Risk prediction of excessive gestational weight gain based on a nomogram model

Risk prediction of excessive gestational weight gain based on a nomogram model | 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 Risk prediction of excessive gestational weight gain based on a nomogram model Linyan He, Xihong Zhou, Jiajun Tang, Min Yao, Li Peng, Sai Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3921018/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Excessive gestational weight gain is a global public health problem with serious and long-term effects on maternal and offspring health. Early identification of at-risk groups and interventions is crucial for controlling weight gain and reducing the incidence of excessive gestational weight gain. Currently, tools for predicting the risk of excessive gestational weight gain are lacking in China. This study aimed to develop a risk-prediction model and screening tool for the early identification of at-risk groups. Methods Convenience sampling was used to select 306 pregnant women who underwent regular obstetric checkups at a tertiary-level hospital in China between January and March 2023. Logistic regression analysis was used to construct the risk-prediction model. The goodness of fit of the model was assessed using the Hosmer-Lemeshow test, and the predictive performance was evaluated using the area under the receiver operating characteristic (ROC) curve. R4.3.1 software was used to create a nomogram. Results The prevalence of excessive gestational weight gain was 49.53%. Logistic regression analysis revealed that prepregnancy overweight (odds ratio [OR] = 2.662), obesity (OR = 3.851), and primiparity (OR = 5. 134); eating in front of a screen (OR = 5.588); consumption of sugar-sweetened beverages, desserts, and western fast food (> 5 times per week) (OR = 6.733); and pregnancy body image (OR = 1.031) were risk factors for excessive gestational weight gain. Protective motivation to manage pregnancy body mass (OR = 0.979) and duration of moderate-intensity physical activity (OR = 0.234) were protective factors against excessive gestational weight gain. The area under the ROC curve of the model was 0.885, with a maximum Youden index of 0.617, optimal threshold of 0.404, sensitivity of 83.96%, and specificity of 77.78%. The model validation results showed a sensitivity, specificity, and accuracy of 83.33%, 77.27%, and 80.43%, respectively. Conclusion The risk-prediction model developed in this study proved to be effective, providing a valuable basis for early identification and precise intervention in individuals at risk of excessive gestational weight gain. Pregnancy Excessive Gestational Weight Gain Risk-Prediction Model Nomogram Figures Figure 1 Figure 2 Figure 3 Background Gestational weight gain (GWG) refers to the change in weight from before pregnancy to before delivery [ 1 ]. When GWG exceeds the upper limit of the recommended weight gain range, it is called excessive gestational weight gain (EGWG) [ 2 ]. The global incidence rate of EGWG is approximately 47% [ 3 ]. EGWG not only increases the risk of adverse perinatal outcomes such as gestational hypertension, gestational diabetes, dystocia, postpartum haemorrhage, postpartum depression, and postpartum obesity but also increases long-term complications in the offspring, such as obesity, cardiovascular diseases, metabolic diseases, and respiratory diseases [ 4 – 5 ]. This condition has become a major global public health concern. Therefore, the early identification of EGWG risk groups and accurate intervention are crucial for effectively controlling weight gain and reducing the incidence of EGWG.Currently, most studies on pregnancy weight gain, both domestically and internationally, focus on its effects on delivery outcomes, maternal and infant health [ 3 – 5 ], intervention methods for pregnancy weight management [ 6 – 7 ], and influencing factors [ 8 ]. Through this study, we have not only developed a novel risk-prediction model for pregnant women in China but also offered valuable insights for international research conducted within diverse cultural, economic, and social contexts. Although foreign models exist [ 9 ], their applicability to Chinese women remains uncertain due to distinct regional, racial, economic, cultural, dietary, and lifestyle variations. Traditional dietary concepts may influence pregnant women's acceptance of high-calorie, high-fat foods. Work and rest habits in China differ significantly from those of western countries. For instance, certain traditional beliefs can affect pregnant women's attitudes towards rest and work during pregnancy. Social and cultural factors such as family structure, social pressure, and marital attitudes play pivotal roles in the physical health and weight management of pregnant women. In China, family responsibilities and social support can have a distinct influence on lifestyle choices and weight control during pregnancy. Bridging this gap is essential for tailoring interventions to China's unique conditions. This study addresses the absence of China-specific EGWG prediction tools and aims to construct a model based on a comprehensive review of domestic and foreign literature. The goal of this study was to facilitate the early identification of EGWG risk groups and the formulation of targeted prevention strategies, recognising the nuanced factors affecting pregnant Chinese women. Methods Participants and sample size determination Convenience sampling was used to select women in their second trimester of pregnancy who had records and regular prenatal examinations at the Obstetrics Clinic of the Second Xiangya Hospital in Changsha, Hunan Province, from January to March 2023. The survey included pregnant women whose body weights were monitored before full-term delivery. The inclusion criteria were age ≥ 18 years, pregnancy between 14 and 27 + 6 weeks, and live intrauterine birth. The exclusion criteria were twin or multiple pregnancies; mental illness or cognitive impairment; severe anaemia, diabetes, hypertension, thyroid function complications, heart, liver, kidney, or lung diseases, or other pathological conditions affecting weight gain during pregnancy; and inability to complete the investigation independently. Additionally, dropouts and patients lost to follow-up or who experienced early pregnancy termination were excluded. Previous studies [ 8 – 10 ] have shown that EGWG has 4–9 independent risk factors; therefore, it was speculated that no more than 13 risk factors will be included in the risk-prediction model constructed in this study. According to the sample size requirements of the prediction model, at least 5–10 samples [ 11 ] were needed for each risk factor. The incidence of EGWG was 47% [ 3 ]. Accounting for a potential 20% sample loss, the required sample size was approximately (13×5 ÷ 0.47) ÷ 0.8 = 173. According to the sample size requirement [ 12 ] of 7:3 for the modelling and validation groups, 74 samples were required for the validation group. Finally, 306 patients were included in this study, with 214 and 92 patients in the modelling and validation groups, respectively. Materials General information questionnaire Possible risk factors for EGWG were identified by reviewing the domestic and foreign literature using evidence-based methods and expert consultations. A self-developed questionnaire was administered to collect general information. The questionnaire included questions on maternal age, pre-pregnancy body mass index (BMI), place of residence, education level, family monthly income, marital status, employment status, parity, smoking, drinking, dietary patterns, and dietary behaviour. Protective motivation for weight management during pregnancy questionnaire Our research group developed a questionnaire based on the theoretical framework of protection motivation [ 13 ]. Its main purpose was to assess the level of motivation of pregnant women to maintain their weight during pregnancy and their understanding of weight management. The questionnaire consisted of six dimensions: threat perception, response efficacy, self-efficacy, internal and external rewards, and reaction costs. It included 32 items, with higher scores indicating a higher level of protection motivation and a greater likelihood of adopting weight management strategies. The overall Cronbach's α coefficient for the questionnaire was 0.894, and the α coefficient for each dimension ranged from 0.652 to 0.915. The overall test-retest reliability of the questionnaire was 0.947, with each dimension having test-retest reliability ranging from 0.846 to 0.956. Body image scale for pregnant women The Body Image in Pregnancy Scale (BIPS) was developed by Brittany Watson [ 14 ] and translated into Chinese by Weijia Sun [ 15 ] to assess the self-body image of pregnant women in an academic context. The BIPS comprises nine dimensions (35 items): body part dissatisfaction, physical dissatisfaction, dissatisfaction with appearance concerns, dissatisfaction with facial features, attraction to the opposite sex, weight control due to appearance, appearance over body function, appearance-related avoidance behaviour, and dissatisfaction with physiological changes during pregnancy. The total score ranges from 11 to 195, with higher scores indicating greater severity of the body image disorder. The Cronbach's α coefficient of the scale was 0.861, and the Cronbach's α coefficient for each dimension ranged from 0.552 to 0.905. Social Support Rating Scale The Social Support Rating Scale (SSRS) was developed by Xiao Shuiyuan [ 16 ] to evaluate social support received by pregnant women in an academic context. The scale was divided into three dimensions: objective support (three items), subjective support (four items), and support utilisation (three items), resulting in a total of 10 items. The sum of the scores for each dimension constitutes the total score, with higher scores indicating higher levels of social support. The Cronbach's α coefficient of the scale was 0.896, and it has been widely utilised in China since 1987. Edinburgh Postnatal Depression Scale The self-rating Edinburgh Postnatal Depression Scale (EPDS) was developed by Cox et al. [ 17 ] and translated into Chinese by Guo Xiujing [ 18 ] to assess the level of depression among pregnant women. The scale consists of 10 items, with a total score range of 0 to 30. A score of ≤ 5 indicates no or very mild depression, 6–9 indicates mild depression, and ≥ 10 indicates moderate or severe depression. The Cronbach's α coefficient of the scale was 0.76. Studies have demonstrated that the EPDS can be used not only to screen for postpartum depression but also for prenatal depression [ 18 ]. Perceived Stress Scale The Perceived Stress Scale (PSS-10), developed by Cohen et al. [ 19 ] in 1983 and translated into Chinese by Yang et al. [ 20 ], is widely used in psychological assessments to measure individuals’ subjective stress levels. The scale consisted of 10 items. The total score ranges from 0 to 40, with higher scores indicating higher levels of perceived stress. The Cronbach's α coefficient of the scale was 0.734. Physical activity during pregnancy The Pregnancy Physical Activity Questionnaire (PPAQ) developed by Chesan-Taber et al. and translated into Chinese by Yan et al. [ 21 ], was used to assess the duration, frequency, and intensity of physical activity during pregnancy. The PPAQ includes 31 items, including home care activities (14 items), transportation (4 items), sports and exercise (8 items), and occupational activities (5 items). The 31 items were classified based on the metabolic equivalent (MET) values for energy expenditure into sedentary activities (< 1.5 METs), low-intensity physical activity (1.6–2.9 METs), and moderate-to-vigorous-intensity physical activity (≥ 3.0 METs). Each activity was further categorised into six levels, considering duration and frequency, and the corresponding time-weighted coefficients were calculated. The total energy expenditure for each activity was determined by multiplying the energy consumption by the time-weight coefficient. A higher total energy expenditure indicates greater physical activity during pregnancy. The content validity index of the questionnaire was 0.940 and the test-retest reliability was 0.944. The 2020 World Health Organization (WHO) guidelines [ 22 ] on physical activity and sedentary behaviour recommend that pregnant women without contraindications should accumulate at least 150 minutes of moderate-intensity physical activity per week. In this study, engaging in activities with an energy expenditure of 3.0–6.0 METs for more than 2.5 hours per week was defined as meeting the exercise requirements, whereas engaging in activities with an energy expenditure of 3.0–6.0 METs for less than 2.5 hours per week was defined as inadequate exercise. Definition of EGWG According to the recommendations for GWG, which represent the first industry standard for GWG in China [ 1 ], optimal GWG ranges have been established based on pre-pregnancy BMI categories. These categories include underweight (BMI < 18.5 kg/m 2 ), normal weight (18.5 kg/m 2 ≤ BMI < 24.0 kg/m 2 ), overweight (24.0 kg/m 2 ≤ BMI < 28.0 kg/m 2 ), and obesity (BMI ≥ 28.0 kg/m), with corresponding optimal GWG ranges of 11.0–16.0, 8.0–14.0, 7.0–11.0, and 5.0–9.0 kg, respectively [ 1 ]. EGWG was defined as the actual GWG surpassing the established optimal GWG values for the respective BMI categories. Procedures and ethical considerations The hospital’s ethics committee approved this study. Prior to participation, all participants provided written informed consent. To assess the feasibility of the study and identify potential data collection issues, a preliminary experiment involving 10 eligible participants was conducted after obtaining ethical approval with no reported problems. The data collectors were uniformly trained to ensure accuracy and consistency of data collection. A face-to-face questionnaire survey was conducted from 14 to 27 + 6 weeks of gestation when pregnant women were admitted to the Department of Obstetrics and Gynaecology. The survey included a general information questionnaire, pregnancy weight management and protection motivation questionnaire, and the SSRS, BIPS, EPDS, PSS-10, and PPAQ. The investigator immediately collected the questionnaires, checked the authenticity of the data, and eliminated invalid questionnaires with inconsistent options or all the same options. A secondary check by another team member ensured the accuracy of the collected data and recalculated the scores. Pre-pregnancy weight was determined as the average weight during the first 3 months of pregnancy extracted from electronic medical records. Weight before delivery, measured a week before delivery, was obtained by an investigator on the study team before hospitalisation. A total of 321 questionnaires were distributed and 314 valid questionnaires were returned, for an effective recovery rate of 97.8%. Questionnaires with incomplete data were considered invalid and excluded. Statistical analysis Data entry was conducted using Excel, statistical analyses were performed using SPSS version 26.0, and the nomogram model was constructed using R software version 4.3.1. Categorical data are summarised as counts and percentages, and group comparisons were conducted using the chi-square test. Normally distributed continuous data are presented as mean ± standard deviation and analysed using the t -test. For non-normally distributed continuous data, the median and interquartile range were reported, and group comparisons were performed using the Mann-Whitney U test. Independent risk factors were identified using binary logistic regression to construct a risk-prediction model. Model fitting and prediction performance were assessed using the Hosmer-Lemeshow test and the area under the receiver operator characteristic (ROC) curve (AUC). The optimal threshold for the model was determined using the maximum Youden index. The model performance was evaluated using sensitivity, specificity, and accuracy rates. P < 0.05 was considered statistically significant. Results Sample and demographic characteristics of participants In total, 214 pregnant women were included in the modelling group. The mean age was 33years (range, 25-43 years), and the gestational age was 15-27+4 weeks. EGWG occurred in 106 pregnant women, for an incidence of 49.53%. Additional demographic details are presented in Table 1. Univariate analysis of risk factors in the modelling group There were statistically significant differences between the EGWG group and the non-EGWG group in pre-pregnancy BMI, previous parity, eating in front of a screen, frequency of weekly consumption of sugar-sweetened beverages/desserts/western fast food, Pregnancy Weight Management and Protection Motivation Questionnaire score, BIPS score, and time of daily moderate-intensity physical activity ( P < 0.05), as shown in Table 1. Multivariate logistic regression analysis of modelling group and establishment of risk-prediction model Statistically significant factors in the univariate analysis were used as independent variables, and the occurrence of EGWG was used as the dependent variable in the logistic regression analysis. The assignments of independent variables are listed in Table 2. The results of logistic multivariate analysis showed that pre-pregnancy BMI classified as overweight or obesity, no previous delivery (primiparas), eating in front of a screen, consumption of sugar-sweetened beverages/desserts/western fast food more than five times per week, Pregnancy Weight Management and Protection Motivation Questionnaire score, BIPS score, and daily moderate-intensity physical activity time were the main influencing factors for EGWG, as shown in Table 3. Finally, the prediction model was established as follows: P = 1/[1 + exp (-2.259 + 0.979 × overweight pre-pregnancy BMI +1.348 × obese pre-pregnancy BMI + 1.636 × first birth + 1.721 × eating in front of a screen + 1.907 × consumption of sugar-sweetened beverages/desserts/western fast food >5 times/week -0.022 × score of the questionnaire on the motivation of weight management and protection during pregnancy + 0.031 × BIPS score -1.451 × duration of daily moderate-intensity physical activity)]. A nomogram was constructed based on the seven predictors screened using logistic regression, as shown in Figure 1. Each predictor had a corresponding score based on the scoring criteria. The total score of the seven factors projected onto the risk of EGWG location represented the prediction probability of EGWG, where a higher score indicated an elevated risk of EGWG. The Hosmer-Lemeshow test results showed that χ2=5.090 (P=0.748), and there was no significant difference between the predicted value and the observed value, suggesting that the model fitted well. The AUC was used to test the sensitivity and specificity of the model, and the maximum Youden index was used to determine the best critical value for the model [11]. The results showed that the AUC of the model was 0.885 (95% confidence interval [CI]: 0.843-0.928). A maximum Youden index of 0.617 indicated that the optimal critical value for the prediction model was 0.404. The corresponding sensitivity and specificity values were 83.96% and 77.78%, respectively (Fig. 2). Figure 1 Nomogram for predicting excessive gestational weight gain. The risk of predicting the occurrence of EGWG is quantifed as the number of points marked on the axis, the score determined by each variable axis is the number corresponding to the value vertical on the total points scale, and projected the sum of all variables onto the bottom axis, yielding a personalized EGWG risk for each pregnant women Figure 2 ROC curve of risk factor prediction model for EGWG in modeling set. the ROC curve(AUC) of the model was 0.885, when the risk probability is 0.404 as the cutof point,the validation model’s sensitivity and specifcity were 0.839 and 0.778, respectively. Internal verification of prediction model To validate the prediction model internally, a validation group comprising 92 pregnant women was selected. Pregnant women's data were input into the prediction model formula to assess their predictive efficacy. The AUC for the validation group was determined to be 0.853 (95% CI: 0.776-0.930). The optimal critical value identified at a maximum Youden index of 0.593 was 0.648. At this critical value, the sensitivity of the ROC curve was 72.9% and the specificity was 86.4%, as depicted in Figure 3. In the validation group, EGWG occurred in 48 patients (52.17%). Of these, the model correctly identified EGWG in 40 cases but misjudged it in eight cases (83.33% accuracy). For cases in which EGWG did not occur (44 cases), the model correctly identified 34 cases and misjudged 10 cases, resulting in a specificity of 77.27%. The overall accuracy of the model for the validation group was 80.43%. Figure 3 ROC curve of risk factor prediction model for EGWG in validation set.the ROC curve(AUC) of the model was 0.853, when the risk probability is 0.648 as the cutof point,the validation model’s sensitivity and specifcity were 0.729 and 0.864, respectively. Discussion The intricate nature of the risk factors linked to EGWG spans various dimensions, as evidenced by numerous studies worldwide. These dimensions encompass physiological, psychological, cognitive, social, and health behaviours. In our investigation, logistic regression analysis was used to identify predictive factors associated with EGWG. Regarding physiological factors, our findings underscore that pre-pregnancy overweight (odds ratio [OR] = 2.662) and obesity (OR = 3.851) are independent risk factors for EGWG. These results are consistent with those of both domestic and international studies [ 10 , 23 – 24 ]. Individuals frequently contend with an obesogenic dietary environment against the backdrop of rapid global economic growth. Moreover, the prevalent sedentary lifestyle in modern society significantly amplifies overweight and obesity rates among women of reproductive age [ 7 ]. This highlights the importance of prioritising and managing overweight or obese individuals during prenatal care. Initiating weight management interventions ideally before conception is crucial among women of childbearing age. Proactive measures of this nature hold promise for reducing the incidence of EGWG. Among the psychological and cognitive factors, body image emerged as a significant predictor of EGWG (OR = 1.031), whereas the level of protective motivation towards maternal body quality management was found to be a protective factor against EGWG (OR = 0.979). Body image refers to the way individuals perceive their physical appearance and functionality. It is a complex concept shaped by perceptual experiences, beliefs, and emotional attitudes [ 25 ]. Fealy et al. used a psychosocial assessment questionnaire to identify women at risk of EGWG. The questionnaire included four items that focused on body image as a predictive factor [ 26 ]. Another prospective cohort study found that pregnant women with body image disorders were more likely to experience EGWG [ 27 ], which supports the findings of this study. It is worth noting that there is limited research on the topic of pregnant women's body image conducted by Chinese scholars, as the field is still in its early stages [ 28 ]. Therefore, future efforts should focus on understanding pregnant women’s body image experiences and developing effective intervention strategies aimed at enhancing body perception, facilitating adaptation to physical changes during the perinatal period, and promoting overall well-being. Protective motivation towards maternal body quality management aims to stimulate an individual’s awareness of weight management during pregnancy from an internal perspective to encourage healthy behaviours and improve self-management capabilities related to weight control. Several studies have indicated that the level of protective motivation can moderately predict pregnant women's adherence to health behaviours related to body quality management [ 13 ]. A recent empirical study conducted by You et al. on weight management during pregnancy emphasised the significance of motivation as a key determinant with a direct impact on weight management, highlighting the need for strong motivation among pregnant women to initiate and sustain desired behaviours [ 29 ], which aligns with the findings of this study. However, insufficient attention has been paid to assessing the level of protective motivation in pregnant women and limited research has explored its association with weight gain. Consequently, healthcare professionals should incorporate targeted interventions aimed at enhancing pregnant women's awareness of and intrinsic motivation for self-weight management when formulating strategies for effective weight control. In terms of social factors, primiparity was identified as an independent risk factor for EGWG, with an OR of 5.134. Primiparous women exhibit a significantly higher risk of EGWG than multiparous women, which is consistent with the conclusions drawn by numerous domestic and international scholars [ 8 – 10 ]. This disparity may stem from a lack of reproductive experience among primiparous individuals, leading to limited awareness and understanding of weight management. Consequently, targeted health education and guidance tailored specifically for primiparous pregnant women has become imperative compared to those with previous childbirth experiences. Diet and physical activity play crucial roles in influencing pregnancy weight. Numerous studies have consistently established significant correlations between the western [ 30 ], beverage confectionery [ 31 ], and ultra-processed food [ 32 ] dietary patterns and EGWG. These patterns involve a high consumption of sugar, fat, energy-dense foods, and ultra-processed products. Our study revealed an independent increase in EGWG risk (OR = 6.733) associated with the consumption of sugar-sweetened beverages, desserts, and western fast food five or more times per week. This aligns with the findings of the dietary screening questionnaire developed by Hrolfsdottir et al. [ 33 ], who identified the consumption of sugar/artificially sweetened beverages five or more times per week as a dietary risk predictor. Such dietary habits, rich in added sugars and fats, contribute to liver lipid synthesis, elevated serum cholesterol levels, and visceral fat accumulation, thereby increasing the likelihood of overweight or obesity [ 31 ]. Additionally, this study found that pregnant women engaging in screen-based eating behaviours faced a higher risk of EGWG (OR = 5.588), consistent with the findings of McDonald et al. [ 9 ]. Eating while using mobile phones or watching TV may lead to overeating because of distraction, reduced physical activity time, and increased sedentary behaviour, thereby elevating the likelihood of EGWG. Proper dietary nutrition during pregnancy is crucial for weight control, emphasising the importance of promoting healthy dietary patterns and reducing the intake of high-sugar, high-fat, high-energy-density, and ultra-processed foods. Regular assessment of dietary behaviours in pregnant women is essential to promptly address unhealthy habits. In our study, the duration of moderate-intensity physical activity during pregnancy was negatively associated with EGWG (OR = 0.244). Consistent with our findings, a study on exercise interventions for pregnant women revealed that those implementing interventions such as regular exercise and moderate-intensity physical activity during pregnancy exhibited a significantly lower incidence of EGWG than those without such interventions [ 21 ]. Bao et al. [ 34 ] also emphasised that the duration of moderate or high physical activity is an independent influencing factor for weight gain in the third trimester, which is consistent with our study's results. The WHO physical activity and sedentary behaviour guidelines [ 22 ] explicitly state that pregnant women without contraindications should achieve at least 150 minutes of moderate-intensity physical activity per week to meet recommended standards. Our study corroborates this, highlighting that fewer hours of moderate-intensity physical activity during pregnancy are associated with a higher risk of EGWG. Consequently, achieving weight control in pregnant women involves balancing energy intake (eating) and expenditure (exercise). Limitations This study had certain limitations. First, the study participants were recruited exclusively from a single tertiary hospital, which may limit the generalisability of the findings to a broader population. This poses a potential problem for sample representation, because the results may not accurately reflect the characteristics of the entire target population. Additionally, although internal validation indicated promising results, external validation was not conducted to verify the performance of the model on an independent dataset. Future research should involve larger sample sizes and multicentre studies to enhance the predictive accuracy and generalisability of the model. Conclusions High pre-pregnancy BMI (overweight or obesity), being a first-time mother, eating in front of a screen, consuming sugar-sweetened beverages and desserts, frequent consumption of western fast food (> 5 times per week), and having a maternal body image disorder were identified as risk factors for EGWG. However, a high level of motivation for weight management during pregnancy and engaging in moderate-intensity physical activity for a longer duration were protective factors against EGWG. In this study, we developed a risk-prediction model for EGWG in pregnant women using a nomogram. The model demonstrated high sensitivity and specificity, a good fit, and a reliable predictive effect. It can serve as a convenient screening tool for nursing staff to identify individuals at risk for EGWG early and implement accurate interventions, thereby reducing the incidence of EGWG and promoting the health of both mothers and children. Abbreviations GWG Gestational Weight Gain EGWG Excessive Gestational Weight Gain BMI Body mass index PSS The Perceived Stress Scale EPDS The self-rating Edinburgh Postnatal Depression Scale BIPS The Body Image in Pregnancy SSRS The Social Support Rating Scale PPAQ Pregnancy Physical Activity Questionnaire MET Metabolic Equivalent ROC receiver operator characteristic curve Declarations Ethics approval and consent to participate The study was approved by the Ethics Review Committee of Nursing and Behavioral Medicine Research, Xiangya School of Nursing, Central South University (no. E2023183). This study was conducted in accordance with the guidelines of the Declaration of Helsinki. Participation in the study was voluntary, and all participants provided written informed consent before participation. Availability of data and materials The datasets analyzed during the current study are not publicly available due to the metadata containing information that could compromise the patients but are available from the corresponding author on reasonable request. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Natural Science Foundation of Hunan Province[grant number 2023JJ30807]. Authors’contributions All the authors contributed to the work and approved the fnal version of this manuscript. This study was designed by LYH, XHZ. JJT,SL and MY performed the statistical analysis. LYH wrote the manuscript. XHZ reviewed and edited manuscript. All authors read and approved the manuscript. Acknowledgements We would like to thank The Second Xiangya Hospital, Central South University for letting us to conduct this study. 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Ferreira LB, Lobo CV, Miranda AEDS et al. Dietary patterns during pregnancy and gestational weight gain: a systematic review[J]. Rev Bras Ginecol Obstet 2022,44(05):540–7. Cai CJ, Dong HL, Pang XX et al. A Prospective Study of the Relationship Between Dietary Patterns during the Second Trimester of Pregnancy and Gestational Weight Gain[J].J Sichuan Univ (Med Sci Edi),2020,51(06):822–7. Cummings JR, Lipsky LM, Schwedhelm C, et al. Associations of ultra-processed food intake with maternal weight change and cardiometabolic health and infant growth[J]. Int J Behav Nutr Phys Act. 2022;19(1):61. Hrolfsdottir L, Halldorsson TI, Birgisdottir BE, et al. Development of a dietary screening questionnaire to predict excessive weight gain in pregnancy[J]. Matern Child Nutr. 2019;15(1):e12639. Bao YH, Wu C, Zhao RP et al. Moderate-to-vigorous physical activities and gestational weight gains during the second and last trimesters of pregnancy[J]. J Sichuan Univ (Med Sci Edi) 018,49(6):938–43. Tables Table 1 Single factor analysis results of excessive gestational weight gain Variable Non-egwg group ( n =108) EGWG group ( n =106) Test statistics P -value Age [years, M ( P25 , P75 )] 33 (29,38) 33 (29,38) 0.173 * 0.863 Pre-pregnancy BMI[example (percentage,%)] 14.153 # 0.003 Low weight 15 (13.89) 11 (10.38) Normal weight 53 (49.07) 29 (27.36) Overweight 27 (25.00) 42 (39.62) Obesity 13 (12.04) 24 (22.64) Level of education [example (percentage,%)] 4.921 # 0.178 Junior high school or below 10 (9.25) 18 (16.98) High school or secondary school 17 (15.74) 23 (21.71) College or bachelor's degree 71 (65.74) 57 (53.77) Master's degree or above 10 (9.25) 8 (7.54) Marital status [example (percentage,%)] 2.626 # 0.622 Unmarried 4 (3.70) 2 (1.89) Married 90 (83.33) 90 (84.91) Divorce 5 (4.63) 8 (7.54) Widowed 1 (0.93) 0 (0) Separation 8 (7.41) 6 (5.66) Place of residence [example (percentage,%)] 0.056 # 0.812 Towns 84 (77.78) 81 (76.42) Rural 24 (22.22) 25 (23.58) Monthly income [example (percentage,%)] 3.383 # 0.336 15000 9 (8.33) 13 (12.27) Previous parity [example (percentage,%)] 31.454 # 0.000 0 time 17 (15.74) 55 (51.89) 1 time 46 (42.59) 24 (22.64) 2 or more times 45 (41.67) 27 (25.47) Note :* denotes z-score; # is the c² value. Table 1(Continued) Single factor analysis results of excessive gestational weight gain Variable Non-egwg group ( n =108) EGWG group ( n =106) Test statistics P -value Smoking [example (percentage,%)] 0.001 # 0.981 No 105 (97.22) 103 (97.17) Yes 3 (2.78) 3 (2.83) Drinking [example (percentage,%)] / / No 108 (100) 106 (100) Yes 0 (100) 0 (0) Eating in front of a screen [example (percentage,%)] 28.563 # 0.000 No 71 (65.74) 31 (29.25) Yes 37 (34.26) 75 (70.75) Habit of eating snacks or snacks [example (percentage,%)] 0.160 # 0.689 No 60 (55.56) 56 (52.83) Yes 48 (44.44) 50 (47.17) Consumption of sugar-sweetened beverages,desserts, and western fast food [example (percentage,%)] 34.306 # 0.000 5 times per week 14 (12.96) 48 (45.28) PSS-10 score [score, M ( P25 , P75 )] 14 (10.25, 18) 14 (10, 18) 0.065 * 0.948 EPDS score [score, M ( P25 , P75 )] 8 (5, 9) 10 (5, 9) 0.468 * 0.640 Protective Motivation for Weight Management during Pregnancy Questionnaire score [score, M ( P25 , P75 )] 125.5 (109.25, 140) 114.5 (98127). 3.892 * 0.000 BIPS score [score, M ( P25 , P75 )] 91.5 (82106.75) 122 (90141). 5.899 * 0.000 SSRS score [score, M ( P25 , P75 )] 38.5 (30.25, 45) 38 (31,44.25) 0.137 * 0.891 Note :* denotes z-score; # is the c² value. Table 1(Continued) Single factor analysis results of excessive gestational weight gain Variable Non-egwg group ( n =108) EGWG group ( n =106) Test statistics P -value Total daily energy consumption [cards, M ( P25 , P75 )] 32.33 (27.31, 35.71) 31.57 (27.7, 36.81) 0.151 * 0.880 Daily physical activity time [hours, M ( P25 , P75 )] Daily sedentary time 20 (18.23, 21.9) 19.2 (17.6, 21.23) 1.650 * 0.099 Daily light exercise time 3.5 (1.83, 5.58) 4 (2.28, 6.05) 1.116 * 0.264 Daily moderate exercise time 1.15 (0.5, 1.6) 0.65 (0.18, 1.1) 4.154 * 0.000 Daily heavy exercise time 0 (0, 0) 0 (0, 0) 0.013 * 0.989 Whether the activity is up to standard [example (percentage,%)] 0.949 # 0.330 Under par 31 (28.71) 37 (34.91) Up to par 77 (71.29) 69 (65.09) Note :* denotes z-score; # is the c² value. BMI Body mass index, PSS The Perceived Stress Scale, EPDS The self-rating Edinburgh Postnatal Depression Scale, BIPS The Body Image in Pregnancy ,SSRS The Social Support Rating Scale Table 2 The assignment of the independent variable Independent variable Assignment method Pre-pregnancy BMI Normal weight =1, low weight =2, overweight =3, and obese =4 Previous parity 0 = 1, 1 = 2, 2 or more =3 Eating in front of a screen No =0, yes =1 Consumption of sugar-sweetened beverages, desserts, and western fast food 5 times per week =4 Protective Motivation for Weight Management during Pregnancy Questionnaire score Original value BIPS score Original value Duration of moderate intensity physical activity Original value BMI Body mass index, BIPS The Body Image in Pregnancy Table 3 Logistic regression analysis results of excessive gestational weight gain Items β values Standard error Wald c² value P value OR value 95%CI Constant 2.259 1.695 1.776 0.183 0.104 - Pre-pregnancy BMI Normal weight - - 9.322 0.025 - - Low weight 0.375 0.675 0.308 0.579 0.687 0.183 ~ 2.582 Overweight 0.979 0.478 4.192 0.041 2.662 1.043 ~ 6.797 Obesity 1.348 0.571 5.573 0.018 3.851 1.257 ~ 11.795 Previous parity 2 or more births - - 12.655 0.002 - - 1 time 0.070 0.481 0.021 0.885 1.072 0.418 ~ 2.752 0 times 1.636 0.521 9.856 0.002 5.134 1.849 ~ 14.256 Eating in front of a screen 1.721 0.429 16.122 0.000 5.588 2.413 ~ 12.942 Consumption of sugar-sweetened beverages, desserts, and western fast food 5 times per week 1.907 0.579 10.852 0.001 6.733 2.165 ~ 20.942 Protective Motivation for Weight Management during Pregnancy Questionnaire score 0.022 0.011 3.890 0.049 0.979 0.958 ~ 1 BIPS score 0.031 0.009 12.849 0.000 1.031 1.014 ~ 1.048 Moderate intensity physical activity 1.451 0.365 15.820 0.000 0.234 0.115 ~ 0.479 BMI Body mass index, BIPS The Body Image in Pregnancy Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3921018","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272948039,"identity":"9afd44a8-f1e6-424c-b1b3-d94756ce66dc","order_by":0,"name":"Linyan He","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Linyan","middleName":"","lastName":"He","suffix":""},{"id":272948040,"identity":"916177ce-8959-44a7-bbb2-617a24ecac1b","order_by":1,"name":"Xihong Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACCRBhcABIMB+AiBwgXgtbAilawMp4DIjTIj+7+Zg0T8EdOXP+NR8//mxjkOO7kcD4uQCPFsY5x9IkZxg8M7ac8XazNG8bg7HkjQRm6Rl4tDBL5JhJfDA4nLjhxtltzIxtDEBGAhszDx4tbCAtCWAtZ54xAh1WT1ALD9yW8z1sDECHJRgQ0iIhkZZsOcPgsLHBDTZjaZ5zEoYzzzxslsanRX5G8sHbPH8OyxmcP/zw448yG3m+48kHP+PTgmRfApgEYsYGojQwMPAfIFLhKBgFo2AUjDgAAKGKS/0itLmNAAAAAElFTkSuQmCC","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Xihong","middleName":"","lastName":"Zhou","suffix":""},{"id":272948041,"identity":"660073f2-1906-4fd9-a79e-227414364b93","order_by":2,"name":"Jiajun Tang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jiajun","middleName":"","lastName":"Tang","suffix":""},{"id":272948042,"identity":"5db0766a-0e91-4ba7-a89d-430fd12f16dd","order_by":3,"name":"Min Yao","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Yao","suffix":""},{"id":272948043,"identity":"d0f55a44-b4dd-4bed-9b0f-4681e8db73db","order_by":4,"name":"Li Peng","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Peng","suffix":""},{"id":272948044,"identity":"eee97b21-5912-4934-82e5-3a189b3115e8","order_by":5,"name":"Sai Liu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Sai","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-02-02 13:30:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3921018/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3921018/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51237969,"identity":"0541027a-c37d-49b0-a1a8-1210436d76e6","added_by":"auto","created_at":"2024-02-16 16:47:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1812371,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting excessive gestational weight gain. The risk of predicting the occurrence of EGWG is quantifed as the number of points marked on the axis, the score determined by each variable axis is the number corresponding to the value vertical on the total points scale, and projected the sum of all variables onto the bottom axis, yielding a personalized EGWG risk for each pregnant women\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3921018/v1/2983431f8ff4467474eae482.png"},{"id":51237968,"identity":"a7dc2ea8-79ba-40d2-a39a-28df3d2ea453","added_by":"auto","created_at":"2024-02-16 16:47:27","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49247,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of risk factor prediction model for EGWG in modeling set. the ROC curve(AUC) of the model was 0.885, when the risk probability is 0.404 as the cutof point,the validation model’s sensitivity and specifcity were 0.839 and 0.778, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3921018/v1/d5075083f025c7205f15a62c.jpeg"},{"id":51237970,"identity":"6b40f5ca-f409-441a-8fc3-94d4bc48fcf9","added_by":"auto","created_at":"2024-02-16 16:47:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47519,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of risk factor prediction model for EGWG in validation set.the ROC curve(AUC) of the model was 0.853, when the risk probability is 0.648 as the cutof point,the validation model’s sensitivity and specifcity were 0.729 and 0.864, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3921018/v1/be7ff58579d7692ae86649f2.jpeg"},{"id":62020465,"identity":"a7d499de-4df7-42c7-ae74-3c0d40d43cf2","added_by":"auto","created_at":"2024-08-08 09:34:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1363597,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3921018/v1/8c8800b2-fa67-45f7-a43a-72212246f8e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk prediction of excessive gestational weight gain based on a nomogram model","fulltext":[{"header":"Background","content":"\u003cp\u003eGestational weight gain (GWG) refers to the change in weight from before pregnancy to before delivery [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. When GWG exceeds the upper limit of the recommended weight gain range, it is called excessive gestational weight gain (EGWG) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The global incidence rate of EGWG is approximately 47% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. EGWG not only increases the risk of adverse perinatal outcomes such as gestational hypertension, gestational diabetes, dystocia, postpartum haemorrhage, postpartum depression, and postpartum obesity but also increases long-term complications in the offspring, such as obesity, cardiovascular diseases, metabolic diseases, and respiratory diseases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This condition has become a major global public health concern. Therefore, the early identification of EGWG risk groups and accurate intervention are crucial for effectively controlling weight gain and reducing the incidence of EGWG.Currently, most studies on pregnancy weight gain, both domestically and internationally, focus on its effects on delivery outcomes, maternal and infant health [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], intervention methods for pregnancy weight management [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and influencing factors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Through this study, we have not only developed a novel risk-prediction model for pregnant women in China but also offered valuable insights for international research conducted within diverse cultural, economic, and social contexts. Although foreign models exist [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], their applicability to Chinese women remains uncertain due to distinct regional, racial, economic, cultural, dietary, and lifestyle variations. Traditional dietary concepts may influence pregnant women's acceptance of high-calorie, high-fat foods. Work and rest habits in China differ significantly from those of western countries. For instance, certain traditional beliefs can affect pregnant women's attitudes towards rest and work during pregnancy. Social and cultural factors such as family structure, social pressure, and marital attitudes play pivotal roles in the physical health and weight management of pregnant women. In China, family responsibilities and social support can have a distinct influence on lifestyle choices and weight control during pregnancy. Bridging this gap is essential for tailoring interventions to China's unique conditions. This study addresses the absence of China-specific EGWG prediction tools and aims to construct a model based on a comprehensive review of domestic and foreign literature. The goal of this study was to facilitate the early identification of EGWG risk groups and the formulation of targeted prevention strategies, recognising the nuanced factors affecting pregnant Chinese women.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and sample size determination\u003c/h2\u003e \u003cp\u003eConvenience sampling was used to select women in their second trimester of pregnancy who had records and regular prenatal examinations at the Obstetrics Clinic of the Second Xiangya Hospital in Changsha, Hunan Province, from January to March 2023. The survey included pregnant women whose body weights were monitored before full-term delivery. The inclusion criteria were age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, pregnancy between 14 and 27\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e weeks, and live intrauterine birth. The exclusion criteria were twin or multiple pregnancies; mental illness or cognitive impairment; severe anaemia, diabetes, hypertension, thyroid function complications, heart, liver, kidney, or lung diseases, or other pathological conditions affecting weight gain during pregnancy; and inability to complete the investigation independently. Additionally, dropouts and patients lost to follow-up or who experienced early pregnancy termination were excluded.\u003c/p\u003e \u003cp\u003ePrevious studies [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] have shown that EGWG has 4\u0026ndash;9 independent risk factors; therefore, it was speculated that no more than 13 risk factors will be included in the risk-prediction model constructed in this study. According to the sample size requirements of the prediction model, at least 5\u0026ndash;10 samples [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] were needed for each risk factor. The incidence of EGWG was 47% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Accounting for a potential 20% sample loss, the required sample size was approximately (13\u0026times;5\u0026thinsp;\u0026divide;\u0026thinsp;0.47)\u0026thinsp;\u0026divide;\u0026thinsp;0.8\u0026thinsp;=\u0026thinsp;173. According to the sample size requirement [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] of 7:3 for the modelling and validation groups, 74 samples were required for the validation group. Finally, 306 patients were included in this study, with 214 and 92 patients in the modelling and validation groups, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMaterials\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eGeneral information questionnaire\u003c/h2\u003e \u003cp\u003ePossible risk factors for EGWG were identified by reviewing the domestic and foreign literature using evidence-based methods and expert consultations. A self-developed questionnaire was administered to collect general information. The questionnaire included questions on maternal age, pre-pregnancy body mass index (BMI), place of residence, education level, family monthly income, marital status, employment status, parity, smoking, drinking, dietary patterns, and dietary behaviour.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eProtective motivation for weight management during pregnancy questionnaire\u003c/h2\u003e \u003cp\u003eOur research group developed a questionnaire based on the theoretical framework of protection motivation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Its main purpose was to assess the level of motivation of pregnant women to maintain their weight during pregnancy and their understanding of weight management. The questionnaire consisted of six dimensions: threat perception, response efficacy, self-efficacy, internal and external rewards, and reaction costs. It included 32 items, with higher scores indicating a higher level of protection motivation and a greater likelihood of adopting weight management strategies. The overall Cronbach's α coefficient for the questionnaire was 0.894, and the α coefficient for each dimension ranged from 0.652 to 0.915. The overall test-retest reliability of the questionnaire was 0.947, with each dimension having test-retest reliability ranging from 0.846 to 0.956.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBody image scale for pregnant women\u003c/h2\u003e \u003cp\u003eThe Body Image in Pregnancy Scale (BIPS) was developed by Brittany Watson [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and translated into Chinese by Weijia Sun [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] to assess the self-body image of pregnant women in an academic context. The BIPS comprises nine dimensions (35 items): body part dissatisfaction, physical dissatisfaction, dissatisfaction with appearance concerns, dissatisfaction with facial features, attraction to the opposite sex, weight control due to appearance, appearance over body function, appearance-related avoidance behaviour, and dissatisfaction with physiological changes during pregnancy. The total score ranges from 11 to 195, with higher scores indicating greater severity of the body image disorder. The Cronbach's α coefficient of the scale was 0.861, and the Cronbach's α coefficient for each dimension ranged from 0.552 to 0.905.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSocial Support Rating Scale\u003c/h2\u003e \u003cp\u003eThe Social Support Rating Scale (SSRS) was developed by Xiao Shuiyuan [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] to evaluate social support received by pregnant women in an academic context. The scale was divided into three dimensions: objective support (three items), subjective support (four items), and support utilisation (three items), resulting in a total of 10 items. The sum of the scores for each dimension constitutes the total score, with higher scores indicating higher levels of social support. The Cronbach's α coefficient of the scale was 0.896, and it has been widely utilised in China since 1987.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEdinburgh Postnatal Depression Scale\u003c/h2\u003e \u003cp\u003eThe self-rating Edinburgh Postnatal Depression Scale (EPDS) was developed by Cox et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and translated into Chinese by Guo Xiujing [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] to assess the level of depression among pregnant women. The scale consists of 10 items, with a total score range of 0 to 30. A score of \u0026le;\u0026thinsp;5 indicates no or very mild depression, 6\u0026ndash;9 indicates mild depression, and \u0026ge;\u0026thinsp;10 indicates moderate or severe depression. The Cronbach's α coefficient of the scale was 0.76. Studies have demonstrated that the EPDS can be used not only to screen for postpartum depression but also for prenatal depression [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003ePerceived Stress Scale\u003c/h2\u003e \u003cp\u003eThe Perceived Stress Scale (PSS-10), developed by Cohen et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in 1983 and translated into Chinese by Yang et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], is widely used in psychological assessments to measure individuals\u0026rsquo; subjective stress levels. The scale consisted of 10 items. The total score ranges from 0 to 40, with higher scores indicating higher levels of perceived stress. The Cronbach's α coefficient of the scale was 0.734.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePhysical activity during pregnancy\u003c/h2\u003e \u003cp\u003eThe Pregnancy Physical Activity Questionnaire (PPAQ) developed by Chesan-Taber et al. and translated into Chinese by Yan et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], was used to assess the duration, frequency, and intensity of physical activity during pregnancy. The PPAQ includes 31 items, including home care activities (14 items), transportation (4 items), sports and exercise (8 items), and occupational activities (5 items). The 31 items were classified based on the metabolic equivalent (MET) values for energy expenditure into sedentary activities (\u0026lt;\u0026thinsp;1.5 METs), low-intensity physical activity (1.6\u0026ndash;2.9 METs), and moderate-to-vigorous-intensity physical activity (\u0026ge;\u0026thinsp;3.0 METs). Each activity was further categorised into six levels, considering duration and frequency, and the corresponding time-weighted coefficients were calculated. The total energy expenditure for each activity was determined by multiplying the energy consumption by the time-weight coefficient. A higher total energy expenditure indicates greater physical activity during pregnancy. The content validity index of the questionnaire was 0.940 and the test-retest reliability was 0.944. The 2020 World Health Organization (WHO) guidelines [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] on physical activity and sedentary behaviour recommend that pregnant women without contraindications should accumulate at least 150 minutes of moderate-intensity physical activity per week. In this study, engaging in activities with an energy expenditure of 3.0\u0026ndash;6.0 METs for more than 2.5 hours per week was defined as meeting the exercise requirements, whereas engaging in activities with an energy expenditure of 3.0\u0026ndash;6.0 METs for less than 2.5 hours per week was defined as inadequate exercise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of EGWG\u003c/h2\u003e \u003cp\u003eAccording to the recommendations for GWG, which represent the first industry standard for GWG in China [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], optimal GWG ranges have been established based on pre-pregnancy BMI categories. These categories include underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal weight (18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;24.0 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (24.0 kg/m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;28.0 kg/m\u003csup\u003e2\u003c/sup\u003e), and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28.0 kg/m), with corresponding optimal GWG ranges of 11.0\u0026ndash;16.0, 8.0\u0026ndash;14.0, 7.0\u0026ndash;11.0, and 5.0\u0026ndash;9.0 kg, respectively [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. EGWG was defined as the actual GWG surpassing the established optimal GWG values for the respective BMI categories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eProcedures and ethical considerations\u003c/h2\u003e \u003cp\u003e The hospital\u0026rsquo;s ethics committee approved this study. Prior to participation, all participants provided written informed consent. To assess the feasibility of the study and identify potential data collection issues, a preliminary experiment involving 10 eligible participants was conducted after obtaining ethical approval with no reported problems.\u003c/p\u003e \u003cp\u003eThe data collectors were uniformly trained to ensure accuracy and consistency of data collection. A face-to-face questionnaire survey was conducted from 14 to 27\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e weeks of gestation when pregnant women were admitted to the Department of Obstetrics and Gynaecology. The survey included a general information questionnaire, pregnancy weight management and protection motivation questionnaire, and the SSRS, BIPS, EPDS, PSS-10, and PPAQ. The investigator immediately collected the questionnaires, checked the authenticity of the data, and eliminated invalid questionnaires with inconsistent options or all the same options. A secondary check by another team member ensured the accuracy of the collected data and recalculated the scores. Pre-pregnancy weight was determined as the average weight during the first 3 months of pregnancy extracted from electronic medical records. Weight before delivery, measured a week before delivery, was obtained by an investigator on the study team before hospitalisation. A total of 321 questionnaires were distributed and 314 valid questionnaires were returned, for an effective recovery rate of 97.8%. Questionnaires with incomplete data were considered invalid and excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData entry was conducted using Excel, statistical analyses were performed using SPSS version 26.0, and the nomogram model was constructed using R software version 4.3.1. Categorical data are summarised as counts and percentages, and group comparisons were conducted using the chi-square test. Normally distributed continuous data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and analysed using the \u003cem\u003et\u003c/em\u003e-test. For non-normally distributed continuous data, the median and interquartile range were reported, and group comparisons were performed using the Mann-Whitney \u003cem\u003eU\u003c/em\u003e test. Independent risk factors were identified using binary logistic regression to construct a risk-prediction model. Model fitting and prediction performance were assessed using the Hosmer-Lemeshow test and the area under the receiver operator characteristic (ROC) curve (AUC). The optimal threshold for the model was determined using the maximum Youden index. The model performance was evaluated using sensitivity, specificity, and accuracy rates. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSample and demographic characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 214 pregnant women were included in the modelling group. The mean age was 33years (range, 25-43 years), and the gestational age was 15-27+4 weeks. EGWG occurred in 106 pregnant women, for an incidence of 49.53%. Additional demographic details are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate analysis of risk factors in the modelling group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were statistically significant differences between the EGWG group and the non-EGWG group in pre-pregnancy BMI, previous parity, eating in front of a screen, frequency of weekly consumption of sugar-sweetened beverages/desserts/western fast food, Pregnancy Weight Management and Protection Motivation Questionnaire score, BIPS score, and time of daily moderate-intensity physical activity (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), as shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate logistic regression analysis of modelling group and establishment of risk-prediction model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistically significant factors in the univariate analysis were used as independent variables, and the occurrence of EGWG was used as the dependent variable in the logistic regression analysis. The assignments of independent variables are listed in Table 2. The results of logistic multivariate analysis showed that pre-pregnancy BMI classified as overweight or obesity, no previous delivery (primiparas), eating in front of a screen, consumption of sugar-sweetened beverages/desserts/western fast food more than five times per week, Pregnancy Weight Management and Protection Motivation Questionnaire score, BIPS score, and daily moderate-intensity physical activity time were the main influencing factors for EGWG, as shown in Table 3. Finally, the prediction model was established as follows: P = 1/[1 + exp (-2.259 + 0.979 \u0026times; overweight pre-pregnancy BMI +1.348 \u0026times; obese pre-pregnancy BMI + 1.636 \u0026times; first birth + 1.721 \u0026times; eating in front of a screen + 1.907 \u0026times; consumption of sugar-sweetened beverages/desserts/western fast food \u0026gt;5 times/week -0.022 \u0026times; score of the questionnaire on the motivation of weight management and protection during pregnancy + 0.031 \u0026times; BIPS score -1.451 \u0026times; duration of daily moderate-intensity physical activity)]. A nomogram was constructed based on the seven predictors screened using logistic regression, as shown in Figure 1. Each predictor had a corresponding score based on the scoring criteria. The total score of the seven factors projected onto the risk of EGWG location represented the prediction probability of EGWG, where a higher score indicated an elevated risk of EGWG.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Hosmer-Lemeshow test results showed that \u0026chi;2=5.090 (P=0.748), and there was no significant difference between the predicted value and the observed value, suggesting that the model fitted well. The AUC was used to test the sensitivity and specificity of the model, and the maximum Youden index was used to determine the best critical value for the model [11]. The results showed that the AUC of the model was 0.885 (95% confidence interval [CI]: 0.843-0.928). A maximum Youden index of 0.617 indicated that the optimal critical value for the prediction model was 0.404. The corresponding sensitivity and specificity values were 83.96% and 77.78%, respectively (Fig. 2).\u003c/p\u003e\n\u003cp\u003eFigure 1 Nomogram for predicting excessive gestational weight gain. The risk of predicting the occurrence of EGWG is quantifed as the number of points marked on the axis, the score determined by each variable axis is the number corresponding to the value vertical on the total points scale, and projected the sum of all variables onto the bottom axis, yielding a personalized EGWG risk for each pregnant women\u003c/p\u003e\n\u003cp\u003eFigure 2 \u0026nbsp; ROC curve of risk factor prediction model for EGWG in modeling set. the ROC curve(AUC) of the model was 0.885, when the risk probability is 0.404 as the cutof point,the validation model\u0026rsquo;s sensitivity and specifcity were 0.839 and 0.778, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInternal verification of prediction model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the prediction model internally, a validation group comprising 92 pregnant women was selected. Pregnant women\u0026apos;s data were input into the prediction model formula to assess their predictive efficacy. The AUC for the validation group was determined to be 0.853 (95% CI: 0.776-0.930). The optimal critical value identified at a maximum Youden index of 0.593 was 0.648. At this critical value, the sensitivity of the ROC curve was 72.9% and the specificity was 86.4%, as depicted in Figure 3. In the validation group, EGWG occurred in 48 patients (52.17%). Of these, the model correctly identified EGWG in 40 cases but misjudged it in eight cases (83.33% accuracy). For cases in which EGWG did not occur (44 cases), the model correctly identified 34 cases and misjudged 10 cases, resulting in a specificity of 77.27%. The overall accuracy of the model for the validation group was 80.43%.\u003c/p\u003e\n\u003cp\u003eFigure 3 \u0026nbsp; ROC curve of risk factor prediction model for EGWG in validation set.the ROC curve(AUC) of the model was 0.853, when the risk probability is 0.648 as the cutof point,the validation model\u0026rsquo;s sensitivity and specifcity were 0.729 and 0.864, respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe intricate nature of the risk factors linked to EGWG spans various dimensions, as evidenced by numerous studies worldwide. These dimensions encompass physiological, psychological, cognitive, social, and health behaviours. In our investigation, logistic regression analysis was used to identify predictive factors associated with EGWG.\u003c/p\u003e \u003cp\u003eRegarding physiological factors, our findings underscore that pre-pregnancy overweight (odds ratio [OR]\u0026thinsp;=\u0026thinsp;2.662) and obesity (OR\u0026thinsp;=\u0026thinsp;3.851) are independent risk factors for EGWG. These results are consistent with those of both domestic and international studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Individuals frequently contend with an obesogenic dietary environment against the backdrop of rapid global economic growth. Moreover, the prevalent sedentary lifestyle in modern society significantly amplifies overweight and obesity rates among women of reproductive age [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This highlights the importance of prioritising and managing overweight or obese individuals during prenatal care. Initiating weight management interventions ideally before conception is crucial among women of childbearing age. Proactive measures of this nature hold promise for reducing the incidence of EGWG.\u003c/p\u003e \u003cp\u003eAmong the psychological and cognitive factors, body image emerged as a significant predictor of EGWG (OR\u0026thinsp;=\u0026thinsp;1.031), whereas the level of protective motivation towards maternal body quality management was found to be a protective factor against EGWG (OR\u0026thinsp;=\u0026thinsp;0.979). Body image refers to the way individuals perceive their physical appearance and functionality. It is a complex concept shaped by perceptual experiences, beliefs, and emotional attitudes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Fealy et al. used a psychosocial assessment questionnaire to identify women at risk of EGWG. The questionnaire included four items that focused on body image as a predictive factor [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Another prospective cohort study found that pregnant women with body image disorders were more likely to experience EGWG [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which supports the findings of this study. It is worth noting that there is limited research on the topic of pregnant women's body image conducted by Chinese scholars, as the field is still in its early stages [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Therefore, future efforts should focus on understanding pregnant women\u0026rsquo;s body image experiences and developing effective intervention strategies aimed at enhancing body perception, facilitating adaptation to physical changes during the perinatal period, and promoting overall well-being. Protective motivation towards maternal body quality management aims to stimulate an individual\u0026rsquo;s awareness of weight management during pregnancy from an internal perspective to encourage healthy behaviours and improve self-management capabilities related to weight control. Several studies have indicated that the level of protective motivation can moderately predict pregnant women's adherence to health behaviours related to body quality management [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A recent empirical study conducted by You et al. on weight management during pregnancy emphasised the significance of motivation as a key determinant with a direct impact on weight management, highlighting the need for strong motivation among pregnant women to initiate and sustain desired behaviours [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], which aligns with the findings of this study. However, insufficient attention has been paid to assessing the level of protective motivation in pregnant women and limited research has explored its association with weight gain. Consequently, healthcare professionals should incorporate targeted interventions aimed at enhancing pregnant women's awareness of and intrinsic motivation for self-weight management when formulating strategies for effective weight control.\u003c/p\u003e \u003cp\u003eIn terms of social factors, primiparity was identified as an independent risk factor for EGWG, with an OR of 5.134. Primiparous women exhibit a significantly higher risk of EGWG than multiparous women, which is consistent with the conclusions drawn by numerous domestic and international scholars [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This disparity may stem from a lack of reproductive experience among primiparous individuals, leading to limited awareness and understanding of weight management. Consequently, targeted health education and guidance tailored specifically for primiparous pregnant women has become imperative compared to those with previous childbirth experiences.\u003c/p\u003e \u003cp\u003eDiet and physical activity play crucial roles in influencing pregnancy weight. Numerous studies have consistently established significant correlations between the western [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], beverage confectionery [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and ultra-processed food [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] dietary patterns and EGWG. These patterns involve a high consumption of sugar, fat, energy-dense foods, and ultra-processed products. Our study revealed an independent increase in EGWG risk (OR\u0026thinsp;=\u0026thinsp;6.733) associated with the consumption of sugar-sweetened beverages, desserts, and western fast food five or more times per week. This aligns with the findings of the dietary screening questionnaire developed by Hrolfsdottir et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], who identified the consumption of sugar/artificially sweetened beverages five or more times per week as a dietary risk predictor. Such dietary habits, rich in added sugars and fats, contribute to liver lipid synthesis, elevated serum cholesterol levels, and visceral fat accumulation, thereby increasing the likelihood of overweight or obesity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, this study found that pregnant women engaging in screen-based eating behaviours faced a higher risk of EGWG (OR\u0026thinsp;=\u0026thinsp;5.588), consistent with the findings of McDonald et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Eating while using mobile phones or watching TV may lead to overeating because of distraction, reduced physical activity time, and increased sedentary behaviour, thereby elevating the likelihood of EGWG. Proper dietary nutrition during pregnancy is crucial for weight control, emphasising the importance of promoting healthy dietary patterns and reducing the intake of high-sugar, high-fat, high-energy-density, and ultra-processed foods. Regular assessment of dietary behaviours in pregnant women is essential to promptly address unhealthy habits.\u003c/p\u003e \u003cp\u003eIn our study, the duration of moderate-intensity physical activity during pregnancy was negatively associated with EGWG (OR\u0026thinsp;=\u0026thinsp;0.244). Consistent with our findings, a study on exercise interventions for pregnant women revealed that those implementing interventions such as regular exercise and moderate-intensity physical activity during pregnancy exhibited a significantly lower incidence of EGWG than those without such interventions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Bao et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] also emphasised that the duration of moderate or high physical activity is an independent influencing factor for weight gain in the third trimester, which is consistent with our study's results.\u003c/p\u003e \u003cp\u003eThe WHO physical activity and sedentary behaviour guidelines [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] explicitly state that pregnant women without contraindications should achieve at least 150 minutes of moderate-intensity physical activity per week to meet recommended standards. Our study corroborates this, highlighting that fewer hours of moderate-intensity physical activity during pregnancy are associated with a higher risk of EGWG. Consequently, achieving weight control in pregnant women involves balancing energy intake (eating) and expenditure (exercise).\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study had certain limitations. First, the study participants were recruited exclusively from a single tertiary hospital, which may limit the generalisability of the findings to a broader population. This poses a potential problem for sample representation, because the results may not accurately reflect the characteristics of the entire target population. Additionally, although internal validation indicated promising results, external validation was not conducted to verify the performance of the model on an independent dataset. Future research should involve larger sample sizes and multicentre studies to enhance the predictive accuracy and generalisability of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHigh pre-pregnancy BMI (overweight or obesity), being a first-time mother, eating in front of a screen, consuming sugar-sweetened beverages and desserts, frequent consumption of western fast food (\u0026gt;\u0026thinsp;5 times per week), and having a maternal body image disorder were identified as risk factors for EGWG. However, a high level of motivation for weight management during pregnancy and engaging in moderate-intensity physical activity for a longer duration were protective factors against EGWG. In this study, we developed a risk-prediction model for EGWG in pregnant women using a nomogram. The model demonstrated high sensitivity and specificity, a good fit, and a reliable predictive effect. It can serve as a convenient screening tool for nursing staff to identify individuals at risk for EGWG early and implement accurate interventions, thereby reducing the incidence of EGWG and promoting the health of both mothers and children.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGestational Weight Gain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEGWG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExcessive Gestational Weight Gain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Perceived Stress Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEPDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe self-rating Edinburgh Postnatal Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Body Image in Pregnancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Social Support Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPAQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePregnancy Physical Activity Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMetabolic Equivalent\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operator characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Review Committee of Nursing and Behavioral Medicine Research, Xiangya School of Nursing, Central South University (no. E2023183). This study was conducted in accordance with the guidelines of the Declaration of Helsinki. Participation in the study was voluntary, and all participants provided written informed consent before participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are not publicly available due to the metadata containing information that could compromise the patients but are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Hunan Province[grant number 2023JJ30807].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors contributed to the work and approved the fnal version of this manuscript. This study was designed by LYH, XHZ. JJT,SL and MY performed the statistical analysis. LYH wrote the manuscript. XHZ reviewed and edited manuscript. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank The Second Xiangya Hospital, Central South University for letting us to conduct this study. Our deepest gratitude also goes to study participants, data collectors, and supervisor for their invaluable support to make this study real.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.\u003c/p\u003e\n\u003cp\u003e2Clinical Nursing Teaching and Research Section, The Xiangya School of Nursing, Central South University, Changsha, Hunan, China.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNational Health Commission of the People's Republic of China. Standard of recommendation for weight gain during pregnancy period:WS/T801-2022[EB/OL].(2022-7-28)[2022-11-01]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nhc.gov.cn/wjw/fyjk/202208/864ddc16511148819168305d3e576de9.shtml\u003c/span\u003e\u003cspan address=\"http://www.nhc.gov.cn/wjw/fyjk/202208/864ddc16511148819168305d3e576de9.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChinese Nutrition Society. Weight monitoring and evaluation during pregnancy period of Chinese women TCN/SS009-2021[EB/OL].(202-09-01)[2023-10-01]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cnsoc.org/otherNotice/392100200.html\u003c/span\u003e\u003cspan address=\"https://www.cnsoc.org/otherNotice/392100200.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldstein RF, Abell SK, Ranasinha S et al. Association of gestational weight gain with maternal and infant outcomes:a systematic review and meta-analysis[J]. JAMA 2017,317(21):2207\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao X, Yang HX. Influence of maternal obesity and excessive weight gain during pregnancy outcome and longterm health in offspring:a review[J]. 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Arch Public Health. 2022;80(1):129.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcdonald SD, Yu ZM, Van BS et al. Prediction of excess pregnancy weight gain using psychological, physical, and social predictors: a validated model in a prospective cohort study[J]. PLoS One 2020,15(6):e233774.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen ZK, Xing Y, Tong XM et al. Analysis of the influencing factors and the adverse effect of gestational weight gain maternal and infant health in Beijing[J]. Chin J Health Manage 2021,15(03):284\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu LL, Niu HY, Han XY, et al. The development and application of a risk prediction model for extracorporeal circuit clotting during continuous renal replacement therapy[J]. Chin J Nurs. 2023;58(15):1845\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen YY, Zhang ZM, Zuo QQ, et al. Construction and validation of a prediction model for the risk of cognitive frailty among the elderly in a community[J]. Chin J Nurs. 2022;57(02):197\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng MY, Zhou XH, HOU YP et al. Development and psychometric evaluation of protective motivation for weight management during pregnancy questionnaire[J]. Chin Nurs Manag 2021,21(08):1169\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatson B, Fuller-Tyszkiewicz M, Broadbent J et al. Development and Validation of a Tailored Measure of Body Image for Pregnant Women[J]. Psychol Assess, 2017, 29(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun WJ. Reliability and validity of the Chinese version of Body Image in Pregnancy Scale[D]. Changchun:Jilin University; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao SY. Theoretical basis and research application of Social Support Rating Scale[J]. J Chin Psychol Med 1994,4(2):98\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox JL, Holden R, Detection JM. Sagovsky of postnatal depression:development of the 10-item Edinburgh Postnatal Depression Scale[J]. Br J Psychiatry. 1987;150:782\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo XJ, Wang YQ, Liu Y, et al. Study on the optimal critical value of the Edinburgh Postnatal Depression Scale in the screening of antenatal depression[J]. Chin J Nurs. 2009;44(9):808\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen S, Kamarck T, Mermelstein R. A global measure of perceived stress[J]. J Health Soc Behav. 1983;24:385\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang TZ, Huang HT. An epidemiological study on stress among urban residents in social transition period[J]. Chin J Epidemiol 2003,24(9):11\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhao Y, Dong SW, et al. Reliability and validity of the Chinese version of the Pregnancy Physical Activity Questionnaire(PPAQ)[J]. Chin J Nurs. 2013;48(9):825\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO guidelines on physical activity and sedentary behaviour[M]. Geneva:World Health Organization; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaray SM, Sumption LA, Pearson RM, et al. Risk factors for excessive gestational weight gain in a UK population: a biopsychosocial model approach[J]. BMC Pregnancy Childbirth. 2021;21(1):43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDolin CD, Gross RS, Deierlein AL, et al. Predictors of gestational weight gain in a low-income hispanic population: sociodemographic characteristics, health behaviors, and psychosocial stressors[J]. Int J Environ Res Public Health. 2020;17(1):352.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi KM, Li ZZ, Zhao Y et al. Hot topics and trends in CiteSpace-based research on maternal body image[J]. J Nurses Train 2023,38(09):816\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFealy S, Leigh L, Hazelton M, et al. Translation of the weight-related behaviours questionnaire into a short-form psychosocial assessment tool for the detection of women at risk of excessive gestational weight gain[J]. Int J Environ Res Public Health. 2021;18(18):9522.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinami J-PNA, Eitoku M. Lack of concern about body image and health during pregnancy linked to excessive gestational weight gain and small-for-gestational-age deliveries: the Japan Environment and Children\u0026rsquo;s Study[J]. BMC Pregnancy Childbirth. 2021;21(1):396.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu QY, Wang Z, Xiao TY et al. Research progress on maternal body dissatisfaction[J]. Chin Nurs Res 2022,36(04):679\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou H, Wang YY, Zhang C, et al. Empirical validation of the information-motivation-behavioral skills model of gestational weight management behavior: a framework for intervention. BMC Public Health. 2023;23:130.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira LB, Lobo CV, Miranda AEDS et al. Dietary patterns during pregnancy and gestational weight gain: a systematic review[J]. Rev Bras Ginecol Obstet 2022,44(05):540\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai CJ, Dong HL, Pang XX et al. A Prospective Study of the Relationship Between Dietary Patterns during the Second Trimester of Pregnancy and Gestational Weight Gain[J].J Sichuan Univ (Med Sci Edi),2020,51(06):822\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCummings JR, Lipsky LM, Schwedhelm C, et al. Associations of ultra-processed food intake with maternal weight change and cardiometabolic health and infant growth[J]. Int J Behav Nutr Phys Act. 2022;19(1):61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHrolfsdottir L, Halldorsson TI, Birgisdottir BE, et al. Development of a dietary screening questionnaire to predict excessive weight gain in pregnancy[J]. Matern Child Nutr. 2019;15(1):e12639.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBao YH, Wu C, Zhao RP et al. Moderate-to-vigorous physical activities and gestational weight gains during the second and last trimesters of pregnancy[J]. J Sichuan Univ (Med Sci Edi) 018,49(6):938\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Single factor analysis results of excessive gestational weight gain\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-egwg group (\u003cem\u003en\u003c/em\u003e=108)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEGWG group (\u003cem\u003en\u003c/em\u003e=106)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge [years,\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eP25\u003c/em\u003e,\u003cem\u003eP75\u003c/em\u003e)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e33 (29,38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e33 (29,38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.173 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-pregnancy BMI[example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e14.153\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eLow weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e15 (13.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e11 (10.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e53 (49.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e29 (27.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e27 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e42 (39.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e13 (12.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e24 (22.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of education [example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e4.921\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eJunior high school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e10 (9.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e18 (16.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eHigh school or secondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e17 (15.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e23 (21.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eCollege or bachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e71 (65.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e57 (53.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaster\u0026apos;s degree or above\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e10 (9.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e8 (7.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status [example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e2.626\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e4 (3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e90 (83.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e90 (84.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eDivorce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e5 (4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e8 (7.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eSeparation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e8 (7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e6 (5.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence [example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eTowns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e84 (77.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e81 (76.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e24 (22.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e25 (23.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly income [example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e3.383\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e14 (12.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e10 (9.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e4000-8000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e30 (27.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e21 (19.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e8001-15000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e55 (50.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e62 (58.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 15000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e9 (8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e13 (12.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious parity [example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e31.454\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e0 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e17 (15.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e55 (51.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e1 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e46 (42.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e24 (22.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e2 or more times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e45 (41.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e27 (25.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Note :* denotes z-score; # is the c\u0026sup2; value.\u003c/p\u003e\n\u003cp\u003eTable 1(Continued) Single factor analysis results of excessive gestational weight gain\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003eNon-egwg group (\u003cem\u003en\u003c/em\u003e=108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\"\u003e\n \u003cp\u003eEGWG group (\u003cem\u003en\u003c/em\u003e=106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\"\u003e\n \u003cp\u003eTest statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking [example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e105 (97.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e103 (97.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking [example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e108 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e106 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e0 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEating in front of a screen [example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e28.563\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e71 (65.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e31 (29.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e37 (34.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e75 (70.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHabit of eating snacks or snacks [example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.160\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e60 (55.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e56 (52.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e48 (44.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e50 (47.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsumption of sugar-sweetened beverages,desserts,\u003c/strong\u003e\u003cstrong\u003eand western fast food\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;[example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e34.306\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;1 time per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e33 (30.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e13 (12.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eOnce or twice a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e36 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e17 (16.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e3-4 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e25 (23.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e28 (26.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;5 times per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e14 (12.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e48 (45.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSS-10 score [score,\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eP25\u003c/em\u003e,\u003cem\u003eP75\u003c/em\u003e)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e14 (10.25, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e14 (10, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.065 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEPDS score [score,\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eP25\u003c/em\u003e,\u003cem\u003eP75\u003c/em\u003e)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e8 (5, 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e10 (5, 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.468 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtective Motivation for Weight Management during Pregnancy Questionnaire score [score,\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eP25\u003c/em\u003e,\u003cem\u003eP75\u003c/em\u003e)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e125.5 (109.25, 140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\"\u003e\n \u003cp\u003e114.5 (98127).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e3.892 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIPS score [score,\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eP25\u003c/em\u003e,\u003cem\u003eP75\u003c/em\u003e)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e91.5 (82106.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e122 (90141).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e5.899 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSSRS score [score,\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eP25\u003c/em\u003e,\u003cem\u003eP75\u003c/em\u003e)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e38.5 (30.25, 45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e38 (31,44.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.137 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote :* denotes z-score; # is the c\u0026sup2; value.\u003c/p\u003e\n\u003cp\u003eTable 1(Continued) Single factor analysis results of excessive gestational weight gain\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003eNon-egwg group (\u003cem\u003en\u003c/em\u003e=108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\"\u003e\n \u003cp\u003eEGWG group (\u003cem\u003en\u003c/em\u003e=106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\"\u003e\n \u003cp\u003eTest statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal daily energy consumption [cards,\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eP25\u003c/em\u003e,\u003cem\u003eP75\u003c/em\u003e)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e32.33 (27.31, 35.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\"\u003e\n \u003cp\u003e31.57 (27.7, 36.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.151 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDaily physical activity time [hours,\u003cem\u003eM\u003c/em\u003e(\u003cem\u003eP25\u003c/em\u003e,\u003cem\u003eP75\u003c/em\u003e)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eDaily sedentary time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e20 (18.23, 21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\"\u003e\n \u003cp\u003e19.2 (17.6, 21.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e1.650 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eDaily light exercise time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e3.5 (1.83, 5.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\"\u003e\n \u003cp\u003e4 (2.28, 6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e1.116 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eDaily moderate exercise time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e1.15 (0.5, 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\"\u003e\n \u003cp\u003e0.65 (0.18, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e4.154 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eDaily heavy exercise time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\"\u003e\n \u003cp\u003e0 (0, 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\"\u003e\n \u003cp\u003e0 (0, 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.013 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhether the activity is up to standard [example (percentage,%)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e0.949\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eUnder par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e31 (28.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e37 (34.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.62676056338028%\" valign=\"top\"\u003e\n \u003cp\u003eUp to par\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp\u003e77 (71.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12676056338028%\" valign=\"top\"\u003e\n \u003cp\u003e69 (65.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091549295774648%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote :* denotes z-score; # is the c\u0026sup2; value. \u003cem\u003eBMI\u003c/em\u003e Body mass index,\u003cem\u003ePSS\u003c/em\u003e The Perceived Stress Scale,\u003cem\u003eEPDS\u0026nbsp;\u003c/em\u003eThe self-rating Edinburgh Postnatal Depression Scale,\u003cem\u003eBIPS\u0026nbsp;\u003c/em\u003eThe Body Image in Pregnancy\u003cem\u003e,SSRS\u003c/em\u003e The Social Support Rating Scale\u003c/p\u003e\n\u003cp\u003eTable 2 The assignment of the independent variable\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.136929460580916%\" valign=\"top\"\u003e\n \u003cp\u003eIndependent variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.863070539419084%\" valign=\"top\"\u003e\n \u003cp\u003eAssignment method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.136929460580916%\" valign=\"top\"\u003e\n \u003cp\u003ePre-pregnancy BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.863070539419084%\" valign=\"top\"\u003e\n \u003cp\u003eNormal weight =1, low weight =2, overweight =3, and obese =4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.136929460580916%\" valign=\"top\"\u003e\n \u003cp\u003ePrevious parity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.863070539419084%\" valign=\"top\"\u003e\n \u003cp\u003e0 = 1, 1 = 2, 2 or more =3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.136929460580916%\" valign=\"top\"\u003e\n \u003cp\u003eEating in front of a screen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.863070539419084%\" valign=\"top\"\u003e\n \u003cp\u003eNo =0, yes =1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.136929460580916%\" valign=\"top\"\u003e\n \u003cp\u003eConsumption of sugar-sweetened beverages, desserts, and western fast food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.863070539419084%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 1 time per week =1, 1-2 times per week =2, 3-4 times per week =3, \u0026gt; 5 times per week =4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.136929460580916%\"\u003e\n \u003cp\u003eProtective Motivation for Weight Management during Pregnancy Questionnaire score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.863070539419084%\"\u003e\n \u003cp\u003eOriginal value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.136929460580916%\"\u003e\n \u003cp\u003eBIPS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.863070539419084%\" valign=\"top\"\u003e\n \u003cp\u003eOriginal value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.136929460580916%\"\u003e\n \u003cp\u003eDuration of moderate intensity physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.863070539419084%\" valign=\"top\"\u003e\n \u003cp\u003eOriginal value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eBMI\u003c/em\u003e Body mass index, \u003cem\u003eBIPS\u003c/em\u003e The Body Image in Pregnancy\u003c/p\u003e\n\u003cp\u003eTable 3 Logistic regression analysis results of excessive gestational weight gain\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003evalues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\"\u003e\n \u003cp\u003eWald\u0026nbsp;c\u0026sup2; value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\"\u003e\n \u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e2.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e1.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e1.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-pregnancy BMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e9.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003eLow weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.183 ~ 2.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e4.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e2.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.043 ~ 6.797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e1.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e5.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e3.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.257 ~ 11.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious parity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e2 or more births\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e12.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e1 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.418 ~ 2.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e0 times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e1.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e9.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e5.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.849 ~ 14.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEating in front of a screen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e1.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e16.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e5.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.413 ~ 12.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e\u003cstrong\u003eConsumption of sugar-sweetened beverages, desserts, and western fast food\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e\u0026lt;1 time per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\"\u003e\n \u003cp\u003e12.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e1-2 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e1.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.465 ~ 4.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e3-4 times a week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e3.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e2.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.932 ~ 9.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e\u0026gt;5 times per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e1.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e10.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e6.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.165 ~ 20.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtective Motivation for Weight Management during Pregnancy Questionnaire score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e3.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.958 ~ 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIPS score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e12.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.014 ~ 1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.898032200357783%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate intensity physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.701252236135957%\" valign=\"top\"\u003e\n \u003cp\u003e1.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.311270125223613%\" valign=\"top\"\u003e\n \u003cp\u003e15.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.37567084078712%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.88908765652952%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.115 ~ 0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eBMI\u003c/em\u003e Body mass index, \u003cem\u003eBIPS\u003c/em\u003e The Body Image in Pregnancy\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pregnancy, Excessive Gestational Weight Gain, Risk-Prediction Model, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-3921018/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3921018/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eExcessive gestational weight gain is a global public health problem with serious and long-term effects on maternal and offspring health. Early identification of at-risk groups and interventions is crucial for controlling weight gain and reducing the incidence of excessive gestational weight gain. Currently, tools for predicting the risk of excessive gestational weight gain are lacking in China. This study aimed to develop a risk-prediction model and screening tool for the early identification of at-risk groups.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eConvenience sampling was used to select 306 pregnant women who underwent regular obstetric checkups at a tertiary-level hospital in China between January and March 2023. Logistic regression analysis was used to construct the risk-prediction model. The goodness of fit of the model was assessed using the Hosmer-Lemeshow test, and the predictive performance was evaluated using the area under the receiver operating characteristic (ROC) curve. R4.3.1 software was used to create a nomogram.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of excessive gestational weight gain was 49.53%. Logistic regression analysis revealed that prepregnancy overweight (odds ratio [OR]\u0026thinsp;=\u0026thinsp;2.662), obesity (OR\u0026thinsp;=\u0026thinsp;3.851), and primiparity (OR\u0026thinsp;=\u0026thinsp;5. 134); eating in front of a screen (OR\u0026thinsp;=\u0026thinsp;5.588); consumption of sugar-sweetened beverages, desserts, and western fast food (\u0026gt;\u0026thinsp;5 times per week) (OR\u0026thinsp;=\u0026thinsp;6.733); and pregnancy body image (OR\u0026thinsp;=\u0026thinsp;1.031) were risk factors for excessive gestational weight gain. Protective motivation to manage pregnancy body mass (OR\u0026thinsp;=\u0026thinsp;0.979) and duration of moderate-intensity physical activity (OR\u0026thinsp;=\u0026thinsp;0.234) were protective factors against excessive gestational weight gain. The area under the ROC curve of the model was 0.885, with a maximum Youden index of 0.617, optimal threshold of 0.404, sensitivity of 83.96%, and specificity of 77.78%. The model validation results showed a sensitivity, specificity, and accuracy of 83.33%, 77.27%, and 80.43%, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe risk-prediction model developed in this study proved to be effective, providing a valuable basis for early identification and precise intervention in individuals at risk of excessive gestational weight gain.\u003c/p\u003e","manuscriptTitle":"Risk prediction of excessive gestational weight gain based on a nomogram model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-16 16:47:22","doi":"10.21203/rs.3.rs-3921018/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cca91d60-b51d-4269-9085-70e6a7647579","owner":[],"postedDate":"February 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-08T09:26:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-16 16:47:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3921018","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3921018","identity":"rs-3921018","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}