Childhood Obesity最新文献

Interventions in community settings, where children spend substantial out of school time, may enhance access to evidence-based lifestyle interventions. The Boys and Girls Club of Lawrence (BGCL) and New Balance Foundation Obesity Prevention Center at Boston Children's Hospital partnered to revise, enact, and evaluate BGCL's existing Healthy Living Club and then flexibly expand the program to increase access. The BGCL is within walking distance of three public housing communities and easily accessible to members, of whom 90% identify as Hispanic. The interventions comprised nutrition sessions and either fitness activity sessions (N+FA Cycle 1, n = 63, 26 hours; N+FA Cycle 2, n = 94, 27 hours) or academic basketball practices (N+AB Cycle 2, n = 99, 72-80 hours), leveraging time already in the schedule where fitness could be intentionally promoted by coaches. Among children aged 8-15 years, mean [95% confidence interval (CI)] changes (beginning to end) in percentage above the BMI median were significant [N+FA Cycle 1: -2.4 (-4.1, -0.8); N+FA Cycle 2: -4.3 (-5.4, -3.1); N+AB Cycle 2: -5.5 (-6.9, -4.1)]. Lifestyle interventions, implemented with flexibility in existing programs, had beneficial impact, indicating potential of community-academic partnerships.

Background: To examine longitudinal associations of early neighborhood Child Opportunity Index 2.0 (COI) with children's BMI trajectories and identify whether household economic resources moderate relations of COI in infancy/toddlerhood and the preschool years to longitudinal BMI growth between 2 and 12 years. Methods: Family data (n = 1091) were drawn from the Family Life Project, a longitudinal study of families residing in rural high-poverty areas. Neighborhood COI was obtained for each developmental period: infancy/toddlerhood (2-15 months) and the preschool years (2-5 years). BMIs were created from anthropometrics collected at six time points. Results: Higher neighborhood COIs during the infancy/toddlerhood (β = -0.0130, p < 0.01) and preschool years (β = -0.0093, p < 0.05) were associated with lower BMI at 5 years of age; although the latter became nonsignificant after adjusting for infancy/toddlerhood COI. Both household income and time spent in poverty moderated associations of infancy/toddlerhood exposure to neighborhood COI with BMI change. Among children residing in not poor households, higher neighborhood level child opportunity was associated with a slower increase in BMI from 2 to 12 years (β = -0.0369, p < 0.05), and a lower BMI at 12 years (β = -0.0395, p < 0.05). Conclusions: Neighborhood COI during the infant and toddler years is longitudinally associated with child growth, and long-term associations are evident among children residing in not poor households. Future work is needed to better understand how family and neighborhood-level resources interact to influence obesity risk, particularly for those at high risk.

Background: There are now four FDA-approved anti-obesity medications (AOMs) for youth ≥12 years, which can be effective therapies to treat obesity and obesity-related comorbidities. Objectives: This study describes parent/guardian (caregiver) openness to using AOMs for adolescents with obesity and evaluates factors that may contribute to openness. Methods: Caregivers of adolescents aged 12-17 years were surveyed. Self-reported height, weight, demographic information, family, and personal history of obesity or obesity-related comorbidities were collected. Participants rated their openness to starting an AOM for their child for obesity alone or obesity-related comorbidities on a 7-point Likert scale. A Likert rating of less than 4 was considered "less open" versus 4-7 was considered "more open." Results: A total of 344 participants completed the survey. Average openness toward AOM use for obesity as the only indication (as opposed to comorbid conditions) was 3.2 ± 1.74. Caregivers who were knowledgeable that the FDA-approved AOM use in adolescents had greater odds of being open to using these medications compared with caregivers who were not knowledgeable (odds ratio: 2.18; 95% confidence interval: 1.25-2.86). Conclusions: Caregivers reported openness to starting an AOM if they had prior knowledge of these medications, highlighting the need for family education on AOM use and indications.

Background: Amblyopia is the most common cause of vision loss in children. Amblyopia has been associated with impaired depth perception but little attention has been paid to the extent to which amblyopia increases the risk of obesity. Methods: Public-use data from the 1999-2008 National Health and Nutrition Examination Survey were used. Analyses were limited to children aged 12-18, who had a visual examination, and a best corrected visual acuity (BCVA) of at least 20/40 in the better-seeing eye. Amblyopia was defined as two or more-line interocular difference in BCVA. Obesity was defined as Body Mass Index (BMI) or body fat percentage (BFP) ≥95th percentile for age and gender. Sedentary lifestyle was defined as cardiovascular fitness level (CFL) rating of "low." We used Mantel-Haenszel odds ratios (ORs) to examine the relative prevalence of obesity in children with/without amblyopia. Results: Adolescents with amblyopia (n = 360) were more likely than those without (n = 7935) to have a high BMI [OR = 1.56; 95% confidence interval (CI): 1.24-1.98; p < 0.001]. The associations with either high BFP (OR = 1.20; 95% CI: 0.86-1.56, p = 0.167) or low CFL (OR = 1.15; 95% CI: 0.83-1.57; p = 0.267) were not statistically significant but in the direction of a priori hypotheses. Conclusions: This analysis of population-based data suggests that adolescents with amblyopia may be at higher risk of having obesity. Given the high prevalence of amblyopia and the range of morbidities associated with childhood obesity, targeted interventions to reduce the risk of obesity among children with amblyopia could be warranted.

Existing studies that have demonstrated a positive association between obesity and depression have been among adults, did not utilize the Patient Health Questionnaire (PHQ), or were conducted in a homogenous patient population. In this retrospective longitudinal cohort study of patients >11 and <18 years old with obesity in one health system we analyzed associations between change in BMI between two BMI measurements and PHQ-9 scores using chi-square and Kruskal-Wallis tests. We used PHQ-9 scores dichotomized at

Background: Promoting healthy eating and physical activity in early childhood education and care (ECEC) is recommended within guidelines and supported by health promotion programs; however, implementation is suboptimal. Evidence suggests implementation within the sector varies over time; however, this has not been empirically examined in relation to implementation barriers. This study aims to: (1) describe changes in the prevalence of, and barriers to, implementation of priority healthy eating and physical activity practices; and (2) explore the associations between such barriers and implementation. Methods: This was a repeated cross-sectional study over an 8-month period. A cross-section of 150-180 Australian ECEC services were prospectively randomly sampled for each month (April-November 2023), with 1127 ECEC services sampled in total and 20% of services sampled twice. Services reported via survey their implementation of two priority practices: (1) healthy menu standards and (2) educating and engaging parents in child physical activity. They also reported on implementation status, implementation stage, and five core implementation barriers. Results: Overall, 716 services completed 809 surveys. There were no significant differences in the prevalence of implementation or general trends in barriers to implementation of the two priority practices across that time. Services reporting less barriers were significantly more likely to be implementing the priority practices, and services in more advanced implementation stages were significantly less likely to report barriers. Conclusions: To enhance the implementation of priority practices in ECEC services, key barriers to implementation need to be understood and targeted to progress services through to advanced implementation stages.

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.