Abstract
Background
Existing studies have shown that parental health literacy is closely associated with the development of childhood overweight and obesity. Childhood overweight and obesity, in turn, can lead to a range of health outcomes, including adult obesity, type 2 diabetes, and advanced bone age. However, no studies have yet examined the relationship between parental health literacy and advanced bone age in overweight and obese children.
Methods
Overweight and obese children were classified into two groups based on whether their bone age was advanced. Baseline characteristics between the two groups were compared. Multivariate logistic regression was used to analyze the association between health literacy and advanced bone age. Multivariate linear regression was performed for sensitivity analyses, with sequential adjustment for age, sex, BMI SDS, and other factors. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative ability of parental health literacy for advanced bone age. Finally, stratified analyses were conducted to assess differences in the strength of the association across subgroups.
Results
Higher levels of parental health literacy were associated with a lower risk of advanced bone age in children. Paternal health literacy was influenced by confounding factors, whereas maternal health literacy showed a more consistent association across the fitted models. However, the formal comparison showed no statistically significant difference between the regression coefficients for maternal and paternal health literacy (χ2 = 0.444, p = 0.505). Exploratory ROC curve analysis showed that adding maternal health literacy to a model based on basic characteristics improved discriminative ability (AUC = 0.734), compared with adding paternal health literacy (AUC = 0.717) or combining both parents’ health literacy (AUC = 0.733). The negative association between maternal health literacy and advanced bone age was more pronounced among mothers with higher educational levels.
Conclusion
These findings suggest that, in pediatric nursing practice, parental health literacy, particularly maternal health literacy, can be improved through health education and other approaches to better support healthy skeletal growth and development in overweight and obese children.
Keywords: age determination by skeleton, health literacy, parents, pediatric nursing, pediatric obesity
Introduction
Childhood overweight and obesity have become a major global public health issue, with its prevalence increasing year by year. Studies have shown that in China, the prevalence of overweight and obesity among children increased from 11.7% in 1991 to 25.2% in 2011, with primary school-aged children and boys identified as high-risk groups (1). A study of school-aged children aged 5–12 years in India reported that the prevalence of obesity was 38.4% (2). This further highlights the widespread prevalence of childhood obesity across different countries and regions and its public health significance. Obesity has significant impacts on children’s physical health and growth and development. In terms of metabolism, obesity is closely associated with insulin resistance (3), type 2 diabetes (4), and dyslipidemia. In the cardiovascular system, it can lead to elevated blood pressure and early atherosclerotic changes (5, 6). In the skeletal and musculoskeletal systems, it may result in flat feet (7), genu valgum, abnormal bone mineral density, and advanced bone age. Advanced bone age refers to a rate of skeletal maturation that exceeds chronological age (8), which may lead to reduced adult height and adversely affect physical and mental health (9, 10). Therefore, exploring the factors influencing advanced bone age in overweight and obese children has important clinical significance.
Health literacy is defined as an individual’s ability to understand, access, and use health information to improve their own and others’ well-being. It plays an important role in the management of chronic diseases (11). A review has shown that health literacy is recognized as a fundamental and modifiable factor in the development and maintenance of obesity (12). Low levels of health literacy, including limited ability to read and interpret food labels and estimate portion sizes, can contribute to disordered eating behaviors (DEB) and obesity (13, 14) As children have limited cognitive and information-processing abilities, they rely heavily on parental guidance and support in health-related decision-making. Therefore, parental health literacy is crucial to children’s health outcomes.
Parental health literacy may influence the skeletal growth and development of overweight and obese children through multiple biological and behavioral pathways. Parents with higher health literacy may be more likely to understand health information related to nutrition, physical activity, sleep, and weight management and translate it into daily family health behaviors, such as appropriate dietary management and physical activity, thereby reducing children’s energy intake and body weight. Children’s body weight and metabolism may influence skeletal maturation through multiple biological pathways, including insulin/IGF-1-related signaling (15), leptin, and the hypothalamic–pituitary-gonadal (HPG) axis (16). Therefore, parental health literacy may be indirectly associated with advanced bone age by influencing children’s health behaviors and weight-related metabolic status.
Previous studies have demonstrated that childhood obesity is strongly associated with parental factors (17–19), and many studies have also analyzed factors influencing advanced bone age in overweight and obese children (20, 21). However, few studies have focused on parental health literacy. Therefore, whether parental health literacy influences skeletal maturation in overweight and obese children remains insufficiently explored. This study aims to investigate this association and provide a nursing evidence base for interventions in child growth and development.
Methods
Study population
This was a cross-sectional study including children who underwent bone age assessment at our hospital between January 2022 and January 2025.
Inclusion criteria
School-aged children aged 6–12 years;
According to the World Health Organization (WHO) Child and Adolescent Growth Standards, children with a BMI SDS ≥ 1 were classified as overweight, and those with a BMI SDS ≥ 2 were classified as obese (22);
Both parents completed the health literacy questionnaire;
Left-hand wrist radiography was performed and bone age assessment results were available.
Exclusion criteria
Other diseases that may affect growth and development, such as clinically diagnosed central precocious puberty (CPP) (23) and growth hormone deficiency;
Secondary obesity;
Use of glucocorticoids, growth hormone, or sex hormones within the past three months;
End-stage malignancy or severe systemic diseases such as severe hepatic or renal insufficiency;
Missing key data.
Both parental health literacy assessments and the child’s bone-age assessment were completed during the same outpatient visit. Parents completed the HLS19-Q12 questionnaire before receiving the child’s bone-age assessment results. The detailed participant inclusion and exclusion process is shown in Supplementary Figure 1.
Quality control
All imaging data were independently reviewed by two qualified pediatric radiologists who were blinded to clinical information. Skeletal maturation stages were assessed, and each bone’s maturity score was calculated according to the TW3 (Tanner–Whitehouse 3) bone age assessment system. The scores of individual bones were summed to obtain a total score, which was then converted into bone age. In cases of disagreement, consensus was reached through discussion. The intraclass correlation coefficient (ICC) was calculated to assess inter-rater agreement. The agreement between the two radiologists was excellent (ICC = 0.949, 95% CI: 0.923–0.971). Missing data for “residence” were observed in 15 participants (7.14%) and were handled using multiple imputation by chained equations (MICE), and 20 imputed datasets were generated. Regression analyses were performed separately in each imputed dataset, and the estimates from the imputed datasets were pooled according to Rubin’s rules using the “pool” function in the R mice package. All other variables included in the analyses had no missing data.
Definition and calculation of key variables
Residential areas were classified according to the urban–rural classification codes issued by the National Bureau of Statistics of China based on the participants’ residential addresses. The 15th digit of the classification code indicates the category, with “1” representing urban residence and “2” representing rural residence. Children’s BMI was calculated as weight (kg) divided by height squared (m2). BMI SDS was calculated using the childsds package in R software by inputting the child’s age, sex, and BMI. Based on previous literature, advanced bone age was defined as the difference between bone age (BA) and chronological age (CA). When BA − CA > 1, it was classified as advanced bone age; otherwise, it was classified as non-advanced bone age (24).
Study instruments
This study used the 12-item Health Literacy Survey Questionnaire (HLS19-Q12) to assess parental health literacy. The questionnaire was developed by the WHO Health Literacy Measurement Action Network (M-POHL) and has been applied in China (25). The HLS19-Q12 consists of a 12-cell matrix (three health domains × four health management tasks). Respondents rate the perceived difficulty of each item using a 4-point Likert scale (1 = very difficult, 4 = very easy). A valid response rate of ≥80% was required for inclusion. The final scores were categorized into four levels: excellent, sufficient, problematic, and inadequate. In this study, health literacy was included as a categorical variable in the analysis, with the final results classified into four levels: Excellent, Sufficient, Problematic, and Inadequate (Supplementary Table 1). Excellent, Sufficient, Problematic, and Inadequate health literacy were coded as 3, 2, 1, and 0, respectively, with higher values indicating higher health literacy. The Cronbach’s α coefficients of the HLS19-Q12 for fathers and mothers were 0.800 and 0.812, respectively, indicating good internal consistency.
Covariate selection
We selected age, sex, BMI SDS, Height SDS, Tanner stage, parental age, parental educational level, and residence as covariates. Specifically, age, sex, BMI, and height are important determinants of skeletal maturation and pubertal development in children and are closely related to bone age development. Parental age and parental educational level are associated with parents’ ability to obtain health information and their health literacy, while residence mainly reflects differences in the socioeconomic and healthcare resource environment of the family and may be associated with parental health literacy and children’s health status. Tanner stage is closely related to pubertal development and skeletal maturation. All of these factors may influence the association between parental health literacy and children’s bone age development; therefore, they were prespecified as potential confounders and included in the multivariable models for adjustment.
Statistical analysis
Statistical analyses were performed using R software (version 4.4.1). After testing for normality, continuous variables with a normal distribution were presented as mean ± standard deviation, and comparisons between groups were performed using independent-samples t-tests. Non-normally distributed variables were expressed as median (interquartile range), and group comparisons were conducted using the Mann–Whitney U test. Categorical variables were analyzed using the chi-square test or Fisher’s exact test when expected frequencies were small, and are presented as counts and percentages [n (%)]. Multivariate logistic regression analysis was conducted with the presence of advanced bone age as the dependent variable. Tanner stages I–III were coded as 0–2, while parental education levels of “Junior high school or below,” “High school/technical secondary school,” and “College degree or above” were coded as 0, 1, and 2, respectively. First, only paternal/maternal health literacy was included. Then BMI SDS was adjusted. Subsequently, age, sex, and BMI SDS were adjusted. Finally, all baseline characteristics (age, sex, BMI SDS, Height SDS, Tanner stage, parental age, parental educational level, and residence) were adjusted to evaluate the association between parental health literacy and advanced bone age. Multiple linear regression analysis was performed using bone age difference (BA−CA) as a continuous outcome variable, with the same adjustment strategy to verify the robustness of the primary findings. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminative ability of models including: (1) baseline characteristics only (ROC-1); (2) baseline characteristics plus maternal or paternal health literacy (ROC-2; ROC-3); and (3) baseline characteristics plus both paternal and maternal health literacy (ROC-4). The optimal cutoff value for the ROC analysis was determined by maximizing the Youden index. All statistical tests were two-sided, and a p value < 0.05 was considered statistically significant. Interaction analyses were performed using multivariable logistic regression models. The models included the main effects of parental health literacy and maternal educational level, as well as their product term. To balance confounding control and model complexity, the interaction models were additionally adjusted for age, sex, and BMI SDS. Parental health literacy was coded from 0 to 3, and maternal educational level was coded from 0 to 2 and treated as an ordinal variable. The statistical significance of the interaction-term coefficient was assessed using the Wald test. Because maternal educational level was modeled as an ordinal variable, the interaction term represented a 1-degree-of-freedom test for a linear interaction trend rather than a global test of interaction across educational categories. To assess the robustness of the assumption that the association with advanced bone age is relatively consistent for each one-level increase in parental health literacy when health literacy was coded as an ordinal variable from 0 to 3, we additionally performed a sensitivity analysis by treating parental health literacy as a categorical variable, with Inadequate as the reference category.
Post hoc power analysis
A post hoc power analysis was performed based on the available sample of 210 participants and was used to describe the statistical power of the univariable analyses. For continuous variables, the statistical power was 0.946, assuming a medium effect size (Cohen’s d = 0.50) and a two-sided significance level of 0.05. For categorical variables, assuming a medium effect size (Cohen’s w = 0.30) and a significance level of 0.05, the statistical power was 0.992 for 2 × 2 contingency tables and 0.980 for 2 × 3 contingency tables. These results indicated that the present study had adequate statistical power with the available sample size.
Results
Baseline characteristics of overweight and obese children and their parents in the non-advanced and advanced bone age groups
A total of 210 children were included in the study, among whom 90 (42.9%) were identified as having advanced bone age. The results showed that multiple indicators differed significantly between the advanced bone age group and the non-advanced group, including BMI SDS, Tanner stage, age, and maternal education level (p < 0.05). Specifically, BMI SDS and Tanner stage were higher in the advanced bone age group, whereas age and maternal education level were lower compared with the non-advanced group (Table 1). We also compared the baseline characteristics between participants included in the study and those excluded due to missing key data. The results showed that there were no statistically significant differences in the main baseline characteristics between the two groups (Supplementary Table 2). Among the 210 fathers, 12 (5.7%) were classified as having Inadequate health literacy, 38 (18.1%) as Problematic, 99 (47.1%) as Sufficient, and 61 (29.0%) as Excellent. Among the 210 mothers, 7 (3.3%) were classified as having Inadequate health literacy, 36 (17.1%) as Problematic, 90 (42.9%) as Sufficient, and 77 (36.7%) as Excellent.
Table 1.
Comparison of the basic characteristics of obese children with non-advanced and advanced bone age and their parents.
| Characteristics | Non-advanced group (n = 120) | Advanced group (n = 90) | p value |
|---|---|---|---|
| BMI SDS | 2.3 (2.0–2.6) | 2.4 (2.1–2.7) | 0.030 |
| Age (years) | 9.4 (8.6–10.0) | 8.8 (8.2–9.7) | 0.013 |
| Sex | 0.531 | ||
| Male | 73 (60.83%) | 50 (55.56%) | |
| Female | 47 (39.17%) | 40 (44.44%) | |
| Height SDS | 0.47 ± 0.66 | 0.49 ± 0.76 | 0.816 |
| Tanner stage | 0.044 | ||
| I | 73 (60.83%) | 44 (48.89%) | |
| II | 40 (33.33%) | 32 (35.56%) | |
| III | 7 (5.83%) | 14 (15.56%) | |
| Fathers’ age (years) | 39 (38–41) | 39 (37–40) | 0.306 |
| Mothers’ age (years) | 37 (35–39) | 37 (35–38) | 0.601 |
| Fathers’ educational level | 0.093 | ||
| Junior high school or below | 13 (10.83%) | 10 (11.11%) | |
| High school/technical secondary school | 61 (50.83%) | 58 (64.44%) | |
| College degree or above | 46 (38.33%) | 22 (24.44%) | |
| Mothers’ educational level | 0.003 | ||
| Junior high school or below | 8 (6.67%) | 8 (8.89%) | |
| High school/technical secondary school | 77 (64.17%) | 73 (81.11%) | |
| College degree or above | 35 (29.17%) | 9 (10%) | |
| Residence | 0.789 | ||
| Urban | 93 (77.5%) | 72 (80%) | |
| Rural | 27 (22.5%) | 18 (20%) |
Continuous variables with a normal distribution are presented as mean ± standard deviation (SD) and were compared using independent-samples t-tests. Non-normally distributed continuous variables are presented as median (interquartile range, IQR) and were compared using the Mann–Whitney U test. Categorical variables are presented as counts and percentages [n (%)] and were compared using the chi-square test or Fisher’s exact test when appropriate.
Association between paternal health literacy and advanced bone age
Multicollinearity analysis showed that the variance inflation factors (VIFs) for all variables were slightly above 1 and well below 5, indicating no evidence of multicollinearity (Supplementary Table 3). In the model including only paternal health literacy, paternal health literacy was significantly negatively associated with advanced bone age (OR = 0.912, p = 0.024). After adjustment for BMI SDS, this negative association remained significant (OR = 0.920, p = 0.039). After further adjustment for age and sex, the negative association also remained significant (OR = 0.916, p = 0.030). However, after adjusting for all baseline characteristics, the association between paternal health literacy and advanced bone age was no longer statistically significant (OR = 0.930, p = 0.073). These results indicate that for each one-level increase in paternal health literacy, the odds of advanced bone age in children decreased by 7.0–8.8%. Multiple linear regression analysis further indicated a negative association between paternal health literacy and the outcome (β = −0.180 to −0.159, p = 0.046–0.085), although statistical significance was not consistent. After stepwise adjustment for confounding factors, this association was no longer significant (Table 2).
Table 2.
Association between fathers’ health literacy and advanced bone age.
| Model | Term | Multivariable logistic regression | Multiple linear regression | ||||
|---|---|---|---|---|---|---|---|
| β | p value | OR | 95%CI | β | p value | ||
| Model 1 | Fathers’ health literacy | −0.092 | 0.024 | 0.912 | 0.842–0.987 | −0.180 | 0.046 |
| Model 2 | Fathers’ health literacy | −0.084 | 0.039 | 0.920 | 0.850–0.995 | −0.167 | 0.064 |
| BMI SDS | 0.160 | 0.034 | 1.174 | 1.013–1.360 | 0.254 | 0.130 | |
| Model 3 | Fathers’ health literacy | −0.087 | 0.030 | 0.916 | 0.847–0.991 | −0.173 | 0.055 |
| BMI SDS | 0.164 | 0.029 | 1.178 | 1.018–1.363 | 0.257 | 0.124 | |
| Age | −0.072 | 0.018 | 0.931 | 0.878–0.987 | −0.137 | 0.043 | |
| Sex | −0.052 | 0.445 | 0.949 | 0.831–1.085 | −0.047 | 0.755 | |
| Model 4 | Fathers’ health literacy | −0.073 | 0.073 | 0.930 | 0.859–1.006 | −0.159 | 0.085 |
| BMI SDS | 0.141 | 0.058 | 1.152 | 0.996–1.332 | 0.203 | 0.227 | |
| Age | −0.058 | 0.056 | 0.944 | 0.889–1.001 | −0.122 | 0.076 | |
| Sex | −0.056 | 0.411 | 0.946 | 0.828–1.080 | −0.056 | 0.714 | |
| Height SDS | 0.049 | 0.303 | 1.050 | 0.957–1.153 | 0.144 | 0.184 | |
| Tanner | 0.088 | 0.082 | 1.092 | 0.990–1.204 | 0.139 | 0.224 | |
| Fathers’ age | −0.019 | 0.183 | 0.981 | 0.954–1.009 | −0.055 | 0.094 | |
| Mothers’ age | −0.006 | 0.652 | 0.994 | 0.967–1.021 | −0.003 | 0.926 | |
| Fathers educational | −0.076 | 0.161 | 0.927 | 0.834–1.030 | −0.144 | 0.240 | |
| Mothers educational | −0.170 | 0.010 | 0.844 | 0.742–0.960 | −0.288 | 0.054 | |
| Residence | 0.040 | 0.621 | 1.041 | 0.888–1.220 | −0.033 | 0.858 | |
Association between maternal health literacy and advanced bone age
Similar to the analysis of paternal health literacy, in the model including only maternal health literacy, maternal health literacy was significantly negatively associated with advanced bone age (OR = 0.893, p = 0.007). After adjustment for BMI SDS, this negative association remained statistically significant (OR = 0.902, p = 0.014). After further adjustment for age and sex, the association remained significant (OR = 0.907, p = 0.020). Unlike paternal health literacy, after adjustment for all baseline characteristics, the negative association remained statistically significant (OR = 0.899, p = 0.010). These results indicate that for each one-level increase in maternal health literacy, the odds of advanced bone age in children decreased by 9.3–10.7%. Multiple linear regression analysis further supported these findings, showing a consistent negative association between maternal health literacy and the outcome (β = −0.254 to −0.247, p = 0.006–0.008), with statistical significance maintained across all models with different covariate adjustments (Table 3).
Table 3.
Association between mothers’ health literacy and advanced bone age.
| Model | Term | Multivariable logistic regression | Multiple linear regression | ||||
|---|---|---|---|---|---|---|---|
| β | p value | OR | 95%CI | β | p value | ||
| Model 1 | Mothers’ health literacy | −0.113 | 0.007 | 0.893 | 0.823–0.969 | −0.254 | 0.006 |
| Model 2 | Mothers’ health literacy | −0.103 | 0.014 | 0.902 | 0.831–0.979 | −0.239 | 0.011 |
| BMI SDS | 0.154 | 0.041 | 1.166 | 1.007–1.351 | 0.235 | 0.160 | |
| Model 3 | Mothers’ health literacy | −0.097 | 0.020 | 0.907 | 0.837–0.984 | −0.230 | 0.014 |
| BMI SDS | 0.158 | 0.035 | 1.171 | 1.012–1.355 | 0.238 | 0.153 | |
| Age | −0.067 | 0.027 | 0.935 | 0.882–0.992 | −0.127 | 0.060 | |
| Sex | −0.039 | 0.571 | 0.962 | 0.842–1.099 | −0.017 | 0.911 | |
| Model 4 | Mothers’ health literacy | −0.106 | 0.010 | 0.899 | 0.830–0.974 | −0.247 | 0.008 |
| BMI SDS | 0.130 | 0.080 | 1.139 | 0.985–1.316 | 0.174 | 0.296 | |
| Age | −0.051 | 0.087 | 0.950 | 0.896–1.007 | −0.107 | 0.115 | |
| Sex | −0.044 | 0.514 | 0.957 | 0.839–1.092 | −0.029 | 0.850 | |
| Height SDS | 0.026 | 0.582 | 1.026 | 0.935–1.126 | 0.092 | 0.393 | |
| Tanner | 0.085 | 0.087 | 1.089 | 0.988–1.201 | 0.133 | 0.239 | |
| Fathers’ age | −0.018 | 0.202 | 0.982 | 0.955–1.010 | −0.053 | 0.101 | |
| Mothers’ age | −0.004 | 0.778 | 0.996 | 0.969–1.024 | 0.002 | 0.937 | |
| Fathers educational | −0.090 | 0.096 | 0.914 | 0.823–1.016 | −0.176 | 0.149 | |
| Mothers educational | −0.199 | 0.002 | 0.820 | 0.723–0.929 | −0.352 | 0.016 | |
| Residence | 0.047 | 0.560 | 1.048 | 0.895–1.227 | −0.017 | 0.926 | |
Sensitivity analysis
Because Tanner stage is closely related to skeletal maturation and may lie on the causal pathway through which parental health literacy influences skeletal development in children, we further conducted a sensitivity analysis excluding Tanner stage. The results showed that after removing Tanner stage, the OR for maternal health literacy changed from 0.899 to 0.897, while that for paternal health literacy changed from 0.930 to 0.928, with only minimal changes in the effect estimates (Supplementary Table 4). These findings suggest that Tanner stage did not materially affect the association between parental health literacy and advanced bone age. When parental health literacy was entered into the multivariable logistic regression model as a categorical variable, the ORs for advanced bone age for fathers in the Problematic, Sufficient, and Excellent groups compared with the Inadequate group were 0.658 (p = 0.009), 0.588 (p < 0.001), and 0.631 (p = 0.002), respectively. For maternal health literacy, the corresponding ORs were 0.950 (p = 0.794), 0.705 (p = 0.061), and 0.688 (p = 0.046), respectively (Supplementary Table 5). Overall, higher levels of health literacy were associated with lower odds of advanced bone age, and the direction of the associations was generally consistent with that observed in the ordinal analysis.
Joint analysis of parental health literacy
To compare whether the associations of parental health literacy with advanced bone age differed significantly, maternal and paternal health literacy were simultaneously included in the multivariable logistic regression model. Maternal health literacy was significantly negatively associated with advanced bone age (OR = 0.911, 95% CI: 0.838–0.991, p = 0.031), whereas paternal health literacy was not significantly associated with advanced bone age (OR = 0.953, 95% CI: 0.878–1.035, p = 0.254) (Supplementary Table 6). However, a formal comparison of the two regression coefficients using a linear hypothesis test showed no statistically significant difference between them (χ2 = 0.444, p = 0.505).
ROC curve analysis of the discriminative ability of paternal and maternal health literacy for advanced bone age
In the ROC-1 model including only baseline characteristics, the AUC was 0.711, with a sensitivity of 0.800 and specificity of 0.550. After adding paternal health literacy (ROC-2), the AUC increased to 0.717, while sensitivity decreased to 0.711 and specificity increased to 0.642. After adding maternal health literacy to the baseline model (ROC-3), the AUC increased to 0.734, sensitivity increased to 0.867, and specificity decreased to 0.533. In the model including both paternal and maternal health literacy (ROC-4), the AUC was 0.733, with sensitivity of 0.833 and specificity of 0.550, which was similar to ROC-3 (Table 4; Figures 1A–D). DeLong’s test showed that the difference in AUC between ROC-3 and ROC-1 was statistically significant (p = 0.012), whereas no statistically significant differences were observed between ROC-2 and ROC-1 or between ROC-4 and ROC-1. These exploratory ROC results indicate that adding maternal health literacy contributed most to improving model AUC, while combining both parents’ health literacy did not further improve discriminatory performance.
Table 4.
ROC parameters.
| ROC item | AUC | 95%AUC | Best-threshold | Youden | Sensitivity | Specificity |
|---|---|---|---|---|---|---|
| ROC-1 | 0.711 | 0.642–0.780 | 0.383 | 0.350 | 0.800 | 0.550 |
| ROC-2 | 0.717 | 0.648–0.786 | 0.415 | 0.353 | 0.711 | 0.642 |
| ROC-3 | 0.734 | 0.667–0.800 | 0.342 | 0.400 | 0.867 | 0.533 |
| ROC-4 | 0.733 | 0.666–0.800 | 0.357 | 0.383 | 0.833 | 0.550 |
Footnote: ROC-1: Baseline characteristics. ROC-2: Baseline characteristics plus paternal health literacy. ROC-3: Baseline characteristics plus maternal health literacy. ROC-4: Baseline characteristics plus both paternal and maternal health literacy.
Figure 1.
ROC curves for models with different combinations of parental health literacy and baseline characteristics. (A) Baseline characteristics (ROC-1). (B) Baseline characteristics plus paternal health literacy (ROC-2). (C) Baseline characteristics plus maternal health literacy (ROC-3). (D) Baseline characteristics plus both paternal and maternal health literacy (ROC-4).
Stratified analysis
The above analyses showed that maternal educational level differed most markedly between the advanced bone age group and the non-advanced bone age group (p = 0.003). To further explore whether maternal educational level influenced the association between maternal health literacy and advanced bone age, we conducted stratified analyses according to maternal educational level. The results showed that when maternal education level was junior high school or below, or high school/secondary vocational school, neither maternal nor paternal health literacy was significantly associated with advanced bone age. However, when maternal education level was college or above, maternal health literacy was significantly negatively associated with advanced bone age (OR = 0.833, p = 0.007), whereas paternal health literacy did not reach statistical significance. Interaction analyses showed that the interaction between maternal educational level and maternal health literacy was statistically significant (P for interaction = 0.047), whereas the interaction between maternal educational level and paternal health literacy was not significant (P for interaction = 0.885) (Table 5; Figure 2).
Table 5.
Stratified analysis by maternal educational level.
| Subgroup | Term | β | p value | OR | 95%CI | P for interaction |
|---|---|---|---|---|---|---|
| Junior high school or below | Mothers’ health literacy | −0.103 | 0.642 | 0.902 | 0.600–1.360 | 0.047 |
| Fathers’ health literacy | −0.647 | 0.128 | 0.524 | 0.261–1.050 | 0.885 | |
| High school/technical secondary school | Mothers’ health literacy | −0.079 | 0.120 | 0.924 | 0.837–1.020 | |
| Fathers’ health literacy | −0.032 | 0.486 | 0.968 | 0.885–1.060 | ||
| College degree or above | Mothers’ health literacy | −0.183 | 0.007 | 0.833 | 0.735–0.944 | |
| Fathers’ health literacy | −0.131 | 0.179 | 0.878 | 0.728–1.057 |
Figure 2.
Stratified odds ratios for the association between parental health literacy and advanced bone age by maternal educational level.
Discussion
This study found that BMI SDS was higher in the advanced bone age group, but the underlying mechanism is complex. The prevailing view suggests that the IGF-1/insulin pathway plays an important role. A classic animal study demonstrated that IGF-1 is not an “auxiliary factor” but a core hormone for skeletal growth with a threshold-dependent effect (26). In overweight and obese children, increased free IGF-1 levels and enhanced IGF-1 signaling promote skeletal growth and development (27). In addition, some studies have suggested that the adrenal axis may act as an early driving factor. For example, Cornelis J. de Groot et al. showed that dehydroepiandrosterone sulfate (DHEAS) is significantly positively correlated with bone age SDS, whereas insulin shows no significant association with bone age (20). The higher Tanner stage in the advanced bone age group is mainly due to premature activation of the hypothalamic–pituitary–gonadal (HPG) axis. Overweight and obese children have higher leptin levels (28), and leptin can directly act on the HPG axis, promoting its early activation, thereby leading to earlier pubertal onset and increased sex hormone secretion.
After separate analyses of paternal and maternal health literacy, we found that paternal health literacy was significantly negatively associated with advanced bone age in unadjusted or minimally adjusted models, whereas this association was no longer significant after adjustment for additional factors, suggesting that the relationship may be influenced by confounding factors. Indicating the need to consider more comprehensive family- and individual-level factors when interpreting the role of paternal health literacy. The association between maternal health literacy and advanced bone age was more consistent, showing a significant negative association regardless of whether baseline characteristics were adjusted for. However, the formal comparison of the maternal and paternal health literacy regression coefficients in the joint model showed no statistically significant difference between them. Therefore, maternal health literacy cannot be considered to have a stronger or more direct association with advanced bone age than paternal health literacy. The ROC curve analysis also supported this finding, with the most notable increase in AUC observed after adding maternal health literacy to the model. However, these models were not subjected to internal validation or calibration assessment and should therefore be considered exploratory. The more consistent association between maternal health literacy and advanced bone age does not imply that mothers play a more central role than fathers in children’s health behaviors. Other factors may also contribute to this finding, such as mothers’ involvement in children’s routine healthcare, socioeconomic factors, and differences in the measurement of parental health literacy.
Stratified analysis revealed that maternal educational level moderated the association between health literacy and advanced bone age. The association between maternal health literacy and advanced bone age was more pronounced among mothers with higher educational levels. This suggests that higher education may enhance the ability to translate health literacy into actual health-related behaviors. In contrast, among mothers with lower educational levels, this association may be attenuated due to limited capacity to understand health information and to implement appropriate health behaviors. Given the relatively small sample size within some educational strata, this finding should be regarded as exploratory and interpreted with caution.
These findings have important implications for nursing practice. Maternal health literacy may represent an important factor associated with advanced bone age in children. Future studies could further investigate the relationship between mother-centered health education, nutritional management, obesity prevention, and skeletal maturation in children.
This study also has several limitations. First, as a cross-sectional study, causal inferences cannot be established, and only associations can be identified. All participants were recruited from a university-affiliated hospital, which may differ from the general community population. Second, other relevant confounding factors, including dietary habits, physical activity, household income, duration and severity of obesity, insulin resistance, growth velocity, and pubertal history, were not fully considered. These factors may be associated with both parental health literacy and skeletal maturation in children; therefore, the associations observed in this study should be interpreted with caution. Third, health literacy was assessed using a questionnaire, which may introduce self-report bias. Urban–rural residence cannot fully capture household socioeconomic differences. In this study, it should be considered as a comprehensive indicator of the social environmental context. Although we conducted a sensitivity analysis excluding Tanner stage, the possibility of overadjustment cannot be completely ruled out. The number of outcome events was relatively limited compared with the number of independent variables, which may have increased the risk of model overfitting and reduced the stability of the effect estimates. Therefore, the results of this study should be interpreted with caution. Future studies should adopt a longitudinal study design to further clarify the role of Tanner stage in the association between parental health literacy and advanced bone age.
Conclusion
The association between paternal health literacy and advanced bone age varied across the different adjustment models, showing less consistency. The negative association between maternal health literacy and advanced bone age was more consistent across the fitted models and was more pronounced among mothers with higher educational levels. However, the formal comparison of the maternal and paternal health literacy regression coefficients showed no statistically significant difference between their effect estimates (χ2 = 0.444, p = 0.505).
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the China International Medical Foundation 2024 Research Grant for the Professional Development of Young and Mid-Career Physicians in Pediatric Endocrinology (No. Z-2019-41-2405).
Footnotes
Edited by: Maryam Yazdi, University of Leicester, United Kingdom
Reviewed by: Pablo Perez-Martinez, Carlos III Health Institute (ISCIII), Spain
Shiori Minabe, Tohoku University, Japan
Jaimini Sarkar, University of Mumbai, India
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by the Ethics Committee of the First Affiliated Hospital of Air Force Medical University (Approval No. KY20254040-1). The studies were conducted in accordance with the local legislation and institutional requirements. The ethics committee/institutional review board waived the requirement of written informed consent for participation from the participants or the participants’ legal guardians/next of kin because the retrospective nature of the study and the use of anonymized data.
Author contributions
DW: Writing – original draft. SW: Writing – original draft. JL: Writing – review & editing. LA: Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2026.1946206/full#supplementary-material
References
- 1.Jia P, Xue H, Zhang J, Wang Y. Time trend and demographic and geographic disparities in childhood obesity prevalence in China-evidence from twenty years of longitudinal data. Int J Environ Res Public Health. (2017) 14:4. doi: 10.3390/ijerph14040369, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Kumar P, Agarwal M. Analysis of prevalence of obesity among school going children of 5 12 years age group. Int J Life Sci. (2023) 12:2168–70. [Google Scholar]
- 3.Luo Y, Luo D, Li M, Tang B. Insulin resistance in pediatric obesity: from mechanisms to treatment strategies. Pediatr Diabetes. (2024) 2024:2298306. doi: 10.1155/2024/2298306, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Badr M, El-Rabaa G, Freiha M, Kędzia A, Niechciał E. Endocrine consequences of childhood obesity: a narrative review. Front Endocrinol (Lausanne). (2025) 16:1584861. doi: 10.3389/fendo.2025.1584861, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Drozdz D, Alvarez-Pitti J, Wójcik M, Borghi C, Gabbianelli R, Mazur A, et al. Obesity and cardiometabolic risk factors: from childhood to adulthood. Nutrients. (2021) 13:4176. doi: 10.3390/nu13114176, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Salama M, Balagopal B, Fennoy I, Kumar S. Childhood obesity, diabetes. And cardiovascular disease risk. J Clin Endocrinol Metab. (2023) 108:3051–66. doi: 10.1210/clinem/dgad361, [DOI] [PubMed] [Google Scholar]
- 7.Molina-Garcia P, Miranda-Aparicio D, Ubago-Guisado E, Alvarez-Bueno C, Vanrenterghem J, Ortega FB. The impact of childhood obesity on joint alignment: a systematic review and meta-analysis. Phys Ther. (2021) 101:7. doi: 10.1093/ptj/pzab066, [DOI] [PubMed] [Google Scholar]
- 8.Cavallo F, Mohn A, Chiarelli F, Giannini C. Evaluation of bone age in children: a mini-review. Front Pediatr. (2021) 9:9. 580314. doi: 10.3389/fped.2021.580314, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Lee PA. Short adult height after rapid-tempo puberty: when is it too late to treat? J Clin Res Pediatr Endocrinol. (2024) 16:235–42. doi: 10.4274/jcrpe.galenos.2024.2024-1-13, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Sopher AB, Jean AM, Zwany SK, Winston DM, Pomeranz CB, Bell JJ, et al. Bone age advancement in prepubertal children with obesity and premature adrenarche: possible potentiating factors. Obesity (Silver Spring). (2011) 19:1259–64. doi: 10.1038/oby.2010.305, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Van Der Gaag M, Heijmans M, Spoiala C, Rademakers J. The importance of health literacy for self-management: a scoping review of reviews. Chronic Illn. (2022) 18:234–54. doi: 10.1177/17423953211035472, [DOI] [PubMed] [Google Scholar]
- 12.Upton A, Spirou D, Craig M, Saul N, Winmill O, Hay P, et al. Health literacy and obesity: a systematic scoping review. Obes Rev. (2025) 26:e13904. doi: 10.1111/obr.13904, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Silva P. Food and nutrition literacy: exploring the divide between research and practice. Foods. (2023) 12:14. doi: 10.3390/foods12142751, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Taylor MK, Sullivan DK, Ellerbeck EF, Gajewski BJ, Gibbs HD. Nutrition literacy predicts adherence to healthy/unhealthy diet patterns in adults with a nutrition-related chronic condition. Public Health Nutr. (2019) 22:2157–69. doi: 10.1017/s1368980019001289, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Pinhas-Hamiel O, Benary D, Mazor-Aronovich K, Ben-Ami M, Levy-Shraga Y, Boyko V, et al. Advanced bone age and hyperinsulinemia in overweight and obese children. Endocr Pract. (2014) 20:62–7. doi: 10.4158/ep13193.Or, [DOI] [PubMed] [Google Scholar]
- 16.Nieuwenhuis D, Pujol-Gualdo N, Arnoldussen IC, Kiliaan AJ. Adipokines: a gear shift in puberty. Obes Rev. (2020) 21:e13005. doi: 10.1111/obr.13005, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Chari R, Warsh J, Ketterer T, Hossain J, Sharif I. Association between health literacy and child and adolescent obesity. Patient Educ Couns. (2014) 94:61–6. doi: 10.1016/j.pec.2013.09.006 [DOI] [PubMed] [Google Scholar]
- 18.Mech P, Hooley M, Skouteris H, Williams J. Parent-related mechanisms underlying the social gradient of childhood overweight and obesity: a systematic review. Child Care Health Dev. (2016) 42:603–24. doi: 10.1111/cch.12356, [DOI] [PubMed] [Google Scholar]
- 19.Sarkar J, Sarkar C. Association between overweight and central obesity in women of reproductive age and overweight in children under five years of age. Epidemiol Biostat Public Health. (2025) 19:CD015875. doi: 10.54103/2282-0930/26847 [DOI] [Google Scholar]
- 20.De Groot CJ, Van Den Berg A, Ballieux B, Kroon HM, Rings E, Wit JM, et al. Determinants of advanced bone age in childhood obesity. Horm Res Paediatr. (2017) 87:254–63. doi: 10.1159/000467393, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Oh MS, Kim S, Lee J, Lee MS, Kim YJ, Kang KS. Factors associated with advanced bone age in overweight and obese children. Pediatr Gastroenterol Hepatol Nutr. (2020) 23:89–97. doi: 10.5223/pghn.2020.23.1.89, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.De Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. (2007) 85:660–7. doi: 10.2471/blt.07.043497, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Latronico AC, Roberts SA, Alonzo M, Argente J, Canton APM, Carel JC, et al. Central precocious puberty: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. (2026) 111:2101–44. doi: 10.1210/clinem/dgag168, [DOI] [PubMed] [Google Scholar]
- 24.Lee HS, Shim YS, Jeong HR, Kwon EB, Hwang JS. The association between bone age advancement and insulin resistance in Prepubertal obese children. Exp Clin Endocrinol Diabetes. (2015) 123:604–7. doi: 10.1055/s-0035-1559795, [DOI] [PubMed] [Google Scholar]
- 25.Liu R, Zhao Q, Yu M, Chen H, Yang X, Liu S, et al. Measuring general health literacy in Chinese adults: validation of the HLS(19)-Q12 instrument. BMC Public Health. (2024) 24:1036. doi: 10.1186/s12889-024-17977-1, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Yakar S, Rosen CJ, Beamer WG, Ackert-Bicknell CL, Wu Y, Liu JL, et al. Circulating levels of IGF-1 directly regulate bone growth and density. J Clin Invest. (2002) 110:771–81. doi: 10.1172/jci15463, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Martín-Rivada Á, Martos-Moreno G, Guerra-Cantera S, Campillo-Calatayud A, Oxvig C, Frystyk J, et al. Prepubertal children with obesity have high free IGF-1 levels and accelerated growth despite reduced pappalysin levels. J Clin Endocrinol Metab. (2025) 110:e622–9. doi: 10.1210/clinem/dgae288 [DOI] [PubMed] [Google Scholar]
- 28.Shalitin S, Phillip M. Role of obesity and leptin in the pubertal process and pubertal growth--a review. Int J Obes Relat Metab Disord. (2003) 27:869–74. doi: 10.1038/sj.ijo.0802328, [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.