Charles T. Wood

Childhood obesity is a major risk factor for adult cardiovascular disease. Current obesity-prediction models were not developed in diverse populations and do not include heterogeneous social, environmental, and climate factors that may impact body mass index across the full pediatric spectrum. Additionally, they consider only the immediate neighborhood within which a child lives, ignoring contextual factors from expanded (ie, distal) neighborhoods. This study uses expanded neighborhoods’ social, environmental, and climate data to improve individual-level body mass index prediction–from underweight through obesity–using a novel machine learning approach. We obtained demographic and clinical data from the electronic health records of the Duke University Health System, identifying 12,226 children aged 6-18 years in Durham County, North Carolina, with body mass index data from 2014 to 2022. Participants’ data were linked to socioeconomic and environmental information at the census block group level. We captured expanded neighborhood effects with a graph neural network and combined this information with individual-level factors to predict body mass index. Our model predicted body mass index more accurately than simpler models for children aged 6–11 (R2 = 0.234, mean absolute error = 3.352, root mean square error = 4.370) and 12–18 (R2 = 0.147, mean absolute error = 4.980, root mean square error = 6.343) using all features. Key predictive factors identified included rent burden, poverty rate, and tree coverage. This research highlights the value of including broader socioeconomic and environmental factors in body mass index prediction, offering insights that could guide targeted, neighborhood-level interventions.

Background: Pediatricians support families in establishing healthy behaviors, yet there is limited information on valid and pragmatic questionnaires for assessing weight-related health behaviors in early childhood (≤24 months). The objective of this review is to summarize the evidence on parent-reported measures of weight-related health behaviors and/or parenting practices (e.g., feeding practices, dietary intake, sleep duration/routine, physical activity/sedentary behavior, and screen time) in early childhood. Methods: We conducted a scoping review by searching MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, and Cochrane from inception to May 2025 (Open Science Framework Protocol https://doi.org/10.17605/OSF.IO/ME5KW ). Articles included were peer reviewed, original research, published in English, and parent-reported measures of weight-related health behaviors in early childhood that reported psychometric results and/or were used in primary care. Trained reviewers systematically extracted details on the setting, questionnaire, and psychometrics. Findings were summarized using descriptive analysis. Results: Our search yielded 26,642 articles with a final data set of 223 articles. Of the 100 unique questionnaires identified, most (90%) assessed parents’ feeding practices and/or children’s dietary intake; few assessed sleep duration/routine (13%), screen time (13%), and/or physical activity/sedentary behavior (12%). Of the 52 articles describing questionnaires used in primary care, 35% reported psychometric analyses. Conclusions: Most questionnaires measuring weight-related health behaviors in early childhood assessed parent feeding practices and/or children’s dietary intake. Few questionnaires used in primary care reported psychometric results. Comprehensive, valid, reliable, and pragmatic questionnaires of health behaviors in early childhood are needed in the primary care settings to standardize screening for obesity risk in early childhood.

Existing studies that have demonstrated a positive association between obesity and depression have been among adults, did not utilize the Patient Health Questionnaire (PHQ), or were conducted in a homogenous patient population. In this retrospective longitudinal cohort study of patients >11 and <18 years old with obesity in one health system we analyzed associations between change in BMI between two BMI measurements and PHQ-9 scores using chi-square and Kruskal-Wallis tests. We used PHQ-9 scores dichotomized at </≥5 as the outcome in logistic regression models to calculate the adjusted odds of having a higher PHQ-9 score for each increase in BMI per month. One-unit higher BMI change per month was associated with 2.52 times higher odds of PHQ-9 score over 5 (95% CI: 1.57-4.05) after adjusting for sex, baseline BMI, age, race, ethnicity, language, and insurance. BMI changes are associated with an increased risk of higher PHQ-9 scores. Close attention to depression screening in this population may be an important addition to other routine screening in pediatric patients with obesity.

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: Electronic health record–based clinical decision support (CDS) tools can facilitate the adoption of evidence into practice. Yet, the impact of CDS beyond single-site implementation is often limited by dissemination and implementation barriers related to site- and user-specific variation in workflows and behaviors. The translation of evidence-based CDS from initial development to implementation in heterogeneous environments requires a framework that assures careful balancing of fidelity to core functional elements with adaptations to ensure compatibility with new contexts. Objective: This study aims to develop and apply a framework to guide tailoring and implementing CDS across diverse clinical settings. Methods: In preparation for a multisite trial implementing CDS for pediatric overweight or obesity in primary care, we developed the User-Centered Framework for Implementation of Technology (UFIT), a framework that integrates principles from user-centered design (UCD), human factors/ergonomics theories, and implementation science to guide both CDS adaptation and tailoring of related implementation strategies. Our transdisciplinary study team conducted semistructured interviews with pediatric primary care clinicians and a diverse group of stakeholders from 3 health systems in the northeastern, midwestern, and southeastern United States to inform and apply the framework for our formative evaluation. Results: We conducted 41 qualitative interviews with primary care clinicians (n=21) and other stakeholders (n=20). Our workflow analysis found 3 primary ways in which clinicians interact with the electronic health record during primary care well-child visits identifying opportunities for decision support. Additionally, we identified differences in practice patterns across contexts necessitating a multiprong design approach to support a variety of workflows, user needs, preferences, and implementation strategies. Conclusions: UFIT integrates theories and guidance from UCD, human factors/ergonomics, and implementation science to promote fit with local contexts for optimal outcomes. The components of UFIT were used to guide the development of Improving Pediatric Obesity Practice Using Prompts, an integrated package comprising CDS for obesity or overweight treatment with tailored implementation strategies. Trial Registration: ClinicalTrials.gov NCT05627011; https://clinicaltrials.gov/study/NCT05627011

BACKGROUND Electronic health record–based clinical decision support (CDS) tools can facilitate the adoption of evidence into practice. Yet, the impact of CDS beyond single-site implementation is often limited by dissemination and implementation barriers related to site- and user-specific variation in workflows and behaviors. The translation of evidence-based CDS from initial development to implementation in heterogeneous environments requires a framework that assures careful balancing of fidelity to core functional elements with adaptations to ensure compatibility with new contexts. OBJECTIVE This study aims to develop and apply a framework to guide tailoring and implementing CDS across diverse clinical settings. METHODS In preparation for a multisite trial implementing CDS for pediatric overweight or obesity in primary care, we developed the User-Centered Framework for Implementation of Technology (UFIT), a framework that integrates principles from user-centered design (UCD), human factors/ergonomics theories, and implementation science to guide both CDS adaptation and tailoring of related implementation strategies. Our transdisciplinary study team conducted semistructured interviews with pediatric primary care clinicians and a diverse group of stakeholders from 3 health systems in the northeastern, midwestern, and southeastern United States to inform and apply the framework for our formative evaluation. RESULTS We conducted 41 qualitative interviews with primary care clinicians (n=21) and other stakeholders (n=20). Our workflow analysis found 3 primary ways in which clinicians interact with the electronic health record during primary care well-child visits identifying opportunities for decision support. Additionally, we identified differences in practice patterns across contexts necessitating a multiprong design approach to support a variety of workflows, user needs, preferences, and implementation strategies. CONCLUSIONS UFIT integrates theories and guidance from UCD, human factors/ergonomics, and implementation science to promote fit with local contexts for optimal outcomes. The components of UFIT were used to guide the development of Improving Pediatric Obesity Practice Using Prompts, an integrated package comprising CDS for obesity or overweight treatment with tailored implementation strategies. CLINICALTRIAL ClinicalTrials.gov NCT05627011; https://clinicaltrials.gov/study/NCT05627011