Childhood Obesity最新文献

Background: The purpose of this study was to determine the reliability of anthropometric measurements between two trained anthropometrists working in a team and one trained anthropometrist working with a child's parent/caregiver in a primary health care setting. Study Design: An observational study to determine measurement reliability was conducted in a primary care child research network in Canada. In total, 120 children 0-5 years old had their anthropometric measurement taken twice by two trained anthropometrists working in a team and twice by one trained anthropometrist working with a child's parent/caregiver. Inter- and intra-observer reliability was calculated using the technical error of measurement (TEM), relative TEM (%TEM), and the coefficient of reliability (R). Results: The %TEM values for length/height and weight were <2%, and the R coefficient values were >0.99, indicating a high degree of inter- and intra-observer reliability. The TEM values demonstrated a high degree of reliability for inter- and intra-observer measurement of length/height in comparison with other anthropometric measurement parameters. However, there was greater variation seen in the length measurement for children 0 to <2 years of age and in arm circumference measurement across both age-groups. Conclusion(s): This study suggests that anthropometric measurement taken by one trained anthropometrist with the assistance of a parent/caregiver is reliable. These findings provide evidence to support inclusion of a child's parent/caregiver with anthropometric measurement collection in clinical setting(s) to enhance feasibility and efficiency and reduce the research costs of including a second trained anthropometrist.

Factors associated with change in percent body fat (%BF) of children in pediatric weight management (PWM) care may differ from those associated with change in weight status. Objective: To describe %BF and weight status at initial visits to 14 PWM sites, identify differences by sex, and evaluate factors associated with change over 6 months. Methods: Initial visits of 2496 males and 2821 females aged 5-18 years were evaluated. %BF was measured using bioelectrical impedance analysis. Results: Sex-specific logistic regressions [806 males (32.3%), 837 females (29.7%)] identified associations with primary outcomes: lower %BF and metabolically impactful ≥5-point drop in percent of the 95th BMI percentile (%BMIp95) over 6 months. At the initial visit, males had lower %BF and higher %BMIp95 than females. Over 6 months, males had significantly (p < 0.001) greater median drop in %BF (-1.4% vs. -0.4%) and %BMIp95 (-3.0% vs. -1.9%) and a higher frequency of decreased %BF (68.9% vs. 57.8%), but similar percentage with ≥5-point %BMIp95 drop (36.5% vs. 32.4%; p = 0.080). For males, factors significantly associated with decreased %BF (older age, ≥6 visits, lack of developmental or depression/anxiety concerns) were not related to having a ≥5-point %BMIp95 drop. For females, lack of depression/anxiety concern was significantly associated with decreased %BF but was not associated with ≥5-point %BMIp95 drop. Conclusions: There are differences by sex in initial visit %BF and %BMIp95 and in characteristics associated with changes in these measures. PWM interventions should consider evaluating body composition and sex-stratifying outcomes.

Background: Understanding social determinants of health (SDOH) that may be risk factors for childhood obesity is important to developing targeted interventions to prevent obesity. Prior studies have examined these risk factors, mostly examining obesity as a static outcome variable. Methods: We extracted electronic health record data from 2012 to 2019 for a children's health system that includes two hospitals and wide network of outpatient clinics spanning five East Coast states in the United States. Using data-driven and algorithmic clustering, we have identified distinct BMI-percentile classification groups in children from 0 to 7 years of age. We used two separate algorithmic clustering methods to confirm the robustness of the identified clusters. We used multinomial logistic regression to examine the associations between clusters and 27 neighborhood SDOHs and compared positive and negative SDOH characteristics separately. Results: From the cohort of 36,910 children, five BMI-percentile classification groups emerged: always having obesity (n = 429; 1.16%), overweight most of the time (n = 15,006; 40.65%), increasing BMI percentile (n = 9,060; 24.54%), decreasing BMI percentile (n = 5,058; 13.70%), and always normal weight (n = 7,357; 19.89%). Compared to children in the decreasing BMI percentile and always normal weight groups, children in the other three groups were more likely to live in neighborhoods with higher poverty, unemployment, crowded households, single-parent households, and lower preschool enrollment. Conclusions: Neighborhood-level SDOH factors have significant associations with children's BMI-percentile classification and changes in classification. This highlights the need to develop tailored obesity interventions for different groups to address the barriers faced by communities that can impact the weight and health of children living within them.

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.

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.