Cultural Adaptation and Psychometric Evaluation of the Preference for Intuition and Deliberation in Eating Decision-making Scale among Chinese People with Cardiovascular Disease

Cultural Adaptation and Psychometric Evaluation of the Preference for Intuition and Deliberation in Eating Decision-making Scale among Chinese People with Cardiovascular Disease | 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 Research Article Cultural Adaptation and Psychometric Evaluation of the Preference for Intuition and Deliberation in Eating Decision-making Scale among Chinese People with Cardiovascular Disease Huixia Huang, Menglei Wang, Guiping Hu, Ping Lin, Xueqin Gao, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7199291/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 Background Healthy diet plays a critical role in the prevention, treatment, and prognosis of cardiovascular disease. The Preference for Intuition and Deliberation in Eating Decision-Making Scale (E-PID) was developed to evaluate individuals' preferences for intuitive and deliberative styles when making dietary decision. Our study aims to culturally adapt it into Chinese and test its psychometric properties in 1463 people with cardiovascular disease (CVD). Methods The original E-PID was translated into Chinese using the Brislin translation model. 1463 patients were recruited from a hospital from July 2024 to December 2024. The psychometric properties of the Chinese version of the E-PID were assessed through item analyses, composite reliability, test re-test reliability, measurement invariance (MI), factorial validity, discriminative validity and criterion-related validity. Results Item analyses indicated that no item deletion was necessary. Both exploratory factor analysis (EFA) and exploratory graph analysis (EGA) ( n = 704) supported the two-factor structure of the 7-item original scale, and confirmatory factor analysis (CFA)indicated that the scale demonstrated a satisfactory model fit. Psychometric properties showed strong internal consistency, sufficient criterion-related validity and test-retest reliability over a six-week period. The results also demonstrated that the Chinese version of the E-PID maintained good measurement properties across gender, supporting its applicability for examining gender-related differences. Conclusion The E-PID showed sufficient psychometric properties in a Chinese sample, making it a valid instrument for assessing dietary decision-making preferences among people with cardiovascular disease in China and can serve as an effective tool for both clinical practice and research. Dietary behavior Intuitive eating Psychometric properties Deliberation EGA Decision-making Cardiovascular disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In recent years, cardiovascular disease (CVD) has remained one of the leading causes of death and disability in China, posing a substantial public health burden [ 1 ]. Among the various modifiable behavioral risk factors, unhealthy dietary patterns play a critical role in the onset, progression, and prognosis of CVD [ 2 , 3 ]. Consequently, optimizing dietary behavior is a key component of secondary prevention strategies for this high-risk population. Dietary behavior is not only shaped by nutritional knowledge or access to healthy food but is also deeply influenced by individuals’ cognitive and decision-making styles [ 4 , 5 ]. Emerging evidence suggests that people differ in how they make dietary decisions: some rely more on gut feelings and automatic responses (intuitive style), while others engage in reflective thinking and deliberate reasoning (deliberative style) [ 6 ]. These individual tendencies termed dietary decision-making preferences can significantly influence diet quality, intervention adherence, and ultimately, long-term health outcomes [ 5 , 7 – 9 ]. Understanding and identifying such cognitive preferences is therefore critical for designing tailored dietary interventions that align with patients’ natural decision-making inclinations, thereby playing an important role in the secondary prevention of cardiovascular disease. However, most existing dietary behavior assessment tools used in China primarily focus on emotional eating, external cues, or restrained eating [ 10 , 11 ], and fail to capture the cognitive dimension of dietary decision-making. This leaves a significant gap in our understanding of how individuals navigate food choices based on instinctive vs. rational processing. Particularly in patients with CVD, whose dietary decisions affect disease outcomes, assessing these decision-making preferences is of high clinical relevance. To bridge this gap, König et al. [ 5 ] developed the Preference for Intuition and Deliberation in Eating Decision-Making Scale (E-PID), a tool engineered to access individual preferences toward intuitive or deliberative approaches when making dietary decisions. Notably, Brazilian researchers have also undertaken the cultural adaptation and validation of the E-PID [ 12 ], and both original German version and the Brazilian adaptation have confirmed its robust psychometric properties and support its two-factor structure. To our knowledge, the E-PID has not yet been culturally adapted or validated in China, limiting its application in Chinese clinical and research settings. Given the unique cultural, cognitive, and behavioral characteristics of Chinese CVD patients [ 13 , 14 ], there is a clear need to develop a Chinese version of the E-PID through rigorous translation, cultural adaptation, and psychometric evaluation. Such an instrument would not only fill an important methodological gap but also provide researchers and clinicians with a culturally relevant tool for assessing cognitive decision-making styles in dietary behavior. This could facilitate the development of more effective, individualized dietary interventions in CVD care. Moreover, in addition to traditional psychometric approaches, the current study introduces Exploratory Graph Analysis (EGA), a modern network-based technique that allows for the data-driven identification of dimensional structures without reliance on strict theoretical assumptions [ 15 , 16 ]. Additionally, EGA incorporates a bootstrap procedure to assess the consistency and robustness of its findings [ 17 ]. Moreover, a supplementary advantage of EGA is that it facilitates the accessible visualization, the attribution of the dimensionality of an item can be determined visually by using a network graph coded in different colors. By employing EGA alongside conventional factor analysis, the study seeks to gain deeper insights into the structural validity of the Chinese version of the E-PID. In sum, this study aimed to (1) translate and culturally adapt the two-factor E-PID scale into Chinese, ensuring linguistic and conceptual equivalence; and (2) validate the psychometric properties of the Chinese version of the E-PID scale in a cardiovascular disease (CVD) patient population. This was achieved through a comprehensive evaluation of its dimensionality using both Exploratory Factor Analysis (EFA) and EGA, as well as assessments of reliability and convergent validity; (3) assess the measurement invariance across gender, thereby testing whether the scale performs consistently across male and female subgroups and establishing its applicability for gender-based comparisons in future dietary behavior research. Methods Cultural Adaptation and Translation of the E-PID The original scale authors granted permission for the adaptation of the E-PID. We conducted the translation and back-translation process in accordance with Brislin’s guidelines. Step 1 forward translation: Three PhDs from the fields of nursing, nutrition, psychology, together with a medically trained bilingual expert, independently conducted the forward translation and cultural adaptation of the scale. Step 2 reconciliation: One nursing master’s graduate with overseas study experience consolidated these into the initial translation (T1). Step 3 back translation: One English master’s graduate and two nursing specialists unfamiliar with the original scale independently back-translated T1 into English. Subsequently, another nursing master’s graduate reviewed and consolidated three versions to create the back-translated version (BT1). Step 4 Expert review: BT1 was submitted to the original author for review. Based on the feedback, ambiguous items were refined, resulting in the revised version T2. Subsequently, six nursing experts with extensive experience in clinical practice and education (all holding a master’s degree or above and with at least 10 years of nursing experience) were invited to review T2 by Delphi method. They were asking to provide suggestions focusing on content appropriateness, semantic accuracy, and the standardization of medical expressions. Based on their feedback, resulting in the pre-final version of the E-PID. Step 5 Pilot testing: Conducted a pilot rest with 20 patients with cardiovascular disease using the pre-final version E-PID. After completing the scale, the patients were interviewed by the researcher to collect feedback on item comprehension, clarity of expression, and cultural appropriateness. The scale items were modified based on insights from the interviews, ultimately resulting in the finalized Chinese version of the E-PID, which includes 7 items. Participants This cross-sectional, non-interventional study was conducted at the cardiology department of a hospital in China from July 2024 to December 2024. Patients were eligible if they met the following criteria: (1) no intellectual disabilities and language impairments and able to complete questionnaire assessment, (2) over 18 years old. Exclusion criteria include: (1) refusal to participate, (2) undergoing diet-related nutritional therapy, (3) a current diagnosis of a severe psychiatric disorder. The minimum required sample size was 10 participants per item, although a larger sample was preferred [ 18 ]. Finally, 1463 participants were recruited. Procedures The researcher explained each item of the scale, and the participant responded accordingly. When necessary, the researcher provided assistance by documenting responses for participants with sensory or literacy impairments. All participants signed an informed consent form and voluntarily took part in the study without any compensation or incentives. Measures Demographic information Participants were further asked to report their sex, age, height (cm) and weight (kg), place of residence, living pattern, educational attainment, current occupation status, monthly household net income, smoking status, drinking status. Self-reported height and weight were collected to calculate the body mass index (BMI = kg/m 2 ). Participants’ demographic information is outlined in Table 1 . Table 1 Descriptive characteristics of participants. Variables First Split-half for EFA, EGA ( n = 704) Second Split-half for CFA( n = 759) P-Value Sex, n (%) 0.72 Men 424 (60.2) 464 (61.1) Women 280 (39.8) 295 (38.9) Age group, n (%) 0.52 18–44 46 (6.5) 39 (5.1) 45–59 245 (34.8) 258 (34.0) ≧ 60 413 (58.7) 462 (60.9) Residence, n (%) 0.66 Urban 288 (40.9) 319 (42.0) Non-urban 416 (59.1) 440 (58.0) Living with, n (%) 0.58 Alone 20 (2.8) 17 (2.2) Partner 517 (73.4) 541 (71.3) Children 50 (7.1) 63 (8.3) Partner and Children 117 (16.6) 138 (18.2) Educational attainment, n (%) 0.76 Primary school or Illiterate 147 (20.9) 175 (23.1) Junior high school 374 (53.1) 399 (52.6) High school 119 (16.9) 120 (15.8) University 64 (9.1) 65 (8.6) Monthly household net income in CNY (yuan), n (%) 0.96 ¥0 - ¥2000 384 (54.5) 418 (55.1) ¥2000 - ¥5000 276 (39.2) 295 (38.9) > ¥5000 44 (6.3) 46 (6.1) Smoking status, n (%) 0.51 Never 405 (57.5) 429 (56.5) Current 198 (28.1) 232 (30.6) Former 101 (14.3) 98 (12.9) Drinking status, n (%) 0.70 Never 600 (85.2) 667 (87.9) Current 70 (9.9) 63 (8.3) Former 34 (4.8) 29 (3.8) BMI, n (%) 0.18 < 18.5 14 (2.0) 22 (2.9) 18.5–23.9 229 (32.5) 278 (36.6) 24-27.9 321 (45.6) 311 (41.0) ≥ 28 124 (17.6) 130 (17.1) Bedridden, exact weight unknown 16 (2.3) 18 (2.4) CNY: Chinese Yuan; BMI: Body Mass Index. Preference for Intuition and Deliberation in Eating Decision-Making Scale(E-PID) The E-PID is a two-factor scale adapted by König et al. to effectively assess eating-related decision-making either through intuition or deliberation [ 5 ], and contains two subscales: Preference for Intuition (3 items) and Preference for Deliberation (4 items). All items are rated on a 5-point Likert scale from 1 I do not agree to 5 I agree, higher mean scores of the subscales reflect greater tendency of one’s eating-related intuitive or deliberation decisive-making. The English version of the E-PID scale has good reliability and validity with the Cronbach’s α of 0.79 for Preference for Intuition and 0.82 for Preference for Deliberation. Participants completed the Chinese version of the E-PID, which had undergone translation and cultural adaptation into Chinese. Intuitive Eating Scale-2 (IES-2) Tylka and Kroon initially developed the IES in 2006 and updated it in 2013 into the 23-item IES-2 [ 19 ], which was used to capture individual differences in intuitive eating. The Chinese version of the IES-2 with Cronbach’s α of 0.95 and a test–retest correlation of 0.89 among cardiovascular disease patients was included in the present study to examine construct validity of E-PID [ 20 ]. This 23-item IES-2 consists of 4 subscales: (1) Unconditional Permission to Eat (6 items); (2) Eating for Physical Rather Than Emotional Reasons (8 items); (3) Reliance on Hunger and Satiety Cues (6 items); and (4) Body-Food Choice Congruence (3 items). In the current study, participants were asked to rate each item from strongly disagree (1 score) to strongly agree (5 score). The higher their scores, the greater their level of intuitive eating. Brief Self-control Scale (BSCS) General self-control was assessed using the BSCS, which demonstrated good validity [ 21 ]. The BSCS consists of 7 items and 2 subscales answered with a 5-point Likert scale: (1) Self-Discipline (3 items); (2) Impulse Control (4 items). Higher scores indicate a higher level of self-control. Statistical analyses The data were analyzed using SPSS 27.0, Mplus 8.3 and R 4.4.1. The full sample was randomly divided into two datasets using SPSS 27.0, dataset 1 which consisted of 704 samples and was used for EFA and EGA; and dataset 2 which consisted of 759 samples and was used for CFA. In addition, the full sample was used for all statistical analyses in the subsequent data analysis unless otherwise specified. The univariate normality was assessed by using skewness <|2| and kurtosis <|7| [ 22 ]. Descriptive statistics summarized item means and response frequencies. Item analysis included three methods: the correlation coefficient method, in which items with item-total correlations of r 0.05 were deleted; the corrected item-total correlation (CITC) method, which compared Cronbach’s α before and after removing items, if Cronbach’s α increased significantly after deletion, the item was removed [ 23 ]; and independent samples t-test between the top and bottom 27% of subscale mean scores, with non-significant items excluded [ 24 ]. Before conducting EFA, the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s Test of Sphericity were performed to assess sampling adequacy (KMO > 0.70 and p 1 were extracted via using principal component analysis with varimax rotation. Factors retention was based on eigenvalues, cumulative variance contribution, and scree plot analysis [ 26 ]. EGAnet package in R 4.4.1 was used to model EGA process, combining the GLASSO network estimation [ 27 ] and the Walktrap algorithm to uncover the number of item clusters and their internal structure [ 28 ]. Subsequently, the bootEGA was used to perform 5,000 bootstrap samples to assess the stability of the EGA results. The key indicators include the consistency of the number of dimensions, structural consistency, and item replication index which must be greater than or equal to 0.75. Confirmatory factor analysis (CFA) was applied using weighted least squares mean and variance adjusted (WLSMV) estimation. Model fitting was evaluated with TLI, CFI and GFI ≥ 0.95; RMSEA < 0.08; and RMR < 0.05 [ 29 ]. Although χ 2 / df < 3 is commonly used, it wasn’t considered here due to its sensitivity to large sample sizes. Convergent validity of the two subscale of the E-PID for different genders was assessed through Pearson correlation analysis, p -value > 0.05 indicated non-significant changes [ 30 ]. Reliability was evaluated using Cronbach’s α, McDonald’s ω, and test-retest reliability. McDonald's ω (> 0.70) was considered a robust indicator of internal consistency [ 31 ]. Multi-group CFA and independent samples t-tests were conducted to measure the invariance of E-PID across gender. Four nested models were compared: Configural (A), Metric (B), Scalar (C), and Strict (D) Invariance model. If both ∆CFI and ∆RMSEA between B vs. A, C vs. B, and D vs. C were all < 0.01, then invariance holds [ 32 ]. Independent samples t-tests assessed subscale differences, with p < 0.05 (two-tailed) indicating statistical significance. Results Response frequency of each item in the Chinese version of E-PID We used the convenience sampling method for data collection, total 1463 participant’s data collected. Table S1 shows the frequency (percentage), mean, SD, skewness and kurtosis of each score for all E-PID items. The skewness and kurtosis of the distribution of E-PID scores are within the accepted range, and there is no violation of the normality of the scale totals. The percentages for each score are shown in Fig. 1 . In preference for intuition (item1-item3), most patients would choose a score of 3 and 4, and in preference for deliberation (item4-item7), most of the options were clustered around scores of 2, 3, and 4. Item analysis Item analysis revealed that all items in the Preference for Intuition dimension were positively correlated with the total subscale score (r = 0.89 − 0.84, ps < 0.001), and all items in the Preference for Deliberation dimension were positively correlated with their total score (r = 0.71–0.81, ps < 0.001). Corrected item total correlation coefficients were also computed for each item within the full scale and their respective subscales. The Cronbach’s α coefficients ranged from 0.64 to 0.71 for the total scale, 0.75 to 0.80 for the Preference for Intuition subscale, and 0.66 to 0.72 for the Preference for Deliberation subscale when each item was deleted. Since the internal consistency coefficients decreased following item deleting, no items were removed. Finally, independent samples t-tests were conducted by comparing the top and bottom 27% of respondents based on subscale mean scores. For the Preference for Intuition subscale, the high groups (≥ 4) and the low 27% (≤ 3) were compared; for the Preference for Deliberation subscale, the high group (≥ 3.25) and the low group (≤ 2.75) were used. The results showed that all items had significant differences between the two groups, with t-values ranging from 20.629 to 62.476 (all p < 0.001), indicating good item discrimination and no need for item removal. Exploratory factor analysis Exploratory factor analysis (EFA) was performed on Dataset 1. Bartlett’s test of sphericity was statistically significant (p < 0.001), and the KMO value was 0.76, indicating adequate sampling and strong inter-item correlations. Parallel analysis supported a two-factor solution with factor 1 and factor 2 contributing 40.58% and 27.41% of the variance, respectively, explaining a total of 67.99% (Table 2 ). Table 2 E-PID items and factor loadings for EFA sample ( n = 704). Preference for intuition Preference for deliberation Mean SD Item1 0.85 3.67 0.91 Item2 0.88 3.26 0.99 Item3 0.89 3.06 1.07 Item4 0.80 3.27 0.61 Item5 0.73 2.02 0.57 Item6 0.76 2.70 0.63 Item7 0.80 3.78 0.77 %variance accounted for 39.84 27.70 Cronbach’s α 0.85 0.78 Mean 3.33 2.94 SD 0.87 0.5 Exploratory graph analysis EGA detected two dimensions (Fig. 2 ), namely dimension 1 (including INT1, INT2 and INT3), and dimension 2 (including DEL1, DEL2, DEL3 and DEL4). In the network plot, nodes represent the 7 items, and edge thickness reflects the strength of association; the thicker green edges indicate the stronger positive relationships. EGA’s bootstrap results demonstrated high stability, both dimensions were detected with a frequency of 1.00; all items were categorized into their respective dimensions with a frequency of 1.00 ( Figure S 1 ); and the structural consistency of both dimensions was also 1.00. These findings provide strong support for the robust 2-dimensional structure of the Chinese version of the E-PID. Confirmatory factor analysis CFA was conducted to validate the previously established structure using Dataset 2. Given the sensitivity of χ 2 / df to sample size, it was excluded from model evaluation. Model fit was assessed using CFI, GFI, TLI, RMSEA and SRMR. The results showed that the E-PID model fit well with CFI = 0.99, GFI = 0.99, TLI = 0.98, RMSEA = 0.059 (90% CI [0.041, 0.077]) and SRMR = 0.026 (Fig. 3 ). Content Validity The results demonstrated that the I-CVI values of each item ranged from 0.83 to 1.00, with an S-CVI of 0.97, supporting the scale’s robust content validity. Convergent validity The E-PID subscales demonstrated satisfactory convergent validity. Specifically, the intuitive preference subscale showed a significant positive correlation with the IES-2 (r = 0.55, p < 0.001), while the deliberative preference subscale exhibited strong correlated with the BSCS (r = 0.66, p < 0.001), further supporting the convergent validity of the scale (Fig. 4 ). Darker shades indicate stronger correlations, blue for positive and red for negative. The area of each square reflects the significance of the p -value, the larger the area, the smaller the p-value (more significant). Internal consistency and test-retest reliability Cronbach’s α values and their 95% CI for the total E-PID, Preference for Intuition and Preference for Deliberation were 0.71 (95% CI [0.68, 0.75]), 0.83 (95% CI [0.80, 0.86]) and 0.75 (95% CI [0.73, 0.77]) respectively, indicating acceptable internal consistency. Test-retest reliability assessed 32 participants over a 6-week interval showed strong temporal stability with results of 0.86 (95% CI [0.72, 0.93]) for the Preference for Intuition, and 0.79 (95% CI [0.61, 0.89]) for the Preference for Deliberation. Additionally, McDonald's ω values further supported reliability, with values for the E-PID, preference for intuition and preference for deliberation at 0.81 (95% CI [0.78, 0.84]), 0.87 (95% CI [0.85, 0.90]) and 0.73 (95% CI [0.70, 0.77]) respectively. These results demonstrate the good reliability of the Chinese version of E-PID. Measurement invariance tests and gender differences Measurement invariance of the E-PID across genders was tested via multi-group CFA. Model fit remained consistent across configural, metric, scalar and strict invariance model, supporting full measurement invariance of the Chinese version of the E-PID scale across genders (Table 3 ). Table 3 Measurement invariance test of the Chinese version of the E-PID scale ( n = 1463). Model χ 2 df CFI RMSEA Model Comparisons ∆CFI ∆RMSEA Gender A: configural invariance 79.268 26 0.983 0.053 B: metric invariance 87.079 31 0.982 0.050 B vs. A 0.001 0.003 C: scalar invariance 97.000 36 0.980 0.048 C vs. B 0.002 0.002 D: strict invariance 120.049 43 0.975 0.049 D vs. C 0.005 -0.001 After confirming that the E-PID had measurement invariance by gender, independent samples t-tests were conducted to compare the two subscales for gender differences. The results revealed that men scored slightly higher on Preference for Intuition was in men (M = 3.39, SD = 0.83) than women (M = 3.33, SD = 0.85), t (1463) = 1.25, p > 0.05; and slightly on Preference for Deliberation (M = 2.96, SD = 0.50) than women (M = 2.99, SD = 0.50), t (1463) = -1.21, p > 0.05. Discussion Given the lack of validated assessment tools for dietary decision-making preference in the CVD population, present study is the first culturally adapted version of the E-PID scale to be translated into Chinese, culturally adapted, and evaluated for its psychometric properties [ 33 ]. The results provide initial evidence for the psychometric soundness of the Chinese version E-PID confirming its two-factor structure with seven items, internal consistency, criterion-related validity, and temporal stability. These findings contribute to the growing interest in cognitive approaches to dietary decision-making and fill an important gap in culturally validated assessment tools for chronic disease dietary management in Chinese populations. Both EFA and CFA analyses supported the original two-factor structure of the E-PID and good fit indices [ 5 ] distinguishing between preference for intuition (3 items) and preference for deliberation (4 items) in eating decisions. Notably, the factor structure identified through EGA further confirmed the robustness of its two-factor model, offering strong visual and statistical support for dimensionality. This is especially relevant as the original study by König et al. relied solely on confirmatory approaches [ 5 ]. The use of EGA represents a methodological advancement by allowing theory-free discovery of latent structures [ 34 ], which is particularly advantageous in a new cultural context such as China, where eating behaviors are shaped by unique social and health belief systems. The two subscales demonstrated different associations with criterion-related measures, supporting their conceptual distinctiveness. The Preference for Intuition subscale was positively correlated with scores on the Intuitive Eating Scale-2 [ 19 ], suggesting that individuals who trust internal cues such as hunger, satiety, and sensory pleasure are more likely to adopt intuitive eating decision-making styles. This correlation highlights the cultural resonance of intuitive eating in a traditionally collectivist society like China, where food carries deep social meaning and is embedded in family rituals [ 35 ]. During questionnaire time, participants scoring high on intuitive eating frequently referenced ideas such as “eat when hungry” or “I eat what I grow, it’s healthy” rooted in traditional Chinese medical discourses like Yin-Yang balance and holistic harmony [ 36 ]. In contrast, the Preference for Deliberation subscale showed a positive association with self-control, indicating that individuals who prioritize planning and rational assessment in dietary decisions tend to have higher levels of general self-regulation. While deliberation is generally beneficial for managing chronic diseases, excessive cognitive control may lead to guilt, social isolation or social stress, particularly during festival communal meals involving symbolic or high-calorie foods [ 37 ]. These culturally specific tensions suggest that promoting deliberation must be balanced with sensitivity to the emotional and social dimensions of eating in Chinese culture [ 35 , 38 ]. Internal consistency for both subscales was adequate, with Cronbach’ α and McDonald’s ω coefficients exceeding 0.70. Test-retest reliability over a six-week interval confirmed the scale’s temporal stability. These results demonstrate that the Chinese version E-PID is both internally consistent and stable over time, making it suitable for use in both clinical and research settings. Several culturally nuanced response patterns emerged. The item “ I think about my eating plans and goals more often than others do我比其他人更常考虑自己的饮食计划和目标 ” received lower scores in the Chinese CVD patients compared to the German and Brazilian validations. This may reflect culturally rooted values of modesty and humility, where overt self-assertion and social comparison are generally discouraged. In traditional Chinese culture, even individuals confident in their dietary decisions may be reluctant to express superiority over others due to prevailing social norms that valorize self-restraint and deference [ 39 ]. Moreover, response distributions tended to cluster around the midpoint with relatively few participants selecting extreme values (1 or 5 points). This response pattern aligns with prior research documenting Chinese participants’ preference for moderate responses [ 40 ]. Such moderation may reflect the influence of Confucian cultural values, particularly the emphasis on harmony, restraint, and the “Doctrine of the Mean” (Zhong Yong), which encourages balanced and non-extreme expressions in both thought and behavior [ 41 ]. Particularly, this response style was not evenly distributed across the two subscales. Items within the Preference for Intuition dimension exhibited more extreme responses compared to those in the Preference for Deliberation dimension. This may suggest that intuitive behaviors evoke stronger personal resonance or emotional engagement, leading to more confident responses. In contrast, deliberation may be perceived as normative or socially regulated resulting in more neutral or cautious answers. From a CVD-related perspective, patients with CVD often prioritize direct medical treatments such as pharmacotherapy and surgical interventions, while considering dietary management a secondary and supportive strategy. Consequently, dietary decisions are frequently driven by intuitive or habitual patterns rather than deliberate planning. Moreover, depressive symptoms are common among the CVD patients with a reported prevalence of 30–50% [ 42 ]. These emotional factors may contribute to a negative cognitive bias such as underestimating the long-term benefits of dietary changes or hold fatalistic beliefs such as “even a healthy diet won't help”. Taken together, these results underscore the importance of cultural context in interpreting self-report data. Future studies may benefit from incorporating methods to adjust for response styles or using qualitative approaches to better understand how cultural scripts shape participants’ answers of scale items. It is also recommended to include subgroup analyses, particularly for the group of CVD patients with concomitant depressive symptoms, to enable the development of more tailored and effective interventions in future clinical practice. Strengths This study makes several important contributions. First, it is the first to adapt and validate the E-PID within a Chinese clinical population extending its applicability to non-Western contexts. Second, the integration of EGA with traditional EFA and CFA enhances the robustness of the factor structure and provides methodological innovation. Third, the inclusion of culturally relevant criterion measures, such as intuitive eating and self-control, ensures meaningful interpretation within the Chinese dietary health context. Limitations Several limitations should be acknowledged in present study. First, the sample was limited to cardiovascular disease patients in northeastern China, which may limit generalizability. Future research should examine diverse populations across different regions and age groups, including healthy individuals. Second, data collection through self-report may affect the accuracy and objectivity of responses due to social expectations or recall bias. Third, this study only examined criterion-related validity, other aspects such as predictive validity remain to be tested. Moreover, cultural constructs like Confucian familism, traditional food symbolism, or fatalistic health beliefs were not quantitatively measured, restricting our understanding of their moderating roles. Future research may incorporate qualitative interview to explore how respondents comprehend, interpret, and cognitively process scale items particularly during cross-cultural adaptation, so that to improve the rigor and precision of content validation. In conclusion, this study provides initial evidence supporting the reliability and validity of the Chinese version of the E-PID in a cardiovascular disease population. The scale offers a culturally appropriate and psychometrically sound tool for assessing intuitive and deliberative tendencies in dietary decision-making. By capturing these dual cognitive styles, the E-PID can inform tailored dietary interventions and enhance culturally responsive nutritional care in clinical settings. Conclusions The validated Chinese E-PID scale, with 2-factor and 7-item structure, offers a culturally adapted tool to assess intuitive and deliberative dietary decision-making among CVD patients in both clinical and community settings. Abbreviations E-PID The Preference for Intuition and Deliberation in Eating Decision-Making Scale CVD Cardiovascular Disease EFA Exploratory Factor Analysis EGA Exploratory Graph Analysis CFA Confirmatory Factor Analysis MI Measurement Invariance CFI Comparative Fit Index GFI Goodness-of-Fit Index TLI Tucker-Lewis Index RMSEA Root Mean Square Error of Approximation IES-2 Intuitive Eating Scale-2 UPE Unconditional Permission to Eat EPR Eating for Physical Rather than Emotional Reasons RHSC Reliance on Hunger and Satiety Cues B-FCC Body-Food Choice Congruence BSCS Brief Self-control Scale BMI Body Mass Index PCA Principal Component Analysis WLSMVA Weighted Least Squares Mean and Variance Adjusted Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki guidelines and received approval from the Medical Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (Approval number: YJSKY2024-406). All participants provided written informed consent. Consent for publication Not Applicable. Availability of data and materials The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. Competing interests All authors declare that they have no conflict of interest. Funding This research was funded by the Young Foundation Project Program of State Key Laboratory of Frigid Zone Cardiovascular Diseases, Harbin Medical University [grant number HD202401]; and Heilongjiang Natural Science Foundation Joint Guidance Project [grant number LH2023G008]. Authors’ contribution HH: Writing - original draft, Writing - review & editing, Methodology, Formal analysis, Data curation, Visualization, Conceptualization, Translation. MW: Investigation, Formal analysis. GH : Writing - review & editing, Software. PL: Writing - review & editing, Conceptualization. XG: Writing - review & editing, Funding acquisition. ZZ: Writing - review & editing. YW: Writing - review & editing. LL: Visualization. XM: Data curation. XH: Validation. GL: Writing - review & editing, Supervision, Project administration, Funding acquisition, Conceptualization. All authors read and approved the final manuscript. Acknowledgments We gratefully acknowledge the contribution of all participants. References Hu SS. Epidemiology and current management of cardiovascular disease in China. J Geriatr Cardiol. 2024;21(4):387–406. Mente A, Dehghan M, Rangarajan S, et al. Diet, cardiovascular disease, and mortality in 80 countries. Eur Heart J. 2023;44(28):2560–79. Yu E, Malik VS, Hu FB. Cardiovascular Disease Prevention by Diet Modification: JACC Health Promotion Series. J Am Coll Cardiol. 2018;72(8):914–26. Brown DJ, Charlesworth J, Hagger MS et al. A Dual-Process Model Applied to Two Health-Promoting Nutrition Behaviours. Behav Sci (Basel). 2021;11(12). König LM, Sproesser G, Schupp HT, et al. Preference for Intuition and Deliberation in Eating Decision-making: Scale validation and associations with eating behaviour and health. Br J Health Psychol. 2021;26(1):109–31. Kuo WJ, Sjöström T, Chen YP, et al. Intuition and deliberation: two systems for strategizing in the brain. Science. 2009;324(5926):519–22. Sproesser G, Aulbach M, Gültzow T, et al. Do nutrition knowledge, food preferences, and habit strength moderate the association between preference for intuition and deliberation in eating decision-making and dietary intake? Appl Psychol Health Well Being. 2023;15(3):957–82. Hensley-Hackett K, Bosker J, Keefe A, et al. Intuitive Eating Intervention and Diet Quality in Adults: A Systematic Literature Review. J Nutr Educ Behav. 2022;54(12):1099–115. Sire T, Carbonneau N, Lemieux S, et al. Associations between intuitive eating, overall diet quality, and physical health indicators: Results of the PREDISE study. Appetite. 2025;207:107904. Qiuyue L, Man Y, Zhi W, et al. Research progress on intuitive eating concept. Chin Nurs Res. 2021;35(01):115–8. Wu S, Cai T, Luo X. Validation of the Dutch Eating Behavior Questionnaire (DEBQ) in a sample of Chinese adolescents. Psychol Health Med. 2017;22(3):282–8. Resende TRO, de Sousa EM, Alvarenga MDS, et al. Psychometric Properties of the Preference for Intuition and Deliberation in Eating Decision-Making Scale among Brazilian Adult Women. Nutrients. 2024;16:19. Chan JY. Multicultural nursing. Dietary beliefs of Chinese patients. Nurs Stand. 1995;9(27):30–4. Winham DM. Culturally tailored foods and CVD prevention. Am J Lifestyle Med. 2009;3(1):s64–8. Epskamp S, Maris G, Waldorp LJ et al. Network Psychometrics. The Wiley Handbook of Psychometric Testing2018. pp. 953 – 86. Christensen AP, Gross GM, Golino HF, et al. Exploratory Graph Analysis of the Multidimensional Schizotypy Scale. Schizophr Res. 2019;206:43–51. Christensen AP, Golino H. Estimating the Stability of Psychological Dimensions via Bootstrap Exploratory Graph Analysis: A Monte Carlo Simulation and Tutorial. Psych. 2021;3(3):479–500. Yang Z, Chen F, Lu Y, et al. Psychometric evaluation of medication safety competence scale for clinical nurses. BMC Nurs. 2021;20(1):165. Tylka TL, Van Kroon AM. The Intuitive Eating Scale-2: item refinement and psychometric evaluation with college women and men. J Couns Psychol. 2013;60(1):137–53. Junhua L, Minjuan W, Guohe F et al. Localization of Intuitive Eating Scale-2 and Its Application in Chinese Obese Patients with Primary Hypertension. 2021. Luo T, Cheng L, Qin L, et al. Reliability and validity of Chinese version of brief self-control scale. Chin J Clin Psychol. 2021;29(1):83–6. Kim HY. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restor Dent Endod. 2013;38(1):52–4. Jaarsma T, Arestedt KF, Mårtensson J, et al. The European Heart Failure Self-care Behaviour scale revised into a nine-item scale (EHFScB-9): a reliable and valid international instrument. Eur J Heart Fail. 2009;11(1):99–105. Wang X, Zhang C, Qi Y, et al. Digital Health Literacy Questionnaire for Older Adults: Instrument Development and Validation Study. J Med Internet Res. 2025;27:e64193. Tajalli S, Ashghali Farahani M, Hamzekhani M, et al. Does the Farsi version of attitude toward plagiarism questionnaire have acceptable psychometric properties? J Med Ethics Hist Med. 2022;15:1. Manzar MD, Hameed UA, Alqahtani M, et al. Obstructive sleep apnea screening in young people: Psychometric validation of a shortened version of the STOP-BANG questionnaire using categorical data methods. Ann Thorac Med. 2020;15(4):215–22. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195–212. Golino H, Shi D, Christensen AP, et al. Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychol Methods. 2020;25(3):292–320. Wolf MG, McNeish D. dynamic: An R Package for Deriving Dynamic Fit Index Cutoffs for Factor Analysis. Multivar Behav Res. 2023;58(1):189–94. Putnick DL, Bornstein MH. Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research. Dev Rev. 2016;41:71–90. Trizano-Hermosilla I, Alvarado JM. Best Alternatives to Cronbach's Alpha Reliability in Realistic Conditions: Congeneric and Asymmetrical Measurements. Front Psychol. 2016;7:769. Chen FF. Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance. Struct Equation Modeling: Multidisciplinary J. 2007;14(3):464–504. Beaton DE, Bombardier C, Guillemin F, et al. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976). 2000;25(24):3186–91. Golino HF, Epskamp S. Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE. 2017;12(6):e0174035. Yan Y, Petersen JD, Lin L, et al. A qualitative study of food sociality in three provinces of South China: social functions of food and dietary behavior. Front Nutr. 2023;10:1058764. Wongvibulsin S, Lee SS, Hui KK. Achieving Balance Through the Art of Eating: Demystifying Eastern Nutrition and Blending it with Western Nutrition. J Tradit Complement Med. 2012;2(1):1–5. Hagerman CJ, Stock ML, Beekman JB, et al. The ironic effects of dietary restraint in situations that undermine self-regulation. Eat Behav. 2021;43:101579. Kuijer RG, Boyce JA, Marshall EM. Associating a prototypical forbidden food item with guilt or celebration: relationships with indicators of (un)healthy eating and the moderating role of stress and depressive symptoms. Psychol Health. 2015;30(2):203–17. Xiong M, Wang F, Cai R. Development and Validation of the Chinese Modesty Scale (CMS). Front Psychol. 2018;9:2014. Chen C, Lee S-y, Stevenson HW. Response Style and Cross-Cultural Comparisons of Rating Scales Among East Asian and North American Students. Psychol Sci. 1995;6(3):170–5. Gao R, Huang S, Yao Y, et al. Understanding Zhongyong Using a Zhongyong Approach: Re-examining the Non-linear Relationship Between Creativity and the Confucian Doctrine of the Mean. Front Psychol. 2022;13:903411. Ren Y, Yang H, Browning C, et al. Prevalence of depression in coronary heart disease in China: a systematic review and meta-analysis. Chin Med J (Engl). 2014;127(16):2991–8. Additional Declarations No competing interests reported. Supplementary Files TableS1Responsefrequency.docx FigureSIwithlegend.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7199291","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509813529,"identity":"4b1bb9f2-ca52-4e2c-a709-ff3687ccdb27","order_by":0,"name":"Huixia Huang","email":"","orcid":"","institution":"College of Nursing of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huixia","middleName":"","lastName":"Huang","suffix":""},{"id":509813531,"identity":"295f7ec4-65d2-4a18-944d-c0f37a9b8dd6","order_by":1,"name":"Menglei Wang","email":"","orcid":"","institution":"College of Nursing of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Menglei","middleName":"","lastName":"Wang","suffix":""},{"id":509813534,"identity":"b1f62010-9367-4250-833b-96182b87a743","order_by":2,"name":"Guiping Hu","email":"","orcid":"","institution":"Sichuan Institute of Industrial Technology","correspondingAuthor":false,"prefix":"","firstName":"Guiping","middleName":"","lastName":"Hu","suffix":""},{"id":509813535,"identity":"7361f50d-284a-44db-93fa-133721d94500","order_by":3,"name":"Ping Lin","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Lin","suffix":""},{"id":509813537,"identity":"0e3fdfe8-a00d-431f-8c92-db3a586235e8","order_by":4,"name":"Xueqin Gao","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xueqin","middleName":"","lastName":"Gao","suffix":""},{"id":509813545,"identity":"4c61bf18-ad3e-4ec7-ae79-2c3175ee7d31","order_by":5,"name":"Zhenjuan Zhao","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenjuan","middleName":"","lastName":"Zhao","suffix":""},{"id":509813546,"identity":"12ff9feb-efa0-43f2-a099-69de9870ac17","order_by":6,"name":"Yini Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yini","middleName":"","lastName":"Wang","suffix":""},{"id":509813547,"identity":"5b71df51-d918-4d4e-9434-daa1e8eb50e2","order_by":7,"name":"Ling Li","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Li","suffix":""},{"id":509813548,"identity":"ddf71f27-1697-417c-9345-ef9532b0480c","order_by":8,"name":"Xinrui Ma","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinrui","middleName":"","lastName":"Ma","suffix":""},{"id":509813549,"identity":"99abc3f7-1728-4227-b907-97c01fa0964a","order_by":9,"name":"Xuejia Huang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuejia","middleName":"","lastName":"Huang","suffix":""},{"id":509813550,"identity":"a5691f31-cde8-4303-a3c5-6cf32a866856","order_by":10,"name":"Guojie Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACPgaGhAOSfyTk2NibDxCnhY2BIfGBZYOFMR/PsQSitTAbVDZUJM6TyFEgUgv/gmcSN3dIpLcx5DAw/KjYRoQWiQdpkjPPSOS2MZw9wNhz5jYxWg6kSUuwAbUw9iUwM7YRq+UPm0Q6GzOPAZFa+BuSDSTbJBLY2IjWIgEMZIkzEoZtPGwJB4nyCz//mYQDEhV18vLzHx988KOCCC0MEjkJcPYBItSDrDlOpMJRMApGwSgYuQAAqR45E6M0BBMAAAAASUVORK5CYII=","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Guojie","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-07-23 19:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7199291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7199291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90898556,"identity":"0bc6ed16-bc16-4bff-9b33-b0255a9c7679","added_by":"auto","created_at":"2025-09-09 11:54:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39320,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of options for each item of E-PID (n = 1463).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7199291/v1/564125751f74ed5bdbad53ae.png"},{"id":90898576,"identity":"7e09a57b-7c52-424c-8c34-0472d385253e","added_by":"auto","created_at":"2025-09-09 11:54:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58585,"visible":true,"origin":"","legend":"\u003cp\u003eChinese version of E-PID - graphical network (n = 704).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7199291/v1/752e03feece8cd063829caf1.png"},{"id":90898515,"identity":"ea420938-cc17-4198-bfeb-c233a004438a","added_by":"auto","created_at":"2025-09-09 11:53:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38771,"visible":true,"origin":"","legend":"\u003cp\u003eThe CFA model for the Chinese version of E-PID\u003cem\u003e (n = 759).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7199291/v1/c58db4d63718588a1a647afb.png"},{"id":90898548,"identity":"4753777b-5ac8-48d2-8870-254fa7348fee","added_by":"auto","created_at":"2025-09-09 11:53:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90354,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between E-PID subscales and Criterion-related variables (\u003cem\u003en = \u003c/em\u003e1463).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7199291/v1/2737e84a8c75d0a2ab0b1c58.png"},{"id":103402396,"identity":"ebe29b2b-5727-4ad6-803a-a7f98578f2ff","added_by":"auto","created_at":"2026-02-25 09:28:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1311290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7199291/v1/d1e188d1-478d-4d36-9f2e-1f9343084728.pdf"},{"id":90898555,"identity":"561c8652-391d-4e8b-bf9b-91445a06d2cd","added_by":"auto","created_at":"2025-09-09 11:54:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14678,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1Responsefrequency.docx","url":"https://assets-eu.researchsquare.com/files/rs-7199291/v1/26583428d156f2ef32ec3447.docx"},{"id":90898552,"identity":"88709f96-0660-4115-a90f-badb14bcaa97","added_by":"auto","created_at":"2025-09-09 11:53:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":204630,"visible":true,"origin":"","legend":"","description":"","filename":"FigureSIwithlegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-7199291/v1/895217532012ae52e98c6469.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cultural Adaptation and Psychometric Evaluation of the Preference for Intuition and Deliberation in Eating Decision-making Scale among Chinese People with Cardiovascular Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn recent years, cardiovascular disease (CVD) has remained one of the leading causes of death and disability in China, posing a substantial public health burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among the various modifiable behavioral risk factors, unhealthy dietary patterns play a critical role in the onset, progression, and prognosis of CVD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, optimizing dietary behavior is a key component of secondary prevention strategies for this high-risk population.\u003c/p\u003e\u003cp\u003eDietary behavior is not only shaped by nutritional knowledge or access to healthy food but is also deeply influenced by individuals\u0026rsquo; cognitive and decision-making styles [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Emerging evidence suggests that people differ in how they make dietary decisions: some rely more on gut feelings and automatic responses (intuitive style), while others engage in reflective thinking and deliberate reasoning (deliberative style) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These individual tendencies termed dietary decision-making preferences can significantly influence diet quality, intervention adherence, and ultimately, long-term health outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Understanding and identifying such cognitive preferences is therefore critical for designing tailored dietary interventions that align with patients\u0026rsquo; natural decision-making inclinations, thereby playing an important role in the secondary prevention of cardiovascular disease.\u003c/p\u003e\u003cp\u003eHowever, most existing dietary behavior assessment tools used in China primarily focus on emotional eating, external cues, or restrained eating [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and fail to capture the cognitive dimension of dietary decision-making. This leaves a significant gap in our understanding of how individuals navigate food choices based on instinctive vs. rational processing. Particularly in patients with CVD, whose dietary decisions affect disease outcomes, assessing these decision-making preferences is of high clinical relevance.\u003c/p\u003e\u003cp\u003eTo bridge this gap, K\u0026ouml;nig et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] developed the Preference for Intuition and Deliberation in Eating Decision-Making Scale (E-PID), a tool engineered to access individual preferences toward intuitive or deliberative approaches when making dietary decisions. Notably, Brazilian researchers have also undertaken the cultural adaptation and validation of the E-PID [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and both original German version and the Brazilian adaptation have confirmed its robust psychometric properties and support its two-factor structure. To our knowledge, the E-PID has not yet been culturally adapted or validated in China, limiting its application in Chinese clinical and research settings.\u003c/p\u003e\u003cp\u003eGiven the unique cultural, cognitive, and behavioral characteristics of Chinese CVD patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], there is a clear need to develop a Chinese version of the E-PID through rigorous translation, cultural adaptation, and psychometric evaluation. Such an instrument would not only fill an important methodological gap but also provide researchers and clinicians with a culturally relevant tool for assessing cognitive decision-making styles in dietary behavior. This could facilitate the development of more effective, individualized dietary interventions in CVD care.\u003c/p\u003e\u003cp\u003eMoreover, in addition to traditional psychometric approaches, the current study introduces Exploratory Graph Analysis (EGA), a modern network-based technique that allows for the data-driven identification of dimensional structures without reliance on strict theoretical assumptions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, EGA incorporates a bootstrap procedure to assess the consistency and robustness of its findings [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, a supplementary advantage of EGA is that it facilitates the accessible visualization, the attribution of the dimensionality of an item can be determined visually by using a network graph coded in different colors. By employing EGA alongside conventional factor analysis, the study seeks to gain deeper insights into the structural validity of the Chinese version of the E-PID.\u003c/p\u003e\u003cp\u003eIn sum, this study aimed to (1) translate and culturally adapt the two-factor E-PID scale into Chinese, ensuring linguistic and conceptual equivalence; and (2) validate the psychometric properties of the Chinese version of the E-PID scale in a cardiovascular disease (CVD) patient population. This was achieved through a comprehensive evaluation of its dimensionality using both Exploratory Factor Analysis (EFA) and EGA, as well as assessments of reliability and convergent validity; (3) assess the measurement invariance across gender, thereby testing whether the scale performs consistently across male and female subgroups and establishing its applicability for gender-based comparisons in future dietary behavior research.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eCultural Adaptation and Translation of the E-PID\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe original scale authors granted permission for the adaptation of the E-PID. We conducted the translation and back-translation process in accordance with Brislin\u0026rsquo;s guidelines.\u003c/p\u003e\u003cp\u003eStep 1 forward translation: Three PhDs from the fields of nursing, nutrition, psychology, together with a medically trained bilingual expert, independently conducted the forward translation and cultural adaptation of the scale.\u003c/p\u003e\u003cp\u003eStep 2 reconciliation: One nursing master\u0026rsquo;s graduate with overseas study experience consolidated these into the initial translation (T1).\u003c/p\u003e\u003cp\u003eStep 3 back translation: One English master\u0026rsquo;s graduate and two nursing specialists unfamiliar with the original scale independently back-translated T1 into English. Subsequently, another nursing master\u0026rsquo;s graduate reviewed and consolidated three versions to create the back-translated version (BT1).\u003c/p\u003e\u003cp\u003eStep 4 Expert review: BT1 was submitted to the original author for review. Based on the feedback, ambiguous items were refined, resulting in the revised version T2. Subsequently, six nursing experts with extensive experience in clinical practice and education (all holding a master\u0026rsquo;s degree or above and with at least 10 years of nursing experience) were invited to review T2 by Delphi method. They were asking to provide suggestions focusing on content appropriateness, semantic accuracy, and the standardization of medical expressions. Based on their feedback, resulting in the pre-final version of the E-PID.\u003c/p\u003e\u003cp\u003eStep 5 Pilot testing: Conducted a pilot rest with 20 patients with cardiovascular disease using the pre-final version E-PID. After completing the scale, the patients were interviewed by the researcher to collect feedback on item comprehension, clarity of expression, and cultural appropriateness. The scale items were modified based on insights from the interviews, ultimately resulting in the finalized Chinese version of the E-PID, which includes 7 items.\u003c/p\u003e\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis cross-sectional, non-interventional study was conducted at the cardiology department of a hospital in China from July 2024 to December 2024. Patients were eligible if they met the following criteria: (1) no intellectual disabilities and language impairments and able to complete questionnaire assessment, (2) over 18 years old. Exclusion criteria include: (1) refusal to participate, (2) undergoing diet-related nutritional therapy, (3) a current diagnosis of a severe psychiatric disorder. The minimum required sample size was 10 participants per item, although a larger sample was preferred [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Finally, 1463 participants were recruited.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProcedures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe researcher explained each item of the scale, and the participant responded accordingly. When necessary, the researcher provided assistance by documenting responses for participants with sensory or literacy impairments. All participants signed an informed consent form and voluntarily took part in the study without any compensation or incentives.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasures\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDemographic information\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants were further asked to report their sex, age, height (cm) and weight (kg), place of residence, living pattern, educational attainment, current occupation status, monthly household net income, smoking status, drinking status. Self-reported height and weight were collected to calculate the body mass index (BMI\u0026thinsp;=\u0026thinsp;kg/m\u003csup\u003e2\u003c/sup\u003e). Participants\u0026rsquo; demographic information is outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eDescriptive characteristics of participants.\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\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst Split-half for EFA, EGA (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;704)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSecond Split-half for CFA(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;759)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP-Value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e424 (60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e464 (61.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e280 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e295 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e245 (34.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e258 (34.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e≧\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e413 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e462 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e288 (40.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e319 (42.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-urban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416 (59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e440 (58.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e517 (73.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e541 (71.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartner and Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e117 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e138 (18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational attainment, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school or Illiterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e147 (20.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175 (23.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e374 (53.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e399 (52.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e119 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMonthly household net income in CNY (yuan), \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026yen;0 - \u0026yen;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e384 (54.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e418 (55.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026yen;2000 - \u0026yen;5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e276 (39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e295 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt; \u0026yen;5000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e405 (57.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e429 (56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e198 (28.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e232 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e101 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98 (12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrinking status, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e600 (85.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e667 (87.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e229 (32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e278 (36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24-27.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e321 (45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e311 (41.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e124 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130 (17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBedridden, exact weight unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eCNY: Chinese Yuan; BMI: Body Mass Index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePreference for Intuition and Deliberation in Eating Decision-Making Scale(E-PID)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe E-PID is a two-factor scale adapted by K\u0026ouml;nig et al. to effectively assess eating-related decision-making either through intuition or deliberation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and contains two subscales: Preference for Intuition (3 items) and Preference for Deliberation (4 items). All items are rated on a 5-point Likert scale from 1 I do not agree to 5 I agree, higher mean scores of the subscales reflect greater tendency of one\u0026rsquo;s eating-related intuitive or deliberation decisive-making. The English version of the E-PID scale has good reliability and validity with the Cronbach\u0026rsquo;s α of 0.79 for Preference for Intuition and 0.82 for Preference for Deliberation. Participants completed the Chinese version of the E-PID, which had undergone translation and cultural adaptation into Chinese.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntuitive Eating Scale-2 (IES-2)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTylka and Kroon initially developed the IES in 2006 and updated it in 2013 into the 23-item IES-2 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which was used to capture individual differences in intuitive eating. The Chinese version of the IES-2 with Cronbach\u0026rsquo;s α of 0.95 and a test\u0026ndash;retest correlation of 0.89 among cardiovascular disease patients was included in the present study to examine construct validity of E-PID [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This 23-item IES-2 consists of 4 subscales: (1) Unconditional Permission to Eat (6 items); (2) Eating for Physical Rather Than Emotional Reasons (8 items); (3) Reliance on Hunger and Satiety Cues (6 items); and (4) Body-Food Choice Congruence (3 items). In the current study, participants were asked to rate each item from strongly disagree (1 score) to strongly agree (5 score). The higher their scores, the greater their level of intuitive eating.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBrief Self-control Scale (BSCS)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGeneral self-control was assessed using the BSCS, which demonstrated good validity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The BSCS consists of 7 items and 2 subscales answered with a 5-point Likert scale: (1) Self-Discipline (3 items); (2) Impulse Control (4 items). Higher scores indicate a higher level of self-control.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data were analyzed using SPSS 27.0, Mplus 8.3 and R 4.4.1. The full sample was randomly divided into two datasets using SPSS 27.0, dataset 1 which consisted of 704 samples and was used for EFA and EGA; and dataset 2 which consisted of 759 samples and was used for CFA. In addition, the full sample was used for all statistical analyses in the subsequent data analysis unless otherwise specified.\u003c/p\u003e\u003cp\u003eThe univariate normality was assessed by using skewness \u0026lt;|2| and kurtosis \u0026lt;|7| [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Descriptive statistics summarized item means and response frequencies. Item analysis included three methods: the correlation coefficient method, in which items with item-total correlations of r\u0026thinsp;\u0026lt;\u0026thinsp;0.4 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 were deleted; the corrected item-total correlation (CITC) method, which compared Cronbach\u0026rsquo;s α before and after removing items, if Cronbach\u0026rsquo;s α increased significantly after deletion, the item was removed [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; and independent samples t-test between the top and bottom 27% of subscale mean scores, with non-significant items excluded [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBefore conducting EFA, the Kaiser-Meyer-Olkin (KMO) test and Bartlett\u0026rsquo;s Test of Sphericity were performed to assess sampling adequacy (KMO\u0026thinsp;\u0026gt;\u0026thinsp;0.70 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Factors with eigenvalue\u0026thinsp;\u0026gt;\u0026thinsp;1 were extracted via using principal component analysis with varimax rotation. Factors retention was based on eigenvalues, cumulative variance contribution, and scree plot analysis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEGAnet package in R 4.4.1 was used to model EGA process, combining the GLASSO network estimation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and the Walktrap algorithm to uncover the number of item clusters and their internal structure [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Subsequently, the bootEGA was used to perform 5,000 bootstrap samples to assess the stability of the EGA results. The key indicators include the consistency of the number of dimensions, structural consistency, and item replication index which must be greater than or equal to 0.75.\u003c/p\u003e\u003cp\u003eConfirmatory factor analysis (CFA) was applied using weighted least squares mean and variance adjusted (WLSMV) estimation. Model fitting was evaluated with TLI, CFI and GFI\u0026thinsp;\u0026ge;\u0026thinsp;0.95; RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.08; and RMR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although χ\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003edf\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;3 is commonly used, it wasn\u0026rsquo;t considered here due to its sensitivity to large sample sizes.\u003c/p\u003e\u003cp\u003eConvergent validity of the two subscale of the E-PID for different genders was assessed through Pearson correlation analysis, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicated non-significant changes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eReliability was evaluated using Cronbach\u0026rsquo;s α, McDonald\u0026rsquo;s ω, and test-retest reliability. McDonald's ω (\u0026gt;\u0026thinsp;0.70) was considered a robust indicator of internal consistency [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMulti-group CFA and independent samples t-tests were conducted to measure the invariance of E-PID across gender. Four nested models were compared: Configural (A), Metric (B), Scalar (C), and Strict (D) Invariance model. If both ∆CFI and ∆RMSEA between B vs. A, C vs. B, and D vs. C were all \u0026lt;\u0026thinsp;0.01, then invariance holds [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Independent samples t-tests assessed subscale differences, with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed) indicating statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eResponse frequency of each item in the Chinese version of E-PID\u003c/b\u003e\u003c/p\u003e\u003cp\u003e We used the convenience sampling method for data collection, total 1463 participant\u0026rsquo;s data collected. \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e shows the frequency (percentage), mean, SD, skewness and kurtosis of each score for all E-PID items. The skewness and kurtosis of the distribution of E-PID scores are within the accepted range, and there is no violation of the normality of the scale totals. The percentages for each score are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In preference for intuition (item1-item3), most patients would choose a score of 3 and 4, and in preference for deliberation (item4-item7), most of the options were clustered around scores of 2, 3, and 4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eItem analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eItem analysis revealed that all items in the Preference for Intuition dimension were positively correlated with the total subscale score (r\u0026thinsp;=\u0026thinsp;0.89\u0026thinsp;\u0026minus;\u0026thinsp;0.84, \u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and all items in the Preference for Deliberation dimension were positively correlated with their total score (r\u0026thinsp;=\u0026thinsp;0.71\u0026ndash;0.81, \u003cem\u003eps\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eCorrected item total correlation coefficients were also computed for each item within the full scale and their respective subscales. The Cronbach\u0026rsquo;s α coefficients ranged from 0.64 to 0.71 for the total scale, 0.75 to 0.80 for the Preference for Intuition subscale, and 0.66 to 0.72 for the Preference for Deliberation subscale when each item was deleted. Since the internal consistency coefficients decreased following item deleting, no items were removed.\u003c/p\u003e\u003cp\u003eFinally, independent samples t-tests were conducted by comparing the top and bottom 27% of respondents based on subscale mean scores. For the Preference for Intuition subscale, the high groups (\u0026ge;\u0026thinsp;4) and the low 27% (\u0026le;\u0026thinsp;3) were compared; for the Preference for Deliberation subscale, the high group (\u0026ge;\u0026thinsp;3.25) and the low group (\u0026le;\u0026thinsp;2.75) were used. The results showed that all items had significant differences between the two groups, with t-values ranging from 20.629 to 62.476 (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating good item discrimination and no need for item removal.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExploratory factor analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eExploratory factor analysis (EFA) was performed on Dataset 1. Bartlett\u0026rsquo;s test of sphericity was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the KMO value was 0.76, indicating adequate sampling and strong inter-item correlations. Parallel analysis supported a two-factor solution with factor 1 and factor 2 contributing 40.58% and 27.41% of the variance, respectively, explaining a total of 67.99% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eE-PID items and factor loadings for EFA sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;704).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePreference for intuition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePreference for deliberation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e%variance accounted for\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eExploratory graph analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEGA detected two dimensions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), namely dimension 1 (including INT1, INT2 and INT3), and dimension 2 (including DEL1, DEL2, DEL3 and DEL4). In the network plot, nodes represent the 7 items, and edge thickness reflects the strength of association; the thicker green edges indicate the stronger positive relationships. EGA\u0026rsquo;s bootstrap results demonstrated high stability, both dimensions were detected with a frequency of 1.00; all items were categorized into their respective dimensions with a frequency of 1.00 (\u003cb\u003eFigure S 1\u003c/b\u003e); and the structural consistency of both dimensions was also 1.00. These findings provide strong support for the robust 2-dimensional structure of the Chinese version of the E-PID.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConfirmatory factor analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCFA was conducted to validate the previously established structure using Dataset 2. Given the sensitivity of χ\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003edf to\u003c/em\u003e sample size, it was excluded from model evaluation. Model fit was assessed using CFI, GFI, TLI, RMSEA and SRMR. The results showed that the E-PID model fit well with CFI\u0026thinsp;=\u0026thinsp;0.99, GFI\u0026thinsp;=\u0026thinsp;0.99, TLI\u0026thinsp;=\u0026thinsp;0.98, RMSEA\u0026thinsp;=\u0026thinsp;0.059 (90% CI [0.041, 0.077]) and SRMR\u0026thinsp;=\u0026thinsp;0.026 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eContent Validity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe results demonstrated that the I-CVI values of each item ranged from 0.83 to 1.00, with an S-CVI of 0.97, supporting the scale\u0026rsquo;s robust content validity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConvergent validity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe E-PID subscales demonstrated satisfactory convergent validity. Specifically, the intuitive preference subscale showed a significant positive correlation with the IES-2 (r\u0026thinsp;=\u0026thinsp;0.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the deliberative preference subscale exhibited strong correlated with the BSCS (r\u0026thinsp;=\u0026thinsp;0.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), further supporting the convergent validity of the scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDarker shades indicate stronger correlations, blue for positive and red for negative. The area of each square reflects the significance of the \u003cem\u003ep\u003c/em\u003e-value, the larger the area, the smaller the p-value (more significant).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInternal consistency and test-retest reliability\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCronbach\u0026rsquo;s α values and their 95% CI for the total E-PID, Preference for Intuition and Preference for Deliberation were 0.71 (95% CI [0.68, 0.75]), 0.83 (95% CI [0.80, 0.86]) and 0.75 (95% CI [0.73, 0.77]) respectively, indicating acceptable internal consistency. Test-retest reliability assessed 32 participants over a 6-week interval showed strong temporal stability with results of 0.86 (95% CI [0.72, 0.93]) for the Preference for Intuition, and 0.79 (95% CI [0.61, 0.89]) for the Preference for Deliberation. Additionally, McDonald's ω values further supported reliability, with values for the E-PID, preference for intuition and preference for deliberation at 0.81 (95% CI [0.78, 0.84]), 0.87 (95% CI [0.85, 0.90]) and 0.73 (95% CI [0.70, 0.77]) respectively. These results demonstrate the good reliability of the Chinese version of E-PID.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurement invariance tests and gender differences\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMeasurement invariance of the E-PID across genders was tested via multi-group CFA. Model fit remained consistent across configural, metric, scalar and strict invariance model, supporting full measurement invariance of the Chinese version of the E-PID scale across genders (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eMeasurement invariance test of the Chinese version of the E-PID scale (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1463).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel Comparisons\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e∆CFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e∆RMSEA\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA: configural invariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB: metric invariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eB vs. A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC: scalar invariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eC vs. B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD: strict invariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eD vs. C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-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\u003eAfter confirming that the E-PID had measurement invariance by gender, independent samples t-tests were conducted to compare the two subscales for gender differences. The results revealed that men scored slightly higher on Preference for Intuition was in men (M\u0026thinsp;=\u0026thinsp;3.39, SD\u0026thinsp;=\u0026thinsp;0.83) than women (M\u0026thinsp;=\u0026thinsp;3.33, SD\u0026thinsp;=\u0026thinsp;0.85), t (1463)\u0026thinsp;=\u0026thinsp;1.25, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; and slightly on Preference for Deliberation (M\u0026thinsp;=\u0026thinsp;2.96, SD\u0026thinsp;=\u0026thinsp;0.50) than women (M\u0026thinsp;=\u0026thinsp;2.99, SD\u0026thinsp;=\u0026thinsp;0.50), t (1463) = -1.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGiven the lack of validated assessment tools for dietary decision-making preference in the CVD population, present study is the first culturally adapted version of the E-PID scale to be translated into Chinese, culturally adapted, and evaluated for its psychometric properties [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The results provide initial evidence for the psychometric soundness of the Chinese version E-PID confirming its two-factor structure with seven items, internal consistency, criterion-related validity, and temporal stability. These findings contribute to the growing interest in cognitive approaches to dietary decision-making and fill an important gap in culturally validated assessment tools for chronic disease dietary management in Chinese populations.\u003c/p\u003e\u003cp\u003eBoth EFA and CFA analyses supported the original two-factor structure of the E-PID and good fit indices [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] distinguishing between preference for intuition (3 items) and preference for deliberation (4 items) in eating decisions. Notably, the factor structure identified through EGA further confirmed the robustness of its two-factor model, offering strong visual and statistical support for dimensionality. This is especially relevant as the original study by K\u0026ouml;nig et al. relied solely on confirmatory approaches [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The use of EGA represents a methodological advancement by allowing theory-free discovery of latent structures [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which is particularly advantageous in a new cultural context such as China, where eating behaviors are shaped by unique social and health belief systems.\u003c/p\u003e\u003cp\u003eThe two subscales demonstrated different associations with criterion-related measures, supporting their conceptual distinctiveness. The Preference for Intuition subscale was positively correlated with scores on the Intuitive Eating Scale-2 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], suggesting that individuals who trust internal cues such as hunger, satiety, and sensory pleasure are more likely to adopt intuitive eating decision-making styles. This correlation highlights the cultural resonance of intuitive eating in a traditionally collectivist society like China, where food carries deep social meaning and is embedded in family rituals [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. During questionnaire time, participants scoring high on intuitive eating frequently referenced ideas such as \u0026ldquo;eat when hungry\u0026rdquo; or \u0026ldquo;I eat what I grow, it\u0026rsquo;s healthy\u0026rdquo; rooted in traditional Chinese medical discourses like Yin-Yang balance and holistic harmony [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn contrast, the Preference for Deliberation subscale showed a positive association with self-control, indicating that individuals who prioritize planning and rational assessment in dietary decisions tend to have higher levels of general self-regulation. While deliberation is generally beneficial for managing chronic diseases, excessive cognitive control may lead to guilt, social isolation or social stress, particularly during festival communal meals involving symbolic or high-calorie foods [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. These culturally specific tensions suggest that promoting deliberation must be balanced with sensitivity to the emotional and social dimensions of eating in Chinese culture [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInternal consistency for both subscales was adequate, with Cronbach\u0026rsquo; α and McDonald\u0026rsquo;s ω coefficients exceeding 0.70. Test-retest reliability over a six-week interval confirmed the scale\u0026rsquo;s temporal stability. These results demonstrate that the Chinese version E-PID is both internally consistent and stable over time, making it suitable for use in both clinical and research settings.\u003c/p\u003e\u003cp\u003eSeveral culturally nuanced response patterns emerged. The item \u0026ldquo;\u003cb\u003eI think about my eating plans and goals more often than others do我比其他人更常考虑自己的饮食计划和目标\u003c/b\u003e\u0026rdquo; received lower scores in the Chinese CVD patients compared to the German and Brazilian validations. This may reflect culturally rooted values of modesty and humility, where overt self-assertion and social comparison are generally discouraged. In traditional Chinese culture, even individuals confident in their dietary decisions may be reluctant to express superiority over others due to prevailing social norms that valorize self-restraint and deference [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, response distributions tended to cluster around the midpoint with relatively few participants selecting extreme values (1 or 5 points). This response pattern aligns with prior research documenting Chinese participants\u0026rsquo; preference for moderate responses [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Such moderation may reflect the influence of Confucian cultural values, particularly the emphasis on harmony, restraint, and the \u0026ldquo;Doctrine of the Mean\u0026rdquo; (Zhong Yong), which encourages balanced and non-extreme expressions in both thought and behavior [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Particularly, this response style was not evenly distributed across the two subscales. Items within the Preference for Intuition dimension exhibited more extreme responses compared to those in the Preference for Deliberation dimension. This may suggest that intuitive behaviors evoke stronger personal resonance or emotional engagement, leading to more confident responses. In contrast, deliberation may be perceived as normative or socially regulated resulting in more neutral or cautious answers.\u003c/p\u003e\u003cp\u003eFrom a CVD-related perspective, patients with CVD often prioritize direct medical treatments such as pharmacotherapy and surgical interventions, while considering dietary management a secondary and supportive strategy. Consequently, dietary decisions are frequently driven by intuitive or habitual patterns rather than deliberate planning. Moreover, depressive symptoms are common among the CVD patients with a reported prevalence of 30\u0026ndash;50% [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These emotional factors may contribute to a negative cognitive bias such as underestimating the long-term benefits of dietary changes or hold fatalistic beliefs such as \u0026ldquo;even a healthy diet won't help\u0026rdquo;.\u003c/p\u003e\u003cp\u003eTaken together, these results underscore the importance of cultural context in interpreting self-report data. Future studies may benefit from incorporating methods to adjust for response styles or using qualitative approaches to better understand how cultural scripts shape participants\u0026rsquo; answers of scale items. It is also recommended to include subgroup analyses, particularly for the group of CVD patients with concomitant depressive symptoms, to enable the development of more tailored and effective interventions in future clinical practice.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study makes several important contributions. First, it is the first to adapt and validate the E-PID within a Chinese clinical population extending its applicability to non-Western contexts. Second, the integration of EGA with traditional EFA and CFA enhances the robustness of the factor structure and provides methodological innovation. Third, the inclusion of culturally relevant criterion measures, such as intuitive eating and self-control, ensures meaningful interpretation within the Chinese dietary health context.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged in present study. First, the sample was limited to cardiovascular disease patients in northeastern China, which may limit generalizability. Future research should examine diverse populations across different regions and age groups, including healthy individuals. Second, data collection through self-report may affect the accuracy and objectivity of responses due to social expectations or recall bias. Third, this study only examined criterion-related validity, other aspects such as predictive validity remain to be tested. Moreover, cultural constructs like Confucian familism, traditional food symbolism, or fatalistic health beliefs were not quantitatively measured, restricting our understanding of their moderating roles. Future research may incorporate qualitative interview to explore how respondents comprehend, interpret, and cognitively process scale items particularly during cross-cultural adaptation, so that to improve the rigor and precision of content validation.\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides initial evidence supporting the reliability and validity of the Chinese version of the E-PID in a cardiovascular disease population. The scale offers a culturally appropriate and psychometrically sound tool for assessing intuitive and deliberative tendencies in dietary decision-making. By capturing these dual cognitive styles, the E-PID can inform tailored dietary interventions and enhance culturally responsive nutritional care in clinical settings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe validated Chinese E-PID scale, with 2-factor and 7-item structure, offers a culturally adapted tool to assess intuitive and deliberative dietary decision-making among CVD patients in both clinical and community settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eE-PID\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe Preference for Intuition and Deliberation in Eating Decision-Making Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCardiovascular Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eExploratory Factor Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEGA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eExploratory Graph Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMeasurement Invariance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eComparative Fit Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGFI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGoodness-of-Fit Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTLI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTucker-Lewis Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIES-2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntuitive Eating Scale-2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUPE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUnconditional Permission to Eat\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEPR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEating for Physical Rather than Emotional Reasons\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRHSC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReliance on Hunger and Satiety Cues\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eB-FCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody-Food Choice Congruence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBSCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBrief Self-control Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWLSMVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWeighted Least Squares Mean and Variance Adjusted\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki guidelines and received approval from the Medical Ethics Committee of the Second Affiliated Hospital of Harbin Medical University (Approval number:\u0026nbsp;YJSKY2024-406). All participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Young Foundation Project Program of State Key Laboratory of Frigid Zone Cardiovascular Diseases,\u0026nbsp;Harbin Medical University [grant number HD202401]; and Heilongjiang Natural Science Foundation Joint Guidance Project [grant number LH2023G008].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHH:\u003c/strong\u003e Writing - original draft, Writing - review \u0026amp; editing, Methodology, Formal analysis, Data curation, Visualization, Conceptualization, Translation.\u0026nbsp;\u003cstrong\u003eMW:\u003c/strong\u003e Investigation, Formal analysis.\u0026nbsp;\u003cstrong\u003eGH\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Writing - review \u0026amp; editing, Software.\u0026nbsp;\u003cstrong\u003ePL:\u0026nbsp;\u003c/strong\u003eWriting - review \u0026amp; editing, Conceptualization.\u0026nbsp;\u003cstrong\u003eXG:\u0026nbsp;\u003c/strong\u003eWriting - review \u0026amp; editing, Funding acquisition.\u0026nbsp;\u003cstrong\u003eZZ:\u0026nbsp;\u003c/strong\u003eWriting - review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eYW:\u0026nbsp;\u003c/strong\u003eWriting - review \u0026amp; editing.\u0026nbsp;\u003cstrong\u003eLL:\u0026nbsp;\u003c/strong\u003eVisualization.\u0026nbsp;\u003cstrong\u003eXM:\u003c/strong\u003e Data curation.\u0026nbsp;\u003cstrong\u003eXH:\u003c/strong\u003e Validation.\u0026nbsp;\u003cstrong\u003eGL:\u0026nbsp;\u003c/strong\u003eWriting - review \u0026amp; editing, Supervision, Project administration, Funding acquisition, Conceptualization. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the contribution of all participants. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHu SS. Epidemiology and current management of cardiovascular disease in China. J Geriatr Cardiol. 2024;21(4):387\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMente A, Dehghan M, Rangarajan S, et al. Diet, cardiovascular disease, and mortality in 80 countries. Eur Heart J. 2023;44(28):2560\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu E, Malik VS, Hu FB. Cardiovascular Disease Prevention by Diet Modification: JACC Health Promotion Series. J Am Coll Cardiol. 2018;72(8):914\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown DJ, Charlesworth J, Hagger MS et al. A Dual-Process Model Applied to Two Health-Promoting Nutrition Behaviours. Behav Sci (Basel). 2021;11(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK\u0026ouml;nig LM, Sproesser G, Schupp HT, et al. Preference for Intuition and Deliberation in Eating Decision-making: Scale validation and associations with eating behaviour and health. Br J Health Psychol. 2021;26(1):109\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuo WJ, Sj\u0026ouml;str\u0026ouml;m T, Chen YP, et al. Intuition and deliberation: two systems for strategizing in the brain. Science. 2009;324(5926):519\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSproesser G, Aulbach M, G\u0026uuml;ltzow T, et al. Do nutrition knowledge, food preferences, and habit strength moderate the association between preference for intuition and deliberation in eating decision-making and dietary intake? Appl Psychol Health Well Being. 2023;15(3):957\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHensley-Hackett K, Bosker J, Keefe A, et al. Intuitive Eating Intervention and Diet Quality in Adults: A Systematic Literature Review. J Nutr Educ Behav. 2022;54(12):1099\u0026ndash;115.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSire T, Carbonneau N, Lemieux S, et al. Associations between intuitive eating, overall diet quality, and physical health indicators: Results of the PREDISE study. Appetite. 2025;207:107904.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQiuyue L, Man Y, Zhi W, et al. Research progress on intuitive eating concept. Chin Nurs Res. 2021;35(01):115\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu S, Cai T, Luo X. Validation of the Dutch Eating Behavior Questionnaire (DEBQ) in a sample of Chinese adolescents. Psychol Health Med. 2017;22(3):282\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eResende TRO, de Sousa EM, Alvarenga MDS, et al. Psychometric Properties of the Preference for Intuition and Deliberation in Eating Decision-Making Scale among Brazilian Adult Women. Nutrients. 2024;16:19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChan JY. Multicultural nursing. Dietary beliefs of Chinese patients. Nurs Stand. 1995;9(27):30\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWinham DM. Culturally tailored foods and CVD prevention. Am J Lifestyle Med. 2009;3(1):s64\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp S, Maris G, Waldorp LJ et al. Network Psychometrics. The Wiley Handbook of Psychometric Testing2018. pp. 953\u0026thinsp;\u0026ndash;\u0026thinsp;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChristensen AP, Gross GM, Golino HF, et al. Exploratory Graph Analysis of the Multidimensional Schizotypy Scale. Schizophr Res. 2019;206:43\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChristensen AP, Golino H. Estimating the Stability of Psychological Dimensions via Bootstrap Exploratory Graph Analysis: A Monte Carlo Simulation and Tutorial. Psych. 2021;3(3):479\u0026ndash;500.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang Z, Chen F, Lu Y, et al. Psychometric evaluation of medication safety competence scale for clinical nurses. BMC Nurs. 2021;20(1):165.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTylka TL, Van Kroon AM. The Intuitive Eating Scale-2: item refinement and psychometric evaluation with college women and men. J Couns Psychol. 2013;60(1):137\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJunhua L, Minjuan W, Guohe F et al. Localization of Intuitive Eating Scale-2 and Its Application in Chinese Obese Patients with Primary Hypertension. 2021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo T, Cheng L, Qin L, et al. Reliability and validity of Chinese version of brief self-control scale. Chin J Clin Psychol. 2021;29(1):83\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim HY. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restor Dent Endod. 2013;38(1):52\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaarsma T, Arestedt KF, M\u0026aring;rtensson J, et al. The European Heart Failure Self-care Behaviour scale revised into a nine-item scale (EHFScB-9): a reliable and valid international instrument. Eur J Heart Fail. 2009;11(1):99\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, Zhang C, Qi Y, et al. Digital Health Literacy Questionnaire for Older Adults: Instrument Development and Validation Study. J Med Internet Res. 2025;27:e64193.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTajalli S, Ashghali Farahani M, Hamzekhani M, et al. Does the Farsi version of attitude toward plagiarism questionnaire have acceptable psychometric properties? J Med Ethics Hist Med. 2022;15:1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManzar MD, Hameed UA, Alqahtani M, et al. Obstructive sleep apnea screening in young people: Psychometric validation of a shortened version of the STOP-BANG questionnaire using categorical data methods. Ann Thorac Med. 2020;15(4):215\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50(1):195\u0026ndash;212.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGolino H, Shi D, Christensen AP, et al. Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychol Methods. 2020;25(3):292\u0026ndash;320.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWolf MG, McNeish D. dynamic: An R Package for Deriving Dynamic Fit Index Cutoffs for Factor Analysis. Multivar Behav Res. 2023;58(1):189\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePutnick DL, Bornstein MH. Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research. Dev Rev. 2016;41:71\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrizano-Hermosilla I, Alvarado JM. Best Alternatives to Cronbach's Alpha Reliability in Realistic Conditions: Congeneric and Asymmetrical Measurements. Front Psychol. 2016;7:769.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen FF. Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance. Struct Equation Modeling: Multidisciplinary J. 2007;14(3):464\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeaton DE, Bombardier C, Guillemin F, et al. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976). 2000;25(24):3186\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGolino HF, Epskamp S. Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE. 2017;12(6):e0174035.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan Y, Petersen JD, Lin L, et al. A qualitative study of food sociality in three provinces of South China: social functions of food and dietary behavior. Front Nutr. 2023;10:1058764.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWongvibulsin S, Lee SS, Hui KK. Achieving Balance Through the Art of Eating: Demystifying Eastern Nutrition and Blending it with Western Nutrition. J Tradit Complement Med. 2012;2(1):1\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHagerman CJ, Stock ML, Beekman JB, et al. The ironic effects of dietary restraint in situations that undermine self-regulation. Eat Behav. 2021;43:101579.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuijer RG, Boyce JA, Marshall EM. Associating a prototypical forbidden food item with guilt or celebration: relationships with indicators of (un)healthy eating and the moderating role of stress and depressive symptoms. Psychol Health. 2015;30(2):203\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiong M, Wang F, Cai R. Development and Validation of the Chinese Modesty Scale (CMS). Front Psychol. 2018;9:2014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen C, Lee S-y, Stevenson HW. Response Style and Cross-Cultural Comparisons of Rating Scales Among East Asian and North American Students. Psychol Sci. 1995;6(3):170\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao R, Huang S, Yao Y, et al. Understanding Zhongyong Using a Zhongyong Approach: Re-examining the Non-linear Relationship Between Creativity and the Confucian Doctrine of the Mean. Front Psychol. 2022;13:903411.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen Y, Yang H, Browning C, et al. Prevalence of depression in coronary heart disease in China: a systematic review and meta-analysis. Chin Med J (Engl). 2014;127(16):2991\u0026ndash;8.\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":"Dietary behavior, Intuitive eating, Psychometric properties, Deliberation, EGA, Decision-making, Cardiovascular disease","lastPublishedDoi":"10.21203/rs.3.rs-7199291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7199291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHealthy diet plays a critical role in the prevention, treatment, and prognosis of cardiovascular disease. The Preference for Intuition and Deliberation in Eating Decision-Making Scale (E-PID) was developed to evaluate individuals' preferences for intuitive and deliberative styles when making dietary decision. Our study aims to culturally adapt it into Chinese and test its psychometric properties in 1463 people with cardiovascular disease (CVD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe original E-PID was translated into Chinese using the Brislin translation model. 1463 patients were recruited from a hospital from July 2024 to December 2024. The psychometric properties of the Chinese version of the E-PID were assessed through item analyses, composite reliability, test re-test reliability, measurement invariance (MI), factorial validity, discriminative validity and criterion-related validity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eItem analyses indicated that no item deletion was necessary. Both exploratory factor analysis (EFA) and exploratory graph analysis (EGA) (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;704) supported the two-factor structure of the 7-item original scale, and confirmatory factor analysis (CFA)indicated that the scale demonstrated a satisfactory model fit. Psychometric properties showed strong internal consistency, sufficient criterion-related validity and test-retest reliability over a six-week period. The results also demonstrated that the Chinese version of the E-PID maintained good measurement properties across gender, supporting its applicability for examining gender-related differences.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe E-PID showed sufficient psychometric properties in a Chinese sample, making it a valid instrument for assessing dietary decision-making preferences among people with cardiovascular disease in China and can serve as an effective tool for both clinical practice and research.\u003c/p\u003e","manuscriptTitle":"Cultural Adaptation and Psychometric Evaluation of the Preference for Intuition and Deliberation in Eating Decision-making Scale among Chinese People with Cardiovascular Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 11:53:46","doi":"10.21203/rs.3.rs-7199291/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":"40e067f8-189c-4845-b95f-cdce7c3aba62","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T09:27:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 11:53:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7199291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7199291","identity":"rs-7199291","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}