Examining alternatives for assessing the effect of exercise on health-related consumption decisions: a questionnaire survey

Examining alternatives for assessing the effect of exercise on health-related consumption decisions: a questionnaire survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Examining alternatives for assessing the effect of exercise on health-related consumption decisions: a questionnaire survey Ziqi Wang, Simin Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8427153/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The current study explores the "exercise effect" on large groups of people via a survey-oriented method of epidemiology. Our sampling method started with 312 students at the China University of Mining and Technology. The participants were divided into a low-level physical activity group (n=156) and a high-level physical activity group (n=156). To test the reliability of the questionnaire by using different scoring models, we applied the concordance correlation coefficient (CCC). Compared with less physically active people, more physically active people presented less general risk perceptions and more health-conscious purchasing behavior (risk perception: P = 0.018; purchasing behavior: P < 0.001). Additionally, the results were consistent when the stringent scoring criterion was used (risk perception: P = 0.041; purchasing behavior: P < 0.001). The results at the dimension level revealed significant differences between groups in terms of perceived susceptibility (P = 0.018), perceived severity (P = 0.031), health-related worry (P = 0.022), preventive purchase intention (P < 0.001), health-related product preferences (P = 0.004), and health-related long-term investment in consumption (P = 0.009). The results indicate that questionnaire-based methods substitute for complex methods, concerning the effect of exercise on the perception of risk and the purchase of health-related products. Health sciences/Health care Health sciences/Medical research Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors Concordance correlation analysis Short-term physical activity Questionnaire surveys Risk perception Health-related purchasing behavior Consistency Figures Figure 1 Figure 2 Introduction A steadily increasing proportion of health decisions made by consumers are increasingly informed by how individuals judge risk and uncertainty1-4. This is particularly pertinent with respect to health prevention decisions, the use of health-enhancing products, and the consumption of health-enhancing services5. For example, the literature shows that these decisions are not driven by purely objective judgments of decisions on health outcomes; instead, they remain highly influenced by fluctuating psychological and physiological states6,7. Across this range of psychological and physiological states, exercise has emerged as an important factor that has the potential to cause rapid changes in affect, cognition, and control. Many studies have verified the beneficial effects of regular physical exercise on physical and emotional well-being as well as on behavioral choices8-10. However, more recent empirical evidence suggests that acute interventions involving physical exercise can help achieve specific effects on cognitive performance, control of emotions, and risk-taking behavior11-13. The transient effect of physical exercise has been proven to affect behavioral choices regarding the effects of risk-taking on financial resources, the use of substances, and reward evaluation14. Nevertheless, the role of those transient effects on healthy consumption choices has yet to be examined. Risk perception is an important psychological process that forms the basis of health-related consumption decisions15-16. There is a divergence between individuals' risk perceptions and objective risk, but risk perceptions play a vital role in forming the choices they make and the products they consume17. Studies have shown that exercise has the potential to modify risk perception and vulnerability/sensitivity to risk18-19. This is achieved through an improvement in perceived physical competence and control, as well as through increasing risk awareness and motivations to adopt health-related considerations. Nevertheless, the majority of studies undertaken within this field have been concerned with risk perception or behavior20. There appears to be a noticeable absence of research focused on investigating how risk perception and behavior affect one another, particularly with regard to short-term exercise regimens21-22. In addition, while certain studies have proposed connections between exercise and behavioral elements, primarily those related to purchasing, the majority of studies have focused on long-term exercise behaviors23-25. This provides very little insight into how a single, short exercise bout might elicit behaviors related to purchasing decisions. Interestingly, the mediating process of risk perception, wherein transient physical activity is linked to health-related purchasing outcomes via a risk assessment procedure, has yet to be reviewed26,27. Notably, the scope of risk perception is precisely the area where the physiological state of the consumer interfaces with evaluations to drive purchasing outcomes of a behavioral nature with attendant economic consequences. Therefore, the objective of this research study is to present a comprehensive, scientifically supported analysis of the current available evidence concerning what has come to be known as the ‘exercise effect’ in relation to perceived risk. While it focuses primarily on the specialized effect of short-term exercise intervention on risk perception, it further explores ways in which changes in these factors of risk perception could influence health purchase decisions. Through scientific research studies conducted from both experimental and observational perspectives in relation to risk perception, we hope to provide insight into the interlinkage of exercise behavior patterns with those of purchasing patterns in relation to health. Methods Study participants and design For this study, cross-sectional analysis was performed through a structured questionnaire. Prior to data collection, the research was approved by the Institutional Review Board of China University of Mining and Technology, and consent was obtained from all study participants. All methods were performed in accordance with the relevant guidelines and regulations (e.g., the Declaration of Helsinki). A total of 312 participants were drawn from China University of Mining and Technology. The participants included both undergraduate and postgraduate students. The inclusion criteria included participants aged between 18 and 30 years and those who were full-time students. The exclusion criteria included participants with a diagnosed psychiatric disorder, participants with chronic cardiovascular disease, participants with physical ailments that would make it difficult for them to take part in physical activities, and other relevant factors. Furthermore, participants were excluded if they did not complete the attention-check questions. A structured self-administered questionnaire was used to collect the data. The participants responded to the questionnaire either in the class environment or via the internet, depending on the available facilities. Using the structured self-administered questionnaire, information about short-term engagement in physical activities, risk perceptions, health-related purchases, and health-related buying intentions as well as some demographic information, such as level, age, and perceptions of health, was collected. The participants were then grouped according to their level of involvement in short-term exercise activities to establish the level of involvement across the activity categories. The three questionnaires were administered in one session, with all the answers remaining anonymous before the commencement of the data analysis. Questionnaire development Every participant was required to answer a self-designed, structured research questionnaire before data collection started. The research questionnaire was divided into two separate sections. The first section contained some personal data, such as age, gender, academic level, and self-perception of personal health, and was intended as a proposed descriptive characteristic of the population under investigation, functioning as a proposed control in the data analyses. Section two covered the key variables that were studied: recent physical activity involvement, perceived health-related risk, and health-related purchasing. For physical activity involvement, the questions included the intensity of the physical activity accomplished within the last seven days prior to the study. For example, “How often have you engaged in moderate or vigorous physical activity lasting at least 10 minutes during the past week?” Questions were carefully developed for the other two research variables. The answers were rated on a five-point Likert-type scale, with 1 indicating strongly disagree and 5 indicating strongly agree. For the items measuring the frequency of the behavior, the scale consisted of 1 (never) and 5 (very often). A response of zero was assumed if any item was unanswered. A composite measure for any construct was obtained by adding all the responses to the items loading on that construct. A larger composite score represented greater engagement in physical activities, greater risk perceptions about health, and greater proclivity toward health-related purchases. Physical activity manipulation To evaluate the level of physical activity experienced by the respondents, the research used a self-assessment technique that was incorporated into a structured questionnaire. The respondents are required to recall their experience with physical activity in the last seven days prior to administering the survey. For the evaluation process, physical activity can be defined as any activity that is moderate or vigorously intense and results in increased heart rate and/or breath rate28. Examples of physical activities are brisk walking, cycling, and running. This was referred to as the physical activity assessment section. In this section, the respondents were asked a series of questions with the aim of determining the prevalence of their involvement in physical activities. Some sample questions included “How often have you participated in moderate or vigorous physical activity lasting at least 10 minutes in the past seven days?” and “On average, how intense was your physical activity?” With respect to the first question on frequency, the response categories included one to five with the description “never” to “very often,”, respectively. With respect to the intensity question, the response categories included one to five with the description “very light” to “very intense,”, respectively. Using the data provided, a composite score of physical activity engagement was derived by adding the scores from two items related to the frequency and intensity of physical activity. A higher composite score indicates more engagement with physical activity. By using the composite scores, the participants were classified on the basis of their exposure. Risk perception and purchasing behavior assessment These findings were generated by employing standardized self-report techniques within the questionnaire, which focused on risk perception and health-associated purchasing decisions. In an attempt to determine perceived risk, a set of questions was employed to explore perceptions of vulnerability to health risks, as well as perceptions of the severity of health threats. For these questions, a statement about personal beliefs about health risks and how serious they might be was given. To analyze health-related consumer purchasing behavior, scenario-based research questions were designed to replicate how decisions are made in everyday situations involving health-related products. For each scenario, the respondent indicated their intention and choice in purchasing health-related products and recorded answers via a five-point Likert scale ranging from strongly disagree to strongly agree. Composite scores for risk perception and purchasing behavior were derived by adding the responses to the respective items. A higher score indicates a greater level of risk perception for health as well as the tendency to purchase health-related items. There were some predefined criteria to combine the scores, and only the complete scores were considered for the analyses. Quality control (QC) of data collection and measurement Quality control techniques were implemented to the highest standards of stringency in all respects, from data collection to analysis, to completely preclude the possibility that the data were susceptible to being affected by unreliable answers. The questionnaires, each and every one of them, underwent stringent screening under specified circumstances to preclude the possibility of careless answers influencing the outcome. Prior to engaging in data processing, questionnaires were screened on specified criteria related to data of questionable validity. A crucial aspect of the quality control process for the measurement technique was the integration of attention-check questions and the examination of response times. These two factors are important for ensuring the validity of the method. Any form that failed more than once on the attention-check questions or was finished in less than the stipulated number of times was removed from the analysis process. For every aspect of the data analysis, two researchers worked separately to ensure a better level of objectivity and reliability of the results. Moreover, to avoid the possibility of errors while analyzing the data, the entire process of scoring and coding was performed as per a predefined protocol, which was established prior to commencing the experimental stage. To estimate the reliability of the data, the internal consistency was tested systematically. Cronbach’s alpha was used to determine the reliability of the scales for physical activity, risk perception, and purchasing behavior. Statistical analysis For the analyses, all the calculations were conducted via R software version 4.3.1. For the continuous variables, which seemed to be roughly normal, the results are presented in terms of the mean and standard deviation. However, for variables showing skewness, their presentation was in terms of the median and interquartile range. Finally, the categorical variables are presented in terms of their count and percentage. In the case of two groups, the independent samples t test was used if the necessary assumptions were met. Otherwise, the Mann‒Whitney U test was conducted. For the comparison of the categories, the chi-square test was conducted. To analyze the associations between physical activity, perceived risk, and health-related purchasing decisions, the Pearson correlation test and the Spearman correlation test were conducted. These analyses were carried out on the basis of the nature of the variables. A mediation analysis was performed on the basis of regression analysis, which is applicable for OLS regression equations, to determine whether there was a mediating effect on risk perception. The bootstrapping technique was used to estimate the indirect effect, with 5,000 resamples. Multicollinearity was checked through the VIF, and the assumptions for the regression models were finished. All the statistical tests were two-tailed, and the level of statistical significance was set to less than 0.05. Results Baseline characteristics of the study participants Table 1 presents the basic characteristics of the participants in the study. In total, 312 participants were ultimately included in the final analysis. The participants in the study were classified into two groups on the basis of their composite score for physical activity, which included the lower physical activity group and the higher physical activity group. There were no statistically significant differences in terms of age, sex, body mass index, or self-rated health between these two groups, as all P values were greater than 0.05. Table 1 Baseline characteristics of the study participants. Note that the P value indicates the group difference between physical activity groups. Indices Lower PA group (n = 156) Higher PA group (n = 156) P value Age (years, mean ± SE) 21.84 ± 0.29 22.11 ± 0.27 0.412 Gender (male/female) 68/88 71/85 0.735 BMI (kg/m², mean ± SE) 21.63 ± 0.41 21.47 ± 0.38 0.628 Self-rated health (mean ± SE) 3.62 ± 0.07 3.68 ± 0.06 0.481 Figure 1 shows the comparison of the two scores across the different groups of participants on the basis of their physical activity in relation to the scores of risk perception and health-related purchasing behavior. The data indicate that individuals in the high-level physical activity group tend to have low risk perception health scores but high health purchasing intention scores compared with those in the low-level physical activity group. To develop an informed perspective on the intensity of distinction between these groups, further tests of comparison were conducted on two different levels of classification concerning the overall indicators. On every level of these tests, there has been a noticeable distinction between these groups, which fall under physical activity, particularly in health-driven consumer behavior. Table 2 Differences in risk perception and purchasing behavior under different scoring thresholds. Note that higher scores indicate higher perceived risk or stronger purchasing intention. Scoring threshold Lower PA vs Higher PA (Risk perception) Lower PA vs Higher PA (Purchasing behavior) Standard scoring P = 0.018 P < 0.001 Conservative scoring P = 0.041 P < 0.001 Correlation analysis revealed that participating in short-term physical activity is generally associated with lowering risk perceptions. This is because the relationship is indicated by a negative correlation coefficient of r = − 0.24 (P < 0.001). Conversely, the relationship between physical activity and purchasing behavior related to personal and family healthcare was positive, as indicated by a correlation coefficient of r = 0.31 (P < 0.001). In addition, risk perception is related to nonparticipation in purchasing behavior related to personal and family healthcare, as indicated by the negative correlation coefficient of r = − 0.28 (P < 0.001). Study participants and physical activity–related outcomes Discrepancies in outcome measures between different physical activity groups. Within the subjects that participated in this research, a number of regions were established, as depicted in Fig. 1 , where the characteristics of risk perception tended to be delineated. These traits tended to be most observable among members in the lower physical activity stratum. In contrast, subjects within the stratum of high physical activity displayed a decreased perception of risk with respect to susceptibility to health problems and were highly confident in their choices concerning health. Concerning the buying intentions for issues pertaining to health, the data show that the group of people categorized as having higher activity levels exhibited a greater intention to buy preventative health care products and services that promote healthier eating. That is, the group categorized as having lower activity was more anxious and less sure about their health risks than the group categorized as having higher activity, who were less concerned about their health risks. The discrepancy in the total questionnaire scores between different groups. Marked differences were observed among the participants in terms of their levels of physical activity, as calculated by the scores from the questionnaire. These differences depend on the scoring standards used, as indicated in Table 2 . When standard scoring standards were used, those participants ranked into higher physical activity groups demonstrated a markedly lower mean for the total risk perception score, whereas they demonstrated a markedly higher mean for health-related purchasing behaviors compared with those ranked into lower physical activity groups (P < 0.05). When more strict standards for scoring were used, participants ranked into higher physical activity groups demonstrated a significantly different mean for risk perception, although the gap regarding purchasing behaviors was smaller, with a statistically significant difference being maintained. Discrepancies in the dimensions of questionnaire scores among different groups. Further analysis of the dimensions of the questionnaire revealed interesting differences between the groups on the basis of level of physical activity for a variety of constructs. In a pairwise test of difference, when individuals who had lower levels of physical activity were compared with individuals who had been identified as exercising at a higher level, there were significant differences, including perceived susceptibility (P = 0.018), perceived severity (P = 0.031), and health-related worries (P = 0.022). There were also significant differences found for preventive purchase intentions (P < 0.001), preferences for health-related merchandise (P = 0.004), and health-related behaviors for an extended period of time (P = 0.009). Study participants and questionnaire scores Validity and reliability testing of questionnaires. The internal consistency of the research instrument is high since the alpha value is 0.842. Additionally, Bartlett’s test of sphericity is significant since the P value is less than 0.001. This determines that the correlation matrix is not an identity matrix. Furthermore, the Kaiser‒Meyer‒Olkin (KMO) sample adequacy is 0.758. All the above measures indicate the adequacy of the construct validity and sample adequacy. Differences in total questionnaire scores among different groups. The data analysis of the total scores of the questionnaire revealed a significant difference across the groups in terms of physical activity. In particular, as shown in Table 3 , individuals with a high level of physical activity have significantly lower scores for total risk perception and significantly higher scores for health-related purchasing behaviors than individuals with a lower level of physical activity. This relationship is highly significant (P value is less than 0.001). Most significantly, such discrepancies have been found to be significant despite performing similar subgroup analyses. Table 3 The discrepancy in the total scores of the questionnaire among different physical activity groups. Questionnaire Lower PA vs Higher PA Alternative grouping comparison Total score P < 0.001 P < 0.001 Concordance correlation analysis of questionnaire scores across physical activity groups. This result from the concordance correlation coefficient (CCC) analysis shows that there is a very high level of consistency in the scores obtained from the questionnaire among the various groups, in that the intervals are very narrow, as exhibited by the 95% confidence intervals of the concordance estimates shown in Table 4 . Specifically, for people classified in the lower physical activity category, the CCC value is 0.84, with a 95% CI ranging from 0.78–0.89, whereas in the higher category, the CCC value is 0.87, with a 95% CI ranging from 0.82–0.91. For the assessment of a new grouping or method of scoring, the CCC value was 0.89, with a 95% CI ranging from 0.85–0.93. From the above findings, it may be concluded that the index between the questionnaire scores, when examined under varying conditions, is relatively low. A CCC value greater than 0.80, as per the general acceptance criterion for the CCC, reveals a satisfactory index of concordance. In conclusion, the results from the concordance correlation analysis show a strong level of concordance among the scores within the two physical activity groups across the different methods of analysis. Table 4 Concordance correlation analysis of questionnaire scores across physical activity groups Group CCC Lower limit of 95% CI Upper limit of 95% CI Lower physical activity group 0.84 0.782 0.891 Higher physical activity group 0.872 0.821 0.913 Alternative grouping criterion 0.889 0.852 0.934 Discussion On the basis of the empirical evidence generated from conducting research, there is a strong positive link between short-term physical activity and health-oriented consumer behavior. This basically indicates that as a person becomes increasingly involved in short-term physical activities, he/she becomes less concerned about health risks and manifests intense intentions toward purchasing health-oriented products. On the other hand, as a person becomes increasingly involved in short-term physical activities, his health risks escalate, and his intentions toward purchasing health-oriented products diminish. Furthermore, from the concordance correlation analysis, there is a certain level of consistency, which indicates that, to a large extent, the questionnaire method can also be treated as a substitute for more complex methods in risk perception, health-related purchasing, or consumption decisions. However, existing research has highlighted not only the role of education but also the role of physical activities in the cognitive and emotional processes of health-related decisions. For example, moderate physical activity influences individuals’ emotional states, attentiveness, or judgment, which in turn results in their perception of health-related threats29-32. Similarly, in our analysis, there are certain significant differences between individuals who perform physical activities and those who do not. Furthermore, there are also significant health-related purchasing behaviors in these two groups. Risk perception is very important in regard to health care decisions, considering that it influences a variety of cognitive and behavioral processes33,34. It determines how a person perceives and estimates a certain risk, what drives a person toward preventive behaviors, and what choices he or she makes among a variety of available options35-37. Owing to rising health concerns, governments and health care organizations have started developing interventions aimed at promoting healthy living among people38. Numerous interventions are intended to empower a person to live a physically active lifestyle as a primary approach39,40. Despite these interventions, unhealthy behaviors among the population are still common. Given the background mentioned above, it is also important to note the significance of this study in presenting a new method of analyzing the relationship between physical exercise and risk perception, including connected issues related to purchasing decisions with a focus on a healthy lifestyle. This is linked to a large number of studies related to physical exercise and decision-making, taking a risk perspective, using experimental and lab studies, and analyzing the process of decision-making associated with risk perception via a classical method of behavioral studies combined with survey studies of a healthy lifestyle41-43. They reported a commonly accepted fact: a healthy lifestyle has a significantly important impact on risk perception and, consequently, on decision-making processes. The designed questionnaire used in this research has been proven effective in determining whether the respondents have participated in any physical activities in recent years. The questionnaire is also effective in determining patterns in relation to health-related consumption behavior. Moreover, the results obtained through various methodologies have proven to be consistent. From the analyzed information on the basis of the collected data, the use of questionnaires seems to be an effective and preferable approach for determining risk perception and consumption behavior. Moreover, this methodology may provide a distinguished framework for making these determinations in contrast to the experimental approach preferred in the design of this research. However, one of the drawbacks of the current study is that it has a relatively small sample size. This was due to the factors that have already been identified, so this study cannot conduct an in-depth analysis to determine which of the variables in the questionnaires are valid and reliable. Moreover, there is still room for improvement in terms of studying other variables of behavior, in addition to the scope of the main study. For example, there could be studies on general behavioral traits in terms of lifestyle and overall behaviors in terms of eating choices, in addition to actions that directly relate to purchases. In general, the current study offers a new perspective for investigating ways and means by which physical exercise could affect risk perceptions and health-related purchases by individuals. In other words, this paper builds a robust foundation that could open avenues for future studies regarding this issue. Using the survey approach to conduct research, this study provides an effective screening tool for identifying the implications of physical exercise in relation to both behavior and psychological aspects. As an area for future research, it would be beneficial, and a larger, representative sample is needed. A strong research design would be essential for determining pathways through which health risks associated with physical activities affect perceptions of those health risks and how those health risks affect purchasing decisions. One of the goals of such research would be lowering health risks and fostering healthy decision-making choices. As such, one of the most important recommendations is to use physical activities as a component of a communication approach that emphasizes healthy food choices. The implementation of these activities will be highly effective for enhancing health outcomes because the approach will teach consumers how to make healthy food choices. Conclusion This paper examines the possibility of substituting the complex and expensive method of laboratory experiments with a data collection method using questionnaires. This method can be applied when evaluating the effects of short-term physical exercise on risk perception and the purchase of healthcare by individuals. The possibility of substituting the method of laboratory experiments with another method is illustrated with the introduction of the concept of questionnaires. Declarations The study protocol was approved by the Institutional Review Board of China University of Mining and Technology. Written informed consent was obtained from all participants prior to data collection. Competing interests The authors declare no competing interests. Funding Not applicable. Author Contribution Z.W. conceived and designed the study. Z.W. collected the data and performed the statistical analyses. Z.W. drafted the manuscript. S.C. contributed to data interpretation and revised the manuscript. All authors reviewed the final version of the manuscript. Data Availability The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. References Lerner, J. S. & Keltner, D. Fear, anger, and risk. J. 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Psychol. 5 , 85–89. https://doi.org/10.1016/j.copsyc.2015.03.012 (2015). Sobkow, A., Zaleskiewicz, T., Petrova, D., Garcia-Retamero, R. & Traczyk, J. Worry, risk perception, and controllability predict intentions toward COVID-19 preventive behaviors. Front. Psychol. 11 , 582720. https://doi.org/10.3389/fpsyg.2020.582720 (2020). Van der Pligt, J. Risk perception and self-protective behavior. Eur. Psychol. 1 , 34–43. https://doi.org/10.1027/1016-9040.1.1.34 (1996). Renner, B., Schüz, B. & Sniehotta, F. F. Preventive health behavior and adaptive accuracy of risk perceptions. Risk Anal. 28 , 741–748. https://doi.org/10.1111/j.1539-6924.2008.01047.x (2008). Zhang, C. Q., Zhang, R., Schwarzer, R. & Hagger, M. S. A meta-analysis of the health action process approach. Health Psychol. 38 , 623. https://doi.org/10.1037/hea0000728 (2019). Mauro, A., Bruland, D. & Latteck, Ä. D. With enthusiasm and energy throughout the day: promoting a physically active lifestyle in people with intellectual disability by using a participatory approach. Int. J. Environ. Res. Public. Health . 18 , 12329. https://doi.org/10.3390/ijerph182312329 (2021). Parkin, B. L. A behavioral and brain science perspective on decision making in sport (Doctoral dissertation, University College London) (2017). Stephan, Y., Boiche, J., Trouilloud, D., Deroche, T. & Sarrazin, P. The relation between risk perceptions and physical activity among older adults: a prospective study. Psychol. Health . 26 , 887–897. https://doi.org/10.1080/08870446.2010.509798 (2011). Connolly, C. P. et al. The influence of risk perceptions and efficacy beliefs on leisure-time physical activity during pregnancy. J. Phys. Act. Health . 13 , 494–503. https://doi.org/10.1123/jpah.2015-0358 (2016). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8427153","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":593399700,"identity":"34cbbe31-5cbf-4e97-9abe-609f640874e4","order_by":0,"name":"Ziqi Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3RvQrCMBDA8ZNAXKJZryj1FQKFToKv0qC4CbpIB4dCpR3Ud3HyY1MDmeLu4KAIzro5Kjgqbd0c8pvzh9wdgGX9IdpQm+0tbLJWzO+nIBzlJ1Wk8oKm64oyeOJkdH7iIvNEZbzzBAffOY9JgY/Vpj460UYuCXRDGVHg6STITup6iIPVUa5j0Ae5qgOa/Tw7gfYCHXOVkSolB2koCOzlJYGPlUS9EkL7MiEFEuy8xk+UJxSlUCxh+r1kJ2YEA6NZ7iyNNH6fknNTuj/CkcvTWXbygf323LIsy/rqCYkKSqrTSPcDAAAAAElFTkSuQmCC","orcid":"","institution":"China University of Mining and Technology","correspondingAuthor":true,"prefix":"","firstName":"Ziqi","middleName":"","lastName":"Wang","suffix":""},{"id":593399708,"identity":"5453b559-125b-4877-8b62-18eba1c1f8d0","order_by":1,"name":"Simin Chen","email":"","orcid":"","institution":"China University of Mining and Technology","correspondingAuthor":false,"prefix":"","firstName":"Simin","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-12-22 16:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8427153/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8427153/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103179285,"identity":"e14a8511-be24-4406-8836-86e83234b1a4","added_by":"auto","created_at":"2026-02-22 17:09:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":154015,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of the research questionnaire\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8427153/v1/d1da7fc369a3626313950822.png"},{"id":103179286,"identity":"c4d16cc1-d67f-48fe-8651-5aac3c0a9fec","added_by":"auto","created_at":"2026-02-22 17:09:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14134,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of risk perception and health-related purchasing behavior across physical activity groups.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8427153/v1/f932175611652751423ef2b1.png"},{"id":107505035,"identity":"ae1d796c-1eda-4baf-93fc-bf5661985098","added_by":"auto","created_at":"2026-04-22 06:42:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":517274,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8427153/v1/2c0ea370-bfa2-4e7a-b1d0-322e19ee4f81.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Examining alternatives for assessing the effect of exercise on health-related consumption decisions: a questionnaire survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA steadily increasing proportion of health decisions made by consumers are increasingly informed by how individuals judge risk and uncertainty1-4. This is particularly pertinent with respect to health prevention decisions, the use of health-enhancing products, and the consumption of health-enhancing services5. For example, the literature shows that these decisions are not driven by purely objective judgments of decisions on health outcomes; instead, they remain highly influenced by fluctuating psychological and physiological states6,7. Across this range of psychological and physiological states, exercise has emerged as an important factor that has the potential to cause rapid changes in affect, cognition, and control.\u003c/p\u003e \u003cp\u003eMany studies have verified the beneficial effects of regular physical exercise on physical and emotional well-being as well as on behavioral choices8-10. However, more recent empirical evidence suggests that acute interventions involving physical exercise can help achieve specific effects on cognitive performance, control of emotions, and risk-taking behavior11-13. The transient effect of physical exercise has been proven to affect behavioral choices regarding the effects of risk-taking on financial resources, the use of substances, and reward evaluation14. Nevertheless, the role of those transient effects on healthy consumption choices has yet to be examined.\u003c/p\u003e \u003cp\u003eRisk perception is an important psychological process that forms the basis of health-related consumption decisions15-16. There is a divergence between individuals' risk perceptions and objective risk, but risk perceptions play a vital role in forming the choices they make and the products they consume17. Studies have shown that exercise has the potential to modify risk perception and vulnerability/sensitivity to risk18-19. This is achieved through an improvement in perceived physical competence and control, as well as through increasing risk awareness and motivations to adopt health-related considerations.\u003c/p\u003e \u003cp\u003eNevertheless, the majority of studies undertaken within this field have been concerned with risk perception or behavior20. There appears to be a noticeable absence of research focused on investigating how risk perception and behavior affect one another, particularly with regard to short-term exercise regimens21-22. In addition, while certain studies have proposed connections between exercise and behavioral elements, primarily those related to purchasing, the majority of studies have focused on long-term exercise behaviors23-25. This provides very little insight into how a single, short exercise bout might elicit behaviors related to purchasing decisions. Interestingly, the mediating process of risk perception, wherein transient physical activity is linked to health-related purchasing outcomes via a risk assessment procedure, has yet to be reviewed26,27. Notably, the scope of risk perception is precisely the area where the physiological state of the consumer interfaces with evaluations to drive purchasing outcomes of a behavioral nature with attendant economic consequences.\u003c/p\u003e \u003cp\u003eTherefore, the objective of this research study is to present a comprehensive, scientifically supported analysis of the current available evidence concerning what has come to be known as the \u0026lsquo;exercise effect\u0026rsquo; in relation to perceived risk. While it focuses primarily on the specialized effect of short-term exercise intervention on risk perception, it further explores ways in which changes in these factors of risk perception could influence health purchase decisions. Through scientific research studies conducted from both experimental and observational perspectives in relation to risk perception, we hope to provide insight into the interlinkage of exercise behavior patterns with those of purchasing patterns in relation to health.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants and design\u003c/h2\u003e \u003cp\u003eFor this study, cross-sectional analysis was performed through a structured questionnaire. Prior to data collection, the research was approved by the Institutional Review Board of China University of Mining and Technology, and consent was obtained from all study participants. All methods were performed in accordance with the relevant guidelines and regulations (e.g., the Declaration of Helsinki).\u003c/p\u003e \u003cp\u003eA total of 312 participants were drawn from China University of Mining and Technology. The participants included both undergraduate and postgraduate students. The inclusion criteria included participants aged between 18 and 30 years and those who were full-time students. The exclusion criteria included participants with a diagnosed psychiatric disorder, participants with chronic cardiovascular disease, participants with physical ailments that would make it difficult for them to take part in physical activities, and other relevant factors. Furthermore, participants were excluded if they did not complete the attention-check questions.\u003c/p\u003e \u003cp\u003eA structured self-administered questionnaire was used to collect the data. The participants responded to the questionnaire either in the class environment or via the internet, depending on the available facilities. Using the structured self-administered questionnaire, information about short-term engagement in physical activities, risk perceptions, health-related purchases, and health-related buying intentions as well as some demographic information, such as level, age, and perceptions of health, was collected.\u003c/p\u003e \u003cp\u003eThe participants were then grouped according to their level of involvement in short-term exercise activities to establish the level of involvement across the activity categories. The three questionnaires were administered in one session, with all the answers remaining anonymous before the commencement of the data analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuestionnaire development\u003c/h3\u003e\n\u003cp\u003eEvery participant was required to answer a self-designed, structured research questionnaire before data collection started. The research questionnaire was divided into two separate sections. The first section contained some personal data, such as age, gender, academic level, and self-perception of personal health, and was intended as a proposed descriptive characteristic of the population under investigation, functioning as a proposed control in the data analyses.\u003c/p\u003e \u003cp\u003eSection two covered the key variables that were studied: recent physical activity involvement, perceived health-related risk, and health-related purchasing. For physical activity involvement, the questions included the intensity of the physical activity accomplished within the last seven days prior to the study. For example, \u0026ldquo;How often have you engaged in moderate or vigorous physical activity lasting at least 10 minutes during the past week?\u0026rdquo; Questions were carefully developed for the other two research variables.\u003c/p\u003e \u003cp\u003eThe answers were rated on a five-point Likert-type scale, with 1 indicating strongly disagree and 5 indicating strongly agree. For the items measuring the frequency of the behavior, the scale consisted of 1 (never) and 5 (very often). A response of zero was assumed if any item was unanswered. A composite measure for any construct was obtained by adding all the responses to the items loading on that construct. A larger composite score represented greater engagement in physical activities, greater risk perceptions about health, and greater proclivity toward health-related purchases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePhysical activity manipulation\u003c/h3\u003e\n\u003cp\u003eTo evaluate the level of physical activity experienced by the respondents, the research used a self-assessment technique that was incorporated into a structured questionnaire. The respondents are required to recall their experience with physical activity in the last seven days prior to administering the survey. For the evaluation process, physical activity can be defined as any activity that is moderate or vigorously intense and results in increased heart rate and/or breath rate28. Examples of physical activities are brisk walking, cycling, and running.\u003c/p\u003e \u003cp\u003eThis was referred to as the physical activity assessment section. In this section, the respondents were asked a series of questions with the aim of determining the prevalence of their involvement in physical activities. Some sample questions included \u0026ldquo;How often have you participated in moderate or vigorous physical activity lasting at least 10 minutes in the past seven days?\u0026rdquo; and \u0026ldquo;On average, how intense was your physical activity?\u0026rdquo; With respect to the first question on frequency, the response categories included one to five with the description \u0026ldquo;never\u0026rdquo; to \u0026ldquo;very often,\u0026rdquo;, respectively. With respect to the intensity question, the response categories included one to five with the description \u0026ldquo;very light\u0026rdquo; to \u0026ldquo;very intense,\u0026rdquo;, respectively.\u003c/p\u003e \u003cp\u003eUsing the data provided, a composite score of physical activity engagement was derived by adding the scores from two items related to the frequency and intensity of physical activity. A higher composite score indicates more engagement with physical activity. By using the composite scores, the participants were classified on the basis of their exposure.\u003c/p\u003e\n\u003ch3\u003eRisk perception and purchasing behavior assessment\u003c/h3\u003e\n\u003cp\u003eThese findings were generated by employing standardized self-report techniques within the questionnaire, which focused on risk perception and health-associated purchasing decisions. In an attempt to determine perceived risk, a set of questions was employed to explore perceptions of vulnerability to health risks, as well as perceptions of the severity of health threats. For these questions, a statement about personal beliefs about health risks and how serious they might be was given.\u003c/p\u003e \u003cp\u003eTo analyze health-related consumer purchasing behavior, scenario-based research questions were designed to replicate how decisions are made in everyday situations involving health-related products. For each scenario, the respondent indicated their intention and choice in purchasing health-related products and recorded answers via a five-point Likert scale ranging from strongly disagree to strongly agree.\u003c/p\u003e \u003cp\u003eComposite scores for risk perception and purchasing behavior were derived by adding the responses to the respective items. A higher score indicates a greater level of risk perception for health as well as the tendency to purchase health-related items. There were some predefined criteria to combine the scores, and only the complete scores were considered for the analyses.\u003c/p\u003e\n\u003ch3\u003eQuality control (QC) of data collection and measurement\u003c/h3\u003e\n\u003cp\u003eQuality control techniques were implemented to the highest standards of stringency in all respects, from data collection to analysis, to completely preclude the possibility that the data were susceptible to being affected by unreliable answers. The questionnaires, each and every one of them, underwent stringent screening under specified circumstances to preclude the possibility of careless answers influencing the outcome. Prior to engaging in data processing, questionnaires were screened on specified criteria related to data of questionable validity.\u003c/p\u003e \u003cp\u003eA crucial aspect of the quality control process for the measurement technique was the integration of attention-check questions and the examination of response times. These two factors are important for ensuring the validity of the method. Any form that failed more than once on the attention-check questions or was finished in less than the stipulated number of times was removed from the analysis process.\u003c/p\u003e \u003cp\u003eFor every aspect of the data analysis, two researchers worked separately to ensure a better level of objectivity and reliability of the results. Moreover, to avoid the possibility of errors while analyzing the data, the entire process of scoring and coding was performed as per a predefined protocol, which was established prior to commencing the experimental stage.\u003c/p\u003e \u003cp\u003eTo estimate the reliability of the data, the internal consistency was tested systematically. Cronbach\u0026rsquo;s alpha was used to determine the reliability of the scales for physical activity, risk perception, and purchasing behavior.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor the analyses, all the calculations were conducted via R software version 4.3.1. For the continuous variables, which seemed to be roughly normal, the results are presented in terms of the mean and standard deviation. However, for variables showing skewness, their presentation was in terms of the median and interquartile range. Finally, the categorical variables are presented in terms of their count and percentage.\u003c/p\u003e \u003cp\u003eIn the case of two groups, the independent samples t test was used if the necessary assumptions were met. Otherwise, the Mann‒Whitney U test was conducted. For the comparison of the categories, the chi-square test was conducted. To analyze the associations between physical activity, perceived risk, and health-related purchasing decisions, the Pearson correlation test and the Spearman correlation test were conducted. These analyses were carried out on the basis of the nature of the variables.\u003c/p\u003e \u003cp\u003eA mediation analysis was performed on the basis of regression analysis, which is applicable for OLS regression equations, to determine whether there was a mediating effect on risk perception. The bootstrapping technique was used to estimate the indirect effect, with 5,000 resamples. Multicollinearity was checked through the VIF, and the assumptions for the regression models were finished.\u003c/p\u003e \u003cp\u003eAll the statistical tests were two-tailed, and the level of statistical significance was set to less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the basic characteristics of the participants in the study. In total, 312 participants were ultimately included in the final analysis. The participants in the study were classified into two groups on the basis of their composite score for physical activity, which included the lower physical activity group and the higher physical activity group. There were no statistically significant differences in terms of age, sex, body mass index, or self-rated health between these two groups, as all P values were greater than 0.05.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study participants. \u003cem\u003eNote\u003c/em\u003e that the P value indicates the group difference between physical activity groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower PA group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher PA group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68/88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71/85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the comparison of the two scores across the different groups of participants on the basis of their physical activity in relation to the scores of risk perception and health-related purchasing behavior. The data indicate that individuals in the high-level physical activity group tend to have low risk perception health scores but high health purchasing intention scores compared with those in the low-level physical activity group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo develop an informed perspective on the intensity of distinction between these groups, further tests of comparison were conducted on two different levels of classification concerning the overall indicators. On every level of these tests, there has been a noticeable distinction between these groups, which fall under physical activity, particularly in health-driven consumer behavior.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in risk perception and purchasing behavior under different scoring thresholds. \u003cem\u003eNote\u003c/em\u003e that higher scores indicate higher perceived risk or stronger purchasing intention.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScoring threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower PA vs Higher PA\u003c/p\u003e \u003cp\u003e(Risk perception)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower PA vs Higher PA\u003c/p\u003e \u003cp\u003e(Purchasing behavior)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard scoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConservative scoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis revealed that participating in short-term physical activity is generally associated with lowering risk perceptions. This is because the relationship is indicated by a negative correlation coefficient of r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.24 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, the relationship between physical activity and purchasing behavior related to personal and family healthcare was positive, as indicated by a correlation coefficient of r\u0026thinsp;=\u0026thinsp;0.31 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, risk perception is related to nonparticipation in purchasing behavior related to personal and family healthcare, as indicated by the negative correlation coefficient of r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.28 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants and physical activity\u0026ndash;related outcomes\u003c/h2\u003e \u003cp\u003eDiscrepancies in outcome measures between different physical activity groups.\u003c/p\u003e \u003cp\u003eWithin the subjects that participated in this research, a number of regions were established, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, where the characteristics of risk perception tended to be delineated. These traits tended to be most observable among members in the lower physical activity stratum. In contrast, subjects within the stratum of high physical activity displayed a decreased perception of risk with respect to susceptibility to health problems and were highly confident in their choices concerning health.\u003c/p\u003e \u003cp\u003eConcerning the buying intentions for issues pertaining to health, the data show that the group of people categorized as having higher activity levels exhibited a greater intention to buy preventative health care products and services that promote healthier eating. That is, the group categorized as having lower activity was more anxious and less sure about their health risks than the group categorized as having higher activity, who were less concerned about their health risks.\u003c/p\u003e \u003cp\u003eThe discrepancy in the total questionnaire scores between different groups.\u003c/p\u003e \u003cp\u003eMarked differences were observed among the participants in terms of their levels of physical activity, as calculated by the scores from the questionnaire. These differences depend on the scoring standards used, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. When standard scoring standards were used, those participants ranked into higher physical activity groups demonstrated a markedly lower mean for the total risk perception score, whereas they demonstrated a markedly higher mean for health-related purchasing behaviors compared with those ranked into lower physical activity groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When more strict standards for scoring were used, participants ranked into higher physical activity groups demonstrated a significantly different mean for risk perception, although the gap regarding purchasing behaviors was smaller, with a statistically significant difference being maintained.\u003c/p\u003e \u003cp\u003eDiscrepancies in the dimensions of questionnaire scores among different groups.\u003c/p\u003e \u003cp\u003eFurther analysis of the dimensions of the questionnaire revealed interesting differences between the groups on the basis of level of physical activity for a variety of constructs. In a pairwise test of difference, when individuals who had lower levels of physical activity were compared with individuals who had been identified as exercising at a higher level, there were significant differences, including perceived susceptibility (P\u0026thinsp;=\u0026thinsp;0.018), perceived severity (P\u0026thinsp;=\u0026thinsp;0.031), and health-related worries (P\u0026thinsp;=\u0026thinsp;0.022). There were also significant differences found for preventive purchase intentions (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), preferences for health-related merchandise (P\u0026thinsp;=\u0026thinsp;0.004), and health-related behaviors for an extended period of time (P\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants and questionnaire scores\u003c/h2\u003e \u003cp\u003eValidity and reliability testing of questionnaires.\u003c/p\u003e \u003cp\u003eThe internal consistency of the research instrument is high since the alpha value is 0.842. Additionally, Bartlett\u0026rsquo;s test of sphericity is significant since the P value is less than 0.001. This determines that the correlation matrix is not an identity matrix. Furthermore, the Kaiser‒Meyer‒Olkin (KMO) sample adequacy is 0.758. All the above measures indicate the adequacy of the construct validity and sample adequacy.\u003c/p\u003e \u003cp\u003eDifferences in total questionnaire scores among different groups.\u003c/p\u003e \u003cp\u003eThe data analysis of the total scores of the questionnaire revealed a significant difference across the groups in terms of physical activity. In particular, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, individuals with a high level of physical activity have significantly lower scores for total risk perception and significantly higher scores for health-related purchasing behaviors than individuals with a lower level of physical activity. This relationship is highly significant (P value is less than 0.001). Most significantly, such discrepancies have been found to be significant despite performing similar subgroup analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe discrepancy in the total scores of the questionnaire among different physical activity groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuestionnaire\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower PA vs Higher PA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlternative grouping comparison\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConcordance correlation analysis of questionnaire scores across physical activity groups.\u003c/p\u003e \u003cp\u003eThis result from the concordance correlation coefficient (CCC) analysis shows that there is a very high level of consistency in the scores obtained from the questionnaire among the various groups, in that the intervals are very narrow, as exhibited by the 95% confidence intervals of the concordance estimates shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSpecifically, for people classified in the lower physical activity category, the CCC value is 0.84, with a 95% CI ranging from 0.78\u0026ndash;0.89, whereas in the higher category, the CCC value is 0.87, with a 95% CI ranging from 0.82\u0026ndash;0.91. For the assessment of a new grouping or method of scoring, the CCC value was 0.89, with a 95% CI ranging from 0.85\u0026ndash;0.93.\u003c/p\u003e \u003cp\u003eFrom the above findings, it may be concluded that the index between the questionnaire scores, when examined under varying conditions, is relatively low. A CCC value greater than 0.80, as per the general acceptance criterion for the CCC, reveals a satisfactory index of concordance.\u003c/p\u003e \u003cp\u003eIn conclusion, the results from the concordance correlation analysis show a strong level of concordance among the scores within the two physical activity groups across the different methods of analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcordance correlation analysis of questionnaire scores across physical activity groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower limit of 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper limit of 95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower physical activity group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher physical activity group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlternative grouping criterion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOn the basis of the empirical evidence generated from conducting research, there is a strong positive link between short-term physical activity and health-oriented consumer behavior. This basically indicates that as a person becomes increasingly involved in short-term physical activities, he/she becomes less concerned about health risks and manifests intense intentions toward purchasing health-oriented products. On the other hand, as a person becomes increasingly involved in short-term physical activities, his health risks escalate, and his intentions toward purchasing health-oriented products diminish.\u003c/p\u003e \u003cp\u003eFurthermore, from the concordance correlation analysis, there is a certain level of consistency, which indicates that, to a large extent, the questionnaire method can also be treated as a substitute for more complex methods in risk perception, health-related purchasing, or consumption decisions.\u003c/p\u003e \u003cp\u003eHowever, existing research has highlighted not only the role of education but also the role of physical activities in the cognitive and emotional processes of health-related decisions. For example, moderate physical activity influences individuals\u0026rsquo; emotional states, attentiveness, or judgment, which in turn results in their perception of health-related threats29-32. Similarly, in our analysis, there are certain significant differences between individuals who perform physical activities and those who do not. Furthermore, there are also significant health-related purchasing behaviors in these two groups.\u003c/p\u003e \u003cp\u003eRisk perception is very important in regard to health care decisions, considering that it influences a variety of cognitive and behavioral processes33,34. It determines how a person perceives and estimates a certain risk, what drives a person toward preventive behaviors, and what choices he or she makes among a variety of available options35-37. Owing to rising health concerns, governments and health care organizations have started developing interventions aimed at promoting healthy living among people38. Numerous interventions are intended to empower a person to live a physically active lifestyle as a primary approach39,40. Despite these interventions, unhealthy behaviors among the population are still common.\u003c/p\u003e \u003cp\u003eGiven the background mentioned above, it is also important to note the significance of this study in presenting a new method of analyzing the relationship between physical exercise and risk perception, including connected issues related to purchasing decisions with a focus on a healthy lifestyle. This is linked to a large number of studies related to physical exercise and decision-making, taking a risk perspective, using experimental and lab studies, and analyzing the process of decision-making associated with risk perception via a classical method of behavioral studies combined with survey studies of a healthy lifestyle41-43. They reported a commonly accepted fact: a healthy lifestyle has a significantly important impact on risk perception and, consequently, on decision-making processes.\u003c/p\u003e \u003cp\u003eThe designed questionnaire used in this research has been proven effective in determining whether the respondents have participated in any physical activities in recent years. The questionnaire is also effective in determining patterns in relation to health-related consumption behavior. Moreover, the results obtained through various methodologies have proven to be consistent. From the analyzed information on the basis of the collected data, the use of questionnaires seems to be an effective and preferable approach for determining risk perception and consumption behavior. Moreover, this methodology may provide a distinguished framework for making these determinations in contrast to the experimental approach preferred in the design of this research.\u003c/p\u003e \u003cp\u003eHowever, one of the drawbacks of the current study is that it has a relatively small sample size. This was due to the factors that have already been identified, so this study cannot conduct an in-depth analysis to determine which of the variables in the questionnaires are valid and reliable. Moreover, there is still room for improvement in terms of studying other variables of behavior, in addition to the scope of the main study. For example, there could be studies on general behavioral traits in terms of lifestyle and overall behaviors in terms of eating choices, in addition to actions that directly relate to purchases.\u003c/p\u003e \u003cp\u003eIn general, the current study offers a new perspective for investigating ways and means by which physical exercise could affect risk perceptions and health-related purchases by individuals. In other words, this paper builds a robust foundation that could open avenues for future studies regarding this issue. Using the survey approach to conduct research, this study provides an effective screening tool for identifying the implications of physical exercise in relation to both behavior and psychological aspects. As an area for future research, it would be beneficial, and a larger, representative sample is needed. A strong research design would be essential for determining pathways through which health risks associated with physical activities affect perceptions of those health risks and how those health risks affect purchasing decisions. One of the goals of such research would be lowering health risks and fostering healthy decision-making choices.\u003c/p\u003e \u003cp\u003eAs such, one of the most important recommendations is to use physical activities as a component of a communication approach that emphasizes healthy food choices. The implementation of these activities will be highly effective for enhancing health outcomes because the approach will teach consumers how to make healthy food choices.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis paper examines the possibility of substituting the complex and expensive method of laboratory experiments with a data collection method using questionnaires. This method can be applied when evaluating the effects of short-term physical exercise on risk perception and the purchase of healthcare by individuals. The possibility of substituting the method of laboratory experiments with another method is illustrated with the introduction of the concept of questionnaires.\u003c/p\u003e "},{"header":"Declarations","content":" \u003cp\u003e The study protocol was approved by the Institutional Review Board of China University of Mining and Technology. Written informed consent was obtained from all participants prior to data collection.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.W. conceived and designed the study. Z.W. collected the data and performed the statistical analyses. Z.W. drafted the manuscript. S.C. contributed to data interpretation and revised the manuscript. All authors reviewed the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLerner, J. S. \u0026amp; Keltner, D. Fear, anger, and risk. \u003cem\u003eJ. Pers. Soc. 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Health\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 494\u0026ndash;503. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1123/jpah.2015-0358\u003c/span\u003e\u003cspan address=\"10.1123/jpah.2015-0358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Concordance correlation analysis, Short-term physical activity, Questionnaire surveys, Risk perception, Health-related purchasing behavior, Consistency","lastPublishedDoi":"10.21203/rs.3.rs-8427153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8427153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The current study explores the \"exercise effect\" on large groups of people via a survey-oriented method of epidemiology. Our sampling method started with 312 students at the China University of Mining and Technology. The participants were divided into a low-level physical activity group (n=156) and a high-level physical activity group (n=156). To test the reliability of the questionnaire by using different scoring models, we applied the concordance correlation coefficient (CCC). Compared with less physically active people, more physically active people presented less general risk perceptions and more health-conscious purchasing behavior (risk perception: P = 0.018; purchasing behavior: P \u003c 0.001). Additionally, the results were consistent when the stringent scoring criterion was used (risk perception: P = 0.041; purchasing behavior: P \u003c 0.001). The results at the dimension level revealed significant differences between groups in terms of perceived susceptibility (P = 0.018), perceived severity (P = 0.031), health-related worry (P = 0.022), preventive purchase intention (P \u003c 0.001), health-related product preferences (P = 0.004), and health-related long-term investment in consumption (P = 0.009). The results indicate that questionnaire-based methods substitute for complex methods, concerning the effect of exercise on the perception of risk and the purchase of health-related products.","manuscriptTitle":"Examining alternatives for assessing the effect of exercise on health-related consumption decisions: a questionnaire survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 17:08:31","doi":"10.21203/rs.3.rs-8427153/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17bf766a-235b-4724-8e3d-fb3f1fc36142","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63144727,"name":"Health sciences/Health care"},{"id":63144728,"name":"Health sciences/Medical research"},{"id":63144729,"name":"Biological sciences/Psychology"},{"id":63144730,"name":"Social science/Psychology"},{"id":63144731,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-22T06:42:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 17:08:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8427153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8427153","identity":"rs-8427153","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}