Abstract
This study aimed to evaluate the short-term (24 weeks of pregnancy) and long-term (pre-delivery) effects of a 12-week comprehensive nutritional literacy intervention (from week 12 to 24 of pregnancy) on pregnant women’s dietary behavior and weight. A pre-registered, two-arm, single-blind randomized controlled trial was conducted, enrolling 88 pregnant women at 12 weeks of gestation, who were randomly assigned to either the control group or the intervention group. Both groups received routine obstetric care, while the intervention group additionally received a 12-week personalized dietary intervention guided by Nutbeam’s health literacy framework.The intervention was tailored based on nutritional literacy assessments and lasted until 24 weeks of gestation. The primary outcome measures were nutrition literacy, restrained eating behavior, emotional eating behavior, and external eating behavior before delivery. Secondary outcomes included dietary balance indices and gestational weight gain. A total of 83 participants completed the analysis. The comprehensive dietary intervention significantly improved the overall nutritional literacy of pregnant women before delivery (51.00 ± 6.08 vs. 44.88 ± 6.34, P < 0.001), with significant improvements in knowledge literacy (31.75 ± 3.36 vs. 27.96 ± 3.92, P < 0.001) and behavioral literacy (6.77 ± 2.85 vs. 5.59 ± 2.54, P = 0.024). In terms of eating behavior, the intervention group had significantly higher scores for restrictive eating (32.90 ± 7.68 vs. 28.32 ± 8.24, P = 0.005) and external eating (32.57 ± 5.80 vs. 30.17 ± 5.93, P = 0.033) compared to the control group. The dietary quality index showed significant improvements in the intervention group in areas such as organ meats (P < 0.001), seafood (P = 0.01), algae (P = 0.008), nuts (P < 0.001), dairy products (P < 0.001), water intake (P < 0.001), and food variety (P < 0.001). Furthermore, the intervention group experienced significantly less weight gain during pregnancy than the control group (13.21 ± 3.61 kg vs. 16.18 ± 4.70 kg, P = 0.002). The comprehensive nutritional literacy intervention implemented in early pregnancy significantly improved pregnant women’s nutritional literacy levels in both the short and long term, optimized dietary behaviors, improved dietary quality, and effectively controlled weight gain during pregnancy. The study protocol follows the CONSORT guidelines and has been registered with the Chinese Clinical Trial Registry (ChiCTR2300075082).
Keywords: Pregnancy, Health literacy, Eating behavior, Weight, Nutrition, Digital, Intervention
Subject terms: Nutrition, Public health, Weight management
Introduction
Maternal and Child Health (MCH) is a key global health indicator. In middle-income countries such as China, the focus of maternal and child health has gradually shifted from merely reducing mortality to optimizing maternal and infant health outcomes1. Gestational Weight Gain (GWG) is an important indicator of normal fetal development. The U.S. Institute of Medicine (IOM) has proposed appropriate GWG ranges based on pre-pregnancy Body Mass Index (BMI), and China has adjusted these recommendations according to its own BMI classification standards2.
In China, as society develops, the proportion of overweight and obese pregnant women has been rising, a trend consistent with global patterns. Monitoring data from 2010 to 2012 indicate a high prevalence of excessive GWG among Chinese pregnant women3. The 2020 Report on the Status of Nutrition and Chronic Diseases of Chinese Residents further highlights that the prevalence of overweight and obesity is rising across all urban age groups4. Studies have shown that although 60.4% of Chinese pregnant women had a normal pre-pregnancy weight, 54.97% still exceeded the recommended GWG range based on the 2009 IOM guidelines. Similar findings from other studies in China suggest that most pregnant women fail to meet the IOM’s recommended GWG targets5. Additionally, a 2018 review reported that due to traditional dietary patterns lacking vitamin-rich foods, Chinese pregnant women commonly experience deficiencies in vitamin D, calcium, iron, and other essential micronutrients, which may pose deeper underlying health risks6.
Abnormal GWG is closely associated with various adverse pregnancy outcomes and pregnancy-related complications, such as induced labor, variations in delivery timing and mode, maternal and infant mortality, stillbirth, congenital anomalies, fetal growth restriction, and preterm birth (PTB)7-9. Studies have shown that excessive GWG increases the risk of cesarean delivery, postpartum weight retention, and large-for-gestational-age (LGA) infants, whereas insufficient GWG raises the likelihood of small-for-gestational-age (SGA) infants and PTB10,11. At the same time, micronutrient deficiencies and macronutrient excesses significantly increase the incidence of gestational complications12. Research has found that approximately 32.9% of pregnant women experience calf cramps due to nutritional issues, over 3% develop gestational hypertension, and the incidence of gestational diabetes mellitus (GDM) is rising13. These health challenges are particularly pronounced in mainland China, where the GDM rate reaches 14.8%14, the LGA incidence is 7.3%15, and China ranks second worldwide in preterm births16.
Dietary behavior during pregnancy is a crucial factor affecting nutritional balance. However, studies on dietary interventions during pregnancy have yielded mixed results, with some online dietary interventions showing no significant effect17-21. These inconclusive outcomes may be attributed to intervention designs failing to effectively address the key barriers pregnant women face in adopting healthy eating habits22. While more complex and longer-term interventions are more likely to yield significant health benefits, their high costs limit the feasibility of widespread implementation. Therefore, more efficient and practical strategies may be necessary to overcome key barriers preventing pregnant women from establishing healthy dietary behaviors, focusing interventions within an appropriate time window.
Recent literature suggests that one of the key underlying barriers to healthy dietary behavior is low nutrition literacy23-25. Nutrition literacy is not merely knowledge of dietary guidelines; it encompasses the ability to access, understand, evaluate, and apply nutrition information in everyday contexts. Enhancing nutrition literacy may serve as a strategic entry point to improve dietary behavior and, in turn, maternal and neonatal health outcomes. Therefore, this study proposes the development and implementation of a health literacy–based intervention, the Comprehensive Dietary Intervention Program (CDIP), aimed at improving nutrition literacy, promoting healthy dietary behavior, and optimizing pregnancy-related outcomes among urban pregnant women in China. Based on the conceptual model, this study prioritizes nutrition literacy and dietary behavior as primary behavioral outcomes, reflecting the direct targets of the intervention. GWG and GDM are examined as secondary clinical outcomes, serving as important public health indicators that reflect the downstream impact of improved literacy and behavior. This distinction is made to align both with theoretical considerations and practical maternal health priorities in China.
Background
Nutrition literacy: A modifiable determinant of dietary behavior
While traditional dietary interventions emphasize the delivery of nutritional knowledge, evidence increasingly suggests that knowledge alone is insufficient to drive sustainable behavior change among pregnant women26,27. The concept of nutrition literacy, an extension of health literacy within the dietary context, addresses this gap by encompassing not only knowledge but also the ability to access, comprehend, evaluate, and apply nutritional information in real-life settings. Low nutrition literacy has been identified as a key barrier to healthy dietary behavior, particularly in pregnant populations where conflicting information, limited decision-making capacity, and family influence are prevalent28,29.
The Nutbeam Health Literacy Model provides a useful framework for operationalizing nutrition literacy into three dimensions30: Functional literacy: basic skills in reading, understanding, and following dietary advice. Interactive literacy: the ability to communicate, seek guidance, and engage with health professionals or family members regarding dietary choices. Critical literacy: the ability to critically analyze and apply nutritional information to one’s specific context and needs.
This multi-dimensional understanding supports the development of more holistic and effective interventions that move beyond knowledge dissemination to include skill-building and critical thinking (Fig. 1). Interventions designed around this model can empower pregnant women to take a more active role in managing their dietary behavior, leading to more meaningful and sustained improvements.
Fig. 1.
Components of the comprehensive dietary intervention program base on nutbeam’s health literacy model.
Self-care theory and the role of nutrition literacy in pregnancy
Orem’s Self-Care Theory offers another important perspective for understanding dietary behavior in pregnancy31. The theory defines self-care agency as an individual’s capacity to care for themselves in order to maintain health and well-being. During pregnancy, dietary practices—such as food selection, meal preparation, and nutritional monitoring—are primarily performed in the home setting, and thus fall squarely within the domain of self-care.
In this context, nutrition literacy may be conceptualized as a specialized form of self-care agency. Women with high nutrition literacy are more capable of making informed dietary decisions, adhering to nutritional guidelines, and seeking support when needed. This aligns with Orem’s assertion that enhancing self-care capabilities leads to better health behaviors and outcomes. Interventions that strengthen nutrition literacy can therefore be viewed as mechanisms for enhancing self-care during pregnancy.
Conceptual framework and hypothesis logic
Drawing on these theoretical foundations, the current study proposes a conceptual framework in which the Comprehensive Dietary Intervention Program (CDIP) improves nutrition literacy (primary outcome), which subsequently leads to improvements in healthy dietary behavior (intermediate outcome) and optimizes clinical outcomes such as gestational weight gain, dietary quality, and gestational diabetes mellitus risk (secondary outcomes).
The logic model guiding this research can be summarized as follows (Fig. 2): Enhancing nutrition literacy strengthens self-care capacity. Improved self-care capacity leads to healthier eating behavior. Healthier dietary behavior leads to improved nutrition-related pregnancy outcomes.
Fig. 2.
Conceptual framework of the comprehensive dietary intervention program (CDIP-CUPW): pathways from nutrition literacy to behavior change and pregnancy outcomes in Urban Chinese pregnant women.
This model underscores the critical role of nutrition literacy as both an intervention target and a mediator between education and clinical effect. By incorporating both the Nutbeam and Orem frameworks, the CDIP is positioned to address cognitive, behavioral, and structural determinants of dietary health among pregnant women.
The study
Aim(S) and objective
The primary aim of this study is to evaluate the effectiveness of the Comprehensive Dietary Intervention Program (CDIP) in improving nutrition literacy and promoting healthy dietary behavior among pregnant women in urban China.
Specifically, the study seeks to: assess whether the CDIP significantly improves nutrition literacy (including functional, interactive, and critical literacy). Determine whether the CDIP promotes healthier dietary behaviors during pregnancy. Examine whether the CDIP contributes to improved nutritional status, including dietary quality and appropriate GWG. Evaluate the potential of the CDIP in preventing gestational diabetes mellitus (GDM).
Technical terminology used to describe the aim
In this study, nutrition literacy is defined based on Nutbeam’s tripartite model of health literacy, encompassing functional, interactive, and critical domains. Healthy dietary behavior refers to self-regulated eating practices that align with national dietary recommendations for pregnancy. Nutritional status is assessed through dietary quality indices and total GWG. GDM prevention is evaluated based on incidence rates recorded during the third trimester.
Methodology
Design
This study employed a two-arm, single-blind randomized controlled trial (RCT) design. The trial was conducted in accordance with the CONSORT guidelines and was prospectively registered with the Chinese Clinical Trial Registry (Registration No.: ChiCTR2300075082; Date: 24/08/2023). Ethical approval was obtained from the Ethics Committee of the Affiliated People’s Hospital of Jiangsu University (Approval No. K-2023007-Y). All procedures adhered to the Declaration of Helsinki.
Participants were randomly assigned to either the intervention group (CDIP) or the control group (routine prenatal care). Outcome assessments were conducted at three time points: t1 (baseline, 12 weeks’ gestation), t2 (post-intervention, 24 weeks’ gestation), andt3 (follow-up, before delivery at 37–40 weeks’ gestation).
Instrument with validity and reliability
Nutrition Literacy was assessed using the Nutrition Literacy Assessment Instrument for Pregnant Women (NLAI-P), which demonstrated high reliability (Cronbach’s α = 0.82) and strong content validity (CVI = 0.98, CVR = 0.97). Confirmatory factor analysis supported structural validity (χ²/df = 1.82, GFI = 0.86, RMSEA = 0.046)5.
Eating Behavior was measured using the validated Chinese version of the Dutch Eating Behavior Questionnaire (DEBQ-C), with strong internal consistency (total α = 0.94; emotional α = 0.96; external α = 0.88; restrained α = 0.91)32,33.
Dietary Quality was assessed through a pregnancy-specific Food Frequency Questionnaire (FFQ-P) and evaluated using the Diet Balance Index for Pregnancy (DBI-P)34. Reliability indicators included test-retest ICCs for nutrients (0.24–0.58) and Pearson correlations for food and nutrient items (0.28–0.59)35.
GWG was tracked using standardized procedures from pre-pregnancy to delivery.
Gestational diabetes mellitus (GDM) was diagnosed based on the 75 g 2-hour oral glucose tolerance test (OGTT), conducted between 24 and 28 gestational weeks, following the criteria recommended by the Chinese Guidelines for the Diagnosis and Treatment of Pregnancy Complications (2020 edition).
Sampling and recruitment
Participants were recruited from the obstetric outpatient clinic of Changzhou No. 2 People’s Hospital between August and November 2023. A random sampling strategy was applied through the hospital’s intelligent medical system. All participants provided written informed consent before enrollment.
Inclusion and exclusion criteria
Inclusion criteria were age between 18 and 35 years, pre-pregnancy Body Mass Index (BMI) between 18.5 kg/m² and 24 kg/m², primigravida with a singleton pregnancy, gestational age no more than 12 weeks, ability to use WeChat, and at least one family member other than the pregnant woman responsible for cooking. Exclusion criteria included diabetes, uncontrolled hypertension, thyroid disorders, cardiovascular diseases, cancer, pulmonary diseases, severe gastrointestinal diseases, a history of eating disorders or bariatric surgery, severe mental illnesses, a history of mood or anxiety disorders in the past three months, substance abuse, and threatened miscarriage.
Sample size and power
Sample size was calculated using G*Power based on the expected effect size (Cohen’s d = 0.63) from a prior study36. A total of 80 participants (40 per group) were required to achieve 80% power at a 5% significance level. Accounting for a 10% attrition rate, the final sample size was increased to 88 (44 per group).
Randomization and blinding
Participants were randomly assigned to either the intervention group or the control group using simple randomization with a 1:1 allocation ratio. The random sequence was generated by a computer and implemented by an independent researcher who was not involved in participant recruitment or outcome assessment.
The intervention group received both offline and online support from trained midwives, including individualized dietary guidance and follow-up. The control group received routine prenatal care as per standard practice. To prevent contamination between groups, participants in the intervention group were physically separated during face-to-face consultations and managed independently online.
Intervention overview
Routine care
All participants received routine prenatal care in accordance with national Chinese medical guidelines, encompassing four scheduled antenatal visits between 12 and 24 weeks of gestation. These visits included standard physical examinations, biochemical assessments, and recommended screening tests.
Under routine care, pregnancy-related health education primarily addressed topics such as childbirth preparation and breastfeeding. Structured nutritional education was not routinely provided, thereby requiring pregnant women to seek dietary guidance independently from healthcare providers or external resources.
Comprehensive dietary intervention program (CDIP)
The intervention employed a phased approach emphasizing knowledge acquisition, skill development, and practical application, and combined face-to-face consultations with digital support tools. The overall objective was to integrate evidence-based dietary recommendations within the context of participants’ sociocultural environments.
Phase 1: In-person consultation (at 12 weeks of gestation)
A structured 30–40-minute, one-on-one consultation was conducted to establish individualized goals and strategies. Key components included:
Baseline nutritional literacy assessment
Participants’ baseline nutritional literacy was evaluated using a standardized assessment tool. Dimensions included understanding of essential nutrients, self-monitoring capabilities, and decision-making related to dietary choices.
Identification of barriers to information utilization
Participants were encouraged to identify challenges encountered in accessing, interpreting, and applying dietary information. Personalized strategies were introduced to address these barriers, such as the use of simplified educational materials and the engagement of family members.
Guided education on nutritional standards
National dietary standards and recommendations for appropriate GWG were presented in accessible language. Participants were instructed on aligning their dietary choices with these guidelines and were provided culturally relevant examples of nutrient-dense meals.
Skill-Building through practical exercises
Using food models and meal-planning templates, participants practiced translating guidelines into daily meal choices. Strategies to manage common challenges—such as cravings and healthier substitutions—were introduced to facilitate long-term adherence.
Motivational engagement
Participants were informed about subsequent online interventions offering continuous support, flexibility, and prompt feedback. Incentives (e.g., free fetal heart rate monitoring) were provided to foster sustained engagement.
Phase 2: Digital follow-up (16–24 weeks of gestation)
Following the initial consultation, participants transitioned to an online platform designed to reinforce nutritional literacy through interactive content and regular feedback. Key activities included:
Interactive educational modules
Short video segments (10–15 min) addressing self-monitoring of weight gain, macronutrient balance, and energy intake management were made available. The goal was to deepen participants’ comprehension and facilitate practical application of dietary guidelines.
Family engagement and support
An online session at 16 weeks of gestation included participants and primary household meal preparers. This session aimed to improve family-level support for healthy eating practices, ensuring sustained dietary changes within the home environment.
Progress monitoring and feedback
Dietary intake and GWG were monitored biweekly. Personalized feedback was provided through virtual consultations, enabling participants to understand their progress relative to established targets and make informed adjustments.
Adaptive meal planning
Meal plans were regularly updated based on participants’ progress. Nutrient deficiencies, excessive weight gain, or other emerging challenges were addressed by integrating more plant-based options, adjusting portion sizes, or incorporating nutrient-dense foods.
Sustained motivation
Regular reminders, motivational messages, and milestone celebrations were delivered via the online platform to reinforce adherence and acknowledge improvements.
Post-24-week care
Following completion of the intervention at 24 weeks, participants in both the control and intervention arms continued to receive standard antenatal care per national guidelines. Additionally, all participants were offered on-demand dietary consultations until delivery. This service aimed to address residual or newly emerging nutritional concerns, thereby ensuring that maternal and fetal nutritional requirements were met throughout the remainder of the pregnancy.
Treatment fidelity program
Intervention fidelity in this study followed the framework established by the American National Institutes of Health’s Behavior Change Consortium (BCC). The intervention was designed based on a conceptual model of health literacy and relevant behavior change techniques (BCTs). Standardized content covering 11 topics was developed using theoretical evidence, and the frequency and duration of interventions were systematically determined. Midwives delivering the intervention were required to hold registered midwife and maternal-child clinical nursing specialist certificates, undergo training in nutrition and communication skills, and pass qualification assessments. Training was further reinforced through professional retraining by registered dietitians, and obstetrician consultants were assessed on pregnancy weight gain standards. Only those who passed evaluations were permitted to conduct interventions, with weekly monitoring ensuring quality.
The intervention delivery process followed a structured protocol, with providers using a standardized checklist to verify content coverage, appropriate delivery methods, and attendance tracking. Participant adherence was promoted through automated reminders and follow-up calls if needed. After each session, brief interviews assessed participants’ satisfaction, comprehension, and implementation of the intervention. Self-monitoring reports and feedback assisted in evaluating the actual application of intervention strategies. To enhance adherence, participants were incentivized with a free testing program upon completion, and data collection was coordinated with routine antenatal visits to minimize additional hospital visits.
Follow-up and data collection
In this study, the follow-up period was extended to include a third assessment at t3 (before delivery). Data were collected at three time points: baseline (t1) at 12 weeks of gestation before the intervention, post-intervention (t2) at 24 weeks of gestation after the completion of the CDIP, and before delivery (t3) at gestational weeks 37–40. At each time point, nutrition literacy, eating behavior, dietary quality, and GWG were assessed using the validated instruments.
Statistical analysis
The study used SPSS 28.0 for data analysis, applying descriptive statistics (mean, standard deviation, median, and interquartile range) to analyze variable distributions. Baseline data comparisons between groups were conducted using an independent sample t-test for normally distributed continuous variables, while the Mann-Whitney U test or Chi-square test was used for non-normally distributed variables. Within-group comparisons before and after the intervention were analyzed using a paired sample t-test or a paired Wilcoxon signed-rank test. Post-intervention comparisons between groups were conducted using one-way analysis of covariance (One-way ANCOVA) or the Mann-Whitney U test, depending on normality and homogeneity of variance assumptions. Frequency data were analyzed using Fisher’s exact test.
A modified intention-to-treat (mITT) approach was adopted. Participants were analyzed according to their original group allocation, but those with missing outcome data were excluded from the analysis. This approach is acknowledged as a limitation in the discussion.
Human subject protection
The study protocol and informed consent documents were reviewed and approved by REDACTED before the trial commenced. The trial adhered to ethical principles of informed consent, confidentiality, and participant protection. The evaluation researcher provided eligible pregnant women with detailed information on the study’s background, objectives, procedures, potential risks and benefits, alternative treatment options, data confidentiality, compensation, and participant rights. Only after ensuring full comprehension and voluntary agreement did participants sign two identical informed consent forms—one for the researcher and one for themselves.
Results
General characteristics of participants
In this study, the dropout rates in the intervention and control groups were 4.5% and 6.8%, respectively, which were within the acceptable range for longitudinal studies (see Fig. 3).
Fig. 3.
Recruitment intervention and follow-up flowchart.
A comparison of baseline data between the routine care group and the CDIP group showed no significant differences in variables such as age, baseline gestational weeks, pre-pregnancy BMI, education level, household annual income, and dietary preferences between the two groups (see Table 1).
Table 1.
Baseline characteristics of participants in both groups (N = 88).
| Variable | Category | Control group (n = 44) | Intervention group (n = 44) | Statistic (Z/t/χ²) | p-value |
|---|---|---|---|---|---|
| Education level | Below associate degree | 15(35.1%) | 9(20.5%) | -1.494a | 0.135 |
| Associate/bachelor’s degree | 26(59.1%) | 30(68.2%) | |||
| Above bachelor’s degree | 3(6.8%) | 5(11.4%) | |||
| Household annual Income (10,000 CNY) | < 10 | 12(27.3%) | 5(11.4%) | -1.441a | 0.149 |
| 10–20 | 20(45.5%) | 27(61.4%) | |||
| 21–40 | 10(22.7%) | 3(6.8%) | |||
| > 40 | 2(4.5%) | 5(11.4%) | |||
| Dietary preference | Hunan cuisine | 4(9.1%) | 4(0.1%) | 0.232b | 0.972 |
| Sichuan cuisine | 6(13.6%) | 5(11.4%) | |||
| Anhui cuisine | 8(18.2%) | 7(15.9%) | |||
| Jiangsu cuisine | 26(59.1%) | 28(63.6%) | |||
| Pre-pregnancy BMI | 21.28(20.23 ~ 22.60) | 20.54(19.57 ~ 22.24) | -1.836a | 0.066 | |
| Age (years) | 26.14 ± 2.33 | 26.89 ± 3.47 | 1.190c | 0.237 | |
| Baseline Gestational Age (weeks) | 11.82 ± 0.40 | 11.89 ± 0.42 | 0.743c | 0.459 |
Continuous variables are presented as mean ± standard deviation (for normal distribution) or median (25-75%) (for non-normal distribution). Categorical variables are presented as frequency (percentage). a Mann-Whitney U test, b Chi-square test, c independent t-test.
Baseline (t1, 12 weeks of gestation) key variable comparison
A comparison of key variables, including nutrition literacy, eating behavior, gestational weight gain before the intervention, and healthy eating index, showed no statistically significant differences between the routine care group and the CDIP group at baseline (see Table 2).
Table 2.
Comparison of key variables at baseline (t1, 12 weeks of Gestation).
| Variable | Possible range | Control group (n = 44) | Intervention group (n = 44) | Statistic (Z/t) | |||
|---|---|---|---|---|---|---|---|
| Actual min–Max | Mean ± SD / Median (25-75%) | Actual min–Max | Mean ± SD / Median (25-75%) | ||||
| Nutrition literacy score | |||||||
| Total score | 0～76 | 14.00～62.60 | 43.18 ± 9.24 | 20.00～56.10 | 43.28 ± 7.25 | .056b | 0.955 |
| Knowledge literacy | 0～46 | 11.50～40.00 | 26.53 ± 6.57 | 13.50～37.00 | 26.77 ± 5.31 | .187b | 0.852 |
| Behavioral literacy | 0～14 | 50～9.50 | 4.93 ± 2.22 | 1.00～10.00 | 4.36 ± 1.89 | -1.291b | 0.200 |
| Skill literacy | 0～16 | 2.00～15.70 | 13 (10.35～14.05) | 3.00～15.00 | 13(10.23～14.48) | − .367a | 0.713 |
| Eating behavior score | |||||||
| Restrained eating behavior | 0～50 | 14.00～43.00 | 29.20 ± 7.61 | 13.00～40.00 | 28.86 ± 6.82 | − .221b | 0.825 |
| Emotional eating behavior | 0～65 | 13.00～60.00 | 26.11 ± 8.82 | 13.00～51.00 | 27.20 ± 8.96 | − .575b | 0.566 |
| External eating behavior | 1～50 | 13.00～46.00 | 30.11 ± 7.05 | 21.00～42.00 | 30.36 ± 5.64 | .184b | 0.855 |
| Healthy eating index | |||||||
| Food variety | -13～0 | 6～10 | 8.50 (8.00～10.00) | 6～11 | 8.50(8.00～9.00) | − .107a | 0.915 |
| Grains & tubers | -12～12 | -8～5 | -4.00 (-5.00～3.00) | -8～10 | -4.00 (-5.00～1.25) | − .328a | 0.743 |
| Meat & Poultry | -4～4 | -4～4 | -1.00 (-2.00～0) | -4～4 | 0 (-1.00～2.00) | -1.463a | 0.143 |
| Animal blood or liver | -6～0 | -6～0 | -6.00(-6.0～6.00) | -6～0 | -6.00(-6.00～6.00) | − .575a | 0.565 |
| Seafood | -4～0 | -4～0 | -4.00 (-4.00～2.00) | -4～0 | -3.00 (-4.00～1.00) | -1.789a | 0.074 |
| Eggs | -4～4 | -4～1 | -2.00 (-4.00～0) | -4～4 | -2.00 (-4.00～0) | -1.235a | 0.217 |
| Soy & soy products | -3～0 | -3～0 | -1.00 (-2.00～0) | -3～0 | -2.00 (-3.00～1.00) | -1.783a | 0.075 |
| Vegetables | -6～0 | -5～0 | 0 (0～0) | -5～0 | 0 (-2.00～0) | -1.307a | 0.191 |
| Seaweed | -4～0 | -4～0 | -2.00 (-2.00～2.00) | -4～0 | -2.00 (-2.00～2.00) | − .633a | 0.527 |
| Fruits | -6～6 | -4～6 | 5.00 (2.00～6.00) | -3～6 | 5.00 (0～6.00) | -1.140a | 0.254 |
| Nuts | -3～0 | -3～0 | -2.00 (-3.00～0) | -3～0 | -3.00 (-3.00～0) | − .600a | 0.548 |
| Dairy products | -6～0 | -6～0 | -4.00 (-5.00～2.00) | -6～0 | -3.00 (-5.00～1.00) | -1.286a | 0.198 |
| Water | -12～0 | -10～0 | -3.00 (-5.00～2.00) | -10～0 | -3.00 (-5.00～0) | − .702a | 0.483 |
| Oil | 0～4 | 0～2 | 0(0～1.50) | 0～2 | 0(0～2.00) | − .476a | 0.634 |
| Salt | 0～4 | 0～4 | 2.00(0–2.00) | 0～4 | 2.00(0～2.00) | − .217a | 0.828 |
| Weight gain (kg) (Weeks 0–12) | - | -7.00～8.00 | − 0.01 ± 3.18 | -7.00～5.00 | 0.37 ± 2.37 | .635b | 0.527 |
Continuous variables are presented as mean ± standard deviation (SD) for normal distributions or median (25–75%) for non-normal distributions. a Mann-Whitney U test, b Independent t-test.
Post-intervention (t2, 24 weeks of gestation) key variable comparison
After the intervention (at 24 weeks of gestation), the CDIP group showed significantly higher scores in total nutrition literacy, knowledge literacy, behavioral literacy, and skill literacy compared to the routine care group (all p-values < 0.001). In eating behavior, the CDIP group had higher restrictive eating behavior scores (p < 0.001), with no significant differences in emotional and external eating behaviors (p > 0.05).
Regarding the healthy eating index, the intervention group had healthier eating patterns in food variety, grains and tubers, seafood, fruits and vegetables, nuts, dairy products, and water intake (p < 0.05), while oil and salt intake showed no significant differences. Additionally, gestational weight gain from 12 to 24 weeks was significantly lower in the intervention group than in the routine care group (p = 0.014, see Table 3).
Table 3.
Comparison of key variables after intervention (t2, 24 weeks of Gestation).
| Variable | Control Group (n = 44 | Intervention Group (n = 44) | Statistic (Z/t) | p-value | ||
|---|---|---|---|---|---|---|
| Actual Min–Max | Mean ± SD / Median (25–75%) | Actual Min–Max | Mean ± SD / Median (25–75%) | |||
| Nutrition Literacy Score | ||||||
| Total score | 18.20～63.10 | 43.55 ± 9.58 | 34.30～66.00 | 53.39 ± 6.60 | 16.038b | < 0.001 |
| Knowledge literacy | 13.50～40.50 | 26.89 ± 6.46 | 23.00～42.50 | 33.05 ± 4.70 | 12.633b | < 0.001 |
| Behavioral literacy | 0～9.00 | 5.19 ± 2.42 | 3.00～12.00 | 6.77 ± 2.15 | 5.774b | < 0.001 |
| Skill literacy | 2.20～16.10 | 12.30±10.45～14.10 | 3.80～16.10 | 14.35 (12.75～15.55) | -3.234a | 0.001 |
| Eating behavior score | ||||||
| Restrained eating behavior | 13.00～42.00 | 28.79 ± 7.96 | 15.00～46.00 | 31.61 ± 7.28 | -4.123b | < 0.001 |
| Emotional eating behavior | 13.00～44.00 | 26.07 ± 8.82 | 13.00～45.00 | 26.30 ± 8.75 | -1.196b | 0.118 |
| External eating behavior | 13.00～49.00 | 30.12 (6.46) | 21.00～42.00 | 30.45 ± 4.99 | .298b | 0.383 |
| Healthy eating index | ||||||
| Food variety | 7～13 | 9 (9.00～11.00) | 6～13 | 12.00 (11.00～13.00) | -5.305a | < 0.001 |
| Grains & tubers | -8～12 | -3.00 (-4.00～.75) | -8～0 | -4.00 (-5.00～2.00) | 2.031a | 0.042 |
| Meat & poultry | -5～4 | 0 (-.25～3.00) | -2～4 | 1.00 (0～1.00) | − .031a | 0.975 |
| Animal blood or liver | -6～0 | -6 (-6.00～6.00) | -6～0 | -6.00 (-6.00～2.00) | -5.030a | < 0.001 |
| Seafood | -4～0 | -3.00 (-4.00～0) | -4～0 | -2.00 (-3.00～2.00) | -2.181a | 0.029 |
| Eggs | -4～4 | 0 (0～2.00) | -4～4 | 0 (0～0) | .074a | 0.941 |
| Soy & soy products | -4～0 | -2 (-3.25～0) | -4～0 | -1 (-2.00～0) | -1.708a | 0.088 |
| Vegetables | -4～0 | 0 (-2.00～0) | -4～0 | 0 (0～0) | − .218a | 0.827 |
| Seaweed | -4～0 | -2.00 (-2.00～0) | -4～0 | 0 (-1.50～0) | -2.994a | 0.003 |
| Fruits | -6～6 | 2.00 (0～6.00) | -4～6 | 0 (0～0) | 3.925a | < 0.001 |
| Nuts | -3～0 | -3.00 (-3～1.75) | -3～0 | 0 (0～0) | -6.056a | < 0.001 |
| Dairy products | -6～0 | -1.00 (-4.00～1.00) | -6～0 | 0 (-1.00～0) | -5.195a | < 0.001 |
| Water | -10～0 | -3.00 (-5.00～0) | -10～0 | 0 (-3.00～0) | -3.083a | 0.002 |
| Oil | 0～2 | 0 (0～2.00) | 0～2 | 0 (0～0) | -1.407a | 0.160 |
| Salt | 0～4 | 2.00 (0～2.00) | 0～4 | 2.00 (0～2.00) | 1.075a | 0.282 |
| Weight gain (kg) (Weeks 12–24) | 20～12.00 | 5.98 ± 2.78 | 3.10～8.50 | 4.97 ± 1.33 | -2.220c | 0.014 |
Continuous variables are presented as mean ± standard deviation (SD) for normal distributions or median (25–75%) for non-normal distributions. a Mann-Whitney U test,b NCOVA (adjusted for baseline data), c independent t-test. Analyses were conducted on complete cases only (modified intention-to-treat analysis).
Follow-up (t3, before delivery) key variable comparison
At the follow-up stage (before delivery), the CDIP group maintained significantly higher scores in total nutrition literacy, behavioral literacy, and skill literacy (all p-values < 0.05). Restrictive eating behavior scores remained significantly higher in the intervention group (p = 0.005), with no significant differences in emotional or external eating behaviors (p > 0.05).
Additionally, gestational weight gain in the intervention group was significantly lower than in the routine care group (p = 0.002), although no significant differences were found in neonatal birth weight (p = 0.684). The incidence of GDM was lower in the intervention group, though not statistically significant (see Table 4).
Table 4.
Comparison of gestational weeks at delivery, neonatal weight, nutrition literacy score, eating behavior score, dietary quality index score, gestational weight gain, and gestational diabetes mellitus (GDM) between the two groups during the Follow-up Period.
| Variable | Control group (n = 44 | Intervention group (n = 44) | Statistic (Z/t) | p-value | ||
|---|---|---|---|---|---|---|
| Actual min–max | Mean ± SD / Median (25–75%) | Actual min–max | Mean ± SD / Median (25–75%) | |||
| Nutrition literacy score | ||||||
| Total score | 31.80～59.30 | 44.88 ± 6.34 | 35.80～65.00 | 51.00 ± 6.08 | 4.488c | < 0.001 |
| Knowledge literacy | 18.00～36.50 | 12.80（8.60～13.60） | 24.50～41.50 | 12.80（11.65～13.80）d | 1031.00d | 0.061 |
| Behavioral literacy | 0～10.00 | 5.59 ± 2.54 | 1.50～12.50 | 6.77 ± 2.85 | 2.006c | 0.024 |
| Skill literacy | 4.60～14.80 | 27.96 ± 3.92 | 4.40～16.00 | 31.75 ± 3.36 | 4.724c | < 0.001 |
| Eating behavior score | ||||||
| Restrained eating behavior | 16.00～50.00 | 28.32 ± 8.24 | 17.00～48.00 | 32.90 ± 7.68 | 2.624c | 0.005 |
| Emotional eating behavior | 13.00～65.00 | 21.02 ± 9.45 | 13.00～51.00 | 23.40 ± 8.98 | 1.176c | 0.121 |
| External eating behavior | 17.00～50.00 | 30.17 ± 5.93 | 18.00～41.00 | 32.57 ± 5.80 | 1.864c | 0.066 |
| Healthy eating index | ||||||
| Food Variety | 7～13 | 9.00（9.00～10.00） | 6～13 | 12.00（10.25～12.00） | -4.997d | < 0.001 |
| Grains & Tubers | -8～6 | 0.00（-4.00～1.00） | -6～1 | -3.00（-4.00～1.00) | -1.833d | 0.033 |
| Meat & Poultry | -2～4 | 0.00（0.00 ～3.00） | -2～4 | 0.00（0.00～4.00） | − 0.776d | 0.219 |
| Animal blood or liver | -6～0 | -6.00（-6.00～4.00） | -6～0 | -3.00（-4.00～2.00） | -4.942d | < 0.001 |
| Seafood | -4～0 | -2.00（-3.00～0.00） | -4～2 | 0.00（-2.00～0.00） | -2.319d | 0.010 |
| Eggs | -4～4 | 0.00（0.00 ～ 2.00） | -4～4 | 0.00（0.00～0.00) | -1.150d | 0.078 |
| Soy & soy products | -4～0 | -2.00（-3.00～0.00） | -4～0 | -1.00（-2.00～0.00) | -1.429d | 0.068 |
| Vegetables | -3～0 | 0.00（-2.00～0.00） | -3～0 | 0.00（0.00～0.00） | -1.153d | 0.125 |
| Seaweed | -4～0 | -2.00 (-2.00～0.00) | -4～0 | 0.00（-2.00～0.00） | -2.410d | 0.008 |
| Fruits | 0～6 | 4.00（2.00～6.00） | -4～6 | 0.00（0.00～2.00） | -5.132d | < 0.001 |
| Nuts | -3～0 | -3.00（-3.00～0.00） | -3～0 | -0.50 (-1.00 ～0.00) | -4.192d | < 0.001 |
| Dairy products | -5～0 | -2.00（-4.00～1.00） | -6～0 | 0.00（-1.00～0.00） | -4.533d | < 0.001 |
| Water | -10～0 | -3.00（-5.00～2.00） | -7～0 | -3.00（-3.00～0.00） | -3.878d | < 0.001 |
| Oil | 0～4 | 0.00（0.00～2.00） | 0～2 | 0.00（0.00～0.00） | -1.878d | 0.030 |
| Salt | 0～2 | 2.00（0.00～2.00） | 0～4 | 2.00（0.00～2.00） | -1.097d | 0.136 |
| Gestational age at delivery (weeks) | 37～41+1 | 39.73 ± 1.25 | 36+5～41 | 39.78 ± 0.88 | .185a | 0.854 |
| Neonatal birth weight (g) | 2650.00～4450.00 | 3367.07 ± 430.35 | 2550.00～4200.00 | 3 332.14 ± 343.02 | − .408a | 0.684 |
| Gestational Weight Gain (kg) (Weeks 0–Full-Term Delivery) | 6.50～26.00 | 16.18 ± 4.70 | 6.50～21.50 | 13.21 ± 3.61 | -3.237a | 0.002 |
| Gestational Diabetes Mellitus (GDM) | – | 5 (12.2%) | – | 2 (4.8%) | .678b | 0.410 |
Continuous variables are presented as mean ± standard deviation (SD) for normal distributions or median (25–75%) for non-normal distributions.Categorical variables are presented as frequency (percentage). a Independent t-test, b Adjusted chi-square test, c One-way ANCOVA, using baseline data as covariates, d Mann-Whitney U test. Analyses were conducted on complete cases only (modified intention-to-treat analysis).
Furthermore, based on the 2009 Institute of Medicine (IOM) guidelines, a significantly higher proportion of participants in the intervention group achieved appropriate gestational weight gain compared to the control group (68.2% vs. 43.2%, χ² = 6.951, p = 0.031). The proportion of excessive weight gain was notably lower in the intervention group (18.2% vs. 43.2%) (see Table 5).
Table 5.
Comparison of gestational weight gain (GWG) categories according to IOM guidelines between control and intervention Groups.
| Category | Control Group (n = 44) | Intervention Group (n = 44) | Statistic (χ²) | p-value |
|---|---|---|---|---|
| Appropriate | 19(43.2%) | 30(68.2%) | 6.951b | 0.031 |
| Excessive | 19(43.2%) | 8(18.2%) | ||
| Inadequate | 6(13.6%) | 6(13.6%) |
b Adjusted chi-square test; Analyses were conducted on complete cases only (modified intention-to-treat analysis).
Discussion
The attrition rate in this study was relatively low, mainly occurring in late pregnancy (T3) due to preterm birth or delivery at a non-designated hospital. Compared to similar studies, the attrition rate in this study was significantly lower37, indicating high participant compliance. This could be attributed to personalized weight monitoring reminders and dietary counseling services provided during the intervention. Previous research has demonstrated that continuous communication and personalized services effectively enhance adherence to pregnancy interventions38, and the findings of this study align with these conclusions.
This study showed that the CDIP intervention significantly improved nutrition literacy among urban pregnant women in China, particularly in total nutrition literacy, behavioral literacy, and skill literacy. These findings are consistent with previous studies, which suggest that nutrition education effectively enhances nutrition literacy and helps pregnant women better acquire, understand, and apply this information39. However, although the intervention group achieved significant improvements in nutrition literacy at T2 and T3, the improvement in skill literacy was slower, possibly indicating that the development of advanced skills requires a longer period of practice. This finding is consistent with prior research, which suggests that skill literacy improvement tends to be slower than knowledge literacy and requires long-term interventions and diverse strategies40. At T3, the difference in knowledge literacy between the intervention and control groups was not statistically significant, suggesting that the short-term effects of the intervention may not be sustained, highlighting the necessity of knowledge reinforcement and long-term education40.
The CDIP intervention significantly improved restrictive eating behavior among pregnant women, mainly due to repeated reminders about nutrition education, weight monitoring, and weight gain standards. This finding aligns with previous research, demonstrating that personalized interventions effectively guide pregnant women in adopting healthy eating behaviors41. However, the intervention did not significantly improve emotional eating behavior or external eating behavior, which may be due to insufficient intervention on emotional fluctuations and external environmental factors. Previous studies have also indicated that emotional eating behavior is influenced by complex factors such as emotional health and social support, which are difficult to change in the short term42. Therefore, future research should strengthen interventions targeting emotional regulation and environmental factors, potentially through multidimensional strategies to better modify these behaviors.
The CDIP intervention group demonstrated a more diverse food selection, particularly in the intake of liver, animal blood, seafood, nuts, and dairy products. This is consistent with previous research findings, suggesting that nutrition education effectively improves dietary quality, especially in cases where dietary structures are imbalanced43. Additionally, at T3, the CDIP intervention group continued to show healthier dietary trends in terms of food variety, seafood, nuts, dairy products, and water intake, indicating the long-term impact of the intervention. This finding supports previous research that suggests improved nutrition literacy leads to stable and lasting dietary behavior changes. Nutrition literacy is not merely about acquiring knowledge in the short term; it also involves the ability to make health decisions, understand, and apply information. Once these abilities are shaped and internalized, they are more likely to be sustained over the long term44. However, salt intake did not show significant changes, which may be closely related to individual taste preferences and cultural habits. This suggests that salt intake is strongly influenced by personal habits, and future interventions may need to integrate psychological and sociological strategies to further improve salt consumption.
The CDIP intervention demonstrated significant effectiveness in gestational weight management, with the intervention group showing better weight gain control. Particularly at T3, the overall gestational weight gain in the intervention group remained significantly lower than in the control group, indicating the persistence of the intervention effects. This finding is consistent with previous studies, supporting the notion that personalized weight monitoring and dietary guidance help control excessive weight gain45. However, it is important to note that gestational weight management is influenced by multiple factors, including individual differences, cultural background, and lifestyle46. Despite these influences, this study demonstrated that the CDIP intervention effectively reduced both excessive and insufficient gestational weight gain, helping pregnant women maintain a healthy weight while reducing the risk of pregnancy complications.
It is important to note that the present study included only pregnant women with normal pre-pregnancy BMI. This decision was made to reduce bias related to metabolic dysregulation and unmeasured disordered eating tendencies, which may be more prevalent among overweight or obese individuals. However, this limits the generalizability of the findings. Future research should extend the intervention to high-BMI populations, using adapted assessment tools and targeted behavioral strategies to address their specific risks and needs.
Furthermore, the phased intervention model adopted in this study was designed with both personalization and scalability in mind. By combining a single in-person consultation with digital follow-ups via WeChat—a widely used platform in China—and aligning sessions with routine antenatal visits, the intervention minimized logistical and time burdens for both healthcare providers and pregnant women. Importantly, although the intervention lasted only 12 weeks (from 12 to 24 gestational weeks), its positive effects on nutrition literacy, dietary behavior, and gestational weight gain persisted until delivery. This suggests a favorable cost–benefit ratio in terms of time investment and clinical outcomes. The practical design and sustained effect of this intervention highlight its feasibility for integration into routine care pathways, especially in resource-limited primary care settings.
Regarding the diagnosis of GDM, the difference in diagnosis rates between the CDIP intervention group and the routine care group was not statistically significant, although the diagnosis rate in the intervention group was lower than in the control group. The small sample size may have contributed to the lack of statistical significance47. Nevertheless, the CDIP intervention may have a potentially positive role in GDM management by improving dietary behavior and weight management. Future research should expand the sample size to further explore the clinical impact of different intervention strategies on GDM.
Despite physical isolation during intervention delivery, potential contamination through informal interactions in shared clinical spaces or community settings cannot be entirely ruled out. This is acknowledged as a limitation of the study. Additionally, while the study adhered to the intention-to-treat principle, missing data were handled using complete case analysis, which partially deviates from the strict definition of this principle. Future studies may consider using multiple imputation techniques to more rigorously address this issue. Furthermore, in line with the cultural context in China, all participants were legally married at enrollment, and no unmarried or cohabitating pregnancies were observed. As a result, marital status showed no variation in the sample and was not included in the statistical analysis. Nevertheless, we recognize that marital and family support may influence dietary behaviors through joint decision-making and caregiving roles. Future research may consider comparing outcomes across diverse family structures and cultural backgrounds.
Although a modified intention-to-treat approach was used in this study, missing outcome data were handled using complete case analysis. This analytical strategy excludes participants lost to follow-up, which may introduce selection bias and reduce the generalizability of the findings. Future studies should consider more rigorous approaches to missing data, such as multiple imputation, to better preserve the benefits of randomization and ensure full adherence to ITT principles.
Conclusion
This study indicates that the CDIP intervention has potential benefits in improving pregnant women’s nutrition literacy, modifying dietary behaviors, optimizing weight gain, and managing diabetes. Although some results did not reach statistical significance, the long-term impact of the intervention on pregnancy health remains noteworthy. Future research should further optimize interventions by considering individual differences and cultural backgrounds to enhance their overall effectiveness.
Acknowledgements
We would like to thank all of the Affiliated Changzhou No. 2. People’s Hospital of Nanjing Medical University staff who contributed to reviewing the program content and updating and delivering the program.
Author contributions
Author Contributions: Conceptualization, Q.L.; methodology, Q.L.; formal analysis, Q.L, and J.W.; investigation, Q.L, and J.W.; data curation, Q.L, and J.W.; writing—original draft preparation, Q.L, and J.W.; writing—review and editing, Q.L, and J.W.; project administration, Q.L.; funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by the Social Development Guidance Science and Technology Program of Zhenjiang, Jiangsu Province, China (Grant No.: FZ2024066).
Data availability
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy issues.
Declarations
Competing interests
The authors declare no competing interests.
Informed consent statement
Informed consent was obtained from all subjects involved in the study.
Institutional review board statement
Approval was obtained from the Clinical Research Ethics Committee of the Affiliated Changzhou No. 2. People’s Hospital of Nanjing Medical University, Changzhou, China. ([2023]KY107-01, 20 July 2023).
Declaration of generative AI in scientific writing
Generative AI and AI-assisted technologies should only be used in the writing process to improve the readability and language of the manuscript.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy issues.