Literature Review

Prevention and Management of Childhood Obesity and its Psychological and Health Comorbidities

Justin D. Smith, PhD1, Emily Fu, MPH2, Marissa Kobayashi, MHS3

1Department of Psychiatry and Behavioral Sciences, Department of Preventive Medicine, and Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, 750 N. Lake Shore Drive, Illinois, 60611, USA

2Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, 750 N. Lake Shore Drive, Chicago, Illinois, 60611, USA

3Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th Street, Suite 1009, Miami, FL 33136. Phone: (305) 972-9961

Abstract

Childhood obesity has become a global pandemic in developed countries, leading to a host of

medical conditions that contribute to increased morbidity and premature death. The causes of

obesity in childhood and adolescence are complex and multifaceted, presenting researchers and

clinicians with myriad challenges in preventing and managing the problem. This chapter reviews

the state-of-the-science for understanding the etiology of childhood obesity, the preventive

interventions and treatment options for overweight and obesity, and the medical complications and

co-occurring psychological conditions that result from excess adiposity, such as hypertension,

non-alcoholic fatty liver disease, and depression. Interventions across the developmental span,

varying risk levels, and service contexts (e.g., community, school, home, and healthcare systems)

are reviewed. Future directions for research are offered with an emphasis on translational issues

for taking evidence-based interventions to scale in a manner that reduce the public health burden

of the childhood obesity pandemic.

Keywords

adiposity; childhood obesity; health psychology; prevention; research translation

1.0 INTRODUCTION

Influenced by genetics, biology, psychosocial factors, and health behaviors, overweight and

obesity (OW/OB) in childhood is a complex public health problem affecting the majority of

developed countries worldwide. Additionally, the key contributors to obesity—poor diet and

physical inactivity—are among the leading causes of preventable youth deaths, chronic

[email protected].

DISCLOSURE STATEMENT Justin D. Smith is co-developer of the Family Check-Up® 4 Health intervention for childhood obesity. The authors are not aware of any other affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

HHS Public Access Author manuscript Annu Rev Clin Psychol. Author manuscript; available in PMC 2020 May 29.

Published in final edited form as: Annu Rev Clin Psychol. 2020 May 07; 16: 351–378. doi:10.1146/annurev-clinpsy-100219-060201.

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disease, and economic health burden (Friedemann et al 2012, Hamilton et al 2018). Despite

the remarkable need to prevent childhood obesity and to intervene earlier to prevent excess

weight gain in later developmental periods, few interventions have demonstrated long-

lasting effects or been implemented at such a scale to have an appreciable public health

impact (Hales et al 2018).

In this review, we describe the extent and nature of the childhood obesity pandemic, present

conceptual and theoretical models for understanding its etiology, and take a translational-

developmental perspective in reviewing intervention approaches within and across

developmental stages and in the various contexts in which childhood OW/OB interventions

are delivered. We pay particular attention to co-occurring psychological conditions

intertwined with OW/OB for children, adolescents, and their families as they relate to both

development/etiology and to intervention. For this reason, our review begins with

interventions aimed at prevention and moves to management and treatment options for

obesity and its psychological and medical comorbidities. Then, we discuss the state-of-the-

science and expert recommendations for interventions to prevent and manage childhood

OW/OB and what it would take to implement current evidence-based programs at scale.

Last, we end by discussing identified gaps in the literature to inform future directions for

research and the translation of research findings to real-world practice that can curb the

pandemic. For readability, we use the term “interventions for the prevention and

management of childhood OW/OB” to capture an array of approaches referred to by a

variety of monikers in the literature, including primary prevention, prevention of excess

weight gain, weight loss intervention, weight management, and treatment of obesity. More

specific labels are used when needed.

2.0 EPIDEMIOLOGY OF CHILDHOOD OBESITY

Childhood OW/OB is determined by the child’s height and weight to calculate body mass

index (BMI), which is adjusted according to norms based on the child’s age and gender.

BMI between the 85th and 94th percentile is in the “overweight” range, whereas BMI ≥ 95th

percentile for age and gender is in the “obese” range (Centers for Disease Control and

Prevention [CDC] 2018). Rates of obesity among children and adolescents in developed

countries worldwide, collected in 2013, were 12.9% for boys and 13.4% for girls (Ng et al

2014). In the United States (US) from 1999–2016, 18.4% of children ages 2–19 years had

obesity, and 5.2% had severe obesity, defined as BMI ≥120% of the 95th percentile for age

and gender (Skinner et al 2018). The prevalence of obesity has increased between 2011–

2012 and 2015–2016 in children ages 2–5 and 16–19 years (Hales et al 2018). Being in the

obese range during childhood or adolescence makes the youth five times more likely to be

obese in adulthood compared to peers who maintain a healthy weight (Simmonds et al

2016). Compared to obesity, severe obesity is strongly linked with greater cardiometabolic

risk, adult obesity, and premature death (Skinner et al 2015).

OW/OB and its health consequences are disproportionately distributed across the US, with a

higher prevalence among children of disadvantaged racial and socioeconomic backgrounds.

Rates of OW/OB are significantly higher among Non-Hispanic black and Hispanic children

compared to Non-Hispanic White children (e.g., Hales et al 2018). Such disparities are

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particularly pronounced among severe obesity, where 12.8% of African American children,

and 12.4% of Hispanic children have severe obesity compared to 5.0% of Non-Hispanic

White children (Hales et al 2018). Youth in low socioeconomic households are more likely

to develop OW/OB compared to their counterparts in high socioeconomic households. In

2011–2014, 18.9% of children ages 2–19 living in the lowest income group (≤130% of

Federal Poverty Line) had obesity, whereas 10.9% of children in the highest income group

(>350% Federal Poverty Line) had obesity (Ogden et al 2018). Influences on multiple

socioecological levels put racially diverse children of low socioeconomic status (SES) at

higher risk of developing OW/OB, which is further exacerbated by limited access to health

services that can prevent excess weight gain and its sequelae.

3.0 ETIOLOGY OF CHILDHOOD OBESITY

At the most basic level, childhood OW/OB emerges from consuming more calories than

expended, resulting in excess weight gain and an excess body fat. Caloric imbalance is the

result of, and can be further exacerbated by, a range of obesogenic behaviors. That is,

behaviors that are highly correlated with excess weight gain. The most common obesogenic

behaviors are high consumption of sugar sweetened beverages and low-nutrient, high

saturated fat foods, low levels of physical activity and high levels of sedentary behaviors,

and shortened sleep duration (e.g., Sisson et al 2016). Diet, physical activity, screen time,

and sleep patterns are influenced by a myriad of factors and interactions involving genetics,

interpersonal relationships, environment, and community (e.g., Russell & Russell 2019,

Smith et al 2018d). Children living in the United States commonly consume the “Western

Diet,” known as a diet high in calories, rich in sugars, trans and saturated fats, salt and food

additives, and low in complex carbohydrates, and vitamins. Poor sleep patterns, defined as

short duration and late timing, can contribute to obesity through changing levels of appetite-

regulating hormones, and irregular eating patterns including late night snacking and eating

(Miller et al 2015). Children who experience shortened night time sleep from infancy to

school age are at increased risk of developing OW/OB compared to same-aged children

sleeping average, age-specific hours (e.g., Taveras et al 2014). Research indicates that

children with higher rates of screen time also consume high levels of energy-dense snacks,

beverages, and fast food, and fewer fruits and vegetables, and screen time is hypothesized to

affect food and beverage consumption through distracted eating, reducing feelings of satiety

or fullness, and exposure to advertisements for junk food (sweet and salty, calorically-dense

foods) (Robinson et al 2017). Screen time can also negatively affect children’s sleeping

patterns, and is correlated with sedentary behaviors (e.g., watching television, playing video

games) (Hale & Guan 2015).

3.1 Conceptual Models for Understanding and Addressing Childhood OW/OB

Conceptualizing development of childhood OW/OB requires consideration of interplay of

genetic, biological, psychological, behavioral, interpersonal, and environment factors

(Kumar & Kelly 2017). OW/OB interventions are typically designed to account for these

multilevel factors to assist children in achieving expert recommendations for physical

activity and fruit and vegetable consumption, while limiting sugar sweetened beverages

intake and screen time, and regulating sleep patterns (Kakinami et al 2019). Creating

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behavioral change requires understanding of the multi-level interactions to identify

opportunities for intervention to prevent excess weight gain long-term. A variety of

conceptual models exist to explain potential interactions and individual influences leading to

obesogenic behaviors and development of childhood OW/OB, and targets for improving

health behaviors and routines. Importantly, basic science and conceptual models can be

translated to develop effective, targeted intervention programs for prevention of excess

weight gain.

3.1.1 Biopsychosocial model—The biopsychosocial model combines biological foundations in child development with environmental and psychosocial influences to

identify and address mechanisms and processes to prevent and manage development of

childhood OW/OB (Russell & Russell 2019). This model features biological factors, such as

genetics, alongside environmental, psychosocial, and behavioral risk factors (e.g., family

disorganization, parenting skills, feeding practices, child appetite, temperament), and the

development of self-regulation. Such an approach can illustrate developmental processes

interacting with biological underpinnings that can be targeted in prevention and management

interventions for OW/OB. Intervening from a biopsychosocial model involves cognitive

behavioral and behavioral therapy to reframe thoughts and replace unhealthy eating

behaviors with new habits.

3.1.2 Ecological systems theory (EST)—EST embeds individual development and change within multiple proximal and distal contexts and emphasizes the need to understand

how an “ecological niche” can contribute to the development of specific characteristics, and

how such niches are embedded in more distal contexts (Davison & Birch 2001). For

example, a child’s ecological niche can be the family or school, which are embedded in

larger social contexts, such as the community and society. Individual child characteristics,

such as gender and age, interact within and between the family and community context

levels, which all influence development of OW/OB. The EST model presents various

predictors of childhood OW/OB through identifying risk factors moderated by

intraindividual child characteristics. The structure of the EST is present in various studies

examining influences of community exposures and children’s individual attributes on weight

outcomes.

3.1.3 The Six C’s Model—The Six-C’s is a developmental ecological model that includes environmental (family, community, country, societal), personal, behavioral, and

hereditary influences, and a system for categorizing environmental influences, all of which

can be adapted to each stage of child development from infancy to adolescence (Harrison et

al 2011). The Six C’s stand for: cell, child, clan, community, country, and culture, which

represent biology/genetics, personal behaviors, family characteristics, factors outside of the

home including peers and school, state and national-level institutions, and culture-specific

norms, respectively. Each C includes factors that contribute to child obesity that occur and

interact simultaneously throughout child development. For example, among preschool age

children, obesity-predisposing genes (cell), excessive media exposure (child), parent dietary

intake (clan), unhealthful peer food choices (community), national economic recession,

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(country) and oversized portions (culture), are all factors associated with obesity that can

occur simultaneously and interact during this developmental stage.

3.1.3 The developmental cascade model of pediatric obesity—The model described in the Smith et al. (2018b) article offers a longitudinal framework to elucidate the

way cumulative consequences and spreading effects of multiple risk and protective factors,

across and within biopsychosocial spheres and phases of development, can propel children

towards OW/OB outcomes. The cascade model of pediatric obesity (Figure 1) was

developed using a theory-driven model-building approach and a search of the literature to

identify paths and relationships in the model that were empirically based. The model allows

for different pathways and interactions between different combinations of variables and

constructs that contribute to pediatric obesity (equifinality), identifying multi-level risk and

protective factors spanning from the prenatal stage to adolescence stage. The complete

model can, but has yet to, be tested. The model focuses on intra- and inter-individual child

processes and mechanisms (e.g., parenting practices), while acknowledging that individuals

are embedded within the broader ecological systems. St. George et al (in press) then

conducted a systematic review of the intervention literature to elucidate the ways in which

the developmental cascade model of childhood obesity can inform and is informed by

intervention approaches for childhood OW/OB.

3.2 Psychosocial Contributors

3.2.1 Maternal mental and physical health—An emerging body of literature has shown a significant relationship between higher levels of parental stress and youths’ higher

weight status and unhealthy lifestyle behaviors (Tate et al 2015). In a prospective study,

Stout et al (2015) found that fetal exposure to stress, as evidenced by elevated maternal

cortisol and corticotropin-releasing hormone, was related to patterns of increasing BMI over

the first 24 months of life. Children of mothers experiencing psychological distress and

anxiety during pregnancy had higher fat mass, BMI, subcutaneous and visceral fat indices,

liver fat fraction, and risk of obesity at age 10 years compared to those whose mothers did

not (Vehmeijer et al 2019). Early stress can have long-lasting effects, and studies from a

nationally-representative cohort study have shown that postnatal maternal stress during the

first year has a positive longitudinal relationship with the child’s BMI up to age 5 (Leppert

et al 2018), and psychological distress at age 5 was associated with risk of obesity at age 11

in another nationally-representative cohort (Hope et al 2019). Among Hispanic children and

adolescents whose caregivers reported ≥ 3 chronic stressors, Isasi et al (2017) found an

increased likelihood of childhood obesity when compared to those whose parents reported

no chronic stressors. In a systematic review assessing the impact of maternal stress on

children’s weight-related behaviors, O’Connor et al (2017) found mixed evidence for the

relationship specific to dietary intake; however, researchers found consistent evidence for the

detrimental impact on youths’ physical activity and sedentary behavior, which was often

conceptualized as screen time. Understandably, highly stressed parents may have an

increased reliance on convenient fast-food options versus grocery shopping and preparing

fresh and healthy meals for their children and may not have the energy or wherewithal to

support their youths’ physical activity, nor engage in limit-setting behaviors specific to their

children’s screen time.

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One of the few studies using a longitudinal design did not replicate the relationship between

high parental stress and lower levels of youth physical activity, but the relationship held for

high levels of parental stress and increased fast food consumption (Baskind et al 2019).

Interestingly, this study observed an interaction effect on the relationship of high parental

stress and childhood obesity by only low-income households and among ethnic minority

children, specifically non-Hispanic black children—explaining one of the factors that

contributes to healthy disparities for childhood obesity rates in the US. In another study

using a large, prospective cohort, Shankardass et al (2014) found a significant effect of

parental stress on BMI. The researchers also observed a significantly larger effect among

Hispanics versus the total sample population, further noting that the relationship was weaker

and not statistically significant among non-Hispanic children. Due to the salient role of

caregiver stress on child health behaviors, it seems that interventions for childhood OW/OB

should incorporate stress reduction strategies for parents while simultaneously focusing

efforts on reaching racial/ethnic minority families and the economically disadvantaged.

Maternal mental health, most commonly operationalized as depressive symptoms and

diagnosis, relate to children’s risk for OW/OB. The longitudinal effects of postnatal

maternal depressive symptoms predicted obesity risk in preschool-age children, and

unhealthier lifestyle behaviors, such as high TV viewing time and low levels of physical

activity (Benton et al 2015). Children of mothers with severe depression were more likely to

be obese compared to children of mothers with fewer symptoms (Marshall et al 2018).

Maternal mental health could negatively affect child feeding behaviors such that elevated

depressive symptoms in low-income mothers have been associated with increased use of

feeding to soothe children (Savage & Birch 2017). Few interventions for childhood obesity

to date specifically target caregiver depression, but some protocols provide guidance to

engage caregivers in services to manage depression and related stressors (Smith et al 2018c).

3.2.2 Child mental health—Poor self-regulation and related constructs such as reactivity and impulsivity, are prospective obesogenic risk factors (Bergmeier et al 2014,

Smith et al 2018d). A child’s temperament describes behavioral tendencies in reactivity and

self-regulation. Negative reactivity is characterized by a quick response with intense

negative affect, and is difficult to soothe. Infants and children with negative reactivity are at

high risk of excess weight gain, and developing obesity later on and toddlers with low self-

regulation and inability to control impulses or behavior are at increased risk for obesity and

rapid weight over the subsequent nine years compared to toddlers with higher self-regulation

abilities (Graziano et al 2013). Poorer emotional self-regulation at age 3 is an independent

predictor of obesity at age 11 (Anderson et al 2017). On the other hand, the ability to delay

gratification at age 4 is associated with lower BMI 30 years later (Schlam et al 2013). It is

possible that parents of children with difficult temperament experience challenges effectively

managing children’s behaviors and setting limits, leading to irregular health routines and

increased obesity risk (Bergmeier et al 2014, Smith et al 2018d). Further, parents could

overuse food and feeding to soothe children (Anzman-Frasca et al 2012). Throughout

childhood, emotional regulation deficits and other mental health disorders continue to

predict obesity and weight gain. Emotional regulation in conjunction with stress during

childhood is highly linked to low physical activity, emotional eating, irregular and disrupted

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sleep, and later development of obesity (Aparicio et al 2016). A longitudinal study

examining emotional psychopathology in preadolescence saw that boys diagnosed with a

social phobia, panic disorder or dysthymia (persistent depressive disorder) had higher waist

circumference and/or BMI, and girls diagnosed with dysthymia had increased waist

circumference at the three-year follow-up (Aparicio et al 2013). In a prospective study,

overweight children who reported binge eating at ages 6–12 years gained 15% more fat mass

over a period of four years compared to overweight children with no binge eating (Tanofsky-

Kraff et al 2006). The predictive role of mental health on physical health conditions and

subsequent comorbidities can be costly and burdensome. Children with obesity-related

health conditions (e.g., type 2 diabetes, metabolic syndrome) and a comorbid psychiatric

diagnosis (e.g., depressive mood disorder, bipolar disorder, attachment disorder) have higher

healthcare utilization and costs per year compared to children without a comorbid

psychiatric diagnosis (Janicke et al 2009a)

There is an association between OW/OB and depression in childhood and adolescence, but

there is mixed evidence of the directionality of this effect among children and adolescents. A

review of high quality studies by Mühlig et al (2016) saw that among nine studies examining

the influence of depression on weight status, six found no significant influence. Of the

studies that reported significant associations, one study saw effects only among female

adolescents, another only for male adolescents, and a third showed effects of adolescent

depressive symptoms on adult obesity at age 53 years only in women. Conversely, OW/OB

status can have significant influences on risk of low self-esteem and depressive symptoms/

diagnosis in adolescence, as discussed later in this paper.

3.2.3 Stigma/bullying—Weight-related stigma, defined as subtly or overtly having discriminatory actions against individuals with obesity, toward children with obesity can

impair quality of life, and contributes to unhealthy behaviors that can worsen obesity such as

social isolation, decreased physical activity, and avoidance of health care services (Pont et al

2017). Unfortunately, stigma is widespread and tolerated in society, furthering the reach of

negative harm. Children with obesity face explicit weight bias and stigma from multiple

environments including from parents, obesity researchers, clinical settings, and school.

Parents not only demonstrate implicit bias against childhood obesity, but also implicit and

explicit biases against children with obesity (Lydecker et al 2018). Even among obesity

researchers and health professionals, significant implicit and explicit anti-fat bias, and

explicit anti-fat attitudes increased between 2001–2013 (Tomiyama et al 2015). Exposure to

stigma and weight bias can have damaging psychosocial effects on children, such that

stigma can mediate the relationship between BMI, depression, and body dissatisfaction

(Stevens et al 2017).

Weight stigma can also initiate bullying and weight related teasing, which can have serious

psychological consequences such as depression among children, further weight gain and

lessen motivation to change. A nationally representative sample of children ages 10–17

years saw that OW/OB adolescents were at higher odds of being a victim of bullying, and

also higher odds of perpetrating bullying and victimizing others (Rupp & McCoy 2019). The

children at higher odds of engaging in bullying, or being bullied were also at significantly

higher odds of having depression, difficulty making friends, and conduct problems

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compared to OW/OB adolescents who were not bullies or victims of bullying. The

relationship between obesity and bullying needs to be addressed through bullying

engagement, and coping skills for victimization to prevent and manage associated behavioral

and depressive symptoms.

3.2.4 Family functioning and home environment—Evidence suggests a link between general family functioning, parent–child relationships, communication, and use of

positive behavior support strategies and childhood OW/OB (see Smith et al 2017a).

Influence of general parenting styles, as opposed to the more specific feeding styles, have

been extensively studied and linked to children’s diet, physical activity, and weight (Shloim

et al 2015). Children raised with an authoritative (warm and demanding) parenting style had

healthier diet, higher physical activity levels, and lower BMI’s than those raised with the

other styles (Sleddens et al 2011). Parents proactively structuring home environments to

support and positively reinforce healthy dietary and physical activity behaviors also play a

key role in children’s healthy lifestyles (Smith et al 2017b). Children exposed to less

supportive environments consisting of family stress, father absence, maternal depression,

confinement, and unclean home environments at 1 year of age has been associated with high

BMI at age 21 (Bates et al 2018). Taken together, family participation and building parenting

skills can play a salient role in the prevention of childhood OW/OB (Pratt & Skelton 2018,

Wen et al 2011).

4.0 PREVENTION AND MANAGEMENT OF OVERWEIGHT AND OBESITY

This section discusses the state-of-the-science in childhood OW/OB prevention and

management along with salient factors related to their implementation in varied healthcare

delivery systems. The current climate is being shaped by the position of the American

Medical Association. In 2013, the Board voted to classify obesity as a disease that requires

medical attention. This classification aimed to emphasize health risks of obesity, remove

individual blame, and create new implications and opportunities for intervention. This

classification can help to further: 1) a broader public understanding of the obesity condition

and associated stigma; 2) prevention efforts; 3) research for treatment and management; 4)

insurance reimbursement for intervention; and 5) medical education (Kyle et al 2016). In

primary healthcare settings specifically, the US Preventive Services Task Force (USPSTF)

gave childhood obesity screening and family-based intervention a “B” grade for evidence of

effectiveness (US Preventive Services Task Force 2017), which is sufficient to open

insurance reimbursement streams for activities related to the prevention and management of

childhood OW/OB that did not exist before. Reimbursement has been a significant barrier to

uptake of effective interventions and the impact of the USPSTF in removing this impediment

is not yet fully known.

A number of high-quality systematic reviews and meta-analyses have been published in

recent years, which provide the most contemporary perspective of the effectiveness of

interventions for prevention and management, as well as revealing wide variability and

inconsistent findings. For example, Peirson et al (2015a) saw that prevention interventions

were associated with slightly improved weight outcomes compared to control groups in

mixed-weight children and adolescents. However, intervention effects were not consistent

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among each intervention strategy tested, suggesting that specific characteristics of the

interventions, such as setting, participants, dose, and tailoring, should be examined to

determine what is and is not effective in achieving desired outcomes.

Intervention strategies for the prevention and management of child OW/OB occur in various

contexts and within, and in coordination with, multiple service delivery systems. This is due

in large part to the risk factors inherent to familial, school, and community/societal levels.

Relatedly, for prevention in particular, there is some correspondence between the sample

being targeted and the context, such that community and school-based interventions are far

more likely to be universal (sample does not consider weight status) or selective (target

sample is overweight or specifically targeted due to being at-risk for obesity; e.g., ethnic

minority, low income) compared to the indicated (majority of target sample is in the obese

range) models more commonly found in primary and specialty healthcare systems.

Unsurprisingly, the specific intervention targets and behavior change strategies align with

the context and approach (St. George et al in press).

4.1 Community Interventions

Community interventions are defined as incorporating policies and strategies aimed at

reducing the population risk of obesity through legislation, modifications to the built

environment, provision of accessible resources, and changes in economic/pricing/food

subsidies (Bleich et al 2013). Community interventions can involve the use of media,

businesses (e.g., restaurants), community health services, community gardens, community or

recreational centers, city planning, and the local governments (Karacabeyli et al 2018).

Interventions delivered in community settings have the ability to provide high degrees of

access and exposure to strategies and programs to racially diverse, low-income children,

who are at the highest risk of OW/OB. Interventions delivered in community settings can be

effective, but the impact could be diminished through the lower likelihood of intervention

completion due to living in lower socioeconomic circumstances and other obstacles (Fagg et

al 2015).

In comparison to other settings, such as the school and family level, there were fewer studies

conducted at the community level in a recent review (Bleich et al 2018). This may be due to

the numerous challenges and complications involved in building community capacity and

engaging community leaders, stakeholders, community agencies, and city organizations.

Alternatively, it could reflect a greater focus to date on other contexts and intervention

targets, which we discuss in the following sections. To address effectiveness and

sustainability, a combined clinical and community intervention could hold promise,

especially for racially diverse children living in a low-income community, who are most at-

risk. A study by Hoffman et al (2018) showed that an integrated clinic-community model is

feasible and improves physical activity and quality of life when compared to

multidisciplinary treatment only in clinical care settings.

To summarize, there is promise in community-based interventions that involve either the

health clinic and community partnerships or community and school partnerships.

Interventions using a community-based participatory approach and a strong quasi-

experimental design could achieve the long term goal of reducing both child BMI, the

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prevalence of OW/OB in childhood, and remission of obesity in children (Economos &

Hammond 2017).

4.2 School-Based Interventions

School-based interventions are defined as taking place during school hours or after-school

hours for children in kindergarten through high school, and being focused exclusively in the

school or delivered primarily in the school setting with secondary settings of family/home,

primary care, or community (Bleich et al 2018). Considering that the majority of children

spend a significant amount of their day in school, many preventive interventions have

leveraged schools as an entry point to improve the obesogenic environment by promoting

more physical activity in physical education classes and recess, improving school

playgrounds and nutritional options in school cafeterias, and providing healthy lifestyle

education in classes or other school policies (Ickes et al 2014). Previous reviews recommend

using multi-component interventions targeting two or more health behaviors (i.e., physical

activity, dietary outcomes, sedentary behavior) to improve adiposity outcomes when

compared to single-component interventions (e.g., Wang et al 2015). Interestingly, well-

designed school-based studies are effective in improving dietary behavior, but typically do

not see statistically significant differences in child BMI between intervention and control

schools, except for among children who are already in the obese range (Bogart et al 2016).

While increasing fruit, vegetable and water consumption are important, the health behavior

modifications are not sufficient for significant long-term obesity management. A way this

has been addressed is partnerships between schools and community-based interventions

which also engage parents. In a review, Ickes et al (2014) found that less than half of

childhood obesity interventions incorporated parents; of those studies involving parents,

75% demonstrated positive outcomes in reducing BMI or weight status. In a synthesis of

systematic reviews and meta-analyses of school-based interventions, long-term interventions

with a combination of diet and physical activity components and family or parental

involvement significantly reduced weight among children (Khambalia et al 2012). Aligned

with previous research, Bleich et al (2018) found that school-based interventions that used a

multi-component approach of both physical activity and nutrition with some intervention

with families in the home had the largest effects. A systematic review and meta-analysis by

Wang et al (2015) observed that strength of evidence of obesity prevention programs for

children ages 2–18 years was dependent on intervention type, and delivery setting(s).

Strength of evidence was high for physical activity-only interventions delivered in school

settings with home involvement, or combined diet and physical activity interventions

delivered in school settings with home and community involvement. They also found

moderately strong evidence when delivering combined interventions in school-based settings

alone, in schools with home or community component, or in community with a school

component.

Bleich et al (2018) also reviewed a smaller number of pre-school interventions and found

some promise in both single component interventions—focusing solely on physical activity

—and multi-component interventions. In two other reviews evaluating early child care

center-based interventions, both found promising evidence for multi-component

interventions and multiple levels influencing the child, parent, teachers/staff, and class

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(Sisson et al 2016, Ward et al 2017). An exemplar study, Natale et al (2017) conducted an

early childhood multi-level obesity intervention, which included menu modifications at the

child care center, a nutrition and physical activity educational curriculum for preschoolers,

and a healthy meal preparation and role modeling curriculum for parents. At two-years

follow-up, the researchers observed significantly less increase in BMI percentile among the

intervention group versus controls. Overall, strong obesity prevention interventions in early

care and education settings were associated with healthy eating and anthropometric

outcomes, which was further improved by parental engagement. In sum, the preschool and

school contexts hold promise for improving weight-related behaviors and adiposity

outcomes; however, evidence is clear that parents should be engaged in the process of

supporting and reinforcing their children’s health behaviors for these programs to be

maximally effective (Ward et al 2017).

4.3 Family-Based Interventions

The home environment (e.g., family routines, limit setting, household chaos, crowding) has

long been considered one of the most powerful influences on children’s healthy behaviors

and OW/OB outcomes (Bates et al 2018). Playing an integral role in physical activity, diet,

screen time, and sleep, parents can exhibit positive parenting practices (e.g., limit-setting,

role modeling) and provide a healthy, supportive environment (e.g., provisions of fresh fruits

and vegetables), thereby shaping their children’s lifelong habits and preventing the onset of

childhood obesity (for a review see Smith et al 2018d). Family-based interventions are

defined as involving either passive or active parental involvement, often with parents viewed

as the primary or sole agents of change (Sung-Chan et al 2013). Active parental involvement

entails repeated engagement, such as participation in workshops, counseling, or educational

sessions; passive involvement does not integrally involve the parent or guardian (e.g.,

brochures, newsletters).

In a review evaluating family-based interventions for OW/OB prevention, Ash et al (2017)

found a significant increase in the number of family-based interventions with just six studies

published in 2008 compared to 35 studies in 2013. The majority of studies employed

rigorous RCT study designs (73%), but almost two thirds of the studies were short-term and

implemented for less than a year. A fraction of studies occurred in multiple settings and over

half targeted multiple components beyond diet and physical activity, such as screen time or

sleep. Many preventive studies targeting young children (pre-natal to five years old) tend to

use home or primary-care based settings with parental involvement, whereas interventions

targeting older children tended to take place in community- and school-based settings. These

findings are commensurate with the review of St. George et al (in press), which showed a

decrease in parental involvement and family-based intervention strategies with child age.

This dovetails with the conclusions of Kothandan (2014) that family-based interventions

demonstrated effectiveness for children younger than twelve, but for children twelve and up,

school-based interventions were most effective in the short-term.

Regarding preventive interventions specifically, the majority of interventions have been

tested among low SES families and predominantly white families (Ash et al 2017).

Hispanics/Latinx have been well-represented in US intervention studies in comparison to

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other ethnic minorities (i.e., African Americans, Asians, and indigenous groups). Latinx are

particularly well-suited to participate in family-based interventions given their cultural

emphasis on familial values; however, a recent meta-analysis noted diminishing intervention

effects with a higher proportion of Hispanic children (Ling et al 2016), which was attributed

to a lack of culturally competent interventions to address language barriers and dietary

preferences. In addition to incorporating other ethnic minorities and culturally appropriate

interventions, Ash et al (2017) suggested that preventive family-based interventions should

account for non-traditional families and their different needs and family dynamics.

In regard to family dynamics and interactions, poor family functioning has been linked with

an increased risk of obesity, obesogenic behaviors, and adverse health outcomes (e.g., Pratt

& Skelton 2018). Family-based care for childhood OW/OB involves targeting dietary and

physical activity behaviors along with the rules of the family unit, family health routines,

communication, and dynamics (Pratt & Skelton 2018). Existing protocols involve family

counseling for diet and physical activity change in the home environment, with some

approaches also targeting more general parenting and family management skills that have

been found to impact OW/OB status of the child (Smith et al 2018a, Smith et al 2018b,

Smith et al 2017b). Interventions including both parents and children have shown more

positive short and long-term effects on child weight when compared to parent-only

interventions and controls in some studies (Yackobovitch-Gavan et al 2018), whereas others

have found comparable effects for parent-only and child-involved family-based approaches

(Boutelle et al 2017). Further, parent-only interventions have been shown to be more cost-

effective (Janicke et al 2009b). In a meta-analysis evaluating comprehensive behavioral

family lifestyle interventions treating pediatric obesity, Janicke et al (2014) found an overall

standardized effect size of 0.47, which indicates a small-to-moderate effect on BMI. The

dose of treatment (i.e., number of intervention sessions, minutes spent in treatment) was

positively related to the treatment effect, which provides support for the notion that more

intense and longer interventions are associated with better outcomes, a conclusion also made

by (Whitlock et al 2010). In addition, age was a significant moderator for weight outcomes

indicating that older children had larger and more beneficial intervention effects than

younger children.

Specifically, family-based interventions targeting positive behavior support have been used

to address key mechanisms of change specific to promoting children’s healthy lifestyle

behaviors (Smith et al 2017b). Positive behavior support has been identified as a way to

reduce weight gain through improving the caregiver’s ability to support and work with the

child toward a healthier diet and improved physical activity. Long-term prevention trials

using family-based intervention to target positive behavior support found that children

randomized to the intervention had lower BMI in the years following participation (Smith et

al 2015). This finding was particularly promising given that these trials did not explicitly

focus on child weight in any way; thus, prevention of childhood OW/OB was a spillover

effect.

Given the various ways individual, interpersonal, and family health behaviors contribute to

child obesity, a tailored family-based intervention could be effective in identifying specific

family needs and providing appropriate resources. In a family-based tailored intervention,

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Taylor et al (2015) saw that the children of families randomized to the tailored treatment had

significantly lower BMI compared to families in the usual care group. Additionally, children

in the tailored treatment had better dietary behaviors and were more physically active than

children in the treatment as usual group. Smith, Berkel et al. (2018b) adapted the highly

effective and well-known individually-tailored family-based prevention program called the

Family Check-Up® (Dishion et al 2008) to specifically target obesogenic behaviors with the

aim of preventing obesity and excess weight gain in children ages 2 to 12 years. This

adaptation is referred to as the Family Check-Up® 4 Health and is being tested in two large

RCTs in coordination with pediatric primary care (Smith et al 2018a) and with community-

based family resource centers and public schools (Berkel et al 2019) in low-income

neighborhoods with racially/ethnically-diverse families at highest risk for childhood

OW/OB.

4.4 Primary Healthcare

Primary care interventions are defined as health promotion or weight management programs

conducted within or in close coordination with the primary healthcare system. Primary care

is viewed as an ideal, real world environment for weight management interventions because

of accessibility and frequency of visits (i.e., routine well-child visits) (Davis et al 2007). In a

meta-analysis evaluating weight management interventions delivered in primary-care

settings, Mitchell et al (2016) found an overall effect size of 0.26, indicating a small

treatment effect, and a smaller effect than has been found in broader meta-analytic reviews

(e.g., Janicke et al 2014, Whitlock et al 2010). The dose-response relationship was

significant, where the number of treatment contacts, length of treatment in months, and the

number of visits with the pediatrician was associated with larger treatment effects.

A systematic review examining randomized control trials targeting obesity management in

children ages 2–5 years saw five of six interventions, all in ambulatory healthcare settings,

had significant decreases in child weight, with sustained intervention effects through follow-

up (Ling et al 2016). The effective interventions actively involved parents in health

education, group meetings, physical activity sessions, or behavioral therapy.

4.5 Interventions by Developmental Period

In a review of interventions of OW/OB from birth to age 18, St. George et al (in press)

identified 74 distinct interventions reported across the 141 included articles. They were

categorized based on the child’s age at entry into the intervention: prenatal/infancy (< 2

years; n = 4), early childhood (2–5 years; n = 11), childhood (6–11 years; n = 38), early

adolescence (12–15 years; n = 18), and late adolescence (16–18 years; n = 3).

Developmental stage of the child has also been found to align with the strategy, such that

interventions in the prenatal and infancy periods are nearly all universal, whereas during

childhood and adolescence, as compared to early childhood, the burden of disease is larger

and intervention strategies more often target selected and indicated samples with greater

intensity (St. George et al in press).

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5.0 EXPERT RECOMMENDATIONS

5.1 Youth Health Behaviors

It is recommended that children and adolescents aged 6–17 years should achieve ≥ 60

minutes of physical activity each day (Piercy et al 2018). The 2015–2020 Dietary Guidelines for Americans recommend consuming a variety of fruits and vegetables, whole grains, proteins, low-fat dairy products, and limiting intake of sodium, solid fats and added sugars

beginning at age 2 years (DeSalvo et al 2016). Unfortunately, only 21.6% of children 6–19

years reach the recommended 60 minutes of physical activity at least five days per week

(Alliance 2016). Dietary quality impacts weight gain and OW/OB, and it is estimated that

the obesity epidemic largely contributed to statistics showing a declining life expectancy,

which occurred in 2015 for the first time in 30 years (Ludwig 2016).

The American Academy of Pediatrics (AAP) recommends that children under 18 months

should have no screen time aside from video-chatting, and children ages 2–5 years engage in

one hour of screen time per day of high-quality programs with parents. Children ages 6 and

above should have limited media exposure, ≤ 2 hours per day, which should not interfere

with sleep, physical activity, or other health behaviors. The AAP recommends that families

should have “media-free” time together, and “media-free” locations such as in the dining

room or bedroom to avoid interfering with meals and sleep duration (American Academy of

Pediatrics Council on Communications and Media 2016). The World Health Organization

asserts that screen time brings no benefit to children, and infants younger than one year

should have no electronic screen exposure, and children age 2–4 years should not have more

than one hour of daily “sedentary screen time.” In recent years, the portability of screen

devices has led to an overall increase in screen time, with the majority of US youth

exceeding screen time guidelines by a wide margin (averaging more than 7 hours daily)

(Barnett Tracie et al 2018).

The most recent AAP guidelines recommend that children ages 1–2 years sleep 11–14 hours

per 24 hours, children 3–5 sleep 10–13 hours, children 6–12 sleep 9–12 hours, and teenagers

ages 13–18 should regularly sleep 8–10 hours (Paruthi et al 2016). Certain behaviors such as

a regular routine, avoiding large meals close to bedtime, being physically active during the

day time, and eliminating electronic devices in the bedroom are associated with better sleep

(Irish et al 2015). According to the CDC, 60% of middle schoolers and 70% of high

schoolers do not meet regular sleep recommendations.

5.2 Behavioral Intervention

Family-based intervention is recommended by The National Academy of Medicine, the

American Academy of Pediatrics, and the Endocrine Society, among others, as the preferred

approach for the management of OW/OB from infancy to adolescence. Based on a

systematic review, the USPSTF concluded that lifestyle-based weight loss interventions (not

necessarily family-based) consisting of 26 or more hours of intervention engagement are

likely to assist children and adolescents in weight management (O’Connor et al 2017).

Recommendations from a number of expert committees and task forces support targeting the

following behaviors for prevention and management of childhood OW/OB: limiting

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consumption of sugar sweetened beverages, consuming daily recommended fruit and

vegetables, limiting screen time, increasing physical activity, eating breakfast, limiting

eating out at restaurants, encouraging family meals, and limiting portion sizes. The majority

of existing interventions target multiple behaviors, but some have been designed for discrete

behaviors.

5.3 Pharmacologic Intervention

Orlistat is the only FDA-approved medication for treating obesity for pediatric patients ages

12 years and older. Side effects in the gastrointestinal area are common in children, and

further clinical trials are needed to evaluate medication risk and benefits among pediatric

patients (Chao et al 2018). Expert opinion states that Orlistat, in conjunction to lifestyle

changes, leads to modest weight loss and could benefit children in the indicated age range

with obesity but tolerability limits its use (Kelly & Fox 2018). And results are not

unequivocal. In a meta-analysis looking at primary-care based interventions, Peirson et al

(2015b) found a medium effect (standardized effect size [ES] = −0.54) favoring behavioral

interventions when compared to Orlistat plus behavioral intervention components (ES =

−0.43). Additional research is needed on both effectiveness and tolerability in youth.

Additionally, new pharmacologic options continue to be developed and tested and could

reach the market in the next few years if approval is granted (Kelly & Fox 2018).

5.4 Surgical Intervention

The American Society for Metabolic and Bariatric Surgery Pediatric Committee’s best

practice guidelines selection criteria are based on systematic reviews of co-morbidities, risks

and outcomes, important team members, and patient selection. They recommend that

adolescents being considered for a bariatric procedure should have a BMI of ≥35 kg/m2 with

major co-morbidities such as type-2 diabetes mellitus, moderate to severe sleep apnea, or

severe nonalcoholic steatohepatitis (Michalsky et al 2012). Data show that bariatric surgery

in morbidly obese adolescents can greatly impact weight loss, and attenuate or resolve

associated chronic disease. However, adolescents undergoing bariatric surgery should be

assessed for capability to adhere to follow-up care regimens to ensure proper nutrition intake

and care. The committee also recommends a multidisciplinary team for adolescents

undergoing bariatric surgery, which could include an experienced bariatric surgeon, pediatric

specialist, registered dietitian, mental health specialist, care coordinator, and exercise

physiologist.

6.0 CLINICAL IMPLICATIONS OF CO-OCCURRING MEDICAL AND

PSYCHOLOGICAL CONDITIONS

6.1 Co-Occurring Medical Conditions

The pro-inflammatory disease nature of obesity and contributing health behaviors affects

normal physiology and metabolism, and can cause many associated diseases (Gonzalez-

Muniesa et al 2017). If left untreated, obesity can lead to serious health conditions including

type-2 diabetes, cardiovascular disease, asthma, obstructive sleep apnea, high blood

pressure/hypertension, non-alcoholic fatty liver disease, hepatocellular carcinoma, and

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psychosocial problems (e.g., Nobili et al 2015). Recent research indicates increased risk of

cardiovascular disease incidence, morbidity (ischemic heart disease, stroke), and mortality in

adulthood associated with being in the obese BMI range in childhood or adolescence

(Sommer & Twig 2018). Obesity prevention and management interventions in childhood are

imperative for averting the burden of associated comorbidities.

6.1.1 Type-2 diabetes—Children with obesity are four times as likely to develop type-2 diabetes compared to children with a normal BMI (Abbasi et al 2017). Ethnic minority

children of low income are at increased risk, and have limited maintenance and glycemic

control, furthering the probability of developing additional health complications down the

line (Pulgaron & Delamater 2014). Metformin is the main treatment of type-2 diabetes in

youth and adults, though emerging evidence implicates a role in treating children with

obesity and a family history of type-2 diabetes (e.g., Warnakulasuriya et al 2018). Exercise

and lifestyle interventions have had significantly positive health effects in adults, however

trials evaluating effects in youth with type-2 diabetes are limited. Given the data from adult

trials, the American Diabetes Association recommends that youth with type-2 diabetes meet

the 1-hour per day physical activity goal to manage symptoms and decrease health risks

(Colberg et al 2016).

6.1.2 Obstructive sleep apnea—Pediatric obstructive sleep apnea (OSA) involves a child having disrupted breathing due to partially or completely blocked upper airways during

sleep (Narang & Mathew 2012). Obesity confers the most significant risk for OSA. As many

as 60% of children and adolescents with obesity have OSA, or some sort of disrupted

breathing during sleep (Narang & Mathew 2012). Obesity and OSA have additional

comorbidities and impairments including excessive daytime sleepiness, neurocognitive

function, reduced physical activity, cardiovascular burden, and hypertension, further

complicating quality life of children with obesity (Blechner & Williamson 2016). Obesity

management such as increased physical activity and a healthy diet are recommended for

OSA treatment, as well as surgical procedures, if appropriate.

6.1.3 Asthma—Asthma is one of the most common chronic diseases among children and adolescents: 10.1% of children ages 5–14 years had asthma in 2016 (National Center for

Health Statistics 2019). Although both obesity and asthma rates have been increasing, it

does not appear that obesity has been contributing to the increased asthma prevalence rate

(Akinbami et al 2018). This does not discount the risks of obesity on asthma and its unique

effects on asthma symptoms. OW/OB children have been observed to have higher

prevalence of asthma, and exacerbation as early as preschool age compared to normal

weight children (Lang et al 2018). Additionally, OW/OB children have reported distinct

asthma symptoms, such as greater shortness of breath, reduced airway hyperresponsiveness,

and loss of asthma control, compared to normal weight children (Lang et al 2015). The

relationship between asthma and OW/OB should be further investigated.

6.1.4 Hypertension—Hypertension, like obesity, has been increasing among youth and is associated with increased cardiovascular disease risk throughout the lifetime (May et al

2012). The greatest risk factor for pediatric hypertension is elevated BMI (Falkner et al

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2006). About 3% of children in the general population have hypertension, compared to

about 25% of obese children (Shatat & Brady 2018). In a meta-analysis examining

cardiovascular risk factors, compared with normal weight children, systolic blood pressure

was higher by 4.54 mm Hg (n=12169, 8 studies) in overweight children, and by 7.49 mm Hg

(n=8074, 15 studies) in obese children (Friedemann et al 2012). A study examining

childhood hypertension and OW/OB in school children saw that 2.2% of the sample had

hypertension, and 37% of those cases could be attributed to OW/OB status (Chiolero et al

2007). A review shows that children with obesity-related hypertension are at increased risk

of cardiovascular morbidity and mortality (Wuhl 2019). About 3.8%–24.8% of children with

OW/OB have hypertension, though these rates could be higher due to inconsistences and

challenges with diagnoses (Flynn et al 2017). The risks of hypertension on children’s

lifetime health emphasize the importance of preventing obesity early on.

6.1.5 Nonalcoholic fatty liver disease (NAFLD)—NAFLD is the leading cause of liver disease, leading to a shorter life expectancy due to associated comorbidities; one of

which, non-alcoholic steatohepatitis, is projected to be the leading indication for pediatric

liver transplant by 2025 (Charlton et al 2011). Epidemiological studies consistently show

associations between NAFLD and adiposity, unhealthy diet, and sedentary behavior (Dunn

& Schwimmer 2008). Prevalence of NAFLD is especially high in young people who have

obesity such that 22.5%–52.8% of children with obesity have NAFLD compared to 2.6% of

all children (Anderson et al 2015). Child obesity has the highest risk in the development of

NAFLD during childhood (Hays & McGinnis 2018). A longitudinal study of participants

ages 3–18 years were followed for 31 years, and saw that child OW/OB was associated with

increased risk for adult NAFLD (Cuthbertson et al 2018). The associated risk was removed

if participants obtained a normal range BMI by adulthood, emphasizing the salient role of

weight management. The high prevalence of NAFLD among children with obesity, and

effectiveness of weight change in treating this condition, emphasizes the need for prevention

and management of obesity. Smith et al (2017a) found that among children who had

NAFLD, poorer family functioning was significantly related to higher BMI, elevated levels

of cholesterol, HbA1c, and glucose. Their study exposes the critical role of family

functioning on child health, and the importance of using targeted intervention to prevent, and

manage obesity and associated disease using a family-centered approach. Weight being the

most modifiable factor, the mainstay of NAFLD treatment is lifestyle behavior modifications

aimed at weight loss (Marchesini et al 2015).

6.2 Co-Occurring Psychological Conditions

6.2.1 Self-esteem/depression—Children with OW/OB are more likely to experience low self-esteem, and develop depressive symptoms during adolescence compared to normal

weight peers (e.g., Mühlig et al 2016). This relationship can be attributed to multi-level

factors including health behaviors, parenting styles, and family functioning. A review by

Hoare et al (2014) suggests that obesogenic risk factors, such as infrequent physical activity,

sedentary behavior, poor diet quality, and adiposity were associated with depressive

symptoms in adolescents. Conversely, healthier eating patterns were associated with

decreased depressive symptoms. Child eating disorder pathology, emotionally-manipulative

parenting style, and lower child social status have been associated with depressive

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symptomatology among children with OW/OB (Sheinbein et al 2019). Children in poorly

functioning families with low self-esteem participating in weight loss interventions have

been observed to have poor 6-month outcomes, suggesting that multiple social-ecological

factors need to be addressed when targeting depressive symptoms in children with OW/OB

(Taylor et al 2017). Further, negative psychological experiences more generally, such as

trauma and stigma, trigger emotional eating, leading to an ongoing obesity-depression cycle

(Milaneschi et al 2019).

6.2.2 Eating disorders—Children with OW/OB have a high prevalence of disordered eating attitudes and behaviors, which can increase risk of developing eating disorders in

adulthood. A high proportion of adolescents with restrictive eating disorders report a history

of OW/OB (Lebow et al 2015). Additionally, it is estimated that over a quarter of youth with

OW/OB have binge and loss of control eating (He et al 2017). Adolescent girls with OW/OB

experiencing overvaluation of weight—so concerned with weight that self-evaluation is

influenced—are at higher risk of starting to binge eat weekly 2 years later, have more severe

depressive symptoms, and continuous overvaluation (Sonneville et al 2015). The

bidirectional relationship of obesity and eating disorders, including eating disorder

psychopathology, should be properly evaluated during treatment planning.

7.0 IMPLEMENTATION AND RESEARCH TRANSLATION CHALLENGES

One of the abundant challenges for the field is the translation and implementation of

effective interventions to the real-world service delivery systems that can reach those most in

need. This so-called research-practice gap is pronounced in obesity prevention and

management given the preponderance of untested, usual care approaches currently in use;

the persistence of debunked myths about causes and effective intervention approaches (e.g.,

fad diets); and the incongruence between what is being developed by experts and what is

acceptable, feasible, and sustainable in existing systems given the constraints of the

workforce, space, and funding. This says nothing about the consumer of evidence-based

interventions, who historically have had only cursory involvement in the design and

deployment of interventions. This has contributed to low engagement rates and high attrition

from more intensive OW/OB interventions (Lydecker & Grilo 2016). Raising public and

caregiver concern about the risks posed by OW/OB in childhood and adolescence would also

facilitate engagement and retention. Currently, many parents with children with obesity

underestimate their children’s weight (Lydecker & Grilo 2016) and are thus unlikely to seek

intervention or to follow through with a referral for intervention. Add the stigma in society

surrounding obesity and the shame parents experience concerning their child’s weight, and

traditional approaches to care will continue to be underutilized.

While many of the aforementioned conceptual models encapsulate the multiple levels

contributing to childhood obesity, researchers are trying to elucidate which combination of

levels and service contexts have greatest effectiveness, and which implementation strategies

best address the complexity at levels of the community, school, family, and primary care.

Implementation strategies are defined as the methods or techniques used to enhance the

adoption, implementation, and sustainability of a clinical program or practice (Proctor et al

2013). They are the actions taken on agents in the system of care itself, and rarely only on

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the patient or client that is the recipient of the clinical program or practice. The first iteration

of the Childhood Obesity Research Demonstration Projects (CORD 1.0), a program of

research administered by the CDC, examined multi-sector intervention implementation in

schools, community centers, early care and health centers, and pediatric primary care

practices. The three projects around the US, identified the facilitators and barriers of

implementing multi-setting interventions targeting levels of the socioecologial model in

racially diverse, lower-income communities (Dooyema et al 2017). CORD 1.0 projects

identified common implementation barriers in schools, rural communities and community

centers, including staff turnover, limited resources, and competing needs for existing

requirements (such as standardized testing in schools) (Chuang et al 2016, Ganter et al

2017). Interventions in rural communities and multiple settings benefited from engaging

parents and obtaining support from organization members and leadership (Chuang et al

2016, Ganter et al 2017). Facilitators of school interventions included using the principal as

a champion and using students to engage other students (Blaine et al 2017). Low-income

primary care settings showed that only about 27% of referred patients enrolled in the

intervention (Barlow et al 2017). Such knowledge assists in the design of future studies to

develop effective, accessible, and acceptable interventions for those needing it most.

These implementation challenges are not unique to childhood obesity but the complexity of

the problem will require more rapid translation of discoveries in research with bidirectional

input from successes and failures in practice back to researchers. Last, improving the

packaging of evidence-based programs can provide potential implementers with a “ready off

the shelf” product that requires less involvement by the intervention developers, which is a

primary contributor to the high cost of adopting a new program (Jordan et al 2019), and can

arguably aid implementers in delivering interventions with fidelity. This is the goal of the

CDC’s Childhood Obesity Research Demonstration (CORD) 3.0 Project (https://

www.cdc.gov/obesity/strategies/healthcare/cord3.html). However, the scale up penalty—

reduced effects as interventions are widely disseminated and adopted—has been shown in

the childhood obesity literature to be about 75% of efficacy studies (McCrabb et al 2019),

but implementation scientists have argued for dynamic adaptation that retains effectiveness

while also increasing sustainability (e.g., Chambers et al 2013). This is an area in need of

attention as interventions are taken to scale.

8.0 RECOMMENDATIONS FOR FUTURE RESEARCH

Reviews of interventions for childhood OW/OB show variability in effectiveness, often

changing health behaviors but not weight, thus exposing the difficulties of addressing and

managing this public health crisis. There are a number of directions for future research to

improve outcomes and address the challenges of wide-scale implementation.

1) Interventions need to be integrated across systems.

Given the multifaceted, multilevel, and interrelated nature of OW/OB development, if

interventions are to be maximally effective there needs to be an integration of multiple

service systems (primary care, schools, communities, child care, the home) for the delivery

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of multicomponent interventions that utilize behavioral, structural, environmental, policy,

and biomedical approaches.

2) There is no “one size fits all.”

More complex, individual child and family interventions need to be tailored both in terms of

content and implementation strategy to best align with the personal needs of those involved.

This means flexible, adaptive, or modularized intervention protocols addressing the cadre of

potential health behaviors and related individual and familial risk factors of OW/OB present,

and getting the intervention to families in a manner that is engaging, accessible, and has

wide reach.

3) Consider implementation earlier.

Researchers developing interventions for childhood OW/OB ought to consider their

implementability from the beginning using the framework of “designing for dissemination

and implementation” (Dearing et al 2013), which considers the capacities, needs, and

preferences of the end users (service delivery systems, children/families, funding

mechanisms) during design and testing. Another method for speeding translation is to adapt

existing programs for new service contexts and new populations, rather than following the

traditional pipeline of treating something different as “new” and having to establish efficacy

and effectiveness before moving to implement. This concept has been referred to as “scaling

out” (Aarons et al 2017) and it has been applied in childhood OW/OB prevention and

management (Smith et al 2018b). Scaling out is a critical method for implementation

research to address the health inequities and disparities of childhood obesity (McNulty et al

2019).

4) Engage the community to enhance scalability and sustainability.

Berkel et al (in press) engaged a diverse group of stakeholders, including payors, in the

adaptation and delivery processes of a recent trial of the Family Check-Up® 4 Health as a

means of increasing the likelihood of sustained adoption beyond the funded trial. Economos

and Hammond (2017) suggest that community-level research should employ novel

techniques of systems mapping and causal loop diagramming, which can help stakeholders

to visualize the interrelated processes and elements that are relevant to the intervention. They

also suggest using agent-based modeling and other simulation methods to help encapsulate

the complex dynamics involved in implementing successful community-based interventions.

Tailoring strategies to local communities and deepening engagement holds promise in

enhancing sustainability and scalability of community-based interventions.

5) Research rigor—scale up balance.

Future directions should address the shortcomings of less rigorous study designs, which

inherently increases the risk of confounding and presents challenges in attributing changes in

the outcome to intervention effects, but as research translation moves toward scaling up after

establishing effectiveness, this tradeoff is both expected and encouraged to increase external

validity. Additionally, research is needed to determine the appropriate length and dosage of

interventions, along with clear reporting of outcomes, consistency of measures, and long-

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term follow ups (Bleich et al 2018, Ickes et al 2014, St. George et al in press). Echoing

Karacabeyli et al (2018), we also recommend collecting process evaluation and outcome

data in order to understand the complex causal chain and to help bolster inferences in regard

to the effectiveness and implementation of the intervention using hybrid designs .

6) Engagement and participation are critical challenges.

Large community trials in particular often suffer high attrition rates because of mobile

populations who move to different residences, which can impact the ability to track and

communicate with participants. And this relates to effectiveness. Children completing >75%

of a community-based intervention program experienced beneficial change in BMI as well

as associated health behaviors (physical activity, screen time, unhealthy food consumption)

compared with children completing <75% of the program (Hardy et al 2015). A way to

attenuate attrition in research on community-level interventions could be through adjusting

study intervention design. The majority of community-based interventions used a quasi-

experimental design, which is often attributed to practicality and sustainability (Bleich et al

2018, Karacabeyli et al 2018). Interestingly, less rigorous study designs (e.g., quasi-

experimental vs. RCTs) demonstrated significant reductions in child weight (Karacabeyli et

al 2018). By removing randomization, the authors reported that communities with the

resources, engagement/buy-in, and capacity could be selected to participate, which

optimized community support for the obesity intervention efforts through both sustainable

partnerships and buy-in from the community and its champions. This participatory approach

could potentially lead to lasting positive health changes that extend beyond the study period.

In addition, Karacabeyli et al (2018) described the benefits of a quasi-experimental design

which lends itself to selecting at-risk communities that could greatly benefit from

intervention efforts. For example, using a stepped wedge or randomized rollout trial design

where all at-risk communities selected would eventually receive the intervention at different

time periods but none serve as “no intervention” controls (see Landsverk et al 2017).

9. CONCLUSIONS

There are signs that progress is being made in stemming the tide of childhood obesity and

evidence-based interventions are available across development and for various contexts and

systems that affected and at-risk children routinely encounter. Tremendous challenges

remain in connecting the dots between etiology, development, and intervention targets, as

well as when and where to intervene. There needs to be a push to scale up effective

interventions as even small changes in weight can yield significant impact on multiple

cardiometabolic indices (Lloyd-Jones et al 2010) that can improve quality and length of life.

Clinical health psychologists are ideally suited to conduct research on this complex problem

but transdisciplinary teams will be needed to increasingly move the dial.

ACKNOWLEDGEMENTS

The authors wish to thank Sara St. George for feedback on an earlier version of this review and to acknowledge support of this work from the Centers for Disease Control and Prevention (grant U18DP006255) and the United States Department of Agriculture (grant 2018-68001-27550), awarded to Justin Smith and Cady Berkel; and the National Institute on Drug Abuse (grant P30 DA027828), to C. Hendricks Brown, in support of Justin Smith.

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SUMMARY POINTS

1. Childhood obesity is a complex, multidetermined, preventable chronic disease that increases risk for premature death and psychological problems.

2. Evidence-based interventions for obesity are available for all stages of development from birth to 18 years.

3. Specific interventions can be delivered in community, school, home, and healthcare settings depending on the type of strategy and risk level of the

targeted population.

4. Associated co-occurring medical and psychological conditions of childhood obesity present an opportunity for clinical and health psychology researchers

and practitioners.

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FUTURE ISSUES

1. Future research ought to focus on translational considerations from the start and ways to scale up delivery of effective interventions.

2. Research is needed on interventions and their implementation to more effectively reach minority and underserved populations at greatest risk for

obesity.

3. Increasing engagement and retention in childhood obesity interventions is a promising focus for future research.

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Figure 1. Developmental cascade model of pediatric obesity Note. Bold text indicates strongest support based on our review of the literature. Reprinted with permission from Taylor and Francis Group: Originally published in Smith JD, Egan

KN, Montaño Z, Dawson-McClure S, Jake-Schoffman DE, et al. 2018. A developmental

cascade perspective of paediatric obesity: Conceptual model and scoping review. Health Psychology Review 12: 271–293.

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