ABSTRACT
Obesity is a multifactorial chronic disease influenced by behavioral, environmental, genetic, and psychological factors. One primary determinant of childhood obesity is the presence of dietary factors commonly acquired through the shared home food environment, which parents can greatly influence. Thus, the present study examined the similarity in diet quality between children or adolescents with obesity and their mothers. We analyzed baseline data collected from the Intervention for Children and Adolescent obesity via Activity and Nutrition study. Seventy mother–offspring dyads were identified, which included children and adolescents with obesity aged 8–16 years and their mothers living in Seoul and Gyeonggi Province, South Korea. Food or nutrient intake and diet quality were evaluated from 3-day food records. Childhood obesity was defined as body mass index ≥ 95th percentile based on the 2007 Korean National Growth Charts. No significant difference was observed in the diet quality score between children with obesity and their mothers. However, correlation coefficients between mothers and their children’s total Diet Quality Index-International (DQI-I) score (r = 0.30) and subcategories, such as variety (r = 0.29), adequacy (r = 0.43), moderation (r = 0.45), and overall balance (r = 0.30), were positively correlated (p < 0.05). Linear regression analysis of the influence of maternal diet quality on offspring diet quality revealed that the maternal DQI-I score influenced the offspring’s DQI-I score, consistent with our prediction. Further studies with larger and more representative samples are needed to confirm the applicability of our findings to all children and adolescent populations.
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Trial Registration
Clinical Research Information Service Identifier:
KCT0002718
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Keywords: Pediatric obesity; Dietary patterns; Food preferences
INTRODUCTION
The World Health Organization recognizes childhood obesity as one of the most serious global public health challenges of the 21st century. The condition is steadily and widely affecting both developed and developing countries, with a higher prevalence in urban than in rural areas. As of 2015, the global prevalence of overweight children aged 5 years was approximately over 42 million, with nearly half living in Asia and Africa [
1]. In South Korea, the number of children and adolescents with obesity aged 2–18 years old in 2012 was 9.6%, with a notable sex disparity (boys: 10.7%; girls: 8.3%) [
2]. Excessive adiposity in childhood is caused mainly by personal and socioeconomic factors. Present and future individual health problems, such as obesity, type 2 diabetes mellitus, cardiovascular disease, and metabolic comorbidity, are significant factors [
3]. Psychological issues, such as depression, low self-esteem, and body dissatisfaction, also contribute to the development of obesity [
4]. Socioeconomic problems are also associated with adolescent obesity.
The multifactorial causation of this issue consists of behavioral, environmental, genetic, and psychological factors. However, a primary determinant of childhood obesity is dietary factors. Factors influencing children’s eating habits and diet quality include exterior home environments; neighborhood; and urban corner stores, peers, and social factors [
5]. Specifically, childhood dietary behaviors are commonly acquired through observational learning in the home food environment [
6]. Parents influence children’s eating habits, taste preferences, and food choices by modeling eating habits and creating a food environment for children’s early experiences with food and eating [
7]. Environmental factors and eating behaviors established in childhood often persist into adulthood.
Several studies indicate that familial environments, such as parental pressure or control of feeding practices, positively influence children’s eating behaviors. However, evidence remains inconsistent on whether a similarity exists between the eating habits and diet quality of children and their parents. To the best of our knowledge, no prior studies in South Korea have examined this relationship using directly comparable dietary data from children with obesity and their parents.
The present study investigated predictors of diet quality similarity between Korean children or adolescents with obesity and their mothers and explored the associations among these factors.
MATERIALS AND METHODS
Participants
We analyzed baseline data collected from the Intervention for Children and Adolescent obesity via Activity and Nutrition study approved by the Institutional Review Board (IRB) of Hallym University Sacred Heart Hospital (IRB No. 2016-I135). The participants were recruited in January–September 2016 through advertising modalities or by school nurses in Seoul (metropolitan) and Gyeonggi Province (satellite city) in South Korea. The study population included children or adolescents with obesity and their mothers. We excluded participants with missing anthropometric, biochemical, and dietary intake data for children and their mothers. We also excluded individuals with poor literacy. In total, 70 eligible dyads that included children and adolescents aged 8–16 years were recruited. Written informed consent was obtained from both mothers and children. Childhood obesity was defined as age- and sex-specific body mass index (BMI) ≥ 95th percentile based on the 2007 Korean National Growth Charts (Korea Centers for Disease Control and Prevention, 2007).
Measurements
Demographic and lifestyle factors
General personal information (including age, sex, and physical activity) was collected using the International Physical Activity Questionnaire [
8]. Maternal employment status was categorized as employed or unemployed. Maternal smoking status was obtained from self-administered survey questionnaires completed by either parents or children.
Anthropometric measurements
The participants were instructed to wear light clothes and no shoes for the assessment. BMI, lean body mass, body fat, and percent body fat were measured via bioimpedance analysis (InBody 720; InBody Co., Ltd., Seoul, Korea). The BMI Z-score was determined using a formula. Body weight and height were measured using a digital scale (DS-103; Dongsan Jenix Co., Seoul, Korea) to the nearest 0.1 kg and 0.1 cm, respectively. During the assessment, participants were required to stand erect, stare forward, and relax their abdominal muscles at the end of expiration. Waist circumference (WC) was measured horizontally with a non-elastic constant tension tape at the midway between the upper iliac crest and lower last rib to the nearest 0.1 cm. Hip circumference (HC) was measured to the nearest 0.1 cm with a non-stretched measuring tape at the maximum girth over the buttocks and in the standing position with feet placed fairly close together. The waist-to-hip ratio (WHR) was calculated by dividing waist girth (cm) with HC (cm). The waist-to-height ratio (WHtR) was calculated as WC divided by height. For blood pressure measurement, participants were asked to rest in the sitting position for at least 5 minutes before measurement. Systolic and diastolic blood pressure were measured twice on the right arm using an automatic sphygmomanometer (Dinamap Pro100; GE Healthcare, Tampa, FL, USA) and then averaged. All measurement data were collected by trained research staff.
Biochemical parameters
Blood samples were collected from peripheral veins after 8–12 hours of fasting to assess serum biomarker levels. The samples were collected into a serum separation, sodium fluoride, or ethylenediaminetetraacetic acid tube attached with a serial number. The samples were left at ambient room temperature for 10 minutes, after which the serum was separated from whole blood via centrifugation (DSC-1512T; Digisystem Laboratory Instruments Inc., Taipei City, Taiwan) at 3,000 rpm for 10 minutes. Centrifuged blood samples were placed in portable iceboxes and sent for analysis at the Seegene Medical Foundation (Seoul, Korea). Enzymatic triglyceride (TG) levels were measured. Low-density lipoprotein (LDL-cholesterol, mg/dL) and high-density lipoprotein (HDL-cholesterol, mg/dL) levels were measured using a homogeneous enzymatic colorimetric assay. Total cholesterol (TC, mg/dL) levels were measured formulas follows: TC = HDL-Cholesterol + LDL-Cholesterol + [TG/5]. Fasting blood glucose (FBG, mg/dL) levels were measured using the ultraviolet (hexokinase) assay. Insulin (µU/mL) levels were determined using the electrochemiluminescence immune assay method. High sensitivity C-reactive protein (mg/L) levels were measured via immunoturbidimetry.
Dietary intake
Dietary intake was evaluated using the food record method for 3 non-consecutive days, including 2 weekdays and one weekend. The respondents were instructed to complete a self-reported 3-day food record in a food diary. The nutrition expert double-checked the completed records using 3-dimensional food models and measuring cups. We used Computer-Aided Nutritional Analysis Program (CAN-Pro, Web version 5.0, 2016; The Korean Nutrition Society, Seoul, Korea) to assess the typical nutrient intake of the participants based on the food records.
Diet quality
Diet Quality Index-International (DQI-I)
The DQI-I is a tool for assessing diet quality [
9]. The score is based on 4 subcategories: variety, adequacy, moderation, and overall balance. Variety (0–20 points) assessed the diversity of protein sources. Adequacy (0–40 points) evaluated whether nutrient intake met age- and sex-specific recommendations. Moderation (0–30 points) evaluated restricted consumption of certain foods. Overall balance (0–10 points) examined the proportion of energy and fat intake. The component scores were summed to obtain a total score that ranged from 0 to 100, with higher scores indicating a healthier diet.
We investigated the differences and associations among nutrient intake, diet quality, and general characteristics between children or adolescents with obesity and their mothers using the Wilcoxon rank-sum test, Spearman correlation coefficients, and multivariate regression analysis. Data are expressed as mean ± standard deviation (continuous variables) or percentages (categorical variables). Similarities in diet quality were assessed by generating 2 different quartiles of diet quality sets for children and their mothers. Statistical analyses were performed using SAS (Statistical Analysis System, version 9.4; SAS Institute Inc., Cary, NC, USA).
RESULTS
The descriptive characteristics of 70 children/adolescent–mother dyads (61.4% boys and 38.6% girls) according to sex are reported in
Table 1. The mean age of the children was 11.7 ± 3.1 years, with boys being significantly younger than girls (11.4 ± 2.7 years vs. 14.1 ± 3.7 years; p < 0.01). Half (50%) of the mothers graduated from college or graduate school, and 15.6% reported a high income and over 50% working full-time jobs. Most children belonged to middle-income families, and 8.6% had mothers who were current smokers. Nearly two-thirds of the children reported attempting weight loss, whereas 64.3% were eating out with their families 2 or more times per week.
Table 1 Descriptive characteristics of mothers and their children with obesity
Table 1
|
Variables |
Total (n = 70) |
Children’s sex |
|
Boys (n = 43) |
Girls (n = 27) |
|
Child’s age (yr) |
11.7 ± 3.1 |
11.4 ± 2.7 |
14.1 ± 3.7**
|
|
Mother’s age (yr) |
43.4 ± 6.7 |
42.8 ± 8.0 |
43.6 ± 6.5 |
|
Mother’s education level |
|
|
|
|
≤ Elementary school |
6 (8.6) |
3 (4.3) |
3 (4.3) |
|
High school |
31 (44.3) |
20 (28.6) |
11 (15.7) |
|
Higher education |
33 (47.1) |
20 (28.6) |
13 (18.6) |
|
Father’s education level |
|
|
|
|
≤ Elementary school |
13 (18.6) |
8 (11.4) |
5 (7.1) |
|
High school |
17 (24.3) |
11 (15.7) |
6 (8.6) |
|
Higher graduation |
40 (57.1) |
24 (34.3) |
16 (22.9) |
|
Household income†
|
|
|
|
|
Low |
4 (5.7) |
3 (4.3) |
1 (1.4) |
|
Medium |
53 (75.7) |
32 (45.7) |
21 (30.0) |
|
High |
13 (15.6) |
8 (11.4) |
5 (7.1) |
|
Mother’s smoking status |
|
|
|
|
Never |
64 (91.4) |
41 (58.6) |
23 (32.9) |
|
Current |
6 (8.6) |
2 (2.9) |
4 (5.7) |
|
Mother’s employment status |
|
|
|
|
Employed (full-time) |
37 (52.9) |
21 (30.0) |
16 (22.9) |
|
Employed (part-time) |
3 (4.3) |
2 (2.9) |
1 (1.4) |
|
Unemployed |
30 (42.9) |
20 (28.6) |
10 (14.3) |
|
Weight loss in children |
|
|
|
|
Yes |
52 (74.3) |
30 (42.9) |
22 (31.4) |
|
No |
18 (25.7) |
13 (18.6) |
5 (7.1) |
|
Frequency of eating out with family |
|
|
|
|
Rarely |
3 (4.3) |
1 (1.4) |
2 (2.9) |
|
< Once/week |
22 (31.4) |
11 (15.7) |
11 (15.7) |
|
≥ Twice/week |
45 (64.3) |
31 (44.3) |
14 (20.0) |
|
Physical activity (MET week−1)‡
|
|
|
|
|
Inactive |
20 (28.6) |
9 (12.9) |
11 (15.7) |
|
Minimally active |
28 (40.0) |
19 (27.1) |
9 (12.9) |
|
Health-enhancing |
22 (31.4) |
15 (21.4) |
7 (10.0) |
|
Sedentary behaviors (hr)†
|
3.5 ± 2.5 |
3.5 ± 3.5 |
3.0 ± 2.3 |
|
Sleep duration (hr) |
8.8 ± 1.1 |
8.7 ± 1.0 |
8.8 ± 1.2 |
|
Meal speed (min) |
|
|
|
|
< 10 |
16 (22.9) |
12 (17.1) |
4 (5.7) |
|
10–15 |
33 (47.1) |
18 (20.3) |
15 (21.4) |
|
≥ 15 |
21 (30.0) |
13 (18.6) |
8 (11.4) |
No significant sex-based differences were observed in children’s health-related lifestyle factors. Approximately 30.0% of children with obesity were classified in the health-enhancing physical activity group. Two-thirds of children reported eating meals under 15 minutes, while their average sedentary hours, such as time spent playing computer or video games, were 3.5 ± 2.5 hours per day. The children reported an average of 8.8 ± 1.1 hours of sleep per day.
Table 2 presents anthropometric, biochemical, and daily dietary intake data for children with obesity and their mothers, stratified by the children’s sex. Significant sex-based differences (p < 0.05) were observed in WC (boys, 97.6 ± 12.0 cm; girls, 91.0 ± 14.0 cm), WHR (boys, 0.94 ± 0.08 cm; girls, 0.88 ± 0.06 cm), WHtR, and systolic blood pressure (boys, 127.5 ± 22.0 mmHg; girls, 119.5 ± 17.0 mmHg). Among mothers, 55.7% (n = 39) were obese, 14.3% (n = 10) were overweight, and 30.0% (n = 21) had normal weight. Serum TC level was significantly higher in mothers with daughters than in those with sons (p < 0.01). Serum FBG level was significantly higher in boys than in girls (p < 0.01). The average energy intake of boys was 2,422.2 ± 735.2 kcal, whereas that of girls was 2,203.5 ± 637.5 kcal.
Table 2 Anthropometric, biochemical measurement, and dietary intake data of children with obesity and their mothers
Table 2
|
Variables |
Total (n = 70) |
Mother–son dyads (n = 43) |
Mother–daughter dyads (n = 27) |
|
Mother |
Offspring |
Mother |
Son |
Mother |
Daughter |
|
Height (cm) |
159.3 ± 5.8 |
156.3 ± 11.9 |
160.0 ± 6.7 |
154.9 ± 12.4 |
158.0 ± 7.7 |
157.5 ± 10.5 |
|
Weight (kg) |
64.5 ± 15.8 |
76.5 ± 20.8 |
62.7 ± 11.5 |
76.3 ± 23.0 |
65.4 ± 19.0 |
76.9 ± 18.3 |
|
BMI-Z score by maternal BMI status†
|
|
|
|
|
|
|
|
Normal |
21 (30.0) |
2.3 ± 0.7 |
15 (21.4) |
2.2 ± 0.6 |
6 (8.6) |
2.6 ± 0.8 |
|
Overweight |
10 (14.3) |
2.1 ± 0.6 |
8 (11.4) |
2.0 ± 0.5 |
2 (2.9) |
2.6 ± 0.3 |
|
Obese |
39 (55.7) |
2.5 ± 0.6 |
20 (28.6) |
2.3 ± 0.6 |
19 (27.1) |
2.4 ± 0.6 |
|
WC (cm) |
86.8 ± 16.8 |
94.8 ± 12.0 |
85.0 ± 12.4 |
97.6 ± 12.0 |
89.0 ± 14.8 |
91.0 ± 14.0*
|
|
WHR |
0.9 ± 0.1 |
0.9 ± 0.1 |
0.9 ± 0.1 |
0.94 ± 0.08 |
0.9 ± 0.1 |
0.88 ± 0.06*
|
|
% Total body fat |
34.0 ± 9.5 |
41.4 ± 6.1 |
33.6 ± 9.1 |
40.9 ± 6.1 |
36.5 ± 10.3 |
42.0 ± 5.5 |
|
SBP (mmHg) |
115.8 ± 19.5 |
124.3 ± 19.0 |
115.5 ± 22.0 |
127.5 ± 22.0 |
116.0 ± 19.5 |
119.5 ± 17.0*
|
|
DBP (mmHg) |
70.5 ± 12.0 |
67.3 ± 7.0 |
72.0 ± 12.0 |
67.0 ± 9.5 |
68.0 ± 14.0 |
67.5 ± 6.5 |
|
TC (mg/dL) |
193.6 ± 35.4 |
179.5 ± 40.2 |
193.8 ± 38.2 |
181.4 ± 42.2 |
193.0 ± 38.0#
|
172.8 ± 29.4 |
|
LDL-Chol (mg/dL) |
116.0 ± 41.0 |
110.0 ± 29.0 |
116.0 ± 42.0 |
110.0 ± 30.0 |
116.0 ± 34.0 |
108.0 ± 25.0 |
|
HDL-Chol (mg/dL) |
53.5 ± 21.0 |
47.0 ± 11.0 |
57.0 ± 19.0 |
47.0 ± 13.0 |
50.0 ± 21.0 |
45.0 ± 15.0 |
|
TG (mg/dL) |
90.0 ± 54.0 |
100.0 ± 58.0 |
81.0 ± 47.0 |
100.0 ± 58.0 |
104.0 ± 65.0 |
98.0 ± 96.0 |
|
FBG (mg/dL) |
90.0 ± 17.0 |
89.0 ± 8.0 |
90.0 ± 16.0 |
90.0 ± 6.0 |
88.0 ± 16.0 |
85.0 ± 9.0**
|
|
Insulin (µU/mL) |
7.6 ± 5.4 |
20.1 ± 9.9 |
7.6 ± 4.8 |
19.4 ± 8.6 |
7.5 ± 7.1 |
22.0 ± 13.1 |
|
hs-CRP (mg/L) |
0.7 ± 1.3 |
1.6 ± 2.4 |
0.8 ± 1.3 |
1.7 ± 1.9 |
0.6 ± 1.4 |
1.3 ± 2.8 |
|
Energy (kcal) |
1,650.2 ± 441.6 |
2,379.1 ± 632.8 |
1,669.5 ± 399.4 |
2,422.2 ± 735.2 |
1,629.3 ± 482.2 |
2,203.5 ± 637.5*
|
|
Carbohydrate (g) |
209.6 ± 76.5 |
305.4 ± 91.4 |
211.7 ± 72.6 |
323.1 ± 98.6 |
203.5 ± 87.4 |
294.0 ± 69.8 |
|
Protein (g) |
61.9 ± 19.0 |
95.9 ± 38.2 |
61.8 ± 19.4 |
97.8 ± 50.8 |
62.7 ± 20.8 |
89.0 ± 34.1 |
|
Fat (g) |
48.3 ± 21.0 |
81.7 ± 33.4 |
48.3 ± 26.2 |
82.2 ± 36.5 |
48.3 ± 18.4 |
80.6 ± 26.8 |
|
C:P:F |
53.1:15.8:29.1 |
51.3:16.0:32.1 |
54.2:15.7:28.2 |
50.9:16.3:32.1 |
51.4:16.0:30.5 |
51.9:15.6:32.1 |
|
Fiber (g) |
16.8 ± 7.3 |
18.0 ± 8.1 |
18.1 ± 6.5 |
18.8 ± 8.1 |
15.7 ± 9.7 |
16.1 ± 8.1 |
|
DQI-I score (0–100) |
58.4 ± 8.8 |
61.8 ± 7.7 |
59.6 ± 8.7 |
62.9 ± 8.6 |
56.4 ± 9.1 |
59.9 ± 5.9 |
|
Variety (20) |
15.2 ± 2.7 |
15.6 ± 2.5 |
15.3 ± 2.5 |
15.7 ± 2.6 |
15.2 ± 3.2 |
15.4 ± 2.3 |
|
Adequacy (40) |
24.5 ± 6.4 |
26.8 ± 5.0 |
24.8 ± 6.1 |
27.4 ± 5.1 |
24.1 ± 7.1 |
27.4 ± 5.1 |
|
Moderation (30) |
16.8 ± 4.8 |
18.6 ± 4.2 |
17.6 ± 4.9 |
18.9 ± 4.0 |
15.7 ± 4.5 |
18.2 ± 4.6 |
|
Overall balance (10) |
1.7 ± 1.8 |
0.8 ± 1.4 |
1.9 ± 1.8 |
1.0 ± 1.5 |
1.5 ± 1.7 |
0.6 ± 1.2 |
Diet quality scores were determined based on the nutrient intake of children with obesity and their mothers reported in the 3-day food records and children’s self-administered questionnaire. The total and subgroup DQI-I scores did not differ significantly across the groups.
Table 3 presents the correlations between the maternal and child diet quality scores and subgroup scores. Higher DQI-I (r = 0.30; p < 0.05), variety (r = 0.29; p < 0.05), adequacy (r = 0.43; p < 0.01), moderation (r = 0.45; p < 0.001), and overall balance (r = 0.30; p < 0.01) scores were positively correlated with one-to-one matched children’s scores. A weak or moderate correlation was observed, with moderation showing the strongest correlation.
Table 3 Age-adjusted correlation coefficients of the diet quality score between children with obesity and their mothers
Table 3
|
Children/Mother |
DQI-I score |
Variety |
Adequacy |
Moderation |
Overall balance |
|
DQI-I score |
0.30*
|
0.11 |
0.26*
|
0.13 |
0.05 |
|
Variety |
0.27*
|
0.29*
|
0.35**
|
−0.11 |
−0.02 |
|
Adequacy |
0.33*
|
0.24*
|
0.43**
|
−0.14 |
0.08 |
|
Moderation |
−0.11 |
−0.32**
|
−0.32**
|
0.45***
|
−0.09 |
|
Overall balance |
0.16 |
0.17 |
0.18 |
−0.11 |
0.30**
|
Table 4 presents the proportion of agreement, total agreement, and weighted kappa coefficients assessing mother–child diet quality similarity by sex. Daughter–mother dyads showed greater agreement in the DQI-I score, variety, and adequacy than son–mother dyads based on weighted kappa coefficients. However, son–mother dyads exhibited higher agreement in overall balance than daughter–mother dyads (k > 0.20).
Table 4 Similarity in diet quality score between children with obesity and their mothers
Table 4
|
Variables |
Boys |
Girls |
|
Degree of similarity*
|
Total agreement |
Weighted kappa† (95% CI) |
Proportion of agreement*
|
Total agreement |
Weighted kappa† (95% CI) |
|
Q1 |
Q2 |
Q3 |
Q4 |
Q1 |
Q2 |
Q3 |
Q4 |
|
Children’s DQI-I score |
6.9 |
6.9 |
6.9 |
9.3 |
30.2 |
0.07 (−0.16 to 0.29) |
11.1 |
7.4 |
3.7 |
11.1 |
33.3 |
0.25 (−0.03 to 0.52) |
|
Variety |
9.3 |
4.7 |
9.3 |
0.0 |
23.3 |
−0.04 (−0.24 to 0.16) |
7.4 |
7.4 |
14.8 |
7.4 |
37.0 |
0.24 (−0.02 to 0.49) |
|
Adequacy |
6.9 |
4.7 |
6.9 |
9.3 |
27.9 |
0.04 (−0.18 to 0.27) |
7.4 |
7.4 |
7.4 |
14.8 |
37.0 |
0.27 (0.00 to 0.54) |
|
Moderation |
11.6 |
0.0 |
4.7 |
9.3 |
25.6 |
−0.03 (−0.26 to 0.19) |
11.1 |
0.0 |
3.7 |
0.0 |
14.8 |
NA |
|
Overall balance |
20.9 |
0.0 |
6.9 |
9.3 |
37.2 |
0.27 (0.07 to 0.46) |
22.2 |
0.0 |
7.4 |
0.0 |
29.6 |
NA |
Table 5 shows the results for the multivariate regression analysis of maternal and child diet quality score and adiposity indices. A positive association was noted between maternal and offspring DQI-I scores, both unadjusted (β = 0.2) and adjusted (β = 0.19; p < 0.04). No other significant differences were observed for other variables.
Table 5 Multivariate regression analysis of selected variables between children with obesity and their mothers
Table 5
|
Children/Mother |
DQI-I score |
% Body fat |
BMI |
TC |
|
β |
SE |
R2
|
p |
β |
SE |
R2
|
p |
β |
SE |
R2
|
p |
β |
SE |
R2
|
p |
|
DQI-I score |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Unadjusted |
0.20 |
0.09 |
0.06 |
0.04*
|
−0.08 |
0.14 |
0.01 |
0.55 |
−0.07 |
0.20 |
0.00 |
0.71 |
0.00 |
0.03 |
0.00 |
0.93 |
|
Adjusted†
|
0.19 |
0.09 |
0.17 |
0.04*
|
−0.19 |
0.14 |
0.14 |
0.18 |
−0.27 |
0.20 |
0.14 |
0.19 |
−0.01 |
0.03 |
0.12 |
0.69 |
|
% Body fat |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Unadjusted |
0.01 |
0.06 |
0.00 |
0.87 |
0.04 |
0.09 |
0.00 |
0.62 |
0.06 |
0.12 |
0.00 |
0.62 |
−0.02 |
0.02 |
0.02 |
0.32 |
|
Adjusted |
0.02 |
0.06 |
0.16 |
0.73 |
0.07 |
0.08 |
0.24 |
0.39 |
0.12 |
0.12 |
0.25 |
0.32 |
0.00 |
0.02 |
0.24 |
0.75 |
|
BMI-Z score |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Unadjusted |
−0.01 |
0.01 |
0.02 |
0.22 |
−0.01 |
0.01 |
0.01 |
0.51 |
0.00 |
0.01 |
0.00 |
0.99 |
0.00 |
0.00 |
0.04 |
0.12 |
|
Adjusted |
−0.01 |
0.00 |
0.25 |
0.11 |
0.00 |
0.01 |
0.22 |
0.52 |
0.00 |
0.01 |
0.22 |
0.99 |
0.00 |
0.00 |
0.23 |
0.46 |
|
hs-CRP |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Unadjusted |
0.04 |
0.04 |
0.01 |
0.36 |
0.03 |
0.06 |
0.00 |
0.62 |
0.07 |
0.08 |
0.01 |
0.41 |
0.01 |
0.01 |
0.01 |
0.58 |
|
Adjusted |
0.03 |
0.04 |
0.07 |
0.41 |
0.01 |
0.06 |
0.06 |
0.85 |
0.06 |
0.09 |
0.07 |
0.53 |
0.00 |
0.01 |
0.07 |
0.75 |
DISCUSSION
We investigated the similarity in diet quality between children with obesity and their mothers using DQI-I. Aligning with expectations, we found a weak-to-moderate positive association between maternal and child total DQI-I score and subcategories, such as variety, adequacy, moderation, and nutrient intake. In particular, DQI-I score was most significantly correlated with moderation. Female children’s diet quality showed greater similarity with their mothers’ diet compared with that of male children.
Previous studies have also reported weak-to-moderate correlations between dietary intake and diet quality among children and their parents [
10,
11]. The strength of the association between parent–child diet quality and energy intake tends to increase with a narrower age range [
12]. In younger children, food choices are more directly controlled and influenced by parents [
13]. Conversely, the dietary intake of older adolescents is shaped by a broader range of factors, including food environments, peer influence, self-image, and self-esteem, in addition to household influences [
14,
15,
16]. Consistent with our findings, another study involving children and adolescents observed a weak-to-moderate correlation (r = 0.2–0.4) between parents’ and children’s nutrient and dietary intake.
In the present study, the strongest correlation between maternal and child diet quality was observed in the moderation subcategory. Moderation reflects the intake of foods and nutrients such as total fat, sodium, and empty-calorie foods, which are associated with an increased risk of obesity. Children with obesity tend to consume greater quantities of low-quality foods high in fat, sodium, and calories compared with children of normal weight [
17]. One study reported that adolescents aged 8–13 years whose parents regularly consumed soft drinks were 2.9 times more likely to consume soft drinks 5 or more times per week than those whose parents did not. Notably, a strong association was also found between fast food consumption by mothers with obesity and their children’s fast food intake [
18]. These findings underscore the influence of parental junk food consumption on childhood obesity and its detrimental impact on children’s diet quality.
Numerous studies have reported a strong association between obesity and dietary factors in school-aged children, comparing obese and non-obese groups as well as parent–child dyads across both domestic and international populations. These studies emphasize the role of specific dietary components, including fat intake [
19], fruit and vegetable consumption [
20], and eating behaviors such as skipping breakfast [
21], in influencing chronic disease risk. A child’s typical diet is more accurately reflected by the overall pattern of foods consumed daily rather than by individual food items. Therefore, assessing diet quality or dietary patterns offers a more meaningful evaluation of children’s usual dietary intake than analyzing single food items in isolation.
The findings of this study are consistent with those of previous studies indicating that girls exhibit stronger dietary similarities with their parents than boys [
10,
11]. In particular, mother–daughter dyads have shown greater concordance in the consumption of vegetables, fruits [
22,
23,
24], calcium-rich foods, and dairy products. This pattern may be attributed to factors such as repeated exposure and greater accessibility to certain foods at home [
25]. Accordingly, mothers may play a key role in promoting healthier diets for their daughters by consistently providing and encouraging the consumption of vegetables and other nutritious foods within the household setting [
26].
Mothers were selected as the focus of this study because they typically exert a greater influence on children’s diet quality. As primary caregivers, mothers are largely responsible for food purchasing and meal preparation [
27], and they play a key role in shaping children’s exposure to various fruits and vegetables [
28].
This study has several limitations. First, the cross-sectional design allows for the examination of associations but not causal relationships. Therefore, longitudinal cohort studies are needed to clarify the causal relationship between maternal and child diet quality.
This study included only children with obesity and their mothers. Therefore, the findings cannot be compared with those of non-obese, healthy child–mother dyads or generalized to the broader population of children and adolescents. Further research involving larger and more diverse samples, including non-obese, healthy children and their mothers, is needed to confirm and to extend these findings.
Previous studies have demonstrated regional variations in the prevalence of childhood obesity [
29]. However, our study was limited to participants from Seoul and Gyeonggi Province. To improve the generalizability of the findings, future studies should include a broader population from diverse regions across South Korea.
Furthermore, although the association between age- and sex-related prevalence of overweight and obesity and pubertal stage is well documented [
30], the present study did not examine the effects of children’s pubertal maturation.
Despite these limitations, the study has several notable strengths. It is the first to examine the similarity in diet quality between children with obesity and their mothers in Korea. Dietary intake was assessed using non-consecutive 3-day food records, which are considered the gold standard for evaluating usual dietary patterns. Additionally, data were analyzed using a web-based system to compute DQI-I scores, enhancing accuracy and consistency in assessment.
Obesity is a multigenerational and lifelong condition, making its prevention and early treatment critically important. Obesity rates are projected to increase, beginning in early adulthood, and will likely impose a substantial economic burden on governments. In the United States alone, an estimated 92.6 billion dollars are spent annually on obesity-related medical expenses and health services [
29].
This study contributes to the development of family-based, sustainable prevention and management programs for children with obesity that integrate nutrition and lifestyle modifications, such as reducing screen time and increasing physical activity. These strategies have the potential to reverse projected childhood obesity trends and improve long-term health outcomes. Maternal diet quality emerges as a key influencing factor on the diet quality of children with obesity, reinforcing findings from existing research in this field.
Korea Centers for Disease Control and Preventionhttps://doi.org/10.13039/501100003669
2015-ER6401-00
NOTES
-
Funding: This research was supported by the Korea Centers for Disease Control and Prevention (grant number: 2015-ER6401-00).
-
Conflict of Interest: The authors declare that they have no competing interests.
-
Author Contributions:
Conceptualization: Kim J, Lim H.
Data curation: Lee H, Kim J, Kim N.
Funding acquisition: Park KH.
Methodology: Kim J, Lim H.
Supervision: Kim YM, Park KH, Lim H.
Writing - original draft: Lee H.
Writing - review & editing: Kim J, Kim N, Park HG.
ACKNOWLEDGEMENTS
We thank all the study participants, their families, and teachers. We also thank all investigators involved in this project for their support and cooperation and the officers of the Gyeonggi Provincial Government.
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, Nayoung Kim1,2
