The Role of Teachers in Addressing Childhood Obesity: A School-Based Approach

Childhood obesity is one of the most prevalent public health challenges in the United States, and although rates are declining overall, rates among children living in underserved neighborhoods are increasing. This five-year intervention project seeks to empower teachers (n = 92) to invest in their own health and then integrate nutrition concepts into core subjects' lessons in elementary schools. The professional development sessions reflect the concepts in the Whole Child, Whole School, Whole Community model. Results indicate that teachers who attended professional development sessions were more likely to implement nutrition lessons in the classroom (r = 0.54, p < 0.01), and students demonstrated a significant increase in nutrition knowledge (p < 0.001, df = 2, F = 9.66). Investing in school-based programs that ensure teacher well-being and professional development can yield positive benefits for both teachers and students.

Building Machine Learning Models to Correct Self-Reported Anthropometric Measures

Monitoring population obesity risk primarily depends on self-reported anthropometric data prone to recall error and bias. This study developed machine learning (ML) models to correct self-reported height and weight and estimate obesity prevalence in US adults. Individual-level data from 50 274 adults were retrieved from the National Health and Nutrition Examination Survey (NHANES) 1999-2020 waves. Large, statistically significant differences between self-reported and objectively measured anthropometric data were present. Using their self-reported counterparts, we applied 9 ML models to predict objectively measured height, weight, and body mass index. Model performances were assessed using root-mean-square error. Adopting the best performing models reduced the discrepancy between self-reported and objectively measured sample average height by 22.08%, weight by 2.02%, body mass index by 11.14%, and obesity prevalence by 99.52%. The difference between predicted (36.05%) and objectively measured obesity prevalence (36.03%) was statistically nonsignificant. The models may be used to reliably estimate obesity prevalence in US adults using data from population health surveys.

From the clinic to the community: Can health system data accurately estimate population obesity prevalence?

OBJECTIVE

Health system data were assessed for how well they can estimate obesity prevalence in census tracts.

METHODS

Clinical visit data were available from two large health systems (Kaiser Permanente Washington and University of Washington Medicine) in King County, Washington, as were census tract-level obesity prevalence estimates from the Behavioral Risk Factor Surveillance System (BRFSS). The health system data were geocoded to identify each patient's tract of residence, and the cross-sectional concordance between census tract-level obesity prevalence estimates computed from the two health systems in 2005 to 2006 and the concordance between University of Washington Medicine and BRFSS from 2012 to 2016 were assessed.

RESULTS

The spatial distribution of obesity was similar between the health systems (Spearman r = 0.63). The University of Washington Medicine estimates of rank order correlated well with BRFSS estimates (Spearman r = 0.85), though prevalence estimates from BRFSS were lower (mean obesity prevalence = 26% for University of Washington Medicine versus 20% for BRFSS, Wilcoxon rank sum test p < 0.001). Across all data sources, obesity was more prevalent in tracts with less educational attainment.

CONCLUSIONS

Health system clinical weight data can reliably replicate census tract-level spatial patterns in the ranking of obesity prevalence. Health system data may be an efficient resource for geographic obesity surveillance.

Measurement challenges for childhood obesity research within and between Latin America and the United States

Childhood obesity is a major public health challenge across Latin America and the United States. Addressing childhood obesity depends on valid, reliable, and culturally sensitive measurements. Such progress within and between countries of the Americas could be enhanced through better measurement across different age groups, different countries, and in sending and receiving communities. Additionally, better and more comparable measurements could accelerate cross-border collaboration and learning. Here, we present (1) frameworks that influenced our perspectives on childhood obesity and measurement needs across the Americas; (2) a summary of resources and guidance available concerning measurement and adaptation of measures for childhood obesity research; and (3) three major areas that present challenges and opportunities for measurement advances related to childhood obesity, including parental behavior, acculturation, and the potential to incorporate ethnographic methods to identify critical factors related to economics and globalization. Progress to reduce childhood obesity across the Americas could be accelerated by further transnational collaboration aimed at improving measurement for better surveillance, intervention development and evaluation, implementation research, and evaluation of natural experiments. Additionally, there is a need to improve training related to measurement and for improving access to valid and reliable measures in Spanish and other languages common in the Americas.

Effect of School-Based Body Mass Index Reporting in California Public Schools: A Randomized Clinical Trial

IMPORTANCE

Annually, US schools screen millions of students' body mass index (BMI) and report the results to parents, with little experimental evidence on potential benefits and harms.

OBJECTIVE

To determine the impact of school-based BMI reporting on weight status and adverse outcomes (weight stigmatization and weight-related perceptions and behaviors) among a diverse student population.

DESIGN, SETTING, AND PARTICIPANTS

Cluster randomized clinical trial. The Fit Study (2014-2017) randomized 79 California schools to BMI screening and reporting (group 1), BMI screening only (group 2), or control (no BMI screening or reporting [group 3]) in grades 3 to 8. The setting was California elementary and middle schools. Students in grades 3 to 7 at baseline participated for up to 3 years. A modified intent-to-treat protocol was used. Data analysis was conducted from April 13, 2017, to March 26, 2020.

INTERVENTIONS

School staff assessed BMI each spring among students in groups 1 and 2. Parents of students in group 1 were sent a BMI report each fall for up to 2 years.

MAIN OUTCOMES AND MEASURES

Changes in BMI z score and in adverse outcomes (based on surveys conducted each fall among students in grades 4 to 8) from baseline to 1 and 2 years of follow-up.

RESULTS

A total of 28 641 students (14 645 [51.1%] male) in grades 3 to 7 at baseline participated in the study for up to 3 years. Among 6534 of 16 622 students with a baseline BMI in the 85th percentile or higher (39.3%), BMI reporting had no effect on BMI z score change (-0.003; 95% CI, -0.02 to 0.01 at 1 year and 0.01; 95% CI, -0.02 to 0.03 at 2 years). Weight dissatisfaction increased more among students having BMI screened at school (8694 students in groups 1 and 2) than among control participants (5674 students in group 3). Results of the effect of BMI reporting on other adverse outcomes were mixed: compared with the control (group 3), among students weighed at school (groups 1 and 2), weight satisfaction declined more after 2 years (-0.11; 95% CI, -0.18 to -0.05), and peer weight talk increased more after 1 year (0.05; 95% CI, 0.01-0.09); however, concerning weight control behaviors declined more after 1 year (-0.06; 95% CI, -0.10 to -0.02).

CONCLUSIONS AND RELEVANCE

Body mass index reports alone do not improve children's weight status and may decrease weight satisfaction. To improve student health, schools should consider investing resources in evidence-based interventions.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02088086.

Elements of Effective Population Surveillance Systems for Monitoring Obesity in School Aged Children

The continuing high prevalence of child overweight and obesity globally means that it remains the most common chronic health condition in children. Population-based child obesity surveillance systems are critical for monitoring trends in obesity and related behaviours, and determining the overall effect of child obesity prevention strategies. Effective surveillance systems may vary in methods, scope, purpose, objectives, and attributes, and our aim was to provide an overview of child obesity surveillance systems globally, and to highlight main components and other types of survey data that can enhance our understanding of child obesity. Measures of adiposity, including body mass index and waist circumference are essential, but effective surveillance must also include measures of weight-related behaviours, including diet, physical activity, sedentary time, and sleep. While objective measures are desirable, the variability in psychometrics and rapid evolution of wearable devices is potentially problematic for examining long-term trends over time and how behaviours may change. Questionnaires on self-reported behaviours are often used but also have limitations. Because the determinants of obesity are not only functioning at the individual level, some measures of the broader environmental and commercial determinants, including the built and food environments, are useful to guide upstream policy decisions.

Trends in food consumption of schoolchildren from 2nd to 5th grade: a panel data analysis

ABSTRACT Objective To identify trends in food consumption among schoolchildren (2nd-5th grades) from public schools in the city of Florianópolis, Brazil, in a period of three years. Methods Three cross-sectional surveys were carried out in 2013 (n=1,942), 2014 (n=1,989) and 2015 (n=2,418). Dietary intake data were obtained using the Web-Based Food Intake and Physical Activity of Schoolchildren questionnaire. Food items were aggregated to eight food groups. Kruskal-Wallis heterogeneity and trend tests were used to analyze the differences and trends among the mean intake frequency of food groups. Results There were trends to decrease the mean intake frequency of sweets in the total sample (2013: 0.72±0.91; 2014: 0.68±0.87; 2015: 0.67±0.89, p=0.03) which was determined by children between 7-9 years old (2013: 0.69±0.88; 2014: 0.64±0.85; 2015: 0.62±0.87, p=0.02), and boys (2013: 0.75±0.90; 2014: 0.70±0.86; 2015: 0.68±0.88, p=0.03). Younger children also tended to increase the mean intake frequency of fruits and vegetables (2013: 1.03±1.35; 2014: 1.16±1.45; 2015: 1.17±1.41, p=0.03) and those aged ten-12 years decreased their intake of dairy products (2013: 1.32±1.25; 2014: 1.23±1.18; 2015: 1.20±1.20, p=0.05). Conclusion The results suggest positive trends for younger children, with an increased consumption of fruits and vegetables in both sexes and decreased consumption of sweets for boys. Older children reduced their consumption of dairy products over the three-year period of this study.

Influence of school environments on childhood obesity in California

OBJECTIVE

To conduct a state-wide examination of public schools and the school neighborhood as potential targets for environmental public health tracking to address childhood obesity.

METHODS

We examined the relationship of social and physical environmental attributes of the school environment (within school and neighborhood) and childhood obesity in California with machine learning (Random Forest) and multilevel methods. We used data compiled from the California Department of Education, the U.S. Geological Survey, ESRI's Business Analyst, the U.S. Census, and other public sources for ecologic level variables for various years and assessed their relative importance to obesity as determined from the statewide Physical Fitness Test 2003 through 2007 for grades 5, 7, and 9 (n = 5,265,265).

RESULTS

In addition to individual-level race and gender, the following within and school neighborhood variables ranked as the most important model contributors based on the Random Forest analysis and were included in multilevel regressions clustered on the county. Violent crime, English learners, socioeconomic disadvantage, fewer physical education (PE) and fully credentialed teachers, and diversity index were positively associated with obesity while academic performance index, PE participation, mean educational attainment and per capita income were negatively associated with obesity. The most highly ranked built or physical environment variables were distance to the nearest highway and greenness, which were 10th and 11th most important, respectively.

CONCLUSIONS

Many states in the U.S. do not have school-based surveillance programs that collect body mass index data. System-level determinants of obesity can be important for tracking and intervention. The results of these analyses suggest that the school social environment factors may be especially important. Disadvantaged and low academic performing schools have a higher risk for obesity. Supporting such schools in a targeted way may be an efficient way to intervene and could impact both health and academic outcomes. Some of the more important variables, such as having credentialed teachers and participating in PE, are modifiable risk factors.