Childhood Obesity最新文献

Aim: Picky eating is a common appetitive trait reported among children and adolescents and may have detrimental effects on their weight, vegetable, and fruit intake, impacting health status. However, an updated systematic review of the literature and summary of effect estimates is required. This study aims to explore the association between picky eating with weight, vegetable and fruit intake, vegetable-only intake, and fruit-only intake. Methods: A systematic literature search of six electronic scientific databases and data extraction was performed between November 2022 and June 2023. Original articles that examined picky eating in association with weight, vegetable and/or fruit intake were included. PRISMA guidelines were followed and meta-analytical and meta-regression analyses were conducted to compute summary effect estimates and explore potential moderators. PROSPERO registration: CRD42022333043. Results: The systematic review included 59 studies of which 45 studies were included in the meta-analysis. Overall, the summarized effect estimates indicated that picky eating was inversely associated with weight [Cohen's d_z: -0.27, 95% confidence interval (CI): -0.41 to -0.14, p < 0.0001]; vegetable and fruit intakes (Cohen's d_z: -0.35, 95% CI: -0.45, -0.25, p < 0.0001); vegetable-only intake (Cohen's d_z: -0.41, 95% CI: -0.56, -0.26, p < 0.0001), and fruit-only intake (Cohen's d_z: -0.32, 95% CI: -0.45, -0.20, p < 0.0001). Picky eating was positively associated with underweight (Cohen's d_z: 0.46, 95% CI: 0.20, 0.71 p = 0.0008). Conclusion: Although effect sizes were small, picky eating was inversely associated with weight, vegetable, and fruit intakes, and positively associated with underweight in children and adolescents aged birth to 17 years.

Objective: (1) To describe the prevalence of high blood pressure (BP) and the association with BMI in young children with overweight/obesity; (2) to evaluate the accuracy of a single high BP to diagnose sustained hypertension over three visits. Methods: We used pre-intervention data from the Improving Pediatric Obesity Practice Using Prompts (iPOP-UP) trial. We included children aged 3-12 years with BMI ≥85th percentile at well-visits in 2019-2021 at 84 primary care practices in 3 US health systems in the Northeast, Midwest, and South. BP percentiles were calculated from the first visit with BP recorded during the study period. Hypertensive-range BP was defined by the 2017 American Academy of Pediatrics guideline. We tested the association between BMI classification and hypertensive BP using multivariable logistic regression. Results: Of 78,280 children with BMI ≥85th percentile, 76,214 (97%) had BP recorded during the study period (mean 7.4 years, 48% female, 53% with overweight, and 13% with severe obesity). The prevalence of elevated or hypertensive BP was 31%, including 27% in children with overweight and 33%, 39%, and 49% with class I, II, and III obesity, respectively. Higher obesity severity was associated with higher odds of hypertensive BP in the multivariable model. Stage 2 hypertensive BP at the initial visit had specificity of 99.1% (95% confidence interval 98.9-99.3) for detecting sustained hypertension over ≥3 visits. Conclusions: High BP is common in 3- to 12-year-olds with overweight/obesity, with higher obesity severity associated with greater hypertension. Children with overweight/obesity and stage 2 BP are likely to have sustained hypertension and should be prioritized for evaluation. Trial Registration: ClinicalTrials.gov Identifier: NCT05627011.

Background: Adolescent obesity rates continue to rise. A better understanding of who engages in emotional eating, a maladaptive eating style, is needed. Despite emotional eating being a frequent research target, the prevalence of emotional eating in US adolescents is currently unknown. Methods: Nationally representative adolescents (n = 1622, m = 14.48 years, 63.8% non-Hispanic White, 50.6% female) reported eating behaviors in the National Cancer Institute's Family Life, Activity, Sun, Health, and Eating (FLASHE) study. Frequencies and one-way ANOVAs were conducted to examine the rates of emotional eating across demographic and weight status groups. Correlations between emotional eating and dietary intake were examined. Results: In total, 30% of adolescents engaged in emotional eating. Older adolescents (35% of 17-year-olds), females (39%), non-Hispanic White individuals (32%), and adolescents with obesity (44%) had significantly higher rates of emotional eating. Controlling for weight status, greater adolescent emotional eating was correlated with more frequent intake of energy-dense/nutrient-poor foods (β = 0.10, p < 0.001), junk food (β = 0.12, p < 0.001), and convenience foods (β = 0.13, p < 0.001). Conclusions: This study fills a critical gap by providing insight into how common adolescent emotional eating is and highlighting demographic factors that are associated with higher rates. Nearly a third of adolescents in the United States reported eating due to anxiety or sadness, with rates higher in older adolescents, girls, non-Hispanic White adolescents, and adolescents with obesity. Emotional eating was associated with consuming less healthy foods, which conveys immediate and long-term health risks. Practitioners can intervene with emotional eating to reduce obesity and comorbid health risks.

Background: Research is needed to explore inequities in physical activity (PA) and access to healthy eating resources for children on a national scale. This study examined disparities in childhood obesogenic environments across all United States (US) counties by income and race/ethnicity and their interaction with county rurality. Methods: Data for four PA variables (exercise opportunities, school proximity, walkability, crime) and six nutrition variables (grocery stores, farmers markets, fast-food restaurants, full-service restaurants, convenience stores, and births at baby-friendly hospitals) were collected for all US counties (n = 3142) to comprise the Childhood Obesogenic Environment Index (COEI). Variables were ranked and allocated a percentile for each county, and a total obesogenic environment score was created by averaging variable percentiles. Analysis of variance was used to assess differences by tertiles of county-level median household income (low/intermediate/high) and percentage of non-Hispanic (NH) White residents (low/intermediate/high). Interaction tests were used to assess effect modification by rurality, and stratified results were presented for all significant interactions. Results: There were significant differences in COEI values according to tertiles of median household income (F = 260.9, p < 0.0001). Low-income counties (M = 54.3, SD = 8.3) had worse obesogenic environments than intermediate (M = 49.9, SD = 7.9) or high (M = 45.9, SD = 8.8) income counties. There was also a significant interaction between rurality and median household income (F = 13.9, p < 0.0001). Similarly, there were significant differences in COEI values according to tertiles of race/ethnicity (F = 34.5, p < 0.0001), with low percentage NH White counties (M = 51.8, SD = 9.8) having worse obesogenic environment scores than intermediate (M = 48.7, SD = 8.4) or high (M = 49.5, SD = 8.5) NH White counties. There was also a significant interaction between rurality and race/ethnicity (F = 13.9, p < 0.0001). Conclusion: Low-income counties and those with more racial/ethnic minority residents, especially in rural areas, had less supportive PA and healthy eating environments for youth. Targeted policy and environmental approaches that aimed to address concerns specific to underserved communities are needed.

Background: The etiology of obesity is multifaceted, with multiple risk factors occurring during early childhood (e.g., fast food frequency, eating dinner as a family, TV in the bedroom). Many past studies have largely considered obesity risk factors in isolation, when in reality, the risk factors likely cluster together. A latent class analysis can be used to identify patterns in child eating behaviors, parent feeding behaviors, and household habits among preschool-aged children and their families to identify distinct, heterogenous classes and to determine if classes are associated with overweight and obesity. Methods: We used data from a community-based study of 624 three- to five-year-old children and a parent in New Hampshire, from March 2014 to October 2015. Parent-reported data were used to determine frequency of eating behaviors and household habits. Height and weight were objectively measured. Results: Four classes were identified; Class 1: "Healthy/Mildly accommodating," Class 2: "Healthy/Accommodating," Class 3: "Moderately healthy/Moderately accommodating," and Class 4: "Least healthy/Least accommodating." Compared with Class 1, children in Class 4 had increased odds of being overweight or obese [adjusted odds ratio (aOR): 1.64, 95% confidence interval (CI): 1.13-2.15], whereas Classes 2 and 3 were not associated with BMI (Class 2: aOR: 1.24, 95% CI: 0.62-1.86; Class 3: aOR: 1.31, 95% CI: 0.81-1.81). Conclusion: Study findings highlight that child-parent interactions around meals differentially relate to children's weight status given the context of children's eating habits. Most important, our study findings confirm the importance of adapting multiple healthy habits within the home social and physical environment to offset obesity risk in young children.

Background: The role of neighborhood factors in the association between adverse childhood experiences (ACEs) and body mass index (BMI) has not been widely studied. A neighborhood ACEs index (NAI) captures neighborhood environment factors associated with ACE exposure. This study examined associations between BMI and an NAI among New York City (NYC) youth. An exploratory objective examined the NAI geographic distribution across NYC neighborhoods. Methods: Data for students attending NYC public general education schools in kindergarten-12th grade from 2006-2017 (n = 1,753,867) were linked to 25 geospatial datasets capturing neighborhood characteristics for every census tract in NYC. Multivariable hierarchical linear regression tested associations between BMI and the NAI; analyses also were conducted by young (<8 years), school age (8-12 years), and adolescent (>12 years) subgroups. In addition, NAI was mapped by census tract, and local Moran's I identified clusters of high and low NAI neighborhoods. Results: Higher BMI was associated with higher NAI across all sex and age groups, with largest magnitude of associations for girls (medium NAI vs. low NAI: unstandardized β = 0.112 (SE 0.008), standardized β [effect size] = 0.097, p < 0.001; high NAI vs. low NAI: unstandardized β = 0.195 (SE 0.008), standardized β = 0.178, p < 0.001) and adolescents (medium NAI vs. low NAI: unstandardized β = 0.189 (SE 0.014), standardized β = 0.161, p < 0.001, high NAI vs. low NAI: unstandardized β = 0.364 (SE 0.015), standardized β = 0.334, p < 0.001 for adolescent girls; medium NAI vs. low NAI: unstandardized β = 0.122 (SE 0.014), standardized β = 0.095, p < 0.001, high NAI vs. low NAI: unstandardized β = 0.217 (SE 0.015), standardized β = 0.187, p < 0.001 for adolescent boys). Each borough of NYC included clusters of neighborhoods with higher and lower NAI exposure, although clusters varied in size and patterns of geographic dispersion across boroughs. Conclusions: A spatial index capturing neighborhood environment factors associated with ACE exposure is associated with higher BMI among NYC youth. Findings complement prior literature about relationships between neighborhood environment and obesity risk, existing research documenting ACE-obesity associations, and the potential for neighborhood factors to be a source of adversity. Collectively, evidence suggests that trauma-informed place-based obesity reduction efforts merit further exploration as potential means to interrupt ACE-obesity associations.

Background: Childhood obesity is a risk factor for poor cardiovascular, metabolic, and respiratory health. The studies examining influences of socio-ecologic factors on weight trajectories using longitudinal data are limited, often examine single measures (e.g., proximity to parks), and have not examined the specific trajectories of children with obesity. Methods: We examined influences on weight among 1518 children, 6-12 years of age, who had obesity using body mass index (BMI) criteria. BMI slope trajectories were categorized as decreasing, flat, or increasing, with a median of 2.1 years of follow-up. We examined socio-ecologic exposures, stratified by rural and urban settings, using census tracts to map indices, including food access, proximity to parks, normalized difference vegetation index, and area deprivation index (ADI). We used ordinal logistic regression to examine the associations between the socio-ecologic factors and BMI trajectories. Results: Among the 1518 children, 360 (24%) had a decreasing BMI trajectory with the remainder having flat (23%) or increasing (53%) trajectories. Children in rural areas were more likely to live in high disadvantage areas, 85%, compared with urban children, 46%. In the multivariable ordinal model, living in a lower ADI census tract had a 0.78 (95% CI 0.61-0.99) lower odds of being in an increasing BMI slope group, and no other socio-ecologic factor was associated. Conclusions: The area deprivation index captures a range of resources and social context compared with the built environment indicators, which had no association with BMI trajectory. Further work examining how to develop effective interventions in high deprivation areas is warranted.

Background: Adolescents' perceived responsibility for weight management behaviors has yet to be studied in relation to bariatric surgery. The current study examined perceived responsibility to pursue bariatric surgery and engage in specific weight management behaviors among adolescents seeking bariatric surgery and its associations with demographic, family support, and eating disorder symptoms. Methods: Data were collected using retrospective chart review of adolescent bariatric surgery candidates presenting to a tertiary interdisciplinary clinic. Data included demographics and adolescents' self-report of (1) perceived responsibility (i.e., primarily adolescent; primarily parent; shared) for the decision to pursue bariatric surgery and weight management behaviors, (2) family support for eating and exercise behaviors, and (3) eating disorder symptoms. Analyses included one-way analysis of covariance, chi-squared tests, and Kruskal-Wallis tests. Results: Participants reporting primarily teen or shared responsibility for seeking bariatric surgery were older than those reporting primarily parent responsibility (p = 0.023). Teens perceiving primary responsibility for their own healthy eating reported less family encouragement for healthy eating (p = 0.011) and more eating disorder symptoms (p = 0.002) than those reporting primarily parent or shared responsibility. Teens reporting primary responsibility for exercise reported less family encouragement for healthy eating (p = 0.012) compared with those reporting shared responsibility. Conclusions: This study is the first to provide a description of health behavior responsibilities in a sample of adolescents with severe obesity seeking bariatric surgery. Not only will these insights improve our understanding of this population, but it can also inform presurgical discussions with adolescents and their parents.

Background: Understanding how different populations respond to a childhood obesity intervention could help optimize personalized treatment strategies, especially with the goal to reduce disparities in obesity. Methods: We conducted a secondary analysis of the Greenlight Cluster Randomized Controlled Trial, a health communication focused pediatric obesity prevention trial, to evaluate for heterogeneity of treatment effect (HTE) by child biological sex, caregiver BMI, caregiver reported race and ethnicity, primary language, and health literacy. To examine HTE on BMI z-score from 2 to 24 months of age, we fit linear mixed effects models. Results: We analyzed 802 caregiver-child pairs, of which 52% of children were female, 58% of households reported annual family income of <$20,000, and 83% did not have a college degree. We observed evidence to suggest HTE by primary language (p = 0.047 for Spanish vs. English) and the combination of primary language and health literacy (p = 0.01). There was insufficient evidence to suggest that the Greenlight intervention effect differed by biological sex, caregiver BMI, or by race/ethnicity. Conclusions: This HTE analysis found that the Greenlight obesity prevention intervention had a more beneficial effect on child BMI z-score over 2 years for children of caregivers with limited health literacy and for caregivers for whom Spanish was the primary language.