Introduction

Stunting in children under five years of age is a critical and complex issue in Indonesia. The prevalence of stunting is predominantly found in low-and middle-income countries (LMIC), including Indonesia, particularly in rural areas. The national stunting prevalence of 21.6%. The problem is particularly severe in certain regions, such as Jember, East Java, with a stunting prevalence of 34.9%. Most instances of stunting among children under five years of age occur within families whose household incomes are below the region's minimum wage. According to 2023 data from Statistics Indonesia (BPS), 9.4% of the population is living in poverty, and 4.7% of families are impoverished [1]. Poverty can lead to household food insecurity access (HFIA), in addition to socioeconomic status [2] that hinder access to food, affecting their ability to obtain, consume, and meet their nutritional needs [3]. Moreover, households with food insecurity had a greater risk of stunting of children under five in Indonesia [4] influencing feeding habits among stunted children under five years old [5,6]. Therefore, family food insecurity needs to be addressed through strengthening food security as part of stunting prevention efforts, utilizing a nursing process approach.

Additionally, the dietary diversity that children consume is a crucial indicator of diet quality, particularly for children under five years old, a critical period for growth. According to the WHO, children who consume food from diverse food groups have better micronutrient intake, thus are at lower risk of stunting [7]. Low dietary diversity has been shown to be significantly associated with children’s stunting [8,4], while in children under five years of age without Wasting-Stunting, dietary diversity needs are met [9]. Dietary diversity fulfilment in children under five years is influenced by HFIA and family income [8]. In many rural areas, including Indonesia, household consumption patterns are still dominated by carbohydrate sources, with limited access to animal protein, fruits, and vegetables. However, conversely, in rural regions such as Jember, Indonesia, fish prices tend to be comparatively low, and vegetables can often be sourced directly from nearby gardens within the household or village.

Another risk factor for stunting in children under five in rural areas of Indonesia is inadequate sanitation in the family environment [10,11]. Inadequate sanitation—characterized by insufficient toilet facilities, contaminated drinking water, and limited hygiene practices—heightens children's vulnerability to enteric infections that can induce diarrhea [12]. The accessibility of sanitation facilities within the household and the practice of hygienic behaviours, particularly handwashing, may reduce stunting in children under five age [13,8]. Enhancing hygienic practices and access to family environmental sanitation is essential for reducing stunting in children under five years of age.

Family functioning — including aspects such as communication, roles, problem-solving, affective involvement, and affective responsiveness in parenting — plays a central role in determining the nutritional status and growth of children under five years of age. A recent cross-sectional study in rural areas of Indonesia showed that children from families with unhealthy family functioning have a significantly higher risk of stunting: dimensions such as problem-solving, communication, roles, affective responsiveness, and affective involvement were found to significantly increase the odds of stunting (AOR ranging from 2.0 to 2.7) compared to families with healthy family functioning [14]. Parental capabilities (knowledge, time availability, support) influence feeding practices, hygiene, and disease management, which are related to the risk of stunting [15]. Low maternal capabilities (time management, decision-making autonomy) affect child feeding practices, leading to an inability to meet the child's energy-protein needs, which impacts the child's nutritional status [16]. Family functioning in rural Indonesia is often shaped by patriarchal culture and the presence of extended family structures. This influences mothers' decision-making regarding child feeding practices.

Stunting in children under five years of age in rural regions is a multifactorial condition resulting from the interaction between insecure access to food, limited dietary diversity, unhygienic family environments, and suboptimal family functioning. Addressing this requires a holistic and comprehensive approach, and community nursing interventions can play a vital role. Previous studies have identified the issue of stunting and analysed various intervention programs, but there remains a gap in research analysing the modelling that integrates HFIA, dietary diversity, family environmental sanitation, family functioning and stunting in rural areas, which are all shown to be significant contributing factors [17,18,14]. The goal of modelling is to analyse the basic principles of community nursing care that are specific and sensitive to the needs of families with stunted children in rural areas. Therefore, this study aims to explore the factors that influence stunting in children in rural areas of Indonesia, based on the relationship between HFIA, dietary diversity, family environmental sanitation, and family functioning as a model in community health nursing practices in Indonesia.

Methods

Study design

This study utilized a descriptive analytic design and a cross-sectional survey to investigate the correlation between multiple risk variables and the prevalence of stunting in children under five years of age. The survey was conducted between June and August 2025 in two rural areas, East Java, Indonesia. These areas were purposively selected due to their high prevalence of stunting. The data were collected using structured questionnaires and measurements of stunting, supplemented by secondary data from the Community Health Centre, also known as Puskesmas, which included stunting reports.

Participants

The sample size was calculated based on the prevalence of stunting (34.9%) in children aged 1 to 5 years. The estimated population of children under five years of age was thus calculated to be approximately 35,000. The study was conducted in two Puskesmas. According to secondary data from Puskesmas, the number of stunted children under five years of age as of February 2025 was 262 and 339, respectively. The estimated sample size for the study, calculated using a proportion formula with a 95% confidence interval, was 339 children, obtained through proportional random sampling for each portion, calculated separately. This consisted of 157 children from the "Puskesmas Pakusari" and 182 children from the "Puskesmas Ajung". The inclusion criteria for the study were mothers with children registered at the Integrated Health Post, known as Posyandu, who gave their consent to participate in the research. Meanwhile, children with chronic diseases, infectious diseases, or those who were hospitalized were excluded. The sampling process in the two Puskesmas areas is added (Supplementary Figure 1).

Instruments

Socio-Demographic Characteristics: A researcher-developed questionnaire was used to collect data on the socio-demographic characteristics of families and children. It's used to collect data on family characteristics (e.g., age, education, income), characteristics of children under five years of age (e.g., age, birth history, medical history), and family structure. The medical histories of mothers and children under five years of age were cross-referenced with medical records from the Puskesmas and Posyandu records, where available.

The Family Assessment Device (FAD) was utilized to assess family functioning and patterns of transactions among family members. Grounded in the McMaster Model of Family Functioning (MMFF) the FAD consists of 53 items encompassing seven dimensions, ordered as follows: general functioning [GF] (12 items), problem-solving [PS] (five items), communication [C] (six items), roles [R] (eight items), behaviour control [BC] (nine items), affective involvement [AI] (seven items), and affective responsiveness [AR] (six items). Each item was rated using a four-point Likert scale: 1 = strongly agree, 2 = agree, 3 = disagree, and 4 = strongly disagree.

The Family Assessment Device (FAD) was utilized to assess family functioning and patterns of transactions among family members. Grounded in the McMaster Model of Family Functioning (MMFF), the FAD consists of 53 items encompassing seven dimensions, ordered as follows: general functioning [GF] (12 items), problem-solving [PS] (five items), communication [C] (six items), roles [R] (eight items), behaviour control [BC] (nine items), affective involvement [AI] (seven items), and affective responsiveness [AR] (six items). Each item was rated using a four-point Likert scale: 1 = strongly agree, 2 = agree, 3 = disagree, and 4 = strongly disagree [19]. The clinical thresholds for each dimension are as follows: GF ≥ 2.00, PS ≥ 2.20, C ≥ 2.20, R ≥ 2.30, BC ≥ 1.90, AI ≥ 2.10, and AR ≥ 2.20 [20]. The clinical thresholds for each dimension, presented in the same order, are: GF ≥ 2.00, PS ≥ 2.20, C ≥ 2.20, R ≥ 2.30, BC ≥ 1.90, AI ≥ 2.10, and AR ≥ 2.20 [20]. Scores equal to or exceeding the cut-off indicate unhealthy family functioning, whereas scores below the cut-off reflect healthy functioning on the respective dimension [20]. he translation process and psychometric evaluation demonstrated that 47 items were valid, and confirmatory factor analysis indicated that the Indonesian version of the FAD is acceptable [21]. The Cronbach’s alpha coefficient of the Indonesian FAD was 0.84, indicating good internal consistency [22].

Household Food Insecurity Access Scale (HFIAS): The Food Insecurity Experience Scale was used, consisting of nine questions to measure household food insecurity. This questionnaire was developed by the Food and Nutrition Technical Assistance (FANTA) to describe the behaviours and attitudes related to the experiential aspects of food insecurity at the household level [23]. This tool captures a household's experience of food insecurity over the previous four weeks across three domains: anxiety about food supply, inadequate food quality, and insufficient food intake. HFIAS was translated into the Indonesian language, and the survey was administered in a one-on-one interview using the Indonesian language, with good reliability (Cronbach's alpha = 0.831) [24]. Its categorization follows the guidelines set by the FANTA guidelines [23]. Calculate the Household Food Insecurity Access category for each household. 1 = Food Secure, 2=Mildly Food Insecure Access, 3=Moderately Food Insecure Access, 4=Severely Food Insecure Access. HFIA category = 1 if [(Q1a=0 or Q1a=1) and Q2=0 and Q3=0 and Q4=0 and Q5=0 and Q6=0 and Q7=0 and Q8=0 and Q9=0]; HFIA category = 2 if [(Q1a=2 or Q1a=3 or Q2a=1 or Q2a=2 or Q2a=3 or Q3a=1 or Q4a=1) and Q5=0 and Q6=0 and Q7=0 and Q8=0 and Q9=0]; HFIA category = 3 if [(Q3a=2 or Q3a=3 or Q4a=2 or Q4a=3 or Q5a=1 or Q5a=2 or Q6a=1 or Q6a=2) and Q7=0 and Q8=0 and Q9=0]; HFIA category = 4 if [Q5a=3 or Q6a=3 or Q7a=1 or Q7a=2 or Q7a=3 or Q8a=1 or Q8a=2 or Q8a=3 or Q9a=1 or Q9a=2 or Q9a=3].

Dietary Diversity (DD): Dietary diversity was measured using the dietary diversity tool [25], which comprises three indicators: 24-hour recall, 7-day recall, and a 48-hour standard recall technique. The Dietary diversity of children under five years of age was assessed using a questionnaire developed in accordance with the WHO and UNICEF guidelines for evaluating child feeding practices (Minimum Dietary Diversity – MDD) [7]. Consumption was categorized into eight food groups: (1) breast milk; (2) grains, roots, and tubers; (3) legumes and nuts; (4) dairy products; (5) flesh foods; (6) eggs; (7) vitamin A-rich fruits and vegetables; and (8) other fruits and vegetables. A score of '1' was given if a food group was consumed and '0' if not. A total score is divided into three categories: Lowest dietary diversity (≤ 3 food groups), Medium dietary diversity (4 and 5 food groups), and High dietary diversity (≥ 6 food groups) [25].

Family Environmental Sanitation (FES): The assessment of family environmental sanitation is based on the Minister of Health's Decree of the Republic of Indonesia No. 829/Menkes/SK/VII/1999.No. 829/Menkes/SK/VII/1999 [26]. The instrument evaluates three key indicators: housing components, sanitation facilities, and behaviour of the house occupants. A weighted scoring method (Value x Weight) was used for each sub-scale to produce a total score. This score was then categorized as either a healthy (1,068–1,200) or an unhealthy family sanitation environment (<1,068) [18].

Measurement of Stunting: Children aged 1 to 5 years, weight and height (or length for children under 24 months) were measured following the Standard Operational Procedures (SOPs) of the Puskesmas and the Indonesian Minister of Health Decree No. HK.01.07/MENKES/51/2022. All measurements were recorded to two decimal places. This data was entered into the WHO Anthro software to calculate age- and sex-specific Z-scores for height-for-age (H/A), weight-for-age (W/A), and weight-for-height (W/H) based on the WHO Child Growth Standards [27]. For this study, stunting was defined as a height-for-age Z-score of less than -2 standard deviations (SD), and severe stunting was defined as a Z-score of less than -3 SD.

Data collection

A set of questionnaires, accompanied by detailed instructions, was administered and completed by the subject's mother or primary caregiver. The day before data collection, parents were informed by the data collection team about the time and location of data collection. On the day of data collection, participants were provided with a set of questionnaires and informed consent sheets to complete, which were then collected and checked for completeness. During the questionnaire completion process, six enumerators assisted in ensuring that all participants understood the instructions given for the questionnaire. Once the questionnaire was completed, the participants returned it to the researcher and enumerator for re-checking. Each participant who completed the questionnaire received the measurement results, a small reward, and a participation certificate at the end of the research.

Ethics statement

This research was approved by the Health Research Ethics Committee, Faculty of Nursing, Universitas Jember, under certificate No. 3150/UN25.8/KEPK/DL/2025.

Statistical analysis

In this study, we applied Partial Least Squares Structural Equation Modelling (PLS-SEM) with the aid of SmartPLS 3.6 to investigate factors related to stunting. The factors under scrutiny encompassed participant characteristics, food insecurity, dietary diversity, family functioning, and environmental sanitation within the family. The PLS-SEM was conducted in two stages: first, an evaluation of the measurement model (regarding the validity and reliability of the constructs); and second, an evaluation of the structural model (regarding the path coefficients among the constructs). Reliability was confirmed with Cronbach's Alpha values greater than 0.7 [28]. Principal component analysis provided the foundation for PLS estimation. The strength and predictive ability of relationships between latent and observed variables were assessed using path coefficients.

Results

Table 1 shows that most mothers were of productive age, with an average age of 29.24±5.73 years and an average age at pregnancy of 28.01±6.25 years. The majority (83.5%) delivered vaginam, had a primary education (62.5%), and came from low-income families with a monthly income of less than IDR < 1499K (USD 95) (64.9%). Most mothers were married (98.8%) and had an average of 1–2 children, with one under the age of five years. Among children, the average age was 31 months with a mean birth weight of 2861.39±459.87 grams and length of 52.7±3.72 cm; most received exclusive breastfeeding (64%), while the coverage of complete immunization was 51.6%, and 17.7% had a history of illness, and the majority were boys (56%).

The Kolmogorov–Smirnov test was performed for each sub-variable of the family functioning, and all the sub-variables exhibited a normal distribution. Unhealthy family functioning was identified in terms of problem-solving (2.33±0.02) and affective responsiveness (2.21±0.02). Nonetheless, other factors indicated healthy family functioning. The majority of families have a high diversity of dietary patterns, at 97%. This aligns with the HFIA being mild, at 95.8%. However, all of them have unhealthy living conditions, both in terms of facilities and healthy and clean living behaviors.

Table 2 shows that only a few sub-variables had loading factors greater than 0.7, indicating they met the criteria for convergent validity and were retained in the model. Within the mother factors construct, only education (.74) met this criterion. In the child factors construct, only history of illness (.75) showed a loading factor above 0.7. In the family functioning construct, several sub-variables demonstrated strong loadings, including behavior control (.74), affective responsiveness (.91), communication (.82), general functioning (.78), and roles (.75). In the sanitation construct, all sub-variables met the threshold, namely home (.71), behavior (.90), and facilities (.78). Meanwhile, the constructs of dietary diversity, household food insecurity access (HFIA), and stunting each showed loading factors of 1.00.

Figure 1 illustrates that the convergent validity values indicate that the SEM PLS model accurately describes the results, providing a clearer picture of the relationships among the significant variables in the study and offering deeper insights and more accurate data interpretation. However, several indicators were found to be invalid, including those related to children’s stunting.

Table 3 shows that several constructs have a significant relationship with the occurrence of stunting in the tested model. Child factors were found to have significant effects on family functioning (p=.001; β=.36), dietary diversity (p=.010; β=.14), FES (p=.001; β=.24), and HFIA (p=.001; β=-.51), and had a direct effect on stunting (p=.029; β=.14). The model showed that children's conditions and characteristics were significantly associated with family environment and nutritional status. Mother factors showed significant effects on dietary diversity (p=.021; β=-.13) and FES (p=.012; β=.14), highlighting the central role of mothers in managing food intake and household hygiene. In addition, family functioning (p=.003; β=.20) and dietary diversity (p=.001; β=.16) also had significant effects on stunting.

The structural model presented in Figure 2 demonstrated an acceptable level of model fit, as indicated by the goodness-of-fit indices with SRMR<.08 and NFI approaching 1 [29]. These results suggest that the proposed model adequately represents the empirical data and appropriately captures the relationships among the studied constructs. Therefore, the structural model can be considered well-fitted and suitable for explaining the interrelationships between mother factors, child factors, family functioning, dietary diversity, FES, HFIA, and the incidence of stunting.

Discussion

Stunting in rural Indonesian children under five years of age is a multifaceted issue driven by mother and child factors, HFIA, dietary diversity, FES, and family functioning. Our findings emphasize the significance of both mother and child factors, reinforcing the necessity for a comprehensive, family-centred approach to interventions. Dietary diversity is associated with the prevalence of stunting in children under five in rural regions (β=.16; t=0.16; p=.001). The mother's educational attainment is associated with the prevalence of stunting in children under five years of age (loading factor=.74). Indirectly, maternal education influences the provision of dietary diversity for children (β=-.13; t=2.32; p=.021) and the maintenance of hygiene during feeding (β=.14; t=2.52; p=.012), thereby mitigating the risk of illness in children. The implementation of hygiene habits by mothers, particularly handwashing, can prevent stunting in children under five years of age [13,8]. The significant influence of mothers' factors on dietary diversity further emphasizes the mother's pivotal role as a nutritional gatekeeper, where her knowledge and decision-making power directly shape a child's food intake [30]. Our results reinforce the importance of promoting locally sourced, nutrient-rich complementary foods, a strategy proven effective for improving children's feeding practices in similar Indonesian contexts.

Beyond nutrition, our model identified the FES—encompassing both family functioning and FES—as a critical determinant of child growth. The positive association between healthy family functioning and reduced stunting risk supports the concept that cohesive families are better equipped to manage resources, solve problems, and create a supportive atmosphere for child development [14]. Family functioning is substantially associated with the prevalence of stunting in rural regions (β=.20; t=0.20; p=.003). A well-functioning family unit can act as a buffer against the adverse effects of poverty and food insecurity, which are pervasive in many Indonesian communities [14]. Concurrently, the link between poor household sanitation and stunting is well-established, as inadequate sanitation contributes to a higher incidence of infectious diseases that impair nutrient absorption and compromise growth [12,31]. Interestingly, demographic variables such as mothers' age, pregnancy age, and marital status were not significant predictors in our model. This finding may reflect the overriding influence of socioeconomic status in this specific rural population. The majority of mothers in our study had primary education and belonged to low-income households, conditions that are powerfully correlated with stunting children in Indonesia and other developing nations [32,2]. It is plausible that in contexts of widespread economic hardship, the potent effects of poverty and educational limitations mask the more subtle influences of other demographic characteristics.

The primary strength of this study lies in its use of a multifactorial model to analyze the complex pathways leading to stunting in a specific, high-risk agrarian population. However, several limitations must be acknowledged. First, the cross-sectional design enables us to identify significant associations, but it does not permit the establishment of causality. Longitudinal studies are needed to confirm the temporal relationships between these variables. Second, data on family function and dietary intake were based on self-reports, which may be subject to social desirability and recall bias. Finally, our findings are specific to a rural area in Jember, Indonesia, and may not be generalizable to urban populations or other regions with different socioeconomic and cultural contexts. These findings have significant implications for community health nursing practice. Interventions must move beyond single-focus nutritional supplementation and embrace a comprehensive, family-empowerment model. Community nurses are uniquely positioned to promote dietary diversity, develop and deliver educational programs that emphasise the use of diverse, locally available foods for complementary feeding, aligning with cultural practices [33]. Implement family-centred counselling on communication, collaborative problem-solving, and resource management to enhance family resilience, drawing lessons from successful integrated parenting and nutrition programs [34]. Advocate for and connect families with community-based programs aimed at improving household food security, such as home gardening initiatives or local food cooperatives [8].

In conclusion, this study provides evidence that preventing stunting requires creating a supportive family ecosystem. By addressing dietary diversity, family functioning, and family environmental sanitation simultaneously, community nursing interventions can more effectively support child growth and development. Future research should focus on designing and evaluating the efficacy of such integrated, family-centred programs in rural settings to help Indonesia achieve its national goal of reducing stunting prevalence.

Supplementary Materials

Figure 1.

Hypothesised path Structural Equation Model (SEM): The determinants of stunting in children under 5 years of age

Figure 2.

Final path Structural Equation Model (SEM): the determinants of stunting in children under 5 years of Age

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Figure & Data

References

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