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. 2026 Mar 12;65:103445. doi: 10.1016/j.pmedr.2026.103445

Do socioeconomic resources reshape maternal health behaviors? Evidence from a nationally representative network analysis

Kyong Hee Park a, Chulhyo Jeon b,⁎
PMCID: PMC12990352  PMID: 41846587

Abstract

Objective

Maternal health behaviors may co-occur as interconnected patterns. This study, guided by the Family Health Development framework, examined these behaviors as an interconnected system and investigated whether network structure varied by socioeconomic status.

Methods

We conducted a cross-sectional secondary analysis of nationally representative data from Korea (Korea National Health and Nutrition Examination Survey [KNHANES], 2022–2023). Seven binary health behaviors were modeled using pairwise φ-correlation networks, and permutation-based Network Comparison Tests compared income and education strata.

Results

Fruit and vegetable intake and non-smoking were central to the network, showing multiple connections with other health behaviors. Network structure and global connectivity did not significantly differ by household income or maternal education.

Conclusions

Maternal health behaviors operate as an integrated system rather than independent choices in Korea. Targeting central behaviors (dietary behavior and smoking cessation) may yield spillover benefits across multiple health behaviors.

Keywords: Mothers, Health behaviors, Network analysis, Socioeconomic status, Korea National Health and Nutrition Examination Survey, Smoking, Dietary intake

Highlights

  • •

    Maternal health behaviors formed an interconnected network in South Korea.

  • •

    Fruit and vegetable intake and non-smoking were central behaviors.

  • •

    Central behaviors connected diet, stress, physical activity, and health checkups.

  • •

    Network structure did not differ meaningfully by income or educational attainment.

  • •

    Targeting diet and smoking may improve multiple behaviors simultaneously.

1. Introduction

Noncommunicable diseases remain leading causes of premature death globally, due to behaviors including tobacco use, unhealthy diet, harmful alcohol consumption, and physical inactivity (World Health Organization, 2023). These conditions are preventable through interventions targeting multiple risky behaviors (Prochaska and Prochaska, 2011). Family environments shape behavior, with parents' routines influencing children's health habits. Mothers typically assume primary responsibility for health-related decisions (Feinberg et al., 2022). Studies show that lifestyle behaviors cluster in individuals, with smoking, poor nutrition, physical inactivity, and excessive alcohol forming distinct patterns in adults (Meader et al., 2016; Noble et al., 2015). Parents' health behaviors are often concordant within couples (Graham et al., 2016), suggesting maternal behaviors are components of broader lifestyle patterns. Higher family socioeconomic status (SES) correlates with healthier behaviors, while lower status associates with risky behaviors (Gautam et al., 2023; Meader et al., 2016). Most studies quantify risk burden through simple counts but don't reveal behavior interrelationships across socioeconomic levels.

In South Korea, where national screening and health promotion programs target many of these behaviors, understanding maternal behavior clustering across socioeconomic groups may enhance family-level chronic disease prevention strategies.

The Family Health Development (FHD) framework conceptualizes family health as a dynamic, life-course process arising from interactions among family structures, processes, cognitions, and health-related behaviors (Feinberg et al., 2022). From this perspective, maternal health behaviors are plausible pathways through which socioeconomic resources and constraints shape family health trajectories and may influence the health of subsequent generations. Prior FHD-oriented scholarship has applied this framework to organize family-health constructs and guide measurement, underscoring the interdependence of multiple family domains in shaping health and well-being (Ramaswami et al., 2022). Building on this literature, we focus on the health-behavior domain of FHD by examining patterns of co-occurrence among mothers' preventive and lifestyle behaviors and assessing whether these patterns vary by socioeconomic resources. Network analysis provides a parsimonious way to operationalize behavioral interdependence and to identify behaviors that occupy central positions within a behavioral system (Chacha et al., 2025).

Using the FHD framework, this study examined mothers living with unmarried children in South Korea. Using nationally representative data from the 2022 to 2023 Korea National Health and Nutrition Examination Survey (KNHANES), we analyzed seven health behaviors: non-smoking, non-drinking, healthy weight maintenance, physical activity, fruit and vegetable intake, health checkups, and stress levels.

We investigated how mothers' health behaviors form networks and how socioeconomic resources influence their structure by (1) estimating network structure, (2) identifying central behaviors, and (3) comparing networks across income and education levels.

2. Methods

2.1. Study design and population

We conducted a cross-sectional network analysis of KNHANES, analyzing mothers' health behavior networks using pairwise φ-correlations between binary health behaviors. We compared network structure, strength, and centrality across education and income groups using permutation testing. We used bootstrapping to evaluate edge weights and centrality indices, performing sensitivity analyses under alternative specifications.

The KNHANES is a nationally representative survey of the non-institutionalized Korean population by the Korea Disease Control and Prevention Agency (KDCA), using multistage stratified probability sampling from household registries (Oh et al., 2021). The sample comprised married adults aged 18–65 years who resided with at least one unmarried child. After excluding missing data, the final sample included 2973 parents (1780 mothers, 1193 fathers), with analyses focusing on mothers.

The KNHANES protocol is approved by the Institutional Review Board of the Korea Disease Control and Prevention Agency, and all participants provide written informed consent. The present study was a secondary analysis of de-identified KNHANES data and complied with the data curator's guidelines for protection of human subjects, privacy, and confidentiality. This secondary analysis was reviewed by the Institutional Review Board of Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea (IRB No. UC25ZISI0125) and was determined to be exempt from full review.

2.2. Measures

2.2.1. Assessment of health behaviors

Health behaviors were evaluated using seven binary indicators from KNHANES: smoking status, alcohol consumption, physical activity, fruit and vegetable intake, body mass index (BMI)-based weight status, perceived stress, and participation in preventive health checkups. Variables were coded as 1 for health-promoting behavior and 0 for risk level. Smoking status was based on self-reported current smoking, with non-smokers (never or former) coded as health-promoting. Alcohol consumption was assessed using 12-month frequency, with abstinent or infrequent drinkers (≤1 occasion/month) classified as low-risk.

Physical activity compliance followed WHO guidelines: ≥150 min moderate-intensity, 75 min vigorous-intensity, or equivalent combined activity (Bull et al., 2020). Fruit and vegetable intake was assessed as usual consumption frequency over the past year in the KNHANES nutrition survey. Participants reported fruit and vegetable intake separately using a nine-category response scale ranging from “three times per day or more” to “rarely (less than once per month).” Responses were recoded to a 1–9 scale, and a composite fruit and vegetable frequency score was created by summing the fruit and vegetable scores (range: 2–18). Because the score is discrete, quartiles corresponded to the following score ranges: Q1 (2−12), Q2 (13), Q3 (14), and Q4 (15–18). Participants with scores ≥15 (top quartile/approximately the 75th percentile) were classified as having high-frequency fruit and vegetable intake. Obesity was defined as BMI ≥25 kg/m2 according to Korean guidelines, and participants with BMI <25 kg/m2 were coded as non-obese (Haam et al., 2023; World Health Organization Expert Consultation, 2004). Perceived stress was assessed using a single four-point item and dichotomized so that those reporting low or very low stress were classified as having low perceived stress. Participation in the national health checkup program within the past two years was coded as being up to date with preventive health checkups.

2.2.2. Socioeconomic resources

Socioeconomic resources were conceptualized in accordance with the structural domain of the FHD model, which suggests family structure and resources affect health behavior (Feinberg et al., 2022). Two structural indicators were used: (1) household income quartiles and (2) maternal education. Household income was categorized using KNHANES household income quartiles (monthly household income; KRW, South Korean Won): Q1 < 1.12 million KRW/month, Q2 1.12–2.36 million, Q3 2.36–3.85 million, and Q4 ≥ 3.85 million. For subgroup comparisons, participants were grouped into lower income (Q1–Q2) and middle/high income (Q3–Q4). Education was initially coded into four levels (1 = elementary or below, 2 = middle school, 3 = high school, 4 = college or above). As over half had a college degree, education was grouped into lower (levels 1–3) and higher (level 4) to balance group sizes for network comparisons. Both measures represent socioeconomic resources within the FHD framework.

2.3. Statistical analysis

Pairwise φ-correlation networks were estimated to examine interconnections among seven binary health behaviors.

2.3.1. Network estimation

Distinct φ-correlation networks were constructed for mothers using seven binary health behavior variables. The φ coefficient assessed association strength between behavior pairs, suitable for binary variables. Correlation networks have revealed comorbidity patterns in clinical populations (Divo et al., 2015). Networks were visualized using qgraph in R (Epskamp et al., 2012), with nodes representing health behaviors and edges showing φ-correlations. Edge thickness indicates correlation strength (positive in green, negative in red).

2.3.2. Network characterization

Strength, closeness, and betweenness centrality were computed in qgraph, and all centrality values were standardized to z-scores for comparability. Additionally, the expected influence (EI) was computed as an extension of strength centrality, quantifying node influence by summing connection weights (Chen et al., 2021).

2.3.3. Network reliability

Edge-weight accuracy was evaluated using nonparametric bootstrapping (1000 resamples) with 95% confidence intervals.

2.3.4. Network comparison test

Between-group differences in network structure, global strength, and individual edges were tested using the Network Comparison Test (NCT) with 5000 permutations and false discovery rate correction (van Borkulo et al., 2023). Analyses were conducted using IBM SPSS Statistics 30.0 (IBM Corp., Armonk, NY, USA) and R 4.5.2 (R Foundation for Statistical Computing, Vienna, Austria), including the NetworkComparisonTest package.

3. Results

3.1. Participant characteristics

A total of 1780 mothers were included in this study (Table 1), with a mean age of 47.27 ± 8.45 years old. Regarding educational attainment, 56.5% had completed college or higher education, 38.2% were high school graduates, and 5.2% had middle school or below. In household income, 38.3% were in the highest quartile (>75th percentile), followed by 34.6% in the 51st–75th percentile, 21.6% in the 26th–50th percentile, and 5.6% in the lowest quartile (≤25th percentile). For health behaviors, 77.6% had undergone health checkups within the past two years, and 39.8% met the criteria for regular fruit and vegetable intake. Of participants, 54.0% were classified as non-obese (BMI < 25 kg/m2), 20.1% were non-drinkers, and 96.4% were non-smokers. Additionally, 70.4% reported low perceived stress, and 48.2% met regular aerobic physical activity criteria.

Table 1.

Participant characteristics of mothers living with unmarried children in South Korea, 2022–2023 Korea National Health and Nutrition Examination Survey (n = 1780).

Category n % mean ± SD
Sociodemographic characteristics
Age (years) 47.27 ± 8.45
Education level Elementary school or lower 31 1.70
Middle school 63 3.50
High school 680 38.20
College or above 1006 56.50
Household income quartile (percentile) ≤25th 99 5.60
26th–50th 385 21.60
51st–75th 615 34.60
> 75th 681 38.30



Health behaviors
Health checkups Yes 1382 77.60
No 398 22.40
Fruit and vegetable intake Yes 708 39.80
No 1072 60.20
Obesity (body mass index <25 kg/m2) Yes 962 54.00
No 818 46.00
Non-drinking Yes 357 20.10
No 1423 79.90
Non-smoking Yes 1716 96.40
No 64 3.60
Low perceived stress Yes 1254 70.40
No 526 29.60
Aerobic physical activity Yes 858 48.20
No 922 51.80
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Abbreviations: SD Standard deviation.

3.2. Network structure of health behaviors

A φ-correlation network was constructed to elucidate the pairwise associations among the seven binary health behaviors of mothers (Fig. 1A). Of the 21 potential edges, a subset exhibited clear nonzero correlations, suggesting an interconnected behavioral structure in the social network. Correlations were predominantly positive, with few weak negative links observed. After applying false discovery rate (FDR) correction, nine edges remained significant (Fig. 1B). The most robust association was identified between fruit and vegetable intake and smoking (φ = 0.11, q < 0.01). Positive correlations were observed between fruit and vegetable intake and aerobic physical activity (φ = 0.08, q < 0.01), and between smoking and stress (φ = 0.07, q = 0.01). Additionally, fruit and vegetable intake was associated with alcohol consumption (φ = 0.06, q = 0.02) and stress (φ = 0.06, q = 0.02), while obesity was linked to stress (φ = 0.07, q = 0.01). Furthermore, aerobic physical activity was correlated with participation in health checkups (φ = 0.07, q = 0.01) and obesity (φ = 0.06, q = 0.02). The FDR-adjusted network results underscore dietary, smoking, stress-related, and physical activity behaviors among mothers.

Fig. 1.

Fig. 1

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Phi-correlation networks of seven maternal health behaviors in South Korea, Korea National Health and Nutrition Examination Survey, 2022–2023. (A) Full network including all estimated edges. Nodes represent health behaviors and edges correspond to pairwise φ correlations. Edge thickness is proportional to the absolute value of the correlation. (B) Network including only statistically significant edges after false discovery rate correction (q < 0.05).

3.3. Network centrality analysis

Network centrality indices identified the most influential behaviors within mothers' health behavior network (Fig. 2). Strength centrality, the sum of absolute values of edges connected to each node, showed the greatest variability. Fruit and vegetable intake demonstrated the highest strength (0.39), followed by smoking (0.34), aerobic physical activity (0.28), and stress (0.24), indicating that these behaviors were most strongly connected to the rest of the network, regardless of the sign of the association. Alcohol consumption showed the lowest strength (0.16), suggesting a peripheral position.

Fig. 2.

Fig. 2

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Centrality indices (strength, closeness, and betweenness) for seven maternal health behaviors in South Korea, Korea National Health and Nutrition Examination Survey, 2022–2023. Higher values indicate behaviors that are more strongly connected to other behaviors or more central within the network. Closeness values are displayed multiplied by 103 for readability.

The closeness and betweenness centrality indices revealed similar node rankings, with fruit and vegetable intake and smoking consistently emerging as the most central behaviors. These findings underscore the role of these factors as key relational hubs linking multiple health behaviors in mothers.

Expected influence closely mirrored strength (identical node ranking) and is therefore not shown separately (Fig. 2).

3.4. Network reliability

Edge-weight accuracy from bootstrapping is presented in Fig. 3. Bootstrap 95% confidence intervals suggested stable edge estimates for the FDR-significant edges, with the largest effects observed for fruit and vegetable intake–smoking (φ = 0.11, 95% CI 0.07, 0.14) and smoking–health checkups (φ = 0.10, 95% CI 0.04, 0.16).

Fig. 3.

Fig. 3

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Bootstrap estimates of edge-weight precision for seven maternal health behaviors in South Korea, Korea National Health and Nutrition Examination Survey, 2022–2023. Points represent bootstrap mean φ correlations for each edge based on 1000 nonparametric bootstrap resamples, and horizontal lines indicate 95% confidence intervals.

3.5. Network comparison test

Network Comparison Tests (NCT) assessed whether health behavior networks of mothers varied by household income and educational attainment (Supplementary Table S1). Regarding household income, neither the network structure (M = 0.69, p = 0.49) nor global strength (S = 0.38, p = 0.71) exhibited significant differences between the lower- and middle/high-income groups. Local edgewise tests also yielded non-significant results (all p > 0.05), indicating comparable behavioral network structures across income levels.

For educational attainment, analysis showed no significant differences in network structure or global strength. Most local edges were consistent, though the edge between fruit and vegetable intake and aerobic physical activity showed a nominally significant difference at α = 0.05 level (p < 0.05). Given multiple comparisons, this finding should be interpreted cautiously.

4. Discussion

This study investigated mothers' health behaviors within a network, as conceptualized by the FHD framework, and examined variations in socioeconomic status. The analysis revealed that fruit and vegetable intake and non-smoking status were central, connecting to other health activities. These findings suggest that mothers' health behaviors may function as an interconnected system, potentially reflecting shared contextual influences in daily life; however, specific mechanisms (e.g., caregiving burden or work demands) were not directly assessed in this study. Within this system, fruit and vegetable intake has emerged as a fundamental component of family meal preparation, underscoring its centrality in mothers' health behavior network. Mothers often serve as the primary decision-makers for household meals, including menu planning, ingredient selection, and food preparation (Arlinghaus and Laska, 2021). Feeding and meal provision are socially reinforced as maternal roles, linking dietary behaviors to other health practices. The consumption of fruits and vegetables coincided with preventive behaviors like health checkups and stress regulation, reflecting mealtime routines' regulatory function. This aligns with evidence that family meals organize daily life and connect to family health (Mazza et al., 2022; Spagnola and Fiese, 2007). These findings indicate fruit and vegetable intake serves as a structural backbone for family health rather than an isolated dietary choice.

Smoking status is a central behavior within maternal health behavior networks. In this study, smoking correlated with higher stress, reduced preventive routines, and decreased fruit and vegetable consumption, suggesting tobacco use links to emotional strain and fewer health-promoting activities. Previous research has demonstrated that maternal smoking during pregnancy and the child-rearing period increases the risk of miscarriage, preterm birth, low birth weight, neonatal mortality, and respiratory issues in the offspring (Cnattingius, 2004; Diamanti et al., 2019). Parental smoking cessation reduces children's secondhand smoke exposure and future smoking risk (Costa et al., 2023; Leonardi-Bee et al., 2011). From a FHD perspective, smoking cessation represents a shift to non-smoking households, making it central to maternal health networks (Feinberg et al., 2022).

Socioeconomic resources are important for health and family development (Adler and Ostrove, 1999; Barakat and Konstantinidis, 2023; Braveman and Gottlieb, 2014). However, in this study, NCT by household income and educational attainment revealed no statistically significant differences in the overall structure or global connectivity, and only one edge between fruit and vegetable intakes and aerobic physical activity differed according to education level. For the seven behaviors analyzed, these findings suggest that socioeconomic status may influence how often behaviors are performed, but the way mothers connect these behaviors into a routine system can remain broadly similar across socioeconomic status groups. These findings from mothers in a specific context should consider the social environment of family routines (Spagnola and Fiese, 2007).

Physical activity appeared relatively peripheral, consistent with time constraints among mothers (Nomaguchi and Bianchi, 2004). The stress-smoking association suggests smoking serves as an emotional coping mechanism (Baker et al., 2004). In contrast, preventive behaviors tended to cluster together (Poortinga, 2007), consistent with the FHD emphasis on interconnected health-related behaviors.

Although this study did not reveal distinct network structures associated with SES, SES remains a critical upstream factor in understanding family health (Adler and Ostrove, 1999; Braveman and Gottlieb, 2014). The absence of statistically significant NCT differences should not be construed as evidence that socioeconomic status is unimportant; it may reflect limitations of binary measures, the φ-correlation approach, or subgroup sample sizes. Future studies with larger samples and more granular measures may better clarify how socioeconomic conditions relate to mothers' behavioral networks.

The findings have significant implications for policies and practices. The centrality of fruit and vegetable intake and smoking behaviors suggests interventions may yield broader benefits across maternal health networks (Draxten et al., 2014; Pearson et al., 2010). Stress management programs and interventions like childcare support may enhance maternal health behavior (Pilarz, 2021; Timm et al., 2022). Improving access to nutritious foods and providing smoking cessation support can serve as leverage points for family health (Feinberg et al., 2022; Spagnola and Fiese, 2007). Addressing time pressure while enhancing dietary and smoking-related support can improve health and promote population-level equity in family well-being (Braveman and Gottlieb, 2014).

This study has key strengths and limitations. A notable strength is using network analysis within the Family Health Development framework to examine mothers' health behaviors as an interconnected system. The findings are supported by a large, nationally representative sample and permutation-based methods for network invariance. However, the cross-sectional design and self-reported binary measures limit causal inference. Some behaviors (e.g., fruit and vegetable intake frequency and perceived stress) lacked universally accepted thresholds and therefore required distribution-based categorization, which may limit comparability across studies relative to guideline-based behaviors (e.g., aerobic physical activity). The focus on mothers within one cultural context restricts generalizability. Future research should use longitudinal designs, include more behavioral domains and caregivers, and examine links between maternal behavior networks and child and broader family health outcomes.

5. Conclusions

This study using KNHANES data examined mothers' health behaviors within a network as conceptualized by the Family Health Development framework. Fruit and vegetable intake and smoking status emerged as central behaviors connected to preventive checkups, physical activity, weight status, and stress management. These findings support viewing mothers' health behaviors as an interconnected pattern of behaviors rather than independent behaviors considered in isolation.

Socioeconomic resources did not significantly alter the network structure, suggesting socioeconomic status may influence behavior frequency more than their interconnections. However, socioeconomic conditions remain important factors affecting time, stress, and caregiving burden. Future research using longitudinal data and more comprehensive measures is needed to understand how health behavior networks evolve across different contexts.

The centrality of diet and smoking suggests interventions targeting these behaviors may affect broader health networks. Policies supporting healthy food access, family meals, smoking cessation, and flexible work arrangements may help mothers maintain healthier routines, promoting family health equity.

CRediT authorship contribution statement

Kyong Hee Park: Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Chulhyo Jeon: Writing – review & editing, Supervision, Project administration, Methodology, Conceptualization.

Funding

This work was supported by the Catholic Medical Center Research Foundation (Program year 2024; Grant No. 5-2024-B0001-00095).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2026.103445.

Appendix A. Supplementary data

Supplementary material

mmc1.docx (27.4KB, docx)

Data availability

Data will be made available on request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material

mmc1.docx (27.4KB, docx)

Data Availability Statement

Data will be made available on request.

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