A substantial body of research suggests that efforts to prevent pediatric obesity may benefit from targeting not just what a child eats, but how they eat. Specifically, child obesity prevention should include a component that addresses reasons why children have differing abilities to start and stop eating in response to internal cues of hunger and satiety, a construct known as eating self‐regulation . This review summarizes current knowledge regarding how caregivers can be an important influence on children's eating self‐regulation during early childhood. First, we discuss the evidence supporting an association between caregiver feeding and child eating self‐regulation. Second, we discuss what implications the current evidence has for actions caregivers may be able to take to support children's eating self‐regulation. Finally, we consider the broader social, economic, and cultural context around the feeding environment relationship and how this intersects with the implementation of any actions. As far as we are aware, this is the first American Heart Association (AHA) scientific statement to focus on a psychobehavioral approach to reducing obesity risk in young children. It is anticipated that the timely information provided in this review can be used not only by caregivers within the immediate and extended family but also by a broad range of community‐based care providers.

Obesity prevention guidelines recommend children eat ≥ 5 servings of fruits and vegetables, view ≤ 2 h of screen time, participate in 1 h of physical activity, and consume 0 sugar-sweetened beverages daily, commonly known as ‘5-2-1-0’. We sought to determine: the extent to which preschool-aged children attending child care meet these guidelines, predictors of attainment, and associations of attainment with weight status. We analyzed in 2016, 24-hour dietary, physical activity, and screen time data collected in 2009–10 from 398 preschool-aged children in 30 child-care centers in Cincinnati, OH. Dietary intake, screen time and body-mass index (BMI) were obtained by research staff during child care and from parents when at home. Accelerometers measured physical activity. Mixed-effects models and generalized estimating equations were used to determine associations between ‘5-2-1-0’ recommendations, demographic variables, and BMI z-scores. Average child age was 4.3 ± 0.7 years; 26% had a BMI ≥ 85th percentile. Seventeen percent of children with complete dietary data (n = 307) consumed ≥ 5 servings of fruits and vegetables and 50% consumed 0 sugar-sweetened beverages. < 1% with complete physical activity data (n = 386) met the activity recommendation; 81% of children (n = 379) had ≤ 2 h of screen time. Only 1 child met all of the ‘5-2-1-0’ recommendations. There were no consistent demographic predictors of attaining individual recommendations. An additional hour of screen time was associated with a 0.11 (SD 0.06) increase in BMI z-score. Our data suggests there is ample room to increase fruit and vegetable intake and physical activity in preschool-aged children.

Rates of exclusive breastfeeding for 6 months remain low in the United States. Single-parent and step-families, lower income families, non-Hispanic black children, children with exposure to tobacco, and children of mothers with lower education are at greatest risk.

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Overweight and obese children are at an increased risk of remaining obese. The American Academy of Pediatrics recommends addressing healthy habits at well-child checks, but this poses challenges, especially in low-income populations. A clinical innovation project was designed to adapt recommendations in a busy urban clinic and consisted of motivational interviewing, culturally tailored tools, and standardizing documentation. A quasi-experimental design examined innovation outcomes. Of 137 overweight and obese children aged 24 to 66 months, providers’ documentation of weight during well-child check visits improved post-innovation ( P < .01), as did development of healthy habits goals ( P < .001). Families were more likely to return for visits post-innovation ( P = .01). A logistic regression analysis showed that adding body mass index to the problem list and establishing a specific follow-up timeframe most predicted follow-up visits to assess progress ( P < .001). Comprehensive innovations consisting of motivational interviewing, implementation of culturally tailored tools, and standardized documentation can enhance engagement in an urban clinic setting.

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The American Academy of Paediatrics recommends that primary care paediatricians "prescribe" follow-up for weight management between well child checks. We sought to describe rates and predictors of prescribed and actual clinic attendance for weight management in primary care in a predominantly low-income population. A chart review was performed at a large, hospital-based, primary care clinic, where a treatment algorithm for obesity exists. Eligible children were 6 to 12 years of age with a body mass index (BMI) ≥85th percentile and seen for a well child check in 2014. Primary outcomes were the physician prescribing follow-up in primary care and the patient returning for weight management. Multivariable logistic regression was used to identify predictors of prescribing follow-up and predictors of return. Participants included 1339 patients: mean age 9 years (SD: 1.8 years); 53% female; 79% Black; 89% Medicaid-insured; 56% with an obese BMI (vs overweight). Twenty-seven percent of patients were prescribed follow-up in primary care, of which 13% returned (only 4% of the original sample). The odds of the physician prescribing follow-up were greater if the child had obesity (vs overweight), was older, female or non-Medicaid insured. Older and non-Black patients had greater odds of returning.Patients prescribed follow-up within 2 months or less (vs 3-6 months) were also more likely to return (aOR 2.66; CI: 1.34, 5.26). Rates of prescription for weight management in primary care are low and few patients return, even when follow-up is prescribed. Prescribing follow-up at shorter intervals from the index visit (≤ 2 months) may improve patient return.