Improving the Effectiveness and Equity of Child Obesity Interventions
Bibliographic record
Abstract
Effective child obesity interventions remain frustratingly elusive. Obesity continues to affect 1 in 5 children with disproportionate impacts on non-Hispanic Black and Hispanic children.1 In this issue of Pediatrics, Resnicow et al describe the results of a promising multisite randomized controlled trial of a primary care-based, motivational interviewing intervention.2 The multicomponent intervention resulted in a greater increase in BMI percent 95 (BMI percent 95 is the percentage of the 95th percentile and used for tracking change among those with obesity) for intervention children versus children receiving usual care. These findings reflect the complexity of treating obesity in “real-world settings.”First, we need to consider the limitations of motivational interviewing. Motivational interviewing helps parents and their children set goals and identify strategies to motivate themselves to make healthy choices. However, barriers (real and perceived), such as logistics (eg, location, time constraints, transportation), finances (eg, cost of healthy foods and exercise programs), family dynamics, and expectations, can impede motivation.3 To address these barriers, it is important for parents to feel comfortable sharing their challenges with providers and trusting them to help. Additionally, provider knowledge of local resources (eg, programs and organizations that provide free or low-cost health-promoting services) can help support behavior change.4 In the current study, the registered dietitians counseling families were not connected with intervention clinics before the study and lived in communities far away from study participants. This meant that they had to build crucial relationships with parents de novo. Families in obesity treatment prefer tailored recommendations that consider financial resources, logistics, and interpersonal dynamics.3To overcome barriers to behavior change, we must also consider the optimal dose of pediatric obesity interventions and potential consequences. The American Academy of Pediatrics Clinical Practice Guidelines for Pediatric Obesity recommends programs that are high intensity (at least 26 contact hours) and comprehensive (including medical providers, registered dietitians, health behavior specialists, and exercise professionals).5 As Resnicow et al note, their intervention was lower than the recommended number of contact hours. We need to consider whether low-dose pediatric obesity interventions might be detrimental to participants. Adequately addressing a chronic disease as complicated as obesity takes time. Family-based child obesity interventions may disrupt family dynamics, create challenges with navigating social environments, and affect child well-being.6 Further, lack of success in previous obesity treatment programs, because of inadequate dose or other factors, can discourage participation in subsequent treatment opportunities.7Another possible explanation for the study’s outcomes is the internalization of negative body image or weight bias. Even though the authors took measures to minimize weight bias, some aspects of the intervention may have contributed to parents feeling ashamed or judged. For example, during motivational interviewing sessions with providers and registered dietitians, caregivers in the study were asked to grade their child’s behaviors on a scale from A (great/healthy) to F (poor/unhealthy). It is not clear how this practice of self-evaluation affected caregivers’ motivation, expectations, and engagement in the intervention. A qualitative follow-up with participants could reveal how families perceived the intervention and what factors contributed to families’ low fidelity.Most previous studies on motivational interviewing for children with overweight and obesity have been conducted with non-Hispanic white children.8 The subgroup analysis in the current study found that Black and non-Hispanic other children who received the intervention had a significantly greater increase in their BMI versus those who received usual care. The previous efficacy trial by Resnicow et al did not report subgroup analyses.9 The results of the current study suggest that it is important to evaluate how interventions affect different populations, especially those who are disproportionately affected by a condition. Child obesity intervention researchers must closely examine implementation, including reach and efficacy, in groups that are disproportionately affected by child obesity early in intervention development to reduce the likelihood of adverse effects in these groups. Although early-stage studies may lack sufficient power to rigorously examine effect modification by race or ethnicity, qualitative and mixed methods approaches can be used to better elucidate how interventions are experienced by specific populations.10 Additionally, the inclusion of members of populations who experience a disproportionate prevalence of obesity as partners in intervention development and implementation may increase the likelihood that an intervention will improve disparities.11Although pandemics are (we hope) rare, the social, environmental, and economic factors that lead to child obesity are ubiquitous. Interventions focused on individual or family behavior change alone are unlikely to overcome them. Moreover, such interventions may place too much onus on the caregiver to initiate and maintain behavior change against a powerful current of adverse factors. The current study by Resnicow et al reflects the urgent need for child obesity interventions that target the structural factors which contribute to child obesity including socioeconomic, built environment and food policies.12 Such interventions have the potential to yield both effectiveness and equity in real world settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.157 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".