Childhood Obesity最新文献

Background: Families with children who have or are at risk for obesity have differing needs and a one-size-fits-all approach can negatively impact program retention, engagement, and outcomes. Individually tailored interventions could engage families and children through identifying and prioritizing desired areas of focus. Despite literature defining tailoring as individualized treatment informed by assessment of behaviors, intervention application varies. This review aims to exhibit the use of the term "tailor" in pediatric obesity interventions and propose a uniform definition. Methods: We conducted a scoping review following PRISMA-ScR guidelines among peer-reviewed pediatric obesity prevention and management interventions published between 1995 and 2021. We categorized 69 studies into 6 groups: (1) individually tailored interventions, (2) computer-tailored interventions/tailored health messaging, (3) a protocolized group intervention with a tailored component, (4) only using the term tailor in the title, abstract, introduction, or discussion, e) using the term tailor to describe another term, and (5) interventions described as culturally tailored. Results: The scoping review exhibited a range of uses and lack of explicit definitions of tailoring in pediatric obesity interventions including some that deviate from individualized designs. Effective tailored interventions incorporated validated assessments for behaviors and multilevel determinants, and recipient-informed choice of target behavior(s) and programming. Conclusions: We urge interventionists to use tailoring to describe individualized, assessment-driven interventions and to clearly define how an intervention is tailored. This can elucidate the role of tailoring and its potential for addressing the heterogeneity of behavioral and social determinants for the prevention and management of pediatric obesity.

Background: The BMI z-score is a standardized measure of weight status and weight change in children and adolescents. BMI z-scores from various growth references are often considered comparable, and differences among them are underappreciated. Methods: This study reanalyzed data from a weight management clinical study of liraglutide in pubertal adolescents with obesity using growth references from CDC 2000, CDC Extended, World Health Organization (WHO), and International Obesity Task Force. Results: BMI z-score treatment differences varied 2-fold from -0.13 (CDC 2000) to -0.26 (WHO) overall and varied almost 4-fold from -0.05 (CDC 2000) to -0.19 (WHO) among adolescents with high baseline BMI z-score. Conclusions: Depending upon the growth reference used, BMI z-score endpoints can produce highly variable treatment estimates and alter interpretations of clinical meaningfulness. BMI z-scores cited without the associated growth reference cannot be accurately interpreted.

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.

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: Although adolescents with obesity have an increased risk of cardiometabolic disease, a subset maintains a healthy cardiometabolic profile. Unhealthy lifestyle behaviors may determine cardiometabolic risk. We aimed to characterize the lifestyle behaviors of adolescents with obesity, compare differences between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO), and assess associations between lifestyle behaviors and cardiometabolic profiles. Methods: Participants aged 10-18 years with body mass index (BMI) ≥ 95^th percentile were included. Dietary intake (DI) was estimated from 3-day food records, and diet quality (DQ) was assessed using the Healthy Eating Index-Canadian Adaptation. Physical activity (PA), body composition, anthropometrics, blood markers, and blood pressure (BP) were objectively measured. MUO was defined as having high triglycerides, BP, glucose, or low high-density lipoprotein. Regression analyses were performed between lifestyle behaviors and cardiometabolic markers. Results: Thirty-nine participants (BMI z-score 2.8 [2.5-3.5], age 12.5 [10.9-13.5] years, 56.4% female) were included. A high proportion of participants failed to meet lifestyle recommendations, particularly for DQ (94.7%, n = 36), fiber (94.7%, n = 36), and PA (90.9%, n = 30). No differences in lifestyle behaviors were found between MUO (59.0%, n = 22) and MHO (41.0%, n = 16). Protein intake was negatively associated with BMI and waist circumference z-scores, fat mass index, insulin resistance, low-density lipoprotein, and C-reactive protein, whereas higher DQ was associated with lower C-reactive protein. Higher light PA levels were associated with lower total cholesterol and triglycerides. Conclusion: Adolescents with either MUO or MHO displayed low adherence to DQ, DI, and PA recommendations; no differences in lifestyle behaviors were found. Protein intake, DQ, and PA were associated with a healthier cardiometabolic profile.

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: 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: 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: 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: The purpose of this study was to determine how accurately parents measure their preschool child's weight and height with increasing levels of instruction. Methods: Parents measured their child's (n = 30 dyads) weight (own weight scale) and height (soft tape measure) using three levels of instruction: instructional guide (level 1); guide, demonstration video (level 2); and guide, video, and virtual monitoring (level 3), which were compared to researcher measurements (electronic weight scale, Stadiometer). Paired t-tests were used to determine differences between researcher and parent measurements and between the three parent levels. Inaccurate classifications were calculated using parent-measured values for the four categories (underweight, healthy, overweight, obese). Results: Raw mean parent-measured weights (17.4 ± 2.3 kg) differed from researcher by 0.2 kg (level 1), 0.3 kg (level 2), and 0.1 kg (level 3). Raw mean parent-measured heights (104.0 ± 5.9 cm) differed from researcher by 0.9 cm (level 1, p = 0.005), 0.4 cm (level 2, NS), and 0.3 cm (level 3, NS). Across all levels, 48.9% and 65.5% parents overmeasured their children's weights and heights, respectively. Using parent-measured values, 29.4% of children were classified high while 70.5% were classified low. Parents were more likely to make errors if their children were on the borderline between any of the two weight categories. Conclusion: Findings indicate that an instructional guide with demonstration video is helpful in improving the parents' accuracy of their children's weights and heights. More research is needed to determine accuracy in population other than White parents with high education levels and children under overweight and obese category.