A Study on the Influence and Correlation of Dietary Structure and Lifestyle Behavior Patterns on Growth and Development in Children Aged 3–7 Years

A Study on the Influence and Correlation of Dietary Structure and Lifestyle Behavior Patterns on Growth and Development in Children Aged 3–7 Years | 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 A Study on the Influence and Correlation of Dietary Structure and Lifestyle Behavior Patterns on Growth and Development in Children Aged 3–7 Years Bingbing Guo, Lan Jiang, Xinye Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9205134/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective To analyze the relationship between diet and lifestyle behaviors with the growth development of preschool children (aged 3–7) in Wuxi City, providing a scientific basis for implementing a healthy diet and lifestyle. Methods A stratified cluster random sampling method was used to select study participants. Self-administered questionnaires were used to collect demographic information. The dietary diversity score (DDS), dietary behaviors, and current lifestyle were analyzed. Children's growth and development were assessed using BMI-Z scores. Variance analysis and logistic regression were employed to explore the relationships. Results A total of 3727 preschool children were included. The average DDS was 5.07 ± 1.94, with 36.79% of children showing low dietary diversity (DDS ≤ 4). The largest proportion of children (53.13%) had normal growth (-1 ≤ BMI-Z ≤ 1). The children were divided into four groups based on BMI-Z scores, and statistically significant differences were observed across age, current height, current weight, birth weight, DDS, parental BMI, and maternal pre-pregnancy weight (P < 0.05). Low dietary diversity, nighttime snacking, 1–2 hours of daily screen time, and ≥ 2 hours of daily screen time were positively correlated with childhood obesity (P < 0.05). After adjusting for factors such as child’s age, gender, birth weight, parental BMI, family income, number of family members dining together, family type, and primary caregiver, low dietary diversity remained significantly associated with obesity. Conclusion Greater attention should be given to preschool children with low dietary diversity, frequent nighttime snacking, and extended screen time to prevent obesity. Targeted interventions addressing these factors should be considered. Dietary diversity score dietary behavior growth and development children correlation 1 Introduction Childhood growth and development refers to the process of physical and psychological growth from birth to adolescence, encompassing various aspects such as physical, mental, cognitive, and social abilities. In recent years, due to economic development and lifestyle changes, there have been significant shifts in dietary patterns and behaviors, with overnutrition contributing to an increasing prevalence of childhood obesity across various regions. According to the "Chinese Resident Nutrition and Chronic Disease Report (2020)", the prevalence of overweight and obesity among Chinese children and adolescents from 2015 to 2017 has approached 20% [ 1 , 2 ]. By 2030, this rate is projected to reach 28%, with 30.6 million children being overweight and 18.9 million obese [ 3 ]. Childhood obesity has thus garnered widespread attention, as it is an independent risk factor for numerous diseases. Overweight and obese children and adolescents are more likely to develop early signs of hypertension and atherosclerosis [ 4 ], and they are at lifelong increased risk of developing type 2 diabetes and obesity [ 5 ]. Moreover, if children experience early malnutrition followed by excessive weight gain, they face an increased risk of non-communicable diseases due to impaired homeostatic regulation [ 6 ]. According to the "China Childhood Obesity Report", the indirect economic losses caused by obesity in China are expected to reach 8.7% of GDP by 2025 [ 7 ]. Healthy dietary diversity and behavior are fundamental to preventing childhood obesity [ 8 ]. Therefore, understanding the dietary patterns, behaviors, and lifestyle factors that influence childhood obesity and implementing targeted interventions can improve individual lifelong health outcomes. While many studies have focused on the role of specific nutrients in causing obesity [ 9 – 10 ], it has been increasingly recognized that dietary patterns and behaviors are critical factors influencing growth and development. These factors are also widely used to assess the relationship between diet and chronic diseases. Dietary patterns emphasize the combined effects of multiple foods on health, rather than focusing on individual foods or nutrients. This approach better reflects the impact of everyday dietary habits on health [ 11 ]. Dietary diversity scores (DDS) have been employed to evaluate dietary quality and nutritional adequacy [ 12 ]. Several countries have adopted food diversity as a key indicator of a healthy diet [ 13 – 14 ]. China also emphasized the importance of dietary diversity in its "Chinese Dietary Guidelines (2022)", recommending the consumption of at least 12 different foods daily and 25 types of foods weekly [ 14 ]. Although some studies have found a relationship between dietary diversity and healthy weight, others have reported inconsistent findings, particularly in adults and children [ 15 – 17 ]. Currently, the relationship between dietary diversity and weight status in preschool children remains unclear [ 18 – 19 ]. Similarly, additional dietary behaviors, such as nighttime snacking or the consumption of ultra-processed foods, may also influence children's healthy weight. This study aims to investigate the growth and development status (BMI-Z score) of preschool children and explore the correlation between dietary patterns, behaviors, lifestyle habits, and BMI-Z scores. Finally, we will examine the relationship between food diversity and the growth and development of preschool children. 2 Methods 2.1 Participants A stratified cluster random sampling method was used to select guardians of preschool children enrolled in kindergartens across 8 districts of Wuxi City in 2024. In total, 3727 guardians from 18 kindergartens were included in this study. 2.2 Survey Methods and Content The study utilized a self-designed questionnaire based on the multi-indicator cluster survey developed by UNICEF, which has high reliability and validity [ 20 ]. The questionnaire was distributed online, covering the following areas: Child Demographic Information: Gender, age, height, weight, birth weight. Parental Information: Weight, height, education level, occupation, maternal age at pregnancy, pre-pregnancy weight. Household Information: Monthly per capita income, household size, primary guardian type. Child's 24-Hour Dietary Intake: Typical dietary behaviors and lifestyle habits. 2.3 Evaluation Indicators and Definitions Dietary Diversity: Defined according to the "Guidelines for Measuring Household and Individual Dietary Diversity" by the Food and Agriculture Organization (FAO), categorizing daily food intake into 9 groups: cereals, tubers, vegetables, fruits, legumes/nuts, meat, fish, eggs, dairy products. Each group consumed within 24 hours received 1 point, with a maximum score of 9. Low dietary diversity was defined as a score ≤ 4 [ 21 ]. BMI-Z Score: Used to assess children's growth and development, calculated using the World Health Organization (WHO) standards [ 22 ]. A BMI-Z score ≤-1 was considered underweight, -11 as normal, 12 as overweight, and ≥ 2 as obese [ 23 ]. Nighttime Snacking: Defined as consuming food within two hours before bedtime. Ultra-Processed Foods: Included sugary drinks (e.g., soda, milk tea, fruit juices), processed meats (e.g., sausages, bacon), fried foods (e.g., fries, fried chicken), sweets (e.g., candy, ice cream, cakes), and puffed snacks (e.g., chips, biscuits). Daily consumption of one or more of these items was considered daily ultra-processed food intake. Household Type: Defined as single-parent households, remarried families, nuclear families, or extended families. Child Lifestyle Habits: Included daily sleep duration (including naps), daily screen time, and daily outdoor activity duration. 2.4 Quality Control Before conducting the survey, we consulted with pediatricians, kindergarten teachers, and parent representatives to ensure the feasibility and scientific validity of the questionnaire. The questionnaire was designed to include response options for every question and embedded logical error checks to minimize data entry mistakes. Two investigators independently reviewed the collected data, and any discrepancies were resolved by a third party to ensure data quality. 2.5 Statistical Analysis Statistical analysis was conducted using SPSS 19.0 software. Continuous variables were presented as mean ± standard deviation (x ± S), and categorical variables were expressed as percentages (n%). Group comparisons were conducted using analysis of variance (ANOVA), with pairwise comparisons between multiple groups performed using the SNK-q test. Logistic regression was employed to assess the association between dietary diversity, nighttime snacking, ultra-processed food intake, outdoor activity time, screen time, and sleep duration with obesity. After adjusting for variables such as age and gender, logistic regression was used to evaluate the impact of low dietary diversity (yes = 1, no = 0) on childhood obesity (yes = 1, no = 0), with statistical significance set at P < 0.05. 3 Results 3.1 General Characteristics A total of 3780 questionnaires were collected, of which 53 were excluded due to missing information, resulting in 3727 valid responses, with an effective rate of 98.60%. The questionnaires covered kindergartens from all districts of Wuxi City, with the children's ages ranging from 3 to 7 years old, averaging 5.35 ± 0.98 years. There were 1924 boys (51.62%) and 1803 girls (48.38%). The majority of the children (53.13%) had a BMI-Z score between − 1 and 1. The average dietary diversity score (DDS) was 5.07 ± 1.94, with 36.79% of children having low dietary diversity (DDS ≤ 4). 3.2 Characteristics by BMI-Z Group Based on BMI-Z scores, children were divided into four groups: underweight (BMI-Z < -1), normal weight (-1 ≤ BMI-Z < 1), overweight (1 ≤ BMI-Z < 2), and obese (BMI-Z ≥ 2). Comparisons between the four groups revealed significant differences in age (F = 7.936, P < 0.001), height (F = 9.879, P < 0.001), current weight (F = 1360.672, P < 0.001), birth weight (F = 3.037, P = 0.028), DDS (F = 5.657, P < 0.001), maternal BMI (F = 34.735, P < 0.001), maternal pre-pregnancy weight (F = 41.905, P < 0.001), and paternal BMI (F = 55.017, P < 0.001). See Table 1 for details. Table 1 Characteristics of Preschool Children in Different BMI-Z Groups BMI-Z N Age Height Weight Birth weight (kg) DDS Maternal BMI Maternal age during pregnancy Maternal pre-pregnancy weight Paternal BMI ≤-1 663 5.49 ± 1.00 114.83 ± 8.14 17.87 ± 3.44 4.15 ± 11.74 5.03 ± 1.98 23.91 ± 11.58 29.04 ± 6.98 76.81 ± 25.78 25.32 ± 6.96 -1 ~ 1 1980 5.34 ± 0.96 113.46 ± 8.00 20.10 ± 3.69 3.77 ± 5.53 5.18 ± 1.88 24.08 ± 7.76 28.67 ± 4.53 79.87 ± 26.85 25.96 ± 6.34 1 ~ 2 408 5.52 ± 1.01 115.42 ± 8.98 23.19 ± 4.34 3.84 ± 1.28 4.98 ± 1.87 24.63 ± 8.41 28.10 ± 3.94 81.50 ± 30.87 25.94 ± 6.71 ≥ 2 676 5.40 ± 1.00 113.85 ± 9.58 33.84 ± 9.64 4.76 ± 3.68 4.84 ± 2.13 28.05 ± 10.38 28.59 ± 4.80 92.18 ± 35.64 29.88 ± 11.20 F 7.936 9.879 1360.672 3.037 5.657 34.735 1.848 41.905 55.017 P < 0.001 < 0.001 < 0.001 0.028 0.001 < 0.001 0.136 < 0.001 < 0.001 3.3 Association Between Dietary and Lifestyle Behaviors with Obesity Logistic regression analysis was performed to examine the association between various dietary and lifestyle behaviors and obesity. The independent variables included dietary diversity score (using the normal dietary pattern, DDS > 5 group as the reference), nighttime snacking (using the "no" group as the reference), daily consumption of ultra-processed foods (using the "no" group as the reference), daily outdoor activity time (using the > 2 hours/day group as the reference), daily screen time (using the ≤ 0.5 hours group as the reference), and average daily sleep duration (using the < 9 hours group as the reference). The dependent variable was obesity (no = 0, yes = 1). After adjusting for age and gender, we found that low dietary diversity (OR = 1.183, 95%CI: 1.101–1.203), nighttime snacking (OR = 1.109, 95%CI: 1.092–1.512), 1–2 hours of screen time daily (OR = 1.328, 95%CI: 1.094–1.614), and ≥ 2 hours of daily screen time (OR = 1.522, 95%CI: 1.087–2.130) were positively associated with obesity in preschool children (P 5 1.00 1.00 1.00 ≤4 1.098(1.022 ~ 1.159)* 1.210(1.105 ~ 1.238)* 1.183(1.101 ~ 1.203)* Nighttime Snacking No 1.00 1.00 1.00 Yes 1.029(1.082 ~ 1.101)* 1.108(1.009 ~ 1.332)* 1.109(1.092 ~ 1.512)* Ultra-Processed Food No 1.00 1.00 1.00 Yes 1.089(0895 ~ 1.325) 0.896(0.726 ~ 1.107) 0.909(0.787 ~ 1.049) Outdoor Activity Time > 2h/d 1.00 1.00 1.00 ≤ 2h/d 0.927(0.749 ~ 1.147) 0.864(0.683 ~ 1.094) 0.904(0.771 ~ 1.060) Daily Screen Time ≤ 0.5h 1.00 1.00 1.00 0.5-1h 0.849(0.676 ~ 1.066) 1.174(0.917 ~ 1.503) 0.981(0.830 ~ 1.161) 1-2h 1.319(1.0106 ~ 1.723)* 1.351(1.017 ~ 1.795)* 1.328(1.094 ~ 1.614)* ≥ 2h 1.253(0.800 ~ 1.964) 1.980(1.195 ~ 3.280)* 1.522(1.087 ~ 2.130)* Average Daily Sleep Time < 9h 1.00 1.00 1.00 9-13h 0.936(0.689 ~ 1.271) 0.722(0.518 ~ 1.006) 0.850(0.677 ~ 1.067) ≥ 13h 0.738(0.368 ~ 1.481) 1.844(0.938 ~ 3.624) 1.201(0.744 ~ 1.939) *P < 0.05; Obesity (no = 0, yes = 1) was the dependent variable, adjusted for age and gender. 3.4 Logistic Regression Analysis of Dietary Patterns and Childhood Obesity Logistic regression analysis revealed that low dietary diversity was a significant risk factor for obesity, with a crude OR of 1.216 (95% CI: 1.051–1.406). After adjusting for child-related factors such as age, gender, and birth weight, the OR in Model 1 was 1.192 (95% CI: 1.020–1.392). In Model 2, after further adjusting for maternal and paternal BMI, the OR was 1.195 (95% CI: 1.017–1.404). In Model 3, after additionally adjusting for household income, family dining practices, family type, and primary caregiver, the OR was 1.190 (95% CI: 1.010–1.402) (P < 0.05). See Table 3 for details. Table 3 Logistic Regression Analysis of Dietary Diversity and Obesity Model B S.E Wals P OR 95% C.I. Crude OR 0.195 0.074 6.940 0.008 1.216 1.051 ~ 1.406 OR Model 1 0.176 0.079 4.899 0.027 1.192 1.020 ~ 1.392 OR Model 2 0.178 0.082 4.681 0.030 1.195 1.017 ~ 1.404 OR Model 3 0.174 0.084 4.324 0.038 1.190 1.010 ~ 1.402 Model 1: Adjusted for child-related factors (age, gender, birth weight). Model 2: Additionally adjusted for maternal and paternal BMI. Model 3: Additionally adjusted for household income, family dining practices, family type, primary caregiver. 4 Discussion This study used BMI-Z scores to assess the nutritional and developmental status of preschool children. It was found that the rates of overweight (1 ≤ BMI-Z < 2) and obesity (BMI-Z ≥ 2) in children were 10.94% and 18.14%, respectively, which are significantly higher than the national prevalence rates of 6.8% and 3.6% for overweight and obesity in children under six years old during 2015–2019 [ 24 ]. With the rapid socioeconomic development and lifestyle changes in China, particularly the increase in nutrition levels and more diverse diets, the growth and development of children have improved. Although the rates of overweight and obesity among Chinese children are still lower than those in developed countries, the upward trend is comparable. If timely interventions are not implemented, childhood obesity rates could catch up with or even surpass those in developed nations [ 7 ]. The development of childhood obesity is influenced by various factors, including genetics, environment, and dietary behaviors. Although genetic factors play an important role in the onset and progression of obesity, genes do not undergo significant variation during the preschool years (ages 3–7). Therefore, the occurrence of overweight and obesity in children at this stage is mainly attributed to changes in the external environment and social factors. Thus, the prevention and control of childhood obesity should primarily focus on environmental, behavioral, and lifestyle factors. The external environmental factors that contribute to childhood obesity include changes in dietary structure (e.g., an increase in the consumption of high-fat, high-sugar foods) [ 25 ], reduced outdoor activities, and unhealthy eating behaviors. Changes in eating behaviors, such as increased meal frequency, higher intake of ultra-processed foods, and sugary drinks replacing primary energy sources, also play a role. This study primarily explored these factors influencing childhood obesity. In this study, the average dietary diversity score (DDS) for children aged 3–7 was 5.07 ± 1.94, with 36.79% of children having low dietary diversity (DDS ≤ 4). The DDS quantifies the number and variety of food groups consumed in the diet, serving as an effective indicator of micronutrient intake [ 27 ]. A higher DDS reflects a greater variety of nutrient intake, which is beneficial for children's physical development [ 28 ]. Some international studies have shown that higher DDS is associated with better growth outcomes in children [ 29 – 30 ]. We found that low dietary diversity and nighttime snacking were risk factors for childhood obesity. A single dietary pattern that lacks essential high-quality proteins and vegetables while overemphasizing carbohydrates, starches, and fats lays the foundation for obesity in children. A longitudinal study in the U.S. found a negative correlation between weight gain and the consumption of fruits and vegetables [ 31 ]. As household disposable income increases, so does the demand for better-tasting and higher-quality food, beyond merely satisfying hunger. Consequently, the food industry creates and processes food that is lower in nutritional value but more palatable, promoting unhealthy eating habits. Children and adolescents, who generally lack the ability to distinguish between healthy and unhealthy foods, are often influenced by the retail food environment around schools. Studies have shown that the availability of sugary beverages in school stores and the proximity of Western fast food outlets and convenience stores near schools are associated with higher body mass index (BMI) and increased obesity rates [ 32 – 33 ]. In addition, the consumption of ultra-processed foods and various snack products is on the rise in China. A study comparing packaged foods and beverages from 12 countries found that sugar and saturated fat content in China’s packaged foods is among the highest [ 34 ]. A national survey involving 53,151 children and adolescents in China found that 42% consumed sugary drinks at least twice a week, and a cross-sectional analysis of 203 participants revealed a significant association between high consumption of sugary beverages and an increased likelihood of obesity [ 35 – 36 ]. Several studies have also linked the increased consumption of ultra-processed foods and beverages with higher BMI levels [ 37 ]. The survey on nighttime snacking also indicated that foods consumed late at night tend to be high in fat and sugar, making them more appealing to children. This leads to increased meal frequency and reduced dietary diversity, both of which contribute to weight gain. We also found that excessive screen time was another significant risk factor for childhood obesity. Prolonged screen time leads to sedentary behavior and reduced physical activity, both of which are key contributors to obesity. Several studies have confirmed that insufficient physical activity and prolonged sedentary behavior are common among children and adolescents. Although some studies did not find a direct correlation between moderate or high levels of physical activity and lower body weight [ 38 – 39 ], observational studies suggest that children who engage in more physical activity are less likely to be overweight or obese compared to those with insufficient physical activity [ 40 ]. Additionally, a decrease in walking to school has been identified as a predictor of childhood obesity. Thus, we believe that a lack of physical activity, combined with extended screen time, likely increases the risk of obesity in children [ 41 ]. Childhood obesity is itself a disease and is associated with several non-communicable chronic diseases. It is also a major risk factor for adult obesity, placing a significant economic burden on society. Proactive dietary and physical activity interventions have been shown to yield favorable cost-benefit outcomes [ 42 ]. Therefore, strategies for the early prevention and control of childhood obesity should begin at a young age, including educating and managing maternal weight during pregnancy to prevent excessive weight gain in children. A comprehensive obesity prevention system involving government leadership, social participation, and collaboration between schools, families, and communities should be established. Additionally, health policies should target the dietary and lifestyle behaviors that contribute to childhood obesity. Such policies must be implemented with effective supervision, analysis, and evaluation to ensure the reduction of childhood obesity risk factors and lower the prevalence of childhood obesity. Declarations Ethics approval and consent to participate This study was approved by the Ethics Review Committee of Wuxi Maternity and Child Health Care Hospital (No: WXFY-KJC-32). Informed consent was obtained from all participants' legal guardians. All methods were carried out in accordance with relevant guidelines and regulations. Clinical trial number: not applicable. Consent for publication The authors attest that informed consent from all subjects. Competing interests The authors declare no conflict of interest. Acknowledgments Not applicable. Author Contributions Bingbing Guo and Xinye Jiang conceived the study, Lan Jiang and Bingbing Guo performed survey and summary; Bingbing Guo and Xinye Jiang wrote and revised the manuscript. All authors reviewed the manuscript. Funding The work is supported by the Nutrition and Health Research Funding Project of Jiangsu Nutrition Society (JYXE202304) and the Maternal and Child Health Scientific Research Project of Wuxi Municipal Health Commission (FYTG202405). Availability of data and materials The dataset generated during and analyzed during the current study are available from the corresponding author on reasonable request. References Piernas C, Wang D, Du S, Zhang B, Wang Z, Su C, Popkin BM. The double burden of under- and overnutrition and nutrient adequacy among Chinese preschool and school-aged children in 2009–2011. Eur J Clin Nutr. 2015;69(12):1323–9. 10.1038/ejcn.2015.106 . Bureau of Disease Prevention and Control of National Health Commission of the People’s Republic of China. Chinese Residents’ Nutrition and Chronic Disease Report. (2020), 1st ed.; People’s Medical Publishing House: Beijing, China, 2021. Ma G. Report on Childhood Obesity in China. 1st ed. Beijing, China: People’s Medical Publishing House; 2017. Abrignani MG, Lucà F, Favilli S, Benvenuto M, Rao CM, Di Fusco SA, Gabrielli D, Gulizia MM. Cardiovascular Prevention Area, Young Cardiologists Area, and Paediatric Cardiology Task Force of the Associazione Nazionale Medici Cardiologi Ospedalieri (ANMCO), and Heart Care Foundation. Lifestyles and Cardiovascular Prevention in Childhood and Adolescence. Pediatr Cardiol. 2019;40(6):1113–25. 10.1007/s00246-019-02152-w . Barton M. Childhood obesity: a life-long health risk. Acta Pharmacol Sin. 2012;33(2):189–93. 10.1038/aps.2011.204 . Wells JC, Sawaya AL, Wibaek R, Mwangome M, Poullas MS, Yajnik CS, Demaio A. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet. 2020;395(10217):75–88. 10.1016/S0140-6736(19)32472-9 . Zhang N, Ma GS. Interpretation of China Childhood Obesity Report. Acta Nutrimenta Sinica. 2017;39(06):530–4. cnki.acta.nutr.sin.2017.06.005 . World Health Organization. Report of the Commission on Ending Childhood Obesity:Implementation Plan: Executive Summary. World Health Organization: Geneva, Switzerland,; 2017. González-Domínguez Á, Visiedo-García FM, Domínguez-Riscart J, González-Domínguez R, Mateos RM, Lechuga-Sancho AM. Iron Metabolism in Obesity and Metabolic Syndrome. Int J Mol Sci. 2020;21(15):5529. 10.3390/ijms21155529 . Wang L, Liu W, Bi S, Zhou L, Li L. Association between minerals intake and childhood obesity: A cross-sectional study of the NHANES database in 2007–2014. PLoS ONE. 2023;18(12):e0295765. 10.1371/journal.pone.0295765 . Nattagh-Eshtivani E, Pahlavani N, Khosravi M, Sarivi SP, Hakkak AM, Maddahi M. The relationships between dietary patterns and socioeconomic status with anthropometric and body composition indices in Iranian preschool children: A cross-sectional study. Obes Sci Pract. 2023;10(1):e704. 10.1002/osp4.704 . Dello Russo M, Formisano A, Lauria F, Ahrens W, Bogl LH, Eiben G, De Henauw S, Hebestreit A, Intemann T, Hunsberger M, Lissner L, Molnar D, Pala V, Papoutsou S, Santaliestra-Pasias AM, Veidebaum T, Wolters M, Siani A, Russo P, I.Family Consortium. Dietary Diversity and Its Association with Diet Quality and Health Status of European Children, Adolescents, and Adults: Results from the I.Family Study. Foods. 2023;12(24):4458. 10.3390/foods12244458 . Comerford KB, Miller GD, Boileau AC, Masiello Schuette SN, Giddens JC, Brown KA. Global Review of Dairy Recommendations in Food-Based Dietary Guidelines. Front Nutr. 2021;8:671999. 10.3389/fnut.2021.671999 . Chinese Nutrition Society. The Chinese Dietary Guidelines. (2022), 1st ed.; People’s Medical Publishing House: Beijing, China, 2022. Fernandez C, Kasper NM, Miller AL, Lumeng JC, Peterson KE. Association of Dietary Variety and Diversity With Body Mass Index in US Preschool Children. Pediatrics. 2016;137(3):e20152307. 10.1542/peds.2015-2307 . Morris MA, Hulme C, Clarke GP, Edwards KL, Cade JE. What is the cost of a healthy diet? Using diet data from the UK Women's Cohort Study. J Epidemiol Community Health. 2014;68(11):1043–9. 10.1136/jech-2014-204039 . Golpour-Hamedani S, Rafie N, Pourmasoumi M, Saneei P, Safavi SM. The association between dietary diversity score and general and abdominal obesity in Iranian children and adolescents. BMC Endocr Disord. 2020;20(1):181. 10.1186/s12902-020-00662-w . Fanelli Kuczmarski M, Brewer BC, Rawal R, Pohlig RT, Zonderman AB, Evans MK. Aspects of Dietary Diversity Differ in Their Association with Atherosclerotic Cardiovascular Risk in a Racially Diverse US Adult Population. Nutrients. 2019;11(5):1034. 10.3390/nu11051034 . Fernandez C, Kasper NM, Miller AL, Lumeng JC, Peterson KE. Association of Dietary Variety and Diversity With Body Mass Index in US Preschool Children. Pediatrics. 2016;137(3):e20152307. 10.1542/peds.2015-2307 . WHO, UNICEF,IFPRI,UCDavis,FANTA. AED,USAID. Indicators for assessing infant and young child feeding practices[R].Part1:Definition.Geneva:WHO.2008:2–11. Food and Agriculture Organization of the United Nations. Guidelines for measuring household and individual dietary diversity [EB/OL].[2022-03-27]. http://www.fao.org/3/i1983e/ i1983e00. htm. https: //apps.cpeg-gcep.net/quickZ_WHO/ Worldwide trends in. body-mass indexunderweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet (London England). 2017;390:2627–42. Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9(6):373–392. doi: 10.1016/S2213-8587(21)00045-0. Erratum in: Lancet Diabetes Endocrinol. 2021;9(7):e2. 10.1016/S2213-8587(21)00149-2 Su C, Wang H, Wang Z, et al. Trends in dietary fat and cholesterol intake in Chinese children aged 7–17 years across 9 provinces (1991–2009). Chin J Epidemiol. 2012;33(12):1208–12. 10.3760/cma.j.issn.0254-6450.2012.12.002 . Kariippanon KE, Aguilar-Farias N, El Hamdouchi A, Hongyan G, Lubree H, Okely AD, Tremblay MS, Draper CE. The voices of children on movement behaviours: implications for promoting international guidelines to support obesity-prevention efforts. Lancet Glob Health. 2023;11(Suppl 1):S17. 10.1016/S2214-109X(23)00100-6 . Zhao W, Yu K, Tan S, et al. Dietary diversity scores:an indicator of micro-nutrient inadequacy instead of obesity for Chi nese children [J]. BMC Public Health. 2017;17(1):440. 10.1186/s12889-017-4381-x . Meng L, Wang Y, Li T, et al. Dietary diversity and foodvariety in Chinese chlidren aged 3–17 years:are they nega tively associated with dietary micronutrient inadequacy [J]. Nutrients. 2018;10(11):1674. 10.3390/nu10111674 . Frempong RB, Annim SK. Dietary diversity and child malnutrition. Ghana [J] Heliyon. 2017;3(5):00298. 10.1016/j.heliyon.2017.e00298 . Perkins JM, Jayatissa R, Subramanian SV. Dietary diversity and anthropometric status and failure among infants and young children in Sri Lanka [J].Nutrition, 2018, 55–56:76–83. 10.1016/j.nut.2018.03.049 Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med. 2011;364(25):2392–404. 10.1056/NEJMoa1014296 . Zhou S, Cheng Y, Cheng L, Wang D, Li Q, Liu Z, Wang HJ. Association between convenience stores near schools and obesity among school-aged children in Beijing, China. BMC Public Health. 2020;20(1):150. 10.1186/s12889-020-8257-0 . Zheng J, Gao L, Xue H, Xue B, Zhao L, Wang Y, Wei J, Wang Y. Eating-out behaviors, associated factors and associations with obesity in Chinese school children: findings from the childhood obesity study in China mega-cities. Eur J Nutr. 2021;60(6):3003–12. 10.1007/s00394-020-02475-y . Dunford EK, Ni Mhurchu C, Huang L, Vandevijvere S, Swinburn B, Pravst I, Tolentino-Mayo L, Reyes M, L'Abbé M, Neal BC. A comparison of the healthiness of packaged foods and beverages from 12 countries using the Health Star Rating nutrient profiling system, 2013–2018. Obes Rev. 2019;20(Suppl 2):107–15. 10.1111/obr.12879 . Beslay M, Srour B, Méjean C, Allès B, Fiolet T, Debras C, Chazelas E, Deschasaux M, Wendeu-Foyet MG, Hercberg S, Galan P, Monteiro CA, Deschamps V, Calixto Andrade G, Kesse-Guyot E, Julia C, Touvier M. Ultra-processed food intake in association with BMI change and risk of overweight and obesity: A prospective analysis of the French NutriNet-Santé cohort. PLoS Med. 2020;17(8):e1003256. 10.1371/journal.pmed.1003256 . Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, Chung ST, Costa E, Courville A, Darcey V, Fletcher LA, Forde CG, Gharib AM, Guo J, Howard R, Joseph PV, McGehee S, Ouwerkerk R, Raisinger K, Rozga I, Stagliano M, Walter M, Walter PJ, Yang S, Zhou M. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019;30(1):67–e773. 10.1016/j.cmet.2019.05.008 . Vandevijvere S, Jaacks LM, Monteiro CA, Moubarac JC, Girling-Butcher M, Lee AC, Pan A, Bentham J, Swinburn B. Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories. Obes Rev. 2019;20(Suppl 2):10–9. 10.1111/obr.12860 . Dearth-Wesley T, Howard AG, Wang H, Zhang B, Popkin BM. Trends in domain-specific physical activity and sedentary behaviors among Chinese school children, 2004–2011. Int J Behav Nutr Phys Act. 2017;14(1):141. 10.1186/s12966-017-0598-4 . Cui Z, Hardy LL, Dibley MJ, Bauman A. Temporal trends and recent correlates in sedentary behaviours in Chinese children. Int J Behav Nutr Phys Act. 2011;8:93. 10.1186/1479-5868-8-93 . Monda KL, Popkin BM. Cluster analysis methods help to clarify the activity-BMI relationship of Chinese youth. Obes Res. 2005;13(6):1042–51. 10.1038/oby.2005.122 . Sun X, Zhao B, Liu J, Wang Y, Xu F, Wang Y, Xue H. A 3-year longitudinal study of the association of physical activity and sedentary behaviours with childhood obesity in China: The childhood obesity study in China mega-cities. Pediatr Obes. 2021;16(6):e12753. 10.1111/ijpo.12753 . Epub 2020 Nov 22. PMID: 33225582. Zhao LQ. Analysis of the influencing factors of childhood obesity and evaluation of intervention effects. Fudan University Dissertation, 2014. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 05 May, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 29 Mar, 2026 Editor invited by journal 27 Mar, 2026 Editor assigned by journal 25 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 23 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9205134","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614271156,"identity":"fd50d792-698e-4ae3-b99c-e27603c9f400","order_by":0,"name":"Bingbing Guo","email":"","orcid":"","institution":"Wuxi Maternity and Child Health Hospital, Affiliated Women’s Hospital of Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Bingbing","middleName":"","lastName":"Guo","suffix":""},{"id":614271157,"identity":"a70c0c01-a5d1-4369-a638-8c8c8d45779a","order_by":1,"name":"Lan Jiang","email":"","orcid":"","institution":"Wuxi Maternity and Child Health Hospital, Affiliated Women’s Hospital of Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Jiang","suffix":""},{"id":614271158,"identity":"20ab714d-6318-4435-9695-1607c8a84080","order_by":2,"name":"Xinye Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYJCCAxIGYJrxQUJFDWlamA0enDlGmm1skg9bmAkrM7iRY3jAosAuTz4ix6wisYGNgb+9O4GAlrQEoMOSiw3PnDG7kbhDhkHizNkNeLWY3Ug+ANTCnLixvQeo5Qwbg4FELiEtiQ1ALfWJG5t5zAoS25iJ0QK25XDifPYeMwaitNifeQbyy/HEDTzHiiUSzhzjIegXyfYc488Sf6oT589I3vjxR0WNHH97L34tIMAsASQMDnCAI5SHoHIQYPwAJOQb2B8QpXoUjIJRMApGHgAA60lNUno6f9YAAAAASUVORK5CYII=","orcid":"","institution":"Wuxi Maternity and Child Health Hospital, Affiliated Women’s Hospital of Jiangnan University","correspondingAuthor":true,"prefix":"","firstName":"Xinye","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2026-03-24 00:39:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9205134/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9205134/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107705103,"identity":"846ea71d-efe6-4965-bb27-bf0616e02a8e","added_by":"auto","created_at":"2026-04-24 09:08:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":321654,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9205134/v1/2aebf6a3-665a-45d1-b7d7-42c54a5c1c0c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Study on the Influence and Correlation of Dietary Structure and Lifestyle Behavior Patterns on Growth and Development in Children Aged 3–7 Years","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eChildhood growth and development refers to the process of physical and psychological growth from birth to adolescence, encompassing various aspects such as physical, mental, cognitive, and social abilities. In recent years, due to economic development and lifestyle changes, there have been significant shifts in dietary patterns and behaviors, with overnutrition contributing to an increasing prevalence of childhood obesity across various regions. According to the \"Chinese Resident Nutrition and Chronic Disease Report (2020)\", the prevalence of overweight and obesity among Chinese children and adolescents from 2015 to 2017 has approached 20% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By 2030, this rate is projected to reach 28%, with 30.6\u0026nbsp;million children being overweight and 18.9\u0026nbsp;million obese [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Childhood obesity has thus garnered widespread attention, as it is an independent risk factor for numerous diseases. Overweight and obese children and adolescents are more likely to develop early signs of hypertension and atherosclerosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and they are at lifelong increased risk of developing type 2 diabetes and obesity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, if children experience early malnutrition followed by excessive weight gain, they face an increased risk of non-communicable diseases due to impaired homeostatic regulation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. According to the \"China Childhood Obesity Report\", the indirect economic losses caused by obesity in China are expected to reach 8.7% of GDP by 2025 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Healthy dietary diversity and behavior are fundamental to preventing childhood obesity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, understanding the dietary patterns, behaviors, and lifestyle factors that influence childhood obesity and implementing targeted interventions can improve individual lifelong health outcomes.\u003c/p\u003e \u003cp\u003eWhile many studies have focused on the role of specific nutrients in causing obesity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], it has been increasingly recognized that dietary patterns and behaviors are critical factors influencing growth and development. These factors are also widely used to assess the relationship between diet and chronic diseases. Dietary patterns emphasize the combined effects of multiple foods on health, rather than focusing on individual foods or nutrients. This approach better reflects the impact of everyday dietary habits on health [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Dietary diversity scores (DDS) have been employed to evaluate dietary quality and nutritional adequacy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Several countries have adopted food diversity as a key indicator of a healthy diet [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. China also emphasized the importance of dietary diversity in its \"Chinese Dietary Guidelines (2022)\", recommending the consumption of at least 12 different foods daily and 25 types of foods weekly [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although some studies have found a relationship between dietary diversity and healthy weight, others have reported inconsistent findings, particularly in adults and children [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Currently, the relationship between dietary diversity and weight status in preschool children remains unclear [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Similarly, additional dietary behaviors, such as nighttime snacking or the consumption of ultra-processed foods, may also influence children's healthy weight.\u003c/p\u003e \u003cp\u003eThis study aims to investigate the growth and development status (BMI-Z score) of preschool children and explore the correlation between dietary patterns, behaviors, lifestyle habits, and BMI-Z scores. Finally, we will examine the relationship between food diversity and the growth and development of preschool children.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eA stratified cluster random sampling method was used to select guardians of preschool children enrolled in kindergartens across 8 districts of Wuxi City in 2024. In total, 3727 guardians from 18 kindergartens were included in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Survey Methods and Content\u003c/h2\u003e \u003cp\u003eThe study utilized a self-designed questionnaire based on the multi-indicator cluster survey developed by UNICEF, which has high reliability and validity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The questionnaire was distributed online, covering the following areas: Child Demographic Information: Gender, age, height, weight, birth weight. Parental Information: Weight, height, education level, occupation, maternal age at pregnancy, pre-pregnancy weight. Household Information: Monthly per capita income, household size, primary guardian type. Child's 24-Hour Dietary Intake: Typical dietary behaviors and lifestyle habits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Evaluation Indicators and Definitions\u003c/h2\u003e \u003cp\u003eDietary Diversity: Defined according to the \"Guidelines for Measuring Household and Individual Dietary Diversity\" by the Food and Agriculture Organization (FAO), categorizing daily food intake into 9 groups: cereals, tubers, vegetables, fruits, legumes/nuts, meat, fish, eggs, dairy products. Each group consumed within 24 hours received 1 point, with a maximum score of 9. Low dietary diversity was defined as a score\u0026thinsp;\u0026le;\u0026thinsp;4 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBMI-Z Score: Used to assess children's growth and development, calculated using the World Health Organization (WHO) standards [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A BMI-Z score \u0026le;-1 was considered underweight, -11 as normal, 12 as overweight, and \u0026ge;\u0026thinsp;2 as obese [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNighttime Snacking: Defined as consuming food within two hours before bedtime.\u003c/p\u003e \u003cp\u003eUltra-Processed Foods: Included sugary drinks (e.g., soda, milk tea, fruit juices), processed meats (e.g., sausages, bacon), fried foods (e.g., fries, fried chicken), sweets (e.g., candy, ice cream, cakes), and puffed snacks (e.g., chips, biscuits). Daily consumption of one or more of these items was considered daily ultra-processed food intake.\u003c/p\u003e \u003cp\u003eHousehold Type: Defined as single-parent households, remarried families, nuclear families, or extended families.\u003c/p\u003e \u003cp\u003eChild Lifestyle Habits: Included daily sleep duration (including naps), daily screen time, and daily outdoor activity duration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Quality Control\u003c/h2\u003e \u003cp\u003eBefore conducting the survey, we consulted with pediatricians, kindergarten teachers, and parent representatives to ensure the feasibility and scientific validity of the questionnaire. The questionnaire was designed to include response options for every question and embedded logical error checks to minimize data entry mistakes. Two investigators independently reviewed the collected data, and any discrepancies were resolved by a third party to ensure data quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using SPSS 19.0 software. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;S), and categorical variables were expressed as percentages (n%). Group comparisons were conducted using analysis of variance (ANOVA), with pairwise comparisons between multiple groups performed using the SNK-q test. Logistic regression was employed to assess the association between dietary diversity, nighttime snacking, ultra-processed food intake, outdoor activity time, screen time, and sleep duration with obesity. After adjusting for variables such as age and gender, logistic regression was used to evaluate the impact of low dietary diversity (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0) on childhood obesity (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0), with statistical significance set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 General Characteristics\u003c/h2\u003e \u003cp\u003eA total of 3780 questionnaires were collected, of which 53 were excluded due to missing information, resulting in 3727 valid responses, with an effective rate of 98.60%. The questionnaires covered kindergartens from all districts of Wuxi City, with the children's ages ranging from 3 to 7 years old, averaging 5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98 years. There were 1924 boys (51.62%) and 1803 girls (48.38%). The majority of the children (53.13%) had a BMI-Z score between \u0026minus;\u0026thinsp;1 and 1. The average dietary diversity score (DDS) was 5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94, with 36.79% of children having low dietary diversity (DDS\u0026thinsp;\u0026le;\u0026thinsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Characteristics by BMI-Z Group\u003c/h2\u003e \u003cp\u003eBased on BMI-Z scores, children were divided into four groups: underweight (BMI-Z \u0026lt; -1), normal weight (-1\u0026thinsp;\u0026le;\u0026thinsp;BMI-Z\u0026thinsp;\u0026lt;\u0026thinsp;1), overweight (1\u0026thinsp;\u0026le;\u0026thinsp;BMI-Z\u0026thinsp;\u0026lt;\u0026thinsp;2), and obese (BMI-Z\u0026thinsp;\u0026ge;\u0026thinsp;2). Comparisons between the four groups revealed significant differences in age (F\u0026thinsp;=\u0026thinsp;7.936, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), height (F\u0026thinsp;=\u0026thinsp;9.879, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), current weight (F\u0026thinsp;=\u0026thinsp;1360.672, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), birth weight (F\u0026thinsp;=\u0026thinsp;3.037, P\u0026thinsp;=\u0026thinsp;0.028), DDS (F\u0026thinsp;=\u0026thinsp;5.657, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), maternal BMI (F\u0026thinsp;=\u0026thinsp;34.735, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), maternal pre-pregnancy weight (F\u0026thinsp;=\u0026thinsp;41.905, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and paternal BMI (F\u0026thinsp;=\u0026thinsp;55.017, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Preschool Children in Different BMI-Z Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI-Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBirth weight (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMaternal BMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMaternal age during pregnancy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMaternal pre-pregnancy weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePaternal BMI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.83\u0026thinsp;\u0026plusmn;\u0026thinsp;8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;11.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.91\u0026thinsp;\u0026plusmn;\u0026thinsp;11.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.04\u0026thinsp;\u0026plusmn;\u0026thinsp;6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e76.81\u0026thinsp;\u0026plusmn;\u0026thinsp;25.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-1\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.46\u0026thinsp;\u0026plusmn;\u0026thinsp;8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.08\u0026thinsp;\u0026plusmn;\u0026thinsp;7.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e79.87\u0026thinsp;\u0026plusmn;\u0026thinsp;26.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.96\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115.42\u0026thinsp;\u0026plusmn;\u0026thinsp;8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.19\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.63\u0026thinsp;\u0026plusmn;\u0026thinsp;8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e81.50\u0026thinsp;\u0026plusmn;\u0026thinsp;30.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.94\u0026thinsp;\u0026plusmn;\u0026thinsp;6.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.85\u0026thinsp;\u0026plusmn;\u0026thinsp;9.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.84\u0026thinsp;\u0026plusmn;\u0026thinsp;9.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28.05\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92.18\u0026thinsp;\u0026plusmn;\u0026thinsp;35.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e29.88\u0026thinsp;\u0026plusmn;\u0026thinsp;11.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1360.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e55.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association Between Dietary and Lifestyle Behaviors with Obesity\u003c/h2\u003e \u003cp\u003eLogistic regression analysis was performed to examine the association between various dietary and lifestyle behaviors and obesity. The independent variables included dietary diversity score (using the normal dietary pattern, DDS\u0026thinsp;\u0026gt;\u0026thinsp;5 group as the reference), nighttime snacking (using the \"no\" group as the reference), daily consumption of ultra-processed foods (using the \"no\" group as the reference), daily outdoor activity time (using the \u0026gt;\u0026thinsp;2 hours/day group as the reference), daily screen time (using the \u0026le;\u0026thinsp;0.5 hours group as the reference), and average daily sleep duration (using the \u0026lt;\u0026thinsp;9 hours group as the reference). The dependent variable was obesity (no\u0026thinsp;=\u0026thinsp;0, yes\u0026thinsp;=\u0026thinsp;1). After adjusting for age and gender, we found that low dietary diversity (OR\u0026thinsp;=\u0026thinsp;1.183, 95%CI: 1.101\u0026ndash;1.203), nighttime snacking (OR\u0026thinsp;=\u0026thinsp;1.109, 95%CI: 1.092\u0026ndash;1.512), 1\u0026ndash;2 hours of screen time daily (OR\u0026thinsp;=\u0026thinsp;1.328, 95%CI: 1.094\u0026ndash;1.614), and \u0026ge;\u0026thinsp;2 hours of daily screen time (OR\u0026thinsp;=\u0026thinsp;1.522, 95%CI: 1.087\u0026ndash;2.130) were positively associated with obesity in preschool children (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Between Dietary and Lifestyle Behaviors and Obesity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.098(1.022\u0026thinsp;~\u0026thinsp;1.159)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.210(1.105\u0026thinsp;~\u0026thinsp;1.238)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.183(1.101\u0026thinsp;~\u0026thinsp;1.203)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNighttime Snacking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.029(1.082\u0026thinsp;~\u0026thinsp;1.101)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.108(1.009\u0026thinsp;~\u0026thinsp;1.332)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.109(1.092\u0026thinsp;~\u0026thinsp;1.512)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltra-Processed Food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.089(0895\u0026thinsp;~\u0026thinsp;1.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.896(0.726\u0026thinsp;~\u0026thinsp;1.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.909(0.787\u0026thinsp;~\u0026thinsp;1.049)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutdoor Activity Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2h/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2h/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.927(0.749\u0026thinsp;~\u0026thinsp;1.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.864(0.683\u0026thinsp;~\u0026thinsp;1.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.904(0.771\u0026thinsp;~\u0026thinsp;1.060)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily Screen Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.5h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5-1h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.849(0.676\u0026thinsp;~\u0026thinsp;1.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.174(0.917\u0026thinsp;~\u0026thinsp;1.503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.981(0.830\u0026thinsp;~\u0026thinsp;1.161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1-2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.319(1.0106\u0026thinsp;~\u0026thinsp;1.723)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.351(1.017\u0026thinsp;~\u0026thinsp;1.795)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.328(1.094\u0026thinsp;~\u0026thinsp;1.614)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.253(0.800\u0026thinsp;~\u0026thinsp;1.964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.980(1.195\u0026thinsp;~\u0026thinsp;3.280)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.522(1.087\u0026thinsp;~\u0026thinsp;2.130)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Daily Sleep Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;9h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-13h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.936(0.689\u0026thinsp;~\u0026thinsp;1.271)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.722(0.518\u0026thinsp;~\u0026thinsp;1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850(0.677\u0026thinsp;~\u0026thinsp;1.067)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;13h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.738(0.368\u0026thinsp;~\u0026thinsp;1.481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.844(0.938\u0026thinsp;~\u0026thinsp;3.624)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.201(0.744\u0026thinsp;~\u0026thinsp;1.939)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Obesity (no\u0026thinsp;=\u0026thinsp;0, yes\u0026thinsp;=\u0026thinsp;1) was the dependent variable, adjusted for age and gender.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Logistic Regression Analysis of Dietary Patterns and Childhood Obesity\u003c/h2\u003e \u003cp\u003eLogistic regression analysis revealed that low dietary diversity was a significant risk factor for obesity, with a crude OR of 1.216 (95% CI: 1.051\u0026ndash;1.406). After adjusting for child-related factors such as age, gender, and birth weight, the OR in Model 1 was 1.192 (95% CI: 1.020\u0026ndash;1.392). In Model 2, after further adjusting for maternal and paternal BMI, the OR was 1.195 (95% CI: 1.017\u0026ndash;1.404). In Model 3, after additionally adjusting for household income, family dining practices, family type, and primary caregiver, the OR was 1.190 (95% CI: 1.010\u0026ndash;1.402) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression Analysis of Dietary Diversity and Obesity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eS.E\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWals\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% C.I.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.051\u0026thinsp;~\u0026thinsp;1.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR Model 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.020\u0026thinsp;~\u0026thinsp;1.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR Model 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.017\u0026thinsp;~\u0026thinsp;1.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR Model 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.010\u0026thinsp;~\u0026thinsp;1.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 1: Adjusted for child-related factors (age, gender, birth weight). Model 2: Additionally adjusted for maternal and paternal BMI. Model 3: Additionally adjusted for household income, family dining practices, family type, primary caregiver.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study used BMI-Z scores to assess the nutritional and developmental status of preschool children. It was found that the rates of overweight (1\u0026thinsp;\u0026le;\u0026thinsp;BMI-Z\u0026thinsp;\u0026lt;\u0026thinsp;2) and obesity (BMI-Z\u0026thinsp;\u0026ge;\u0026thinsp;2) in children were 10.94% and 18.14%, respectively, which are significantly higher than the national prevalence rates of 6.8% and 3.6% for overweight and obesity in children under six years old during 2015\u0026ndash;2019 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. With the rapid socioeconomic development and lifestyle changes in China, particularly the increase in nutrition levels and more diverse diets, the growth and development of children have improved. Although the rates of overweight and obesity among Chinese children are still lower than those in developed countries, the upward trend is comparable. If timely interventions are not implemented, childhood obesity rates could catch up with or even surpass those in developed nations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The development of childhood obesity is influenced by various factors, including genetics, environment, and dietary behaviors. Although genetic factors play an important role in the onset and progression of obesity, genes do not undergo significant variation during the preschool years (ages 3\u0026ndash;7). Therefore, the occurrence of overweight and obesity in children at this stage is mainly attributed to changes in the external environment and social factors. Thus, the prevention and control of childhood obesity should primarily focus on environmental, behavioral, and lifestyle factors. The external environmental factors that contribute to childhood obesity include changes in dietary structure (e.g., an increase in the consumption of high-fat, high-sugar foods) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], reduced outdoor activities, and unhealthy eating behaviors. Changes in eating behaviors, such as increased meal frequency, higher intake of ultra-processed foods, and sugary drinks replacing primary energy sources, also play a role. This study primarily explored these factors influencing childhood obesity.\u003c/p\u003e \u003cp\u003eIn this study, the average dietary diversity score (DDS) for children aged 3\u0026ndash;7 was 5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94, with 36.79% of children having low dietary diversity (DDS\u0026thinsp;\u0026le;\u0026thinsp;4). The DDS quantifies the number and variety of food groups consumed in the diet, serving as an effective indicator of micronutrient intake [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A higher DDS reflects a greater variety of nutrient intake, which is beneficial for children's physical development [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Some international studies have shown that higher DDS is associated with better growth outcomes in children [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We found that low dietary diversity and nighttime snacking were risk factors for childhood obesity. A single dietary pattern that lacks essential high-quality proteins and vegetables while overemphasizing carbohydrates, starches, and fats lays the foundation for obesity in children. A longitudinal study in the U.S. found a negative correlation between weight gain and the consumption of fruits and vegetables [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. As household disposable income increases, so does the demand for better-tasting and higher-quality food, beyond merely satisfying hunger. Consequently, the food industry creates and processes food that is lower in nutritional value but more palatable, promoting unhealthy eating habits. Children and adolescents, who generally lack the ability to distinguish between healthy and unhealthy foods, are often influenced by the retail food environment around schools. Studies have shown that the availability of sugary beverages in school stores and the proximity of Western fast food outlets and convenience stores near schools are associated with higher body mass index (BMI) and increased obesity rates [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, the consumption of ultra-processed foods and various snack products is on the rise in China. A study comparing packaged foods and beverages from 12 countries found that sugar and saturated fat content in China\u0026rsquo;s packaged foods is among the highest [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A national survey involving 53,151 children and adolescents in China found that 42% consumed sugary drinks at least twice a week, and a cross-sectional analysis of 203 participants revealed a significant association between high consumption of sugary beverages and an increased likelihood of obesity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Several studies have also linked the increased consumption of ultra-processed foods and beverages with higher BMI levels [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The survey on nighttime snacking also indicated that foods consumed late at night tend to be high in fat and sugar, making them more appealing to children. This leads to increased meal frequency and reduced dietary diversity, both of which contribute to weight gain.\u003c/p\u003e \u003cp\u003eWe also found that excessive screen time was another significant risk factor for childhood obesity. Prolonged screen time leads to sedentary behavior and reduced physical activity, both of which are key contributors to obesity. Several studies have confirmed that insufficient physical activity and prolonged sedentary behavior are common among children and adolescents. Although some studies did not find a direct correlation between moderate or high levels of physical activity and lower body weight [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], observational studies suggest that children who engage in more physical activity are less likely to be overweight or obese compared to those with insufficient physical activity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, a decrease in walking to school has been identified as a predictor of childhood obesity. Thus, we believe that a lack of physical activity, combined with extended screen time, likely increases the risk of obesity in children [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChildhood obesity is itself a disease and is associated with several non-communicable chronic diseases. It is also a major risk factor for adult obesity, placing a significant economic burden on society. Proactive dietary and physical activity interventions have been shown to yield favorable cost-benefit outcomes [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, strategies for the early prevention and control of childhood obesity should begin at a young age, including educating and managing maternal weight during pregnancy to prevent excessive weight gain in children. A comprehensive obesity prevention system involving government leadership, social participation, and collaboration between schools, families, and communities should be established. Additionally, health policies should target the dietary and lifestyle behaviors that contribute to childhood obesity. Such policies must be implemented with effective supervision, analysis, and evaluation to ensure the reduction of childhood obesity risk factors and lower the prevalence of childhood obesity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Committee of Wuxi Maternity and Child Health Care Hospital (No: WXFY-KJC-32). Informed consent was obtained from all participants\u0026apos; legal guardians. All methods were carried out in accordance with relevant guidelines and regulations. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors attest that informed consent from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBingbing Guo and Xinye Jiang conceived the study, Lan Jiang and Bingbing Guo performed survey and summary; Bingbing Guo and Xinye Jiang wrote and revised the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work is supported by the Nutrition and Health Research Funding Project of Jiangsu Nutrition Society (JYXE202304) and the Maternal and Child Health Scientific Research Project of Wuxi Municipal Health Commission (FYTG202405).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated during and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePiernas C, Wang D, Du S, Zhang B, Wang Z, Su C, Popkin BM. The double burden of under- and overnutrition and nutrient adequacy among Chinese preschool and school-aged children in 2009\u0026ndash;2011. Eur J Clin Nutr. 2015;69(12):1323\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ejcn.2015.106\u003c/span\u003e\u003cspan address=\"10.1038/ejcn.2015.106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBureau of Disease Prevention and Control of National Health Commission of the People\u0026rsquo;s Republic of China. Chinese Residents\u0026rsquo; Nutrition and Chronic Disease Report. (2020), 1st ed.; People\u0026rsquo;s Medical Publishing House: Beijing, China, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa G. Report on Childhood Obesity in China. 1st ed. Beijing, China: People\u0026rsquo;s Medical Publishing House; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbrignani MG, Luc\u0026agrave; F, Favilli S, Benvenuto M, Rao CM, Di Fusco SA, Gabrielli D, Gulizia MM. Cardiovascular Prevention Area, Young Cardiologists Area, and Paediatric Cardiology Task Force of the Associazione Nazionale Medici Cardiologi Ospedalieri (ANMCO), and Heart Care Foundation. Lifestyles and Cardiovascular Prevention in Childhood and Adolescence. Pediatr Cardiol. 2019;40(6):1113\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00246-019-02152-w\u003c/span\u003e\u003cspan address=\"10.1007/s00246-019-02152-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarton M. Childhood obesity: a life-long health risk. Acta Pharmacol Sin. 2012;33(2):189\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/aps.2011.204\u003c/span\u003e\u003cspan address=\"10.1038/aps.2011.204\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWells JC, Sawaya AL, Wibaek R, Mwangome M, Poullas MS, Yajnik CS, Demaio A. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet. 2020;395(10217):75\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(19)32472-9\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(19)32472-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang N, Ma GS. Interpretation of China Childhood Obesity Report. Acta Nutrimenta Sinica. 2017;39(06):530\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ecnki.acta.nutr.sin.2017.06.005\u003c/span\u003e\u003cspan address=\"http://cnki.acta.nutr.sin.2017.06.005\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Report of the Commission on Ending Childhood Obesity:Implementation Plan: Executive Summary. World Health Organization: Geneva, Switzerland,; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-Dom\u0026iacute;nguez \u0026Aacute;, Visiedo-Garc\u0026iacute;a FM, Dom\u0026iacute;nguez-Riscart J, Gonz\u0026aacute;lez-Dom\u0026iacute;nguez R, Mateos RM, Lechuga-Sancho AM. Iron Metabolism in Obesity and Metabolic Syndrome. Int J Mol Sci. 2020;21(15):5529. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms21155529\u003c/span\u003e\u003cspan address=\"10.3390/ijms21155529\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Liu W, Bi S, Zhou L, Li L. Association between minerals intake and childhood obesity: A cross-sectional study of the NHANES database in 2007\u0026ndash;2014. PLoS ONE. 2023;18(12):e0295765. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0295765\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0295765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNattagh-Eshtivani E, Pahlavani N, Khosravi M, Sarivi SP, Hakkak AM, Maddahi M. The relationships between dietary patterns and socioeconomic status with anthropometric and body composition indices in Iranian preschool children: A cross-sectional study. Obes Sci Pract. 2023;10(1):e704. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/osp4.704\u003c/span\u003e\u003cspan address=\"10.1002/osp4.704\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDello Russo M, Formisano A, Lauria F, Ahrens W, Bogl LH, Eiben G, De Henauw S, Hebestreit A, Intemann T, Hunsberger M, Lissner L, Molnar D, Pala V, Papoutsou S, Santaliestra-Pasias AM, Veidebaum T, Wolters M, Siani A, Russo P, I.Family Consortium. Dietary Diversity and Its Association with Diet Quality and Health Status of European Children, Adolescents, and Adults: Results from the I.Family Study. Foods. 2023;12(24):4458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/foods12244458\u003c/span\u003e\u003cspan address=\"10.3390/foods12244458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComerford KB, Miller GD, Boileau AC, Masiello Schuette SN, Giddens JC, Brown KA. Global Review of Dairy Recommendations in Food-Based Dietary Guidelines. Front Nutr. 2021;8:671999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2021.671999\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2021.671999\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChinese Nutrition Society. The Chinese Dietary Guidelines. (2022), 1st ed.; People\u0026rsquo;s Medical Publishing House: Beijing, China, 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandez C, Kasper NM, Miller AL, Lumeng JC, Peterson KE. Association of Dietary Variety and Diversity With Body Mass Index in US Preschool Children. Pediatrics. 2016;137(3):e20152307. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1542/peds.2015-2307\u003c/span\u003e\u003cspan address=\"10.1542/peds.2015-2307\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris MA, Hulme C, Clarke GP, Edwards KL, Cade JE. What is the cost of a healthy diet? Using diet data from the UK Women's Cohort Study. J Epidemiol Community Health. 2014;68(11):1043\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jech-2014-204039\u003c/span\u003e\u003cspan address=\"10.1136/jech-2014-204039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGolpour-Hamedani S, Rafie N, Pourmasoumi M, Saneei P, Safavi SM. The association between dietary diversity score and general and abdominal obesity in Iranian children and adolescents. BMC Endocr Disord. 2020;20(1):181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12902-020-00662-w\u003c/span\u003e\u003cspan address=\"10.1186/s12902-020-00662-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFanelli Kuczmarski M, Brewer BC, Rawal R, Pohlig RT, Zonderman AB, Evans MK. Aspects of Dietary Diversity Differ in Their Association with Atherosclerotic Cardiovascular Risk in a Racially Diverse US Adult Population. Nutrients. 2019;11(5):1034. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu11051034\u003c/span\u003e\u003cspan address=\"10.3390/nu11051034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandez C, Kasper NM, Miller AL, Lumeng JC, Peterson KE. Association of Dietary Variety and Diversity With Body Mass Index in US Preschool Children. Pediatrics. 2016;137(3):e20152307. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1542/peds.2015-2307\u003c/span\u003e\u003cspan address=\"10.1542/peds.2015-2307\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO, UNICEF,IFPRI,UCDavis,FANTA. AED,USAID. Indicators for assessing infant and young child feeding practices[R].Part1:Definition.Geneva:WHO.2008:2\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFood and Agriculture Organization of the United Nations. Guidelines for measuring household and individual dietary diversity [EB/OL].[2022-03-27]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/3/i1983e/\u003c/span\u003e\u003cspan address=\"http://www.fao.org/3/i1983e/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e i1983e00. htm.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ehttps:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e//apps.cpeg-gcep.net/quickZ_WHO/\u003c/span\u003e\u003cspan address=\"http:////apps.cpeg-gcep.net/quickZ_WHO/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorldwide trends in. body-mass indexunderweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet (London England). 2017;390:2627\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9(6):373\u0026ndash;392. doi: 10.1016/S2213-8587(21)00045-0. Erratum in: Lancet Diabetes Endocrinol. 2021;9(7):e2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2213-8587(21)00149-2\u003c/span\u003e\u003cspan address=\"10.1016/S2213-8587(21)00149-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu C, Wang H, Wang Z, et al. Trends in dietary fat and cholesterol intake in Chinese children aged 7\u0026ndash;17 years across 9 provinces (1991\u0026ndash;2009). Chin J Epidemiol. 2012;33(12):1208\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3760/cma.j.issn.0254-6450.2012.12.002\u003c/span\u003e\u003cspan address=\"10.3760/cma.j.issn.0254-6450.2012.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKariippanon KE, Aguilar-Farias N, El Hamdouchi A, Hongyan G, Lubree H, Okely AD, Tremblay MS, Draper CE. The voices of children on movement behaviours: implications for promoting international guidelines to support obesity-prevention efforts. Lancet Glob Health. 2023;11(Suppl 1):S17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2214-109X(23)00100-6\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(23)00100-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao W, Yu K, Tan S, et al. Dietary diversity scores:an indicator of micro-nutrient inadequacy instead of obesity for Chi nese children [J]. BMC Public Health. 2017;17(1):440. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-017-4381-x\u003c/span\u003e\u003cspan address=\"10.1186/s12889-017-4381-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng L, Wang Y, Li T, et al. Dietary diversity and foodvariety in Chinese chlidren aged 3\u0026ndash;17 years:are they nega tively associated with dietary micronutrient inadequacy [J]. Nutrients. 2018;10(11):1674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu10111674\u003c/span\u003e\u003cspan address=\"10.3390/nu10111674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrempong RB, Annim SK. Dietary diversity and child malnutrition. Ghana [J] Heliyon. 2017;3(5):00298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2017.e00298\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2017.e00298\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerkins JM, Jayatissa R, Subramanian SV. Dietary diversity and anthropometric status and failure among infants and young children in Sri Lanka [J].Nutrition, 2018, 55\u0026ndash;56:76\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.nut.2018.03.049\u003c/span\u003e\u003cspan address=\"10.1016/j.nut.2018.03.049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med. 2011;364(25):2392\u0026ndash;404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1014296\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1014296\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou S, Cheng Y, Cheng L, Wang D, Li Q, Liu Z, Wang HJ. Association between convenience stores near schools and obesity among school-aged children in Beijing, China. BMC Public Health. 2020;20(1):150. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-020-8257-0\u003c/span\u003e\u003cspan address=\"10.1186/s12889-020-8257-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng J, Gao L, Xue H, Xue B, Zhao L, Wang Y, Wei J, Wang Y. Eating-out behaviors, associated factors and associations with obesity in Chinese school children: findings from the childhood obesity study in China mega-cities. Eur J Nutr. 2021;60(6):3003\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00394-020-02475-y\u003c/span\u003e\u003cspan address=\"10.1007/s00394-020-02475-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunford EK, Ni Mhurchu C, Huang L, Vandevijvere S, Swinburn B, Pravst I, Tolentino-Mayo L, Reyes M, L'Abb\u0026eacute; M, Neal BC. A comparison of the healthiness of packaged foods and beverages from 12 countries using the Health Star Rating nutrient profiling system, 2013\u0026ndash;2018. Obes Rev. 2019;20(Suppl 2):107\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/obr.12879\u003c/span\u003e\u003cspan address=\"10.1111/obr.12879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeslay M, Srour B, M\u0026eacute;jean C, All\u0026egrave;s B, Fiolet T, Debras C, Chazelas E, Deschasaux M, Wendeu-Foyet MG, Hercberg S, Galan P, Monteiro CA, Deschamps V, Calixto Andrade G, Kesse-Guyot E, Julia C, Touvier M. Ultra-processed food intake in association with BMI change and risk of overweight and obesity: A prospective analysis of the French NutriNet-Sant\u0026eacute; cohort. PLoS Med. 2020;17(8):e1003256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pmed.1003256\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1003256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, Chung ST, Costa E, Courville A, Darcey V, Fletcher LA, Forde CG, Gharib AM, Guo J, Howard R, Joseph PV, McGehee S, Ouwerkerk R, Raisinger K, Rozga I, Stagliano M, Walter M, Walter PJ, Yang S, Zhou M. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019;30(1):67\u0026ndash;e773. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmet.2019.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2019.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVandevijvere S, Jaacks LM, Monteiro CA, Moubarac JC, Girling-Butcher M, Lee AC, Pan A, Bentham J, Swinburn B. Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories. Obes Rev. 2019;20(Suppl 2):10\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/obr.12860\u003c/span\u003e\u003cspan address=\"10.1111/obr.12860\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDearth-Wesley T, Howard AG, Wang H, Zhang B, Popkin BM. Trends in domain-specific physical activity and sedentary behaviors among Chinese school children, 2004\u0026ndash;2011. Int J Behav Nutr Phys Act. 2017;14(1):141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12966-017-0598-4\u003c/span\u003e\u003cspan address=\"10.1186/s12966-017-0598-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui Z, Hardy LL, Dibley MJ, Bauman A. Temporal trends and recent correlates in sedentary behaviours in Chinese children. Int J Behav Nutr Phys Act. 2011;8:93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1479-5868-8-93\u003c/span\u003e\u003cspan address=\"10.1186/1479-5868-8-93\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonda KL, Popkin BM. Cluster analysis methods help to clarify the activity-BMI relationship of Chinese youth. Obes Res. 2005;13(6):1042\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/oby.2005.122\u003c/span\u003e\u003cspan address=\"10.1038/oby.2005.122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Zhao B, Liu J, Wang Y, Xu F, Wang Y, Xue H. A 3-year longitudinal study of the association of physical activity and sedentary behaviours with childhood obesity in China: The childhood obesity study in China mega-cities. Pediatr Obes. 2021;16(6):e12753. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ijpo.12753\u003c/span\u003e\u003cspan address=\"10.1111/ijpo.12753\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2020 Nov 22. PMID: 33225582.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao LQ. Analysis of the influencing factors of childhood obesity and evaluation of intervention effects. Fudan University Dissertation, 2014.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dietary diversity score, dietary behavior, growth and development, children, correlation","lastPublishedDoi":"10.21203/rs.3.rs-9205134/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9205134/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo analyze the relationship between diet and lifestyle behaviors with the growth development of preschool children (aged 3\u0026ndash;7) in Wuxi City, providing a scientific basis for implementing a healthy diet and lifestyle.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA stratified cluster random sampling method was used to select study participants. Self-administered questionnaires were used to collect demographic information. The dietary diversity score (DDS), dietary behaviors, and current lifestyle were analyzed. Children's growth and development were assessed using BMI-Z scores. Variance analysis and logistic regression were employed to explore the relationships.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 3727 preschool children were included. The average DDS was 5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.94, with 36.79% of children showing low dietary diversity (DDS\u0026thinsp;\u0026le;\u0026thinsp;4). The largest proportion of children (53.13%) had normal growth (-1\u0026thinsp;\u0026le;\u0026thinsp;BMI-Z\u0026thinsp;\u0026le;\u0026thinsp;1). The children were divided into four groups based on BMI-Z scores, and statistically significant differences were observed across age, current height, current weight, birth weight, DDS, parental BMI, and maternal pre-pregnancy weight (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Low dietary diversity, nighttime snacking, 1\u0026ndash;2 hours of daily screen time, and \u0026ge;\u0026thinsp;2 hours of daily screen time were positively correlated with childhood obesity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After adjusting for factors such as child\u0026rsquo;s age, gender, birth weight, parental BMI, family income, number of family members dining together, family type, and primary caregiver, low dietary diversity remained significantly associated with obesity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eGreater attention should be given to preschool children with low dietary diversity, frequent nighttime snacking, and extended screen time to prevent obesity. Targeted interventions addressing these factors should be considered.\u003c/p\u003e","manuscriptTitle":"A Study on the Influence and Correlation of Dietary Structure and Lifestyle Behavior Patterns on Growth and Development in Children Aged 3–7 Years","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 16:52:31","doi":"10.21203/rs.3.rs-9205134/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-05T16:02:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314240183516817704159648996949317569883","date":"2026-04-13T08:57:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T16:05:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-27T07:01:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T11:38:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T11:38:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2026-03-24T00:25:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e7cba437-a890-4c2a-8ae6-82758205eec3","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-05T16:02:43+00:00","index":69,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T16:52:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 16:52:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9205134","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9205134","identity":"rs-9205134","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}