We investigated the relationship among socioeconomic status factors, the risk of anemia, and iron deficiency among school-aged children in Korea. The sample consisted of fourth-grade students aged 10 y recruited from nine elementary schools in Korean urban areas in 2008 (n = 717). Anthropometric and blood biochemistry data were obtained for this cross-sectional observational study. Anemia was defined as hemoglobin levels lower than 11.5 g/dl. Iron deficiency was defined as serum iron levels lower than 40 ug/dl. We also obtained data on parental education from questionnaires and on children's diets from 3-day food diaries. Parental education was categorized as low or high, with the latter representing an educational level beyond high school. Children with more educated mothers were less likely to develop anemia (P = 0.0324) and iron deficiency (P = 0.0577) than were those with less educated mothers. This group consumed more protein (P = 0.0004) and iron (P = 0.0012) from animal sources than did the children of less educated mothers, as reflected by their greater consumption of meat, poultry, and derivatives (P < 0.0001). Logistic regression analysis revealed a significant inverse relationship between maternal education and the prevalence of anemia (odds ratio: 0.52; 95% confidence interval: 0.32, 0.85). As a contributor to socioeconomic status, maternal education is important in reducing the risk of anemia and iron deficiency and in increasing children's consumption of animal food sources.

Summary: Previous studies have reported a relationship between short sleep duration and childhood overweight. Although school‐aged children tend to compensate for weekday sleep deficit by increasing weekend sleep duration, the association between weekend catch‐up sleep and childhood overweight remains unclear. This study aimed to examine the relationship between weekend catch‐up sleep and being overweight in children. A total of 936 school children (48.2% boys) aged 10 or 11 years participated in this school‐based cohort study. Anthropometric measurements including height and body weight were carried out. We obtained data on sleep patterns, lifestyle and parent characteristics using questionnaires. The main outcome measure was childhood overweight. After adjusting for the relevant confounding variables (age, sex, breakfast eating, screen time and parental obesity), longer sleep on weekdays and weekends was associated with decreased odds of childhood overweight (OR: 0.68; 95% CI: 0.54–0.86; OR: 0.64; 95% CI: 0.53–0.77, respectively). Participants with increased catch‐up sleep duration during weekends also had decreased odds of being overweight (OR: 0.67; 95% CI: 0.53–0.85). There was an interaction between weekday sleep duration and weekend catch‐up sleep in relation to childhood overweight, and this effect of weekend catch‐up sleep on being overweight was stronger as the participants slept less on weekdays (P = 0.024). These results indicate that weekend catch‐up sleep is independently associated with decreased risk of being overweight in fifth‐grade students, and this effect can be varied by the weekday sleep duration. A prospective study is required to confirm this observation.

People with obesity (PwO) often struggle to achieve and maintain weight loss. This can perpetuate and/or be influenced by feelings of low motivation. This analysis from ACTION-IO data identified factors associated with PwO motivation to lose weight. PwO completed an online survey in 11 countries. Exploratory multinomial logistic regression analyses identified independent variables associated with self-report of feeling motivated versus not motivated to lose weight. Data from 10,854 PwO were included (5,369 motivated; 3,312 neutral; 2,173 not motivated). Variables associated with feeling motivated versus not motivated included (odds ratio [95% confidence interval]): acknowledgement of healthcare professional (HCP) responsibility to contribute to weight loss (2.32 [1.86-2.88]), comfort in talking to their HCP about weight (1.46 [1.24-1.72), agreement that it is easy to lose weight (1.73 [1.30-2.31]), and a goal of reducing risks from excess weight (1.45 [1.22-1.73]). Conversely, if PwO considered obesity less important than other diseases they were less likely to report feeling motivated (0.49 [0.41-0.58]). PwO who reported being motivated to lose weight were more likely to exercise ≥5 times a week versus <1 time a week (2.77 [2.09-3.68]) than those who reported they were not motivated. Positive interactions with HCPs, self-efficacy, setting goals and knowledge of the importance of weight management, in addition to regular exercising, may increase PwO motivation for weight loss. Appropriate HCP support may help PwO who are ready to engage in weight management. NCT03584191.

The aims of this study were to assess the diagnostic value of the weight-to-height ratio (WHtR) for the detection of obesity and metabolic syndrome (MS) in Korean children and adolescents, and to determine the advantages of WHtR as a population-based screening tool in comparison with other obesity indicators, such as body mass index (BMI) and waist circumference (WC). We performed a cross-sectional analysis of data from 3057 children and adolescents (1625 boys, 1332 girls) aged 10–19 years who were included in the fifth Korean National Health and Nutrition Examination Survey (KNHANES, 2010–2012) up to the second year of the sixth KNHANES (2013–2014). Receiver operation characteristic (ROC) curves were generated to determine the optimal cutoff value and accuracy of WHtR for predicting individual obesity indicators or more than two non-WC components of MS. The area under the ROC curve (AUC) is a measure of the diagnostic power of a test. A perfect test will have an AUC of 1.0, and an AUC equal to 0.5 means that the test performs no better than chance. The optimal WHtR cutoff for the evaluation of general obesity and central obesity was 0.50 in boys and 0.47–0.48 in girls, and the AUC was 0.9. Regarding the assessment of each MS risk factor, the optimal WHtR cutoff was 0.43–0.50 in boys and 0.43–0.49 in girls, and these cutoffs were statistically significant only for the detection of high triglyceride and low High-density lipoprotein (HDL) cholesterol levels. When a pairwise comparison of the AUCs was conducted between WHtR and BMI/WC percentiles to quantify the differences in power for MS screening, the WHtR AUC values (boys, 0.691; girls, 0.684) were higher than those of other indices; however, these differences were not statistically significant (boys, p = 0.467; girls, p = 0.51). The WHtR cutoff value was 0.44 (sensitivity, 67.7%; specificity, 64.6%) for boys and 0.43 (sensitivity, 66.4%; specificity, 66.9%) for girls. There was no significant difference between the diagnostic power of WHtR and that of BMI/WC when screening for MS. Although the use of WHtR was not superior, WHtR is still useful as a screening tool for metabolic problems related to obesity because of its convenience.

The increasing prevalence of childhood obesity is a serious public health problem associated with co-morbidities in adulthood, as well as childhood. This study was conducted to identify associations between total sugar intake and sugar intake from different foods (fruit, milk, and sugar-sweetened beverages (SSBs)), and adiposity and continuous metabolic syndrome scores (cMetS) among Korean children and adolescents using cohort data. The study subjects were children (n = 770) who participated in the 4th year (2008) of the Korean Child–Adolescent Cohort Study (KoCAS). Dietary intake data were collected via three-day 24-h food records, and sugar intake was calculated for the total sugar content of foods using our database compiled from various sources. Anthropometric measurements, assessments of body composition, and blood sample analysis were performed at baseline and at follow-up four years later. The cMetS was calculated based on waist circumference, triglycerides, high-density lipoprotein cholesterol, glucose, and mean arterial blood pressure. According to multiple linear regression analysis, there were no significant associations between total sugar intake and adiposity and cMetS. However, higher intake of fruit sugar at baseline was significantly associated with lower body mass index (BMI) z-scores and body fat percentages at baseline (β = −0.10, p = 0.02 and β = −0.78, p < 0.01, respectively). At follow-up, sugar intake from fruit at baseline was still negatively associated with the above outcomes, but only the relationship with BMI z-scores retained statistical significance (β = −0.08, p < 0.05). There was a significant positive relationship between consumption of sugar from SSBs and cMetS at baseline (β = 0.04, p = 0.02), but that relationship was not observed at follow-up (p = 0.83). Differences in consumption sugars from fruit and SSBs might play an important role in the risk of adiposity and metabolic disease in children and adolescents. Our results suggest that strategies for reducing sugar intake need to target particular food groups. Consequently, this information could be of value to obesity- and metabolic disease-prevention strategies.

Background: New health care services such as smart health care and digital therapeutics have greatly expanded. To effectively use these services, digital health literacy skills, involving the use of digital devices to explore and understand health information, are important. Older adults, requiring consistent health management highlight the need for enhanced digital health literacy skills. To address this issue, it is imperative to develop methods to assess older adults’ digital health literacy levels. Objective: This study aimed to develop a tool to measure digital health literacy. To this end, it reviewed existing literature to identify the components of digital health literacy, drafted preliminary items, and developed a scale using a representative sample. Methods: We conducted a primary survey targeting 600 adults aged 55-75 years and performed an exploratory factor analysis on 74 preliminary items. Items with low factor loadings were removed, and their contents were modified to enhance their validity. Then, we conducted a secondary survey with 400 participants to perform exploratory and confirmatory factor analyses. Results: A digital health literacy scale consisting of 25 items was developed, comprising 4 subfactors: use of digital devices, understanding health information, use and decision regarding health information, and use intention. The model fit indices indicated excellent structural validity (Tucker-Lewis Index=0.924, comparative fit index=0.916, root-mean-square error of approximation=0.088, standardized root-mean-square residual=0.044). High convergent validity (average variance extracted>0.5) and reliability (composite reliability>0.7) were observed within each factor. Discriminant validity was also confirmed as the square root of the average variance extracted was greater than the correlation coefficients between the factors. This scale demonstrates high reliability and excellent structural validity. Conclusions: This study is a significant first step toward enhancing digital health literacy among older adults by developing an appropriate tool for measuring digital health literacy. We expect this study to contribute to the future provision of tailored education and treatment based on individual literacy levels.