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We evaluated the prevalence of high weight status in children ages 0 to 24 months (m) using data from electronic health records (EHR) and NHANES. We also examined relationships between weight status during infancy and obesity at 24 months of age.EHR data from 4 institutions in North and South Carolina included patients born January 1, 2013-October 10, 2017 (N = 147,290). NHANES data included study waves from 1999 to 2018 (unweighted N = 5121). We calculated weight-for-length (WFL), weight-for-age (WFA), and body mass index (BMI), excluding implausible values, and categorized weight status (85th, 85th to95th, or ≥95th percentile), assessing prevalence at birth, 6, 12, 18, and 24 months. Utilizing individual, longitudinal EHR data, we used separate regression models to assess obesity risk at 24 months based on anthropometrics at birth, 6, 12, and 18 months, adjusting for sex, race/ethnicity, insurance, and health system.Prevalence of BMI ≥95th percentile in EHR data at 6, 12, 18, and 24 months were 9.7%, 15.7%, 19.6%, and 20.5%, respectively. With NHANES the prevalence was 11.6%, 15.0%, 16.0%, and 8.4%. For both, the prevalence of high weight status was higher in Hispanic children. In EHR data, high weight status at 6, 12, and 18 months was associated with obesity at 24 months, with stronger associations as BMI category increased and as age increased.High weight status is common in infants and young children, although lower at 24 months in NHANES than EHR data. In EHR data, high BMI at 6, 12, and 18 months was associated with increased risk of obesity at 24 months.

Background Safe and effective treatment exists for childhood obesity, but treatment recommendations have largely not been translated into practice, particularly among racial and ethnic minorities and low-wealth populations. A key gap is meeting the recommended treatment of ≥26 h of lifestyle modification over 6–12 months. Fit Together is an effective treatment model that meets these recommendations by integrating healthcare and community resources. Pediatric providers screen children for obesity, deliver counseling, and treat co-morbidities, while Parks and Recreation partners provide recreation space for a community nutrition and physical activity program. Methods This study will use a hybrid type II implementation-effectiveness design to evaluate the effectiveness of an online implementation platform (the Playbook) for delivering Fit Together. Clinical and community partners in two North Carolina communities will implement Fit Together, using the Playbook, an implementation package designed to facilitate new partnerships, guide training activities, and provide curricular materials needed to implement Fit Together. An interrupted time series design anchored in the Process Redesign Framework will be used to evaluate implementation and effectiveness outcomes in intervention sites. Implementation measures include semi-structured interviews with partners, before and after the implementation of Fit Together, and quantitative measures assessing several constructs within the Process Redesign Framework. The participants will be children 6–11 years old with obesity and their families (n=400). Effectiveness outcomes include a change in child body mass index and physical activity from baseline to 6 and 12 months, as compared with children receiving usual care. Findings will be used to inform the design of a dissemination strategy guided by the PCORI Dissemination Framework. Discussion This project addresses the knowledge-to-action gap by developing evidence-based implementation tools that allow clinicians and communities to deliver effective pediatric obesity treatment recommendations. Future dissemination of these tools will allow more children who have obesity and their families to have access to effective, evidence-based care in diverse communities. Trial registration ClinicalTrials.gov identifier: NCT05455190. Registered on 13 July 2022

Background Obesity is associated with poorer youth fitness. However, little research has examined the magnitude of this relationship in youth with severe obesity. Therefore, we sought to determine the relationship between increasing weight status and fitness within a sample of children and adolescents from New York City public schools. Methods This study utilized longitudinal data from the NYC Fitnessgram dataset years 2010–2018. Height and weight along with fitness were measured annually during physical education classes. Severity of obesity was defined using body mass index relative to the 95th percentile and then categorized into classes. A composite measure of fitness was calculated based on scores for three fitness tests: aerobic capacity, muscular strength, and muscular endurance. To examine the weight status-fitness relationship, repeated measures mixed models with random-intercepts were constructed. Stratified models examined differences by demographic factors. Results The sample included 917,554 youth (51.8% male, 39.3% Hispanic, 29.9% non-Hispanic Black, 14.0%, 4.6%, and 1.6% class I, II and III obesity, respectively). Compared to youth with healthy weight, increasing severity of obesity was associated with decreased fitness: overweight (β = − 0.28, 95% CI:-0.29;-0.28), class I obesity (β = − 0.60, 95% CI:-0.60; − 0.60), class II obesity (β = − 0.94, 95% CI:-0.94; − 0.93), and class III obesity (β = − 1.28; 95% CI:-1.28; − 1.27). Stratified models showed the association was stronger among male and non-Hispanic White youth. Conclusion Findings revealed that more severe obesity was associated with lower fitness. Future research is needed to develop targeted interventions to improve fitness in youth with obesity.

The objective of this technical report is to provide clinicians with evidence-based, actionable information upon which to make assessment and treatment decisions for children and adolescents with obesity. In addition, this report will provide an evidence base to inform clinical practice guidelines for the management and treatment of overweight and obesity in children and adolescents. To this end, the goal of this report was to identify all relevant studies to answer 2 overarching key questions: (KQ1) “What are clinically based, effective treatments for obesity?” and (KQ2) “What is the risk of comorbidities among children with obesity?” See Appendix 1 for the conceptual framework and a priori key questions.

The objective of this technical report is to provide clinicians with actionable evidence-based information upon which to make treatment decisions. In addition, this report will provide an evidence base on which to inform clinical practice guidelines for the management and treatment of overweight and obesity in children and adolescents. To this end, the goal of this report was to identify all relevant studies to answer 2 overarching key questions: (KQ1) “What are effective clinically based treatments for obesity?” and (KQ2) “What is the risk of comorbidities among children with obesity?” See Appendix 1 for the conceptual framework and a priori Key Questions.

Introduction: Fewer than 1/4th of US youth meet physical activity guidelines, leading to physical fitness-related cardiovascular disease disparities tracking into adulthood. Neighborhood opportunity may serve as a critical modifiable factor to reduce youth cardiovascular health-related disparities. The present analysis uses the Child Opportunity Index (COI), a comprehensive measure of children’s neighborhood opportunity, to examine associations between neighborhood context and multiple youth cardiovascular fitness outcomes. Methods: Data were drawn from the NYC FITNESSGRAM (n = 300,000), comprising of fitness data from New York City (NYC) public school youth (grades K-12). Generalized linear mixed models were run to estimate overall and sex-stratified associations between overall COI and individual COI indicators (greenspace, healthy food, walkability, commute time) and youth cardiovascular fitness (body mass index (BMI) percentiles, curl-ups, push-ups, PACER, and sit and reach). Geographically weighted regression (GWR) models explored spatial variation of COI-fitness associations across NYC. Results: Overall COI was associated with improved youth cardiovascular fitness outcomes, with the strongest magnitude of effects across aerobic capacity and muscular strength and endurance measures (PACER: β: 0.15, 95% CI: 0.14, 0.15, curl-ups: β: 0.13, 95% CI: 0.12, 1490.13, and push-ups: β: 0.13, 95% CI: 0.12, 0.13). Greenspace was positively associated with muscular endurance (curl-ups: β: 1.13, 95% CI:0.76, 1.50; push-ups: β: 1.53, 95% CI: 1.21, 1.84), and walkability and commuting time were positively associated with aerobic capacity (PACER: β: 2.11, 95% CI: 1.72, 2.50; β: 2.03, 95% CI: 1.87, 2.19, respectively). Stratified models showed strengthened COI-fitness associations among girls versus boys. GWR models were largely consistent with findings from multilevel models, except for overall COI-BMI associations, which varied in magnitude and direction across regions in NYC. Conclusion: Neighborhood opportunity was associated with multiple measures of youth cardiovascular fitness, though geographic models demonstrate that certain associations vary in magnitude and direction across NYC. Continued research on neighborhood factors and cardiovascular fitness may better inform public health efforts to reduce health disparities modifiable through comprehensive place-based interventions.

Overweight and obesity rates increased dramatically among United States youth from 1999-2016, particularly among adolescents. Promoting physical activity can reduce youth obesity rates, particularly among overweight adolescents, most of whom do not meet guidelines for at least 60 minutes of daily physical activity. Neighborhood public transportation usage is an important environmental factor that has potential to impact adolescent physical activity. We examined the association between neighborhood public transportation usage and adolescent body mass index (BMI) drawing from the Family Life, Activity, Sun, Health, and Eating (FLASHE) study, a cross-sectional national survey conducted in 2014 that assessed cancer preventative behaviors, including physical activity and diet, among adolescents (aged 12-17 years, n=1,737) and their parents. We used linear regression models to test the moderating effects of age, sex, and socioeconomic status and the mediating effect of physical activity on the association between neighborhood public transportation usage and adolescent BMI. The analytic sample included 357 adolescents of middle school (44%) and high school (56%) ages evenly distributed by sex (51% female) with 39% of the sample below 200% of the Federal Poverty Line. On average, adolescents had a BMI percent of the 95 th percentile of 81.77 (17.09) or 22.24 (4.67) BMI and self-reported 71 daily (out-of-school) moderate-to-vigorous activity minutes. While we found no overall association between neighborhood public transportation usage and BMI, we found that low neighborhood public transportation is associated with increased BMI among high school (14% (95% CI 5.72, 21.41)) and male (7.16 (95% CI -13.41, -0.91)) adolescents respectively. We also found that physical activity does not mediate the effect of neighborhood public transportation usage on adolescent BMI. Our findings suggest that our adult reported public transportation measure could be pointing to neighborhood factors, such as poverty or crime, that affect the association between neighborhood public transit and adolescent BMI. Our findings help inform future research on the dynamic relationships between individual and social environmental factors that impact adolescent physical activity.

Blood pressure (BP) assessment and management are important aspects of care for youth with obesity. This study evaluates data of youth with obesity seeking care at 35 pediatric weight management (PWM) programs enrolled in the Pediatric Obesity Weight Evaluation Registry (POWER). Data obtained at a first clinical visit for youth aged 3-17 years were evaluated to: (1) assess prevalence of BP above the normal range (high BP); and (2) identify characteristics associated with having high BP status. Weight status was evaluated using percentage of the 95

The patient-centered medical home (PCMH) strives to improve the quality of care in the primary care setting. Recently, certification programs for patient-centered coordinated care have expanded to subspecialty care. Children with chronic conditions are particularly in need of patient-centered and coordinated care. Our objective was to compare parent perceptions of PCMH elements at primary care and specialty practices for children receiving specialty care.Cross-sectional survey study.We surveyed the parents of children returning for specialty care in a hospital-based pediatric subspecialty clinic on the presence of National Committee for Quality Assurance (NCQA) PCMH elements in their primary care practice and in their main specialty care practice.More parents perceived good appointment access at primary care practices than they did at specialty practices (93% vs 87%, respectively; P.001). They perceived good care coordination and referral follow-up both at primary care and specialty practices (89% vs 88% and 92% vs 92%, respectively). However, parents less frequently perceived the presence of 7 other PCMH elements at primary care practices compared with specialty practices; these included appointment and tests due reminders, distributing handouts, electronic prescribing, sharing test results, surveying experiences, and e-mail capability.Despite an emphasis on PCMHs in primary care settings, parents of children seeking specialty care are more likely to perceive the presence of NCQA PCMH elements in specialty rather than primary care clinics. Future PCMH efforts should address parents' perceptions and interpretations of these services.

This study assessed the prevalence and consistency of high values of weight, height, and BMI considered to be biologically implausible (BIV) using cut points proposed by WHO among 8.8 million low-income children (13.7 million observations).Cross-sectional and longitudinal analyses were performed among 2- to 4-year-olds who were examined from 2008 through 2011.Overall, 2.7% of the body size measurements were classified as BIVs; 95% of these BIVs were very high. Among the subset of children (3.6 million) examined more than once, most of those who initially had a high weight or BMI BIV also had a high BIV at the second examination; odds ratios were250. Based on several alternative classifications of BIVs, the current cut points likely underestimate the prevalence of obesity by about 1%.Many of the extremely high values of body size currently flagged as BIVs are unlikely to be errors. Increasing the z-score cut points or using a percentage of the maximum values in the National Health and Nutrition Examination Survey, could improve the balance between removing probable errors and retaining those that are likely correct.