Danbi Gwon; Ji-Yun Hwang; Jieun Oh

doi:10.5720/kjcn.2024.00311

Articles

Page Path: HOME > Korean J Community Nutr > Volume 30(1); 2025 > Article

Research Article
Nutrition quotient for preschoolers and key impacting factors in Korea: a cross-sectional study on food literacy, social support, and the food environment of primary caregivers: Danbi Gwon¹⁾, Ji-Yun Hwang²⁾, Jieun Oh^3),†; Korean Journal of Community Nutrition 2025;30(1):16-26.
DOI: https://doi.org/10.5720/kjcn.2024.00311
Published online: February 28, 2025

¹⁾Graduate Student, Department of Food and Nutrition, Ewha Womans University, Seoul, Korea

²⁾Professor, Major of Foodservice Management and Nutrition, Sangmyung University, Seoul, Korea

³⁾Professor, College of Science and Industry Convergence, Ewha Womans University, Seoul, Korea

†Corresponding author: Jieun Oh College of Science and Industry Convergence, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemoon-gu, Seoul 03760, Korea Tel: +82-2-3277-6586 Fax: +82-2-3277-6586 Email: oje96@ewha.ac.kr

• Received: December 3, 2024 • Revised: January 20, 2025 • Accepted: February 6, 2025

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

348 Views
21 Download

prev next

Full Article

Download PDF

Abstract
INTRODUCTION
METHODS
RESULTS
DISCUSSION
NOTES
REFERENCES

Abstract

Objectives
This study evaluated the nutrition quotient for preschoolers (NQ-P) and analyzed the impact of key factors, such as caregivers’ food literacy, social support, and food environment, on the eating habits of preschool children in Korea. This study also sought to provide foundational data for developing tailored nutrition education programs by identifying the nutrition education needs of caregivers.
Methods
This study was conducted among caregivers of preschool children (aged 0–6 years) using an online self-administered survey conducted from August 22 to August 28, 2023. A total of 1,116 survey responses were analyzed. This study assessed children’s NQ-P score, caregivers’ food literacy, social support, food environment, and nutritional education needs. Data were analyzed using SPSS 29.0 (IBM Co.).
Results
The average NQ-P score for preschool children was 52, showing a tendency for the balance score to decrease and the moderation score to increase with age. Children from rural and low-income areas exhibited significantly lower NQ-P scores. Caregivers’ food literacy was higher in urban and higher-income groups. Multiple regression analysis revealed that social support, food literacy, income, and food environment significantly affected children's NQ-P scores. The effectiveness of nutrition education varied based on the income level, with nutrition education on healthy eating being the most preferred topic for preschool children.
Conclusion
This study confirmed that caregivers’ food literacy and social support significantly affected preschool children’s nutritional status. This suggests a need for tailored nutritional education and dietary support policies, particularly for low-income and rural populations.
Keywords: food literacy; nutrition quotient for preschoolers; child, preschool; social support

INTRODUCTION

The preschool years represent a critical period of continuous physical, cognitive, and social development during which dietary preferences and eating behaviors are established that often persist into adulthood. Thus, ensuring appropriate nutritional intake and fostering healthy eating habits during this stage is essential [1-4]. In Korea, various dietary issues have been identified among preschoolers, including nutritional imbalances, selective eating, skipping breakfast, and increased consumption of processed foods. These factors contribute to health problems, such as obesity, underweight, and childhood diabetes [5]. According to the 2023 National Health Statistics, 2.9% of children aged 1–2 years and 7.3% of children aged 3–5 years exhibited nutritional deficiencies, while excessive nutrient intake was observed in 4.1% and 3.5%, respectively, potentially impacting their physical development. Additionally, breakfast skipping rates were 6.6% among children aged 1–2 years and 7.1% of children aged 3–5 years, 24.9% of children aged 1–2 years, and 40.4% of children aged 3–5 years consumed at least one out-of-home meal per day [6].

The COVID-19 pandemic has further influenced dietary patterns, leading to a decline in dining out, increased food delivery, and greater reliance on dietary supplements [7]. Restrictions on outdoor activities also resulted in increased screen time and decreased physical activity, significantly altering preschoolers’ daily routines [8]. However, research on the dietary habits of preschoolers, including infants aged 0–1 year, in Korea is limited. Notably, no studies have employed the revised 2021 nutrition quotient for preschoolers (NQ-P). Thus, a precise assessment of dietary habits is needed to evaluate nutritional status and eating behaviors in this population group, along with the development of targeted dietary management strategies and nutrition education programs to support healthy growth and formation of proper eating habits [9].

Primary caregivers play a pivotal role in shaping preschoolers’ dietary patterns. Their food choices and home food environments significantly affect the children’s future eating habits and overall health [10]. Parental influence on their children operates both directly (through parenting behaviors and interactions) and indirectly (through socioeconomic status, occupational status, and living environment, all of which affect parenting attitudes and roles) [11]. Moreover, caregivers’ nutritional knowledge and attitudes have a profound impact on preschoolers’ dietary intake and the formation of appropriate eating habits. Assessing caregivers’ nutritional awareness and knowledge is essential for predicting children’s future health behaviors. Caregivers with higher nutritional awareness are more likely to apply their knowledge to foster proper eating habits among their children [12].

Several factors influence preschoolers’ dietary habits, including socioeconomic status, food literacy, and food environment. As preschoolers’ eating habits are strongly influenced by their primary caregivers, examining factors such as caregivers’ food literacy, social support, and food environment is important. However, studies on these factors are limited. The preschool age is crucial for establishing eating habits that persist into adulthood. This period is not only critical for physical growth and emotional development, but also for nutritional well-being, underscoring the importance of systematic nutrition education in this group and those associated with them [13]. With the growing need for nutrition education programs, studies have examined caregivers’ requirements for such programs. However, most of this research has focused on school-aged children, such as elementary and middle school students, whereas studies on preschoolers’ nutritional education needs have been limited to specific regions or qualitative focus group interviews [14-18].

This study aimed to provide a comprehensive understanding of the dietary environment and nutritional status of preschoolers by conducting a multifaceted analysis of the NQ-P and food literacy, social support, and food environment of their primary caregivers. Furthermore, this study aimed to generate evidence-based policy recommendations for improving nutritional support for preschoolers. Additionally, to facilitate the promotion of healthy eating habits, this study examined the nutritional education needs of primary caregivers, providing a basis for the development of effective evidence-based nutritional education programs.

METHODS

Ethics statement

The study was approved by the institutional review board of Hallym University (IRB No. HIRB-2023-018).

1. Study design

This cross-sectional study was designed in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (https://www.strobe-statement.org/).

2. Study participants and data collection period

This study made use of Data Spring (https://www.d8aspring.com), a professional survey agency that targets primary caregivers of preschoolers across Korea. Participants were selected based on their voluntary agreement to participate in the survey, and their status as primary caregivers was verified using preliminary screening questions. A combination of convenience and regional quota sampling was used, with urban and rural areas accounting for 90% and 10% of the respondents, respectively. The survey was conducted over 7 days (August 22 to 28, 2023). Among the 1,262 voluntary respondents, 146 were excluded because of ineligibility, resulting in a final sample of 1,116 valid responses (response rate: 88.4%).

3. Study measures

1) General characteristics

The survey collected demographic data on the primary caregivers, including their sex, age, relationship with the child(ren) in their care, residential area, and monthly household income. For the preschoolers, information on sex, age, height (cm), and weight (kg) was collected. The body mass index (BMI; kg/m²) was calculated to assess weight status. Following Kim et al. [19] classification criteria, preschoolers were categorized based on age-specific BMI percentiles as follows: underweight (< 5th percentile), normal weight (5th–85th percentile), overweight (85th–95th percentile), and obese (≥ 95th percentile). For ages 0–2 years, overweight was defined as a weight-for-length ≥ 95th percentile on growth charts.

2) Nutrition quotient for preschoolers

The NQ-P is a validated tool used to assess the nutritional status and dietary quality of preschoolers [20, 21]. The NQ-P comprises three domains: balance, moderation, and practice. Each domain comprises 15 items. A weighted scoring system is used to derive the final NQ-P score. Cronbach’s α for the NQ-P in this study was 0.626, confirming its reliability.

3) Primary caregivers’ food literacy, social support, and food environment

Food literacy was assessed using a life-cycle-specific Food Literacy Scale [22-24]. The adult version of this tool consists of 25 items across five domains (total score: 100 points): production (7 items), selection (5 items), preparation and cooking (7 items), intake (3 items), and disposal (3 items). The domain scores were calculated by applying item-specific weights, summing the weighted scores within each domain, and then applying additional domain-specific weights to obtain a total score. Cronbach’s α for the Food Literacy Scale in this study was 0.927, indicating high reliability. To assess social support for dietary practices, previously validated measurement tools were adapted to fit the caregiving context [25]. Participants responded to four items using a 5-point Likert scale regarding the availability of fresh food at home, parental involvement in meal preparation, and snack guidance over the past 3 months. The items included questions regarding the availability of fresh fruits and vegetables at home, availability of fresh milk and dairy products, frequency of meal preparation for preschoolers, and encouragement of healthy snacking (e.g., fruits, vegetables, milk, and yogurt). The Social Support Scale demonstrated a Cronbach’s α value of 0.716, confirming its internal consistency. The food environment was assessed using a 5-item scale developed by Yang and Kim [26]. The five domains of the Food Environment Scale included availability, physical accessibility, affordability, acceptability, and accommodation. Each domain was rated on a 5-point Likert scale, ranging from “strongly disagree” (1 point) to “strongly agree” (5 points). The Food Environment Scale exhibited high reliability, with Cronbach’s alpha = 0.869.

4) Nutrition education needs

Nutritional education needs were assessed based on prior research [17, 18, 27]. The survey included three key items: perceived effectiveness of nutrition education (rated on a 5-point Likert scale from “strongly disagree” to “strongly agree”), participation in nutrition education within the past 3 months (response options: yes/no/unsure), and preferred nutrition education topics (multiple responses allowed).

4. Statistical analysis

All statistical analyses were performed using IBM SPSS Statistics 29.0 (IBM Co.), with statistical significance accepted at P < 0.05. Descriptive statistics were calculated for all the variables. Continuous variables were presented as mean ± standard deviation. Categorical variables are expressed as frequencies (n) and percentages (%). For comparisons of the NQ-P, food literacy, social support, food environment, and nutrition education needs according to the preschoolers’ and caregivers’ characteristics were compared by using independent t-tests and one-way analysis of variance. Chi-square and Duncan’s multiple comparison tests were used for analysis of categorical variables. A stepwise multiple linear regression analysis was conducted to evaluate the impact of primary caregivers’ food literacy, social support, food environment, monthly income, residential area, and the perceived effectiveness of nutritional education on preschoolers’ NQ-P scores. Multiple response frequency analysis was used to identify the caregivers’ preferred nutrition education topics.

RESULTS

1. General characteristics of study participants

Table 1 presents the general characteristics of the participants. Among primary caregivers, females were predominant. Almost two-thirds of participants were in their 30s, followed by those in their 40s, 20s, and 50s. In terms of relationship with the child, 40.9% of the caregivers were fathers and 58.5% were mothers. Regarding the regional distribution, the vast majority of the participants resided in urban areas, whereas only about 10% lived in rural areas. Monthly household income was distributed as follows: most (about one-third) had an income of 400–599 million KRW, followed by those with incomes of 200–399 million KRW, 600–799 million KRW, 800 million KRW, and 200 million KRW, in descending order. Among the preschoolers, just more than half were boys. Each age group (0–1 years, 2–4 years, and 5–6 years) accounted for roughly one-third of the children. Just more than two-thirds of the children were classified as normal weight, while the rest were classified as underweight, overweight, or obese.

2. NQ-P and domain scores

Table 2 presents the total and domain scores of the NQ-P according to sex, age, region, and household income. Significant sex differences were observed in the moderation domain, with boys scoring higher than girls (P < 0.05). Age-related differences were also noted in the total NQ-P score (P < 0.05), balance domain (P < 0.01), and moderation domain (P < 0.001), whereas no significant differences were observed in the practice domain. Balance scores declined with increasing age (P < 0.01), whereas moderation scores increased with age (P < 0.001). Regional analysis revealed that preschoolers in rural areas had significantly lower total NQ-P scores (P < 0.05) and balance domain scores (P < 0.01) than did their urban counterparts. Additionally, household income correlated positively with the total NQ-P scores (P < 0.001), balance scores (P < 0.001), and practice scores (P < 0.05), whereas no significant differences were observed in the moderation domain.

3. Primary caregivers’ food literacy, social support, and food environment scores

Table 3 presents the total scores for primary caregivers’ food literacy, social support, and food environment by urban/rural area and monthly household income. Regarding food literacy, urban caregivers had significantly higher total (P < 0.01), production (P < 0.05), intake (P < 0.01), and disposal (P < 0.05) domain scores. Income-based analysis revealed significantly higher food literacy scores in the higher-income groups across all domains, including total score (P < 0.001), production (P < 0.001), selection (P < 0.001), preparation and cooking (P < 0.001), intake (P < 0.001), and disposal (P < 0.01). Although social support scores showed no significant regional differences, they varied significantly according to income level (P < 0.01), with high-income groups scoring the highest, followed by middle-income, and low-income groups. Food environment analysis showed significantly higher scores in urban versus rural areas (P < 0.001), which increased with higher household income levels (P < 0.001).

4. Impact of primary caregivers’ characteristics on preschoolers’ NQ-P scores

Table 4 presents the correlations between primary caregivers’ characteristics and preschoolers’ NQ-P scores. Significant positive correlations were found between preschoolers’ NQ-P scores and caregivers’ food literacy, social support, food environment, and the perceived effectiveness of nutrition education (all P < 0.001). A stepwise multiple linear regression analysis (Table 5) revealed that caregivers’ social support (P < 0.001), food literacy (P < 0.001), monthly income (P < 0.01), and food environment (P < 0.05) were significant predictors of preschoolers’ NQ-P scores (adjusted R² = 0.188, P < 0.001). Social support, food literacy, and food environment were positively associated and lower income was negatively associated with preschoolers’ NQ-P scores.

5. Nutrition education needs assessment

Table 6 presents primary caregivers’ perceptions of nutrition education effectiveness and recent participation in nutrition education programs. The mean effectiveness score of nutrition education was 3.9, with no significant urban–rural differences. However, income-related differences were observed, with lower-income caregivers rating nutrition education effectiveness lower than did middle- and high-income caregivers (P < 0.01). Regarding participation in nutrition education programs within the past 3 months: about one-quarter reported participation, more than half stated that they had not participated, and the remainder were unsure. Significant urban-rural differences were noted (P < 0.01), whereas no significant differences were observed across income levels.

Table 7 presents caregivers’ preferred nutrition education topics, which included the following, in descending order of preference: healthy eating education, sensory education using food ingredients, food hygiene education, unbalanced diet education, sustainable dietary education, cooking education, traditional food culture education, and other.

DISCUSSION

The preschool years represent a crucial period for establishing lifelong dietary habits, and preschoolers’ eating behaviors are strongly influenced by their primary caregivers. This study thus examined preschoolers’ NQ-P scores and the association thereof with caregivers’ food literacy, social support, food environment, and nutrition education needs.

The findings indicate that the mean NQ-P score was 52.3, with scores for the balance domain of 48.6, moderation domain of 40.4, and practice domain of 59.5. Compared to previous nationwide studies [21], these scores were generally lower, likely due to the inclusion of preschoolers aged 0–1 year, who typically exhibit less independent dietary behaviors. Regional disparities were observed, as preschoolers in rural areas exhibited significantly lower total NQ-P scores (P < 0.05) and balance domain scores (P < 0.01) than did their urban counterparts. These differences may be attributed to limited access to diverse nutritious foods and disparities in the availability of nutrition education in rural settings. Additionally, higher household income was associated with significantly better dietary quality, as indicated by higher total NQ-P (P < 0.001), balance domain (P < 0.001), and practice domain (P < 0.05) scores, consistent with previous studies demonstrating the influence of socioeconomic factors on preschoolers’ dietary behaviors [28, 29]. These findings highlight the need for targeted interventions to reduce disparities between nutrition education and food environments.

The mean food literacy score of the primary caregivers was 62.3, with domain scores of 54.0 for production, 59.0 for selection, 69.0 for preparation and cooking, 62.6 for intake, and 68.6 for disposal. Urban caregivers had significantly higher food literacy scores than did rural caregivers (P < 0.01), and high-income groups exhibited significantly higher food literacy scores than did middle- and low-income groups (P < 0.001), which was consistent with the findings of previous research [30, 31] on the importance of parental food literacy in preschoolers’ nutritional status. The mean social support score was 4.0, with higher scores observed in high-income groups (P < 0.01), consistent with prior findings that low-income households tend to have lower social support and inadequate food environments at home [32]. Given the critical role of the home food environment in child development, government intervention for economically disadvantaged households is necessary. Food environment analysis revealed significant differences across urban–rural locations and across income levels. Urban caregivers reported significantly higher food environment scores across all domains (P < 0.001), while a higher monthly income was significantly associated with greater food environment scores (P < 0.001). These findings align with those of previous studies [33, 34] that reported challenges in accessing fresh food and food desert phenomena in low-income and rural areas.

Analysis of the factors influencing preschoolers’ nutritional status showed that primary caregivers’ social support (P < 0.001), food literacy (P < 0.001), income status (P < 0.01), and food environment (P < 0.05) significantly affected preschoolers’ NQ-P scores (adjusted R² = 0.188, P < 0.001). Primary caregivers play a pivotal role in preschoolers’ dietary habits and meal patterns as caregivers’ food choices and home food environments directly influence their nutritional status [10, 17]. Differences in access to nutritional information, the ability to purchase healthy food, and opportunities to participate in health programs appear to be particularly prominent in high-income households and urban areas. Therefore, nutrition education programs should be tailored to caregivers’ characteristics, and regional and economic circumstances. Specifically, policy support and expanded nutrition education programs are essential for low-income and rural populations, along with concrete measures to enhance fresh food purchasing conditions and to improve access to nutritional information. Previous studies have demonstrated that government-led nutrition support programs, such as the Nutrition Plus program, effectively improve the nutritional status of preschoolers and caregivers [35-37]. Sustained policies and support are needed, including increased financial support and the expansion of nutrition assistance programs, such as Nutrition Plus, food banks, and food vouchers.

Additionally, the development and continuous operation of nutrition education programs involving collaboration among childcare centers, home environments, and children’s food service management support centers should be ensured. For rural areas, policy interventions, such as mobile markets and fresh food delivery services, should be considered to enhance fresh food purchasing environments.

Regarding preferred nutrition education topics, primary caregivers prioritized healthy eating education (20.8%), sensory education using food ingredients (19.3%), food hygiene education (18.5%), and picky eating management (15.9%). These preferences align with previous research [17, 18] and reflect an increased awareness of food hygiene owing to the COVID-19 pandemic. These findings underscore the importance of identifying the nutrition education needs of primary caregivers and developing well-structured, sustainable nutrition education programs.

Limitations

This cross-sectional study based on proxy reporting by primary caregivers and self-administered online surveys using convenience sampling has limitations in establishing clear causal relationships. Additionally, as the existing NQ-P was developed for preschoolers aged 3–5 years, the NQ-P criteria may not be applicable to children aged 0–2 years. Furthermore, the survey’s reliance on primary caregivers’ subjective assessments may not accurately reflect preschoolers’ dietary habits and nutritional status. To address this, objective data collection methods (e.g., meal observations and food intake frequency surveys) should be incorporated. Finally, given that both NQ-P scores and diet-related factors (food literacy, social support, and food environment) showed differences according to monthly household income and residential areas, further research is needed to develop strategies to reduce socioeconomic disparities. Despite these limitations, this study provides valuable baseline data for a comprehensive understanding of preschoolers’ dietary environments and nutritional status, as well as for policy development and support.

Conclusion

This study examined the associations of primary caregivers’ food literacy, social support, and food environment with preschoolers’ NQ-P. These findings indicate that caregivers’ nutritional knowledge, social environment, and access to healthy foods significantly affect preschoolers’ dietary behaviors, underscoring the importance of caregiver-focused nutrition education interventions.

NOTES

CONFLICT OF INTEREST

There are no financial or other issues that might lead to conflict of interest.

FUNDING

This research was supported by a grant from the Ministry of Food and Drug safety (23192영양안063).

DATA AVAILABILITY

Research data is available from the corresponding author upon request.

Table 1.

Characteristics of the study subjects

Variable	Category	Value
Main caregiver (n = 1,116)
Sex	Male	460 (41.2)
Sex	Female	656 (58.8)
Age (year)	20–29	58 (5.2)
	30–39	701 (62.8)
	40–49	346 (31.0)
	50–59	11 (1.0)
Relationship	Father	457 (40.9)
	Mather	653 (58.5)
	Grandparents	4 (0.4)
	Other	2 (0.2)
Region	Urban	997 (89.3)
Region	Rural	119 (10.7)
Education level	High school	116 (10.4)
	University	878 (78.7)
	Graduate	122 (10.9)
Occupation	Office worker	537 (48.1)
	Sale or service	73 (6.6)
	Professional	115 (10.3)
	Self-employed business	54 (4.8)
	Housewives	310 (27.8)
	Others	27 (2.4)
Family income (million KRW/month)	< 200	43 (3.9)
	200–399	297 (26.6)
	400–599	420 (37.6)
	600–799	214 (19.2)
	≥ 800	142 (12.7)
Children (n = 1,116)
Sex	Male	577 (51.7)
Sex	Female	539 (48.3)
Age (year)	0–1	369 (33.1)
	2–4	374 (33.5)
	5–6	373 (33.4)
Weight status	Underweight	133 (11.9)
	Normal weight	762 (68.3)
	Overweight	124 (11.1)
	Obesity	97 (8.7)

n (%).

Table 2.

Scores of nutrition quotient for NQ-P and its factors by sex, age, region and household income

Variable	Total (n = 1,116)	Sex		P-value¹⁾	Age (year)			P-value²⁾	Region		P-value¹⁾	Household income			P-value²⁾
Variable	Total (n = 1,116)	Male (n = 577)	Female (n = 539)	P-value¹⁾	0–1 (n = 369)	2–4 (n = 374)	5–6 (n = 373)	P-value²⁾	Urban (n = 997)	Rural (n = 119)	P-value¹⁾	Upper (n = 356)	Middle (n = 420)	Low (n = 340)	P-value²⁾
NQ-P³⁾	52.3 ± 11.8	52.4 ± 11.9	52.2 ± 11.7	0.788	51.0 ± 12.4^a	52.9 ± 11.7^b	53.0 ± 11.2^b	0.037	52.6 ± 11.7	49.7 ± 12.6	0.013	54.6 ± 11.7^c	52.5 ± 10.7^b	49.5 ± 12.5^a	< 0.001
Balance	48.6 ± 13.7	48.5 ± 13.4	48.7 ± 14.1	0.788	50.0 ± 15.1^b	49.0 ± 12.8^b	46.7 ± 13.1^a	0.003	49.0 ± 13.6	44.8 ± 14.5	0.001	52.2 ± 13.9^c	48.1 ± 12.9^b	45.4 ± 13.8^a	< 0.001
Moderation	40.4 ± 19.9	41.6 ± 20.3	39.1 ± 19.5	0.039	29.4 ± 23.3^a	45.2 ± 16.2^b	46.4 ± 14.5^b	< 0.001	40.3 ± 19.9	40.9 ± 20.8	0.757	39.6 ± 20.1	41.8 ± 19.0	39.5 ± 20.8	0.197
Practice	59.5 ± 18.8	59.4 ± 19.2	59.6 ± 18.5	0.857	59.0 ± 18.6	58.8 ± 19.5	60.7 ± 18.4	0.346	59.8 ± 18.6	57.1 ± 20.3	0.144	61.8 ± 18.3^b	60.1 ± 18.1^b	56.5 ± 20.0^a	0.001

Mean ± SD.

NQ-P, nutrition quotient for preschoolers.

¹⁾P-value was determined by t-test.

²⁾P-value was determined by ANOVA.

³⁾This score encompasses the balance, moderation, practice dimensions of NQ-P.

^a,b,cValues with different superscripts within each row are significantly different at P < 0.05, as determined by Duncan’s multiple comparison test.

Table 3.

Scores of food literacy, social support and food environment by region and household income

Variable	Total (n = 1,116)	Region		P-value¹⁾	Household income			P-value²⁾
Variable	Total (n = 1,116)	Urban (n = 997)	Rural (n = 119)	P-value¹⁾	Upper (n = 356)	Middle (n = 420)	Low (n = 340)	P-value²⁾
Food literacy³⁾	62.3 ± 13.8	62.7 ± 13.7	59.2 ± 14.4	0.008	65.6 ± 13.8^c	62.6 ± 13.3^b	58.7 ± 13.5^a	< 0.001
Production	54.0 ± 21.3	54.5 ± 21.1	49.4 ± 23.1	0.013	57.7 ± 22.1^c	54.6 ± 20.9^b	49.3 ± 20.2^a	< 0.001
Selection	59.0 ± 17.1	59.2 ± 16.9	57.3 ± 19.1	0.259	60.9 ± 18.0^b	59.8 ± 16.8^b	55.9 ± 16.2^a	< 0.001
Preparation and cooking	69.0 ± 15.2	69.2 ± 15.2	67.3 ± 15.0	0.197	72.5 ± 14.8^c	68.5 ± 14.7^b	66.0 ± 15.6^a	< 0.001
Intake	62.6 ± 17.1	63.2 ± 16.8	58.0 ± 18.7	0.002	66.8 ± 16.1^c	62.9 ± 16.1^b	57.9 ± 18.2^a	< 0.001
Disposal	68.6 ± 16.6	69.0 ± 16.4	65.8 ± 17.2	0.044	71.1 ± 16.0^b	68.4 ± 16.1^a	66.4 ± 17.3^a	0.001
Social support⁴⁾	4.0 ± 0.7	4.0 ± 0.7	3.9 ± 0.7	0.053	4.1 ± 0.6^b	4.0 ± 0.7^a	3.9 ± 0.6^a	0.005
Food environment⁵⁾	3.7 ± 0.7	3.7 ± 0.7	3.4 ± 0.8	< 0.001	3.8 ± 0.7^c	3.7 ± 0.7^b	3.5 ± 0.8^a	< 0.001

Mean ± SD.

¹⁾P-value was determined by t-test.

²⁾P-value was determined by ANOVA.

³⁾This score encompasses the balance, moderation, practice dimensions of NQ-P.

⁴⁾Measured using a 5-point Likert scale (never = 1, always = 5).

⁵⁾Measured using a 5-point Likert scale (strongly disagree= 1, strongly agree = 5).

^a,b,cValues with different superscripts within each row are significantly different at P < 0.05, as determined by Duncan’s multiple comparison test.

Table 4.

Correlation analysis of NQ-P, food literacy, social support, food environment and nutrition education effectiveness

	NQ-P	Food literacy	Social support	Food environment	Nutrition education effectiveness
NQ-P	1
Food literacy	0.361^***	1
Social support	0.368^***	0.502^***	1
Food environment	0.255^***	0.372^***	0.399^***	1
Nutrition education effectiveness	0.162^***	0.323^***	0.262^***	0.225^***	1

NQ-P, nutrition quotient for preschoolers.

^***P-value was determined by correlation analysis.

Table 5.

Linear multiple regression analysis to explore factors related to the NQ-P in each domain (stepwise)

Variable	B	SE	β	t	P-value¹⁾	TOL	VIF
(Constant)	21.198	2.255			< 0.001
Social support	1.039	0.146	0.231	7.139	< 0.001	0.695	1.440
Food literacy	0.172	0.028	0.201	6.227	< 0.001	0.699	1.430
Household income (low)²⁾	–2.371	0.705	–0.093	–3.361	0.001	0.960	1.042
Food environment	0.238	0.097	0.074	2.451	0.014	0.794	1.260
F = 65.577; P < 0.001; R² = 0.191; adj. R² = 0.188

NQ-P, nutrition quotient for preschoolers; SE, standard error; TOL, tolerance; VIF, variance inflation factor.

¹⁾P-value was determined by linear multiple regression analysis.

²⁾Reference group: household income (upper).

Table 6.

Scores of nutrition education effectiveness and nutrition education experience by region and household income

Variable	Total (n = 1,116)	Region		P-value	Household income			P-value
Variable	Total (n = 1,116)	Urban (n = 997)	Rural (n = 119)	P-value	Upper (n = 356)	Middle (n = 420)	Low (n = 340)	P-value
Nutrition education effectiveness	3.9 ± 0.9	3.9 ± 0.9	3.8 ± 0.9	0.495¹⁾	3.9 ± 0.9^b	3.9 ± 0.8^b	3.7 ± 0.9^a	0.003²⁾
Nutritional education experience within 3 months				0.004³⁾				0.144³⁾
Yes	296 (26.5)	265 (26.6)	31 (26.0)		93 (26.1)	122 (29.1)	81 (23.8)
No	635 (56.9)	579 (58.1)	56 (47.1)		201 (56.5)	242 (57.6)	192 (56.5)
Not sure	185 (16.6)	153 (15.3)	32 (26.9)		62 (17.4)	56 (13.3)	67 (19.7)

Mean ± SD.

¹⁾P-value was determined by t-test.

²⁾P-value was determined by ANOVA.

³⁾P-value was determined by Chi-square test.

^a,bValues with different superscripts within each row are significantly different at P < 0.05, as determined by Duncan’s multiple comparison test.

Table 7.

Desired nutrition education topics for preschool children

Variable	Total (n = 1,116)	Region		Household income
Variable	Total (n = 1,116)	Urban (n = 997)	Rural (n = 119)	Upper (n = 356)	Middle (n = 420)	Low (n = 340)
Nutrition education on healthy eating¹⁾	654 (20.8)	587 (21.0)	67 (19.2)	213 (21.0)	253 (21.5)	188 (19.7)
Sensory education	606 (19.3)	544 (19.4)	62 (17.8)	186 (18.3)	211 (18.0)	209 (21.9)
Food hygiene	581 (18.5)	516 (18.4)	65 (18.7)	202 (19.9)	219 (18.6)	160 (16.7)
Unbalanced diet	499 (15.9)	449 (16.1)	50 (14.4)	164 (16.2)	184 (15.7)	151 (15.8)
Sustainable diet	330 (10.5)	290 (10.4)	40 (11.5)	103 (10.2)	129 (11.0)	98 (10.2)
Cooking	252 (8.0)	222 (7.9)	30 (8.6)	76 (7.5)	97 (8.2)	79 (8.3)
Traditional food culture	209 (6.6)	178 (6.4)	31 (8.9)	67 (6.6)	75 (6.4)	67 (7.0)
Others	14 (0.4)	11 (0.4)	3 (0.9)	3 (0.3)	7 (0.6)	4 (0.4)
Total	3,145 (100)	2,797 (100)	348 (100)	1,014 (100)	1,175 (100)	956 (100)

n (%).

¹⁾Multiple response.

REFERENCES

1. Seo SJ, Min IJ, Shin HS. Study of eating behavior and food preference in young children: differences by age and gender. J East Asian Soc Dietary Life 2009; 19(5): 659-667.
2. Jang SH, Kim J. A study on the effectiveness of dietary education program based on learning cycle model for young children’s nutrition knowledge, dietary behavior, science process skill and scientific attitude. Korean J Child Educ Care 2017; 17(4): 91-119. Article
3. Kim E, Song B, Ju SY. Dietary status of young children in Korea based on the data of 2013~2015 Korea National Health and Nutrition Examination Survey. J Nutr Health 2018; 51(4): 330-339. Article PDF
4. Sim HM, Han Y, Lee KA. Analysis of the types of eating behavior affecting the nutrition of preschool children: using the dietary behavior test (DBT) and the nutrition quotient (NQ). J Nutr Health 2019; 52(6): 604-617. Article PDF
5. Kim SY, Cha SM. Evaluation of dietary behavior and investigation of the affecting factors among preschoolers in Busan and Gyeongnam area using nutrition quotient for preschoolers (NQ-P). J Nutr Health 2020; 53(6): 596-612. Article PDF
6. Korea Disease Control and Prevention Agency (KDCA). Korea Health Statistics 2023: Korea National Health and Nutrition Examination Survey (KNHANES Ⅸ-2). KDCA; 2024 Dec. Report No. 11-1790387-000796-10.
7. Korea Disease Control and Prevention Agency (KDCA). Continued COVID-19 epidemic and changes in dietary habits. KDCA; 2023 Mar. Report No. 11-1790387-000384-01.
8. Kim JH. Korean children, changes in leisure time use and lifestyle habits before and after COVID-19 [Internet]. Korea Institute of Child Care And Education; 2022 [cited 2025 Jan 14]. Available from: https://repo.kicce.re.kr/handle/2019.oak/5287
9. Lee HJ, Kim JH, Song S. Assessment of dietary behaviors among preschoolers in Daejeon: using nutrition quotient for preschoolers (NQ-P). J Nutr Health 2019; 52(2): 194-205. Article PDF
10. Daniels LA. Feeding practices and parenting: a pathway to child health and family happiness. Ann Nutr Metab 2019; 74(Suppl 2): 29-42. Article PubMed PDF
11. Lee M, Jeong H. A study on the correlation of social support perceived and self-esteem of children. J Educ Res Mokpo National University 2004; 16/17: 123-137.
12. Jung IK, Lee JE. The perception of parents on the eating habits and nutritional education of their elementary school children. Hum Ecol Res 2005; 43(7): 67-77.
13. Yang IS, Kwak TK, Han KS, Kim EK. Needs assessment: nutrition education & training program for day care children. J Korean Soc Diet Cult 1993; 8(2): 103-116.
14. Kim HR, Shin ES, Lyu ES. Mothers’ perceptions on nutrition education for elementary school students in the Busan area I: a demographical factor approach. J Korean Diet Assoc 2008; 14(3): 276-290.
15. Lee MH. A study on the recognition body image and dietary self-efficacy and needs of nutrition education of middle school students by body mass index in Seoul. [master’s thesis]. Hanyang University; 2013.
16. Oh NG, Gwon SJ, Kim KW, Sohn CM, Park HR, Seo JS. Status and need assessment on nutrition & dietary life education among nutrition teachers in elementary, middle and high schools. Korean J Community Nutr 2016; 21(2): 152-164. Article PDF
17. Pyun JS, Lee KH. Study on the correlation between the dietary habits of mothers and their preschoolers and the mother's need for nutritional education for preschoolers. J Korean Diet Assoc 2010; 16(1): 62-76.
18. Kim K. Needs assessment of nutrition education for preschoolers and their parents using focus group interview. J East Asian Soc Dietary Life 2015; 25(1): 20-29. Article
19. Kim JH, Yun S, Hwang SS, Shim JO, Chae HW, Lee YJ, et al.; Committee for the Development of Growth Standards for Korean Children and Adolescents; Committee for School Health and Public Health Statistics, the Korean Pediatric Society; Division of Health and Nutrition Survey, Korea Centers for Disease Control and Prevention. The 2017 Korean National Growth Charts for children and adolescents: development, improvement, and prospects. Korean J Pediatr 2018; 61(5): 135-149. Article PubMed PMC PDF
20. Lee JS, Kang MH, Kwak TK, Chung HR, Kwon S, Kim HY, et al. Development of nutrition quotient for Korean preschoolers (NQ-P): item selection and validation of factor structure. J Nutr Health 2016; 49(5): 378-394. Article PDF
21. Kim HY. Customized health and dietary information development and application. Ministry of Food and Drug Safety; 2021 Nov. Report No. TRKO202200005227.
22. Park D, Park YK, Park CY, Choi MK, Shin MJ. Development of a comprehensive food literacy measurement tool integrating the food system and sustainability. Nutrients 2020; 12(11): 3300.Article PubMed PMC
23. Park D, Choi MK, Park YK, Park CY, Shin MJ. Higher food literacy scores are associated with healthier diet quality in children and adolescents: the development and validation of a two-dimensional food literacy measurement tool for children and adolescents. Nutr Res Pract 2022; 16(2): 272-283. Article PubMed PMC PDF
24. So H, Park D, Choi MK, Kim YS, Shin MJ, Park YK. Development and validation of a food literacy assessment tool for community-dwelling elderly people. Int J Environ Res Public Health 2021; 18(9): 4979.Article PubMed PMC
25. Dewar DL, Lubans DR, Plotnikoff RC, Morgan PJ. Development and evaluation of social cognitive measures related to adolescent dietary behaviors. Int J Behav Nutr Phys Act 2012; 9: 36.Article PubMed PMC PDF
26. Yang N, Kim K. Qualitative study on the perception of community food-accessibility environment among urban older adults. Korean J Community Nutr 2020; 25(2): 137-149. Article PDF
27. Yun JS. A study on satisfaction and needs of nutrition education in elementary school students of Ulsan area. [master’s thesis]. Pukyong National University; 2011.
28. Jang HB, Park JY, Lee HJ, Kang JH, Park KH, Song J. Association between parental socioeconomic level, overweight, and eating habits with diet quality in Korean sixth grade school children. Korean J Nut 2011; 44(5): 416-427. Article
29. Lim JY, Kim JH, Min SH, Lee MH, Lee MJ. Evaluation of dietary behavior among elementary school students in Seoul area using nutrition quotient for children. Korean J Food Cook Sci 2016; 32(1): 84-95. Article
30. de Buhr E, Tannen A. Parental health literacy and health knowledge, behaviours and outcomes in children: a cross-sectional survey. BMC Public Health 2020; 20(1): 1096.Article PubMed PMC
31. Costarell V, Michou M, Panagiotakos DB, Lionis C. Parental health literacy and nutrition literacy affect child feeding practices: a cross-sectional study. Nutr Health 2022; 28(1): 59-68. Article PubMed PDF
32. Yook SM, Hwang JY. A relationship between food environment and food insecurity in households with immigrant women residing in the Seoul metropolitan area. J Nutr Health 2023; 56(3): 264-276. Article PDF
33. Yousefian A, Leighton A, Fox K, Hartley D. Understanding the rural food environment--perspectives of low-income parents. Rural Remote Health 2011; 11(2): 1631.PubMed
34. Moon S. The effects of the food accessibility on fresh food intake and body mass index. [dissertation]. Chung-Ang University; 2018.
35. Kim YS, Kim SR, Jang YH, Kim DS, Kwon KH. A case study on effects of Nutrition-Plus Program - based on infants and children under age 6 and their mothers in Naju region-. J Reg Stud 2011; 19(3): 143-163.
36. Kim HJ, Kim SH. The cost-benefit analysis of the NutriPlus Program in Daejeon Dong-gu health center. Korean J Food Nutr 2015; 28(4): 717-727. Article
37. Song EY, Rho JO. Study on the correlation between nutrition knowledge, dietary attitudes of guardians, and nutritional status of infants and toddlers - Nutrition-Plus program in Jeonju. J Nutr Health 2018; 51(3): 242-253. Article PDF

Figure & Data

REFERENCES

Citations

Citations to this article as recorded by

PubReader
ePub Link

Cite

CITE: export Copy Download Format; Close

Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

Format:

RIS — For EndNote, ProCite, RefWorks, and most other reference management software
BibTeX — For JabRef, BibDesk, and other BibTeX-specific software

Include:

Citation for the content below
Citation and abstract for the content below

Nutrition quotient for preschoolers and key impacting factors in Korea: a cross-sectional study on food literacy, social support, and the food environment of primary caregivers

Korean J Community Nutr. 2025;30(1):16-26. Published online February 28, 2025

DOI: https://doi.org/10.5720/kjcn.2024.00311

XML Download

We recommend

Nutrition quotient for preschoolers and key impacting factors in Korea: a cross-sectional study on food literacy, social support, and the food environment of primary caregivers

Variable	Category	Value
Main caregiver (n = 1,116)
Sex	Male	460 (41.2)
Sex	Female	656 (58.8)
Age (year)	20–29	58 (5.2)
	30–39	701 (62.8)
	40–49	346 (31.0)
	50–59	11 (1.0)
Relationship	Father	457 (40.9)
	Mather	653 (58.5)
	Grandparents	4 (0.4)
	Other	2 (0.2)
Region	Urban	997 (89.3)
Region	Rural	119 (10.7)
Education level	High school	116 (10.4)
	University	878 (78.7)
	Graduate	122 (10.9)
Occupation	Office worker	537 (48.1)
	Sale or service	73 (6.6)
	Professional	115 (10.3)
	Self-employed business	54 (4.8)
	Housewives	310 (27.8)
	Others	27 (2.4)
Family income (million KRW/month)	< 200	43 (3.9)
	200–399	297 (26.6)
	400–599	420 (37.6)
	600–799	214 (19.2)
	≥ 800	142 (12.7)
Children (n = 1,116)
Sex	Male	577 (51.7)
Sex	Female	539 (48.3)
Age (year)	0–1	369 (33.1)
	2–4	374 (33.5)
	5–6	373 (33.4)
Weight status	Underweight	133 (11.9)
	Normal weight	762 (68.3)
	Overweight	124 (11.1)
	Obesity	97 (8.7)

Variable	Total (n = 1,116)	Sex		P-value¹⁾	Age (year)			P-value²⁾	Region		P-value¹⁾	Household income			P-value²⁾
Variable	Total (n = 1,116)	Male (n = 577)	Female (n = 539)	P-value¹⁾	0–1 (n = 369)	2–4 (n = 374)	5–6 (n = 373)	P-value²⁾	Urban (n = 997)	Rural (n = 119)	P-value¹⁾	Upper (n = 356)	Middle (n = 420)	Low (n = 340)	P-value²⁾
NQ-P³⁾	52.3 ± 11.8	52.4 ± 11.9	52.2 ± 11.7	0.788	51.0 ± 12.4^a	52.9 ± 11.7^b	53.0 ± 11.2^b	0.037	52.6 ± 11.7	49.7 ± 12.6	0.013	54.6 ± 11.7^c	52.5 ± 10.7^b	49.5 ± 12.5^a	< 0.001
Balance	48.6 ± 13.7	48.5 ± 13.4	48.7 ± 14.1	0.788	50.0 ± 15.1^b	49.0 ± 12.8^b	46.7 ± 13.1^a	0.003	49.0 ± 13.6	44.8 ± 14.5	0.001	52.2 ± 13.9^c	48.1 ± 12.9^b	45.4 ± 13.8^a	< 0.001
Moderation	40.4 ± 19.9	41.6 ± 20.3	39.1 ± 19.5	0.039	29.4 ± 23.3^a	45.2 ± 16.2^b	46.4 ± 14.5^b	< 0.001	40.3 ± 19.9	40.9 ± 20.8	0.757	39.6 ± 20.1	41.8 ± 19.0	39.5 ± 20.8	0.197
Practice	59.5 ± 18.8	59.4 ± 19.2	59.6 ± 18.5	0.857	59.0 ± 18.6	58.8 ± 19.5	60.7 ± 18.4	0.346	59.8 ± 18.6	57.1 ± 20.3	0.144	61.8 ± 18.3^b	60.1 ± 18.1^b	56.5 ± 20.0^a	0.001

Variable	Total (n = 1,116)	Region		P-value¹⁾	Household income			P-value²⁾
Variable	Total (n = 1,116)	Urban (n = 997)	Rural (n = 119)	P-value¹⁾	Upper (n = 356)	Middle (n = 420)	Low (n = 340)	P-value²⁾
Food literacy³⁾	62.3 ± 13.8	62.7 ± 13.7	59.2 ± 14.4	0.008	65.6 ± 13.8^c	62.6 ± 13.3^b	58.7 ± 13.5^a	< 0.001
Production	54.0 ± 21.3	54.5 ± 21.1	49.4 ± 23.1	0.013	57.7 ± 22.1^c	54.6 ± 20.9^b	49.3 ± 20.2^a	< 0.001
Selection	59.0 ± 17.1	59.2 ± 16.9	57.3 ± 19.1	0.259	60.9 ± 18.0^b	59.8 ± 16.8^b	55.9 ± 16.2^a	< 0.001
Preparation and cooking	69.0 ± 15.2	69.2 ± 15.2	67.3 ± 15.0	0.197	72.5 ± 14.8^c	68.5 ± 14.7^b	66.0 ± 15.6^a	< 0.001
Intake	62.6 ± 17.1	63.2 ± 16.8	58.0 ± 18.7	0.002	66.8 ± 16.1^c	62.9 ± 16.1^b	57.9 ± 18.2^a	< 0.001
Disposal	68.6 ± 16.6	69.0 ± 16.4	65.8 ± 17.2	0.044	71.1 ± 16.0^b	68.4 ± 16.1^a	66.4 ± 17.3^a	0.001
Social support⁴⁾	4.0 ± 0.7	4.0 ± 0.7	3.9 ± 0.7	0.053	4.1 ± 0.6^b	4.0 ± 0.7^a	3.9 ± 0.6^a	0.005
Food environment⁵⁾	3.7 ± 0.7	3.7 ± 0.7	3.4 ± 0.8	< 0.001	3.8 ± 0.7^c	3.7 ± 0.7^b	3.5 ± 0.8^a	< 0.001

	NQ-P	Food literacy	Social support	Food environment	Nutrition education effectiveness
NQ-P	1
Food literacy	0.361^***	1
Social support	0.368^***	0.502^***	1
Food environment	0.255^***	0.372^***	0.399^***	1
Nutrition education effectiveness	0.162^***	0.323^***	0.262^***	0.225^***	1

Variable	B	SE	β	t	P-value¹⁾	TOL	VIF
(Constant)	21.198	2.255			< 0.001
Social support	1.039	0.146	0.231	7.139	< 0.001	0.695	1.440
Food literacy	0.172	0.028	0.201	6.227	< 0.001	0.699	1.430
Household income (low)²⁾	–2.371	0.705	–0.093	–3.361	0.001	0.960	1.042
Food environment	0.238	0.097	0.074	2.451	0.014	0.794	1.260
F = 65.577; P < 0.001; R² = 0.191; adj. R² = 0.188

Variable	Total (n = 1,116)	Region		P-value	Household income			P-value
Variable	Total (n = 1,116)	Urban (n = 997)	Rural (n = 119)	P-value	Upper (n = 356)	Middle (n = 420)	Low (n = 340)	P-value
Nutrition education effectiveness	3.9 ± 0.9	3.9 ± 0.9	3.8 ± 0.9	0.495¹⁾	3.9 ± 0.9^b	3.9 ± 0.8^b	3.7 ± 0.9^a	0.003²⁾
Nutritional education experience within 3 months				0.004³⁾				0.144³⁾
Yes	296 (26.5)	265 (26.6)	31 (26.0)		93 (26.1)	122 (29.1)	81 (23.8)
No	635 (56.9)	579 (58.1)	56 (47.1)		201 (56.5)	242 (57.6)	192 (56.5)
Not sure	185 (16.6)	153 (15.3)	32 (26.9)		62 (17.4)	56 (13.3)	67 (19.7)

Variable	Total (n = 1,116)	Region		Household income
Variable	Total (n = 1,116)	Urban (n = 997)	Rural (n = 119)	Upper (n = 356)	Middle (n = 420)	Low (n = 340)
Nutrition education on healthy eating¹⁾	654 (20.8)	587 (21.0)	67 (19.2)	213 (21.0)	253 (21.5)	188 (19.7)
Sensory education	606 (19.3)	544 (19.4)	62 (17.8)	186 (18.3)	211 (18.0)	209 (21.9)
Food hygiene	581 (18.5)	516 (18.4)	65 (18.7)	202 (19.9)	219 (18.6)	160 (16.7)
Unbalanced diet	499 (15.9)	449 (16.1)	50 (14.4)	164 (16.2)	184 (15.7)	151 (15.8)
Sustainable diet	330 (10.5)	290 (10.4)	40 (11.5)	103 (10.2)	129 (11.0)	98 (10.2)
Cooking	252 (8.0)	222 (7.9)	30 (8.6)	76 (7.5)	97 (8.2)	79 (8.3)
Traditional food culture	209 (6.6)	178 (6.4)	31 (8.9)	67 (6.6)	75 (6.4)	67 (7.0)
Others	14 (0.4)	11 (0.4)	3 (0.9)	3 (0.3)	7 (0.6)	4 (0.4)
Total	3,145 (100)	2,797 (100)	348 (100)	1,014 (100)	1,175 (100)	956 (100)

Table 1. Characteristics of the study subjects

n (%).

Table 2. Scores of nutrition quotient for NQ-P and its factors by sex, age, region and household income

Mean ± SD.

NQ-P, nutrition quotient for preschoolers.

P-value was determined by t-test.

P-value was determined by ANOVA.

This score encompasses the balance, moderation, practice dimensions of NQ-P.

a,b,c

Values with different superscripts within each row are significantly different at P < 0.05, as determined by Duncan’s multiple comparison test.

Table 3. Scores of food literacy, social support and food environment by region and household income

Mean ± SD.

P-value was determined by t-test.

P-value was determined by ANOVA.

This score encompasses the balance, moderation, practice dimensions of NQ-P.

Measured using a 5-point Likert scale (never = 1, always = 5).

Measured using a 5-point Likert scale (strongly disagree= 1, strongly agree = 5).

a,b,c

Values with different superscripts within each row are significantly different at P < 0.05, as determined by Duncan’s multiple comparison test.

Table 4. Correlation analysis of NQ-P, food literacy, social support, food environment and nutrition education effectiveness

NQ-P, nutrition quotient for preschoolers.

***

P-value was determined by correlation analysis.

Table 5. Linear multiple regression analysis to explore factors related to the NQ-P in each domain (stepwise)

NQ-P, nutrition quotient for preschoolers; SE, standard error; TOL, tolerance; VIF, variance inflation factor.

P-value was determined by linear multiple regression analysis.

Reference group: household income (upper).

Table 6. Scores of nutrition education effectiveness and nutrition education experience by region and household income

Mean ± SD.

P-value was determined by t-test.

P-value was determined by ANOVA.

P-value was determined by Chi-square test.

a,b

Values with different superscripts within each row are significantly different at P < 0.05, as determined by Duncan’s multiple comparison test.