Effectiveness of a Family-Centered Pediatric Weight Management Program Integrated in Primary Care



 

Veronica Else, RN, NP, MSN1; Qiaoling Chen, MS2; Alan B Cortez, MD3; Corinna Koebnick, PhD2

Perm J 2021;25:20.101 [Full Citation]

https://doi.org/10.7812/TPP/20.101
E-pub: 01/13/2021

ABSTRACT

Introduction: The evidence supporting the effectiveness of weight loss interventions with low to medium intensity is limited.

Objective: To measure the effectiveness of a family-based weight management intervention in pediatric primary care to reduce body weight in children.

Methods: Electronic medical record data of pediatric patients in Kaiser Permanente Orange County, California, who were enrolled in weight management between April 2014 and December 2018 (family-based behavior-changing weight management [FB-WMG], n = 162) and compared with a control group (CG) of patients who were referred but did not enroll (Ref-CG, n = 203) and an area-matched CG also matched by sex, age, zip code, and body mass index (BMI) (Area-CG, n = 287). BMI was measured at the first visit (or index date) and after 6 months.

Results: Children enrolled in the FB-WMG had 5 (interquartile range = 3-6) sessions over the first 6 months of the program. Most FB-WMG patients (69.1%) reduced or maintained BMI over 6 months, compared with 45.8% of Ref-CG (p < 0.001) and 57.8% of Area-CG (p = 0.02). In girls 3 to 12 years of age, 75% of participants reduced or maintained BMI, compared with 42% of Ref-CG (p < 0.001) and 59.8% of Area-CG (p = 0.07). On average, the difference in BMI change over the 6-month follow-up period was −0.85 kg/m2 (95% confidence interval = −1.25 to −0.46 kg/m2) compared with Ref-CG and −0.28 kg/m2 (95% confidence interval = −0.63 to 0.08 kg/m2) and Area-CG.

Conclusion: Low- to moderate-intensity family-based weight management intervention in primary care can be successful after only 6 months compared with a referred control group.

INTRODUCTION

Approximately 18.5% of US youth are obese.1-3 In Southern California, 34% of children between the ages of 2 and 21 were overweight in 2013, and another 18% were obese.4 Lowering body mass index (BMI) and thus reducing the complications of obesity will decrease the burden of obesity-related sequelae and increase quality of life.5,6 Hence, prevention and treatment of obesity in children and adolescents are important public health issues.7,8

Despite clinical and policy efforts to address pediatric obesity, the prevalence remains high with little to no evidence of decline.9 There are few studies showing effective treatment options. The largest metaanalysis done on the subject by the US Preventive Services Task Force reported that high-intensity intervention of 26 hours was required to demonstrate reduced body weight.6,8 Barriers to this extensive treatment include pediatric clinics without the resources to train staff and offer counseling,10,11 unreliable reimbursement and budget restrictions,12 and limited skills of staff to engage and motivate families.13,14

There is evidence that early childhood intervention is most effective.15,16 Multidisciplinary and family-centered approaches show better results than single-component approaches and those providing directive advice, especially when involving nutrition specialists.15,17

The primary aim of the present study was to evaluate the effectiveness of a family-based weight management program for youth between the ages of 2 and 17 years in a pediatric primary care clinic. A secondary aim was to identify subgroups that responded best to intervention.

METHODS

Study Setting and Population

Kaiser Permanente Southern California (KPSC) is an integrated health care system that provided comprehensive health care for about 4.6 million residents of Southern California in 2019. Members receive medical care in 15 hospitals and more than 233 medical offices owned by KPSC in Southern California. Members enroll through their employer or the employer of a family member, individual prepaid plans, or state or federal programs such as Medicaid (Medi-Cal in California) and Medicare. In 2010, KPSC represented approximately 16% of the population in the coverage area.18 The demographic distribution of the KPSC membership largely reflects the distribution in of the underlying the census reference population.18 Clinical care information is captured using an electronic health record system. All administrative and clinical data are linked through a unique medical record number and include membership information, medical encounters, and other health care information. The study protocol was reviewed and approved by the KPSC Institutional Review Board. Informed consent was waived for this study.

Study Design

For this retrospective cohort study, we identified youth (n = 36,224) with obesity (defined as BMI-for-age ≥ 95th percentile)19 between the ages of 3 and 18 years seen at the Kaiser Permanente Medical Offices in Orange County, CA, between April 2014 and December 31, 2018 (Figure 1). Youth were eligible for the intervention if they were obese (BMI-for-age ≥ 95th percentile); did not have type 1 diabetes, insulin-dependent type 2 diabetes, chromosomal anomalies that predispose to obesity (eg, Prader Willi syndrome or Trisomy 21), an eating disorder (including bulimia or binge eating), or prior history of medications or surgery for the treatment of obesity; were without prolonged steroid use (> 6 months) for treatment of a chronic illness; and were not pregnant.

tpj20101f1

Figure 1. Study flowchart.

For the intervention group, we identified youth who were enrolled in a family-based weight management program using International Classification of Diseases, Ninth Revision, Clinical Modification Z71.3 and V65.3 and an internal descriptor of “weight management.” We excluded youth participating in fewer than 3 sessions (n = 521) because we hypothesized that at least 3 sessions were needed to elicit change in behavior.

As control group 1, we identified youth with obesity who were referred to the family-based weight management program but did not participate in any sessions (n = 203). As control group 2, we identified youth who met the criteria but were never referred and not seen for weight management (n = 287).

Family-Based Weight Management Intervention

The weight management program started in 2014 to provide higher-intensity care and better consistency of care. A family-centered approach focused on understanding, engaging, and partnering with families, including children, to enhance their capacities and identify their needs. Because the appointments were private as opposed to a group meeting or class, emotional support was more easily provided, and barriers were more quickly identified. As the core of the program, the provider used motivational interviewing (MI) and cognitive behavioral therapy approaches to optimize participant self-efficacy.20-22

To receive the family-based weight management intervention, youth were referred by their care provider. The program consisted of 30-minute counseling appointments provided by a pediatric nurse practitioner or pediatrician. The visit included the pediatric patient and usually 1 or more parent or caregiver. The parent or caregivers could vary from 1 appointment to another, but the patient would be present at each appointment for measurements and the discussion. Some adolescents were seen alone by choice. The first visit would include a history of food intake, including meals out, exercise, and other activities. An individualized meal plan would be devised to include healthy snack choices and an exercise plan. Handouts in a folder were provided, including Kaiser Permanente-approved literature. If needed, we sent letters to schools, babysitters, and other caretakers asking them to the support the family in their plan. The core of the intervention used the 5-4-3-2-1-GO! tool developed by the Consortium to Lower Obesity in Chicago Children.23-25 This plan implemented 5 fruits/vegetables daily, 4 glasses of water daily, 3 servings of low-fat dairy daily (alternative given for lactose-intolerant children or those who refuse dairy), 2 hours or less of screen time per day, 1 hour physical activity per day, and 0 sugared drinks. The healthy plate materials were adopted.26 These guidelines were reviewed at each visit. A 6-month weight loss goal was set based on growth charts, age, and other factors, but stabilization of BMI was considered as success.5,8 The standard for success in children and adolescents is different than in adults, in whom a substantial loss of weight would be required to show effectiveness. A child can gain weight and still decrease BMI if they gain less and are still growing.19

At follow-up visits, a written food diary was reviewed and recorded; and if a food diary was not available, the information was obtained by memory recall. Encouragement was given for positive changes, regardless of change in weight. Problem areas were identified if possible, and solutions were discussed. The number of appointments varied, but the patient needed at least 3 visits within 6 months to be included in the intervention group. Completion of the program was at the discretion of the patient and their family, but the average was 4 to 6 visits within a 1-year period.

Efforts to train all providers in MI techniques are ongoing and not fully implemented. Informal training has been provided to many but not all pediatric care providers, and they are currently not certified in MI with standardized quality controls and regular training. Care was consistently provided by the same providers. The fidelity of the intervention was not measured but was maintained by regular team meetings.

Study Groups and Study Outcome

The analytic cohort consists of 3 groups: 1) a group of children who received a family-based behavior-changing weight management intervention (FB-WMG), 2) a control group of children referred by their provider who did not participate in the intervention (Ref-CG), and 3) an area-matched control group (Area-CG) also matched for sex, race, BMI, zip code, and time of first visit (allowing ± 6 months around the window of their match). We used a 2-round matching approach. In the first round, we matched 214 out of 227 children using index date (± 183 days), age (± 1 year), BMI (± 1 percentile of BMI-for-age), and exact zip code. For the second round, another 11 out 13 children were matched using index date (± 365 days), age (± 2 year), BMI (± 1 percentile of BMI-for-age), and exact zip code. Index date for FB-WMG was at first appointment. For Ref-CG, the referral date (or nearest office visit date with BMI) was used as index date. For Area-CG, the index date was the date of the BMI measure used for matching.

Height and weight were measured in light clothing without shoes and used to calculate sex-specific BMI-for-age.19 Obesity was defined as BMI-for-age ≥ 95th percentile. BMI was measured at the index point and approximately 6 months later. Two approaches were used to determine BMI at 6 months after their index date. For 600 children (143 FB-WMG, 183 Ref-CG, 274 Area-CG), a BMI measured during a regular outpatient visit with 1 month before and 3 months after the 6-month outcome was used. For 52 children (19 FB-WMG, 20 Ref-CG, 13 Area-CG), no BMI could be identified that met these criteria, and BMI was imputed using a linear regression of 1 BMI measure assessed within 3 months before and 1 BMI measure assessed within 6 months after the 6-month outcome.

Covariates

Blood pressure at baseline (or index date) was extracted from electronic medical records. Although we did not study blood pressure, labs orders and referrals were done if appropriate and per Kaiser Permanente guidelines. We obtained race and ethnicity information from health plan administrative records and birth records. We categorized race/ethnicity as non-Hispanic White; Hispanic (regardless of race); African American; Asian or Pacific Islander; and other, multiple, or unknown race/ethnicity. We used median household income and education in the patient’s residential census tract as area-based measures of socioeconomic status.27,28 Census-tract household income was classified using the individual’s likelihood of a median household income of <$45,000, $45,001 to $80,000, and $80,000 or more. Neighborhood education was categorized using an individual’s likelihood of an education with some college or higher. We used insurance through government health care assistance programs (yes/no), such as MediCal, as an additional proxy for socioeconomic status.

Statistical Analysis

Baseline characteristics of the study cohort were presented for all 3 study groups (FB-WMG, Ref-CG, and Area-CG) using means with standard deviation or medians with interquartile range (Q1-Q3) for continuous variables as appropriate and number of observations with percentage for categorical variables. Differences in characteristics between groups were assessed using Student t-test, χ2 test, or Fisher’s exact test contrasting intervention against control groups.

We conducted univariate analyses for the overall cohort and for strata defined by age, sex, and by state-subsidized insurance coverage. The primary outcome measure was change of BMI (kg/m2) between baseline and 6 months after. The secondary outcome was the odds of maintaining or reducing BMI after 6 months from baseline. Differences in change of BMI between the intervention and control groups were calculated, and confidence intervals (CIs) were derived using univariate linear regressions with robust standard error. Univariate logistic regressions with robust standard error were conducted to compare the likelihood of maintaining or reducing BMI between study groups. Odds ratios and 95% CIs were reported.

Multivariable linear regression and logistic regression with robust standard error were used to estimate the effect of weight management program adjusting for age, sex, race/ethnicity, baseline BMI, state-subsidized insurance coverage, and length of KPSC membership. For simplicity and consistency, we included all 3 study groups in 1 model. To account for the matching process, we performed additional analyses using separate mixed linear regression and conditional logistic regression to compare the intervention group and control group 2. The results were essentially consistent and did not affect the overall conclusion. All analyses were performed using SAS statistical software version 9.4 (SAS Institute Inc, Cary, NC).

Results

FB-WMG participants (n = 162) were similar to matched Area-CG (n = 287) and Ref-CG with respect to age, sex, race, and neighborhood education, but FB-WMG participants had a higher proportion of children with state-subsidized health insurance and were from neighborhoods with higher median household income (Table 1). BMI-for-age at baseline was 98th percentile across all groups.

Table 1. Demographic characteristics of family-based behavior-changing weight management intervention group, referred control group, and area control group at baseline

        p-value
  FB-WMG (n = 162) Ref-CG (n = 203) Area-CG (n = 287) FB-WMG vs Ref-CG FB-WMG vs Area-CG
Age, n (%)       0.35 0.89
 3-8 y 40 (24.7) 54 (26.6) 70 (24.4)    
 9-12 y 61 (37.7) 83 (40.9) 104 (36.2)    
 13-18 y 61 (37.7) 66 (32.5) 113 (39.4)    
Sex, n (%)       0.002 0.79
 Girls 89 (54.9) 78 (38.4%) 154 (53.7)    
 Boys 73 (45.1) 125 (61.6%) 133 (46.3%)    
Baseline BMI-for-age percentile, mean (SD) 98.8 (1.0) 98.2 (1.24) 98.5 (1.01) < 0.001 0.02
Race/ethnicity, n (%)
 White 26 (16.0) 46 (22.7) 67 (23.3)    
 Black 6 (3.7) 4 (2.0) 9 (3.1)    
 Hispanic 119 (73.5) 131 (64.5) 185 (64.5)    
 Asian/Pacific Islander 11 (6.8) 15 (7.4) 16 (5.6)    
 Other/multiple 0 (0.0) 7 (3.4) 10 (3.5)    
State-subsidized insurance, n (%) 95 (58.6) 100 (49.3) 129 (44.9) 0.07 0.01
Child’s KPSC membership duration (y), median (Q1-Q3) 6 (2.9-8.6) 5.4 (1.8-8.0) 5.9 (2.6-8.9) 0.06 0.41
Blood pressure percentile, median (Q1, Q3)
 Diastolic 67 (51.0-78.0) 63 (41.0-80.0) 67 (42.0-84.0) 0.12 0.61
 Systolic 88 (68.0-94.0) 86 (68.0-93.5) 88 (71.5-95.0) 0.49 0.73
Neighborhood education (college degree and higher)
 0%-50% 61 (37.7) 92 (45.3) 132 (46.0) 0.13 0.22
 51%-75% 61 (37.7) 77 (37.9) 91 (31.7)    
 76%-100% 40 (24.7) 34 (16.7) 64 (22.3)    
Neighborhood median household income       0.01 0.03
 ≤ $45,000 22 (13.6) 34 (16.7) 44 (15.3)    
 $45,001-80,000 69 (42.6) 112 (55.2) 153 (53.3)    
 > $80,000 71 (43.8) 57 (28.1) 90 (31.4)    

Area-CG = area control group; BMI = body mass index; FB-WMG = family-based behavior-changing weight management intervention group; KPSC = Kaiser Permanente Southern California; Ref-CG = referred control group.

Maintaining or Reducing BMI after 6 Months

Overall, 69.1% of FB-WMG reduced or maintained BMI compared with baseline and adjusted for growth, compared with 45.8% of Ref-CG and 57.8% Area-CG (all p < 0.05; see Table S1 in the Supplemental Material at www.thepermanentejournal.org/files/2021/20.101.supp.pdf;). Among girls between 3 and 12 years of age, 75% reduced or maintained their BMI at 6 months in FB-WMG compared with 42.3% in Ref-CG and 59.8% in Area-CG. Unadjusted odds ratio (OR) to reduce or maintain BMI at 6 months for FB-WMG was 2.65 (95% CI = 1.72-4.08) compared with Ref-CG and 1.63 (95% CI = 1.09-2.45) compared with Area-CG (see Table S1 in the Supplemental Material at www.thepermanentejournal.org/files/2021/20.101.supp.pdf, Figure 2). The unadjusted OR to reduce or maintain BMI at 6 months varied by sex, age, and insurance type. After adjusting for age, sex, race/ethnicity, baseline BMI, state-subsidized insurance coverage, and length of KPSC membership, the OR to reduce or maintain BMI at 6 months was lower for Ref-CG (OR = 0.39; 95% CI = 0.25-0.61) and Area-CG (OR = 0.65; 95% CI = 0.42-0.98) compared with FB-WMG (Table 2).

tpj20101f2

Figure 2. Odds ratio of reducing or maintaining BMI at 6 months after baseline for the family-based weight management intervention group (FB-WMG) compared with eligible and referred controls (Ref-CG, A) and area-matched controls (Area-CG, B).

Table 2. Multivariable adjusted odds ratio (95% confidence interval) of family-based behavior-changing weight management intervention group, referred control group, and area control group participants to reduce/maintain body mass index or to reduce body mass index at 6 mo compared with baseline

  Reduced or maintained BMI Δ BMI (kg/m2)
  OR (95% CI) p value β (95% CI) p value
Study group   < 0.001   < 0.001
FB-WMG 1.00 (Ref)   (Ref)  
Ref-CG 0.39 (0.25 to 0.61) < 0.001 0.82 (0.43 to 1.21) < 0.001
Area-CG 0.65 (0.42 to 0.98) 0.04 0.27 (−0.13 to 0.66) 0.19

OR (95% CI) and p value were estimated using multivariable logistic regression with robust standard error adjusted for age, sex, race/ethnicity, baseline BMI, state-subsidized insurance coverage, and length of Kaiser Permanente Southern California (KPSC) membership. Estimates of β (95% CI) and p value were obtained using multivariable linear regression with robust standard error adjusted for age, sex, race/ethnicity, baseline BMI, state-subsidized insurance coverage, and length of KPSC membership.

Area-CG = area control group; BMI = body mass index; CI = confidence interval; FB-WMG = family-based behavior-changing weight management intervention group; Ref-CG = referred control group.

Change in BMI after 6 Months

Children in the FB-WMG lost more weight than children in the Ref-CG (p < 0.001) but not than in the Area-CG (p = 0.13; see Table S2 in the Supplemental Material at www.thepermanentejournal.org/files/2021/20.101.supp.pdf).The unadjusted difference in differences between FB-WMG and Ref-CG was −0.85 kg/m2 (95% CI = −1.23 to −0.46 kg/m2) and −0.28 kg/m2 (95% CI = −0.63 to 0.08 kg/m2) between FB-WMG and Area-CG. The unadjusted difference in differences in girls aged 3 to 12 years was −1.25 kg/m2 (95% CI = −1.85 to −0.65 kg/m2) compared with Ref-CG and 0.16 kg/m2 (95% CI = −0.84 to −0.52 kg/m2) compared with Area-CG (Figure 3). After adjusting for age, sex, race/ethnicity, baseline BMI, state-subsidized insurance coverage, and length of KPSC membership, Ref-CG (β = 0.82 kg/m2; 95% CI = 0.43-1.21 kg/m2) lost significantly less BMI than FB-WMG (Table 2). The difference in differences between FB-WMG and Area-CG (β = 0.27 kg/m2; 95% CI = −0.13 to 0.66 kg/m2) was not significant.

tpj20101f3

Figure 3. Difference in differences (DID) in BMI at 6 months after baseline for the family-based weight management intervention group (FB-WMG) compared with eligible and referred controls who never participated (Ref-CG, A) and matched area controls (Area-CG, B).

DISCUSSION

In the present study, a low- to moderate-intensity, family-based weight management intervention in primary care successfully supported almost 70% of participating children in reducing or maintaining BMI after only 6 months, compared with 45% to 58 %in the control groups. However, the effectiveness varied slightly by sex and age, with the appearance of marginally higher weight loss in girls and younger children than in boys or teens.

The success of the primary care-based weight management program was more pronounced when comparing participants with eligible and referred controls who never participated in the program (Ref-CG) than compared with eligible matched controls who were never referred by their primary care provider (Area-CG). This finding was unexpected because we assumed that Ref-CG children and their parents discussed the importance of weight management with their provider and agreed to be referred into to the program, which may have raised their awareness of potential weight issues, whereas no such indication was documented in the EMR of the Area-CG children. Simulation studies predict that less than 10% of children with obesity will not become obese adults.29 We can only speculate that Ref-CG children may have been at higher risk for further weight gain identified by their provider compared with Area-CG.

Recent studies suggest that patients who were identified as obese may experience trauma from stigma, and parents may feel like they have failed.30 Identifying a child with obesity without providing tools to manage body weight may further contribute to the poor lifestyle and health behaviors that providers are trying to prevent.31,32 One may speculate that stigma and feeling of failure may explain why patients who had access to weight management resources but did not receive the intervention showed a stronger difference than patients who received the interventions than patients who were never referred. Some children or parents in the Ref-CG may have felt stigmatized and therefore did not return for support in managing their child’s weight. On the other hand, children in FB-WMG and Ref-CG were derived from the pool of patients, referred by their medical provider after consultation, and perceived as at higher risk for weight gain than children who were not referred. This would support the hypothesis that Ref-CG subjects, not Area-CG subjects, are more suitable to serve as controls than children in FB-WMG.

A strength of the current intervention was the a multicomponent approach, involvement of a nutrition specialist, family participation, and individual consultation, which allows for a patient-centered and tailored approach with a personalized meal and exercise program in contrast to group-based intervention.33,34 The intervention complied with general recommendations for a treatment duration of at least 6 months.33

For some patients, a co-pay share of cost may have been required, which may be a barrier to participation. In the present study, almost two-thirds of patients in the intervention had state-subsidized health plans such as Medi-Cal, which waives the co-pay. Patients with state-subsidized health plans may have benefited in this respect by having access to the program with lower financial burden. Because children from a lower income background are at higher risk for obesity,1,35 the program was an opportunity to serve a group with fewer resources. The effectiveness of the intervention was comparable between patients with state-subsidized health plans and those who were on private or self-funded plans.

A metaanalysis by Sim et al.36 suggested that brief pediatric primary care interventions to screen and counsel for obesity were only associated with marginal effects on BMI. The US Preventive Services Task Force found that lifestyle-based interventions were associated with reduced body weight compared with controls but estimated that about 26 hours of contact were needed to achieve a significant benefit.6,8 In the present study, patients received between 1.5 hours and 3 hours of contact over 6 months. Pharmaceutical weight-loss therapy or bariatric surgery were not part of the intervention. Despite this relatively low dose, this family-based, multicomponent program, which was integrated in a pediatric primary care clinic, achieved a reduction in BMI when compared with REF-CG. Although the long-term effectiveness of the program has yet to be shown, the results are encouraging. Although we concede that a higher dose (ie, increased contact time) would likely show greater benefit, it may also be cost and time constrictive for the patient and their family, especially in patients with low socio-economic background who are prone to a high attrition.37,38

The effectiveness of the program in the present study with an overall reduction in BMI of −0.85 kg/m2 (95% CI = −1.23 to −0.46) was slightly larger than the effect observed in a metaanalysis of interventions involving nutrition specialists (−0.66 kg/m2; standard deviation = −1.99),34 other comparable primary care-based programs with 0 to 5 h of contact time (−0.04 to −0.13 86 kg/m2),39-41 and the effect shown in a motivational interviewing intervention in pediatric care practices (−0.66 kg/m2).42 A 3-year, clinic-based behavioral program in Sweden also associated greater response with younger age groups. After 3 years of intervention, 44% of children 6 to 9 years of age reduced their BMI, but only 20% of teenagers between 10 and 13 years of age and 8% of teenagers between 14 and 16 years of age reduced their BMI.15

The weight reductions achieved are clinically relevant. In younger children, smaller changes in body weight are sufficient to obtain a healthy weight compared with older children.15,43 Younger children are less likely to present with comorbid conditions that could complicate treatment.44 Moreover, early intervention may prevent the low self-esteem associated with obesity in older children that may contribute to lower success of interventions in older children who are obese.45 If younger children respond best to weight management interventions and benefit most, this age group may be where resources should be focused until effective approaches are available that are tailored to the needs of older children.

Failing to reduce BMI is not equal to failure to adopt healthy lifestyle behaviors.46 Many fitness programs that are school and community based do not measure BMI, and some believe that measurements stigmatize the children. Identifying families that are ready to make behavior changes and training providers in strategies to encourage and structure change in lifestyle behaviors may be most important tasks.46,47

Study Limitations

Due to the nature of the observational study design and the lack of randomization into a treatment group, we cannot exclude unmeasured differences such as lack of motivation between the intervention and the control groups.48 Referred controls may have had risk factors that acted as barriers to treatment.

This intervention was conducted in a real-world pediatric care environment, and participation was not randomized. However, randomized, controlled trials with their restrictive inclusion and exclusion criteria are usually not fully representative of an unselected real-world population.48,49 Although the results from the present study may better represent routine practice compared with the idealized conditions of randomized, controlled trials, methodological and study design issues, such as the risk of confounding and bias, may have occurred, which limits our ability to attribute any differences between the intervention and control groups to the intervention itself as opposed to other factors.49,50 Furthermore, the small sample size of the subgroups may have prevented us from identifying subpopulations that benefited more or less from the intervention, and we lacked key information about the choice of therapy and the differences in this choice across groups.

In conclusion, a low- to moderate-intensity, family-based weight management intervention including a nutrition specialist in primary care may be successful after only 6 months. Because the effectiveness varied slightly by sex and age, tailored approaches may be necessary for older children and boys. Longer-term outcome evaluation of the program and a cost-effectiveness studies are necessary to determine the ideal dosage while weighing the costs of high-intensity interventions as suggested by the US Preventive Services Task Force.

Disclosure Statement

The author(s) have no conflicts of interest to disclose.

Financial Disclosure

None.

Sponsor:

This study was supported by a grant from the Regional Research Committee of Kaiser Permanente Southern California (KP-RR #20181108). IRB approval: KPSC IRB #12018.

Author Affiliations

1Southern California Permanente Medical Group, Kaiser Permanente Yorba Linda Medical Offices, Yorba Linda, CA

2Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA

3Southern California Permanente Medical Group, Kaiser Permanente Tustin Ranch, Tustin, CA

Corresponding Author

Veronica Else, RN, NP, MSN (Veronica.C.Else@kp.org)

How to Cite this Article

Else V, Chen Q, Cortez AB, Koebnick C. Effectiveness of a family-centered pediatric weight management program integrated in primary care. Perm J 2021;25:20.101. DOI: 10.7812/TPP/20.101

References

1. Ogden CL, Fryar CD, Hales CM, Carroll MD, Aoki Y, Freedman DS. Differences in obesity prevalence by demographics and urbanization in US children and adolescents, 2013-2016. JAMA 2018 Jun;319(23):2410-8. DOI: https://doi.org/10.1001/jama.2018.5158, PMID:29922826

2. Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007-2008 to 2015-2016. JAMA 2018 Apr;319(16):1723-5. DOI: https://doi.org/10.1001/jama.2018.3060, PMID:29570750

3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014 Feb;311(8):806-14. DOI: https://doi.org/10.1001/jama.2014.732, PMID:24570244

4. Koebnick C, Mohan YD, Li X, Young DR. Secular trends of overweight and obesity in young southern californians 2008-2013. J Pediatr 2015 Dec;167(6):1264-71.e2. DOI: https://doi.org/10.1016/j.jpeds.2015.08.039, PMID:26421485

5. Kumar S, Kelly AS. Review of childhood obesity: From epidemiology, etiology, and comorbidities to clinical assessment and treatment. Mayo Clin Proc 2017 Feb;92(2):251-65. DOI: https://doi.org/10.1016/j.mayocp.2016.09.017, PMID:28065514

6. Grossman DC, Grossman DC, Bibbins-Domingo K, et al. Screening for obesity in children and adolescents: US Preventive Services Task Force recommendation statement. JAMA 2017 Jun;317(23):2417-26. DOI: https://doi.org/10.1001/jama.2017.6803, PMID:28632874

7. Caplan W, Histon T, Pettay HS. An overview of the care management institute’s weight management and obesity initiative. Perm J 2003 Summer;3(3):41-7. DOI: https://doi.org/10.7812/TPP/03-058

8. O’Connor EA, Evans CV, Burda BU, Walsh ES, Eder M, Lozano P. Screening for obesity and intervention for weight management in children and adolescents: Evidence report and systematic review for the US Preventive Services Task Force. JAMA 2017 Jun;317(23):2427-44. DOI: https://doi.org/10.1001/jama.2017.0332, PMID:28632873

9. Skinner AC, Ravanbakht SN, Skelton JA, Perrin EM, Armstrong SC. Prevalence of obesity and severe obesity in US children, 1999-2016. Pediatrics 2018 Mar;141(3):e20173459. DOI: https://doi.org/10.1542/peds.2017-3459, PMID:29483202

10. Wilfley DE, Staiano AE, Altman M, et al. Improving access and systems of care for evidence-based childhood obesity treatment: Conference key findings and next steps. Obesity (Silver Spring) 2017 Jan;25(1):16-29. DOI: https://doi.org/10.1002/oby.21712

11. Imoisili OE, Goodman AB, Dooyema CA, Harrison MR, Belay B, Park S. Screening and referral for childhood obesity: Adherence to the U.S. Preventive Services Task Force recommendation. Am J Prev Med 2019 Feb;56(2):179-186. DOI: https://doi.org/10.1016/j.amepre.2018.10.003

12. Mead E, Brown T, Rees K, et al. Diet, physical activity and behavioural interventions for the treatment of overweight or obese children from the age of 6 to 11 years. Cochrane Database Syst Rev 2017 Jun;6:CD012651. DOI: https://doi.org/10.1002/14651858.CD012651, PMID:28639319

13. Klein JD, Sesselberg TS, Johnson MS, et al. Adoption of body mass index guidelines for screening and counseling in pediatric practice. Pediatrics 2010 Feb;125(2):265-72. DOI: https://doi.org/10.1542/peds.2008-2985, PMID:20083518

14. Jelalian E, Boergers J, Alday CS, Frank R. Survey of physician attitudes and practices related to pediatric obesity. Clin Pediatr (Phila) 2003 Apr;42(3):235-45. DOI: https://doi.org/10.1177/000992280304200307, PMID:12739922

15. Danielsson P, Kowalski J, Ekblom Ö, Marcus C. Response of severely obese children and adolescents to behavioral treatment. Arch Pediatr Adolesc Med 2012 Dec;166(12):1103-8. DOI: https://doi.org/10.1001/2013.jamapediatrics.319, PMID:23108856

16. Altman M, Wilfley DE. Evidence update on the treatment of overweight and obesity in children and adolescents. J Clin Child Adolesc Psychol 2014 Dec;44(4):521-37. DOI: https://doi.org/10.1080/15374416.2014.963854, PMID:25496471

17. Vallabhan MK, Jimenez EY, Nash JL, et al. Motivational interviewing to treat adolescents with obesity: A meta-analysis. Pediatrics 2018 Nov;142(5):e20180733. DOI: https://doi.org/10.1542/peds.2018-0733, PMID:30348753

18. Koebnick C, Langer-Gould AM, Gould MK, et al. Sociodemographic characteristics of members of a large, integrated health care system: Comparison with US Census Bureau data. Perm J 2012 Summer;16(3):37-41. DOI: https://doi.org/10.7812/tpp/12-031, PMID:23012597

19. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC growth charts for the United States: Methods and development. Vital Health Stat 2002 May;11(246):1-190, PMID:11183293.

20. Resnicow K, Baskin M, Rahotep S, Periasamy S, Rollnick S. Motivational interviewing in health promotion & behavioral medicine settings. In: Handbook of motivational counseling: Motivating people for changeed. Cox W, Klinger E, editors. Sussex, UK: John Wiley and Sons: 2003; p 457-79.

21. Resnicow K, Davis R, Rollnick S. Motivational interviewing for pediatric obesity: Conceptual issues and evidence review. J Am Diet Assoc 2006 Dec;106(12):2024-33. DOI: https://doi.org/10.1016/j.jada.2006.09.015, PMID:17126634

22. Bandura A. Health promotion by social cognitive means. Health Educ Behav 2004 Apr;31(2):143-64. DOI: https://doi.org/10.1177/1090198104263660, PMID:15090118

23. Stahl CE, Necheles JW, Mayefsky JH, Wright LK, Rankin KM. 5-4-3-2-1 go! Coordinating pediatric resident education and community health promotion to address the obesity epidemic in children and youth. Clin Pediatr (Phila) 2011 Mar;50(3):215-24. DOI: https://doi.org/10.1177/0009922810385106, PMID:21098524

24. Consortium to lower obesity in Chicago children. CLOCC 5-4-3-2-1 GO!Consortium to lower obesity in Chicago children. Accessed 05/03/2020. www.clocc.net.

25. Evans WD, Necheles J, Longjohn M, Christoffel KK. The 5-4-3-2-1 go! Intervention: Social marketing strategies for nutrition. J Nutr Educ Behav 2007 Mar-Apr;39(2 Suppl):S55-9. DOI: https://doi.org/10.1016/j.jneb.2006.08.024, PMID:17336809

26. Post RC, Haven J, Maniscalco S. Setting the table with a healthy plate: Make half your plate fruits and vegetables. J Am Diet Assoc 2011 Nov;111(11):1644-7. DOI: https://doi.org/10.1016/j.jada.2011.09.040, PMID:22027042

27. Chen W, Petitti DB, Enger S. Limitations and potential uses of census-based data on ethnicity in a diverse community. Ann Epidemiol 2004 May;14(5):339-45. DOI: https://doi.org/10.1016/j.annepidem.2003.07.002, PMID:15177273

28. Drewnowski A, Rehm CD, Solet D. Disparities in obesity rates: Analysis by ZIP code area. Soc Sci Med 2007 Dec;65(12):2458-63. DOI: https://doi.org/10.1016/j.socscimed.2007.07.001, PMID:17761378

29. Ward ZJ, Long MW, Resch SC, Giles CM, Cradock AL, Gortmaker SL. Simulation of growth trajectories of childhood obesity into adulthood. N Engl J Med 2017 Nov;377(22):2145-53. DOI: https://doi.org/10.1056/NEJMoa1703860, PMID:29171811

30. Puhl R, Peterson JL, Luedicke J. Motivating or stigmatizing? Public perceptions of weight-related language used by health providers. Int J Obes 2013 Apr;37(4):612-9. DOI: https://doi.org/10.1038/ijo.2012.110

31. Brown I, Flint SW. Weight bias and the training of health professionals to better manage obesity: What do we know and what should we do? Curr Obes Rep 2013;2333-40. https://doi.org/10.1007/s13679-013-0070-y

32. Tailor A, Ogden J. Avoiding the term 'obesity': An experimental study of the impact of doctors' language on patients' beliefs. Patient Educ Couns 2009 Aug;76(2):260-4. DOI: https://doi.org/10.1016/j.pec.2008.12.016, PMID:19167856

33. Henry BW, Ziegler J, Parrott JS, Handu D. Pediatric weight management evidence-based practice guidelines: Components and contexts of interventions. J Acad Nutr Diet 2018 Jul;118(7):1301-1.e23.PMID:29233517. DOI: https://doi.org/10.1016/j.jand.2017.08.007

34. Thompson KL, Chung M, Handu D, et al. The effectiveness of nutrition specialists on pediatric weight management outcomes in multicomponent pediatric weight management interventions: A systematic review and exploratory meta-analysis. J Acad Nutr Diet 2019 May;119(5):799-e43.PMID:30833172. DOI: https://doi.org/10.1016/j.jand.2018.12.008

35. Ogden CL, Carroll MD, Fakhouri TH, et al. Prevalence of obesity among youths by household income and education level of head of household - United States 2011-2014. MMWR Morb Mortal Wkly Rep 2018 Feb;67(6):186-9. DOI: https://doi.org/10.15585/mmwr.mm6706a3, PMID:29447142

36. Sim LA, Lebow J, Wang Z, Koball A, Murad MH. Brief primary care obesity interventions: A meta-analysis. Pediatrics 2016 Oct;138(4):e20160149. DOI: https://doi.org/10.1542/peds.2016-0149, PMID:27621413

37. Zeller M, Kirk S, Claytor R, et al. Predictors of attrition from a pediatric weight management program. J Pediatr 2004 Apr;144(4):466-70. DOI: https://doi.org/10.1016/j.jpeds.2003.12.031, PMID:15069394

38. Skelton JA, Martin S, Irby MB. Satisfaction and attrition in paediatric weight management. Clin Obes 2016 Apr;6(2):143-53. DOI: https://doi.org/10.1111/cob.12138, PMID:27008068

39. van Grieken A, Veldhuis L, Renders CM, et al. Population-based childhood overweight prevention: Outcomes of the 'be active, eat right' study. PLoS One 2013;8(5):e65376. DOI: https://doi.org/10.1371/journal.pone.0065376

40. Taveras EM, Gortmaker SL, Hohman KH, et al. Randomized controlled trial to improve primary care to prevent and manage childhood obesity: The high five for kids study. Arch Pediatr Adolesc Med 2011 Aug;165(8):714-22. DOI: https://doi.org/10.1001/archpediatrics.2011.44, PMID:21464376

41. Wake M, Baur LA, Gerner B, et al. Outcomes and costs of primary care surveillance and intervention for overweight or obese children: The LEAP 2 randomised controlled trial. BMJ, 2009 Sep;339:b3308. DOI: https://doi.org/10.1136/bmj.b3308, PMID:19729418

42. Resnicow K, Harris D, Wasserman R, et al. Advances in motivational interviewing for pediatric obesity: Results of the brief motivational interviewing to reduce body mass index trial and future directions. Pediatr Clin North Am 2016 Jun;63(3):539-62. DOI: https://doi.org/10.1016/j.pcl.2016.02.008, PMID:27261549

43. Epstein LH, Paluch RA, Roemmich JN, Beecher MD. Family-based obesity treatment, then and now: Twenty-five years of pediatric obesity treatment. Health Psychol 2007 Jul;26(4):381-91. DOI: https://doi.org/10.1037/0278-6133.26.4.381, PMID:17605557

44. Goldschmidt AB, Wilfley DE, Paluch RA, Roemmich JN, Epstein LH. Indicated prevention of adult obesity: How much weight change is necessary for normalization of weight status in children? JAMA Pediatr 2013 Jan;167(1):21-6. DOI: https://doi.org/10.1001/jamapediatrics.2013.416, PMID:23129001

45. Strauss RS. Childhood obesity and self-esteem. Pediatrics 2000 Jan;105(1):e15. DOI: https://doi.org/10.1542/peds.105.1.e15, PMID:10617752

46. Armstrong SC, Skinner AC. Defining "success" in childhood obesity interventions in primary care. Pediatrics 2016 Oct;138(4):e20162497. DOI: https://doi.org/10.1542/peds.2016-2497, PMID:27621411

47. Resnicow K, Sonneville KR, Naar S. The heterogeneity of MI interventions studies for treatment of obesity. Pediatrics 2018 Nov;142(5):e20182471. DOI: https://doi.org/10.1542/peds.2018-2471, PMID:30348752

48. Zuidgeest MGP, Goetz I, Groenwold RHH, Irving E, van Thiel GJMW, Grobbee DE. Series: Pragmatic trials and real world evidence: Paper 1. Introduction. J Clin Epidemiol 2017 Aug;88:7-13. DOI: https://doi.org/10.1016/j.jclinepi.2016.12.023, PMID:28549929

49. Knottnerus JA, Tugwell P. Research methods must find ways of accommodating clinical reality, not ignoring it: The need for pragmatic trials. J Clin Epidemiol 2017 Aug;88:1-3. DOI: https://doi.org/10.1016/j.jclinepi.2017.08.012

50. Sox HC, Lewis RJ. Pragmatic trials: Practical answers to "real world" questions. J Am Med Assoc 2016 Sep;316(11):1205-6. DOI: https://doi.org/10.1001/jama.2016.11409, PMID:27654606

Keywords: 

BMI, effectiveness, family-centered, Obesity, weight management

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