Sehee Kim; Sukhee Ahn

doi:10.4040/jkan.24052

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Research Paper
Perceptual Factors Associated with Gestational Weight Gain: A Cross-Sectional Survey: Sehee Kim¹, Sukhee Ahn²; Journal of Korean Academy of Nursing 2024;54(4):495-508.
DOI: https://doi.org/10.4040/jkan.24052
Published online: November 1, 2024

¹Department of Nursing, Pai Chai University, Daejeon, Korea

²College of Nursing, Chungnam National University, Daejeon, Korea

Address reprint requests to : Ahn, Sukhee College of Nursing, Chungnam National University, 266 Munhwa-ro, Jung-gu, Daejeon 35015, Korea Tel: +82-42-580-8324 Fax: +82-42-580-8309 E-mail: sukheeahn@cnu.ac.kr

• Received: April 24, 2024 • Revised: July 24, 2024 • Accepted: September 3, 2024

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License (http://creativecommons.org/licenses/by-nd/4.0) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

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Abstract
INTRODUCTION
METHODS
RESULTS
DISCUSSION
CONCLUSION
Article Information
References

Abstract

Purpose
Recent years have seen an increase in the number of pregnant women whose weight gain during pregnancy exceeds the recommended range. This study was intended to determine the relationships among demographic attributes, key perceptual factors, and gestational weight gain (GWG).
Methods
This cross-sectional study was conducted between April and July 2022. First-time pregnant women beyond 36 weeks of gestation who were recruited via social media completed an online survey. Data were analyzed using one-way ANOVA, chi-square test, and logistic regression, all performed using SPSS software.
Results
Of the 369 participants, 63 (17.1%) exceeded the recommended GWG guidelines, while 148 (40.1%) fell within the recommended range, and the remaining 158 (42.8%) had inadequate GWG. Being overweight or obese before pregnancy significantly increased the risk of excessive GWG (p < .001). This risk was also significantly greater for women with low internal weight locus of control (OR = 0.58, 95% CI 0.41~0.82), high external weight locus of control (OR = 1.75, 95% CI 1.31~2.34), and negative body image (OR = 0.62, 95% CI 0.51~0.75).
Conclusion
The growing trend of excessive GWG among pregnant women is influenced by a combination of prepregnancy body mass index (BMI) and perceptual factors, including weight locus of control and body image. These findings underscore the need to implement weight management intervention strategies before pregnancy, taking into consideration BMI, and to enhance positive body image and internal locus of control.
Key words: Gestational Weight Gain; Pregnancy; Body Mass Index; Prenatal Care

INTRODUCTION

Gestational weight gain (GWG) is expected due to the normal physiological changes associated with pregnancy. Correct GWG is crucial to ensuring favorable pregnancy outcomes [1]. The Institute of Medicine (IOM) established recommendations for GWG based on the mother’s prepregnancy body mass index (BMI). According to these recommendations, underweight, healthy-weight, overweight, and obese women are advised to gain 12.5 to 18.0, 11.5 to 16.0, 7.0 to 11.5, and 5.0 to 9.0 kg, respectively [2].

The IOM recommendations for GWG are the most widely used, but they were developed primarily from observational studies in high-income, mostly Western countries. Also, the IOM recommendations are based on the international BMI classification by the World Health Organization, which differs from the Asia-Pacific BMI criteria. The international BMI cutoff values for overweight and obesity are 25.0 and 30.0 kg/m², respectively, while those for the Asia-Pacific BMI are 23.0 and 25.0 kg/m², respectively [3]. The recognition that Asian populations have different BMI cutoff values raises concerns about the appropriateness of applying IOM recommendations to Asian populations. Studies in Asia countries have proposed considering revised IOM recommendations for GWG in line with the Asian BMI categories [4-6]. However, specific GWG recommendations for Asian populations have not been determined, the Korean Society of Obstetrics and Gynecology currently applies the Asia-Pacific BMI criteria alongside the IOM GWG recommendations in clinical practice in South Korea [6].

A recent systematic review found that the GWG was higher than that recommended by the IOM GWG guidelines in 47.0% of pregnant women, reflecting an increasing prevalence of excessive GWG in the US [7]. This increasing trend is also present in Europe and Asia, where 51.0% and 37.0% of pregnant women exceed the recommended GWG, respectively [8]. Although no studies or aggregated data were found on the prevalence of excessive GWG, the rates of overweight and obesity among women of childbearing age have risen dramatically in Korea [9]. Given that obese women are more likely to gain excessive weight during pregnancy, an increasing trend in excessive GWG in Korea is anticipated [10]. Excessive GWG can put pregnant women at risk of pregnancy complications such as gestational diabetes, preeclampsia, maternal death, and severe maternal morbidity [11,12]. A meta-analysis indicated that long-term health impacts of excessive GWG include postpartum weight retention, with a mean of 4.7 kg at more than 15 years postpartum [13]. Increasing GWG contributes to the increasing prevalence of overweight and obesity in women that in turn increases the risk of metabolic health problems [14]. Exceeding the recommended GWG is identified as a risk factor for infants experiencing conditions such as macrosomia and being large for the gestational age [7]. More importantly, infants born to women with obesity during pregnancy have higher risks of cardiac complications and pulmonary diseases [15].

GWG is a modifiable risk factor that impacts both maternal and infant health, with it being influenced by a multitude of factors. Demographic factors including age, education, income, and prepregnancy BMI have an impact on GWG. In particular, prepregnancy BMI has been consistently associated with excessive GWG in many studies [10]. A higher prevalence of excessive GWG was observed among mothers who were overweight before pregnancy, as was a lower income [10]. Older pregnant women were less likely to exhibit excessive GWG compared with younger (including adolescent) pregnant women [16]. A systematic review of the influence of socioeconomic status on GWG found that less-educated women have a higher risk of exceeding recommended weight-gain limits [17]. While the relationship between these factors and GWG are relatively well known, these factors are not modifiable. Where it is possible to predict the risk of GWG based on demographic factors, changing them through interventions remains a considerable challenge.

Multiple researches have indicated that the modifiable behavioral factors that directly affect GWG were diet and physical activity [18]. Promoting favorable pregnancy outcomes through an appropriate GWG requires focusing on weight-related behaviors such as diet and physical activity. According to the Health Belief Model, health-related actions or behaviors are influenced by people’s beliefs, making this model a useful basis for developing interventions to promote healthy behaviors [19]. Similarly, the Theory of Planned Behavior takes into account the individual’s attitudes and perceptions toward behaviors as accurate predictors of their behavioral intentions and behaviors [20]. These psychological theories connecting beliefs with behaviors explain that the willingness of a person to change their health behaviors primarily comes from their perceptions. Researchers have utilized perceptual factors based on those theories to predict and achieve optimal health-related behaviors [21].

A systematic review investigating factors related to perceptions has found links to excessive GWG [22]. However, that review included insufficient independent studies to allow the effect of each factor on GWG to be evaluated properly leading to inconsistent results among studies. Two of four studies found an association between body image dissatisfaction and excess GWG while the other two reported non-significant associations. Weight locus of control appeared as a protective factor in one study but had a non-significant effect in two others. Self-efficacy showed no association with excess GWG in two studies with a third reporting a negative association. One study examining barriers to healthy eating with excessive GWG was underpowered. Knowledge about weight gain was a risk factor for GWG in one study. Moreover, a few recent studies have produced evidence for perceptual factors being associated with GWG, but the reported results have remained inconsistent [23-26].

The increase in excessive GWG is a global trend and is also expected in Korea. It is a significant women’s health issue as it causes various pregnancy complications and threatens fetal health. Since GWG can be corrected and managed with appropriate interventions, it is important to identify the factors that influence GWG for effective intervention. Understanding perceptual factors that affect GWG is important since they can be modified to achieve improved pregnancy outcomes. Nevertheless, very few reported studies have identified perceptual factors and their influence on GWG, and hence this relationship remains somewhat ambiguous. Moreover, these factors need to be examined in different geographic regions given that most studies have focused on Western countries. Research has confirmed that perceptions are shaped by the cultural context [27]. Therefore, we hypothesized that GWG is affected by perceptual factors including body image, perceived barriers, weight locus of control, self-efficacy, and knowledge of the fetal and maternal risks of excessive GWG along with demographic characteristics. The aim of this study was to determine the associations between demographic characteristics, multiple perceptual factors, and GWG.

METHODS

1. Study design and participants

This cross-sectional study was conducted from April to July 2022. Pregnant women were recruited online via Korean social media platforms due to the COVID-19 pandemic being active. All women who provided verbal consent to participate were reviewed, and those meeting the eligibility criteria submitted an online consent form and completed a questionnaire. In order to ensure that the research conformed with ethical requirements, informed consents were obtained and participant confidentiality was ensured. The following inclusion criteria were applied: (1) nullipara, (2) more than 36 weeks of gestation, (3) single pregnancy, (4) not having chronic conditions such as hypertension, cardiopulmonary conditions, or diabetes before becoming pregnant, (5) not having pregnancy complications, and (6) not being admitted to hospital. The criteria were determined based on a low-risk subgroup. Setting the threshold at 36 weeks minimizes the inclusion of preterm births which might confound the analysis due to different medical complications and growth patterns. Additionally, many research articles on GWG and pregnancy outcomes include pregnancies starting from 36 weeks to ensure comprehensive data analysis [28,29]. The data were obtained using a self-administered questionnaire that collected information on current weight, prepregnancy weight, perceptions regarding body image, perceived barrier, weight locus of control, self-efficacy, and knowledge of the fetal and maternal risks of excessive GWG, and demographic information such as age, education, income, and occupation. The sample size for the correlation coefficient was calculated using G*Power (version 3.1). In the absence of an effect size from a similar study, a medium effect size of .17 derived from social psychology research was utilized and a power of .90 was set to determine a sample size of 359 [30]. Additionally, sample size calculations based on odds ratios (ORs) from a previous study yielded a range of 200 to 600 due to varying ORs across multiple perceptual factors [24]. Considering the sample size of 320 used in the previous study, it was concluded that a sample size of 300 to 400 would be appropriate for this research. The study initially enrolled 377 women, and after excluding 8 of them due to missing data (physical activity hour and rest hour), the final analysis was applied to a group of 369 women.

2. Measures

1) Questionnaire on perceptions related to weight

The questionnaire measured the following 5 perceptual factors across 60 individual questions: (1) self-efficacy (29 questions), which measured the degree of confidence in changing diet and exercise behaviors [23], (2) weight locus of control (4 questions), which measured the degree to which a person feels that behavior change is within their personal control (internal weight locus of control, 2 questions) or outside their personal control (external weight locus of control, 2 questions) [23,31], (3) perceived barriers to healthy behaviors (12 questions), which measured the degree to which a person experiences barriers that impede their ability to engage in healthy behaviors despite having the motivation to do so [23], (4) knowledge of the fetal and maternal risks of excessive GWG (10 questions), which measured the degree of awareness of a risk of excessive GWG [32], and (5) body image (5 modified questions), which measured body dissatisfaction among pregnant women [33,34]. Regarding factor 5, the original Body Attitudes Questionnaire consists of 44 items. In a study that evaluated the suitability of this tool for pregnant women, the appropriate items identified for pregnant women in this study were evaluated and used following expert review. Factors 1 and 3 were tools developed by de Jersey et al. [23] to measure self-efficacy regarding diet and exercise behaviors, and perceived barriers to healthy behaviors in pregnant women. Factor 2 was a tool developed by Saltzer [31] for obesity research which was later evaluated for suitability for use in pregnant women by de Jersey et al. [23]. Factor 4 was a tool developed by Ledoux et al. [32] to study knowledge factors related to appropriate weight gain during pregnancy. Factors 1, 2, 3, and 5 use a 1 to 5 numerical rating Likert scale with 1 representing the lowest and 5 representing the highest. The score range for factor 1 is 29 to 145, for factor 2 is 4 to 20, for factor 3 is 12 to 60, and for factor 5 is 5 to 25. Factor 4 consists of 10 true/false questions, with a score of 2 points for true and 1 point for false, giving an overall range of 10 to 20 points. We obtained approval for the tool from all authors via email.

The questionnaire was translated from English to Korean through a rigorous process involving two English experts. To ensure reliability, these experts conducted independent translation and back-translation. The back-translated version was then compared with the original to resolve any discrepancies. The Korean version of the questionnaire was reviewed for validity by an expert panel comprising four professors in women’s health nursing. Feedback was gathered through written evaluations where experts assessed the relevance and clarity of each item of measurement tools. Disagreements were resolved through discussion and consensus leading to revisions to enhance the tool’s validity. A pilot testing was conducted with target population.

The reliability of the questions for each factor was reported previously [23,32-34] and also demonstrated in this study. The internal consistency for each of the five factor scales varied from modest (Cronbach’s α ranging from .50 to < .70 to high (α ≥ .70) as follows: (1) self-efficacy (eating, α = .91; activity, α = .92 in the previous study [23] and .94 in this study), (2) weight locus of control (α = .51 in the previous study [23] and .70 in this study), (3) perceived barriers (eating, α = .72; activity, α = .74 in the previous study [23] and .81 in this study), (4) knowledge of the risks of excessive GWG (α = .86 in the previous study [32] and .81 in this study), (5) and body image (α = .87 for full scale; for pregnant women: α = .62~.93 in the previous study [33,34] and α = .74 in this study).

2) Weight and demographic measures

The maternal current weight and height were self-reported based on the records of measurements made at the most-recent prenatal visit. Participants were first asked about their weight at the time when their pregnancy was initially confirmed. The prepregnancy BMI was calculated using self-reported weight and height. The GWG was calculated by subtracting the most-current weight from the reported prepregnancy weight. The prepregnancy BMI was classified according to the Asia-Pacific BMI categories as follows: underweight (BMI < 18.5 kg/m²), normal weight (BMI = 18.5~22.9 kg/m²), and overweight or obese (BMI = 23.0~ 24.9 kg/m² or BMI > 25.0 kg/m²). The guidelines for GWG followed those in the American IOM 2009 Nutrition in Pregnancy Guidelines. According to these recommendations, underweight, normal-weight, overweight, and obese women are advised to gain 12.5~18.0, 11.5~16.0, 7.0~11.5, and 5.0~9.0 kg, respectively [2]. A self-administered questionnaire was used to collect information on age, education, occupation, income, and other factors.

3. Data analysis

Data were analyzed using IBM^® SPSS^® Statistics 26.0 (IBM Co.). The distributions of all continuous variables were assessed for normality. Descriptive statistical analyses were applied to demographic characteristics. The GWG for each participant was calculated as the difference between the weight at the time of the initial confirmation of pregnancy and the most-recent weight, and was classified as inadequate, normal, or excessive. The cutoff weight value was based on the recommendations of the American IOM 2009 guidelines and Asia-Pacific BMI categories [2]. One-way ANOVA and the chi-square test were used to compare proportions of participants meeting the GWG guidelines across different covariates. Crude and adjusted binary logistic regression analyses were used to identify the risk factors for excessive GWG compared with inadequate or normal GWG. Prepregnancy BMI and other demographic covariates were included in the regression model to account for their potential confounding effects. Crude logistic regression was applied to all potential variables simultaneously in each analysis. The logistic regression model was then adjusted by including covariates to control for potential confounding factors.

The results were presented as crude and adjusted ORs and 95% confidence intervals (CIs). Tests for multicollinearity revealed that this was not present between variables, and so all variables were included in the regression analysis. Two-tailed p-values of < .050 were considered indicative of statistical significance.

4. Ethical consideration

Ethical approval was obtained from the Chungnam National University Institutional Review Board (No. 202201-SB-002-01). The results obtained in the study were reported in accordance with the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines.

RESULTS

1. Weight and demographic predictors of excessive gestational weight gain

The demographic characteristics of 369 participants were categorized based on GWG in Table 1. The majority of participants were aged between 25 and 34 years with an average age of 31.4 years accounting for 64.8% of the total participants while the group aged between 35 and 44 years had an average age of 36.8 years representing 35.2%. Regarding education level, 344 participants (93.2%) had an associate degree or higher, and 95 participants (25.8%) were working while pregnant. The most common household income range was 2,000,000 to 3,990,000 KRW with 138 participants (37.4%). During pregnancy, 3 participants (0.8%) were smokers and 2 participants (0.5%) consumed alcohol. Most of the participants were identified as having an either underweight or normal prepregnancy BMI, were educated to college level or above, were not working currently, and neither smoked nor drank alcohol. The average prepregnancy weight of the participants was 55.04 kg (± 7.07), and the current average weight is 66.94 kg (± 7.91) indicating an average weight gain of 11.90 kg (± 4.08) during the pregnancy period. The 369 participants included 63 (17.1%) who had an excessive GWG while 158 (42.8%) had an inadequate GWG.

The differences in GWG groups according to participants’ characteristics were shown in Table 2. Among the participants, 42.9% with an overweight or obese prepregnancy BMI experienced excessive GWG, compared with only 6.3% of those with an underweight prepregnancy BMI. There were significant differences in prepregnancy BMI (p < .001) but not in age, education, income, or occupation among GWG groups. The statistical results for drinking alcohol and smoking could not be interpreted due to the large imbalances in the proportions within the population. The frequencies of participants in each IOM GWG category according to prepregnancy BMI are shown in Figure 1, which indicates that participants were classified according to the Asia-Pacific BMI criteria as opposed to the international BMI criteria. It was observed that the obese prepregnancy BMI group as defined by the Asia-Pacific BMI criteria experienced a higher rate of excessive GWG than did the overweight group, which was not evident in the graph depicting the international BMI.

2. Perceptual predictors of excessive gestational weight gain

The risk factors for excessive GWG compared with inadequate or normal GWG were analyzed using multivariate binary logistic regression (Table 3). The goodness-of-fit for both the crude and adjusted logistic regression models was assessed using the Hosmer and Lemeshow test. The crude model had a chi-square (χ²) value of 7.47 and a p-value of .487, indicating a good fit. Its Nagelkerke R² was .63 explaining 63% of the variance. The adjusted model showed an χ² value of 6.54 with a p-value of .586, also indicating a good fit. Its Nagelkerke R² was .60.

In the crude model, prepregnancy BMI was found to be the only demographic variable associated with excessive GWG. The risk of excessive GWG was significantly higher in women who were overweight or obese before pregnancy (p < .001). The perceptual factors of self-efficacy, weight locus of control, body image, and knowledge of the fetal risks of excessive GWG were significantly associated with excessive GWG. Excessive GWG occurred when pregnant women had low self-efficacy for healthy eating (OR = 0.96, 95% CI 0.93~0.99) and physical activity (OR = 0.95, 95% CI 0.92~0.98), low internal weight control (OR = 0.49, 95% CI 0.39~0.61), high external weight locus of control (OR = 1.78, 95% CI 1.47~2.14), negative body image (OR = 0.60, 95% CI 0.52~0.69), and lower level of knowledge of the fetal risks of excessive GWG (OR = 0.86, 95% CI 0.76~0.99).

In the adjusted model, the factors identified as increasing the risk of excessive GWG were being overweight or obese before pregnancy (p < .001) and having low internal weight locus of control (OR = 0.58, 95% CI 0.41~0.82), high external locus of control (OR = 1.75, 95% CI 1.31~2.34), and negative body image (OR = 0.62, 95% CI 0.51~0.75).

DISCUSSION

This study examined the risk factors associated with excessive GWG focusing on weight, demographic, and the perceptual risk factors such as self-efficacy, perceived, body image, knowledge, and weight locus of control. The factors found to increase the risk of excessive GWG included being overweight or obese before pregnancy, a low internal weight locus of control, a high external locus of control, and a negative body image. Self-efficacy and knowledge of risks of excessive GWG were not statistically significant.

In this study, 17.1% of participants exceeded the IOM recommendations for GWG, which is much lower than the proportion in Western countries such as the US and UK. In contrast, 42.8% of the participants had an inadequate GWG, which is a much higher proportion than in Western countries. A systematic review found that most pregnant women in Asian countries achieved a lower GWG than the IOM recommendations [4]. Most Asian pregnant women have an inadequate GWG relative to the IOM recommendations, and both excessive and inadequate GWG are linked to worse pregnancy outcomes such as cesarean sections, gestational diabetes, and being either large or small for the gestational age [4]. This review therefore highlights that the IOM recommendations for GWG might not be appropriate for Asian populations due to different body compositions, and hence that GWG limits for different BMI levels need to be revised specifically for Asian populations.

Prepregnancy BMI is a well-known predictor of GWG. A high prepregnancy BMI has been consistently linked to excessive GWG in numerous previous studies [35-37]. Although the statistical results include wide CIs when logistic regression analyses are applied to the relationship between prepregnancy BMI and GWG, they still support the presence of a correlation between high prepregnancy BMI and excessive GWG. This study advocate for the necessity of prepregnancy counseling for overweight and obese women and for the implementation of individualized GWG recommendations optimal pregnancy health. This situation emphasizes the need for targeted interventions and guidance on diet and physical activity to manage weight for pregnant women, especially among those with a higher prepregnancy BMI.

A woman’s perceptions may impact her weight-related behaviors that affect GWG [19]. We found that self-efficacy in the crude model and the weight locus of control in the adjusted model were significant factors influencing whether the GWG recommended by the IOM was achieved. A previous systematic review showed inconsistent results regarding the relationship between weight locus of control, self-efficacy, and excessive GWG [22]. However, this study provides evidence that individuals with higher self-efficacy are less likely to experience excessive GWG, indicating that confidence in managing weight during pregnancy may play a protective role. A high internal weight locus of control is also linked to a decreased risk, highlighting the importance of personal agency in weight management. A low external weight locus of control is associated with a higher risk of excessive GWG, suggesting that attributing weight control to external factors may increase susceptibility to gaining excess weight. These results underscore the importance of interventions that enhance self-efficacy and promote an internal locus of control to prevent excessive GWG. However, it is necessary to understand the relationship between self-efficacy and locus of control to develop effective intervention strategies. Researchers have consistently demonstrated a strong association between self-efficacy and locus of control, with self-efficacy tending to be higher in individuals with an internal locus of control and lower in those with an external locus of control [38-40]. Thus, as interconnected individual beliefs, self-efficacy and locus of control can influence each other and potentially enhance desired behaviors. Locus of control as a personal trait tends to be a fixed characteristic and may be challenging to modify through interventions. However, self-efficacy is a modifiable factor. Since self-efficacy and locus of control exhibit a reciprocal influence, enhancing self-efficacy can potentially improve locus of control. This reciprocal relationship suggests that strengthening self-efficacy can complementarily enhance positive outcomes. This relationship has also been indirectly revealed in several other studies [41,42]. A previous study provided evidence for positive associations between healthy eating and physical activity self-efficacy and achieving healthy GWG while finding that a reduced probability of meeting weight gain guidelines was attributed to an external weight locus of control [24]. Therefore, it is necessary to identify pregnant women with a high external weight locus of control before or during pregnancy and implement enhanced self-efficacy nursing interventions for them.

Another factor that strongly influenced GWG in this study was body image. The results of this study indicate that a positive body image is significantly associated with a lower risk of excessive GWG. This finding suggests that individuals who perceive their bodies positively may be more likely to engage in behaviors that prevent excessive weight gain during pregnancy. Understanding the role of body image in influencing weight-related behaviors can help tailor interventions to promote healthy GWG. The natural changes in a woman’s body during pregnancy is a natural process and can have a profound impact on body image. It is evident that body image significantly influences weight gain in the general population [43]. A recent systematic review examining the association between body image and GWG over the past five years found that poor body image was associated with a higher GWG [44]. Moreover, body image has a positive predictive effect on self-efficacy, and some scholars suggest that body image influences physical exercise behavior through self-efficacy, indicating a mediating role of self-efficacy between body image and behavior [45]. The increasing recognition of the importance of body image may serve as a crucial target for health interventions. Interventions such as mindfulness, cognitive-behavioral therapy, and body-positive approaches have shown promising results in improving body image [46-48]. Improving body image through interventions is also expected to positively affect self-efficacy.

These psychologically oriented interventions have not been widely applied to pregnant women for managing GWG. Most weight-related interventions have instead focused directly on diet and physical activity. To achieve ideal weight gain during pregnancy, it is essential to emphasize assessments of prepregnancy BMI and weight locus of control prior to pregnancy and to implement of tailored interventions that enhance self-efficacy and body image during pregnancy.

Perceived barriers to healthy eating and physical activity are not significantly associated with the risk of excessive GWG in this study. While pregnant individuals acknowledge perceived barriers, these barriers might not be strong enough to affect actual behavior change, especially if individuals are receiving support or guidance through structured programs or interventions. For instance, a study found that even during challenging circumstances such as the COVID-19 pandemic, perceived barriers did not significantly impact GWG [49]. This indicates that other factors may play a more crucial role in influencing GWG than perceived barriers alone. It emphasizes the need to focus on other determinants such as prepregnancy BMI, self-efficacy, locus of control, and body image that could impact weight gain during pregnancy for developing more comprehensive intervention strategies.

1. Nursing implications

Health care providers (HCPs) should advocate and participate in prepregnancy counseling sessions for overweight and obese women. This includes providing information on the risks associated with excessive GWG and discussing strategies for achieving optimal pregnancy health through individualized GWG recommendations. Given that self-efficacy is a key predictor of behavioral changes, including dietary modification, physical activity, and weight control, HCPs should incorporate strategies that enhance self-efficacy in prenatal interventions. This could involve setting achievable goals, providing positive reinforcement, and facilitating peer support groups. HCPs need to assess and understand an individual’s locus of control before implementing tailored interventions. This understanding can guide the development of personalized strategies that empower women to take control of their weight management. There is a need to broaden the scope of interventions beyond diet and physical activity to include those that address body image. HCPs should consider integrating mindfulness practices, cognitive-behavioral therapy, and body-positive approaches into prenatal care. These interventions can help improve women’s perceptions of their bodies which is crucial for mental and physical health during pregnancy. By addressing these implications, nursing practices can significantly contribute to improved health outcomes for pregnant women, particularly in managing GWG effectively.

2. Limitations and suggestions

The inconsistent sample sizes among the prepregnancy BMI groups and the considerable variability in prepregnancy BMI mean that there are potential limitations in interpreting the results of this study. The GWG was measured over 36 weeks, which might not accurately reflect the total GWG before giving birth because women gain the most weight during the third trimester, and the speed of GWG differs between subjects. The relatively low reliability of Rotter’s Locus of Control scale (4 items) is stable over a considerable period [50]. This can occur in research tools with a small number of items. Therefore, the reliability of the Weight Locus of Control scale was deemed acceptable and used in this study. The study design made it difficult to determine whether there were causal associations between the investigated variables and GWG. The GWG during the first or second trimester may already have affected perceptions about self-efficacy and body image when the survey was conducted. Future studies, therefore, need to determine perceptions before or during early pregnancy or track their changes throughout pregnancy. Most of the participants in this study were Korean, ages 25 to 35 years, and were educated to college level or above; hence, they did not accurately represent the vulnerable population. Future research should aim to include more diverse populations to ensure that the included samples of women are representative.

CONCLUSION

This study has highlighted the increasing health problem of pregnant women with excessive GWG and several of its influencing factors. It has demonstrated the complexity of GWG involving multiple perceptual factors. The results suggest relationships exist between prepregnancy BMI, perceptual factors, and excessive GWG. The study has provided evidence for potential intervention strategies to improve pregnancy outcomes through effective weight-management practices. Future research is necessary to examine the trajectory of changes in perceptual factors and GWG throughout pregnancy to determine the causal relationship in large and diverse cohorts.

Article Information

CONFLICTS OF INTEREST

The authors declared no conflict of interest.

ACKNOWLEDGEMENTS

None.

FUNDING

This work was supported by the National Research Foundation of Korea (NRF No. 2020R1A2C201086511).

DATA SHARING STATEMENT

Please contact the corresponding author for data availability.

AUTHOR CONTRIBUTIONS

Conceptualization or/and Methodology: Kim S & Ahn S.

Data curation or/and Analysis: Kim S.

Funding acquisition: Ahn S.

Investigation: Kim S.

Project administration or/and Supervision: Kim S & Ahn S.

Resources or/and Software: Kim S.

Validation: Kim S. Visualization: Kim S.

Writing original draft or/and Review & Editing: Kim S & Ahn S.

Fig. 1.

Proportion of gestational weight gain adequacy with different body mass index criteria. (A) GWG recommendation with Asia Pacific prepregnancy BMI. (B) GWG recommendation with WHO international prepregnancy BMI.

Table 1.

General Characteristics of Participants (N = 369)

Variables	Categories	M ± SD	n (%)
Age (yr)		33.3 ± 3.26
	25~34		239 (64.8)
	35~44		130 (35.2)
Gestational age (wk)	36~41	37.4 ± 1.00
Education	Secondary		25 (6.8)
	College		292 (79.1)
	Graduate		52 (14.1)
Occupation	Full time		74 (20.1)
	Part time (h)		10 (2.7)
	Self-employed		11 (3.0)
	Quitting work due to pregnancy		74 (20.1)
	Maternity leave		137 (37.1)
	Housewife (include students)		63 (17.0)
Household income (10,000 KRW/mo)	< 200		17 (4.6)
	200~399		138 (37.4)
	400~599		131 (35.5)
	≥ 600		83 (22.5)
Smoking	Yes		3 (0.8)
Smoking	No		366 (99.2)
Drinking	Yes		2 (0.5)
Drinking	No		367 (99.5)
Body weight (kg)	Prepregnancy	55.04 ± 7.07
Body weight (kg)	Current	66.94 ± 7.91
Prepregnancy BMI (kg/m²)	< 18.5		59 (16.0)
	18.5~22.9		257 (69.6)
	23.0~24.9		28 (7.6)
	≥ 25.0		25 (6.8)
Gestational weight gain (kg)	Total	11.90 ± 4.08	369 (100.0)
	Inadequate	8.80 ± 1.97	158 (42.8)
	Normal	13.22 ± 2.09	148 (40.1)
	Excessive	17.63 ± 3.86	63 (17.1)

BMI = Body mass index; KRW = Korean won; M = Mean; SD = Standard deviation.

Table 2.

Characteristics of Participants in Gestational Weight Gain Categories (N = 369)

Characteristics	Gestational weight gain			p-value
	Inadequate (n = 158)	Normal (n = 148)	Excessive (n = 63)
	M ± SD or n (%)
Age (yr)^†	33.1 ± 3.16	33.4 ± 3.51	33.6 ± 2.90	.569
Gestational age (wk)^†	37.3 ± 0.94	37.5 ± 1.01	37.4 ± 1.10	.330
Prepregnancy BMI (kg/m²)^††
< 18.5	26 (16.5)	29 (19.6)	4 (6.3)	< .001
18.5~22.9	126 (79.7)	99 (66.9)	32 (50.8)
23.0~24.9	6 (3.8)	12 (8.1)	10 (15.9)
≥ 25.0	0 (0.0)	8 (5.4)	17 (27.0)
Education^††
Secondary	9 (5.7)	8 (5.4)	8 (12.7)	.085
College	120 (75.9)	124 (83.8)	48 (76.2)
Graduate	29 (18.4)	16 (10.8)	7 (11.1)
Income (10,000 KRW/mo)^††
< 200	7 (4.4)	8 (5.4)	2 (3.2)	.748
200~399	55 (34.8)	56 (37.8)	27 (42.8)
400~599	56 (35.5)	56 (37.8)	19 (30.2)
≥ 600	40 (25.3)	28 (19.0)	15 (23.8)
Occupation^††
Yes	43 (27.2)	35 (23.6)	17 (27.0)	.752
No	115 (72.8)	113 (76.4)	46 (73.0)
Drinking^††
Yes	1 (0.6)	1 (0.7)	0 (0.0)	.812
No	157 (99.4)	147 (99.3)	63 (100.0)
Smoking^††
Yes	1 (0.6)	0 (0.0)	2 (3.2)	.060
No	157 (99.4)	148 (100.0)	61 (96.8)

BMI = Body mass index; KRW = Korean won; M = Mean; SD = Standard deviation.

^†One-way ANOVA.

^††Chi-square test.

Table 3.

Multivariate Binary Logistic Regression Determining Risk Factors of Excessive Gestational Weight Gain (N = 369)

Variables	Crude B	SE	Exp. (B)	95% CI	p-value	Adjusted B	SE	Exp. (B)	95% CI	p-value
Age	0.04	0.04	1.04	0.95~1.13	.404
Pre-pregnancy BMI (kg/m²)
< 18.5	1^†					1^†
18.5~22.9	0.67	0.55	1.96	0.66~5.76	.224	0.94	0.76	2.55	0.58~11.30	.217
23.0~24.9	2.03	0.65	7.64	2.13~27.36	.002	3.48	0.97	32.60	4.84~219.51	< .001
≥ 25.0	3.38	0.67	29.22	7.82~109.13	< .001	4.64	0.97	103.25	15.53~686.41	< .001
Education
Secondary	1^†
College	– 0.87	0.46	0.42	0.17~1.02	.056
Graduate	– 1.11	0.59	0.33	0.10~1.05	.061
Income (10,000 KRW/mo)
< 200	1^†
200~399	0.60	0.78	1.82	0.39~8.46	.442
400~599	0.24	0.79	1.27	0.27~6.02	.761
≥ 600	0.50	0.81	1.65	0.34~8.01	.532
Occupation
Yes	1^†
No	– 0.08	0.31	0.93	0.50~1.71	.805
Self-efficacy
Healthy eating	– 0.04	0.01	0.96	0.93~0.99	.003	0.01	0.03	1.01	0.96~1.06	.619
Physical activity	– 0.05	0.02	0.95	0.92~0.98	< .001	– 0.05	0.03	0.95	0.91~1.00	.051
Perceived barrier
Healthy eating	– 0.01	0.02	0.99	0.96~1.04	.768
Physical activity	0.02	0.02	1.02	0.99~1.05	.290
Weight locus of control
Internal	– 0.72	0.11	0.49	0.39~0.61	< .001	– 0.55	0.18	0.58	0.41~0.82	.002
External	0.58	0.10	1.78	1.47~2.14	< .001	0.56	0.15	1.75	1.31~2.34	< .001
Body image	– 0.51	0.07	0.60	0.52~0.69	< .001	– 0.48	0.10	0.62	0.51~0.75	< .001
Knowledge
Maternal risk of EGWG	0.07	0.22	1.07	0.70~1.63	.758
Fetal risk of EGWG	– 0.15	0.07	0.86	0.76~0.99	.032	0.02	0.11	1.02	0.83~1.25	.853

BMI = Body mass index; CI = Confidence interval; EGWG = Excessive gestational weight gain; Exp. = Exponential; KRW = Korean won; SE = Standard error.

^†Reference.

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Perceptual Factors Associated with Gestational Weight Gain: A Cross-Sectional Survey

J Korean Acad Nurs. 2024;54(4):495-508. Published online November 1, 2024

DOI: https://doi.org/10.4040/jkan.24052

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Perceptual Factors Associated with Gestational Weight Gain: A Cross-Sectional Survey

Fig. 1. Proportion of gestational weight gain adequacy with different body mass index criteria. (A) GWG recommendation with Asia Pacific prepregnancy BMI. (B) GWG recommendation with WHO international prepregnancy BMI.

Fig. 1.

Perceptual Factors Associated with Gestational Weight Gain: A Cross-Sectional Survey

Variables	Categories	M ± SD	n (%)
Age (yr)		33.3 ± 3.26
	25~34		239 (64.8)
	35~44		130 (35.2)
Gestational age (wk)	36~41	37.4 ± 1.00
Education	Secondary		25 (6.8)
	College		292 (79.1)
	Graduate		52 (14.1)
Occupation	Full time		74 (20.1)
	Part time (h)		10 (2.7)
	Self-employed		11 (3.0)
	Quitting work due to pregnancy		74 (20.1)
	Maternity leave		137 (37.1)
	Housewife (include students)		63 (17.0)
Household income (10,000 KRW/mo)	< 200		17 (4.6)
	200~399		138 (37.4)
	400~599		131 (35.5)
	≥ 600		83 (22.5)
Smoking	Yes		3 (0.8)
Smoking	No		366 (99.2)
Drinking	Yes		2 (0.5)
Drinking	No		367 (99.5)
Body weight (kg)	Prepregnancy	55.04 ± 7.07
Body weight (kg)	Current	66.94 ± 7.91
Prepregnancy BMI (kg/m²)	< 18.5		59 (16.0)
	18.5~22.9		257 (69.6)
	23.0~24.9		28 (7.6)
	≥ 25.0		25 (6.8)
Gestational weight gain (kg)	Total	11.90 ± 4.08	369 (100.0)
	Inadequate	8.80 ± 1.97	158 (42.8)
	Normal	13.22 ± 2.09	148 (40.1)
	Excessive	17.63 ± 3.86	63 (17.1)

Characteristics	Gestational weight gain			p-value
	Inadequate (n = 158)	Normal (n = 148)	Excessive (n = 63)
	M ± SD or n (%)
Age (yr)^†	33.1 ± 3.16	33.4 ± 3.51	33.6 ± 2.90	.569
Gestational age (wk)^†	37.3 ± 0.94	37.5 ± 1.01	37.4 ± 1.10	.330
Prepregnancy BMI (kg/m²)^††
< 18.5	26 (16.5)	29 (19.6)	4 (6.3)	< .001
18.5~22.9	126 (79.7)	99 (66.9)	32 (50.8)
23.0~24.9	6 (3.8)	12 (8.1)	10 (15.9)
≥ 25.0	0 (0.0)	8 (5.4)	17 (27.0)
Education^††
Secondary	9 (5.7)	8 (5.4)	8 (12.7)	.085
College	120 (75.9)	124 (83.8)	48 (76.2)
Graduate	29 (18.4)	16 (10.8)	7 (11.1)
Income (10,000 KRW/mo)^††
< 200	7 (4.4)	8 (5.4)	2 (3.2)	.748
200~399	55 (34.8)	56 (37.8)	27 (42.8)
400~599	56 (35.5)	56 (37.8)	19 (30.2)
≥ 600	40 (25.3)	28 (19.0)	15 (23.8)
Occupation^††
Yes	43 (27.2)	35 (23.6)	17 (27.0)	.752
No	115 (72.8)	113 (76.4)	46 (73.0)
Drinking^††
Yes	1 (0.6)	1 (0.7)	0 (0.0)	.812
No	157 (99.4)	147 (99.3)	63 (100.0)
Smoking^††
Yes	1 (0.6)	0 (0.0)	2 (3.2)	.060
No	157 (99.4)	148 (100.0)	61 (96.8)

Variables	Crude B	SE	Exp. (B)	95% CI	p-value	Adjusted B	SE	Exp. (B)	95% CI	p-value
Age	0.04	0.04	1.04	0.95~1.13	.404
Pre-pregnancy BMI (kg/m²)
< 18.5	1^†					1^†
18.5~22.9	0.67	0.55	1.96	0.66~5.76	.224	0.94	0.76	2.55	0.58~11.30	.217
23.0~24.9	2.03	0.65	7.64	2.13~27.36	.002	3.48	0.97	32.60	4.84~219.51	< .001
≥ 25.0	3.38	0.67	29.22	7.82~109.13	< .001	4.64	0.97	103.25	15.53~686.41	< .001
Education
Secondary	1^†
College	– 0.87	0.46	0.42	0.17~1.02	.056
Graduate	– 1.11	0.59	0.33	0.10~1.05	.061
Income (10,000 KRW/mo)
< 200	1^†
200~399	0.60	0.78	1.82	0.39~8.46	.442
400~599	0.24	0.79	1.27	0.27~6.02	.761
≥ 600	0.50	0.81	1.65	0.34~8.01	.532
Occupation
Yes	1^†
No	– 0.08	0.31	0.93	0.50~1.71	.805
Self-efficacy
Healthy eating	– 0.04	0.01	0.96	0.93~0.99	.003	0.01	0.03	1.01	0.96~1.06	.619
Physical activity	– 0.05	0.02	0.95	0.92~0.98	< .001	– 0.05	0.03	0.95	0.91~1.00	.051
Perceived barrier
Healthy eating	– 0.01	0.02	0.99	0.96~1.04	.768
Physical activity	0.02	0.02	1.02	0.99~1.05	.290
Weight locus of control
Internal	– 0.72	0.11	0.49	0.39~0.61	< .001	– 0.55	0.18	0.58	0.41~0.82	.002
External	0.58	0.10	1.78	1.47~2.14	< .001	0.56	0.15	1.75	1.31~2.34	< .001
Body image	– 0.51	0.07	0.60	0.52~0.69	< .001	– 0.48	0.10	0.62	0.51~0.75	< .001
Knowledge
Maternal risk of EGWG	0.07	0.22	1.07	0.70~1.63	.758
Fetal risk of EGWG	– 0.15	0.07	0.86	0.76~0.99	.032	0.02	0.11	1.02	0.83~1.25	.853

Table 1. General Characteristics of Participants (N = 369)

BMI = Body mass index; KRW = Korean won; M = Mean; SD = Standard deviation.

Table 2. Characteristics of Participants in Gestational Weight Gain Categories (N = 369)

BMI = Body mass index; KRW = Korean won; M = Mean; SD = Standard deviation.

†

One-way ANOVA.

††

Chi-square test.

Table 3. Multivariate Binary Logistic Regression Determining Risk Factors of Excessive Gestational Weight Gain (N = 369)

BMI = Body mass index; CI = Confidence interval; EGWG = Excessive gestational weight gain; Exp. = Exponential; KRW = Korean won; SE = Standard error.