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Preplanned Studies: Unhealthy Eating Behaviors During Pregnancy and Gestational Weight Gain — Huai’an City, Jiangsu Province, China, 2020–2021

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  • Summary

    What is already known about this topic?

    Maintaining a healthy diet and appropriate weight during pregnancy is crucial for both the expectant mother and the fetus. Unhealthy eating behaviors (UEBs) such as eating out frequently are becoming increasingly prevalent across the globe. However, there is a dearth of research investigating the relationship between UEBs and gestational weight gain (GWG) specifically in the context of Chinese women.

    What is added by this report?

    The study revealed that a majority of pregnant women reported experiencing one or more UEBs such as eating fast, eating three meals irregularly, eating away from home, and skipping breakfast. A positive association was also observed between the number of UEBs and elevated odds of experiencing excessive GWG.

    What are the implications for public health practice?

    The uptake of emerging UEBs is prevalent among pregnant women in China. It is recommended that healthy eating behavior become the focal point of gestational weight management in clinical practice. Moreover, preconception care should take into account customized health education and promotion programs.

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  • Funding: This study was funded by grants to XX from China Medical Board Open Competition Program (21-416), and grants to XZ from Jiangsu Maternal and Child Health Program (F201932), Key Medical Program of Jiangsu Health Commission (ZD2021044), Huai’an Science and Technology Program (HAB202043)
  • [1] Bi Y, Wang J, Duan YF, Pang XH, Jiang S, Zhao LY, et al. Gestational weight gain and associated factors of Chinese women in the second and third trimester of pregnancy in 2015. J Hyg Res 2022;51(3):392 − 6,416. http://dx.doi.org/10.19813/j.cnki.weishengyanjiu.2022.03.008 (In Chinese). CrossRef
    [2] CNS. Dietary guidelines for Chinese residents 2022. Beijing: People’s Medical Publishing House. 2022; p. 3. (In Chinese). 
    [3] Ohkuma T, Hirakawa Y, Nakamura U, Kiyohara Y, Kitazono T, Ninomiya T. Association between eating rate and obesity: a systematic review and meta-analysis. Int J Obes 2015;39(11):1589 − 96. http://dx.doi.org/10.1038/ijo.2015.96CrossRef
    [4] León-Muñoz LM, García-Esquinas E, Soler-Vila H, Guallar-Castillón P, Banegas JR, Rodríguez-Artalejo F. Unhealthy eating behaviors and weight gain: a prospective study in young and middle-age adults. Obesity 2016;24(5):1178 − 84. http://dx.doi.org/10.1002/oby.21477CrossRef
    [5] CNS. Weight monitoring and evaluation during pregnancy period of Chinese women: group standard T/CNSS 009-2021. 2021. https://www.cnsoc.org/otherNotice/392100200.html (In Chinese). https://www.cnsoc.org/otherNotice/392100200.html
    [6] Yang MT, Feng QY, Chen C, Chen SJ, Guo YS, Su DP, et al. Healthier diet associated with reduced risk of excessive gestational weight gain: a Chinese prospective cohort study. Mater Child Nutr 2023;19(3):e13397. http://dx.doi.org/10.1111/mcn.13397CrossRef
    [7] Xie JT, Han Y, Peng L, Zhang JJ, Gong XJ, Du Y, et al. BMI growth trajectory from birth to 5 years and its sex-specific association with prepregnant BMI and gestational weight gain. Front Nutr 2023;10:1101158. http://dx.doi.org/10.3389/fnut.2023.1101158CrossRef
    [8] Fowles ER, Timmerman GM, Bryant M, Kim S. Eating at fast-food restaurants and dietary quality in low-income pregnant women. West J Nurs Res 2011;33(5):630 − 51. http://dx.doi.org/10.1177/0193945910389083CrossRef
    [9] Maimaiti M, Zhao XY, Jia MH, Ru Y, Zhu SK. How we eat determines what we become: opportunities and challenges brought by food delivery industry in a changing world in China. Eur J Clin Nutr 2018;72(9):1282 − 6. http://dx.doi.org/10.1038/s41430-018-0191-1CrossRef
    [10] Kominiarek MA, Peaceman AM. Gestational weight gain. Am J Obstet Gynecol 2017;217(6):642 − 51. http://dx.doi.org/10.1016/j.ajog.2017.05.040CrossRef
  • TABLE 1.  Characteristics of pregnant women based on recommended GWG in Huai'an City, Jiangsu Province, China, 2020–2021.

    CharacteristicTotalInadequateAdequateExcessiveP-value*
    (N=8,218)(N=490)(N=3,143)(N=4,585)
    Age (mean±SD)28.2±4.828.0±5.228.2±4.828.2±4.80.503
    Pre-pregnancy weight (mean±SD)58.7±9.259.2±10.857.0±8.359.7±9.4<0.001
    Trimester weight (mean±SD)73.3±10.164.1±9.568.3±7.577.7±9.4<0.001
    Total GWG (mean±SD)14.6±5.54.8±3.711.2±2.218.0±4.4<0.001
    Pre-pregnancy BMI, n (%)<0.001
    Underweight638 (7.8)73 (14.9)292 (9.3)273 (6.0)
    Normal weight5,127 (62.4)239 (48.8)2,201 (70.0)2,687 (58.6)
    Overweight1,900 (23.1)132 (26.9)532 (16.9)1,236 (27.0)
    Obese553 (6.7)46 (9.4)118 (3.8)389 (8.5)
    Parity, n (%)0.575
    03,350 (40.8)198 (40.4)1,287 (40.9)1,865 (40.7)
    14,050 (49.3)239 (48.8)1,565 (49.8)2,246 (49.0)
    ≥2818 (10.0)53 (10.8)291 (9.3)474 (10.3)
    Education, n (%)0.002
    Middle school and below528 (6.4)46 (9.4)189 (6.0)293 (6.4)
    High school or technical secondary school4,758 (57.9)273 (55.7)1,766 (56.2)2,719 (59.3)
    Junior college and above2,932 (35.7)171 (34.9)1,188 (37.8)1,573 (34.3)
    Employment status, n (%)0.665
    Unemployed179 (2.2)10 (2.0)67 (2.1)102 (2.2)
    Employed or self-employed3,713 (45.2)217 (44.3)1,392 (44.3)2,104 (45.9)
    Others4,326 (52.6)263 (53.7)1,684 (53.6)2,379 (51.9)
    Distribution of family income (n, %)0.007
    Quartile 1 (lowest)1,964 (23.9)151 (30.8)749 (23.8)1,064 (23.2)
    Quartile 22,088 (25.4)115 (23.5)814 (25.9)1,159 (25.3)
    Quartile 32,113 (25.7)116 (23.7)775 (24.7)1,222 (26.7)
    Quartile 4 (highest)2,053 (25.0)108 (22.0)805 (25.6)1,140 (24.9)
    Residential area, n (%)0.046
    Urban5,663 (68.9)313 (63.9)2,177 (69.3)3,173 (69.2)
    Rural2,555 (31.1)177 (36.1)966 (30.7)1,412 (30.8)
    Physical Activity, n (%)0.687
    Rarely722 (8.8)48 (9.8)289 (9.2)385 (8.4)
    1–2 times/ week606 (7.4)32 (6.5)222 (7.1)352 (7.7)
    3–5 times/ week1,576 (19.2)88 (18.0)606 (19.3)882 (19.2)
    Everyday5,314 (64.7)322 (65.7)2,026 (64.5)2,966 (64.7)
    Number of UEBs, n (%)<0.001
    03,973 (48.4)234 (47.8)1,620 (51.5)2,119 (46.2)
    13,391 (41.3)206 (42.0)1,246 (39.6)1,939 (42.3)
    ≥2854 (10.4)50 (10.2)277 (8.8)527 (11.5)
    Eating fast, n (%)0.001
    No7,094 (86.3)433 (88.4)2,761 (87.8)3,900 (85.1)
    Yes1,124 (13.7)57 (11.6)382 (12.2)685 (14.9)
    Eating three meals regularly, n (%)0.108
    Regular8,002 (97.4)477 (97.3)3,075 (97.8)4,450 (97.1)
    Irregular216 (2.6)13 (2.7)68 (2.2)135 (2.9)
    Eating away from home, n (%)0.013
    Rarely4,859 (59.1)289 (59.0)1,921 (61.1)2,649 (57.8)
    ≥1 times/week3,359 (40.9)201 (41.0)1,222 (38.9)1,936 (42.2)
    Skipping breakfast, n (%)0.028
    Rarely7,666 (93.3)448 (91.4)2,958 (94.1)4,260 (92.9)
    ≥1 times/week552 (6.7)42 (8.6)185 (5.9)325 (7.1)
    Abbreviation: BMI=body mass index; GWG=gestational weight gain; UEBs=unhealthy eating behaviors; SD=standard deviation.
    * Differences between groups were assessed using the χ2 test for categorical variables and ANOVA for continuous variables.
    Download: CSV

    TABLE 2.  Associations between UEBs and GWG among pregnant women in Huai'an City, Jiangsu Province, China, 2020–2021.

    Unhealthy eating behaviorsCrude model (95% CI)*Fully adjusted model (95% CI)
    Inadequate (N=490)Excessive (N=4,585)Inadequate (N=490)Excessive (N=4,585)
    Number of UEBs
    0RefRefRefRef
    11.15 (0.94–1.40)1.19 (1.08–1.31)1.13 (0.92–1.38)1.18 (1.07–1.30)
    ≥21.25 (0.90–1.74)1.45 (1.24–1.71)1.14 (0.81–1.60)1.35 (1.14–1.59)
    P-trend0.102<0.0010.259<0.001
    Individual UEBs§
    Eating speed
    Not fastRefRefRefRef
    Fast0.95 (0.71–1.28)1.27 (1.11–1.45)0.86 (0.64–1.16)1.15 (1.00–1.32)
    Eating three meals regularly
    RegularRefRefRefRef
    Irregular1.23 (0.68–2.25)1.37 (1.02–1.84)1.01 (0.54–1.90)1.23 (0.90–1.68)
    Eating away from home
    RarelyRefRefRefRef
    ≥1 times/week1.09 (0.90–1. 33)1.15 (1.05–1.26)1.09 (0.89–1.34)1.13 (1.03–1.25)
    Skipping breakfast
    RarelyRefRefRefRef
    ≥1 times/week1.50 (1.06–2.13)1.22 (1.01–1.47)1.37 (0.95–1.98)1.10 (0.90–1.34)
    Abbreviation: UEBs=unhealthy eating behaviors; GWG=gestational weight gain; CI=confidence interval.
    * Crude model: unadjusted.
    Adjusted for the maternal age, pre-pregnancy BMI, levels of education, employment status, family income, area of residence, physical activity, and parity.
    § Data for individual UEBs were gleaned from a comprehensive model that incorporated all four UEBs, in addition to other relevant covariates. These covariates included factors such as maternal age, pre-pregnancy BMI, educational attainment, employment status, family income, geographic living area, level of physical activity, and parity.
    Download: CSV

    TABLE 3.  Associations between the number of UEBs and GWG across different pre-pregnancy BMI categories among pregnant women in Huai’an City, Jiangsu Province, China, 2020–2021.

    Number of UEBsCrude model (95% CI)*Fully adjusted model (95% CI)
    InadequateExcessiveInadequateExcessive
    Underweight
    0RefRefRefRef
    11.30 (0.76–2.24)1.03 (0.73–1.47)1.23 (0.69–2.19)1.14 (0.78–1.65)
    ≥21.19 (0.48–2.99)1.47 (0.84–2.56)1.18 (0.45–3.09)1.59 (0.89–2.86)
    Normal weight
    0RefRefRefRef
    10.99 (0.75–1.32)1.13 (1.00–1.27)1.00 (0.76–1.35)1.12 (1.00–1.27)
    ≥21.36 (0.84–2.18)1.54 (1.25–1.90)1.35 (0.83–2.19)1.53 (1.24–1.89)
    Overweight
    0RefRefRefRef
    11.41 (0.94–2.11)1.33 (1.07–1.66)1.40 (0.92–2.13)1.32 (1.06–1.66)
    ≥20.93 (0.50–1.74)0.95 (0.69–1.30)0.91 (0.48–1.72)0.91 (0.66–1.02)
    Obese
    0RefRefRefRef
    11.05 (0.50–2.20)1.64 (1.04–2.57)1.12 (0.51–2.46)1.54 (0.95–2.49)
    ≥20.79 (0.26–2.41)1.76 (0.95–3.26)0.97 (0.30–3.12)1.54 (0.80–2.97)
    Abbreviation: UEBs=unhealthy eating behaviors; GWG=gestational weight gain; CI=confidence interval; BMI=body mass index.
    * Crude model: unadjusted.
    Fully adjusted model: adjusted for age, education, maternal employment status, family income, residential area, physical activity, and parity.
    Download: CSV

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Unhealthy Eating Behaviors During Pregnancy and Gestational Weight Gain — Huai’an City, Jiangsu Province, China, 2020–2021

View author affiliations

Summary

What is already known about this topic?

Maintaining a healthy diet and appropriate weight during pregnancy is crucial for both the expectant mother and the fetus. Unhealthy eating behaviors (UEBs) such as eating out frequently are becoming increasingly prevalent across the globe. However, there is a dearth of research investigating the relationship between UEBs and gestational weight gain (GWG) specifically in the context of Chinese women.

What is added by this report?

The study revealed that a majority of pregnant women reported experiencing one or more UEBs such as eating fast, eating three meals irregularly, eating away from home, and skipping breakfast. A positive association was also observed between the number of UEBs and elevated odds of experiencing excessive GWG.

What are the implications for public health practice?

The uptake of emerging UEBs is prevalent among pregnant women in China. It is recommended that healthy eating behavior become the focal point of gestational weight management in clinical practice. Moreover, preconception care should take into account customized health education and promotion programs.

  • 1. School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou City, Zhejiang Province, China
  • 2. Health Care Department, Affiliated Hospital of Yangzhou University Huai’an Maternal and Child Health Care Center, Huai’an City, Jiangsu Province, China
  • 3. School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, Australia
  • Corresponding authors:

    Xiaoqin Zhu, zhuxiaoqinha@163.com

    Xiaolin Xu, xiaolin.xu@zju.edu.cn

  • Funding: This study was funded by grants to XX from China Medical Board Open Competition Program (21-416), and grants to XZ from Jiangsu Maternal and Child Health Program (F201932), Key Medical Program of Jiangsu Health Commission (ZD2021044), Huai’an Science and Technology Program (HAB202043)
  • Online Date: September 01 2023
    Issue Date: September 01 2023
    doi: 10.46234/ccdcw2023.147
  • Both excessive and inadequate gestational weight gain (GWG) are related to adverse maternal and neonatal health outcomes. Recent studies indicate an escalating trend in the prevalence of excessive GWG in China. One such study, using data from the China Nutrition and Health Surveys, reported that 57% of women had excessive GWG, and 13.7% of women had inadequate GWG (1). Currently, there appears to be a rise in unhealthy eating behaviors (UEBs) among the Chinese youth population such as skipping breakfast, eating fast, or frequently eating away from home. Multiple dietary guidelines globally have emphasized the critical role of avoiding UEBs to optimize GWG (2). However, very few studies have explored the relationship between these UEBs and GWG, particularly in relation to Chinese women and the newly released GWG guidelines from the Chinese Nutrition Society (CNS). The present study utilized data from 8,218 pregnant women in Huai’an City, Jiangsu Province collected between 2020–2021 to estimate the association between UEBs and GWG. The results indicated that 51.7% of women reported one or more UEBs and in addition, 55.8% of women experienced excessive GWG. Both single and multiple UEBs were found to be associated with excessive GWG. These findings underscore the public health necessity and clinical relevance of considering UEBs in the management and intervention of gestational weight, as well as in health education as part of preconception care.

    The study sample comprised 27,923 pregnant women who gave birth between July 1, 2020, and June 30, 2021, in Huai’an, Jiangsu Province. The population distribution was approximately equal in terms of rural and urban residents and economic statuses. The per capita gross domestic product in Huai’an paralleled the national average. These individuals’ profiles were logged in the Maternity Information System (MIS), which includes data on basic maternal characteristics, maternal disease history, pregnancy outcomes, and basic neonatal and anthropomorphic characteristics. Of these constituents, 8,218 were solicited to take part in the Grandmothers, Mothers, and Their Children’s Health (GMATCH) inquiry, and their data was incorporated in the present analysis. Comprehensive characteristics for the participating and non-participating individuals are depicted in Supplementary Table S1. This investigation received approval from the Huai’an Maternal and Child Health Care Center Ethics Committee (Approval Number: 2021060), and written informed consent was acquired from all enrollees before the study initiation.

    Four eating behaviors were assessed through a self-administered questionnaire. The conditions encompassed: eating fast (less than 15 minutes), eating meals on an irregular basis, eating away from home at least once a week, and skipping breakfast at least once a week. UEBs were classified as eating fast (under 15 minutes) (3), eating three meals irregularly (2), eating away from home at least once weekly (4), and skipping breakfast at least once weekly (4). To ensure adequate sample size, the number of UEBs was categorized into three groups: 0, 1, and at least 2. Pre-pregnancy body mass index (BMI) was computed using self-reported height and weight data prior to pregnancy. BMI ranges were subdivided into underweight (below 18.5 kg/m2), normal weight (between 18.5 and 23.9 kg/m2), overweight (between 24.0 and 27.9 kg/m2), and obese (28 kg/m2 or greater) (5). GWG was calculated as the differential between pre-pregnancy weight, as self-reported, and the weight measured prior to delivery. GWG was categorized into three groups — inadequate, appropriate, and excessive — in accordance with CNS guidelines (5).

    The relationships between individual and multiple UEBs and GWG were analyzed using logistic regression, presenting odds ratios (ORs) along with a 95% confidence interval (CI). For our reference group, we considered adequate GWG. Factors including maternal age, pre-pregnancy BMI, educational attainment, employment status, family income, geographical residence, physical activity level, parity, eating fast, eating three meals irregularly, eating away from home, and skipping breakfast were accounted for when adjusting the regression models. To test for a linear trend, we modeled UEB categories as a continuous variable. Stratified analyses were undertaken to assess if pre-pregnancy BMI altered the associations between multiple UEBs and GWG. Given the high prevalence of excessive GWG, using logistic regression could potentially lead to overestimated associations, hence suggesting the use of modified Poisson regression as an alternate solution. We implemented a modified Poisson regression to ascertain the relative risk association by categorizing the inadequate and adequate into a non-excessive group, measured against the excessive group. Our computations were performed using SAS (version 9.1, SAS Institute, Cary, NC), and we deemed P-values less than 0.05 to be statistically significant.

    Among 8,218 pregnant participants, 55.8%, 38.2%, and 6.0% experienced excessive GWG, adequate GWG, and inadequate GWG, respectively. Out of these, 4,245 women (51.7%) reported one or more UEBs. Within this subgroup, 40.9% reported eating away from home at least one time per week. Among the women with one or more UEBs, 2,466 (58.1%) and 256 (6.0%) demonstrated excessive and inadequate GWG respectively. Women who experienced excessive GWG tended to have an elevated pre-pregnancy weight, increased weight gain during pregnancy, higher total GWG, and were typically normal weight or overweight prior to pregnancy. These women were also likely to exhibit higher education levels, elevated income, rural residency, and frequently engage in UEBs such as eating fast, eating away from home at least one time per week, and skipping breakfast at least one time per week (Table 1).

    CharacteristicTotalInadequateAdequateExcessiveP-value*
    (N=8,218)(N=490)(N=3,143)(N=4,585)
    Age (mean±SD)28.2±4.828.0±5.228.2±4.828.2±4.80.503
    Pre-pregnancy weight (mean±SD)58.7±9.259.2±10.857.0±8.359.7±9.4<0.001
    Trimester weight (mean±SD)73.3±10.164.1±9.568.3±7.577.7±9.4<0.001
    Total GWG (mean±SD)14.6±5.54.8±3.711.2±2.218.0±4.4<0.001
    Pre-pregnancy BMI, n (%)<0.001
    Underweight638 (7.8)73 (14.9)292 (9.3)273 (6.0)
    Normal weight5,127 (62.4)239 (48.8)2,201 (70.0)2,687 (58.6)
    Overweight1,900 (23.1)132 (26.9)532 (16.9)1,236 (27.0)
    Obese553 (6.7)46 (9.4)118 (3.8)389 (8.5)
    Parity, n (%)0.575
    03,350 (40.8)198 (40.4)1,287 (40.9)1,865 (40.7)
    14,050 (49.3)239 (48.8)1,565 (49.8)2,246 (49.0)
    ≥2818 (10.0)53 (10.8)291 (9.3)474 (10.3)
    Education, n (%)0.002
    Middle school and below528 (6.4)46 (9.4)189 (6.0)293 (6.4)
    High school or technical secondary school4,758 (57.9)273 (55.7)1,766 (56.2)2,719 (59.3)
    Junior college and above2,932 (35.7)171 (34.9)1,188 (37.8)1,573 (34.3)
    Employment status, n (%)0.665
    Unemployed179 (2.2)10 (2.0)67 (2.1)102 (2.2)
    Employed or self-employed3,713 (45.2)217 (44.3)1,392 (44.3)2,104 (45.9)
    Others4,326 (52.6)263 (53.7)1,684 (53.6)2,379 (51.9)
    Distribution of family income (n, %)0.007
    Quartile 1 (lowest)1,964 (23.9)151 (30.8)749 (23.8)1,064 (23.2)
    Quartile 22,088 (25.4)115 (23.5)814 (25.9)1,159 (25.3)
    Quartile 32,113 (25.7)116 (23.7)775 (24.7)1,222 (26.7)
    Quartile 4 (highest)2,053 (25.0)108 (22.0)805 (25.6)1,140 (24.9)
    Residential area, n (%)0.046
    Urban5,663 (68.9)313 (63.9)2,177 (69.3)3,173 (69.2)
    Rural2,555 (31.1)177 (36.1)966 (30.7)1,412 (30.8)
    Physical Activity, n (%)0.687
    Rarely722 (8.8)48 (9.8)289 (9.2)385 (8.4)
    1–2 times/ week606 (7.4)32 (6.5)222 (7.1)352 (7.7)
    3–5 times/ week1,576 (19.2)88 (18.0)606 (19.3)882 (19.2)
    Everyday5,314 (64.7)322 (65.7)2,026 (64.5)2,966 (64.7)
    Number of UEBs, n (%)<0.001
    03,973 (48.4)234 (47.8)1,620 (51.5)2,119 (46.2)
    13,391 (41.3)206 (42.0)1,246 (39.6)1,939 (42.3)
    ≥2854 (10.4)50 (10.2)277 (8.8)527 (11.5)
    Eating fast, n (%)0.001
    No7,094 (86.3)433 (88.4)2,761 (87.8)3,900 (85.1)
    Yes1,124 (13.7)57 (11.6)382 (12.2)685 (14.9)
    Eating three meals regularly, n (%)0.108
    Regular8,002 (97.4)477 (97.3)3,075 (97.8)4,450 (97.1)
    Irregular216 (2.6)13 (2.7)68 (2.2)135 (2.9)
    Eating away from home, n (%)0.013
    Rarely4,859 (59.1)289 (59.0)1,921 (61.1)2,649 (57.8)
    ≥1 times/week3,359 (40.9)201 (41.0)1,222 (38.9)1,936 (42.2)
    Skipping breakfast, n (%)0.028
    Rarely7,666 (93.3)448 (91.4)2,958 (94.1)4,260 (92.9)
    ≥1 times/week552 (6.7)42 (8.6)185 (5.9)325 (7.1)
    Abbreviation: BMI=body mass index; GWG=gestational weight gain; UEBs=unhealthy eating behaviors; SD=standard deviation.
    * Differences between groups were assessed using the χ2 test for categorical variables and ANOVA for continuous variables.

    Table 1.  Characteristics of pregnant women based on recommended GWG in Huai'an City, Jiangsu Province, China, 2020–2021.

    Compared to women reporting zero UEBs, those with one and two or more UEBs demonstrated an increased likelihood of excessive GWG by 18% (OR=1.18, 95% CI: 1.07–1.30) and 35% (OR=1.35, 95% CI: 1.14–1.59) respectively (Ptrend<0.001) (Table 2). Each individual UEB was linked with increased odds of excessive GWG. Notably, eating fast (OR=1.15, 95% CI: 1.00–1.32) and eating away from home (OR=1.13, 95% CI: 1.03–1.25) were associated with excessive GWG even in fully adjusted models.

    Unhealthy eating behaviorsCrude model (95% CI)*Fully adjusted model (95% CI)
    Inadequate (N=490)Excessive (N=4,585)Inadequate (N=490)Excessive (N=4,585)
    Number of UEBs
    0RefRefRefRef
    11.15 (0.94–1.40)1.19 (1.08–1.31)1.13 (0.92–1.38)1.18 (1.07–1.30)
    ≥21.25 (0.90–1.74)1.45 (1.24–1.71)1.14 (0.81–1.60)1.35 (1.14–1.59)
    P-trend0.102<0.0010.259<0.001
    Individual UEBs§
    Eating speed
    Not fastRefRefRefRef
    Fast0.95 (0.71–1.28)1.27 (1.11–1.45)0.86 (0.64–1.16)1.15 (1.00–1.32)
    Eating three meals regularly
    RegularRefRefRefRef
    Irregular1.23 (0.68–2.25)1.37 (1.02–1.84)1.01 (0.54–1.90)1.23 (0.90–1.68)
    Eating away from home
    RarelyRefRefRefRef
    ≥1 times/week1.09 (0.90–1. 33)1.15 (1.05–1.26)1.09 (0.89–1.34)1.13 (1.03–1.25)
    Skipping breakfast
    RarelyRefRefRefRef
    ≥1 times/week1.50 (1.06–2.13)1.22 (1.01–1.47)1.37 (0.95–1.98)1.10 (0.90–1.34)
    Abbreviation: UEBs=unhealthy eating behaviors; GWG=gestational weight gain; CI=confidence interval.
    * Crude model: unadjusted.
    Adjusted for the maternal age, pre-pregnancy BMI, levels of education, employment status, family income, area of residence, physical activity, and parity.
    § Data for individual UEBs were gleaned from a comprehensive model that incorporated all four UEBs, in addition to other relevant covariates. These covariates included factors such as maternal age, pre-pregnancy BMI, educational attainment, employment status, family income, geographic living area, level of physical activity, and parity.

    Table 2.  Associations between UEBs and GWG among pregnant women in Huai'an City, Jiangsu Province, China, 2020–2021.

    In the analysis stratified by pre-pregnancy BMI, among women with a normal pre-pregnancy BMI, those reporting ≥2 UEBs showed a significantly larger propensity for excessive GWG (OR=1.53, 95% CI: 1.24–1.90) in the fully adjusted model. Furthermore, overweight women reporting one UEB manifested larger odds of excessive GWG (OR=1.32, 95% CI: 1.06–1.66) (Table 3). The results of the modified Poisson regression were congruous with those of the odds ratio from the logistic regression. Detailed outcomes are presented in the Supplementary Tables S2S3.

    Number of UEBsCrude model (95% CI)*Fully adjusted model (95% CI)
    InadequateExcessiveInadequateExcessive
    Underweight
    0RefRefRefRef
    11.30 (0.76–2.24)1.03 (0.73–1.47)1.23 (0.69–2.19)1.14 (0.78–1.65)
    ≥21.19 (0.48–2.99)1.47 (0.84–2.56)1.18 (0.45–3.09)1.59 (0.89–2.86)
    Normal weight
    0RefRefRefRef
    10.99 (0.75–1.32)1.13 (1.00–1.27)1.00 (0.76–1.35)1.12 (1.00–1.27)
    ≥21.36 (0.84–2.18)1.54 (1.25–1.90)1.35 (0.83–2.19)1.53 (1.24–1.89)
    Overweight
    0RefRefRefRef
    11.41 (0.94–2.11)1.33 (1.07–1.66)1.40 (0.92–2.13)1.32 (1.06–1.66)
    ≥20.93 (0.50–1.74)0.95 (0.69–1.30)0.91 (0.48–1.72)0.91 (0.66–1.02)
    Obese
    0RefRefRefRef
    11.05 (0.50–2.20)1.64 (1.04–2.57)1.12 (0.51–2.46)1.54 (0.95–2.49)
    ≥20.79 (0.26–2.41)1.76 (0.95–3.26)0.97 (0.30–3.12)1.54 (0.80–2.97)
    Abbreviation: UEBs=unhealthy eating behaviors; GWG=gestational weight gain; CI=confidence interval; BMI=body mass index.
    * Crude model: unadjusted.
    Fully adjusted model: adjusted for age, education, maternal employment status, family income, residential area, physical activity, and parity.

    Table 3.  Associations between the number of UEBs and GWG across different pre-pregnancy BMI categories among pregnant women in Huai’an City, Jiangsu Province, China, 2020–2021.

    • This study discovered an elevated incidence of excessive GWG, with 55% of women in Huai’an City, Jiangsu Province, exceeding the Chinese Nutrition Society (CNS) guideline. Compared to prior studies adhering to the CNS guideline, this rate surpasses that found in Chengdu City, Sichuan Province (46%), yet it is somewhat lower than the rate in Xuzhou City, Jiangsu Province (61%) (6-7). Furthermore, the incidence aligns closely with the rate for China (57%) as reported according to the National Academy of Medicine guideline (1). Consequently, these results suggest that excessive GWG is prevalent among Chinese women, with a heightened occurrence in the developed region of Jiangsu Province.

      The results of this study revealed an association between both individual and combined UEBs and excessive GWG, underscoring the significance of UEBs in the management and prevention of GWG. To our knowledge, this marks the first investigation quantifying the relationships between multiple UEBs and GWG; preceding research generally centered on individual behaviors. A previous study encompassing 50 low-income pregnant women in the United States demonstrated that those with a higher frequency of dining at fast-food establishments had a higher likelihood of experiencing excessive GWG (8). The association between eating fast and excessive GWG partially mirrored a prior study, wherein a meta-analysis posited that eating fast was linked to an elevated risk of obesity with a pooled OR of 2.15 (CI: 1.84–2.51) (4).

      The association between multiple UEBs and excessive GWG aligns with prior research, which showed that UEBs amongst the Spanish population tend to coincide, and an accumulation of these UEBs can result in a greater risk of excessive body weight (4). Significantly, China has undergone rapid urbanization, leading to increased work hours and a decrease in time available for individuals to cook. This has been further escalated by the emerging online food delivery market in China, which has enhanced the availability and convenience of away-from-home food, potentially encouraging those partaking in multiple UEBs (9). This environmental influence can impact not only pregnant women but also those providing care for them, potentially cultivating multiple UEBs including eating away from home and eating fast, thereby increasing the risk of excessive GWG.

      This study acknowledges certain limitations. First, data on UEBs were self-reported, potentially leading to recall bias or skewing towards socially desirable behaviors. Yet, a standardized questionnaire for UEBs is not available. This approach aligns with previous studies using similar questionnaires but varying design cut points, such as “number of times per month or per week” (4). Second, the self-reporting of pre-pregnancy height and weight could introduce potential bias to GWG and BMI measurements. However, these discrepancies are considered minor and still present an accurate representation of true BMI and GWG (10). Third, the study’s participant demographic may not be entirely representative of the broader Chinese population due to recognized income disparities between western and eastern regions, such as Huai’an. Fourth, the absence of collected dietary data in this study curtails our ability to fully understand the association between UEBs and GWG. Finally, the cross-sectional nature of this study restricts its capacity to establish a causal relationship between UEBs and GWG.

      In summary, there is an increased risk of excessive GWG among women exhibiting UEBs, particularly those of normal weight prior to their pregnancies. Notably, the odds of excessive GWG seem to amplify as the frequency of UEBs escalates. As such, intervening in UEBs provides a cost-effective approach to endorse healthier pregnancies. For instance, healthcare professionals could customize dietary plans and/or physical activities based on a woman’s pre-pregnancy BMI. Furthermore, the promotion of wholesome eating behaviors could reinforce the prospects of healthier pregnancies. On a broader scale, policy interventions should consider tackling the growing trend of consuming food outside of the home, or at least enhancing the nutritional quality of such meals to cultivate a healthier food environment, especially for prospective mothers.

    • The authors declare no conflicts of interest.

    • We would like to thank all pregnant women participated and lab members who provided insightful suggestions for this study.

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