November 2025

Conference Connections: Highlighting NCCOR’s Work to Advance Strategic Priorities in Children’s Nutrition, Physical Activity, and Obesity Prevention

November 2025, NCCOR

This fall and winter, NCCOR is excited to be attending several conferences where – through oral sessions, symposia, and poster sessions – we will share highlights from the Collaborative’s most recent efforts to build evidence and advance practice to reduce childhood obesity in the United States. Below, we recap NCCOR’s presence at the American Public Health Association’s (APHA) 2025 Annual Meeting and ObesityWeek® and share how you can connect with NCCOR at the Annual Conference on the Science of Dissemination and Implementation (D&I) in Health in December.

APHA 2025

NCCOR enjoyed seeing familiar faces and connecting with new partners at the APHA 2025 Annual Meeting in Washington, DC, earlier this month. At our brand new, redesigned booth, we shared updated resources designed to support researchers, evaluation scientists, practitioners, and students to enhance their effectiveness, efficiency, and impact. Visitors at the booth expressed strong interest in resources like the Implementation Scorecard, Measures Registry, and learning guides from NCCOR’s Obesity-Related Policy, Systems, and Environmental Research in the U.S. (OPUS) workshop, and over 170 individuals signed up for our monthly e-newsletter. We also delivered two successful oral presentations, both drawing on learnings from the OPUS workshop, as part of a session on policy, systems, and environmental (PSE) change approaches to improving nutrition and food systems. The presentations described federally funded PSE-related work that has advanced the field to date and highlighted key themes from the OPUS presentations along with a bold call to action for catalyzing progress in obesity-related PSE research.

ObesityWeek®

NCCOR was excited to partner with The Obesity Society (TOS) on a joint symposium during ObesityWeek® in early November. The session, titled, “Obesity-related Policy, Systems, and Environmental Research in the United States” highlighted NCCOR’s work to advance multisectoral, community-engaged research to reduce childhood obesity. Speakers shared takeaways from the 2024 OPUS workshop, including best practices for translating evidence into effective, scalable prevention strategies.

Annual Conference on the Science of D&I in Health

NCCOR looks forward to attending the Annual Conference on the Science of Dissemination and Implementation (D&I) in Health in National Harbor, Maryland from December 14-17. During this gathering of individuals on the frontlines of D&I science, NCCOR will present posters highlighting two new tools:

• Models, Measures, and Methods Poster Session: “Using qualitative document analysis combined with expert consultation to consolidate research opportunities: Developing the Physical Activity Research Opportunities (PARO) framework.”
  • This poster will detail the five-step qualitative document analysis and expert consultation process used to develop the PARO framework. The PARO framework synthesizes opportunities for physical activity research into a single resource and serves as a guide of where efforts can be most impactful in physical activity promotion.
• Public Health and Prevention Poster Session: “The Development of the Implementation Scorecard: A Decision-Making Companion for Public Health Practitioners.”
  • This poster describes how formative assessments with practitioners implementing childhood obesity, nutrition, and physical activity interventions informed NCCOR’s new scorecard tool and case study, which is designed to increase the uptake of implementation science strategies.

We hope to see you at the conference in December and invite you to tag us on X @NCCOR or on LinkedIn at linkedin.com/company/nccor/! If you’re not able to attend, all of these tools and resources are available on our website at www.nccor.org.

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NCCOR Toolbox: Last Month's NCCOR Webinar Now Available Online

November 2025, NCCOR

On October 14, 2025, NCCOR hosted a Connect & Explore webinar titled “Turning Evidence into Action: Using NCCOR’s Implementation Scorecard to Strengthen Childhood Obesity, Nutrition, and Physical Activity Interventions.” NCCOR’s new Implementation Scorecard was informed by the experiences and challenges faced by practitioners implementing childhood nutrition, physical activity, and obesity prevention interventions. During this 1-hour webinar, Taren Massey-Swindle, PhD, Associate Professor at the University of Arkansas for Medical Sciences College of Medicine, discussed the development of the scorecard. Teddy Swenson, MPH, Senior Manager of Active Design at the New York City Department of Health and Mental Hygiene, then shared her perspective on how the tool can benefit practitioners as they move through the four phases of implementing new interventions or programs. View the recording here.

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State of Obesity Report 2025: Better Policies for a Healthier America

October 16, 2025, Trust for America’s Health

New data found that nineteen states had adult obesity rates at or above 35 percent in 2024, down from 23 states the prior year, a first time decrease in the number of states at or above the 35 percent level for this dataset.

However, according to the report authors, this progress is limited and at risk due in part to recent federal actions to claw back and reduce funding for public health programs, eliminate programs and lay off experts that work on chronic disease prevention, and limit access to nutrition supports.

The report, based in part on TFAH’s analysis of 2024 data from the Centers for Disease Control and Prevention’s (CDC) Behavioral Risk Factor Surveillance System  (BRFSS), and data from the 2021-2023 National Health and Nutrition Examination Survey (NHANES), shows that while the rise in the number of U.S. adults with obesity slowed in some states, the nation’s overall obesity rate continues to be alarmingly high. Nationally, 4 in 10 American adults have obesity.

The report highlights the federal programs that seek to address rising rates of chronic diseases like obesity. However, the president’s fiscal year (FY) 2026 budget request proposes the near total elimination of the National Center for Chronic Disease Prevention and Health Promotion at CDC. The center’s work includes cornerstone programs that fund state, local, tribal, and territorial efforts to address and prevent obesity, diabetes, heart disease and stroke, and other chronic diseases.

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Counting Bites with AI Might One Day Help Prevent Childhood Obesity

October 16, 2025, EurekAlert!

The faster a child takes bites during a meal or snack, the greater risk they have for developing obesity, according to researchers in the Penn State Department of Nutritional Sciences. But research into this association is often limited to small studies in laboratory environments, largely because counting a child’s bite rate is difficult; it requires someone to watch videos of a child eating and manually record each bite.

To make bite rate counting possible for larger studies and in different environments, researchers from the Penn State Departments of Nutritional Sciences and of Human Development and Family Studies collaborated to develop an artificial intelligence (AI) model that measures bite rate.

A pilot study — recently published in Frontiers in Nutrition — demonstrated that the system is currently about 70% as successful as human bite counters. While it requires more development, the researchers said the AI model shows promise to help researchers — and eventually parents and health professionals — identify when children need to slow or otherwise adjust the ways they eat.

Eating too quickly and obesity risk

“When we eat quickly, we don’t give our digestive track time to sense the calories,” said Kathleen Keller, professor and Helen A. Guthrie Chair of nutritional sciences at Penn State and co-author of this study. “The faster you eat, the faster it goes through your stomach, and the body cannot release hormones in time to let you know you are full. Later, you may feel like you have overeaten, but when this behavior repeats, faster eaters are at greater risk for developing obesity.”

Faster bite rate, especially when combined with larger bite size, is associated with higher obesity rates among children, according to previous research from Keller’s laboratory group. Other studies have demonstrated that larger bite size may also be a risk factor for choking.

“Bite rate is often the target behavior for interventions aimed at slowing eating rate,” said Alaina Pearce, research data management librarian at Penn State and co-author of this research. “This is because bite rate is a stable characteristic of children’s eating style that can be targeted to reduce their eating rate, intake and ultimately risk for obesity.”

Measuring bite rate is tedious, labor-intensive work, meaning it is expensive, which often limits the amount of data considered in bite rate studies, according to Keller, a Penn State Social Science Research Institute co-funded faculty member.

Leveraging technology to keep children healthy

To address that problem, Yashaswini Bhat, doctoral candidate in nutritional sciences and lead author on the study, wanted to develop the first AI bite counter for use in studies of children’s eating behaviors.

“I have an interest in AI and data science, but I had never developed a system like this one,” Bhat said.

She collaborated with Timothy Brick, associate professor of human development and family studies at Penn State and study co-author, to build a system that could identify children’s faces in a video with multiple people and then detect individual bites when a child was eating.

“An experienced and knowledgeable collaborator like Dr. Brick was invaluable to this project,” Bhat said.

The researchers used 1,440 minutes of videos from Keller’s Food and Brain Study, a National Institute of Diabetes and Digestive and Kidney Diseases-funded study of the neural mechanisms that may influence overeating in children. The video footage included 94 seven- to nine-year-old children consuming four meals on separate occasions with varying amounts of identical foods.

The researchers identified bites in 242 of the videos by watching the videos and noting each bite. They then used that information to train the AI model. Once the model was able to identify events that appeared to be bites, the researchers had it evaluate 51 other videos from the same data set. The researchers then compared the bites identified by the model to see if they matched the bites coded by research assistants.

A successful first step

“The system we developed was very successful at identifying the children’s faces,” Bhat said. “It also did an excellent job identifying bites when it had a clear, unobstructed view of a child’s face.”

The system, however, is not yet ready for widespread use, according to Bhat. Results demonstrated that the model was roughly 97% as successful as a human at identifying a child’s face in the video but was about 70% as successful as a human at identifying every bite.

“The system was less accurate when a child’s face was not in full view of the camera or when a child chewed on their spoon or played with their food, as often happens toward the end of a meal,” Bhat said. “As one might imagine, this type of behavior is much more common among children than it is with adults. Chewing on a utensil sometimes appeared to be a bite, and this complicated the task for the AI model.”

While more work is needed, the researchers said that this study represents a successful pilot test. With more training, they said the system — called ByteTrack — will more accurately identify bites and learn to ignore other actions, like sipping a beverage.

“The eventual goal is to develop a robust system that can function in the real world,” Bhat said. “One day, we might be able to offer a smartphone app that warns children when they need to slow their eating so they can develop healthy habits that last a lifetime.”

The National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of General Medical Sciences, the Penn State Institute for Computational and Data Sciences, and the Penn State Clinical and Translational Science Institute funded this research.

[Source]

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Family Ties and Health: Exploring the Link Between Parents’ Social Networks and Child Obesity

October 7, 2025, Childhood Obesity

Background

African American (AA) and Hispanic children are at high risk of developing obesity. Social networks are powerful drivers of health behaviors and outcomes. Parent social network characteristics may influence child health behaviors and obesity risk.

Methods

This is a cross-sectional, observational study of parents of AA and Hispanic children 2–4 years. The social network survey was adapted from The Important and Health Matters Social Network Battery (PhenX Toolkit). Child health behavior questions were from the National Health and Nutrition Examination Survey. Child body mass index z-score (BMIz) was calculated based on measured height and weight at the child’s most recent well-child visit. Linear regression models identified associations between parent network characteristics, BMIz, and child health behaviors. Participants included 44 parents of Hispanic children and 74 parents of AA children.

Results

Parents of Hispanic children named fewer supportive alters (network members) than parents of AA children (3.7 vs. 5.0, p < 0.1). A higher number of stressful parent relationships was associated with higher child BMIz (β = 0.2, p < 0.05). For parents of AA children, a higher number of supportive alters was associated with more hours of child weekday (β = 0.2) and weekend sleep (β = 0.2) and less child weekday screen time (β = −0.2) (all p < 0.05). For parents of Hispanic children, a higher number of alters with whom the parent discusses important matters was associated with more hours of child weekday sleep (β = 0.3, p < 0.05).

Conclusions

Parent social network characteristics are population-specific and associated with child health behaviors and BMIz. Augmenting supportive parent relationships and stress coping may reduce child obesity risk.

[Source]

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Playing Alone: Recess Physical Activity and Social Interactions of Children with and Without Obesity and Severe Obesity

October 7, 2025, Childhood Obesity

Background

This study aimed to compare recess physical activity, social behaviors, and social/school perceptions among children with obesity and severe obesity and those without obesity.

Methods

Recess observations were done using the System of Observing Children’s Activity and Relationships during Play. School climate surveys and anthropometric measurements were also completed. A total of 414 observations from 160 (52% female) children were collected at four schools in Little Rock, AR, during three semesters from 2023 to 2024. Children were in kindergarten through 5th grade. Linear mixed-effects models were used to estimate adjusted associations.

Results

Thirteen percent of observations reflected children with severe obesity, 21% were from children with obesity, and 66% were from children with a BMI below the 95th percentile on the age- and gender-specific reference growth charts. Overall, children were engaged in moderate–vigorous physical activity 64% of the time and played alone 22% of the time. Positive play was observed 50% of the time. Children with severe obesity played alone 8% (p < 0.05) more than the reference group without obesity. There was no evidence of an inverse association between obesity and physical activity. Surveys from upper elementary children showed no differences in self-reports of being happy, having friends, or enjoying recess by weight status.

Conclusions

While we observed more alone play at recess among children with severe obesity, we did not observe less physical activity or more negative play experiences among children with obesity or severe obesity. Recess appears to be beneficial regardless of weight status, both socially and for physical activity.

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Evaluation of BMI Growth Charts for Children Living with Severe Obesity

October 7, 2025, Childhood Obesity

Introduction

Growth charts were not designed to monitor children and adolescents with severe obesity. We evaluate three commonly used international references and their implications for children with severe obesity and develop a BMI growth chart for children with severe obesity, which we call “Reference-point BMI from adjusted World Health Organization (WHO) population” (R-BMI).

Method

Growth charts from the WHO, International Obesity Task Force, and CDC were reviewed regarding population, statistical method, and cut-offs. We created the R-BMI chart from the WHO population, with adapted adjustment and reference-point cut-offs, and the layout was updated for better readability. Moreover, an interactive web app was developed for this project at the following link https://child-bmi.serve.scilifelab.se/ with the purpose of visually comparing different BMI references for children with obesity.

Results

Three different references for children with severe obesity, with corresponding adjustments, are presented to illustrate implications for researchers and clinicians. Furthermore, R-BMI is presented as a method attempting to address chart challenges related to the extreme BMI. The result is reference curves which share desirable features with established references, while avoiding undesirable curve behavior.

Conclusions

Growth charts present challenges for children living with severe obesity, leading to varying approaches and implications of international references. The proposed R-BMI offers monitoring of children with severe obesity that can be used from birth to adulthood. It relates to adult BMI cut-offs and allows for a terminology, and it has a layout with the potential of highlighting changes which may otherwise go unnoticed.

[Source]

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