BACKGROUND AND OBJECTIVES:

Children who become overweight by age 2 have greater risk of long-term obesity and health problems. The study aim was to assess the effectiveness of a primary care–based intervention on the prevalence of overweight at age 24 months.

METHODS:

In a cluster-randomized trial, sites were randomly assigned to the Greenlight intervention or an attention-control arm. Across 4 pediatric residency clinics, we enrolled infant–caregiver dyads at the 2-month well-child visit. Inclusion criteria included parent English- or Spanish-speaking and birth weight ≥1500 g. Designed with health-literacy principles, the intervention included a parent toolkit at each well-child visit, augmented by provider training in clear-health communication. The primary outcome was proportion of children overweight (BMI ≥85th percentile) at age 24 months. Secondary outcomes included weight status (BMI z score).

RESULTS:

A total of 459 intervention and 406 control dyads were enrolled. In total, 49% of all children were overweight at 24 months. Adjusted odds for overweight at 24 months (treatment versus control) was 1.02 (95% confidence interval [CI]: 0.63 to 1.64). Adjusted mean BMI z score differences (treatment minus control) were −0.04 (95% CI: −0.07 to −0.01), −0.09 (95% CI: −0.14 to −0.03), −0.19 (−0.33 to −0.05), −0.20 (−0.36 to −0.03), −0.16 (95% CI: −0.34 to 0.01), and 0.00 (95% CI −0.21 to 0.21) at 4, 6, 12, 15, 18, and 24 months, respectively.

CONCLUSIONS:

The intervention resulted in less weight gain through age 18 months, which was not sustained through 24 months. Clinic-based interventions may be beneficial for early weight gain, but greater intervention intensity may be needed to maintain positive effects.

What’s Known on This Subject:

One in 5 preschool-aged children is overweight, and 1 in 4 parents has limited literacy skills. Although researchers of previous obesity-prevention studies have shown mixed results, none has examined a health-literacy-informed primary care approach.

What This Study Adds:

The Greenlight intervention resulted in less weight gain through age 18 months, which was not sustained through age 24 months. Primary care interventions can reduce early infant weight gain, but enhancements may be necessary to maintain positive effects over time.

Obesity prevention is a public health priority, and the Centers for Disease Control and Prevention and other leading national health agencies have identified early childhood as a critical period for preventing morbidity across the life course.1,2  More than 20% of US preschool-aged children are overweight, and >10% are obese.3,4  Children overweight by age 24 months are 5 times more likely than nonoverweight children to be overweight as adults.510  Early childhood weight gain disproportionately affects low-income and ethnic-minority families and may contribute to disparities in associated chronic illnesses.5,1114  Nearly 1 in 4 parents has limited literacy skills,15  which is independently associated with child overweight, as well as impaired obesity-related knowledge (eg, food labels, portion sizes and growth charts) and skills (eg, problems mixing infant formula correctly). 1638  In fact, the US Surgeon General identified health literacy as “one of the largest contributors to our nation’s epidemic of overweight and obesity.”39,40  As a result, addressing early childhood obesity may require a low-literacy, family-centered approach.41 

Previous clinical trials to prevent childhood obesity have resulted in varying levels of success.1,4251  Few researchers have targeted low-income families, which are difficult to reach and follow over time, and none have examined the impact of a literacy-informed intervention. In this report, we describe the results of the Greenlight Intervention Study, a cluster-randomized, multisite trial to assess the effectiveness of a low-literacy, primary care–based intervention to prevent overweight during the first 2 years of life.

As described previously, we implemented a pragmatic, cluster-randomized controlled trial design to assess the impact of the Greenlight intervention on the prevalence of overweight at age 24 months and on interval weight trajectories.52  We chose resident physicians as primary intervention messengers, because of the potential for training to impact long-term practice and the aim to target low-income families, since at least 1 in 5 low-income children receives care in resident clinics.53  To avoid intrasite contamination, because residents could not be easily cohorted within each site, randomization occurred at the site level. We stratified by population density, such that the 2 sites serving the highest population density were assigned to different groups. A statistician, blinded to site location, conducted each site’s random assignment to intervention or active control status, using a random number generator in Stata 9.0 (Stata Corp, College Park, TX). Two sites (New York University [NYU] and Vanderbilt University Medical Center [VUMC]) were randomly assigned to receive the intervention, which incorporated health-literacy principles and focused on obesity prevention, and 2 sites (University of Miami [UM] and University of North Carolina at Chapel Hill [UNC]) were assigned to receive the active control arm, which did not apply health-literacy principles and focused on injury prevention. The study was approved by the institutional review boards at each of the participating university medical centers. A Data Safety Monitoring Board, which included participants from each institution, provided study oversight of protocol implementation, including monitoring of recruitment, retention, and outcomes. The study is registered with the national Clinical Trials Registry (clinicaltrials.gov), study no. NCT01040897.

The Greenlight intervention consisted of 2 components: (1) a low-literacy parent educational toolkit, including developmentally-tailored booklets at each well-child visit [WCV]; and (2) provider training in health communication, including modules on teach-back and goal setting.52  Booklets were designed with low-literacy principles to target age-specific behavioral goals, with a focus on child nutrition (eg, promoting breastfeeding, avoiding sweetened beverages, recognizing satiety cues, using appropriate portion sizes) and physical activity (eg, promoting tummy time, avoiding screen time).34,52,5456  Tangible tools, which reinforced these goals, included an infant onesie (reading “I’m Sweet Enough. Please, No Juice!”) at 2-month WCV; a sippy cup (with markings to guide juice dilution) at 9-month WCV; a small snack bowl (for pureed foods) at 12-month WCV; and a placemat (illustrating appropriate portion sizes) at 15- to 18-month WCV. Parents from a variety of racial and ethnic backgrounds were consulted to inform toolkit content and design. (Materials accessible at www.greenlight-program.org.) Provider training modules emphasized clear-health communication techniques, including teach-back, goal-setting, and plain language, with specific examples to support toolkit content (eg, avoiding sweetened beverages, recognizing satiety cues).52  Sites randomized to the control arm received The Injury Prevention Program,57,58  an educational intervention designed by the American Academy of Pediatrics. In each arm, providers received the same number of training hours, parents received the same number of tangible tools, and all materials were provided in English or Spanish. In line with standard practice, in-person or telephone interpreters were provided for those who preferred to conduct the visit in Spanish. Before study initiation, intervention fidelity was reinforced through administration of a 10-item checklist, adapted from previously validated tools,5962  applied to each provider at a WCV encounter by a trained observer. Failure to exceed a preset threshold prompted brief feedback and subsequent observation. Before study initiation, the threshold score was exceeded for 95% of initial observations; of those who required subsequent observation, the threshold score was achieved on the second or third observations; none required a fourth. During the study period, 590 families (68% of those randomizly assigned, 74% of those analyzed) completed at least 6 WCVs.

Between April 2010 and October 2014, we enrolled the child’s primary caregiver (“parent”) at the child’s 2-month WCV, with intervention and assessment at each of 7 American Academy of Pediatrics–recommended preventive care visits (2, 4, 6, 9, 12, 15 and/or 18, and 24 months). Each parent provided informed consent before study enrollment.

Eligibility criteria included a child presenting for a 2-month WCV, parent’s ability to speak Spanish or English, and no specific plans to leave the clinic within 2 years. Infant exclusion criteria included gestational age <34 weeks, birth weight <1500 g, enrollment weight less than third percentile on World Health Organization (WHO) growth curve,63,64  or chronic medical problem causing potential problems with feeding or weight gain. Parent exclusion criteria included age <18 years, serious mental or neurologic illness, or poor corrected visual acuity.

Trained, bilingual research assistants conducted interviews in English or Spanish, on the basis of parent preference, at baseline and at each successive WCV. Acceptable timing for each WCV was 6 to 15 weeks (for 2-month WCV), 16 to 20 weeks (for 4-month WCV), 21 to 32 weeks (for 6-month WCV), 33 to 51 weeks (for 9-month WCV), 52 to 57 weeks (for 12-month WCV), 58 to 67 weeks (for 15-month WCV), 68 to 91 weeks (for 18-month WCV), and 92 to 125 weeks (for 24-month WCV). For missed WCVs, parents were contacted by telephone. Weight and length measurements were collected by clinic staff, who were trained on the basis of guidelines from the Agency for Healthcare Research and Quality. Chart abstraction was completed after the 24-month WCV.

The primary outcome was the proportion of children overweight at 24 months. We defined “overweight” as a BMI ≥85th percentile (adjusting for age and sex) according to WHO growth curves.63  At each WCV, child weight status was assessed as a continuous variable (BMI z score) and as a dichotomous variable (BMI ≥85th percentile), each by WHO standards. Although we originally intended to use the Centers for Disease Control and Prevention growth curves, we discovered that many children presented to their 24-month WCV before the chronological age of 24 months, when Centers for Disease Control and Prevention z scores are not available. We therefore used WHO BMI z scores, which are available from age 2 months onward.

Parent-reported measures included child race and ethnicity, Supplemental Nutrition Program for Women, Infants, and Children (WIC) status, annual household income, and caregiver age, health literacy, reading language, and height and weight. Parent health-literacy skills were assessed with the Short Test of Functional Health Literacy in Adults65,66  and the Pediatric Health Literacy Assessment Test.33 

We summarized baseline characteristics by site (UM, NYU, UNC, VUMC) and treatment arm (intervention, control), using percentiles for continuous variables and proportions for categorical variables. Participants were included in the analysis if they had at least 1 episode of follow-up outcome data at 1 of the scheduled clinic visits after randomization. Of the 865 patients randomized at baseline, 802 had at least 1 follow-up visit and remained eligible for analyses. Among those included, we had follow-up data from 78% of all possible WCVs.

We fit repeated measures, marginalized logistic regression models6769  for the binary outcome (BMI ≥85th percentile), and linear mixed models (random intercepts and slopes) for the continuous outcome (BMI z score) to examine site and intervention effects for children from 4 to 24 months of age. For site-specific comparisons, we chose the larger of the 2 control sites (UNC) as referent. To acknowledge clustering by site and to derive the intervention effects, we included in our regression models fixed effects for site, flexible functions of age at each WCV (restricted cubic splines with 2 degrees of freedom), site-by-age interactions, and potential confounders. The treatment-effect estimates were derived from the site-effect estimates. That is, we first estimated site effects over time, and using contrast matrices, we took the difference between the average trajectory in the intervention sites and the average trajectory in the control sites. We used linear contrasts of the site-by-age interactions to ascertain the intervention effect across all ages by subtracting the adjusted average trajectory of the 2 control sites from the adjusted average trajectory of the 2 intervention sites. To adjust for potential confounding due to the small number of sites, we controlled for the following prespecified covariates in each model: baseline child characteristics (sex, race, ethnicity, and the baseline value of the model outcome; ie, BMI ≥85th percentile or BMI z-score at 2-month WCV), baseline caregiver characteristics (age, primary language, annual household income, WIC status, health literacy, and BMI), and the interaction between child age and baseline value of the outcome. The test for treatment-arm effect was based on a Wald test derived from the linear contrasts by using a 2-sided, 0.05 significance level. All repeated measures analyses were clustered by child, rather than by physician, because response dependence was high within children and there was no evidence in the linear mixed model that adding a physician random effect improved model fit. To address missing follow-up data, we conducted multiple imputation with chained equations using predictive mean matching after transforming the data from long to wide format and fitting flexible imputation models.70  We combined estimates from the 25 imputations using Rubin’s rule.71  As a sensitivity analysis, we performed the same analyses, using “weight-for-length z score” as the outcome, which yielded similar results to the BMI z score analysis. In post hoc analysis, we also added the food-insecurity measure to the model, which yielded no change in the results. All analyses were conducted by using R version 3.3.0.67 

We recruited 865 parent–child dyads, 459 at intervention sites and 406 at control sites. Of the 802 in the final analysis, 540 (67%) completed a 24-month WCV. At baseline, children at intervention sites were more likely to be identified as Hispanic (56% vs 43%). Parents at intervention sites were more likely to report, at baseline, a lower age (average 26 vs 28 years), Spanish language as the primary language (39% vs 31%), less than a high school education (29% vs 23%), annual family income <$10 000 (32% vs 30%), and a lower mean BMI (27 vs 28) (See Table 1). At the 24-month WCV, the prevalence of child overweight (BMI ≥85th percentile) was 49% (49% for intervention, 41% for control), and median BMI z score was 1.05 (1.01 for intervention group, 0.78 for control).

TABLE 1

Child and Caregiver (Parent) Characteristics at Baseline

nBy Study SiteBy Intervention
VUMC, n = 230NYU, n = 229UNC, n = 256UM, n = 150Intervention, n = 459Control, n = 406
Child        
 Age, d 864 62 (48 to 78)a 63 (52 to 80) 68 (56 to 86) 62 (45 to 80) 62 (50 to 79) 65 (50 to 85)* 
 Female 865 120 (52) 124 (54) 126 (49) 73 (49) 244 (53) 199 (49) 
 Race and ethnicity        
  Hispanic 430 78 (34) 180 (79) 97 (38) 75 (51) 258 (56) 172 (43)* 
  White, non-Hispanic 155 71 (31) 15 (7) 62 (24) 7 (5) 86 (19) 69 (17) 
  Black, non-Hispanic 240 76 (33) 22 (10) 87 (34) 55 (37) 98 (21) 142 (35) 
  Other, non-Hispanic 37 5 (2) 12 (5) 9 (4) 11 (7) 17 (4) 20 (5) 
 Weight, kg 865 5.3 (4.4 to 6.3) 5.4 (4.6 to 6.3) 5.3 (4.4 to 6.3) 5.2 (4.3 to 6.2) 5.3 (4.5 to 6.3) 5.3 (4.4 to 6.3) 
 Length, cm 864 57.5 (54.5 to 61.0) 57.5 (54.7 to 60.5) 57.0 (53.8 to 60.0) 57.0 (53.3 to 61.0) 57.5 (54.5 to 61.0) 57.0 (53.5 to 60.5)* 
 WHO BMI z score 864 −0.05 (−1.29 to 1.27) 0.06 (−1.03 to 1.40) −0.01 (−1.05 to 1.26) 0.09 (−1.32 to 1.41) 0.02 (−1.16 to 1.38) 0.04 (−1.17 to 1.38) 
 WHO BMI ≥85th percentile 864 47 (21) 61 (27) 75 (29) 42 (28) 108 (24) 117 (29) 
Caregiver (parent)        
 Age, y 858 25 (20 to 33) 28 (22 to 37) 28 (21 to 37) 28 (20 to 37) 26 (20 to 35) 28 (21 to 37)* 
 Female  226 (98) 223 (97) 241 (94) 138 (92) 449 (98) 379 (93) 
 Primary language        
  English 859 175 (76) 106 (46) 172 (68) 104 (71) 281 (61) 276 (69)* 
  Spanish — 55 (24) 123 (54) 82 (32) 42 (29) 178 (39) 124 (31) 
 BMI, kg/m2 764 27 (22 to 36) 26 (22 to 35) 29 (22 to 38) 28 (21 to 37) 27 (22 to 36) 28 (22 to 38)* 
 Education 865       
  Less than HS  51 (22) 82 (36) 65 (25) 27 (18) 133 (29) 92 (23)* 
  HS or some college  153 (67) 104 (45) 144 (56) 78 (52) 257 (56) 222 (55) 
  College  23 (10) 43 (19) 45 (18) 42 (28) 66 (14) 87 (21) 
 Annual household income 852       
  <$10 000  56 (25) 90 (39) 58 (23) 60 (42) 146 (32) 118 (30)* 
  $10 000–19 999  77 (34) 56 (24) 70 (28) 25 (18) 133 (29) 95 (24) 
  $20 000–39 999  61 (27) 57 (25) 59 (23) 25 (18) 118 (26) 84 (21) 
  $40 000–59 999  17 (7) 20 (9) 23 (9) 10 (7) 37 (8) 33 (8) 
  $60 000 or more  14 (6) 5 (2) 25 (10) 18 (13) 19 (4) 43 (11) 
 Enrolled in WIC program, both mother and child 865 200 (87) 212 (93) 194 (76) 123 (82) 412 (90) 317 (78)* 
 Health literacy 855 35 34 35 34 34 35 
  S-TOFHLA score (0–36)  (31–36) (16–36) (29–36) (16–36) (21–36) (20–36) 
nBy Study SiteBy Intervention
VUMC, n = 230NYU, n = 229UNC, n = 256UM, n = 150Intervention, n = 459Control, n = 406
Child        
 Age, d 864 62 (48 to 78)a 63 (52 to 80) 68 (56 to 86) 62 (45 to 80) 62 (50 to 79) 65 (50 to 85)* 
 Female 865 120 (52) 124 (54) 126 (49) 73 (49) 244 (53) 199 (49) 
 Race and ethnicity        
  Hispanic 430 78 (34) 180 (79) 97 (38) 75 (51) 258 (56) 172 (43)* 
  White, non-Hispanic 155 71 (31) 15 (7) 62 (24) 7 (5) 86 (19) 69 (17) 
  Black, non-Hispanic 240 76 (33) 22 (10) 87 (34) 55 (37) 98 (21) 142 (35) 
  Other, non-Hispanic 37 5 (2) 12 (5) 9 (4) 11 (7) 17 (4) 20 (5) 
 Weight, kg 865 5.3 (4.4 to 6.3) 5.4 (4.6 to 6.3) 5.3 (4.4 to 6.3) 5.2 (4.3 to 6.2) 5.3 (4.5 to 6.3) 5.3 (4.4 to 6.3) 
 Length, cm 864 57.5 (54.5 to 61.0) 57.5 (54.7 to 60.5) 57.0 (53.8 to 60.0) 57.0 (53.3 to 61.0) 57.5 (54.5 to 61.0) 57.0 (53.5 to 60.5)* 
 WHO BMI z score 864 −0.05 (−1.29 to 1.27) 0.06 (−1.03 to 1.40) −0.01 (−1.05 to 1.26) 0.09 (−1.32 to 1.41) 0.02 (−1.16 to 1.38) 0.04 (−1.17 to 1.38) 
 WHO BMI ≥85th percentile 864 47 (21) 61 (27) 75 (29) 42 (28) 108 (24) 117 (29) 
Caregiver (parent)        
 Age, y 858 25 (20 to 33) 28 (22 to 37) 28 (21 to 37) 28 (20 to 37) 26 (20 to 35) 28 (21 to 37)* 
 Female  226 (98) 223 (97) 241 (94) 138 (92) 449 (98) 379 (93) 
 Primary language        
  English 859 175 (76) 106 (46) 172 (68) 104 (71) 281 (61) 276 (69)* 
  Spanish — 55 (24) 123 (54) 82 (32) 42 (29) 178 (39) 124 (31) 
 BMI, kg/m2 764 27 (22 to 36) 26 (22 to 35) 29 (22 to 38) 28 (21 to 37) 27 (22 to 36) 28 (22 to 38)* 
 Education 865       
  Less than HS  51 (22) 82 (36) 65 (25) 27 (18) 133 (29) 92 (23)* 
  HS or some college  153 (67) 104 (45) 144 (56) 78 (52) 257 (56) 222 (55) 
  College  23 (10) 43 (19) 45 (18) 42 (28) 66 (14) 87 (21) 
 Annual household income 852       
  <$10 000  56 (25) 90 (39) 58 (23) 60 (42) 146 (32) 118 (30)* 
  $10 000–19 999  77 (34) 56 (24) 70 (28) 25 (18) 133 (29) 95 (24) 
  $20 000–39 999  61 (27) 57 (25) 59 (23) 25 (18) 118 (26) 84 (21) 
  $40 000–59 999  17 (7) 20 (9) 23 (9) 10 (7) 37 (8) 33 (8) 
  $60 000 or more  14 (6) 5 (2) 25 (10) 18 (13) 19 (4) 43 (11) 
 Enrolled in WIC program, both mother and child 865 200 (87) 212 (93) 194 (76) 123 (82) 412 (90) 317 (78)* 
 Health literacy 855 35 34 35 34 34 35 
  S-TOFHLA score (0–36)  (31–36) (16–36) (29–36) (16–36) (21–36) (20–36) 

P values were calculated by using the Pearson χ2 test for categorical variables, the Kruskal Wallis test for continuous variables among sites, and the Wilcoxon rank test for continuous variables between treatment arms. HS, high school; S-TOFHLA, Short Test of Functional Health Literacy in Adults.

a

Values are n (%) for categorical variables and median (10th to 90th percentiles) for continuous variables.

*

P < .05

For the primary outcome, we found no significant difference between the intervention and control groups in the proportion of children overweight at the 24-month WCV, with adjusted odds (intervention versus control) of 1.02 (95% confidence interval [CI]: 0.63 to 1.64). Overall, there was a nonsignificant trend toward lower odds of overweight in the intervention group, compared with the control group, after adjusting for baseline covariates (P = .057) (Fig 1, top left panel.).

FIGURE 1

Overweight status (BMI >85th percentile) and BMI z score at each age, intervention versus control. Intervention effect estimates, from longitudinal regression analyses of overweight status (BMI ≥85th percentile) and BMI z score, between 4 and 24 months. All analyses are adjusted for baseline covariates as described in the Analyses section. The panels on left reveal the differences between the 2 intervention sites and the 2 control sites. The panels on the right reveal the site-specific differences in growth trajectories, with the larger of the 2 control sites (UNC) as referent. We capture site and intervention effects, with adjusted odds ratios for the binary outcome (overweight status) and adjusted mean differences for the continuous outcome (BMI z score). We display age-specific point estimates and CIs. The P values in the lower right corner of each plot correspond to the null hypothesis that growth trajectories are equal overall (across ages). To the extent that trajectories are not equal, P values are small. Two sites (NYU, VUMC) were randomly assigned to receive the intervention, and 2 sites (UM, UNC) were assigned to receive the active control.

FIGURE 1

Overweight status (BMI >85th percentile) and BMI z score at each age, intervention versus control. Intervention effect estimates, from longitudinal regression analyses of overweight status (BMI ≥85th percentile) and BMI z score, between 4 and 24 months. All analyses are adjusted for baseline covariates as described in the Analyses section. The panels on left reveal the differences between the 2 intervention sites and the 2 control sites. The panels on the right reveal the site-specific differences in growth trajectories, with the larger of the 2 control sites (UNC) as referent. We capture site and intervention effects, with adjusted odds ratios for the binary outcome (overweight status) and adjusted mean differences for the continuous outcome (BMI z score). We display age-specific point estimates and CIs. The P values in the lower right corner of each plot correspond to the null hypothesis that growth trajectories are equal overall (across ages). To the extent that trajectories are not equal, P values are small. Two sites (NYU, VUMC) were randomly assigned to receive the intervention, and 2 sites (UM, UNC) were assigned to receive the active control.

Close modal

For the secondary outcome, we found a lower BMI z score in the intervention group compared with the control group through 15 months, marginally lower at 18 months, and no effect at 24 months. Adjusted mean differences (intervention minus control) were −0.04 (95% CI: −0.07 to −0.01), −0.09 (95% CI: −0.14 to −0.03), −0.19 (−0.33 to −0.05), −0.20 (−0.36 to −0.03), −0.16 (95% CI: −0.34 to 0.01), and 0.00 (95% CI −0.21 to 0.21) at 4, 6, 12, 15, 18, and 24 months, respectively. Overall, intervention-site growth trajectories differed from control-site trajectories (P = .002) (Fig 1, bottom left panel.).

To provide deeper insight into the observed intervention effect, we examined child growth trajectories at the site level, with the larger of the 2 control sites (UNC) as referent (Fig 1, bottom right). Overall, children at NYU, VUMC, and UM tended to have lower adjusted BMI z scores when compared with UNC. At 1 intervention site (VUMC), the weight trend decreased over time, with adjusted mean differences of −0.10 (95% CI −0.19 to −0.04) at 6 months, −0.26 (95% CI −0.44 to −0.09) at 12 months, −0.34 (95% CI −0.57 to −0.12) at 18 months, and −0.34 (95% CI −0.60 to −0.07) at 24 months. At the other intervention site (NYU), the weight trend exhibited a “U-shaped” trajectory over time, with adjusted mean differences of −0.16 (95% CI −0.23 to −0.08) at 6 months, −0.35 (95% CI −0.53 to −0.18) at 12 months, −0.34 (95% CI −0.56 to −0.12) at 18 months, and −0.11 (95% CI −0.36 to +0.16) at 24 months. Interestingly, the other control site (UM) exhibited a trend similar to the VUMC site. As a sensitivity analysis, we used “weight-for-length z score” as the outcome and found similar results.

The Greenlight intervention had no impact on the primary outcome, the prevalence of overweight at age 24 months. It did, however, result in differences in earlier childhood weight trajectories that may be clinically meaningful and may inform future interventions. In this study, we introduce new evidence about the effectiveness of clinic-based counseling and literacy-sensitive toolkits during infancy to prevent overweight and obesity.48,72  In addition to addressing an important early childhood precursor of adult health, the Greenlight intervention also provides a model for a health-literacy-informed approach to health behavior change as part of routine primary care.

The Greenlight intervention resulted in less weight gain in the first 18 months of life, which was no longer present by age 24 months. At 12 months, mean BMI z score difference between the intervention and control groups was clinically significant (−0.19), which represents an approximate mean difference of 0.33 kg/m2. One explanation is that the intervention “dose” diminished after 12 months, because of the decreased frequency of WCVs, from 5 during the first year to 2 during the second year. Also, the intervention’s onset at the 2-month WCV may have been “too late,” because many parent behaviors associated with child obesity prevention (eg, breastfeeding, recognizing satiety cues, sleep hygiene, screen time) may be established before the 2-month WCV.73  Other factors may have moderated intervention effect, such as the overall prevalence of child overweight at 24 months (49%), which is higher than most national comparisons. Finally, the Greenlight intervention may have failed to influence some caregiver behaviors,38,4251  particularly given limited provider time at each WCV, or failed to target other potentially important behaviors, such as infant sleep.51 

These findings are subject to limitations that are common to large, pragmatic clinical trials. First, although we randomized by site and adjusted for individual and site characteristics, the observed treatment effects may have been due to site-level confounding that we could not control for in regression analyses. For example, 1 intervention site (VUMC) is located in Tennessee, one of few US states to see an increase in childhood obesity rates during the study period.74  Second, despite adjustment for missing data, a somewhat higher attrition rate in the control sites may limit interpretation of treatment effect.71  Third, the sample included predominantly low-income families, limiting generalizability to higher-income populations. Finally, the intervention period (2–24 months) limits our ability to judge the intervention’s sustained effect into the preschool years, when adiposity rebound occurs and when the effects of obesity prevention may be more clinically meaningful.10  To ascertain this long-term impact, we continued to follow study participants through age 5 years.

Still, the Greenlight Intervention Study provides unique insights for child obesity prevention and health promotion, particularly for low-income communities. These findings, along with those from other clinical trials,7577  support the potential positive impact of primary care interventions to prevent rapid early childhood weight gain. In addition to the reported findings on infant growth trajectories, the Greenlight Intervention Study has provided important information on the early childhood growth trajectories, as well as health behaviors, in low-income and historically underrepresented minority communities.48,72  Conducted across 4 different geographic regions, the study enrolled an often underrepresented group: low-income families, with a variety of English-proficiency and literacy skills. Observational studies of this cohort have provided context for the role that family factors (eg, ethnicity, language, acculturation, locus of control, food insecurity) may play in the early adoption of behaviors that may protect infants from rapid weight gain.73,78,79  Because we found that the overweight prevalence in both groups increased from 12 to 24 months, additional modalities, including asynchronous behavioral supports, may be necessary to achieve public health effectiveness. In ongoing and future analyses, we plan to apply predictive modeling, to explore which behaviors are associated with healthy child weight, and to examine family characteristics that may moderate that relationship.

The Greenlight intervention had no impact on the prevalence of overweight at age 24 months, but it did result in less weight gain during the first 18 months of life. Clinic-based interventions may be beneficial for early weight gain, but greater intervention intensity may be needed to maintain positive weight benefits over time. Pediatric primary care may be an important setting for interventions to prevent childhood overweight, especially given the importance of early rapid weight gain for later child obesity and health risks.2,9,8083  Interventions like Greenlight should be designed to fit real-world time and staffing constraints of pediatric primary care, as well as the social determinants of child health, including food insecurity. With the findings from the Greenlight study, we suggest the need for further research, to inform and improve obesity prevention and health promotion for young children.

We thank the Greenlight Study Team: Duke University: Asheley Skinner, for intellectual work related to study design, implementation, and analysis; Joanne Finkle, for intellectual contributions related to study implementation and measurement; NYU School of Medicine/Bellevue Hospital Center: Alan Mendelsohn, for intellectual work related to study design and implementation; Benard Dreyer, for initial intellectual work related to study design and implementation and toolkit design; Linda van Schaick, for intellectual work related to toolkit design; Mary Jo Messito, for intellectual work related to implementation and and toolkit design; UM/Jackson Memorial Medical Center: Anna Maria Patino-Fernandez, PhD, for intellectual contributions to study design, measures, and implementation; UNC: Tamera Coyne-Beasley (now at University of Alabama), for intellectual work related to study design and implementation; Michael Steiner, for intellectual work related to study design and implementation; Sophie Ravanbakht, for intellectual contributions related to study implementation and measurements; Vanderbilt University: Shari Barkin, for initial intellectual work related to study design and implementation; Sunil Kripilani, for initial intellectual work related to study design and implementation; Andrea Bronaugh, for intellectual contributions related to study implementation and measurements. The development and implementation of the Greenlight Intervention Study were made possible by the following: NYU School of Medicine/Bellevue Hospital Center: Cynthia Osman, Steve Paik (now at Columbia University), Maria Cerra, Evelyn Cruzatte, Dana Kaplan (now at Zucker School of Medicine at Hofstra/Northwell), Omar Baker (now at Riverside Medical Group; Columbia University), Maureen Egan (now at Children’s Hospital Colorado), and Leena Shiwbaren (now at Flatiron Pediatrics); Stanford University: Thomas Robinson, Mairead Mahoney, Kasiemobi Udo-okoye, Pablo Uribe, Nathan Shaw, Chinyelu Nwobu, Catherine Clark, Lauren Wegner, and Michelle Smith; Duke University: Charles Wood and Janna Howard; UNC: Brenda Calderon, Elizabeth Throop (no longer at UNC Chapel Hill), Margaret Kihlstrom, Christina Anderson, Carol Runyan (now at University of Colorado, Denver), and Mariana Garrettson (now no longer at UNC Chapel Hill); UM/Jackson Memorial Medical Center: Lourdes Forster, Randi Sperling, Stephanie White (now at Dartmouth University), Lucila Bloise, Adriana Guzman, and Daniela Quesada; Vanderbilt University: Ayumi Shintani, Sventlana Eden, Marina Margolin, Alexandra Arriaga, Sujata Ayala, Barron Paterson, and Seth Scholer. We also acknowledge support from American Academy of Pediatrics for use of The Injury Prevention Program materials; Greenlight Study Development Team: Duke University: Asheley Skinner, for intellectual work related to study design, implementation, and analysis; Joanne Finkle, for intellectual contributions related to study implementation and measurement; NYU School of Medicine/Bellevue Hospital Center: Alan Mendelsohn, for intellectual work related to study design and implementation; Benard Dreyer, for initial intellectual work related to study design and implementation and toolkit design; Linda van Schaick, for intellectual work related to toolkit design; Mary Jo Messito, for intellectual work related to implementation and toolkit design; UM/Jackson Memorial Medical Center: Anna Maria Patino-Fernandez, PhD, for intellectual contributions to study design, measures, and implementation; UNC: Tamera Coyne-Beasley (now at University of Alabama), for intellectual work related to study design and implementation; Michael Steiner, for intellectual work related to study design and implementation; Sophie Ravanbakht, for intellectual contributions related to study implementation and measurements; Vanderbilt University: Shari Barkin, for initial intellectual work related to study design and implementation; Sunil Kripilani, for initial intellectual work related to study design and implementation; Andrea Bronaugh, for intellectual contributions related to study implementation and measurements.

We will make data available to the scientific community with as few restrictions as feasible, while retaining exclusive use until the publication of major outputs. Data are maintained at Vanderbilt University Medical Center and housed on Research Electronic Data Capture. This includes individual participant data that underlie the results reported in this article. Requests for data, study protocol, analysis plan, and/or analytic code should include a methodologically sound proposal that does not preclude ongoing or planned analyses by the Greenlight Study Team. Research data will be shared in a deidentified data set to protect subject privacy. To gain access, data requesters will need to sign a data-access agreement. Information about Greenlight and the Greenlight toolkit are available through a public Web site: https://www.greenlight-program.org/. Data-sharing requests should be directed to Russell.rothman@vumc.org.

The Greenlight study is registered at clinicaltrials.gov (identifier NCT01040897).

Drs Sanders, Perrin, Yin, and Rothman conceptualized and designed the study and measures, supervised data collection and entry at the University of Miami, University of North Carolina at Chapel Hill, New York University, and Vanderbilt sites, respectively, contributed to data analysis, and drafted; Drs Delamater and Flower supervised data collection and entry at the University of Miami and University of North Carolina at Chapel Hill sites, respectively, helped draft parts of the original manuscript and contributed to data analysis; Mrs Bian co-led the final analytic plan, completed the analytic models, and helped draft parts of the original manuscript; Dr Schildcrout led the final analytic plan, completed the analytic models, and helped draft parts of the original manuscript; and all authors reviewed and revised the original manuscript, approved the final manuscript as submitted, and agree to be accountable for all aspects of the work.

FUNDING: Supported by the Eunice Kennedy Shriver Institute for Child Health and Development, National Institute of Child Health and Human Development (grant R01 HD049794), with supplemental funding from Centers for Disease Control and Prevention and Office of Behavioral and Social Sciences Research (grant R01HD059794-04S1, R01HD059794-04S2). Parts of the study were supported the National Institutes of Health’s National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program (grants 1UL1RR029893, UL1TR000445, and UL1RR025747). During the time the study was conducted, Dr Yin was supported by a grant under the Robert Wood Johnson Foundation Physician Faculty Scholars Program and Health Resources and Services Administration (12-191-1077-Academic Administrative Units in Primary Care) and by funding from the KiDS of NYU Langone Foundation. During the beginning of the study, Dr Perrin was supported by a training grant from National Institute of Child Health and Human Development (grant K23 HD051817). Funded by the National Institutes of Health (NIH)

1
Skinner
AC
,
Ravanbakht
SN
,
Skelton
JA
,
Perrin
EM
,
Armstrong
SC
.
Prevalence of obesity and severe obesity in US children, 1999-2016 [published correction appears in Pediatrics. 2018;142(3):e20181916]
.
Pediatrics
.
2018
;
141
(
3
):
e20173459
2
Cunningham
SA
,
Kramer
MR
,
Narayan
KM
.
Incidence of childhood obesity in the United States
.
N Engl J Med
.
2014
;
370
(
17
):
1660
1661
3
Hales
CM
,
Carroll
MD
,
Fryar
CD
,
Ogden
CL
.
Prevalence of obesity among adults and youth: United States, 2015-2016
.
NCHS Data Brief
.
2017
;(
288
):
1
8
4
Ogden
CL
,
Fryar
CD
,
Martin
CB
, et al
.
Trends in obesity prevalence by race and hispanic origin-1999-2000 to 2017-2018
.
JAMA
.
2020
;
324
(
12
):
1208
1210
5
Nader
PR
,
O’Brien
M
,
Houts
R
, et al
;
National Institute of Child Health and Human Development Early Child Care Research Network
.
Identifying risk for obesity in early childhood
.
Pediatrics
.
2006
;
118
(
3
).
6
Dennison
BA
,
Edmunds
LS
,
Stratton
HH
,
Pruzek
RM
.
Rapid infant weight gain predicts childhood overweight
.
Obesity (Silver Spring)
.
2006
;
14
(
3
):
491
499
7
Taveras
EM
,
Rifas-Shiman
SL
,
Belfort
MB
,
Kleinman
KP
,
Oken
E
,
Gillman
MW
.
Weight status in the first 6 months of life and obesity at 3 years of age
.
Pediatrics
.
2009
;
123
(
4
):
1177
1183
8
Leunissen
RW
,
Kerkhof
GF
,
Stijnen
T
,
Hokken-Koelega
A
.
Timing and tempo of first-year rapid growth in relation to cardiovascular and metabolic risk profile in early adulthood
.
JAMA
.
2009
;
301
(
21
):
2234
2242
9
Singh
AS
,
Mulder
C
,
Twisk
JWR
,
van Mechelen
W
,
Chinapaw
MJM
.
Tracking of childhood overweight into adulthood: a systematic review of the literature
.
Obes Rev
.
2008
;
9
(
5
):
474
488
10
Geserick
M
,
Vogel
M
,
Gausche
R
, et al
.
Acceleration of BMI in early childhood and risk of sustained obesity
.
N Engl J Med
.
2018
;
379
(
14
):
1303
1312
11
Guerrero
AD
,
Mao
C
,
Fuller
B
,
Bridges
M
,
Franke
T
,
Kuo
AA
.
Racial and ethnic disparities in early childhood obesity: growth trajectories in body mass index
.
J Racial Ethn Health Disparities
.
2016
;
3
(
1
):
129
137
12
Polk
S
,
Thornton
RJ
,
Caulfield
L
,
Muñoz
A
.
Rapid infant weight gain and early childhood obesity in low-income Latinos and non-Latinos
.
Public Health Nutr
.
2016
;
19
(
10
):
1777
1784
13
Taveras
EM
,
Gillman
MW
,
Kleinman
K
,
Rich-Edwards
JW
,
Rifas-Shiman
SL
.
Racial/ethnic differences in early-life risk factors for childhood obesity
.
Pediatrics
.
2010
;
125
(
4
):
686
695
14
Taveras
EM
,
Gillman
MW
,
Kleinman
KP
,
Rich-Edwards
JW
,
Rifas-Shiman
SL
.
Reducing racial/ethnic disparities in childhood obesity: the role of early life risk factors
.
JAMA Pediatr
.
2013
;
167
(
8
):
731
738
15
Kutner
M
,
Greenberg
E
,
Jin
Y
,
Paulsen
C
;
US Department of Education
.
Literacy in Everyday Life Results From the 2003 National Assessment of Adult Literacy
. NCES 2007-480.
Washington, DC
:
National Center for Education Statistics
;
2007
16
Nielsen-Bohlman
L
,
Panzer
AM
,
Kindig
DA
, eds..
Health Literacy: A Prescription to End Confusion
.
Washington, DC
:
National Academies Press
;
2004
17
Dewalt
DA
,
Berkman
ND
,
Sheridan
S
,
Lohr
KN
,
Pignone
MP
.
Literacy and health outcomes: a systematic review of the literature
.
J Gen Intern Med
.
2004
;
19
(
12
):
1228
1239
18
Health literacy: report of the Council on Scientific Affairs. Ad Hoc Committee on Health Literacy for the Council on Scientific Affairs, American Medical Association
.
JAMA
.
1999
;
281
(
6
):
552
557
19
Arnold
CL
,
Davis
TC
,
Frempong
JO
, et al
.
Assessment of newborn screening parent education materials
.
Pediatrics
.
2006
;
117
(
5 pt 2
):
S320
S325
20
Baker
DW
,
Parker
RM
,
Williams
MV
, et al
.
The health care experience of patients with low literacy
.
Arch Fam Med
.
1996
;
5
(
6
):
329
334
21
Berkman
ND
,
Dewalt
DA
,
Pignone
MP
, et al
.
Literacy and health outcomes
.
Evid Rep Technol Assess (Summ)
.
2004
;(
87
):
1
8
22
Davis
TC
,
Bocchini
JA
 Jr.
,
Fredrickson
D
, et al
.
Parent comprehension of polio vaccine information pamphlets
.
Pediatrics
.
1996
;
97
(
6, pt 1
):
804
810
23
Davis
TC
,
Mayeaux
EJ
,
Fredrickson
D
,
Bocchini
JA
 Jr.
,
Jackson
RH
,
Murphy
PW
.
Reading ability of parents compared with reading level of pediatric patient education materials
.
Pediatrics
.
1994
;
93
(
3
):
460
468
24
Mayeaux
EJ
 Jr.
,
Murphy
PW
,
Arnold
C
,
Davis
TC
,
Jackson
RH
,
Sentell
T
.
Improving patient education for patients with low literacy skills
.
Am Fam Physician
.
1996
;
53
(
1
):
205
211
25
Montori
VM
,
Rothman
RL
.
Weakness in numbers. The challenge of numeracy in health care
.
J Gen Intern Med
.
2005
;
20
(
11
):
1071
1072
26
Pignone
M
,
DeWalt
DA
,
Sheridan
S
,
Berkman
N
,
Lohr
KN
.
Interventions to improve health outcomes for patients with low literacy. A systematic review
.
J Gen Intern Med
.
2005
;
20
(
2
):
185
192
27
Rothman
R
,
Malone
R
,
Bryant
B
,
Horlen
C
,
DeWalt
D
,
Pignone
M
.
The relationship between literacy and glycemic control in a diabetes disease-management program
.
Diabetes Educ
.
2004
;
30
(
2
):
263
273
28
Rothman
RL
,
DeWalt
DA
,
Malone
R
, et al
.
Influence of patient literacy on the effectiveness of a primary care-based diabetes disease management program
.
JAMA
.
2004
;
292
(
14
):
1711
1716
29
Rothman
RL
,
Malone
R
,
Bryant
B
, et al
.
The Spoken Knowledge in Low Literacy in Diabetes scale: a diabetes knowledge scale for vulnerable patients
.
Diabetes Educ
.
2005
;
31
(
2
):
215
224
30
Schillinger
D
,
Piette
J
,
Grumbach
K
, et al
.
Closing the loop: physician communication with diabetic patients who have low health literacy
.
Arch Intern Med
.
2003
;
163
(
1
):
83
90
31
Huizinga
MM
,
Beech
BM
,
Cavanaugh
KL
,
Elasy
TA
,
Rothman
RL
.
Low numeracy skills are associated with higher BMI
.
Obesity (Silver Spring)
.
2008
;
16
(
8
):
1966
1968
32
Huizinga
MM
,
Carlisle
AJ
,
Cavanaugh
KL
, et al
.
Literacy, numeracy, and portion-size estimation skills
.
Am J Prev Med
.
2009
;
36
(
4
):
324
328
33
Kumar
D
,
Sanders
L
,
Perrin
EM
, et al
.
Parental understanding of infant health information: health literacy, numeracy, and the Parental Health Literacy Activities Test (PHLAT)
.
Acad Pediatr
.
2010
;
10
(
5
):
309
316
34
Houts
PS
,
Doak
CC
,
Doak
LG
,
Loscalzo
MJ
.
The role of pictures in improving health communication: a review of research on attention, comprehension, recall, and adherence
.
Patient Educ Couns
.
2006
;
61
(
2
):
173
190
35
Rothman
RL
,
Housam
R
,
Weiss
H
, et al
.
Patient understanding of food labels: the role of literacy and numeracy
.
Am J Prev Med
.
2006
;
31
(
5
):
391
398
36
Oettinger
MD
,
Finkle
JP
,
Esserman
D
, et al
.
Color-coding improves parental understanding of body mass index charting
.
Acad Pediatr
.
2009
;
9
(
5
):
330
338
37
Yin
HS
,
Dreyer
BP
,
Vivar
KL
,
MacFarland
S
,
van Schaick
L
,
Mendelsohn
AL
.
Perceived barriers to care and attitudes towards shared decision-making among low socioeconomic status parents: role of health literacy
.
Acad Pediatr
.
2012
;
12
(
2
):
117
124
38
Yin
HS
,
Sanders
LM
,
Rothman
RL
, et al
.
Parent health literacy and “obesogenic” feeding and physical activity-related infant care behaviors
.
J Pediatr
.
2014
;
164
(
3
):
577
583.e1
39
Carmona
RH
.
Improving Americans’ health literacy
.
J Am Diet Assoc
.
2005
;
105
(
9
):
1345
40
Carmona
RH
.
Health literacy in America: the role of health care professionals.
In:
American Medical Association House of Delegates Meeting
;
Chicago, Illinois
: June 14,
2003
41
Golan
M
.
Parents as agents of change in childhood obesity–from research to practice
.
Int J Pediatr Obes
.
2006
;
1
(
2
):
66
76
42
American Dietetic Association (ADA)
.
Position of the American Dietetic Association: individual-, family-, school-, and community-based interventions for pediatric overweight
.
J Am Diet Assoc
.
2006
;
106
(
6
):
925
945
43
Bluford
DA
,
Sherry
B
,
Scanlon
KS
.
Interventions to prevent or treat obesity in preschool children: a review of evaluated programs
.
Obesity (Silver Spring)
.
2007
;
15
(
6
):
1356
1372
44
Summerbell
CD
.
The identification of effective programs to prevent and treat overweight preschool children
.
Obesity (Silver Spring)
.
2007
;
15
(
6
):
1341
1342
45
Wen
LM
,
Baur
LA
,
Simpson
JM
, et al
.
Sustainability of effects of an early childhood obesity prevention trial over time: a further 3-year follow-up of the Healthy Beginnings Trial
.
JAMA Pediatr
.
2015
;
169
(
6
):
543
551
46
Lumeng
JC
,
Taveras
EM
,
Birch
L
,
Yanovski
SZ
.
Prevention of obesity in infancy and early childhood: a National Institutes of Health workshop
.
JAMA Pediatr
.
2015
;
169
(
5
):
484
490
47
Daniels
LA
,
Mallan
KM
,
Nicholson
JM
, et al
.
An early feeding practices intervention for obesity prevention
.
Pediatrics
.
2015
;
136
(
1
).
48
Woo Baidal
JA
,
Nelson
CC
,
Perkins
M
, et al
.
Childhood obesity prevention in the women, infants, and children program: outcomes of the MA-CORD study
.
Obesity (Silver Spring)
.
2017
;
25
(
7
):
1167
1174
49
Gross
RS
,
Mendelsohn
AL
,
Yin
HS
, et al
.
Randomized controlled trial of an early child obesity prevention intervention: impacts on infant tummy time
.
Obesity (Silver Spring)
.
2017
;
25
(
5
):
920
927
50
Barkin
SL
,
Heerman
WJ
,
Sommer
EC
, et al
.
Effect of a behavioral intervention for underserved preschool-age children on change in body mass index: a randomized clinical trial
.
JAMA
.
2018
;
320
(
5
):
450
460
51
Paul
IM
,
Savage
JS
,
Anzman-Frasca
S
, et al
.
Effect of a responsive parenting educational intervention on childhood weight outcomes at 3 years of age: the INSIGHT randomized clinical trial
.
JAMA
.
2018
;
320
(
5
):
461
468
52
Sanders
LM
,
Perrin
EM
,
Yin
HS
,
Bronaugh
A
,
Rothman
RL
;
Greenlight Study Team
.
“Greenlight study”: a controlled trial of low-literacy, early childhood obesity prevention
.
Pediatrics
.
2014
;
133
(
6
).
53
Krugman
SD
,
Racine
A
,
Dabrow
S
, et al
;
Continuity Research Network
.
Measuring primary care of children in pediatric resident continuity practices: a Continuity Research Network study
.
Pediatrics
.
2007
;
120
(
2
).
54
Byrne
CD
,
Phillips
DI
.
Fetal origins of adult disease: epidemiology and mechanisms
.
J Clin Pathol
.
2000
;
53
(
11
):
822
828
55
Zhao
M
,
Shu
XO
,
Jin
F
, et al
.
Birthweight, childhood growth and hypertension in adulthood
.
Int J Epidemiol
.
2002
;
31
(
5
):
1043
1051
56
Barker
DJ
.
The developmental origins of adult disease
.
J Am Coll Nutr
.
2004
;
23
(
suppl 6
):
588S
595S
57
Bass
JL
.
TIPP–the first ten years [published correction appears in Pediatrics. 1995;95(4):545]
.
Pediatrics
.
1995
;
95
(
2
):
274
275
58
Gielen
AC
,
Wilson
ME
,
McDonald
EM
, et al
.
Randomized trial of enhanced anticipatory guidance for injury prevention
.
Arch Pediatr Adolesc Med
.
2001
;
155
(
1
):
42
49
59
Carraccio
C
,
Englander
R
.
The objective structured clinical examination: a step in the direction of competency-based evaluation
.
Arch Pediatr Adolesc Med
.
2000
;
154
(
7
):
736
741
60
Makoul
G
.
Essential elements of communication in medical encounters: the Kalamazoo consensus statement
.
Acad Med
.
2001
;
76
(
4
):
390
393
61
Duffy
FD
,
Gordon
GH
,
Whelan
G
, et al.;
Participants in the American Academy on Physician and Patient’s Conference on Education and Evaluation of Competence in Communication and Interpersonal Skills
.
Assessing competence in communication and interpersonal skills: the Kalamazoo II report
.
Acad Med
.
2004
;
79
(
6
):
495
507
62
Makoul
G
.
The SEGUE Framework for teaching and assessing communication skills
.
Patient Educ Couns
.
2001
;
45
(
1
):
23
34
63
WHO Multicentre Growth Reference Study Group
.
WHO Child Growth Standards based on length/height, weight and age
.
Acta Paediatr Suppl
.
2006
;
450
:
76
85
64
Center for Disease Control and Prevention
.
Accurately weighing and measuring infants, children and adolescents.
Available at: https://depts.washington.edu/growth/. Accessed March 23, 2021
65
Parker
RM
,
Baker
DW
,
Williams
MV
,
Nurss
JR
.
The test of functional health literacy in adults: a new instrument for measuring patients’ literacy skills
.
J Gen Intern Med
.
1995
;
10
(
10
):
537
541
66
Baker
DW
,
Williams
MV
,
Parker
RM
,
Gazmararian
JA
,
Nurss
J
.
Development of a brief test to measure functional health literacy
.
Patient Educ Couns
.
1999
;
38
(
1
):
33
42
67
Harrell
FE
.
rms: Regression Modeling Strategies. R package version 5.1-1. 2017. Available at: https://CRAN.R-project.org/package=rms. Accessed October 10, 2018
68
Schildcrout
JS
,
Heagerty
PJ
.
Marginalized models for moderate to long series of longitudinal binary response data
.
Biometrics
.
2007
;
63
(
2
):
322
331
69
Heagerty
PJ
.
Marginally specified logistic-normal models for longitudinal binary data
.
Biometrics
.
1999
;
55
(
3
):
688
698
70
Raghunathan
TW
,
Lepkowksi
JM
,
Van Hoewyk
J
,
Solenbeger
P
.
A multivariate technique for multiply imputing missing values using a sequence of regression models
.
Surv Methodol
.
2001
;
27
:
85
95
71
Little
RJA
,
Rubin
DB
.
Statistical Analysis with Missing Data
, 2nd ed.
Hoboken, NJ
:
Wiley
;
2002
72
Paul
IM
,
Savage
JS
,
Anzman
SL
, et al
.
Preventing obesity during infancy: a pilot study
.
Obesity (Silver Spring)
.
2011
;
19
(
2
):
353
361
73
Perrin
EM
,
Rothman
RL
,
Sanders
LM
, et al
.
Racial and ethnic differences associated with feeding- and activity-related behaviors in infants
.
Pediatrics
.
2014
;
133
(
4
).
74
Centers for Disease Control and Prevention (CDC)
.
Vital signs: obesity among low-income, preschool-aged children–United States, 2008–2011
.
MMWR Morb Mortal Wkly Rep
.
2013
;
62
(
31
):
629
634
75
Golley
RK
,
Magarey
AM
,
Baur
LA
,
Steinbeck
KS
,
Daniels
LA
.
Twelve-month effectiveness of a parent-led, family-focused weight-management program for prepubertal children: a randomized, controlled trial
.
Pediatrics
.
2007
;
119
(
3
):
517
525
76
Savoye
M
,
Shaw
M
,
Dziura
J
, et al
.
Effects of a weight management program on body composition and metabolic parameters in overweight children: a randomized controlled trial
.
JAMA
.
2007
;
297
(
24
):
2697
2704
77
Patrick
K
,
Calfas
KJ
,
Norman
GJ
, et al
.
Randomized controlled trial of a primary care and home-based intervention for physical activity and nutrition behaviors: PACE+ for adolescents
.
Arch Pediatr Adolesc Med
.
2006
;
160
(
2
):
128
136
78
Brown
CL
,
Skinner
AC
,
Yin
HS
, et al
.
Parental perceptions of weight during the first year of life
.
Acad Pediatr
.
2016
;
16
(
6
):
558
564
79
Orr
CJ
,
Ravanbakht
S
,
Flower
KB
.
Associations between food insecurity and parental feeding behaviors of toddlers
.
Acad Pediatr
.
2020
;
20
(
8
):
1163
1169
80
Feldman-Winter
L
,
Burnham
L
,
Grossman
X
,
Matlak
S
,
Chen
N
,
Merewood
A
.
Weight gain in the first week of life predicts overweight at 2 years: a prospective cohort study
.
Matern Child Nutr
.
2018
;
14
(
1
):
e12472
81
Sonnenschein-van der Voort
AM
,
Howe
LD
,
Granell
R
, et al
.
Influence of childhood growth on asthma and lung function in adolescence
.
J Allergy Clin Immunol
.
2015
;
135
(
6
):
1435
1443.e7
82
Howe
LD
,
Chaturvedi
N
,
Lawlor
DA
, et al
.
Rapid increases in infant adiposity and overweight/obesity in childhood are associated with higher central and brachial blood pressure in early adulthood
.
J Hypertens
.
2014
;
32
(
9
):
1789
1796
83
Skilton
MR
,
Marks
GB
,
Ayer
JG
, et al
.
Weight gain in infancy and vascular risk factors in later childhood
.
Pediatrics
.
2013
;
131
(
6
).

Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.