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. 2026 Jan 23;12(1):21–29. doi: 10.33546/bnj.4215

Effect of the Capability-Opportunity-Motivation Behavior (COM-B) model of dietary behavior program on gestational weight gain in Thailand: A randomized controlled trial

Pantipa Buakhai 1, Piyanut Xuto 2,*, Pimpaporn Klunklin 3, Petsunee Thungjaroenkul 4
PMCID: PMC12828563  PMID: 41585845

Abstract

Background

Excessive gestational weight gain can adversely affect maternal and fetal health. Dietary behavior change can help control gestational weight gain and prevent adverse pregnancy outcomes.

Objective

This study aimed to examine the effect of the Capability-Opportunity-Motivation Behavior (COM-B) model of dietary behavior program on gestational weight gain.

Methods

This single-blind randomized controlled trial used a pre-posttest control group design and included 96 pregnant women from a northern province of Thailand. Participants were randomly assigned equally to the experimental and control groups (48 each) using permuted block randomization. The experimental group received a 14-session COM-B model of dietary behavior program, while the control group received usual care. Data were collected via questionnaires between November 2023 and October 2024, and were analyzed using SPSS version 26, employing descriptive and inferential statistical methods.

Results

At 36 weeks’ gestation, pregnant women in the experimental group had a significantly lower mean difference in gestational weight gain based on the Institute of Medicine (IOM) recommendation compared with both their baseline at 20 weeks and the control group. After adjusting for maternal age and education, the experimental group continued to show significantly lower gestational weight gain than the control group. The adjusted intention-to-treat analysis indicated a mean difference of -2.227 kg (95% CI: -3.75 to -0.70; p = 0.005; partial η2 = 0.084), while the adjusted per-protocol analysis showed a mean difference of -2.648 kg (95% CI: -4.31 to -0.99; p = 0.002; partial η2 = 0.110). These results suggest that the COM-B model of dietary behavior program effectively limited gestational weight gain, independent of sociodemographic differences. Even modest reductions in gestational weight gain may contribute to lowering the risk of pregnancy complications such as gestational diabetes and preeclampsia. Dietary behavior change was monitored, but not a predefined secondary outcome.

Conclusion

The COM-B model of dietary behavior program led to minimal but potentially clinically relevant reductions in gestational weight gain. The findings highlight the clinical relevance of nurse-led interventions, underscoring the need for nurse training to implement the program in routine antenatal care.

Trial Registry Number

Thai Clinical Trials Registry (TCTR20230907001)

Keywords: dietary behavior, gestational weight gain, COM-B model, pregnant women, Thailand

Background

Excessive gestational weight gain is a serious issue for pregnant women around the world. Maintaining appropriate weight gain during pregnancy is crucial for achieving favorable birth outcomes and preventing both short- and long-term health complications (Zhou & Liu, 2021). Weight gain during pregnancy is influenced by the pre-pregnancy body mass index (BMI). In 2020, approximately 45.5% of pregnant women worldwide gained weight exceeding the Institute of Medicine (IOM) recommendations (Zhou et al., 2022), and around 20.2% in Asia (Martínez-Hortelano et al., 2020). In Thailand, research conducted in the Eastern region revealed that the percentage of gestational weight gain (GWG) exceeding the recommended standard was 34.3% (Siriarunrat et al., 2018), and as high as 39.8% of pregnant women with a normal BMI had GWG exceeding the standard (11.5–16 kg) (Pongcharoen et al., 2016). This high prevalence of nearly 40% among women seen in antenatal care highlights the potential adverse effects on maternal and fetal health unless interventions to prevent excessive GWG are provided.

The adverse effects on maternal and fetal health occur during the perinatal, labor, and postpartum periods. Maternal health complications include miscarriage, gestational diabetes mellitus (GDM), preeclampsia, gestational hypertension, fatigue, thromboembolic disorders, prolonged labor, cephalopelvic disproportion, cesarean delivery, operative obstetric or assisted birth, preterm labor, postpartum weight retention, and obesity (Al Shekaili et al., 2024; Beyene & Kasahun, 2025; Tam & He, 2021; Wu et al., 2020). The complications of the fetus include large gestational age, fetal death, shoulder dystocia, birth injury, macrosomia, cardiovascular heart disease, obesity, metabolic disorder, and hypertension in children and adults (Al Shekaili et al., 2024; Beyene & Kasahun, 2025; Tam & He, 2021).

Excessive GWG is influenced by several factors, which can be categorized into 1) sociodemographic characteristic factors, 2) psychological factors, and 3) behavioral factors. Sociodemographic factors include young age, low socioeconomic status, high pre-pregnancy BMI, and insufficient knowledge (Darling et al., 2023; Siriarunrat et al., 2018; You et al., 2023). The psychological factors are depression, low social support, and lack of motivation (Siriarunrat et al., 2018), while the behavioral factors are low physical activity, smoking, and inappropriate dietary behavior (Kampan et al., 2021). However, a systematic review showed that dietary interventions alone resulted in a greater reduction in GWG (approximately 2.63 kg) compared to physical activity alone (approximately 1.04 kg) or combined interventions (approximately 1.35 kg) (Teede et al., 2022). Therefore, this study focuses exclusively on a dietary behavior program to maximize the potential effect.

Pregnant women with inappropriate dietary behavior, such as consuming fast foods (Moore et al., 2021), eating high-fat foods, and overeating (Bijlholt et al., 2020), consuming twice as much food as before pregnancy due to the belief that the fetus will be healthy (McDonald et al., 2020), choosing food from own favorite and aversions, consuming low-quality food, and consuming soft drinks but a few fruits and vegetables can experience excessive GWG because inappropriate dietary behavior is related to an imbalance in energy intake and energy expenditure. The excess energy is converted to fat and stored in the body, causing them to gain weight over the IOM recommendation during pregnancy (Bureau of Nutrition of Ministry of Public Health, 2020). Moreover, pregnant women who exceed the recommended GWG often lack knowledge related to food choices (Plante et al., 2020; Willmott et al., 2021), lack information on weight gain in each trimester of gestation, and lack the opportunity to obtain support and advice on a proper and balanced diet during pregnancy (Flannery et al., 2020). Furthermore, dietary behaviors to achieve appropriate weight gain are related to an individual’s capability, opportunity, and motivation (Willmott et al., 2021). This suggests that pregnant women’s knowledge can be improved through appropriate information by creating opportunities for support and advice on dietary behavior and GWG within the IOM recommendations, and by enhancing motivation to maintain sustainable, appropriate dietary behavior change.

However, gaps in GWG control through dietary behavior still exist, as pregnant women often lack the opportunity, motivation, and ability to make long-term dietary changes to manage weight gain. Therefore, new interventions should also focus on boosting motivation for changing dietary habits, in addition to addressing the capability, opportunity, and sustainability of long-term behavior change. To address these gaps, we developed a dietary behavior program for appropriate GWG using the Capability-Opportunity-Motivation Behavior (COM-B) model, which has also been used to understand long-term behavior change (Li & Li, 2025).

The COM-B model, developed by Susan Michie, Maartje M. van Stralen, and Robert West, is used to design behavioral change interventions. There are three components that affect behavior change: capability, motivation, and opportunity related to the target behavior (McClintic et al., 2022). Capability consists of psychological capability that relates to knowledge / psychological strength, skill, or stamina, and physical capability that relates to physical ability, skills, or stamina. Opportunity comprises physical opportunities that facilitate behavior change, such as time, location, resources, and environment, while social opportunity relates to cultural norms, social cues, social structure, and interpersonal influence or support. Lastly, motivation is composed of reflective motivation that involves reflection and planning, and automatic motivation that encompasses desires, impulses, and inhibitions, with behavior as a target behavior (McClintic et al., 2022). The intervention to control GWG should be designed to encompass the main COM-B component for appropriate dietary behavior change. It is necessary to enhance the capability, motivation, and opportunity to change dietary behavior for gaining weight during pregnancy within the IOM recommendations in order to prevent maternal and fetal complications from GWG over the recommended limit.

In the Thai context, inexpensive, energy-dense street foods are widely available, providing pregnant women with easy access to these foods. Moreover, many Thai pregnant women have limited knowledge and practices related to healthy eating and lack adequate healthcare support and motivation for appropriate behavioral change (Hashmi et al., 2018; Nuampa et al., 2023). Therefore, many pregnant women gain weight easily. While the traditional models, such as the Health Belief Model or the Theory of Planned Behavior, primarily emphasize individual knowledge, beliefs, or motivation, they may not provide adequate guidance for translating these factors into practical strategies for behavior change. Such limitations can be addressed by the COM-B model, which integrates capability, opportunity, and motivation, providing a more comprehensive framework for designing effective interventions. Therefore, this model is particularly suitable for creating an intervention for Thai pregnant women. Accordingly, this study aimed to examine the effect of the COM-B model of dietary behavior program for GWG among pregnant women with normal pre-pregnancy BMI.

Methods

Study Design

This study was a randomized controlled trial using a pre-posttest control group design to investigate the COM-B model of dietary behavior program for GWG. This study adhered to the 2025 Consolidated Standards of Reporting Trials (CONSORT) guidelines (Hopewell et al., 2025). Single blinding was used for the participants and the research assistants. Participants were aware only of their assigned group labels (Group 1 or Group 2), and research assistants were blinded to group allocation (experimental and control). Due to the behavioral nature of the intervention, which made complete blinding infeasible, the researcher who delivered the intervention, assessed outcomes, and analyzed the data was not blinded, potentially introducing performance bias. Although the researcher was aware of group allocation, participants and assistants were blinded to group identity to minimize bias. The study was conducted at Naresuan University Hospital, Bang Krathum Hospital, and Bangrakham Hospital, Thailand.

Sample/Participants

G-power 3.1 program (Faul et al., 2009) was used to calculate the sample size with a significance level of 0.05, a power of 0.8, and an effect size calculated from the effect size formula for group difference (d) = (mean of the treatment group - mean of the control group)/standard deviation of the control group. The effect size in previous research was 0.64 (Ruchat et al., 2012). This resulted in a sample size of 40 pregnant women in each group: the experimental and the control. After adding 20% to account for attrition in a randomized controlled trial (Dumville et al., 2006), the sample size was 48 pregnant women per group, for a total of 96. The participants were selected using purposive sampling based on the criteria.

The inclusion criteria for participants were pregnant women with a normal pre-pregnancy BMI (18.5-24.9 kg/m2), gestational age of 16–20 weeks, and cumulative GWG exceeding 0.50 kg/week at enrollment. Only those with a single pregnancy, without obstetric or medical complications, and with a low to middle dietary behavior score (28-83) were eligible. Additional requirements included the ability to read and write Thai, access to a mobile phone with the Line application, and willingness to participate.

The exclusion criteria were participants with complications during pregnancy, including pregnancy-induced hypertension, polyhydramnios, or fetal complications.

Participants were withdrawn from the study based on predefined discontinuation criteria to maintain protocol fidelity and data integrity. Participants who voluntarily withdrew from the program, attended less than 80% of the scheduled intervention sessions, or experienced preterm delivery or were diagnosed with gestational diabetes mellitus (GDM) after 20 weeks of gestation were excluded from further participation.

The random allocation sequence was generated and kept hidden by an independent nurse who was neither the researcher nor the nurse in the antenatal clinic, and who was not involved in data collection or intervention implementation. Permuted block randomization was used to assign participants to the experimental and control groups. A block size of four was selected to produce six balanced patterns between 2Es (experimental) and 2Cs (control): EECC, ECEC, ECCE, CEEC, CECE, CCEE. The allocation was then placed in an opaque sealed envelope. Later, a nurse at the antenatal clinic enrolled participants according to the envelope’s assignment, while the researcher was aware of the group allocation before the program began.

Instruments

The instruments for data collection consisted of demographic questionnaire used to assess individual information, including age, education level, marital status, religion, career, family income, number of family members, smoking history, weight before pregnancy, height, and pre-pregnancy BMI; and obstetric information, including last menstrual period, estimated date of delivery, gestational age (at the date of visit to the antenatal clinic), number of pregnancies, number of abortions, number of deliveries, number of living children, and age of living children.

A weight record form was used to record pregnant women’s weight during their visits to the antenatal clinic. They were weighed using an Omron weighing scale (Model HN-289).

The Twenty-Four-Hour Dietary Recall Record (24-hR) was developed by the Bureau of Nutrition, Department of Health, Ministry of Public Health (Kieudee & Saengrut, 2020). This open-ended format recorded the food list one day before pregnant women visited the antenatal clinic. The food items were entered into the INMUCAL-Nutrients program to calculate energy intake. However, as the 24-hour recall was conducted only once at each time point, the data may be subject to recall bias and may not fully represent participants’ habitual dietary intake.

The Dietary Behavior During Pregnancy Questionnaire was developed by Jantradee (2013) to assess pregnant women’s dietary behavior. It consists of 28 items in three domains: 1) eating patterns, 2) cooking and preparing food, and 3) types of food, with 13 positive items and 15 negative items. Items are rated on a 4-point scale ranging from “always perform” to “never perform.” The total score ranges from 28 to 112, with a higher score indicating better dietary behavior (Jantradee, 2013). The quality of the questionnaire was ensured with a content validity index (CVI) of 0.86 and a Cronbach’s alpha coefficient of 0.83.

Interventions

Experimental group

In addition to usual care, pregnant women in the experimental group received the COM-B model of dietary behavior program, delivered over 14 structured sessions. This program went far beyond usual care by targeting all three components of the COM-B framework. Capability was built through education classes and hands-on training in calculating daily energy intake and arranging food items. The opportunity was supported by distributing dietary pamphlets and providing continuous access to the researcher via the Line application and a mobile phone. Motivation was actively reinforced through the Z-Sizes Ladies application (a 3D body-shape simulation tool), which provided visual feedback on body changes, personalized text messages twice a week (from 20 to 32 weeks of gestation), and direct feedback during antenatal visits. Participants received praise when their dietary behavior and GWG were appropriate and corrective reminders when they were not. Assessments of dietary behavior and GWG were conducted at 20, 28, 32, and 36 weeks of gestational age.

Control group

The participants in the control group received only usual care, including history taking, urine and blood tests, abdominal assessment, weight and blood pressure monitoring, general health advice at each antenatal visit, dental checks, ultrasound, and vaccination according to gestational age. Dietary recommendations were provided by nurses at the first antenatal visit, based on the Mother and Child Health Handbook. This advice was broad and non-personalized. Nutritionist input was provided only for women at high risk of gestational diabetes. In contrast to the experimental group, the control group did not receive personalized feedback, structured education sessions, or continuous follow-up via digital support (e.g., Line, mobile phone, text messages).

Data Collection

Data collection was conducted between November 2023 and October 2024, after the research project was approved by the Research Ethics Committee and a permission letter was received from the directors of all hospitals. The researcher met eligible participants at 20 gestational weeks when they visited the antenatal clinic and built rapport. Then, the researcher provided information about the study and reiterated the participant’s rights before starting the study. The program was started when the pregnant woman’s gestational age was 20 weeks. Moreover, dietary behavior changes for appropriate gestational weight gain during the program were monitored at 28, 32, and 36 weeks of gestation in both the experimental and control groups. After that, GWG was assessed at 36 weeks of gestational age.

Data Analysis

Data were analyzed using SPSS version 26. The normality of the data distribution was checked and confirmed (p > 0.05) by the Kolmogorov-Smirnov test. Descriptive statistics were used to summarize demographic data. Baseline characteristics between groups were compared using an independent t-test for continuous variables and a Chi-square test for categorical variables. The primary outcome, the mean difference of GWG with IOM recommendation at 36 weeks between the groups, was analyzed using an independent t-test. A dependent t-test was used to assess changes within the experimental group from baseline to 36 weeks. Both intention-to-treat (ITT) and per-protocol (PP) analyses were performed. ITT was performed on 48 participants in both the experimental and control groups. Missing data were handled using the Last Observation Carried Forward (LOCF) imputation method. PP analysis was performed on 43 participants in the experimental group and 42 in the control group who completed the program over a four-month period. All statistical tests were two-tailed with a significance level set at p < 0.05. Analysis of covariance (ANCOVA) was performed to adjust for baseline imbalances between groups. The covariates included age and education level, which differed significantly between the groups at baseline. Adjusted mean differences and effect sizes were reported for both ITT and PP analyses.

Ethical Considerations

Ethical approval was obtained from the Research Ethics Committee of the Faculty of Nursing Chiang Mai University (Study code: 2566–EXP084). The trial was registered with the Thai Clinical Trials Registry (TCTR20230907001) prior to participant enrolment (https://www.thaiclinicaltrials.org/#). Informed consent assured patients of anonymity, freedom to withdraw from the study at any time, and data security.

Results

Participants

The study took place between November 2023 and October 2024 with 96 pregnant women. Finally, 85 people (88.54%) remained until the program completion for a period of four months (experimental group n = 43, control group n = 42). There were 11 participants (11.45%) who dropped out of the study (experimental group n = 5, control group n = 6) due to migration to other provinces, GDM, preterm delivery, and lost to follow-up (Figure 1). However, no adverse events were reported in any group throughout the program.

Figure 1.

Figure 1

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CONSORT flow chart

Although participants were randomly assigned, there were significant differences in baseline characteristics between the groups. The control group was, on average, older (27.85 vs. 25.23 years, p = 0.01) and had a higher level of education (p = 0.033) than the experimental group (Table 1). These differences represented potential confounders. The other demographics showed no significant differences between the experimental and control groups. However, to explore the potential influence of these imbalances, an exploratory post hoc subgroup analysis was conducted. These analyses were not prespecified and should be interpreted cautiously. The participants were divided by age (≤ 30 and > 30 years) and by education (lower or diploma and higher education). The results indicated that older pregnant women generally gained more weight than younger pregnant women, although the difference was not apparent at 20 weeks. Conversely, no meaningful differences in GWG were observed between pregnant women with lower and higher levels of education across gestation. These findings from the subgroup were preliminary.

Table 1.

Demographic data of pregnant women in the experimental group and the control group (N = 96)

Characteristics Experimental Group (n = 48) Control Group (n = 48) p
Age (years) 0.010a
Mean (SD) 25.23 (4.57) 27.85 (5.16)
Range 20-34 20-34
Education level, n (%) 0.033b
Primary school 1 (2.1) 6 (12.5)
Junior high school 13 (27.0) 12 (25.0)
Senior high school 19 (39.6) 10 (20.8)
Vocational certificate / Higher vocational diploma 6 (12.5) 3 (6.3)
Bachelor’s degree 9 (18.8) 13 (27.1)
Higher Bachelor’s degree 0 (0) 4 (8.3)
Marital status, n (%) 0.513b
Married 46 (95.8) 46 (95.8)
Separated 2 (4.2) 1 (2.1)
Divorced 0 (0) 1 (2.1)
Religion, n (%) —
Buddhist 48 (100) 48 (100)
Occupation, n (%) 0.073c
Employed 24 (50.0) 16 (33.3)
Unemployed 24 (50.0) 32 (66.7)
Occupation intensity level, n (%) 1.000b
Low 40 (83.3) 40 (83.3)
Moderate to high 8 (16.7) 8 (16.7)
Family income (Baht/month), n (%) 0.226b
<10,000 6 (12.5) 13 (27.0)
10,001–15,000 12 (25.0) 9 (18.8)
15,001–20,000 12 (25.0) 7 (14.6)
>20,001 18 (37.5) 19 (39.6)
Number of family members, n (%) 0.500c
2 9 (18.8) 10 (20.8)
>2 39 (81.2) 38 (79.2)
Smoking history, n (%) 0.753c
No 47 (97.9) 47 (97.9)
Yes 1 (2.1) 1 (2.1)
Body weight before pregnancy (kg) 0.538a
Mean (SD) 54.94 (8.35) 55.92 (7.12)
Range 41-75 40-68
Pre-pregnancy BMI (kg/m2) 0.301a
Mean (SD) 21.38 (2.06) 21.79 (1.81)
Range 18.55–24.88 18.55-24.38
Body weight at GA 20 weeks (kg) 0.535a
Mean (SD) 60.28 (8.35) 61.28 (7.26)
Range 45.10-82.10 45.70-75.00
Mean GWG relative to IOM standard at GA 20 weeks 0.740a
Mean (SD) 1.98 (1.99) 1.86 (1.53)
Range 0.10-7.30 0.10-7.50
Gestational age at recruitment (weeks) 0.527a
Mean (SD) 19.17 (1.41) 18.85 (1.68)
Range 16+1-20+6 16-20+6
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Note.

a

Independent t-test.

b

Chi-square test.

c

Fisher’s exact test. “—” indicates no statistical test performed due to uniform distribution. 1 USD = 32.40 baht (date of reference 20/7/25)

Mean Difference of GWG Based on IOM Recommendation

At 36 weeks’ gestation, pregnant women in the experimental group gained significantly less weight than those in the control group, after adjusting for maternal age and education. The ITT analysis revealed an adjusted mean difference of -2.227 kg (95% CI -3.753 to -0.700, p = 0.005, partial η2 = 0.084), while the PP analysis revealed a mean difference of -2.648 kg (95% CI -4.310 to -0.987, p = 0.002, partial η2 = 0.110) (Table 4). These adjusted analyses were defined as the primary outcomes to account for baseline differences between groups.

Table 4.

Adjusted analysis of Gestational Weight Gain (GWG) at 36 weeks controlling for age and education level

Analysis Group Comparison Adjusted Mean Difference (kg) 95% CI p Partial η2
ITT Intervention vs. Control -2.227 -3.753 to -0.700 0.005 0.084
PP Intervention vs. Control -2.648 -4.310 to -0.987 0.002 0.110
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Note. p <0.05. ANCOVA adjusted for age and education level. ITT = intention-to-treat analysis. PP = per-protocol analysis.

Unadjusted analyses comparing gestational weight gain (GWG) to the IOM standard were also conducted. In the ITT analysis, the experimental group’s mean GWG was 0.14 kg below the standard (SD = 3.29), while the control group exceeded the standard by 2.10 kg (SD = 3.86), yielding a statistically significant difference (t(94) = -3.071, 95% CI -3.711 to -0.796, Cohen’s d = 0.62, p = 0.003). Similarly, the PP analysis revealed a mean GWG of 0.04 kg below the standard in the experimental group versus 2.55 kg above the standard in the control group (95% CI -4.182 to -1.025, Cohen’s d = 0.71, p = 0.002) (Table 2 and Table 3). These results indicated that the COM-B model dietary behavior program effectively reduced GWG, and the effect remained significant after adjusting for potential confounders.

Table 2.

Weight gain in the experimental group from 20 to 36 weeks (Dependent t-test)

Gestational Age (week) Method Cumulative Mean (SD) Mean Difference with IOM (SD) t p 95% CI Cohen’s d Effect Size
GA 20 → GA 36 ITT 5.33 (2.13)b → 10.98 (3.65)b 1.98 (1.99)b → -0.14 (3.29)b 5.18 <0.001 1.305 to 2.963 0.75 Large
GA 20 → GA 36 PP 5.36 (2.23)d → 10.31 (3.71)d 2.02 (2.08)d → -0.04 (3.43)d 4.58 <0.001 1.159 to 2.989 0.70 Large
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Note.

b

n = 48 participants.

d

n = 43 participants. p <0.05. ITT = intention-to-treat analysis. PP = per-protocol analysis.

Table 3.

Mean difference of weight gain based on IOM between groups at 36 weeks (Independent t-test)

Gestational Age (week) Method Control Group Mean Difference with IOM (SD) Control Group Cumulative Mean (SD) Experimental Group Mean Difference with IOM (SD) Experimental Group Cumulative Mean (SD) t p 95% CI Cohen’s d Effect Size
GA 36 ITT 2.10 (3.86)b 13.18 (4.33)b -0.14 (3.29)b 10.89 (3.65)b -3.07 0.003 -3.711 to -0.796 0.62 Large
GA 36 PP 2.55 (3.87)c 13.31 (4.08)c -0.04 (3.43)d 10.31 (3.71)d -3.28 0.002 -4.182 to -1.025 0.71 Large
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Note. p <0.05. ITT = intention-to-treat analysis. PP = per-protocol analysis.

b

n = 48 participants.

c

n = 42 participants.

d

n = 43 participants.

Although dietary behavior change was not defined as a secondary outcome, dietary behavior was monitored. Pregnant women in the experimental group had higher dietary behavior scores after receiving the program at 28, 32, and 36 weeks of gestation. Similarly, pregnant women who received the COM-B model of dietary behavior program had higher dietary behavior scores than pregnant women who received only usual care (ITT: mean score = 87.50, SD = 6.80 versus 82.47, SD = 6.64; PP: mean score = 87.67, SD = 7.18 versus 81.97, SD = 6.96)

Discussion

This study investigated the effect of the COM-B model of dietary behavior program on GWG among pregnant women. After adjusting for maternal age and education, the results showed that pregnant women who received the program gained less weight than those receiving usual care, with an approximate reduction of 2.227 kg in the mean difference in GWG relative to the IOM recommendation at 36 weeks. This adjusted analysis represents the primary finding of the study. This finding suggests that the effect of the COM-B model of dietary behavior program was not explained by sociodemographic differences, supporting the robustness of the intervention across women with different backgrounds.

Meanwhile, unadjusted analysis showed a decrease of around 0.04-0.14 kg in the mean difference of GWG relative to the IOM recommendation at 36 weeks compared to before participating in the program at 20 weeks of gestation. Moreover, the mean difference of GWG and the IOM recommendation at 36 weeks for pregnant women who received the program was lower than that of pregnant women who received usual care. Even though GWG slightly decreased, this may contribute to a lower cumulative risk of complications such as GDM or pre-eclampsia, as the IOM recommends that GWG within the standard range reduces the risk of GDM and pre-eclampsia (Al Shekaili et al., 2024; Yan et al., 2025). However, we did not directly assess the incidence of pre-eclampsia or GDM. Therefore, further research is required to confirm if these slight variations in GWG lead to improved clinical outcomes. Nonetheless, although statistically significant, the observed difference in GWG (0.04-0.14 kg) was small and may have limited clinical significance. Therefore, these findings should be interpreted with caution, as the magnitude of change may not reflect a substantial clinical effect despite statistical significance.

The COM-B model of dietary behavior programs can enhance pregnant women’s dietary behaviors to control GWG. Pregnant women who participated in the program achieved greater capability through educational classes on food choices, food intake, energy intake, GWG, and training on choosing food items based on personal energy intake. This led to greater knowledge and understanding of how to prepare appropriate dietary behavior. Furthermore, they received a dietary booklet that could increase physical opportunity as a material resource for revision at home. Additionally, pregnant women could contact the researcher via telephone or the Line application, providing interpersonal support in the social opportunity aspect of the COM-B model. Interactions between pregnant women and the researcher created an opportunity for pregnant women to participate in their own healthcare, raising their awareness to perform appropriate healthy behaviors, facilitated by sources of support when needing suggestions on good behaviors.

Additionally, motivation was increased as a result of feedback on body changes related to weight gain during pregnancy through the Z-Sizes Ladies application (Sinthanayothin et al., 2022). Pregnant women were able to decide and plan to adopt appropriate dietary behaviors to control weight gain during pregnancy after viewing a 3D model of body changes. In addition, they received feedback on the results of food item arrangements based on energy intake and GWG within IOM recommendations. Text messages about dietary behavior were sent to pregnant women twice a week, serving as reminders for healthy dietary behaviors, with appropriate frequency, message duration, and content to activate awareness for good dietary behavior change and GWG control (Suwannato et al., 2019). The duration of text message delivery was long enough to motivate pregnant women to choose healthy foods and control their weight gain. Moreover, the researcher praised and expressed appreciation when they could control GWG within the IOM recommendations. Positive feedback from nurses, active listening, and appropriate tone of voice were key to effective weight management during pregnancy (Saarikko et al., 2021).

To promote behavior change, the activities in the COM-B model aimed to enhance capability, opportunity, and motivation to choose healthy foods, maintain a balanced energy intake, and manage appropriate GWG. Motivation related to GWG affects the maintenance of healthy behaviors; in particular, strong motivation can facilitate the initiation and maintenance of positive behaviors. Furthermore, adequate informational support and proficient behavioral skills that increase the ability to perform appropriate behaviors can help control GWG (You et al., 2023). Increasing capability through knowledge and skills in cooking healthy foods, adopting a healthy lifestyle, utilizing health technology to support weight management during pregnancy, and receiving family support to encourage healthy behaviors were all crucial for controlling GWG. In this study, gestational weight began to improve approximately one week after dietary behavior changes were implemented. Weight could decrease by around 0.45 kg if pregnant women consumed fewer than 500 calories per day. With a reduction in accumulated body fat, pregnant women could better manage GWG (Saarikko et al., 2021).

Pregnant women engaging in the program had a lower mean difference of GWG compared to IOM recommendation than pregnant women in usual care. This result was congruent with Garmendia et al. (2017), who found that pregnant women receiving a normative nutrition intervention had lower GWG than those receiving usual care (11.3 kg vs. 11.9 kg). Recent systematic reviews support these results, as Harrison et al. (2023) found that dietary interventions reduced GWG by an average of 3.91 kg, while Dewidar et al. (2023) demonstrated that nutrition counseling increased the likelihood of achieving GWG within recommendations by 1.8 times compared to usual care. Similarly, Li et al. (2024) revealed that pregnant women who received a comprehensive dietary intervention gained less weight within the first 12 weeks of the intervention than pregnant women in the control group (4.97±1.33 vs. 5.98±2.78, p = 0.029, respectively). Pregnant women who received knowledge and practiced skills related to diet were more confident and could apply this knowledge to maintain appropriate dietary behavior for controlling weight gain during pregnancy.

Implications

The results of this study support the effectiveness of the COM-B model of dietary behavior in controlling weight gain in pregnant women. Nurses should implement this program to enhance pregnant women’s practice of appropriate dietary behaviors to control GWG. Importantly, the activities in the COM-B model can increase pregnant women’s knowledge, capability, and awareness of appropriate GWG by changing their dietary behavior. Moreover, this program incorporated contact between healthcare workers and pregnant women to motivate and support favorable health behaviors. However, training should be provided for nurses who will be the key agents in implementing the program with pregnant women in antenatal clinics.

Moreover, technology should be integrated into nursing care because it is cost-effective, efficient, and accessible anytime and anywhere. At the policy and health system levels, the COM-B model for dietary behavior can be progressively incorporated into standard antenatal care, beginning in hospital antenatal clinics. Health administrators can facilitate this integration by incorporating the program into regular health education efforts and offering brief training sessions for nurses. This gradual incorporation might enhance the consistency of maternal nutrition guidance across the healthcare system and improve pregnancy outcomes.

Strengths and Limitations

This research involved various activities to enhance all dimensions of the COM-B model to change dietary behavior and control GWG. The Z-Sizes Ladies application, which displayed the model of body changes due to weight gain, stimulated the desire to control GWG. Pregnant women could also contact the researcher when they had problems with dietary behavior and weight control via the Line application or telephone. Finally, the duration of the outcome measurement was longer than in previous research. The first limitation of this research was the inability to randomize participants, resulting in imbalances in age and education levels across groups. The control group was significantly older and more educated, factors often associated with health behaviors. Higher education is typically associated with better health literacy and outcomes, suggesting the control group may have been more likely to manage their weight effectively, potentially introducing selection bias that underestimated the intervention’s true effect. To reduce this risk in future studies, stratified randomization that accounts for important factors such as age and education level should be used. Second, dietary intake was assessed using a single 24-hour recall, which may not accurately capture habitual diet and may introduce recall bias, as participants might underreport or overreport food intake. Third, the follow-up extended only to 36 weeks’ gestation; no postpartum maternal weight or neonatal outcomes were measured, limiting conclusions about long-term benefits. Additionally, excluding women who developed GDM after 20 weeks reduced generalizability. Furthermore, those who prepare food for pregnant women were not invited to join the study. Finally, there was no assessment or guideline for monitoring physical activity or edema during pregnancy, which could have confounded the research results.

Conclusion

This study demonstrated that, after adjustment for maternal age and education, the COM-B model of dietary behavior program was effective in controlling GWG. The program, supported by technology, is applicable for integration into routine antenatal care, particularly through nurse-led interventions. Future research should use stratified randomization to balance significant baseline factors, such as family members or food preparers, and extend follow-up into the postpartum period to assess long-term outcomes. Finally, a guideline for assessing and controlling physical activity and physical edema should be developed.

Acknowledgment

This study is related to the PhD dissertation project of the first author in the Doctor of Philosophy in Nursing Science program at Chiang Mai University. We thank all participants in the research, and thank Naresuan University Hospital, Bang Krathum Hospital, Bangrakham Hospital, and Chiang Mai University, Thailand.

Funding Statement

Funding None.

Declaration of Conflicting Interest

No conflict of interest to declare in this study.

Author Contribution

Conceptualization: PB, PX, PK, PT.

Data handling: PB, PX.

Experiment design: PB, PX.

Data analysis: PB, PX.

Provision of study materials and equipment: PB, PX, PK, PT.

Study validation: PB, PX, PK, PT.

Supervision: PB, PX, PK, PT.

Data presentation: PB, PX, PK, PT.

Draft preparation: PB, PX.

Study consultation: PB, PX, PK, PT.

Writing and reviewing: PB, PX, PK, PT.

Project administration: PB, PX.

Author Biography

Pantipa Buakhai is a PhD. Student at the Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand.

Assoc. Prof. Dr. Piyanut Xuto, PhD, is an Associate Professor at the Obstetrics and Gynaecology Nursing Department, Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand.

Assoc. Prof. Dr. Pimpaporn Klunklin, PhD, is an Associate Professor at the Pediatric Nursing Department, Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand.

Assoc. Prof. Dr. Petsunee Thungjaroenkul, PhD, is an Associate Professor at the Nursing Administration Department, Faculty of Nursing, Chiang Mai University, Chiang Mai, Thailand.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Declaration of Use of AI in Scientific Writing

Nothing to declare.

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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 datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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