaluation is strongly associated with physical activity levels among adolescents [15] and university students [16]. Therefore, it is of great importance to evaluate individuals’ physical appearance and weight stigma concerns because such psychosocial aspects might help to answer the unresolved issues on physical activity participation.

Most previous studies have evaluated the tendency of avoiding physical activity with simply one or two items [17]. Until recently, there had been no validated psychometric instrument developed which could be practically used in clinical or community settings. Therefore, Bevan et al. [18] developed the Tendency to Avoid Physical Activity and Sport Scale (TAPAS) using rigorous assessment development guidelines. Moreover, the development of the TAPAS is supported by the cyclic obesity/weight-based stigma (COBWEBS) framework [19]. More specifically, the COBWEBS framework proposes that weight-related self-stigma is a trigger for an individual to engage in behaviors that cause weight problems (e.g., eating disorders and physical activity avoidance). The TAPAS assesses physical activity and exercise avoidance that could be due to individuals’ weight-related self-stigma and fits within this framework. Their goal was to create a feasible psychometric instrument which would allow clinicians and researchers to better understand the role of weight and appearance-related concerns in physical activity participation [18].

The TAPAS was first tested in an English-speaking (Australian) undergraduate student population and showed the initial evidence and feasibility [14]. Assessment evaluation is a fundamental process to ensure the reliability and validity of any newly developed instruments [20]. More specifically, how each item’s content reflects the purpose of the instrument, the scale construction, and test dimensionality should be examined to ensure the items in the TAPAS have an appropriate level of efficacy in evaluating the construct it claims to assess. To the best of the present authors’ knowledge, the TAPAS has not been psychometrically evaluated using advanced techniques (i.e., Rasch analysis). Therefore, the present study used the Rasch measurement model to further examine the psychometric properties of the TAPAS. Rasch models adopt probabilistic test theory and its precise calibration for each scale item is beneficial for individual test item examination and overall optimizing the test [21]. Due to its strength and usefulness, it has been used extensively in assessment development in a variety of fields [22]. The present study addresses four specific research questions: (i) How does the rating scale function in the TAPAS? (RQ1), (ii) Do all of the 10 items included in TAPAS demonstrate unidimensionality? (RQ2), (iii) Do ceiling or floor effects exist in the TAPAS? (RQ3), and (iv) Do any of the 10 TAPAS items display differential item functioning (DIF) across gender? (RQ4).

Methods

Participants

After obtaining the approval from the Institutional Review Board of Jiangxi Psychological Consultant Association (IRB ref: JXSXL-2021‐J99), the present authors sent an invitation to 25 universities in mainland China to seek their assistance in data collection. Of these, 19 universities across 13 provinces in mainland China agreed to assist in distributing the survey link. Convenience sampling was conducted using an online survey between August and October 2022 via an online hyperlink or a QR code. The eligibility criteria for participant inclusion were being: (i) an adult (i.e., aged 18 years or above); (ii) a university student in mainland China; and (iii) willing to provide electronic consent for participation.

Translation procedures for TAPAS

As aforementioned, the TAPAS is a newly developed scale developed with an English-speaking population (Australians). Therefore, the present authors used a standard translation procedure to translate the English version of the TAPAS to a Chinese version of the TAPAS [23]. In the first stage of the translation procedure, two bilingual translators who were native Chinese (Mandarin) speakers carried out a forward translation independently. In the second stage, the two translators worked with one of the present authors (C-YL) to reconcile a Chinese TAPAS for back translation. In the third stage, a third bilingual translator who was also a native Chinese speaker and was not aware of the English TAPAS translated the reconciled Chinese TAPAS into English. In the fourth stage, one of the present authors (C-YL) called an expert panel meeting with the composition of all translators and various experts (with expertise in pediatrics, public health, psychometrics, physical activity, and weight) to finalize the Chinese TAPAS with the use of all the TAPAS translations and the original TAPAS. In the final stage, the finalized Chinese TAPAS was sent out to several university students to evaluate the readability, and the Chinese TAPAS was revised with some minor word changes.

MeasuresFootnote 1

Tendency to Avoid Physical Activity and Sport Scale (TAPAS)

The Tendency to Avoid Physical Activity and Sport Scale [18] has a total of 10 items with five items evaluating physical activity and five items evaluating sport contexts. Items are rated on a five-point scale from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater tendency to avoid physical activity or sport. The TAPAS assesses physical activity and sport avoidance associated with weight-related self-stigma. Moreover, the TAPAS has been validated among undergraduate students [14]. The internal consistency of the original TAPAS was excellent (Cronbach’s α = 0.95) [14].

Weight self-stigma questionnaire (WSSQ)

The Weight Self-Stigma Questionnaire is a commonly used instrument that assesses weight-related self-stigma. It has two subscales that assess self-devaluation and fear of enacted stigma, respectively [24,25,26]. Each subscale has six items that are rated on a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). A higher score indicates a higher level of weight-related self-stigma. The Chinese version of the WSSQ was used in the present study which has been shown to have satisfactory psychometric properties [27]. The internal consistency of the original WSSQ was very good (Cronbach’s α = 0.88) [25]. The Cronbach’s α of the WSSQ in the present study was 0.90.

Weight bias internalization scale (WBIS)

The Weight Bias Internalization Scale is a widely used instrument that assesses how an individual accepts weight-based stereotypes [28]. It has 11 items that are self-rated on a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree) [29]. When an individual receives a higher score, it indicates a higher level of weight-related self-stigma. The Chinese version of the WBIS was used in the present study and it has been shown to have satisfactory psychometric properties [30]. The internal consistency of the original WBIS was excellent (Cronbach’s α = 0.90) [29]. The Cronbach’s α of the WBIS in the present study was 0.96.

Data analysis

In the present study, Rasch analysis was used to examine the TAPAS’ rating scaling function, test unidimensionality (i.e., to see if all items assessed the same latent construct in the TAPAS), item hierarchy, ceiling and floor effects, and differential item functioning (DIF) across male and female participants. A previous factor analysis study confirmed the single-factor solution for the TAPAS [18]. Therefore, all 10 items were considered to be assessing the same latent construct in the TAPAS (i.e., physical activity and sport avoidance) for Rasch analysis.

The partial credit model [31] was applied in the Rasch analysis to allow individual calibration for the TAPAS items that might have different rating scales. Additionally, it was investigated whether the five-point Likert scale had sufficient participants (i.e., at least 10) in each rating category as well as whether each rating category advanced monotonically throughout the five-point Likert scale levels.

The test unidimensionality of the TAPAS was examined with the goodness-of-fit statistics derived from the Rasch analysis. Two type of mean-squares (MnSq) statistics were used because they showed the amount of distortion of the underlying test structure of the TAPAS. The outfit MnSq is more sensitive to unexpected outliers. It was expected to be less than 2 in all TAPAS items. The infit MnSq is more sensitive to on-target observations with the expected value of 1.0. Infit MnSq statistics between 0.5 and 1.5 associated with a standardized mean square scored (Zstd) between − 2 to + 2 are desired [32]. If any TAPAS item demonstrated an infit MnSq > 1.5, it is considered a threat to TAPAS’s validity because it may suggest that the items are unpredictable with unmodeled noise [33]. If all the TAPAS items have satisfactory infit MnSq statistics, then the unidimensionality of the test can be supported. Furthermore, the item calibrations derived from the Rasch analysis were used to examine the hierarchy of the TAPAS items.

Additionally, the ceiling and floor effects were examined to see if any participants with extreme scores were outliers, which may affect the reliability of the TAPAS. It was expected that there should be less than 15% of the total participants that would achieve the maximum or the minimum possible scores in the TAPAS to be considered as having no ceiling or floor effects, respectively [34].

Moreover, differential item functioning (DIF) analysis was carried out to investigate the interaction between gender that might cause underlying bias. It is generally acceptable to have less than 5% of items that display DIF to support unidimensionality [35,36,37]. The Rasch-Welch t-statistic was utilized to identify any item with statistically significant DIF (p < .05). DIF contrasts (between male and female participants) less than 0.5 logit are negligible while the contrasts between 0.5 and 1 logit are considered moderate. Contrasts over 1 are defined as substantial [38]. Moreover, according to the Rasch guidelines on the required sample size, each rating category should have a minimum of at least of 10 participants to generate stable estimates [40]. Winsteps version 5.3.3.0 was used to perform the Rasch analysis. Other demographic and descriptive statistics were conducted with IBM SPSS version 28.0.

The concurrent validity for the TAPAS was examined using three external measures: body mass index (BMI), Weight Self-Stigma Questionnaire (WSSQ) and the Weight Bias Internalization Scale (WBIS). Spearman correlation coefficient was used with the criteria that 0.1 to 0.3 = weak correlation; 0.4 to 0.7 = moderate correlation; > 0.7 = strong correlation [39]. Lastly, internal consistency of the TAPAS was examined for all the 10 items using Cronbach’s α.

Results

All participants who reported extreme values for their BMI (below 10 or over 50) and those reporting identical answers to all the questions were removed during data cleaning. Therefore, the final sample comprised 2319 higher education students from mainland China. The majority of them were university students (n = 1872, 80.7%), single (n = 2263, 97.6%), with a mean age of 20.16 years (SD = 1.75), and approximately 57% were females. Students were recruited from diverse disciplines with the top five majors being science (27.2%), engineering (21.6%), education (12.2%), management (11.6%), and art (9.0%). Further demographic details can be found in Table 1.

Table 1 Participants’ demographics (N = 2319)
Full size table

The Rasch analysis showed that the TAPAS had sufficient participants (i.e., over 10) to generate stable estimates for each rating category. Additionally, all 10 items had average measures that advanced monotonically (i.e., the difficulty of each category in the responses increased by order; scoring 1 is easier than scoring 2 and scoring 2 is easier than scoring 3, etc.) using the five-point Likert scale. The monotonical advancement was assessed using the average rating measures (Table 2). Regarding the item fitness statistics, all the test items had outfit MnSq less than the cut-off criteria of 2 along with the desired infit MnSq. However, Item 10 (I would prefer to participate in physical activity in a more private setting) demonstrated infit MnSq misfit with the Zstd above 2 (Table 2), which indicated a misfit in the Rasch model’s expectation. Therefore, this misfitting item was removed and the partial credit Rasch model was rerun with the remaining nine items in the TAPAS. The results (Table 3) showed that all the remaining nine items fitted the Rasch model with acceptable values of infit and outfit MnSq and Zstd. Moreover, the average measures across rating categories advanced monotonically. These findings confirmed the unidimensionality of the nine-item TAPAS and supported the construct validity of the TAPAS. When examining the item hierarchy, the most difficult item (i.e., an item that requires a relatively high trait level to be answered correctly) with the TAPAS was Item 1 (I find myself avoiding participating in sport because of my weight) and the easiest item (i.e., an item that requires a relatively low trait level to be answered correctly) was the Item 5 (I am concerned about what other people think of my appearance when I participate in sport). Table 3 contains the detailed item calibrations.

Table 2 Rasch analyses of the 10 TAPAS items
Full size table
Table 3 Rasch analyses of the nine TAPAS items
Full size table

When examining the response pattern of the participants, it was found that 16 (0.7%) and 302 (13%) out of the 2319 students had achieved the maximum and the minimum TAPAS scores, respectively. Therefore, there were no ceiling or floor effects in the TAPAS, which also confirmed that TAPAS items were well targeted to the current population group. Additionally, no significant DIF in any of the TAPAS items were found relative to gender (Table 4). All the DIF contrasts between male and female participants were between − 0.26 and 0.30 logit, which were within the negligible range.

Table 4 Gender DIF for the nine TAPAS items
Full size table

The correlation between TAPAS and the other three external measures were investigated. The Spearman correlation indicated strong correlations between TAPAS and WSSQ (r = .71, p < .001) and WBIS (r = .76, p < .001). No significant correlation was found between the TAPAS and BMI (Table 5). Lastly, the TAPAS showed excellent internal consistency (Cronbach’s α = 0.96).

Table 5 Spearman’s correlation between TAPAS and external measurements
Full size table

Discussion

To the best of the authors’ knowledge, the present large-scale study of university students across mainland China is the first study to examine the psychometric properties of the Tendency to Avoid Physical Activity and Sport Scale (TAPAS). The study applied a widely used rigorous analysis incorporating modern test theory (i.e., Rasch analysis) [21, 40] to examine the psychometric properties of the newly developed TAPAS to evaluate participants’ tendency to avoid physical activity and sport due to appearance or weight-stigma concerns. The results successfully provided evidence of the TAPAS’ construct validity and confirmed the unidimensionality of the test items in the nine-item version. Also, no ceiling or floor effects were found. Moreover, no DIF was found across male and female populations. Therefore, the TAPAS is a reliable and valid assessment tool that can be used to assess psychosocial concerns regarding appearance and weight-stigma in relation to avoiding physical activity and sport.

For the rating scale functioning, each rating category in the TAPAS substantively defined to represent a higher level of tendency to avoid physical activity and sport. The results showed that all items had average measures that advanced monotonically with the designed Likert scale, which confirmed that the five-point rating scale functioned well in capturing the underlying construct (i.e., tendency to avoid physical activity and sport). In other words, individuals with a lower tendency to engage in physical activity and sport rated lower on the TAPAS compared to individuals with a higher tendency.

When examining the TAPAS item structure, Item 10 (I would prefer to participate in physical activity in a more private setting) showed misfit MnSq with significant Zstd, which indicated that this particular item may be evaluating a different underlying trait that was not intended to be assessed [32]. Previous studies have found that individuals with obesity or weight concerns may exclude themselves from specific exercise settings [41, 42]. This item was originally designed to assess whether individuals would avoid engaging in physical activity in a public, open-space workout setting due to their appearance/weight concerns. However, when closely examining this item, the authors noticed that the term “private setting” stood out. The authors were of the view that this could be a misleading term which could imply a specific setting that only an individual with special privileges or being of high socioeconomic status have access to (e.g., the VIP room in a gym or a reserved room in a community sport center). Therefore, this misfitting item in the Rasch model might have been mis-interpreted and rated differently among the study participants. Consequently, this item was removed from the subsequent analysis. Future studies should consider either revising the item description or further examining whether this item should be retained in the TAPAS.

The item measures derived from the Rasch analysis helped to depict the item hierarchy along the linear construct to express the participants’ tendency to avoid physical activity and sport. The most easily adopted item was Item 5 (I am concerned about what other people think of my appearance when I participate in sport) followed by the Item 4 (I am afraid other people will notice my physical flaws when I participate in sport). The most difficult adopted item was Item 1 (I find myself avoiding participating in sport because of my weight) followed by the Item 6 (I avoid physical activity because I might get teased about my weight). Previous studies have demonstrated that attitudes toward physical activity are the most easily adopted variable and are consistently rated as a significant predictor of physical activity intentions among older adults [43] and students [44]. Item 4 and Item 5 in the TAPAS more concern a person’s attitude about engaging in physical activity, which align with prior findings that individuals’ attitude significantly associated with their intention to engage in physical activity. In contrast, Item 1 and item 6 in the TAPAS more concern the actual behaviors of avoiding physical activity. Pachankis [45] illustrated the psychological stages of how individuals process stigma conditions. Weight-related stigma may result in negative effects and attitude. Subsequently, such negative effects can lead to self-stigmatizing behaviors, such as avoidance or withdrawal from specific behaviors or social interactions [46]. Therefore, the current item hierarchy results were consistent with the prior findings that behavior-relevant items are expected to be the items least likely to be adopted by individuals.

Previous research shows that gender plays a critical role on how individuals perceive their body image [47]. More specifically, although both males and females have concerns regarding their body images, the way it manifests may be quite different (e.g., females feeling pressure to be thinner, and males feeling pressure to be more muscular). One possible explanation regarding no DIF being found in the TAPAS between gender could the “neutral phrasing” in the TAPAS. That is, no descriptions are prone to specific body image concerns of males or females in the TAPAS. Therefore, it can be used consistently with both male and female populations. Multiple surveillance studies which have investigated physical activity levels worldwide show that females have lower physical activity participation in comparison to males [11, 48]. Moreover, females have been found to be significantly more dissatisfied with their bodies than males [47, 49]. Therefore, it is critical that the newly developed TAPAS can be used validly and equitably for both males and females.

Finally, strong and significant associations were found between the TAPAS and the WSSQ, and the TAPAS and the WBIS. These findings are consistent with previous studies where stigma experiences were highly correlated with an increased desire to avoid exercise [14, 50]. These results confirmed the concurrent validity of the TAPAS. The associations between these standardized instruments were expected because they either evaluate the level of weight-related self-stigma or how it is being internalized to the individual’s belief, which are all closely related to their tendency to avoid physical activity and sport due to weight-related concerns. Notably, the TAPAS was not associated with BMI, which means, participants’ body fatness level was not directly relevant to their perspectives of physical activity and sport participation. A prior study indicated that individuals can experience and be aware of weight-related stigma. However, they do not necessarily apply the prejudice to themselves nor accept and internalize the stigma [51]. Therefore, in this case, it does not influence their tendency to take in part of any physical activity or sports. This may explain why physical activity and sport avoidance tendency was not directly related to BMI.

Limitations and future research recommendations

Although the TAPAS was designed to be used across a variety of populations [14, 52], the present study (while large-scale) was a convenience sample that only comprised university students. Therefore, future studies should explore the feasibility of the TAPAS to other population groups with more representative samples. One item (Item 10: I would prefer to participate in physical activity in a more private setting) did not fit the Rasch model. This needs to be further examined in future studies to ensure the item wording does not deviate from the main purpose of the item. Additionally, because individuals change their attitude toward how they view themselves and how they handle others’ judgment, future studies should be conducted to examine potential longitudinal effects. A prior study showed that body dissatisfaction and self-esteem differ across ethnic groups [53], and how individuals perceive their body image varies across different ages [47]. Therefore, further studies are suggested to include individuals from different ethnic groups and from varied life stages. In addition, the present study did not measure actual level of physical activity engagement. It is therefore unclear if the TAPAS and its items actually link to low levels of physical activity engagement. Consequently, future studies are needed to collect actual physical activity data to revalidate the TAPAS. Moreover, the present study did not collect the information as to whether any of the participants were athletes. Given that physical activity is the core behavior of athletes, it is important to know if the TAPAS can be used among athletes. Therefore, future studies may want to examine the psychometric properties of the TAPAS among athlete populations. Last, use of self-report data in relation to a sensitive topic, such as providing height and weight (to calculate BMI), could potentially contribute to self-reporting bias and social desirability bias. Future studies should consider adding objective measurements to increase data validity.

Conclusion

The present study applied Rasch analysis to examine the psychometric properties of the newly developed TAPAS among a university students’ population in mainland China. The study findings suggest that the TAPAS is a valid and reliable self-report psychometric instrument. Using this scale, weight and appearance concerns can be evaluated and then addressed with appropriate interventions to promote greater physical activity participation.

Data Availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Change history

  • 21 November 2023

    This article has been corrected since original publication; please see the linked erratum for further details.

  • 30 November 2023

    A Correction to this paper has been published: https://doi.org/10.1186/s40359-023-01451-5

Notes

  1. please see supplementary file for the Chinese versions of these measures

Abbreviations

BMI:

body mass index

DIF:

differential item functioning

MnSq:

Mean-square

Zstd:

standardized score

TAPAS:

Tendency to Avoid Physical Activity and Sport Scale

WBIS:

Weight Bias Internalization Scale

WSSQ:

Weight Self-Stigma Questionnaire

References

  1. Barbiellini Amidei C, Trevisan C, Dotto M, Ferroni E, Noale M, Maggi S, et al. Association of physical activity trajectories with major cardiovascular diseases in elderly people. Heart. 2022;108:360–6.

    Article  PubMed  Google Scholar 

  2. Jurdana M. Physical activity and cancer risk. Actual knowledge and possible biological mechanisms. Radiol Oncol. 2021;55:7–17.

    Article  PubMed  PubMed Central  Google Scholar 

  3. McTiernan A, Friedenreich CM, Katzmarzyk PT, Powell KE, Macko R, Buchner D, et al. Physical activity in cancer prevention and survival: a systematic review. Med Sci Sports Exerc. 2019;51:1252–61.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Burr JF, Davidson W, Shephard RJ, Eves N. Physical activity in chronic respiratory conditions. Can Fam Physician. 2012;58:761–4.

    PubMed  PubMed Central  Google Scholar 

  5. Eves ND, Davidson WJ. Evidence-based risk assessment and recommendations for physical activity clearance: respiratory disease. Appl Physiol Nutr Metab. 2011;36:80–100.

    Article  Google Scholar 

  6. Hamasaki H. Daily physical activity and type 2 diabetes: a review. World J Diabetes. 2016;7:243.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Gill DL, Hammond CC, Reifsteck EJ, Jehu CM, Williams RA, Adams MM, et al. Physical activity and quality of life. J Prev Med Pub Health. 2013;46(Suppl 1):28–34.

    Article  Google Scholar 

  8. Marquez DX, Aguiñaga S, Vásquez PM, Conroy DE, Erickson KI, Hillman C, et al. A systematic review of physical activity and quality of life and well-being. Transl Behav Med. 2020;10:1098–109.

    Article  PubMed  PubMed Central  Google Scholar 

  9. World Health Organization. Physical activity. 2022. https://www.who.int/news-room/fact-sheets/detail/physical-activity. Accessed 9 Dec 2022.

  10. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob Health. 2018;6:e1077–86.

    Article  PubMed  Google Scholar 

  11. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Global physical activity levels: Surveillance progress, pitfalls, and prospects. The Lancet. 2012;380:247–57.

    Article  Google Scholar 

  12. Withall J, Jago R, Fox KR. Why some do but most don’t. Barriers and enablers to engaging low-income groups in physical activity programmes: a mixed methods study. BMC Public Health. 2011;11:507.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Herazo-Beltrán Y, Pinillos Y, Vidarte J, Crissien E, Suarez D, García R. Predictors of perceived barriers to physical activity in the general adult population: a cross-sectional study. Braz J Phys Ther. 2017;21:44–50.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Bevan N, O’Brien KS, Lin C-Y, Latner JD, Vandenberg B, Jeanes R, et al. The relationship between weight stigma, physical appearance concerns, and enjoyment and tendency to avoid physical activity and sport. Int J Environ Res Public Health. 2021;18:9957.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Miranda VPN, de Morais NS, de Faria ER, Amorim PR dos, Marins S, Franceschini JCB, CC S do et al. Body dissatisfaction, physical activity, and sedentary behavior in female adolescents. Rev Paul Pediatr. 2018;36:482–90.

  16. Hao M, Fang Y, Yan W, Gu J, Hao Y, Wu C. Relationship between body dissatisfaction, insufficient physical activity, and disordered eating behaviors among university students in southern China. BMC Public Health. 2022;22:2054.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Lochbaum M, Gottardy J. A meta-analytic review of the approach-avoidance achievement goals and performance relationships in the sport psychology literature. J Sport Health Sci. 2015;4:164–73.

    Article  Google Scholar 

  18. Bevan N, O’Brien KS, Latner JD, Lin C-Y, Vandenberg B, Jeanes R, et al. Weight stigma and avoidance of physical activity and sport: development of a scale and establishment of correlates. Int J Environ Res Public Health. 2022;19:16370.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Tomiyama AJ. Weight stigma is stressful. A review of evidence for the cyclic Obesity/Weight-Based stigma model. Appetite. 2014;82:8–15.

    Article  PubMed  Google Scholar 

  20. Boateng GO, Neilands TB, Frongillo EA, Melgar-Quiñonez HR, Young SL. Best practices for developing and validating scales for health, social, and behavioral research: a primer. Front Public Health. 2018;6:149.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bond T. Applying the Rasch model - fundamental measurement in the human sciences. 3rd ed. Routledge; 2015.

  22. Boone WJ. Rasch analysis for instrument development: why, when, and how? CBE—Life Sci Educ. 2016;15:rm4.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Maneesriwongul W, Dixon JK. Instrument translation process: a methods review. J Adv Nurs. 2004;48:175–86.

    Article  PubMed  Google Scholar 

  24. Chirawat P, Kamolthip R, Rattaprach R, Nadhiroh SR, Tung SEH, Gan WY, et al. Weight stigma among young adults in Thailand: reliability, validation, and measurement invariance of the thai-translated Weight Self Stigma Questionnaire and Perceived Weight Stigma Scale. Int J Environ Res Public Health. 2022;19:15868.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lillis J, Luoma JB, Levin ME, Hayes SC. Measuring weight self-stigma: the Weight Self-Stigma Questionnaire. Obesity. 2010;18:971–6.

    Article  PubMed  Google Scholar 

  26. Nadhiroh SR, Nurmala I, Pramukti I, Tivany ST, Tyas LW, Zari AP, et al. Weight stigma in indonesian young adults: validating the indonesian versions of the Weight Self-Stigma Questionnaire and Perceived Weight Stigma Scale. Asian J Soc Health Behav. 2022;5:169.

    Article  Google Scholar 

  27. Lin KP, Lee ML. Validating a chinese version of the weight self-stigma Questionnaire for use with obese adults. Int J Nurs Pract. 2017;23:e12537.

    Article  Google Scholar 

  28. Lin C-Y, Imani V, Cheung P, Pakpour AH. Psychometric testing on two weight stigma instruments in Iran: Weight Self-Stigma Questionnaire and Weight Bias internalized Scale. Eat Weight Disord - Stud Anorex Bulim Obes. 2020;25:889–901.

    Article  Google Scholar 

  29. Durso LE, Latner JD. Understanding self-directed stigma: development of the Weight Bias internalization scale. Obesity. 2008;16:80–6.

    Article  Google Scholar 

  30. Chen H, Ye Y. Validation of the Weight Bias internalization scale for Mainland chinese children and adolescents. Front Psychol. 2021;11.

  31. Maters GN. Partial credit model. In: van der Linden WJ, editor. Handbook of item response theory: three volume set. Chapman and Hall; 2021.

  32. Wright BD, Linacre JM. Reasonable mean-square fit values. Rasch Meas Trans. 1994;8:370.

    Google Scholar 

  33. Linacre JM. Fit diagnosis: Infit outfit mean-square standardized. 2022. https://www.winsteps.com/winman/misfitdiagnosis.htm. Accessed 9 Dec 2022.

  34. McHorney CA, Tarlov AR. Individual-patient monitoring in clinical practice: are available health status surveys adequate? Qual Life Res. 1995;4:293–307.

    Article  PubMed  Google Scholar 

  35. Fan C-W, Chang K-C, Lee K-Y, Yang W-C, Pakpour AH, Potenza MN, et al. Rasch modeling and differential item functioning of the self-stigma scale-short version among people with three different psychiatric disorders. Int J Environ Res Public Health. 2022;19:8843.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Saffari M, Fan C-W, Chang Y-L, Huang P-C, Tung SEH, Poon WC, et al. Yale Food Addiction Scale 2.0 (YFAS 2.0) and modified YFAS 2.0 (mYFAS 2.0): Rasch analysis and differential item functioning. J Eat Disord. 2022;10:185.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Zwick R, Thayer DT, Lewis C. An empirical bayes approach to Mantel-Haenszel DIF analysis. J Educ Meas. 1999;36:1–28.

    Article  Google Scholar 

  38. Cameron IM, Scott NW, Adler M, Reid IC. A comparison of three methods of assessing differential item functioning (DIF) in the hospital anxiety Depression Scale: Ordinal logistic regression, Rasch analysis and the Mantel chi-square procedure. Qual Life Res. 2014;23:2883–8.

    Article  PubMed  Google Scholar 

  39. Akoglu H. User’s guide to correlation coefficients. Turk J Emerg Med. 2018;18:91–3.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Linacre JM. Optimizing rating scale category effectiveness. J Appl Meas. 2002;3(1):85–106.

    PubMed  Google Scholar 

  41. Thedinga HK, Zehl R, Thiel A. Weight stigma experiences and self-exclusion from sport and exercise settings among people with obesity. BMC Public Health. 2021;21:565.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Thiel A, John JM, Carl J, Thedinga HK. Weight stigma experiences and physical (in)activity: a biographical analysis. Obes Facts. 2020;13:386–402.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Duarte N, Hughes SL, Paúl C. Theory of planned behavior in predicting physical activity among portuguese older adults with osteoarthritis. Act Adapt Aging. 2022;46:60–72.

    Google Scholar 

  44. Pasi H, Lintunen T, Leskinen E, Hagger MS. Predicting school students’ physical activity intentions in leisure-time and school recess contexts: testing an integrated model based on self-determination theory and theory of planned behavior. PLoS ONE. 2021;16:e0249019.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Pachankis JE. The psychological implications of concealing a stigma: a cognitive-affective-behavioral model. Psychol Bull. 2007;133:328–45.

    Article  PubMed  Google Scholar 

  46. Boyd JE, Otilingam PG, DeForge BR. Brief version of the internalized stigma of Mental Illness (ISMI) scale: psychometric properties and relationship to depression, self esteem, recovery orientation, empowerment, and perceived devaluation and discrimination. Psychiatr Rehabil J. 2014;37:17–23.

    Article  PubMed  Google Scholar 

  47. Quittkat HL, Hartmann AS, Düsing R, Buhlmann U, Vocks S. Body dissatisfaction, importance of appearance, and body appreciation in men and women over the lifespan. Front Psychiatry. 2019;10:864.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Edwards ES, Sackett SC. Psychosocial variables related to why women are less active than men and related health implications. Clin Med Insights Womens Health. 2016;9(Suppl 1):47–56.

    PubMed  PubMed Central  Google Scholar 

  49. Tylka TL, Wood-Barcalow NL. The body appreciation Scale-2: item refinement and psychometric evaluation. Body Image. 2015;12:53–67.

    Article  PubMed  Google Scholar 

  50. Vartanian LR, Shaprow JG. Effects of weight stigma on exercise motivation and behavior: a preliminary investigation among college-aged females. J Health Psychol. 2008;13:131–8.

    Article  PubMed  Google Scholar 

  51. Corrigan PW, Rao D. On the self-stigma of mental illness: stages, disclosure, and strategies for change. Can J Psychiatry. 2012;57:464–9.

    Article  PubMed  Google Scholar 

  52. Saffari M, Chen IH, Huang PC, et al. Measurement invariance and psychometric evaluation of the tendency to avoid physical activity and Sport Scale (TAPAS) among mainland chinese university students. Psychol Res Behav Manag. 2023;16:3821–36.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Duncan MJ, Al-Nakeeb Y, Nevill AM, Jones MV. Body dissatisfaction, body fat and physical activity in british children. Int J Pediatr Obes. 2006;1:89–95.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We sincerely thank all the teaching faculty and research assistants who helped in the present study. We also thank all the participants for their involvement in the present study.

Funding

This research was funded by Asia University Hospital, grant number 11251009, the Ministry of Science and Technology, Taiwan (MOST 110-2410-H-006-115; MOST 111-2410-H-006-100), and the Higher Education Sprout Project, Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University (NCKU). This project was supported by the International Research Collaboration Fund granted by the Department of Social Work, The Chinese University of Hong Kong (Grant number: 19231106). The project was also partially supported by E-Da Hospital (EDAHP109027).

Author information

Author notes

  1. Chia-Wei Fan and Yen-Ling Chang contributed equally to the paper.

Authors and Affiliations

  1. Department of Occupational Therapy, AdventHealth University, 671 Winyah Drive, Orlando, FL, 32803, USA

    Chia-Wei Fan

  2. Department of Family Medicine, Cardinal Tien Hospital, No. 362, Zhongzheng Rd., Xindian Dist, New Taipei, 231403, Taiwan

    Yen-Ling Chang

  3. Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, No. 1, University Rd., East Dist, Tainan, 701401, Taiwan

    Po-Ching Huang & Chung-Ying Lin

  4. Department of Rehabilitation Sciences, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, 11 Yuk Choi Rd., Hung Hom, Kowloon, Hong Kong, China

    Xavier C. C. Fung

  5. Department of Social Work, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Ji-Kang Chen

  6. School of Social Sciences, Monash University, 20 Chancellors Walk, Melbourne, 3800, Australia

    Nadia Bevan & Kerry S. O’Brien

  7. Department of Occupational Therapy, Shu-Zen Junior College of Medicine and Management, 452, Huanqiu Rd., Luzhu Dist, Kaohsiung, 821004, Taiwan

    Ya-Chin Yeh

  8. Division of Colon and Rectal Surgery, Department of Surgery, E-Da Hospital, School of Medicine, College of Medicine, I-Shou University, Kaohsiung, 824, Taiwan

    Hsin-Pao Chen

  9. Chinese Academy of Education Big Data, Qufu Normal University, 57 Jingxuan West Rd, Qufu, 273165, China

    I-Hua Chen

  10. Department of Healthcare Administration, Asia University, Taichung, 41354, Taiwan

    I-Ching Lin

  11. Department of Family Medicine, Asia University Hospital, Taichung, 41354, Taiwan

    I-Ching Lin

  12. Department of Kinesiology, Health, and Leisure, Chienkuo Technology University, Changhua, 50094, Taiwan

    I-Ching Lin

  13. International Gaming Research Unit, Psychology Department, Nottingham Trent University, Nottingham, UK

    Mark D. Griffiths

  14. University of Religions and Denominations, Qom, Iran

    Chung-Ying Lin

  15. Department of Public Health, College of Medicine, National Cheng Kung University, No. 1, University Rd., East Dist, Tainan, 701401, Taiwan

    Chung-Ying Lin

  16. Biostatistics Consulting Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 1, University Rd., East Dist, Tainan, 701401, Taiwan

    Chung-Ying Lin

  17. INTI International University, Negeri Sembilan, Nilai, Malaysia

    Chung-Ying Lin

Authors
  1. Chia-Wei Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yen-Ling Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Po-Ching Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xavier C. C. Fung
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ji-Kang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nadia Bevan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kerry S. O’Brien
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ya-Chin Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hsin-Pao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. I-Hua Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. I-Ching Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mark D. Griffiths
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Chung-Ying Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, C-WF, Y-LC, P-CH, XCCF, J-KC, H-PC, I-CL, C-YL; methodology, NB, KSO, Y-CY, I-HC, MDG; software, C-WF; validation, Y-LC, H-PC, I-CL, C-YL, MDG; formal analysis, C-WF; investigation, Y-LC, J-KC, H-PC, I-HC, I-CL, C-YL; resources, Y-LC, P-CH, H-PC, I-HC, I-CL, C-YL; data curation, I-HC; writing–original draft preparation, C-WF, Y-LC, P-CH, C-YL; writing–review and editing, all authors; visualization, XCCF, J-KC, NB, KSO, Y-CY, H-PC, I-HC, I-CL, MDG, C-YL; project administration, I-CL, C-YL; funding acquisition, J-KC, H-PC, I-CL, C-YL. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Hsin-Pao Chen, I-Ching Lin or Chung-Ying Lin.

Ethics declarations

Ethics approval and consent to participate

The present study was approved by the Institutional Review Board of the Jiangxi Psychological Consultant Association (IRB ref: JXSXL-2021‐J99) and all the participants have signed a written informed consent before participation. All methods were performed in accordance with the Declaration of Helsinki and participants could withdraw consent at any time.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, CW., Chang, YL., Huang, PC. et al. The Tendency to Avoid Physical Activity and Sport Scale (TAPAS): Rasch analysis with differential item functioning testing among a Chinese sample. BMC Psychol 11, 369 (2023). https://doi.org/10.1186/s40359-023-01377-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40359-023-01377-y

Keywords

BMC Psychology

ISSN: 2050-7283

Contact us