Abstract
-
Purpose
Birth weight and birth length are essential indicators of neonatal health and later development. However, evidence from Vietnam on the multidimensional determinants of these outcomes remains limited. Guided by the biopsychosocial model, this study examined factors associated with birth weight and birth length among term newborns.
-
Methods
This retrospective cross-sectional study included 198 term singleton live births at Hai Phong Hospital of Obstetrics and Gynecology, Vietnam. Data were extracted from the medical records of newborns born at the hospital between December 2023 and May 2024. Because the variables did not meet parametric assumptions, the Mann-Whitney U test, Kruskal-Wallis H test, and Spearman correlation coefficient were used as nonparametric alternatives to the independent-samples t-test, analysis of variance, and Pearson correlation coefficient, respectively. Two multiple linear regression models were constructed to identify independent predictors of birth weight and birth length, with statistical significance set at p<.05.
-
Results
In the multivariable analysis, gestational age (β=.37, p<.001), maternal pre-pregnancy weight (β=.24, p<.001), protein supplementation (β=.13, p=.040), and infant sex (β=−.16, p=.008) were significant predictors of birth weight. Paternal height (β=.47, p<.001), gestational age (β=.23, p<.001), maternal height (β=.22, p<.001), and maternal pre-pregnancy weight (β=.16, p=.009) were significant predictors of birth length.
-
Conclusion
Within the biopsychosocial framework, interrelated biological and behavioral factors were associated with birth weight and birth length among term newborns. Prenatal assessment of maternal and paternal characteristics may help inform strategies to support fetal growth, particularly in resource-limited settings.
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Key words: Biopsychosocial models; Birth weight; Body weight; Newborn
INTRODUCTION
Birth weight and birth length are fundamental indicators of neonatal health, survival, and further well-being [
1]. According to the World Health Organization, low birth weight (LBW) is defined as <2,500 g regardless of gestational age [
2]. LBW remains one of the strongest predictors of neonatal mortality, being responsible for approximately 60%–80% of deaths in the first month of life [
3], and is associated with postnatal growth impairment, neurocognitive developmental delay, and a number of adult noncommunicable diseases, including hypertension and type 2 diabetes [
4,
5]. Similarly, birth length reflects intrauterine linear growth and skeletal development, with normal full-term length generally ranging from approximately 49 to 50 cm, although values between 47 and 53 cm are also considered within normal limits [
6].
Globally, approximately 14.7% of live births, equivalent to nearly 20 million newborns, were classified as LBW in 2020 [
7]. Along with weight, birth length provides complementary information on skeletal and linear growth. Departures from gestational age norms may signal intrauterine growth restriction even when the weight is near the threshold [
8]. A prospective study in rural Vietnam showed that maternal nutritional status and gestational weight gain were significant determinants of infant growth in the first 2 years of life [
9]. Thus, maternal anthropometry and gestational nutrition are the major drivers of neonatal and early childhood growth. Despite this, secular improvements in neonatal size have been evident in northern Vietnam; for example, a greater than 1.3 cm increase in average birth length over 2 decades [
10], preterm and LBW infants still contribute to nearly 19% of the national neonatal disease burden. Despite these findings, few Vietnamese studies have examined the joint influence of biological, psychological, and social determinants from both maternal and paternal perspectives on neonatal anthropometric outcomes, especially at the provincial hospital level. Provincial tertiary hospitals, such as the Hai Phong Obstetrics and Gynecology Hospital, Hai Phong serve as regional referral centers for both urban and surrounding rural populations, providing a heterogeneous case mix that better reflects real-world clinical and socioeconomic variations than national surveys or highly specialized central hospitals.
Recent global evidence confirms that the determinants of birth weight and birth length are multifactorial and represent biological, behavioral, psychological, and social dimensions. Guided by Engel’s biopsychosocial (BPS) model [
11], this study conceptualized these determinants within an integrated analytical framework to systematically represent the biological, behavioral/psychological, and social/environmental domains. However, owing to the retrospective design and reliance on medical records, psychological variables (e.g., maternal stress or anxiety) were not available in this study. In the biological sphere, maternal and paternal anthropometric characteristics, such as pre-pregnancy body mass index (BMI), gestational weight gain, and height, directly influence intrauterine growth and newborn size. Accordingly, this domain comprises gestational age, infant sex, maternal pre-pregnancy weight, gestational weight gain, maternal height, parity, and paternal height [
12,
13]. The behavioral/psychological domain was operationalized through maternal nutritional practices (e.g., protein/micronutrient supplementation and dietary patterns) and adherence to antenatal care, factors that may affect fetal growth through neuroendocrine or metabolic pathways [
14]. The social domain encompasses maternal age, education, occupation, household income, healthcare utilization, and structural determinants that shape maternal health behaviors and well-being, thereby indirectly influencing fetal development [
15].
Therefore, this study aims to identify factors associated with birth weight and birth length among term newborns in Vietnam, within the BPS framework. A multidimensional understanding of these factors may inform targeted interventions and health policies to improve maternal and neonatal outcomes in low- and middle-income settings.
METHODS
Ethical statements: This study was approved by the Institutional Review Board of Hai Phong University of Medicine and Pharmacy (approval number: 2550/QD-YDHP-IRB). The need for informed consent was waived due to the retrospective design and anonymized data.
1. Study Design
This study employed a retrospective cross-sectional design to identify factors associated with neonatal anthropometric outcomes. The conduct and reporting of the research adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [
16].
This study’s conceptual framework was guided by Engel’s BPS model [
11], which conceptualized health outcomes as arising from interacting biological, psychological/behavioral, and social determinants. The model was used to structure variable selection and analysis strategies. The biological domain included neonatal characteristics (gestational age and infant sex), maternal anthropometry (pre-pregnancy weight, gestational weight gain, height, and parity), and paternal anthropometry (height and weight). The behavioral/psychological domain comprised maternal nutritional practices, supplement use, smoking exposure, and pregnancy-related health behaviors (psychological factors were not included because of data unavailability). The social domain included maternal age, education, occupation, household income, and healthcare utilization (
Figure 1).
Data were collected from Hai Phong Hospital of Obstetrics and Gynecology, Hai Phong, a tertiary perinatal referral center in northern Vietnam. The hospital is located in a major urban–industrial province with a mixed urban and peri-urban population, serving both city residents and the surrounding districts. The hospital functions as the primary referral institution for high-risk pregnancies in the region while also providing routine obstetric services. Medical records of deliveries occurring between December 2023 and May 2024 were extracted from the hospital’s obstetric and neonatal registries, which included routinely documented maternal, paternal, and neonatal information as part of the standard clinical assessment.
Given their referral role, hospitals manage a substantial proportion of medically complicated pregnancies, limiting the representativeness of the sample relative to the general population. However, the institution maintains standardized clinical protocols and a comprehensive electronic registry system that provides high-quality, systematically recorded perinatal data. Restricting the analysis to term singleton births further reduces the potential bias related to extreme prematurity and severe neonatal complications.
4. Participants and Eligibility Criteria
All deliveries recorded during the study period between December 2023 and May 2024 were screened for eligibility.
The inclusion criteria were as follows: (1) term singleton live births (gestational age, 37–42 weeks), (2) complete records of birth weight and birth length, (3) availability of maternal and paternal sociodemographic and anthropometric data, and (4) delivery at the Hai Phong Hospital of Obstetrics and Gynecology. The exclusion criteria included multiple pregnancies, congenital anomalies, stillbirths, and incomplete or implausible medical records.
5. Sample
The sample size was calculated a priori using G*Power ver. 3.1.9 (Heinrich-Heine-Universität Düsseldorf), using an F-test for linear multiple regression, assuming a medium effect size (f²=.15), an alpha level of .05, and up to 8 predictors; a minimum of 160 participants was required to achieve 95% power. Given the retrospective design, all eligible term singleton births during the study period were included in the analysis with no sampling procedure applied. A total of 198 eligible births met the inclusion criteria and were analyzed, exceeding the minimum required sample size. This approach ensured that the study utilized a fully available dataset and minimized the risk of selective inclusion. The selection process is illustrated in
Figure 2.
1) Outcome variables
(1) Birth weight
Birth weight (g) was measured immediately after delivery using a calibrated digital infant scale, recorded to the nearest 50 g, and analyzed as a continuous variable.
(2) Birth length
Birth length (cm) was measured within 1 hour after delivery as part of routine clinical practice at the hospital using a standardized infantometer, recorded to the nearest 0.1 cm. According to the hospital’s standard neonatal protocol, trained maternity staff obtained 2 consecutive measurements, and the recorded value represented the average of the 2 readings to enhance measurement reliability. It was analyzed as a continuous variable.
2) Independent variables
Variables were grouped according to the BPS model framework.
(1) Biological/clinical factors
Maternal attributes: maternal height, pre-pregnancy weight, and total gestational weight gain were measured as continuous variables. Parity (primiparous vs. multiparous), conception method (natural vs. in vitro fertilization), and medical conditions (gestational diabetes mellitus [GDM] and hypertension) were analyzed as categorical variables. Paternal attributes: paternal height, weight (continuous variables), and presence of medical conditions (yes/no). Neonatal characteristics: Gestational age, measured as a continuous variable (weeks) and categorized into 37–38, 38–39, 39–40, and 40–42 weeks [
17], and infant sex (male/female).
(2) Psychological/behavioral factors
Maternal behaviors and pregnancy management: number of antenatal care (ANC) visits (<10 vs. ≥10) [
18], and use of supplements (iron, calcium, protein). Lifestyle and emotional health proxies: dietary restrictions, vegetarian diet, and number of additional meals. Paternal behaviors: smoking status (yes/no).
(3) Social factors
Maternal sociodemographic characteristics: maternal age, although biologically relevant, was categorized as a sociodemographic variable because it reflects life-course and social context factors influencing health behaviors and healthcare access within the BPS framework (<18, 18–35, >35 years) [
19], education (high school or below vs. above high school), occupation (government employee, worker, self-employed, housewife), and household income (<20 million Vietnam dong [VND]/mo vs. ≥20 million VND/mo). Categorical cutoffs were based on prior perinatal epidemiology literature and local clinical practice.
Data were extracted using a structured abstraction form. Two trained research assistants independently reviewed delivery registries and medical records. A screening log was used to identify all deliveries and assess eligibility. After verification, eligible records were abstracted and entered into an electronic database. Discrepancies between reviewers were identified through comparison and resolved by consensus after rechecking the original medical records. Unresolved cases were adjudicated by a senior investigator. A 10% random subsample was rechecked by a senior researcher to ensure accuracy. The data were examined for completeness, logical consistency, and validity.
8. Statistical Analysis
All collected data were entered into IBM SPSS Statistics ver. 29.0 (IBM Corp.), with a 2-tailed significance level set at p<.05. This study adopted a complete case analysis approach. Continuous variables were summarized as mean±standard deviation or median with interquartile range, as appropriate, while categorical variables were presented as frequencies and percentages. Normality was assessed using the Shapiro-Wilk test and P–P plots. Because several continuous variables violated the assumption of normality, nonparametric tests were applied. Bivariate associations were examined using the Mann-Whitney U test and Kruskal-Wallis H test as alternatives to the independent t-test and analysis of variance (ANOVA), respectively. Spearman’s rank correlation coefficient (rₛ) as an alternative to Pearson’s correlation coefficient were used to assess relationships between continuous variables.
Two multiple linear regression models were constructed to identify independent predictors of birth length and weight. Predictors were selected a priori based on the BPS framework and informed by bivariate analysis (
p<.05). Predictors were entered simultaneously using the Enter method. Linearity was evaluated using scatter plots and partial regression plots, and homoscedasticity was assessed by visual inspection of the standardized residual plots. Model assumptions and multicollinearity were evaluated using residual diagnostics, tolerance (>0.20), and the variance inflation factor (VIF <5) [
20]. Model performance was assessed using the adjusted R
2 and ANOVA F-tests. Results were reported as standardized β coefficients, 95% confidence intervals (CIs), and
p-values, with effect sizes interpreted as small (β≥.10), moderate (β≥.30), and large (β≥.50).
RESULTS
1. General Characteristics of the Study Population
This study included 198 singleton newborns and their parents (
Table 1). In terms of gestational age, 32.3% of the infants were born between 37 and <38 weeks, 47.4% between 38 and <39 weeks, 18.7% between 39 and <40 weeks, and 1.5% between 40 and <42 weeks. Of the newborns, 55.6% were male, and 44.4% were female. The average birth weight was 3,067.3±369.7 g, with weights ranging from 2,000 to 4,600 g. Approximately 83% of the infants fell within the normal birth weight range of 2,500–<3,500 g. The mean birth length was 49.9±1.7 cm, with measurements ranging from 45.60 to 55.10 cm.
Table 1 shows the associations between biopsychosocial factors and neonatal outcomes (weight and length).
1) Biological/clinical factors
In the group comparison analyses, GDM was significantly associated with birth length (U=2,347.5, p=.008) but not birth weight (U=2,902.0, p=.331). Infant sex was significantly associated with birth weight (U=3,783.5, p=.008), with no significant association observed for birth length (p=.275). Gestational age was significantly associated with birth weight (H=47.02, p<.001) and birth length (H=14.72, p=.002). Other biological and clinical factors, including parity, method of conception, hypertension during pregnancy, and paternal medical conditions, were not significantly associated with either outcome (all p>.05).
Correlation analyses further demonstrated significant positive associations between parental anthropometric variables and neonatal outcomes. Maternal height was positively correlated with birth weight (r=.19, p=.008) and length (r=.36, p<.001). Maternal pre-pregnancy weight positively correlated with birth weight (r=.32, p<.001) and birth length (r=.33, p<.001). Gestational weight gain was positively correlated with birth weight (r=.22, p=.001) and birth length (r=.23, p=.001).
Paternal height showed a strong positive correlation with birth length (r=.52, p<.001) but was not correlated with birth weight (p=.224). Paternal weight was positively correlated with birth length (r=.27, p<.001) but not with birth weight (p=.108). Gestational age, which was analyzed as a continuous variable, positively correlated with birth weight (r=.41, p<.001) and birth length (r=.20, p=.004).
In addition, the correlation matrix revealed significant interrelationships among the key predictors (
Supplement 1). Maternal height was positively correlated with maternal pre-pregnancy weight (r=.42,
p<.001), while paternal height was positively correlated with paternal weight (r=.39,
p<.001). Gestational age was also weakly correlated with birth weight (r=.41,
p<.001) and length (r=.20,
p=.004), reinforcing its role as an important biological factor.
2) Psychological/behavioral factors
In the group comparison analyses, calcium supplementation was significantly associated with birth length (U=1,952.0, p=.010) and showed a marginal association with birth weight (p=.054). No protein supplementation was significantly associated with higher birth weight (U=2,489.0, p=.011) but not with birth length (p=.248). Paternal smoking status was significantly associated with lower birth weight (U=3,600.0, p=.032) but not with birth length (p=.763). Iron supplementation, a vegetarian diet, and dietary restrictions were not significantly associated with neonatal outcomes.
In the correlation analysis, the number of additional meals per day was not significantly correlated with birth weight (r=−.12, p=.108) or birth length (r=.05, p=.504).
3) Social factors
In the group comparison analyses, none of the sociodemographic factors, including maternal age, maternal education, maternal occupation, household income, number of ANC visits, or paternal age were significantly associated with birth weight or birth length (all p>.05).
Similarly, correlation analyses showed no significant correlations between maternal age (birth weight: r=−.05,
p=.483; birth length: r=−.12,
p=.104) or paternal age (birth weight: r=.01,
p=.928; birth length: r=−.12,
p=.108) and neonatal outcomes (
Table 1).
Additionally, maternal age showed weak negative correlations with maternal pre-pregnancy weight (r=−.26, p<.001) and gestational weight gain (r=−.22, p=.002), although these were not associated with neonatal outcomes.
3. Multivariable Linear Regression Analysis of Factors Associated with Birth Weight
Multiple regression analysis showed that the model was significant (F(7, 190)=12.81, p<.001), explaining 32.1% of the variance in birth weight (adjusted R2=.30).
The model diagnostics supported the assumptions of linear regression. Multicollinearity was not detected (tolerance ≥0.82; VIF ≤1.22), and the Durbin-Watson statistic (1.70) indicated independence of residuals. The residual plots showed no evidence of nonlinearity or heteroscedasticity. The histogram and P–P plot indicated the approximate normality of the residuals. Cook’s distance values were low (maximum=0.10), suggesting that there were no influential outliers.
Gestational age (B=181.74,
p<.001; 95% CI, 121.15 to 242.33; β=.37) and maternal pre-pregnancy weight (B=11.08,
p<.001; 95% CI, 5.01 to 17.16; β=.24) were the strongest predictors of birth weight. No protein supplementation was also positively associated with birth weight (B=112.37,
p=.040; 95% CI, 5.18 to 219.57; β=.13). Female infants weighed significantly less than males (B=–120.74,
p=.008; 95% CI, –209.93 to –31.55; β=–.16). Maternal height, gestational weight gain, and paternal smoking status were not significant predictors (all
p>.05) (
Table 2).
The regression model for birth length was significant (F(8, 189)=22.58, p<.001), explaining 48.9% of the variance (adjusted R2=.47). The assumptions of linear regression were satisfied. The residuals were approximately normally distributed, as indicated by the histogram and normal P–P plots. Visual inspection of the standardized residuals against the predicted values demonstrated no obvious patterns, supporting linearity and homoscedasticity. No influential outliers were identified (Cook’s distance <0.10, maximum=0.06). Multicollinearity was not observed (tolerance >0.70; VIF <1.40). The Durbin-Watson statistic (1.93) indicated the independence of the residuals.
Paternal height was the strongest predictor of birth length (B=0.15,
p<.001; 95% CI, 0.11 to 0.18; β=.47), followed by gestational age (B=0.53,
p<.001; 95% CI, 0.29 to 0.77; β=.23) and maternal height (B=0.07,
p<.001; 95% CI, 0.04 to 0.11; β=.22). Maternal pre-pregnancy weight also showed a significant positive association (B=0.03,
p=.009; 95% CI, 0.01 to 0.06; β=.16). Gestational weight gain, calcium supplementation, GDM, and paternal weight were not significant predictors of birth length (
p>.05) (
Table 3).
DISCUSSION
This study examined the determinants of birth weight and length in term newborns using a BPS model. Overall, biological factors (e.g., gestational age, infant sex, maternal pre-pregnancy weight, maternal height, and paternal height) influenced neonatal anthropometric outcomes. Behavioral and psychological factors (e.g., protein supplementation) also contributed, whereas social and demographic characteristics were not directly associated with birth size.
In the biological domain, gestational age was independently associated with both birth weight and birth length, whereas infant sex was independently associated only with birth weight. Greater gestational age was associated with greater birth weight and birth length, while male newborns had a higher birth weight than female newborns. These observations are consistent with the international newborn growth standards, which demonstrating clear gestational age- and sex-specific differences in birth weights and length [
8]. Recent population-based data have also shown that male infants are consistently heavier and longer at birth across gestational ages, reinforcing the biological influence of fetal sex on neonatal anthropometry [
21]. Moreover, both maternal and paternal traits play important roles in determining neonatal size. Maternal pre-pregnancy weight, gestational weight gain, and height were positively associated with birth weight and length. This is in agreement with recent evidence showing that both insufficient and excessive gestational weight gain are strongly associated with adverse neonatal outcomes in diverse settings [
22,
23]. Similarly, a large-scale analysis conducted by Dola and Valderrama [
13] in 2024 also cited maternal height, pre-pregnancy weight, and gestational weight gain as strong and consistent predictors of birth weight. These findings suggest that fetal growth is mediated by maternal body composition before and during pregnancy.
Paternal height in our study also showed a strong association with neonatal length. Although most studies conventionally place great emphasis on maternal factors, recent work underlines the fact that paternal anthropometry independently explains variances in neonatal size [
24]. These observations indicate that birth size is influenced not only by the intrauterine environment shaped by the mother, but also by the skeletal growth potential inherited from the father. This may be partly explained by genetic mechanisms; paternal height reflects inherited skeletal growth potential, which is transmitted to the fetus and manifests as greater linear growth at birth. A large Japanese cohort study demonstrated that paternal height was significantly associated with both birth weight and birth length independent of maternal characteristics, suggesting a direct genetic pathway influencing fetal skeletal development [
24,
25]. Clinically, this underscores the importance of recognizing the physical profiles of both parents when optimizing prenatal care.
Beyond biological factors, neonatal outcomes are further shaped by behavioral and psychosocial conditions. In this study, maternal protein supplementation remained a significant independent predictor of birth weight in the adjusted model, although the magnitude of this effect was modest. Interestingly, infants whose mothers reported protein supplementation had a lower birth weight than those whose mothers did not receive supplementation. This finding differs from recent high-quality evidence demonstrating that improved maternal protein energy intake can enhance fetal growth. A recent systematic review and meta-analysis of 24 trials showed that balanced protein–energy supplementation significantly increased birth weight and reduced the risk of low birth weight among pregnant women in diverse settings [
26]. However, in the present study, protein supplementation was assessed only as a binary variable (yes/no) without information on dosage, duration, or nutritional composition. Therefore, the associations observed should be interpreted with caution. One possible explanation is that protein supplementation may have been provided more frequently to women at higher nutritional or obstetric risk, resulting in residual confounding rather than a causal effect of protein supplementation itself. While these findings suggest the potential benefits of adequate protein and energy intake during pregnancy, the current data do not allow for precise conclusions regarding the specific effects or optimal characteristics of supplementation. These findings support the broader role of maternal nutrition in fetal growth and highlight the importance of appropriately designed nutritional interventions during pregnancy. However, psychological factors central to the BPS model were not included in this study because of data limitations. This omission may have limited our ability to fully capture the complex pathways that influence neonatal outcomes.
In this study, no significant relationships were observed between social or demographic variables and neonatal size. This may reflect the relatively homogeneous living conditions and healthcare access within the sampled population. However, research from other low- and middle-income countries (LMIC) indicates that socioeconomic inequality remains an important determinant of LBW [
15]. Engdaw et al. [
14] in 2023 also found that regular antenatal visits greatly reduced the likelihood of LBW in African settings, indicating that healthcare access can offset the disadvantages associated with a lower socioeconomic status. The discrepancy between our findings and those of previous studies likely reflects contextual differences. In our setting, ANC access is relatively uniform, limiting socioeconomic variability. However, in many LMIC contexts, wider disparities in healthcare access and living conditions make social inequalities more evident in neonatal outcomes.
In Vietnam, the low national prevalence of LBW (8.2% [
27], probably reflects nationwide improvements in maternal nutrition and healthcare coverage. However, gaps still exist between rural and urban areas. Quyen et al. [
9] in 2020 found that mothers from rural areas with lower dietary diversity and suboptimal weight gain tend to have smaller babies. These findings show that, although progress at the national level can reduce inequalities, differences in income, education, and environmental conditions at the local level still matter. There is also a growing body of evidence showing that general social and environmental stressors, such as air pollution, food insecurity, and urban stress, interact with biological processes that influence fetal development [
28]. Thus, social factors might not be a direct predictor in statistical models but could shape the environment in which biological and behavioral determinants operate. Therefore, future studies in Vietnam should capture the full context of neonatal health determinants, including environmental exposure, family support, and perceived stress.
The application of the BPS model has helped elaborate on how biological, behavioral, and social factors come together to influence fetal growth. Biological factors provide a physiological basis for growth, while behavioral and psychosocial factors determine how well the biological potential is utilized. These findings have clinical implications for the integration of biological monitoring with behavioral counseling in ANC. Healthy gestational weight gain, balanced diets, micronutrient supplementation, and avoidance of tobacco exposure are essential components of comprehensive prenatal programs. Psychosocial screening and support, including emotional status, family relationships, and partner involvement, are also important. Walsh et al. [
29] in 2019 identified maternal stress and lack of family support as contributors to poor perinatal outcomes, emphasizing the importance of counseling and social support systems in maternal healthcare.
Importantly, given the significant association between paternal height and neonatal birth length observed in this study, prenatal care programs should move beyond a maternal-centered approach to incorporate paternal factors. Integrating fathers into antenatal education and counseling programs may help optimize fetal growth by addressing shared health behaviors, lifestyle factors, and genetic influences. For example, programs could include components such as paternal health screening, lifestyle modifications (e.g., smoking cessation and nutrition), and active participation in antenatal visits [
30]. Such family-centered approaches are consistent with the BPS model and may enhance both maternal and neonatal outcomes by leveraging the combined influence of both parents.
From a policy perspective, the BPS framework provides an important guide for designing family-centered maternal health interventions. Strengthening prenatal education, expanding equitable access to supplements, and developing rural healthcare infrastructure remain national priorities. The promotion of paternal involvement in maternal health programs may provide additional opportunities to promote family cohesion and long-term health outcomes for mothers and children. The use of a multidimensional approach congruent with the BPS model ensures that the biological aspects of pregnancy are considered along with the behavioral and social contexts in which health behaviors occur.
This study had several limitations. First, its retrospective cross-sectional design precluded causal inference and limited the ability to capture longitudinal changes during pregnancy. Future prospective cohort studies across multiple centers and regions should provide more robust evidence. Second, psychological factors (e.g., maternal stress, family support, and air pollution exposure) were not measured. Future studies should incorporate psychological assessments to provide a more comprehensive evaluation of the BPS framework regarding birth outcomes. Third, while paternal height and smoking were included, detailed paternal data (BMI, diet, and occupational stress) were lacking. Future studies should broaden paternal profiling in line with emerging evidence of paternal influence [
31]. Furthermore, the study was subject to potential selection and participation biases related to the data completeness criteria. The requirement for full maternal and paternal sociodemographic and anthropometric data may have inadvertently skewed the analyzed sample toward certain socioeconomic strata. It is plausible that participants with higher socioeconomic backgrounds were more willing to disclose sensitive personal and physical information for research purposes. Conversely, individuals from lower-income or marginalized groups may harbor reservations or negative perceptions regarding data disclosure, leading to underrepresentation. Consequently, a participation bias may have restricted the generalizability of our findings to a broader population. Finally, advanced analytical techniques such as structural equation modeling or machine learning may help unpack the complex interdomain interactions posited by the BPS model.
CONCLUSION
This study highlights the multifactorial nature of neonatal outcomes within a BPS framework, emphasizing the combined influence of maternal and paternal factors on fetal growth. These findings support the need for more comprehensive prenatal care approaches that extend beyond maternal-focused models to include paternal characteristics and family contexts. Strengthening preconception nutrition, optimizing gestational weight management, and incorporating paternal involvement in routine ANC may enhance risk assessment and contribute to improved birth outcomes, particularly in low- and middle-income settings.
ARTICLE INFORMATION
Supplementary material
Figure 1.Conceptual framework illustrates determinants of birth weight and length based on the Engel’s biopsychosocial model. GDM, gestational diabetes mellitus.
Figure 2.Sampling process of associated factors of birth weight and length among term newborns.
Table 1.Bivariate associations of biopsychosocial characteristics with birth weight and length (N=198)
|
Domain/factor |
No. (%) or mean±SD (range) |
Birth weight |
Birth length |
|
Mean±SD |
Test statistic |
p
|
Mean±SD |
Test statistic |
p
|
|
Group comparison analyses |
|
|
|
|
|
|
|
|
Biological/clinical factors |
|
|
|
|
|
|
|
|
Parity |
|
|
4,488.5a)
|
.849 |
|
4,224.5a)
|
.385 |
|
Primiparous |
73 (36.9) |
3,063.5±371.4 |
|
|
50.1±1.9 |
|
|
|
Multiparous |
125 (63.1) |
3,069.4±370.1 |
|
|
49.8±1.6 |
|
|
|
Method of conception |
|
|
997.0a)
|
.864 |
|
940.0a)
|
.632 |
|
Natural conception |
187 (94.4) |
3,068.8±365.8 |
|
|
49.9±1.7 |
|
|
|
In vitro fertilization |
11 (5.6) |
3,040.9±449.9 |
|
|
50.2±2.0 |
|
|
|
Hypertension during pregnancy |
|
|
222.0a)
|
.142 |
|
303.0a)
|
.453 |
|
Yes |
4 (2.0) |
2,875.0±826.1 |
|
|
49.2±4.4 |
|
|
|
No |
194 (98.0) |
3,071.2±357.9 |
|
|
49.9±1.6 |
|
|
|
Gestational diabetes mellitus |
|
|
2,902.0a)
|
.331 |
|
2,347.5a)
|
.008 |
|
Yes |
41 (20.7) |
3,135.3±506.4 |
|
|
50.5±2.0 |
|
|
|
No |
157 (79.3) |
3,049.4±324.4 |
|
|
49.8±1.5 |
|
|
|
Paternal medical conditions |
|
|
495.0a)
|
.094 |
|
697.0a)
|
.691 |
|
Yes |
8 (4.0) |
2,825.0±337.0 |
|
|
49.4±2.7 |
|
|
|
No |
190 (96.0) |
3,077.4±368.3 |
|
|
49.9±1.7 |
|
|
|
Infant sex |
|
|
3,783.5a)
|
.008 |
|
4,402.5a)
|
.275 |
|
Male |
110 (55.6) |
3,127.1±388.9 |
|
|
50.0±1.76 |
|
|
|
Female |
88 (44.4) |
2,992.5±331.2 |
|
|
49.8±1.6 |
|
|
|
Gestational age |
|
|
47.02b)
|
<.001 |
|
14.72b)
|
.002 |
|
37w–<38w |
64 (32.3) |
2,822.8±320.2 |
|
|
49.4±1.8 |
|
|
|
38w–<39w |
94 (47.4) |
3,150.5±311.4 |
|
|
50.0±1.4 |
|
|
|
39w–<40w |
37 (18.7) |
3,233.8±369.5 |
|
|
50.6±1.8 |
|
|
|
40w–<42w |
3 (1.5) |
3,620.0±230.6 |
|
|
52.1±2.0 |
|
|
|
Psychological/behavioral factors |
|
|
|
|
|
|
|
|
No. of antenatal care visits |
|
|
3,053.0a)
|
.497 |
|
3,214.5a)
|
.852 |
|
<10 visits |
42 (21.2) |
3,100.2±332.0 |
|
|
49.9±1.6 |
|
|
|
≥10 visits |
156 (78.8) |
3,058.4±379.6 |
|
|
50.0±1.7 |
|
|
|
Iron supplementation |
|
|
725.0a)
|
.222 |
|
855.5a)
|
.632 |
|
Yes |
188 (94.9) |
3,058.3±367.3 |
|
|
49.9±1.7 |
|
|
|
No |
10 (5.1) |
3,235.0±393.0 |
|
|
50.2±2.0 |
|
|
|
Calcium supplementation |
|
|
2,146.0a)
|
.054 |
|
1,952.0a)
|
.010 |
|
Yes |
165 (83.3) |
3,087.9±364.5 |
|
|
50.1±1.7 |
|
|
|
No |
33 (16.7) |
2,963.9±383.4 |
|
|
49.4±1.5 |
|
|
|
Protein supplementation |
|
|
2,489.0a)
|
.011 |
|
2,949.0a)
|
.248 |
|
Yes |
43 (21.7) |
2,933.0±382.5 |
|
|
49.8±2.0 |
|
|
|
No |
155 (78.3) |
3,104.5±358.4 |
|
|
50.0±1.6 |
|
|
|
Vegetarian diet during pregnancy |
|
|
270.0a)
|
.819 |
|
208.5a)
|
.393 |
|
Yes |
3 (1.5) |
3,116.7±525.2 |
|
|
50.3±0.3 |
|
|
|
No |
195 (98.5) |
3,066.5±368.6 |
|
|
49.9±1.7 |
|
|
|
Dietary restriction during pregnancy |
|
|
2,878.0a)
|
.902 |
|
2,643.5a)
|
.381 |
|
Yes |
36 (18.2) |
3,120.3±488.1 |
|
|
50.2±1.9 |
|
|
|
No |
162 (81.8) |
3,055.5±338.6 |
|
|
49.9±1.7 |
|
|
|
Paternal smoking status |
|
|
3,600.0a)
|
.032 |
|
4,304.5a)
|
.763 |
|
Yes |
68 (34.3) |
3,030.0±362.2 |
|
|
49.9±1.6 |
|
|
|
No |
130 (65.7) |
3,138.4±376.0 |
|
|
49.9±1.8 |
|
|
|
Social factors |
|
|
|
|
|
|
|
|
Maternal age (yr) |
|
|
0.47b)
|
.791 |
|
2.14b)
|
.343 |
|
<18 |
4 (2.0) |
3,050.0±613.7 |
|
|
50.5±2.5 |
|
|
|
18–35 |
160 (80.8) |
3,057.6±366.3 |
|
|
50.0±1.7 |
|
|
|
>35 |
34 (17.2) |
3,114±363.8 |
|
|
49.5±1.6 |
|
|
|
Maternal education |
|
|
4,463.0a)
|
.906 |
|
4,223.0a)
|
.460 |
|
High school or below |
127 (64.1) |
3,060.5±359.0 |
|
|
49.9±1.6 |
|
|
|
Above high school |
71 (35.9) |
3,079.3±390.3 |
|
|
50.1±1.9 |
|
|
|
Maternal occupation |
|
|
2.41b)
|
.491 |
|
6.09b)
|
.108 |
|
Government employee |
46 (23.2) |
3,150.4±427.3 |
|
|
50.6±2.1 |
|
|
|
Worker |
60 (30.3) |
3,085.5±277.8 |
|
|
49.9±1.2 |
|
|
|
Self-employed |
77 (38.9) |
3,014.1±402.4 |
|
|
49.7±1.6 |
|
|
|
Housewife |
15 (7.6) |
3,012.0±297.9 |
|
|
49.3±1.9 |
|
|
|
Household income |
|
|
2,845.0a)
|
.189 |
|
2,829.0a)
|
.175 |
|
<20 million VND/mo |
42 (21.2) |
2,976.7±281.3 |
|
|
49.4±1.5 |
|
|
|
≥20 million VND/mo |
156 (78.8) |
3,098.7±391.7 |
|
|
50.1±1.7 |
|
|
|
Correlation analyses |
|
|
|
|
|
|
|
|
Biological/clinical factors |
|
3,067.3±369.7 |
|
|
49.9±1.7 |
|
|
|
Maternal height (cm) |
156.9±5.1 (145–170) |
|
0.19c)
|
.008 |
|
0.36c)
|
<.001 |
|
Maternal pre-pregnancy weight (kg) |
51.6±7.9 (36–92) |
|
0.32c)
|
<.001 |
|
0.33c)
|
<.001 |
|
Gestational weight gain |
13.0±4.3 (5–27) |
|
0.22c)
|
.001 |
|
0.23c)
|
.001 |
|
Paternal height (cm) |
169.94±5.58 (157–185) |
|
0.09c)
|
.224 |
|
0.52c)
|
<.001 |
|
Paternal weight (kg) |
66.07±8.03 (42–85) |
|
0.12c)
|
.108 |
|
0.27c)
|
<.001 |
|
Gestational age (wk) |
38.32±0.83 (37–41.14) |
|
0.41c)
|
<.001 |
|
0.20c)
|
.004 |
|
Psychological/behavioral factors |
|
3,067.3±369.7 |
–0.12c)
|
.108 |
49.9±1.7 |
0.05c)
|
.504 |
|
No. of additional meals per days |
2.67±0.90 (0–4) |
|
|
|
|
|
|
|
Social factors |
|
3,067.3±369.7 |
|
|
49.9±1.7 |
|
|
|
Maternal age |
29.87±5.9 (16–54) |
|
–0.05c)
|
.483 |
|
–0.12c)
|
.104 |
|
Paternal age |
32.87±5.96 (19–58) |
|
0.01c)
|
.928 |
|
–0.12c)
|
.108 |
Table 2.Multivariable linear regression analysis of factors associated with birth weight (N=198)
|
Factor |
B |
β |
t |
p
|
95% CI |
|
Maternal height |
2.13 |
.03 |
0.45 |
.655 |
−7.28 to 11.55 |
|
Maternal pre-pregnancy weight |
11.08 |
.24 |
3.60 |
<.001 |
5.01 to 17.16 |
|
Gestational weight gain |
6.48 |
.08 |
1.24 |
.217 |
−3.83 to 16.79 |
|
Gestational age |
181.74 |
.37 |
5.92 |
<.001 |
121.15 to 242.33 |
|
Protein supplementationa)
|
112.37 |
.13 |
2.07 |
.040 |
5.18 to 219.57 |
|
Paternal smoking statusb)
|
−47.28 |
−.06 |
−1.01 |
.316 |
−140.00 to 45.44 |
|
Infant sexc)
|
−120.74 |
−.16 |
−2.67 |
.008 |
−209.93 to −31.55 |
|
Model fit |
R2=.32, adjusted R2=.30, F=12.81, p<.001 |
Table 3.Multivariable linear regression analysis of factors associated with birth length (N=198)
|
Factor |
B |
β |
t |
p
|
95% CI |
|
Maternal height |
0.07 |
.22 |
3.84 |
<.001 |
0.04 to 0.11 |
|
Maternal pre-pregnancy weight |
0.03 |
.16 |
2.63 |
.009 |
0.01 to 0.06 |
|
Gestational weight gain |
0.04 |
.09 |
1.69 |
.093 |
−0.01 to 0.08 |
|
Calcium supplementationa)
|
−0.45 |
−.10 |
−1.85 |
.066 |
−0.92 to 0.03 |
|
Gestational diabetes mellitusb)
|
−0.24 |
−.06 |
−1.02 |
.307 |
−0.70 to 0.22 |
|
Paternal height |
0.15 |
.47 |
8.23 |
<.001 |
0.11 to 0.18 |
|
Paternal weight |
0.00 |
−.02 |
−0.32 |
.752 |
−0.03 to 0.02 |
|
Gestational age |
0.53 |
.23 |
4.33 |
<.001 |
0.29 to 0.77 |
|
Model fit |
R2=.49, adjusted R2=.47, F=22.58, p<.001 |
REFERENCES
- 1. Blencowe H, Krasevec J, de Onis M, Black RE, An X, Stevens GA, et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis. Lancet Glob Health. 2019;7(7):e849-e860. https://doi.org/10.1016/S2214-109X(18)30565-5
- 2. World Health Organization (WHO). Global nutrition targets 2025: low birth weight policy brief [Internet]. WHO; 2014 [cited 2026 Jan 3]. Available from: https://www.who.int/publications/i/item/WHO-NMH-NHD-14.5
- 3. UNICEF. 1 in 7 babies worldwide born with a low birthweight: the Lancet Global Health, UNICEF, WHO. UNICEF; 2019.
- 4. Victora CG, Adair L, Fall C, Hallal PC, Martorell R, Richter L, et al. Maternal and child undernutrition: consequences for adult health and human capital. Lancet. 2008;371(9609):340-357. https://doi.org/10.1016/S0140-6736(07)61692-4
- 5. Barker DJ. Fetal origins of coronary heart disease. BMJ. 1995;311(6998):171-174. https://doi.org/10.1136/bmj.311.6998.171
- 6. Jamshed S, Khan F, Chohan SK, Bano Z, Shahnawaz S, Anwar A, et al. Frequency of normal birth length and its determinants: a cross-sectional study in newborns. Cureus. 2020;12(9):e10556. https://doi.org/10.7759/cureus.10556
- 7. World Health Organization (WHO). UNICEF/WHO low birthweight estimates: levels and trends 2000-2020. WHO; 2023.
- 8. Villar J, Cheikh Ismail L, Victora CG, Ohuma EO, Bertino E, Altman DG, et al. International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the INTERGROWTH-21st Project. Lancet. 2014;384(9946):857-868. https://doi.org/10.1016/S0140-6736(14)60932-6
- 9. Quyen PN, Nga HT, Chaffee B, Ngu T, King JC. Effect of maternal prenatal food supplementation, gestational weight gain, and breast-feeding on infant growth during the first 24 months of life in rural Vietnam. PLoS One. 2020;15(6):e0233671. https://doi.org/10.1371/journal.pone.0233671
- 10. Hop le T. Secular trend in size at birth of Vietnamese newborns during the last 2 decades (1980-2000). Asia Pac J Clin Nutr. 2003;12(3):266-270.
- 11. Engel GL. The need for a new medical model: a challenge for biomedicine. Science. 1977;196(4286):129-136. https://doi.org/10.1126/science.847460
- 12. Arabzadeh H, Doosti-Irani A, Kamkari S, Farhadian M, Elyasi E, Mohammadi Y. The maternal factors associated with infant low birth weight: an umbrella review. BMC Pregnancy Childbirth. 2024;24(1):316. https://doi.org/10.1186/s12884-024-06487-y
- 13. Dola SS, Valderrama CE. Exploring parental factors influencing low birth weight on the 2022 CDC natality dataset. BMC Med Inform Decis Mak. 2024;24(1):367. https://doi.org/10.1186/s12911-024-02783-x
- 14. Engdaw GT, Tesfaye AH, Feleke M, Negash A, Yeshiwas A, Addis W, et al. Effect of antenatal care on low birth weight: a systematic review and meta-analysis in Africa, 2022. Front Public Health. 2023;11:1158809. https://doi.org/10.3389/fpubh.2023.1158809
- 15. Mare KU, Andarge GG, Sabo KG, Mohammed OA, Mohammed AA, Moloro AH, et al. Regional and sub-regional estimates of low birth weight and its determinants in 44 low- and middle-income countries: evidence from demographic and health survey data. BMC Pediatr. 2025;25(1):342. https://doi.org/10.1186/s12887-025-05691-9
- 16. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61(4):344-349. https://doi.org/10.1016/j.jclinepi.2007.11.008
- 17. Dahlen HG, Thornton C, Downe S, de Jonge A, Seijmonsbergen-Schermers A, Tracy S, et al. Intrapartum interventions and outcomes for women and children following induction of labour at term in uncomplicated pregnancies: a 16-year population-based linked data study. BMJ Open. 2021;11(6):e047040. https://doi.org/10.1136/bmjopen-2020-047040
- 18. Abid MT, Kulsum U, Sultana S, Razia S, Chowdhury AH. Relationship between antenatal care and pregnancy outcome among participants of a Rural Upazilla Health complex in Bangladesh. Age. 2024;20(17):37-38. https://doi.org/10.36348/sijog.2024.v07i09.012
- 19. Kozuki N, Lee AC, Silveira MF, Sania A, Vogel JP, Adair L, et al. The associations of parity and maternal age with small-for-gestational-age, preterm, and neonatal and infant mortality: a meta-analysis. BMC Public Health. 2013;13(Suppl 3):S2. https://doi.org/10.1186/1471-2458-13-S3-S2
- 20. Akinwande MO, Dikko HG, Samson A. Variance inflation factor: as a condition for the inclusion of suppressor variable(s) in regression analysis. Open J Stat. 2015;5(7):754-767. https://doi.org/10.4236/ojs.2015.57075
- 21. Azcorra H, Dickinson F, Mendez-Dominguez N, Mumm R, Valentín G. Development of birthweight and length for gestational age and sex references in Yucatan, Mexico. Am J Hum Biol. 2022;34(6):e23732. https://doi.org/10.1002/ajhb.23732
- 22. Perumal N, Wang D, Darling AM, Liu E, Wang M, Ahmed T, et al. Suboptimal gestational weight gain and neonatal outcomes in low and middle income countries: individual participant data meta-analysis. BMJ. 2023;382:e072249. https://doi.org/10.1136/bmj-2022-072249
- 23. Huang Z, Tan X, Wang J, Zhang A. Maternal pre-pregnancy body mass index and gestational weight gain on adverse birth outcomes in Chinese newborns: a retrospective study. J Health Popul Nutr. 2024;43(1):165. https://doi.org/10.1186/s41043-024-00652-x
- 24. Deshpande M, Miriam D, Shah N, Kajale N, Angom J, Bhawra J, et al. Influence of parental anthropometry and gestational weight gain on intrauterine growth and neonatal outcomes: findings from the MAI cohort study in rural India. PLOS Glob Public Health. 2023;3(8):e0001858. https://doi.org/10.1371/journal.pgph.0001858
- 25. Takagi K, Iwama N, Metoki H, Uchikura Y, Matsubara Y, Matsubara K, et al. Paternal height has an impact on birth weight of their offspring in a Japanese population: the Japan Environment and Children’s Study. J Dev Orig Health Dis. 2019;10(5):542-554. https://doi.org/10.1017/S2040174418001162
- 26. Iftikhar A, Azam H, Ahmed M, Asad A, Noorani A, Shabbir M, et al. Effect of balanced protein-energy supplementation given to pregnant women on birth outcomes: a systematic review and meta-analysis. Womens Health (Lond). 2025;21:17455057251335366. https://doi.org/10.1177/17455057251335366
- 27. Global Nutrition Report. Country nutrition profile: Viet Nam [Internet]. Global Nutrition Report; 2021 [cited 2026 Jan 3]. Available from: https://globalnutritionreport.org/resources/nutrition-profiles/asia/south-eastern-asia/viet-nam/
- 28. O'Sullivan A, Monk C. Maternal and environmental influences on perinatal and infant development. Future Child. 2020;30(2):11-34. https://doi.org/10.1353/foc.2020.a807759
- 29. Walsh K, McCormack CA, Webster R, Pinto A, Lee S, Feng T, et al. Maternal prenatal stress phenotypes associate with fetal neurodevelopment and birth outcomes. Proc Natl Acad Sci U S A. 2019;116(48):23996-24005. https://doi.org/10.1073/pnas.1905890116
- 30. Tokhi M, Comrie-Thomson L, Davis J, Portela A, Chersich M, Luchters S. Involving men to improve maternal and newborn health: a systematic review of the effectiveness of interventions. PLoS One. 2018;13(1):e0191620. https://doi.org/10.1371/journal.pone.0191620
- 31. Kearns ML, Lahdenperä M, Galante L, Rautava S, Lagström H, Reynolds CM. Association of paternal BMI and diet during pregnancy with offspring birth measures: the STEPS Study. Nutrients. 2025;17(5):866. https://doi.org/10.3390/nu17050866
