The Hidden Health Penalty for the Poor: How Food Delivery Consumption Exacerbates Socio-Metabolic Vulnerability to Drive Obesity in Urban China

The Hidden Health Penalty for the Poor: How Food Delivery Consumption Exacerbates Socio-Metabolic Vulnerability to Drive Obesity in Urban China | 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 The Hidden Health Penalty for the Poor: How Food Delivery Consumption Exacerbates Socio-Metabolic Vulnerability to Drive Obesity in Urban China Zefeng Zhang, Chengying Xu, Ming Huo, Oleksandr Andriiovych Boiko This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7400434/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: Health inequality represents a critical public health challenge amidst China's rapid urbanization. The underlying mechanisms through which prevalent lifestyle changes, such as the surge in online food delivery consumption, contribute to disparate health outcomes across socioeconomic strata remain poorly understood. This study aimed to introduce and validate a novel construct, "Socio-Metabolic Vulnerability (SMV)," to elucidate the pathway linking food delivery habits to obesity and to examine the moderating role of socioeconomic status (SES) in this process. Methods: We conducted a large-scale, cross-sectional study involving 20,135 adults in Wuxi, a representative metropolis in China, using a multi-stage stratified random sampling method. Data on food delivery habits and sociodemographics were collected via questionnaires, while obesity and related metabolic indicators were assessed through anthropometric measurements and biochemical assays. The SMV index was constructed using exploratory factor analysis (EFA) on eight pre-selected social and metabolic variables. A moderated mediation model was employed to test the primary hypotheses. Results: Baseline characteristics revealed a significant social gradient, with individuals of low SES exhibiting higher food delivery frequency, elevated SMV index scores, and greater obesity prevalence (all p < .001). EFA confirmed a robust two-factor structure ("Social" and "Metabolic") for the SMV index, explaining 68.4% of the total variance. Path analysis established that SMV significantly mediated the association between food delivery frequency and BMI (standardized indirect effect β = 0.35). Critically, this mediation pathway was significantly moderated by SES (index of moderated mediation = -0.10, 95% CI: -0.14, -0.07). The conditional indirect effect was over three times stronger in the low-SES group (B = 0.31, 95% CI: 0.26, 0.37) compared to the high-SES group (B = 0.10, 95% CI: 0.07, 0.14). This mechanism was more predictive of severe combined obesity (OR = 1.28 for low-SES group) and was particularly pronounced among men and younger adults. A non-linear, accelerating dose-response relationship was observed. Conclusion: Food delivery consumption appears to drive obesity by exacerbating an individual's underlying Socio-Metabolic Vulnerability. This detrimental health effect is disproportionately amplified among individuals of lower socioeconomic status, uncovering a novel mechanism of health inequality in the context of modern urban lifestyles. Public health interventions must transcend individual-level behavioral counseling to address the structural socioeconomic environments that heighten this vulnerability. Obesity Online Food Delivery Health Inequality Socio-Metabolic Vulnerability Social Determinants of Health China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Health inequality has emerged as a major public health concern in parallel with China's unprecedented economic growth and urbanization, with evidence pointing to a widening gap in health outcomes between different income groups [1].A substantial body of evidence has established socioeconomic status (SES) as afundamental determinant of health, profoundly influencing the prevalence of non-communicable diseases such as metabolic syndrome [2][3][4]. Against this backdrop, obesity has become a nationwide epidemic in China, driven by profound shifts in lifestyle and dietary patterns [5]. In recent years, the proliferation of online food delivery services has reshaped the dietary landscape of urban China [6]. While offering convenience, this phenomenon is increasingly linked to poorer dietary quality and adverse health outcomes, including higher body weight [7][8]. International studies have similarlyreported on the often poor nutritional quality of meals available through theseplatforms [9]. However, existing research has predominantly focused on the direct association between food delivery and health, largely overlooking the potential for socially stratified effects. A critical unanswered question is whether the health risks associated with food delivery consumption are equitably borne, or ifthey constitute a novel pathway that exacerbates health inequality. To address this gap, we introduce the theoretical construct of "Socio-MetabolicVulnerability (SMV)." We define SMV as a composite state of heightened risk,linking adverse social circumstances (e.g., economic pressure, time poverty) with a compromised physiological constitution (e.g., subclinical metabolic dysregulation and low-grade inflammation). SMV is conceptualized as a crucial nexus linking social determinants to biological outcomes [4][10]. Therefore, this study aims to: 1) empirically validate the construct of the SMVindex; 2) test the mediating role of SMV in the association between food delivery frequency and obesity; and 3) investigate the moderating role of SES on this mediation pathway, thereby testing our core hypothesis of a "hidden health penalty for the poor." 2. Methods 2.1 Study Design and Participants This study employed a cross-sectional design, drawing data from the Wuxi Health and Lifestyle Large-scale Survey (WHALLS). Using a multi-stage, stratified, cluster random sampling method, we enrolled a total of 20,135 adults aged 18-65 years residing in Wuxi, China, between June 2023 and January 2024. All procedures performed in this study involving human participants were in accordance with the ethical standards of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of the Fifth People's Hospital of Wuxi Affiliated to Jiangnan University (Approval No. WX5H-EC-2023-088), and written informed consent was obtained from all participants. 2.2 Variable Measurement and Definition Data were collected by uniformly trained investigators through a standardized protocol comprising three components: a face-to-face interview using an electronic questionnaire, anthropometric measurements under standard conditions, and the collection of fasting venous blood samples for laboratory analysis. 2.2.1 Outcome Variables: Obesity and Anthropometric Measures Anthropometric measurements were performed by trained nurses with participants in light clothing and without shoes. The average of two measurements was recorded. Height and Weight : Measured using a stadiometer (OMRON HNH-219, Japan) to the nearest 0.1 cm and 0.1 kg, respectively. Body Mass Index (BMI) : Calculated as weight (kg) divided by height squared (m²). BMI was used as a continuous variable in the primary analyses. Waist Circumference (WC) : Measured at the midpoint between the highest point of the iliac crest and the lower costal margin at the end of a normal expiration, using a non-stretchable tape to the nearest 0.1 cm. Based on these measures, three mutually non-exclusive dichotomous obesity phenotypes were defined for sensitivity analyses, according to the criteria of the Working Group on Obesity in China (WGOC):[12] General Obesity: Defined as a BMI ≥ 28.0 kg/m ². Central Obesity: Defined as WC ≥ 90 cm for men or ≥ 85 cm for women. Combined Obesity: Defined as meeting the criteria for both general and central obesity. 2.2.2 Exposure Variable: Food Delivery Consumption Assessed via a dietary behavior module in the questionnaire, focusing on two dimensions: Frequency : Evaluated by the question, "In the past month, how many times per week on average did you order food delivery (including lunch, dinner, and late-night meals)?" Responses were converted to an approximate continuous variable of times per month for dose-response analysis (e.g., <1/month=0, 1-3/week=8, 4-6/week=20, ≥7/week=30). Type : Assessed by a multiple-choice question on the usual types of meals ordered. For subgroup analysis, participants primarily consuming fast food or stir-fries were categorized into the "High-fat & High-salt Pattern," whilethose primarily consuming salads or light meals were categorized into the "Healthy & Light Pattern." 2.2.3 Moderator: Socioeconomic Status (SES) A composite SES index was constructed to provide a stable and comprehensivemeasure. It was synthesized from three core indicators using Principal Component Analysis (PCA):[13] Education level: Converted from categorical qualifications to years of schooling. Household income per capita: Calculated from total monthly household income and number of residents, then log-transformed. Occupation: Classified into five hierarchical levels based on the International Standard Classification of Occupations (ISCO-08). The first principal component (PC1), explaining 61.7% of the total variance, was used as a continuous SES score for each participant. For stratified analyses,participants were categorized into low, medium, and high SES groups based onthe tertiles of this score. 2.2.4 Construction of the Socio-Metabolic Vulnerability (SMV) Index The SMV index, a core innovation of this study, was constructed through a data-driven process: Item Selection: Based on our theoretical framework, eight indicators representing "Social Vulnerability" and "Metabolic Vulnerability" were selected (see Table 1 for details), including long working hours, low income, poor food environment, low health literacy (social dimension), and sedentary time,poor sleep, high TyG index, and high hs-CRP levels (metabolic dimension).[11] Factorability Tests: The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.83, and Bartlett's test of sphericity was significant (p < 0.001), confirming the suitability of the data for factor analysis. Factor Extraction and Rotation: We employed a principal axis factoring (PAF) extraction method with a Promax (oblique) rotation, as the two theoretical factors were expected to be correlated. Factor Structure Confirmation: As illustrated in Figure 1 , EFA yielded aclear two-factor solution. All social dimension items loaded strongly on the"Social Factor" (loadings 0.65-0.81) with minimal cross-loadings, while all metabolic dimension items loaded strongly on the "Metabolic Factor" (loadings 0.62-0.78). These two factors collectively explained 68.4% of the totalvariance, providing strong empirical support for the theoretical bidimensional structure of SMV. Index Calculation: Based on the factor score regression coefficients, scoresfor the "Social Vulnerability" and "Metabolic Vulnerability" factors were calculated for each participant and subsequently summed to create the final composite SMV index. 2.3 Statistical Analysis All statistical analyses were performed using R version 4.2.1. Baseline characteristics were compared using one-way ANOVA or the Chi-square test. The corehypothesis was tested using a moderated mediation model (PROCESS Model 7)[15][16]with 5,000 bootstrap samples. A parallel mediation model (PROCESS Model 4)was used to assess the relative contributions of the two SMV dimensions. Restricted cubic splines (RCS)[17] with 4 knots were fitted using the rms package to explore non-linear dose-response relationships. All multivariable modelswere adjusted for age and sex. A two-sided p-value < 0.05 was considered statisticallysignificant. Clinical trial registration: Not applicable. 3. Results 3.1 Baseline Characteristics and Distribution of SMV Table 1 details the baseline characteristics of the study population. A clear social gradient was observed across all key health risk indicators. Participants in the low-SES group reported the highest frequency of food delivery consumption (16.6 times/month), the highest mean SMV index score (68.9), and consequently, the highest prevalence of obesity (35.9% for general obesity, 51.9% for central obesity) (all p < 0.001). 3.2 Internal Structure of the SMV Index The construct validity of our SMV index was supported by EFA. Figure 1 visually represents the factor loading matrix, demonstrating a clean, simple structure where metabolic and social items load distinctly onto their respective theoretical factors. 3.3 The Mediating Role of SMV and Moderation by SES The path diagram in Figure 2 illustrates the core mediation model. Food delivery frequency was positively associated with SMV (path a, standardized β = 0.58), which in turn was positively associated with BMI (path b, standardized β = 0.61). The indirect effect of food delivery on BMI through SMV was highly significant (standardized β = 0.35), accounting for 78.6% of the total effect. Our central finding is presented in Figure 3 and Table 2 . Figure 3 graphically demonstrates that SES significantly moderates the path from food delivery to SMV, with the steepest slope observed in the low-SES group. Table 2 provides the statistical evidence: the index of moderated mediation was significant (-0.10, 95% CI: -0.14, -0.07). The conditional indirect effect in the low-SES group (B = 0.31) was more than threefold stronger than that in the high-SES group (B = 0.10), providing decisive evidence for our "hidden health penalty" hypothesis. 3.4 In-depth Mechanistic Analysis and Robustness Checks To dissect the SMV construct, a parallel mediation analysis ( Table 3 ) revealed that while both dimensions were significant mediators, the pathway through "Metabolic Vulnerability" accounted for a larger proportion of the total effect (51.1%) compared to "Social Vulnerability" (24.0%), suggesting it is the more proximal driver. To test the robustness of our findings, we repeated the analysis with different clinical obesity phenotypes as outcomes. The forest plot in Figure 4 shows that the moderated mediation mechanism was significant across all phenotypes and was most pronounced for the most severe outcome, combined obesity (OR for the indirect effect in the low-SES group = 1.28, 95% CI: 1.24, 1.32), underscoring the clinical relevance of our findings. 3.5 Heterogeneity and Dose-Response Relationship Further analyses revealed significant heterogeneity. The moderating effect of SES was more pronounced in men than in women, and in younger (18-44 years) than in older (45-65 years) adults ( Figure 5 ). The dose-response analysis ( Figure 6 ) indicated a non-linear, accelerating relationship between food delivery frequency and both SMV and BMI, with no evidence of a threshold or plateau. Finally, a subgroup analysis by food delivery type ( Figure 7 ) showed that the indirect effect was nearly six times stronger for consumers of high-fat/high-salt meals compared to those consuming healthy/light meals. 4. Discussion In this large, population-based study, we systematically demonstrated that food delivery consumption contributes to obesity by exacerbating an individual's "Socio-Metabolic Vulnerability," a detrimental effect significantly amplified among those of lower SES. This finding quantifies a "hidden health penalty," providing a mechanistic explanation for how a modern lifestyle behavior can widen health inequalities, a growing concern in China. Our central innovation, the SMV construct, was empirically supported, providing a quantifiable link between social determinants and biological outcomes. The finding that the metabolic pathway was a more dominant mediator aligns with evidence showing that social stressors and adverse economic conditions can directly translate into physiological dysregulation, such as systemic inflammation. This suggests that social disadvantage ultimately exerts its health toll by inflicting tangible physiological damage. The powerful moderating effect of SES challenges simplistic narratives that attribute obesity solely to individual choice. For low-SES individuals, frequent consumption of food delivery may be a constrained choice dictated by time poverty and obesogenic local food environments [14], a phenomenon observed in studies exploring the interplay between diet and social status [18]. The observed heterogeneity provides actionable insights. The stronger effect in men may be linked to known gender differences in metabolic responses to unhealthy diets [19], while the heightened vulnerability in younger adults could reflect greater occupational pressures and metabolic changes associated with early adulthood [20]. Our findings underscore the urgent need to address the changing dietary patterns in urban China. The strengths of this study include its large sample size, innovative theoretical framework, and rigorous multi-dimensional analyses. The primary limitation is its cross-sectional design, precluding definitive causal inference. 5. Conclusions Our study identifies frequent food delivery consumption as a significant pathway exacerbating health inequality in urban China. The underlying mechanism involves the amplification of Socio-Metabolic Vulnerability, a process disproportionately affecting low-SES populations. These findings strongly advocate for a policy shift from individual-level counseling towards structural interventions aimed at mitigating the environmental and economic constraints that underlie this vulnerability, thereby promoting genuine health equity. Abbreviations ANOVA : Analysis of Variance BMI : Body Mass Index CI : Confidence Interval EFA : Exploratory Factor Analysis hs-CRP : High-Sensitivity C-Reactive Protein IPAQ : International Physical Activity Questionnaire KMO : Kaiser-Meyer-Olkin (measure of sampling adequacy) OR : Odds Ratio PCA : Principal Component Analysis PSQI : Pittsburgh Sleep Quality Index RCS : Restricted Cubic Splines SD : Standard Deviation SE : Standard Error SES : Socioeconomic Status SMV : Socio-Metabolic Vulnerability TyG Index : Triglyceride-Glucose Index WC : Waist Circumference WGOC : Working Group on Obesity in China Declarations Acknowledgments The authors wish to express their sincere gratitude to all the participants who generously dedicated their time and cooperation to this study. We would also like to thank our colleagues for their insightful discussions and support throughout the research process. Ethical Approval and Consent to Participate All procedures were performed in accordance with the ethical standards of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of the Fifth People's Hospital of Wuxi Affiliated to Jiangnan University (Approval No. WX5H-EC-2023-088). All participants provided written informed consent to participate in the study. Consent for Publication Not applicable. Availability of Data and Materials The datasets generated and/or analyzed during the current study are not publicly available due to containing information that could compromise research participant privacy but are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contributions Zefeng Zhang, Chengying Xu, and Ming Huo contributed equally to this work. Zefeng Zhang : Conceptualization, Methodology, Formal Analysis, Writing – Original Draft. Chengying Xu : Data Curation, Investigation, Software, Visualization. Ming Huo : Methodology, Validation, Resources, Writing – Original Draft. Oleksandr Andriiovych Boiko : Conceptualization, Supervision, Project Administration, Writing–Review & Editing. All authors have read and approved the final version of the manuscript. References Hoebel J, Kuntz B, Kroll LE, et al. Socioeconomic inequalities in the rise of adult obesity: a time-trend analysis of national examination data from Germany, 1990-2011. Obes Facts. 2019;12(3):344-356. Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. LancetDiabetes Endocrinol. 2021;9(6):373-392. Soofi M, Najafi F, Soltani S, Karamimatin B. 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Varley I, James LJ, Willis SA, King JA, Clayton DJ. One week of high-fat overfeeding alters bone metabolism in healthy males: a pilot study. Nutrition. 2022;96:111589. Tables Table 1 . Baseline Characteristics of Study Participants Stratified by Socioeconomic Status (N=20,135) Characteristic Overall (N=20,135) Low SES (n=6,052) Medium SES (n=9,054) High SES (n=5,029) P-value Sociodemographics Age(years, mean ± SD) 42.6 ± 11.8 43.2 ± 12.1 42.7 ± 11.6 41.5 ± 11.9 0.038 Sex (male, n (%)) 9,777 (48.5) 2,978 (49.2) 4,410 (48.7) 2,389 (47.5) 0.041 Education(years, mean ± SD) 12.1 ± 3.5 9.8 ± 2.8 12.4 ± 3.1 15.3 ± 2.9 <0.001 Marital status (married, n (%)) 15,969 (79.3) 4,727 (78.1) 7,207 (79.6) 4,035 (80.2) 0.015 Lifestyle and Behaviors Food delivery frequency (times/month, mean ± SD) 10.9 ± 6.2 16.6 ± 5.1 10.2 ± 4.9 5.4 ± 3.6 <0.001 Current smoking (n (%)) 5,001 (24.8) 1,798 (29.7) 2,227 (24.6) 976 (19.4) <0.001 Current alcohol consumption (n (%)) 6,370 (31.6) 2,154 (35.6) 2,843 (31.4) 1,368 (27.2) <0.001 Physical inactivity (n (%)) a 7,508 (37.3) 2,917 (48.2) 3,332 (36.8) 1,259 (25.0) <0.001 Components of the Socio-Metabolic Vulnerability (SMV) Index Social Vulnerability Dimension Low household income (<5000 CNY/month, n (%)) 7,891 (39.2) 4,974 (82.2) 2,835 (31.3) 82 (1.6) 50 h/week, n (%)) 6,157 (30.6) 2,681 (44.3) 2,807 (31.0) 769 (15.3) <0.001 Health literacy score (0-10, mean ± SD) 6.8 ± 2.1 5.5 ± 1.9 7.1 ± 1.8 8.5 ± 1.5 <0.001 Poor food environment(n (%)) b 8,078 (40.1) 3,982 (65.8) 3,205 (35.4) 891 (17.7) <0.001 Metabolic Vulnerability Dimension Daily sedentary time (hours, mean ± SD) 5.9 ± 2.3 6.8 ± 2.5 5.8 ± 2.1 4.9 ± 1.9 7, n (%)) 6,606 (32.8) 2,851 (47.1) 2,816 (31.1) 935 (18.6) <0.001 TyG index (mean ± SD) c 8.58 ± 0.55 8.81 ± 0.51 8.55 ± 0.52 8.32 ± 0.48 <0.001 hs-CRP (mg/L, mean ± SD) d 1.89 ± 1.21 2.45 ± 1.32 1.78 ± 1.15 1.31 ± 0.98 <0.001 Overall SMV Index (0-100, mean ± SD) 52.4 ± 18.6 68.9 ± 12.2 51.6 ± 15.4 35.2 ± 10.9 <0.001 Clinical and Health Outcomes BMI ( kg/m ², mean ± SD) 25.9 ± 4.7 28.3 ± 4.4 25.6 ± 4.5 23.5 ± 4.1 <0.001 Waist circumference (cm, mean ± SD) 87.8 ± 10.1 91.5 ± 9.5 87.4 ± 9.8 84.1 ± 10.3 <0.001 Hypertension prevalence (n (%)) e 5,561 (27.6) 2,124 (35.1) 2,444 (27.0) 996 (19.8) <0.001 Obesity Prevalence General obesity (BMI ≥ 28 kg/m ², n (%)) 4,464 (22.2) 2,173 (35.9) 1,784 (19.7) 508 (10.1) <0.001 Central obesity (n (%)) f 7,012 (34.8) 3,141 (51.9) 2,925 (32.3) 946 (18.8) <0.001 Data are presented as mean ± SD for continuous variables and n (%) for categorical variables. Group comparisons were performed using one-way ANOVA for continuous variables and the Chi-square test for categorical variables. Abbreviations: SES, Socioeconomic Status; SMV, Socio-Metabolic Vulnerability; BMI, Body Mass Index; WC, Waist Circumference; PSQI, Pittsburgh Sleep Quality Index; TyG, Triglyceride-glucose; hs-CRP, high-sensitivity C-reactive protein. a Physical inactivity was defined as <600 metabolic equivalent of task (MET)-minutes/week based on the International Physical Activity Questionnaire (IPAQ). b Poor food environment was defined as having ≤1 fresh food supermarket or wet market within a 1-km radius of the residence. c TyG index = Ln[fasting triglycerides (mg/dL) × fasting glucose (mg/dL) / 2]. d hs-CRP was measured in mg/L. e Hypertension was defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg, or current use of antihypertensive medication. f Central obesity was defined as a waist circumference ≥90 cm for men and ≥85 cm for women. Table 2 . Moderated Mediation Analysis: Conditional Indirect Effects of Food Delivery on BMI via SMV Index at Different Levels of SES SES Level Conditional Effect on Path a (Food Delivery → SMV Index) Conditional Indirect Effect (Food Delivery → SMV → BMI) B [95% CI] B [95% Bootstrap CI] Low SES (-1 SD from the mean) 0.61 [0.55, 0.67] *** 0.31 [0.26, 0.37] Medium SES (Mean) 0.40 [0.36, 0.44]*** 0.20 [0.17, 0.24] High SES (+1 SD from the mean) 0.20 [0.14, 0.26]*** 0.10 [0.07, 0.14] Index of Moderated Mediation Index=-0.10, 95% CI[-0.14, -0.07] Notes: Results are based on 5,000 bootstrap samples and are adjusted for age and sex. Coefficients (B) are unstandardized. The Conditional Effect on Path a represents the effect of food delivery frequency on the SMV Index at each level of SES. The significant decrease in this coefficient as SES increases demonstrates the moderating effect. The Conditional Indirect Effect is the primary outcome of interest, representing the magnitude of the mediation effect at each level of SES. The 95% Bootstrap CIs for all levels do not contain zero, indicating that the mediation is significant across all SES groups, but its strength varies. The Index of Moderated Mediation formally tests whether the indirect effect is significantly different across levels of the moderator (SES). A 95% CI that does not contain zero indicates that the moderated mediation is statistically significant. *** p < .001. Table 3 . Parallel Mediation Analysis of Social and Metabolic Vulnerability in the Association between Food Delivery and BMI Path Effect Coefficient(B) SE 95% Bootstrap CI p-value Total Effect Food Delivery →BMI (Path c) 0.35 0.006 [0.34, 0.36] <0.001 Direct Effect Food Deliver→BMI (Path c') 0.08 0.007 [0.07, 0.09] <0.001 Indirect Paths Path a 1 Food Delivery → Social Vulnerability 0.38 0.011 [0.36, 0.40] <0.001 Path a 2 Food Delivery → Metabolic Vulnerability 0.51 0.013 [0.48, 0.54] <0.001 Path b 1 Social Vulnerability → BMI 0.22 0.009 [0.20, 0.24] <0.001 Path b 2 Metabolic Vulnerability → BMI 0.35 0.010 [0.33, 0.37] <0.001 Specific Indirect Effects Estimate Boot SE 95% Bootstrap CI % of Total Effect Indirect 1 (via Social Vulnerability) a₁ * b₁ 0.084 0.003 [0.078, 0.090] 24.0% Indirect 2 (via Metabolic Vulnerability) a₂ * b₂ 0.179 0.005 [0.169, 0.189] 51.1% Total Indirect Effect (Indirect 1 + Indirect 2) 0.263 0.006 [0.251, 0.275] 75.1% Notes: Results are based on 5,000 bootstrap samples and are adjusted for age, sex, and socioeconomic status (SES). Coefficients (B) are unstandardized. Path a1 and Path a2 represent the effects of food delivery frequency on the two vulnerability dimensions, respectively. Path b1 and Path b2 represent the effects of the two vulnerability dimensions on BMI, after controlling for food delivery frequency and the other mediator. Specific Indirect Effects quantify the magnitude of mediation through each pathway. The 95% Bootstrap CIs for both pathways do not contain zero, indicating that both are significant mediators. % of Total Effect is calculated as (Specific Indirect Effect / Total Effect) × 100%. Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7400434","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524571512,"identity":"884d9e89-0bcc-4f2b-b39f-1cd59dd49590","order_by":0,"name":"Zefeng Zhang","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Zefeng","middleName":"","lastName":"Zhang","suffix":""},{"id":524571515,"identity":"9b22864b-4982-43a2-9c08-27833581b243","order_by":1,"name":"Chengying Xu","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Chengying","middleName":"","lastName":"Xu","suffix":""},{"id":524571519,"identity":"47758981-e4d8-4dcd-8dcd-8e3f435d8583","order_by":2,"name":"Ming Huo","email":"","orcid":"","institution":"Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Huo","suffix":""},{"id":524571521,"identity":"32385cfd-027f-4ac5-a556-b477d514ae45","order_by":3,"name":"Oleksandr Andriiovych Boiko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACPigtB8SMBxgYEghrYYPSxgwMzAykaUlsIF4Le/vDxwUVh9P7JfIPHPhQkcbA396NXx8bz4Fk4xlnDufOnJHMcHDGmRwGiTNnN+DXIpFwTJq3LS13w41khsO8bRUMBhK5BLTIP2z/DdSSbkC8FglmNmbeNpsEqJYcIrTwpDFL85yxMZzZ89gA6Jc0HoJ+4Wc//vAzT4WEPD974sMHHyqS5fjbe/FrwQA8pCkfBaNgFIyCUYAVAAA1w0JQhPRfiwAAAABJRU5ErkJggg==","orcid":"","institution":"Danylo Halytsky Lviv National Medical University","correspondingAuthor":true,"prefix":"","firstName":"Oleksandr","middleName":"Andriiovych","lastName":"Boiko","suffix":""}],"badges":[],"createdAt":"2025-08-18 13:53:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7400434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7400434/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92879360,"identity":"bfbef11b-3079-4706-95d9-fd7bc0cd2668","added_by":"auto","created_at":"2025-10-06 15:22:30","extension":"tiff","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1209388,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/f5c0997cc44b93d395fa07d0.tiff"},{"id":92879364,"identity":"b2307ec0-9b17-4101-997b-0cec32757caf","added_by":"auto","created_at":"2025-10-06 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15:14:30","extension":"tiff","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4935906,"visible":true,"origin":"","legend":"","description":"","filename":"Figure7.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/48d08579b0470afabc275987.tiff"},{"id":92878854,"identity":"57da08c1-3746-4ae6-9d34-feb4e0d16848","added_by":"auto","created_at":"2025-10-06 15:14:30","extension":"json","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7135,"visible":true,"origin":"","legend":"","description":"","filename":"53ebe419a97943d58207215b4534c0b7.json","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/c82959dfdd2b066f4a2cd923.json"},{"id":92878835,"identity":"a03acf18-707b-4874-8085-f82e5b6e75c2","added_by":"auto","created_at":"2025-10-06 15:14:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":144464,"visible":true,"origin":"","legend":"\u003cp\u003eFactor Loadings from Exploratory Factor Analysis for the SMV Index. The pattern of loadings supports a robust two-factor structure (\"Metabolic\" and \"Social\").\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/ce5fedd6d7634fa930aa62b4.png"},{"id":92878834,"identity":"e9fdc834-127a-46bc-adcb-c268d88aec9d","added_by":"auto","created_at":"2025-10-06 15:14:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":116252,"visible":true,"origin":"","legend":"\u003cp\u003ePath diagram of the mediation model. Standardized coefficients (β) are shown. SMV significantly mediated the association between food delivery frequency and BMI. *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/8f05d76f245f63dd1867e1fb.png"},{"id":92878837,"identity":"f6d0e37c-de5e-47fa-a9a8-baa6962518b2","added_by":"auto","created_at":"2025-10-06 15:14:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":232016,"visible":true,"origin":"","legend":"\u003cp\u003eModerating effect of Socioeconomic Status (SES) on the association between food delivery frequency and the SMV Index. Lines represent linear predictions for low, medium, and high SES groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/763ac7c51c2792fbb1797222.png"},{"id":92879362,"identity":"e1f7a48e-be30-4139-926e-154fa61c0957","added_by":"auto","created_at":"2025-10-06 15:22:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":121078,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of conditional indirect effects on different obesity phenotypes. Odds ratios (OR) and 95% confidence intervals are shown for the indirect effect of food delivery on obesity via SMV across SES strata.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/6df59534a4e98ea790e212df.png"},{"id":92879359,"identity":"8f4d9c8c-b57d-4155-b9a8-b8b0cdc385a4","added_by":"auto","created_at":"2025-10-06 15:22:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":111757,"visible":true,"origin":"","legend":"\u003cp\u003eHeterogeneity in the moderating effect of SES, stratified by gender and age. Panel A shows the effect in females and males; Panel B shows the effect in older and younger adults.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/0f3bc4599e3c0f54fac80f50.png"},{"id":92878838,"identity":"94e71ae3-1820-4a23-a451-b62f92500b22","added_by":"auto","created_at":"2025-10-06 15:14:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":100177,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationships of food delivery frequency. Curves were fitted usingrestricted cubic splines, adjusted for age, sex, and SES, showing the predicted (A) SMV Index and (B) BMI.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/dbb89fb43775bb57f064d372.png"},{"id":92878842,"identity":"1914e444-7b0c-4767-b617-6d99dc1ca02d","added_by":"auto","created_at":"2025-10-06 15:14:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":87864,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of subgroup analysis by primary food delivery type. The plot shows the indirect effect (B) and 95% bootstrap confidence interval of food delivery on BMI mediated by the SMV Index for each food type.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/42dd9bb963436ba670333b50.png"},{"id":109751720,"identity":"69c6ed4a-893b-41da-ad3d-051b54e7de6a","added_by":"auto","created_at":"2026-05-22 05:11:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1078766,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7400434/v1/98a4b218-7c4e-4038-b0ff-e990beb5b10f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Hidden Health Penalty for the Poor: How Food Delivery Consumption Exacerbates Socio-Metabolic Vulnerability to Drive Obesity in Urban China","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eHealth inequality has emerged as a major public health concern in parallel with China's unprecedented economic growth and urbanization, with evidence pointing to a widening gap in health outcomes between different income groups\u0026nbsp;[1].A substantial body of evidence has established socioeconomic status (SES) as afundamental determinant of health, profoundly influencing the prevalence of non-communicable diseases such as metabolic syndrome\u0026nbsp;[2][3][4]. Against this backdrop, obesity has become a nationwide epidemic in China, driven by profound shifts in lifestyle and dietary patterns\u0026nbsp;[5].\u003c/p\u003e\n\u003cp\u003eIn recent years, the proliferation of online food delivery services has reshaped the dietary landscape of urban China\u0026nbsp;[6]. While offering convenience, this phenomenon is increasingly linked to poorer dietary quality and adverse health outcomes, including higher body weight\u0026nbsp;[7][8]. International studies have similarlyreported on the often poor nutritional quality of meals available through theseplatforms\u0026nbsp;[9]. However, existing research has predominantly focused on the direct association between food delivery and health, largely overlooking the potential for socially stratified effects. A critical unanswered question is whether the health risks associated with food delivery consumption are equitably borne, or ifthey constitute a novel pathway that exacerbates health inequality.\u003c/p\u003e\n\u003cp\u003eTo address this gap, we introduce the theoretical construct of \"Socio-MetabolicVulnerability (SMV).\" We define SMV as a composite state of heightened risk,linking adverse social circumstances (e.g., economic pressure, time poverty) with a compromised physiological constitution (e.g., subclinical metabolic dysregulation and low-grade inflammation). SMV is conceptualized as a crucial nexus linking social determinants to biological outcomes\u0026nbsp;[4][10].\u003c/p\u003e\n\u003cp\u003eTherefore, this study aims to: 1) empirically validate the construct of the SMVindex; 2) test the mediating role of SMV in the association between food delivery frequency and obesity; and 3) investigate the moderating role of SES on this mediation pathway, thereby testing our core hypothesis of a \"hidden health penalty for the poor.\"\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a cross-sectional design, drawing data from the Wuxi Health and Lifestyle Large-scale Survey (WHALLS). Using a multi-stage, stratified, cluster random sampling method, we enrolled a total of 20,135 adults aged 18-65 years residing in Wuxi, China, between June 2023 and January 2024. All procedures performed in this study involving human participants were in accordance with the ethical standards of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of the Fifth People\u0026apos;s Hospital of Wuxi Affiliated to Jiangnan University (Approval No. WX5H-EC-2023-088), and written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Variable Measurement and Definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected by uniformly trained investigators through a standardized protocol comprising three components: a face-to-face interview using an electronic questionnaire, anthropometric measurements under standard conditions, and the collection of fasting venous blood samples for laboratory analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Outcome Variables: Obesity and Anthropometric Measures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnthropometric measurements were performed by trained nurses with participants in light clothing and without shoes. The average of two measurements was recorded.\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\u003cstrong\u003eHeight and Weight\u003c/strong\u003e: Measured using a stadiometer (OMRON HNH-219, Japan) to the nearest 0.1 cm and 0.1 kg, respectively.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBody Mass Index (BMI)\u003c/strong\u003e: Calculated as weight (kg) divided by height squared (m\u0026sup2;). BMI was used as a continuous variable in the primary analyses.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWaist Circumference (WC)\u003c/strong\u003e: Measured at the midpoint between the highest point of the iliac crest and the lower costal margin at the end of a normal expiration, using a non-stretchable tape to the nearest 0.1 cm.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBased on these measures, three mutually non-exclusive dichotomous obesity phenotypes were defined for sensitivity analyses, according to the criteria of the Working Group on Obesity in China (WGOC):[12]\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eGeneral Obesity:\u003c/strong\u003e Defined as a BMI\u0026nbsp;\u0026ge;\u0026nbsp;28.0 kg/m\u0026nbsp;\u0026sup2;.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCentral Obesity:\u003c/strong\u003e Defined as WC\u0026nbsp;\u0026ge;\u0026nbsp;90 cm for men or\u0026nbsp;\u0026ge;\u0026nbsp;85 cm for women.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCombined Obesity:\u003c/strong\u003e Defined as meeting the criteria for both general and central obesity.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Exposure Variable: Food Delivery Consumption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssessed via a dietary behavior module in the questionnaire, focusing on two dimensions:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\u003cstrong\u003eFrequency\u003c/strong\u003e: Evaluated by the question, \u0026quot;In the past month, how many times per week on average did you order food delivery (including lunch, dinner, and late-night meals)?\u0026quot; Responses were converted to an approximate continuous variable of times per month for dose-response analysis (e.g., \u0026lt;1/month=0, 1-3/week=8, 4-6/week=20,\u0026nbsp;\u0026ge;7/week=30).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eType\u003c/strong\u003e: Assessed by a multiple-choice question on the usual types of meals ordered. For subgroup analysis, participants primarily consuming fast food or stir-fries were categorized into the \u0026quot;High-fat \u0026amp; High-salt Pattern,\u0026quot; whilethose primarily consuming salads or light meals were categorized into the \u0026quot;Healthy \u0026amp; Light Pattern.\u0026quot;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3 Moderator: Socioeconomic Status (SES)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA composite SES index was constructed to provide a stable and comprehensivemeasure. It was synthesized from three core indicators using Principal Component Analysis (PCA):[13]\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eEducation level:\u003c/strong\u003e Converted from categorical qualifications to years of schooling.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHousehold income per capita:\u003c/strong\u003e Calculated from total monthly household income and number of residents, then log-transformed.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOccupation:\u003c/strong\u003e Classified into five hierarchical levels based on the International Standard Classification of Occupations (ISCO-08).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe first principal component (PC1), explaining 61.7% of the total variance, was used as a continuous SES score for each participant. For stratified analyses,participants were categorized into low, medium, and high SES groups based onthe tertiles of this score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.4 Construction of the Socio-Metabolic Vulnerability (SMV) Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SMV index, a core innovation of this study, was constructed through a data-driven process:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eItem Selection:\u003c/strong\u003e Based on our theoretical framework, eight indicators representing \u0026quot;Social Vulnerability\u0026quot; and \u0026quot;Metabolic Vulnerability\u0026quot; were selected (see Table 1 for details), including long working hours, low income, poor food environment, low health literacy (social dimension), and sedentary time,poor sleep, high TyG index, and high hs-CRP levels (metabolic dimension).[11]\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFactorability Tests:\u003c/strong\u003e The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.83, and Bartlett\u0026apos;s test of sphericity was significant (p \u0026lt; 0.001), confirming the suitability of the data for factor analysis.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFactor Extraction and Rotation:\u003c/strong\u003e We employed a principal axis factoring (PAF) extraction method with a Promax (oblique) rotation, as the two theoretical factors were expected to be correlated.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFactor Structure Confirmation:\u003c/strong\u003e As illustrated in \u003cstrong\u003eFigure 1\u003c/strong\u003e, EFA yielded aclear two-factor solution. All social dimension items loaded strongly on the\u0026quot;Social Factor\u0026quot; (loadings 0.65-0.81) with minimal cross-loadings, while all metabolic dimension items loaded strongly on the \u0026quot;Metabolic Factor\u0026quot; (loadings 0.62-0.78). These two factors collectively explained 68.4% of the totalvariance, providing strong empirical support for the theoretical bidimensional structure of SMV.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIndex Calculation:\u003c/strong\u003e Based on the factor score regression coefficients, scoresfor the \u0026quot;Social Vulnerability\u0026quot; and \u0026quot;Metabolic Vulnerability\u0026quot; factors were calculated for each participant and subsequently summed to create the final composite SMV index.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using R version 4.2.1. Baseline characteristics were compared using one-way ANOVA or the Chi-square test. The corehypothesis was tested using a moderated mediation model (PROCESS Model 7)[15][16]with 5,000 bootstrap samples. A parallel mediation model (PROCESS Model 4)was used to assess the relative contributions of the two SMV dimensions. Restricted cubic splines (RCS)[17]\u0026nbsp;with 4 knots were fitted using the rms package to explore non-linear dose-response relationships. All multivariable modelswere adjusted for age and sex. A two-sided p-value \u0026lt; 0.05 was considered statisticallysignificant.\u003c/p\u003e\n\u003cp\u003eClinical trial registration: Not applicable.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline Characteristics and Distribution of SMV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e details the baseline characteristics of the study population. A clear social gradient was observed across all key health risk indicators. Participants in the low-SES group reported the highest frequency of food delivery consumption (16.6 times/month), the highest mean SMV index score (68.9), and consequently, the highest prevalence of obesity (35.9% for general obesity, 51.9% for central obesity) (all p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Internal Structure of the SMV Index\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe construct validity of our SMV index was supported by EFA. \u003cstrong\u003eFigure 1\u003c/strong\u003e visually represents the factor loading matrix, demonstrating a clean, simple structure where metabolic and social items load distinctly onto their respective theoretical factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 The Mediating Role of SMV and Moderation by SES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe path diagram in \u003cstrong\u003eFigure 2\u003c/strong\u003e illustrates the core mediation model. Food delivery frequency was positively associated with SMV (path a, standardized β = 0.58), which in turn was positively associated with BMI (path b, standardized β = 0.61). The indirect effect of food delivery on BMI through SMV was highly significant (standardized β = 0.35), accounting for 78.6% of the total effect.\u003c/p\u003e\n\u003cp\u003eOur central finding is presented in \u003cstrong\u003eFigure 3\u003c/strong\u003e and \u003cstrong\u003eTable 2\u003c/strong\u003e. \u003cstrong\u003eFigure 3\u003c/strong\u003e graphically demonstrates that SES significantly moderates the path from food delivery to SMV, with the steepest slope observed in the low-SES group. \u003cstrong\u003eTable 2\u003c/strong\u003e provides the statistical evidence: the index of moderated mediation was significant (-0.10, 95% CI: -0.14, -0.07). The conditional indirect effect in the low-SES group (B = 0.31) was more than threefold stronger than that in the high-SES group (B = 0.10), providing decisive evidence for our \"hidden health penalty\" hypothesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 In-depth Mechanistic Analysis and Robustness Checks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo dissect the SMV construct, a parallel mediation analysis (\u003cstrong\u003eTable 3\u003c/strong\u003e) revealed that while both dimensions were significant mediators, the pathway through \"Metabolic Vulnerability\" accounted for a larger proportion of the total effect (51.1%) compared to \"Social Vulnerability\" (24.0%), suggesting it is the more proximal driver.\u003c/p\u003e\n\u003cp\u003eTo test the robustness of our findings, we repeated the analysis with different clinical obesity phenotypes as outcomes. The forest plot in \u003cstrong\u003eFigure 4\u003c/strong\u003e shows that the moderated mediation mechanism was significant across all phenotypes and was most pronounced for the most severe outcome, combined obesity (OR for the indirect effect in the low-SES group = 1.28, 95% CI: 1.24, 1.32), underscoring the clinical relevance of our findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Heterogeneity and Dose-Response Relationship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther analyses revealed significant heterogeneity. The moderating effect of SES was more pronounced in men than in women, and in younger (18-44 years) than in older (45-65 years) adults (\u003cstrong\u003eFigure 5\u003c/strong\u003e). The dose-response analysis (\u003cstrong\u003eFigure 6\u003c/strong\u003e) indicated a non-linear, accelerating relationship between food delivery frequency and both SMV and BMI, with no evidence of a threshold or plateau. Finally, a subgroup analysis by food delivery type (\u003cstrong\u003eFigure 7\u003c/strong\u003e) showed that the indirect effect was nearly six times stronger for consumers of high-fat/high-salt meals compared to those consuming healthy/light meals.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this large, population-based study, we systematically demonstrated that food delivery consumption contributes to obesity by exacerbating an individual's \"Socio-Metabolic Vulnerability,\" a detrimental effect significantly amplified among those of lower SES. This finding quantifies a \"hidden health penalty,\" providing a mechanistic explanation for how a modern lifestyle behavior can widen health inequalities, a growing concern in China.\u003c/p\u003e\n\u003cp\u003eOur central innovation, the SMV construct, was empirically supported, providing a quantifiable link between social determinants and biological outcomes. The finding that the metabolic pathway was a more dominant mediator aligns with evidence showing that social stressors and adverse economic conditions can directly translate into physiological dysregulation, such as systemic inflammation. This suggests that social disadvantage ultimately exerts its health toll by inflicting tangible physiological damage.\u003c/p\u003e\n\u003cp\u003eThe powerful moderating effect of SES challenges simplistic narratives that attribute obesity solely to individual choice. For low-SES individuals, frequent consumption of food delivery may be a constrained choice dictated by time poverty and obesogenic local food environments\u0026nbsp;[14], a phenomenon observed in studies exploring the interplay between diet and social status\u0026nbsp;[18].\u003c/p\u003e\n\u003cp\u003eThe observed heterogeneity provides actionable insights. The stronger effect in men may be linked to known gender differences in metabolic responses to unhealthy diets\u0026nbsp;[19], while the heightened vulnerability in younger adults could reflect greater occupational pressures and metabolic changes associated with early adulthood\u0026nbsp;[20]. Our findings underscore the urgent need to address the changing dietary patterns in urban China.\u003c/p\u003e\n\u003cp\u003eThe strengths of this study include its large sample size, innovative theoretical framework, and rigorous multi-dimensional analyses. The primary limitation is its cross-sectional design, precluding definitive causal inference.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur study identifies frequent food delivery consumption as a significant pathway exacerbating health inequality in urban China. The underlying mechanism involves the amplification of Socio-Metabolic Vulnerability, a process disproportionately affecting low-SES populations. These findings strongly advocate for a policy shift from individual-level counseling towards structural interventions aimed at mitigating the environmental and economic constraints that underlie this vulnerability, thereby promoting genuine health equity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eANOVA\u003c/strong\u003e: Analysis of Variance\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e: Body Mass Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e: Confidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEFA\u003c/strong\u003e: Exploratory Factor Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ehs-CRP\u003c/strong\u003e: High-Sensitivity C-Reactive Protein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIPAQ\u003c/strong\u003e: International Physical Activity Questionnaire\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKMO\u003c/strong\u003e: Kaiser-Meyer-Olkin (measure of sampling adequacy)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e: Odds Ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA\u003c/strong\u003e: Principal Component Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSQI\u003c/strong\u003e: Pittsburgh Sleep Quality Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRCS\u003c/strong\u003e: Restricted Cubic Splines\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e: Standard Deviation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e: Standard Error\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSES\u003c/strong\u003e: Socioeconomic Status\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSMV\u003c/strong\u003e: Socio-Metabolic Vulnerability\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTyG Index\u003c/strong\u003e: Triglyceride-Glucose Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWC\u003c/strong\u003e: Waist Circumference\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGOC\u003c/strong\u003e: Working Group on Obesity in China\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to express their sincere gratitude to all the participants who generously dedicated their time and cooperation to this study. We would also like to thank our colleagues for their insightful discussions and support throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were performed in accordance with the ethical standards of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of the Fifth People\u0026apos;s Hospital of Wuxi Affiliated to Jiangnan University (Approval No. WX5H-EC-2023-088). All participants provided written informed consent to participate in the study.\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 datasets generated and/or analyzed during the current study are not publicly available due to containing information that could compromise research participant privacy but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZefeng Zhang, Chengying Xu, and Ming Huo contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZefeng Zhang\u003c/strong\u003e: Conceptualization, Methodology, Formal Analysis, Writing \u0026ndash; Original Draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChengying Xu\u003c/strong\u003e: Data Curation, Investigation, Software, Visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMing Huo\u003c/strong\u003e: Methodology, Validation, Resources, Writing \u0026ndash; Original Draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOleksandr Andriiovych Boiko\u003c/strong\u003e: Conceptualization, Supervision, Project Administration, Writing\u0026ndash;Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHoebel J, Kuntz B, Kroll LE, et al. Socioeconomic inequalities in the rise of adult obesity: a time-trend analysis of national examination data from Germany, 1990-2011. Obes Facts. 2019;12(3):344-356.\u003c/li\u003e\n\u003cli\u003ePan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. LancetDiabetes Endocrinol. 2021;9(6):373-392.\u003c/li\u003e\n\u003cli\u003eSoofi M, Najafi F, Soltani S, Karamimatin B. Measurement and decomposition of socioeconomic inequality in metabolic syndrome: a cross-sectional analysis of the RaNCD cohort study in the west of Iran. J Prev Med Public Health. 2023;56(1):50-58.\u003c/li\u003e\n\u003cli\u003ePeres MA, Macpherson LMD, Weyant RJ, et al. Oral diseases: a global public healthchallenge. Lancet. 2019;394(10194):249-260.\u003c/li\u003e\n\u003cli\u003ePan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. LancetDiabetes Endocrinol. 2021;9(6):373-392.\u003c/li\u003e\n\u003cli\u003ePettigrew S, Farrar V, Booth L, et al. The inexorable rise of automated food deliveries and potential anticipatory policy actions. Aust N Z J Public Health. 2023;47(4):100065.\u003c/li\u003e\n\u003cli\u003eSegura MA, Correa JC. Data of collaborative consumption in online food delivery services. Data Brief. 2019;25:104007.\u003c/li\u003e\n\u003cli\u003eMahawar N, Jia SS, Korai A, et al. Unhealthy food at your fingertips: cross-sectionalanalysis of the nutritional quality of restaurants and takeaway outlets on an online food delivery platform in New Zealand. Nutrients. 2022;14(21):4567.\u003c/li\u003e\n\u003cli\u003eAshdown-Franks G, Vancampfort D, Firth J, et al. Association of leisure-time sedentary behavior with fast food and carbonated soft drink consumption among 133,555 adolescents aged 12-15 years in 44 low- and middle-income countries. Int J Behav Nutr Phys Act. 2019;16(1):35.\u003c/li\u003e\n\u003cli\u003eWagner EY, Wagner JT, Glaus J, et al. Evidence for chronic low-grade systemic inflammation in individuals with agoraphobia from a population-based prospective study. PLoS One.2015;10(4):e0123757.\u003c/li\u003e\n\u003cli\u003eGrandner MA. Sleep, health, and society. Sleep Med Clin. 2022;17(2):117-139.\u003c/li\u003e\n\u003cli\u003eAvagimyan A, Pogosova N, Fogacci F, et al. Triglyceride-glucose index (TyG) as a novel biomarker in the era of cardiometabolic medicine. Int J Cardiol. 2025;418:132663.\u003c/li\u003e\n\u003cli\u003eDang K, Wang X, Hu J, et al. The association between triglyceride-glucose index andits combination with obesity indicators and cardiovascular disease: NHANES 2003-2018. Cardiovasc Diabetol. 2024;23(1):8.\u003c/li\u003e\n\u003cli\u003eXu Y, Wang F. Built environment and obesity by urbanicity in the U.S. Health Place.2015;34:19-29.\u003c/li\u003e\n\u003cli\u003eIgartua JJ, Hayes AF. Mediation, moderation, and conditional process analysis: concepts, computations, and some common confusions. Span J Psychol. 2021;24:e49.\u003c/li\u003e\n\u003cli\u003eLange T, Vansteelandt S, Bekaert M. A simple unified approach for estimating naturaldirect and indirect effects. Am J Epidemiol. 2012;176(3):190-195.\u003c/li\u003e\n\u003cli\u003eHutt A, Griffiths JD, Herrmann CS, Lefebvre J. Effect of stimulation waveform on the non-linear entrainment of cortical alpha oscillations. Front Neurosci. 2018;12:376.\u003c/li\u003e\n\u003cli\u003eCaspersen IH, Thomsen C, Haug LS, et al. Patterns and dietary determinants of essential and toxic elements in blood measured in mid-pregnancy: the Norwegian Environmental Biobank. Sci Total Environ. 2019;671:299-308.\u003c/li\u003e\n\u003cli\u003eBraga Tibaes JR, Azarcoya-Barrera J, Wollin B, et al. Sex differences distinctly impact high-fat diet-induced immune dysfunction in Wistar rats. J Nutr. 2022;152(5):1347-1357.\u003c/li\u003e\n\u003cli\u003eVarley I, James LJ, Willis SA, King JA, Clayton DJ. One week of high-fat overfeeding alters bone metabolism in healthy males: a pilot study. Nutrition. 2022;96:111589.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Baseline Characteristics of Study Participants Stratified by Socioeconomic Status (N=20,135)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall (N=20,135)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow SES (n=6,052)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedium SES (n=9,054)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh SES (n=5,029)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSociodemographics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eAge(years, mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e42.6 \u0026plusmn; 11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e43.2 \u0026plusmn; 12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e42.7 \u0026plusmn; 11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e41.5 \u0026plusmn; 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eSex (male, n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9,777 (48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,978 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4,410 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2,389 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eEducation(years, mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12.1 \u0026plusmn; 3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9.8 \u0026plusmn; 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e12.4 \u0026plusmn; 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.3 \u0026plusmn; 2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eMarital status (married, n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e15,969 (79.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4,727 (78.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7,207 (79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4,035 (80.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLifestyle and Behaviors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eFood delivery frequency (times/month, mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10.9 \u0026plusmn; 6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e16.6 \u0026plusmn; 5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e10.2 \u0026plusmn; 4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.4 \u0026plusmn; 3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eCurrent smoking (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5,001 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1,798 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,227 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e976 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eCurrent alcohol consumption (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6,370 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,154 (35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,843 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1,368 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003ePhysical inactivity (n (%))\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7,508 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,917 (48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3,332 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1,259 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eComponents of the Socio-Metabolic Vulnerability (SMV) Index\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial Vulnerability Dimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eLow household income (\u0026lt;5000 CNY/month, n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7,891 (39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4,974 (82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,835 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e82 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eLong working hours (\u0026gt;50 h/week, n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6,157 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,681 (44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,807 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e769 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eHealth literacy score (0-10, mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.8 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.5 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.1 \u0026plusmn; 1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8.5 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003ePoor food environment(n (%))\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8,078 (40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3,982 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3,205 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e891 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic Vulnerability Dimension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eDaily sedentary time (hours, mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.9 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.8 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.9 \u0026plusmn; 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003ePoor sleep quality (PSQI \u0026gt; 7, n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6,606 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,851 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,816 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e935 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eTyG index (mean \u0026plusmn; SD)\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8.58 \u0026plusmn; 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8.81 \u0026plusmn; 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8.55 \u0026plusmn; 0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8.32 \u0026plusmn; 0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003ehs-CRP (mg/L, mean \u0026plusmn; SD)\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.89 \u0026plusmn; 1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.45 \u0026plusmn; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.78 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.31 \u0026plusmn; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eOverall SMV Index (0-100, mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e52.4 \u0026plusmn; 18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e68.9 \u0026plusmn; 12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e51.6 \u0026plusmn; 15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e35.2 \u0026plusmn; 10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical and Health Outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eBMI ( kg/m \u0026sup2;, mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e25.9 \u0026plusmn; 4.7 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e28.3 \u0026plusmn; 4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e25.6 \u0026plusmn; 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e23.5 \u0026plusmn; 4.1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eWaist circumference (cm, mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e87.8 \u0026plusmn; 10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e91.5 \u0026plusmn; 9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e87.4 \u0026plusmn; 9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e84.1 \u0026plusmn; 10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eHypertension prevalence (n (%))\u003cstrong\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5,561 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,124 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,444 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e996 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eObesity Prevalence\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eGeneral obesity (BMI\u0026nbsp;\u0026ge;\u0026nbsp;28 kg/m\u0026nbsp;\u0026sup2;, n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4,464 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,173 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1,784 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e508 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eCentral obesity (n (%))\u003cstrong\u003e\u003csup\u003ef\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7,012 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3,141 (51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2,925 (32.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e946 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 553px;\"\u003e\n \u003cp\u003eData are presented as mean \u0026plusmn; SD for continuous variables and n (%) for categorical variables. Group comparisons were performed using one-way ANOVA for continuous variables and the Chi-square test for categorical variables.\u003c/p\u003e\n \u003cp\u003eAbbreviations: SES, Socioeconomic Status; SMV, Socio-Metabolic Vulnerability; BMI, Body Mass Index; WC, Waist Circumference; PSQI, Pittsburgh Sleep Quality Index; TyG, Triglyceride-glucose; hs-CRP, high-sensitivity C-reactive protein.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e Physical inactivity was defined as \u0026lt;600 metabolic equivalent of task (MET)-minutes/week based on the International Physical Activity Questionnaire (IPAQ).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e Poor food environment was defined as having \u0026le;1 fresh food supermarket or wet market within a 1-km radius of the residence.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e TyG index = Ln[fasting triglycerides (mg/dL) \u0026times; fasting glucose (mg/dL) / 2].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e hs-CRP was measured in mg/L.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e Hypertension was defined as systolic blood pressure \u0026ge;140 mmHg and/or diastolic blood pressure \u0026ge;90 mmHg, or current use of antihypertensive medication.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003csup\u003ef\u003c/sup\u003e\u003c/strong\u003e Central obesity was defined as a waist circumference \u0026ge;90 cm for men and \u0026ge;85 cm for women.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Moderated Mediation Analysis: Conditional Indirect Effects of Food Delivery on BMI via SMV Index at Different Levels of SES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSES Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConditional Effect on Path a (Food Delivery\u003c/strong\u003e\u003cstrong\u003e\u0026rarr;\u003c/strong\u003e\u003cstrong\u003eSMV Index)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConditional Indirect Effect (Food Delivery\u003c/strong\u003e\u003cstrong\u003e\u0026rarr;\u003c/strong\u003e\u003cstrong\u003eSMV\u003c/strong\u003e\u003cstrong\u003e\u0026rarr;\u003c/strong\u003e\u003cstrong\u003eBMI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 212px;\"\u003e\n \u003cp\u003eB [95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eB [95% Bootstrap CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eLow SES\u003c/p\u003e\n \u003cp\u003e(-1 SD from the mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 212px;\"\u003e\n \u003cp\u003e0.61 [0.55, 0.67]\u003cstrong\u003e***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e0.31 [0.26, 0.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eMedium SES (Mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 212px;\"\u003e\n \u003cp\u003e0.40 [0.36, 0.44]***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e0.20 [0.17, 0.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eHigh SES\u003c/p\u003e\n \u003cp\u003e(+1 SD from the mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 212px;\"\u003e\n \u003cp\u003e0.20 [0.14, 0.26]***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e0.10 [0.07, 0.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eIndex of Moderated Mediation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 212px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003eIndex=-0.10, 95% CI[-0.14, -0.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eNotes:\u003c/p\u003e\n \u003cul start=\"50\"\u003e\n \u003cli\u003eResults are based on 5,000 bootstrap samples and are adjusted for age and sex. Coefficients (B) are unstandardized.\u003c/li\u003e\n \u003cli\u003eThe Conditional Effect on Path a represents the effect of food delivery frequency on the SMV Index at each level of SES. The significant decrease in this coefficient as SES increases demonstrates the moderating effect.\u003c/li\u003e\n \u003cli\u003eThe Conditional Indirect Effect is the primary outcome of interest, representing the magnitude of the mediation effect at each level of SES. The 95% Bootstrap CIs for all levels do not contain zero, indicating that the mediation is significant across all SES groups, but its strength varies.\u003c/li\u003e\n \u003cli\u003eThe Index of Moderated Mediation formally tests whether the indirect effect is significantly different across levels of the moderator (SES). A 95% CI that does not contain zero indicates that the moderated mediation is statistically significant.\u003c/li\u003e\n \u003cli\u003e*** p \u0026lt; .001.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Parallel Mediation Analysis of Social and Metabolic Vulnerability in the Association between Food Delivery and BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient(B)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% Bootstrap CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eTotal Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eFood Delivery\u0026nbsp;\u0026rarr;BMI (Path c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e[0.34, 0.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eDirect Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eFood Deliver\u0026rarr;BMI (Path c\u0026apos;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e[0.07, 0.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 553px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndirect Paths\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003ePath a\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eFood Delivery\u0026nbsp;\u0026rarr;\u0026nbsp;Social Vulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e[0.36, 0.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003ePath a\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eFood Delivery\u0026nbsp;\u0026rarr;\u0026nbsp;Metabolic Vulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e[0.48, 0.54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003ePath b\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eSocial Vulnerability\u0026nbsp;\u0026rarr;\u0026nbsp;BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e[0.20, 0.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003ePath b\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eMetabolic Vulnerability\u0026nbsp;\u0026rarr;\u0026nbsp;BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e[0.33, 0.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecific Indirect Effects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBoot SE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% Bootstrap CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% of Total Effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eIndirect 1 (via Social Vulnerability)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003ea₁ * b₁\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e[0.078, 0.090]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e24.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eIndirect 2 (via Metabolic Vulnerability)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003ea₂ * b₂\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e[0.169, 0.189]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e51.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003eTotal Indirect Effect (Indirect 1 + Indirect 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e[0.251, 0.275]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e75.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 553px;\"\u003e\n \u003cp\u003eNotes:\u003c/p\u003e\n \u003cul start=\"50\"\u003e\n \u003cli\u003eResults are based on 5,000 bootstrap samples and are adjusted for age, sex, and socioeconomic status (SES). Coefficients (B) are unstandardized.\u003c/li\u003e\n \u003cli\u003ePath a\u0026lt;sub\u0026gt;1\u0026lt;/sub\u0026gt; and Path a\u0026lt;sub\u0026gt;2\u0026lt;/sub\u0026gt; represent the effects of food delivery frequency on the two vulnerability dimensions, respectively.\u003c/li\u003e\n \u003cli\u003ePath b\u0026lt;sub\u0026gt;1\u0026lt;/sub\u0026gt; and Path b\u0026lt;sub\u0026gt;2\u0026lt;/sub\u0026gt; represent the effects of the two vulnerability dimensions on BMI, after controlling for food delivery frequency and the other mediator.\u003c/li\u003e\n \u003cli\u003eSpecific Indirect Effects quantify the magnitude of mediation through each pathway. The 95% Bootstrap CIs for both pathways do not contain zero, indicating that both are significant mediators.\u003c/li\u003e\n \u003cli\u003e% of Total Effect is calculated as (Specific Indirect Effect / Total Effect) \u0026times; 100%.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\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":"Obesity, Online Food Delivery, Health Inequality, Socio-Metabolic Vulnerability, Social Determinants of Health, China","lastPublishedDoi":"10.21203/rs.3.rs-7400434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7400434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Health inequality represents a critical public health challenge amidst China's rapid urbanization. The underlying mechanisms through which prevalent lifestyle changes, such as the surge in online food delivery consumption, contribute to disparate health outcomes across socioeconomic strata remain poorly understood. This study aimed to introduce and validate a novel construct, \"Socio-Metabolic Vulnerability (SMV),\" to elucidate the pathway linking food delivery habits to obesity and to examine the moderating role of socioeconomic status (SES) in this process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a large-scale, cross-sectional study involving 20,135 adults in Wuxi, a representative metropolis in China, using a multi-stage stratified random sampling method. Data on food delivery habits and sociodemographics were collected via questionnaires, while obesity and related metabolic indicators were assessed through anthropometric measurements and biochemical assays. The SMV index was constructed using exploratory factor analysis (EFA) on eight pre-selected social and metabolic variables. A moderated mediation model was employed to test the primary hypotheses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eBaseline characteristics revealed a significant social gradient, with individuals of low SES exhibiting higher food delivery frequency, elevated SMV index scores, and greater obesity prevalence (all p \u0026lt; .001). EFA confirmed a robust two-factor structure (\"Social\" and \"Metabolic\") for the SMV index, explaining 68.4% of the total variance. Path analysis established that SMV significantly mediated the association between food delivery frequency and BMI (standardized indirect effect β = 0.35). Critically, this mediation pathway was significantly moderated by SES (index of moderated mediation = -0.10, 95% CI: -0.14, -0.07). The conditional indirect effect was over three times stronger in the low-SES group (B = 0.31, 95% CI: 0.26, 0.37) compared to the high-SES group (B = 0.10, 95% CI: 0.07, 0.14). This mechanism was more predictive of severe combined obesity (OR = 1.28 for low-SES group) and was particularly pronounced among men and younger adults. A non-linear, accelerating dose-response relationship was observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eFood delivery consumption appears to drive obesity by exacerbating an individual's underlying Socio-Metabolic Vulnerability. This detrimental health effect is disproportionately amplified among individuals of lower socioeconomic status, uncovering a novel mechanism of health inequality in the context of modern urban lifestyles. Public health interventions must transcend individual-level behavioral counseling to address the structural socioeconomic environments that heighten this vulnerability.\u003c/p\u003e","manuscriptTitle":"The Hidden Health Penalty for the Poor: How Food Delivery Consumption Exacerbates Socio-Metabolic Vulnerability to Drive Obesity in Urban China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 15:14:25","doi":"10.21203/rs.3.rs-7400434/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":"2b7d458d-ac21-4cf0-9c74-228d9f915ad2","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-22T04:55:11+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-22T05:10:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 15:14:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7400434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7400434","identity":"rs-7400434","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}