outline for childhood obesity research paper

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• Published: 18 May 2023

Child and adolescent obesity

• Natalie B. Lister   ORCID: orcid.org/0000-0002-9148-8632 1 , 2 ,
• Louise A. Baur   ORCID: orcid.org/0000-0002-4521-9482 1 , 3 , 4 ,
• Janine F. Felix 5 , 6 ,
• Andrew J. Hill   ORCID: orcid.org/0000-0003-3192-0427 7 ,
• Claude Marcus   ORCID: orcid.org/0000-0003-0890-2650 8 ,
• Thomas Reinehr   ORCID: orcid.org/0000-0002-4351-1834 9 ,
• Carolyn Summerbell 10 &
• Martin Wabitsch   ORCID: orcid.org/0000-0001-6795-8430 11  

Nature Reviews Disease Primers volume  9 , Article number:  24 ( 2023 ) Cite this article

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• Paediatric research

The prevalence of child and adolescent obesity has plateaued at high levels in most high-income countries and is increasing in many low-income and middle-income countries. Obesity arises when a mix of genetic and epigenetic factors, behavioural risk patterns and broader environmental and sociocultural influences affect the two body weight regulation systems: energy homeostasis, including leptin and gastrointestinal tract signals, operating predominantly at an unconscious level, and cognitive–emotional control that is regulated by higher brain centres, operating at a conscious level. Health-related quality of life is reduced in those with obesity. Comorbidities of obesity, including type 2 diabetes mellitus, fatty liver disease and depression, are more likely in adolescents and in those with severe obesity. Treatment incorporates a respectful, stigma-free and family-based approach involving multiple components, and addresses dietary, physical activity, sedentary and sleep behaviours. In adolescents in particular, adjunctive therapies can be valuable, such as more intensive dietary therapies, pharmacotherapy and bariatric surgery. Prevention of obesity requires a whole-system approach and joined-up policy initiatives across government departments. Development and implementation of interventions to prevent paediatric obesity in children should focus on interventions that are feasible, effective and likely to reduce gaps in health inequalities.

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Introduction.

The prevalence of child and adolescent obesity remains high and continues to rise in low-income and middle-income countries (LMICs) at a time when these regions are also contending with under-nutrition in its various forms 1 , 2 . In addition, during the COVID-19 pandemic, children and adolescents with obesity have been more likely to have severe COVID-19 requiring hospitalization and mechanical ventilation 3 . At the same time, the pandemic was associated with rising levels of childhood obesity in many countries. These developments are concerning, considering that recognition is also growing that paediatric obesity is associated with a range of immediate and long-term negative health outcomes, a decreased quality of life 4 , 5 , an increased presentation to health services 6 and increased economic costs to individuals and society 7 .

Body weight is regulated by a range of energy homeostatic and cognitive–emotional processes and a multifactorial interplay of complex regulatory circuits 8 . Paediatric obesity arises when multiple environmental factors — covering preconception and prenatal exposures, as well as broader changes in the food and physical activity environments — disturb these regulatory processes; these influences are now widespread in most countries 9 .

The treatment of obesity includes management of obesity-associated complications, a developmentally sensitive approach, family engagement, and support for long-term behaviour changes in diet, physical activity, sedentary behaviours and sleep 10 . New evidence highlights the role, in adolescents with more severe obesity, of bariatric surgery 11 and pharmacotherapy, particularly the potential for glucagon-like peptide 1 (GLP1) receptor agonists 12 .

Obesity prevention requires a whole-system approach, with policies across all government and community sectors systematically taking health into account, avoiding harmful health impacts and decreasing inequity. Programmatic prevention interventions operating ‘downstream’ at the level of the child and family, as well as ‘upstream’ interventions at the level of the community and broader society, are required if a step change in tackling childhood obesity is to be realized 13 , 14 .

In this Primer, we provide an overview of the epidemiology, causes, pathophysiology and consequences of child and adolescent obesity. We discuss diagnostic considerations, as well as approaches to its prevention and management. Furthermore, we summarize effects of paediatric obesity on quality of life, and open research questions.

Epidemiology

Definition and prevalence.

The World Health Organization (WHO) defines obesity as “abnormal or excessive fat accumulation that presents a risk to health” 15 . Paediatric obesity is defined epidemiologically using BMI, which is adjusted for age and sex because of the physiological changes in BMI during growth 16 . Global prevalence of paediatric obesity has risen markedly over the past four decades, initially in high-income countries (HICs), but now also in many LMICs 1 .

Despite attempts to standardize the epidemiological classification, several definitions of paediatric obesity are in use; hence, care is needed when comparing prevalence rates. The 2006 WHO Child Growth Standard, for children aged 0 to 5 years, is based on longitudinal observations of multiethnic populations of children with optimal infant feeding and child-rearing conditions 17 . The 2007 WHO Growth Reference is used for the age group 5–19 years 18 , and the 2000 US Centers for Disease Control and Prevention (CDC) Growth Charts for the age group 2–20 years 19 . The WHO and CDC definitions based on BMI-for-age charts are widely used, including in clinical practice. By contrast, the International Obesity Task Force (IOTF) definition, developed from nationally representative BMI data for the age group 2–18 years from six countries, is used exclusively for epidemiological studies 20 .

For the age group 5–19 years, between 1975 and 2016, the global prevalence of obesity (BMI >2 standard deviations (SD) above the median of the WHO growth reference) increased around eightfold to 5.6% in girls and 7.8% in boys 1 . Rates have plateaued at high levels in many HICs but have accelerated in other regions, particularly in parts of Asia. For the age group 2–4 years, between 1980 and 2015, obesity prevalence (IOTF definition, equivalent to an adult BMI of ≥30 kg/m 2 ) increased from 3.9% to 7.2% in boys and from 3.7% to 6.4% in girls 21 . Obesity prevalence is highest in Polynesia and Micronesia, the Middle East and North Africa, the Caribbean and the USA (Fig.  1 ). Variations in prevalence probably reflect different background levels of obesogenic environments, or the sum total of the physical, economic, policy, social and cultural factors that promote obesity 22 . Obesogenic environments include those with decreased active transport options, a ubiquity of food marketing directed towards children, and reduced costs and increased availability of nutrient-poor, energy-dense foods. Particularly in LMICs, the growth of urbanization, new forms of technology and global trade have led to reduced physical activity at work and leisure, a shift towards Western diets, and the expansion of transnational food and beverage companies to shape local food systems 23 .

Maps showing the proportions of children and adolescents living with overweight or obesity (part  a , boys; part b , girls) according to latest available data from the Global Obesity Observatory . Data might not be comparable between countries owing to differences in survey methodology.

The reasons for varying sex differences in prevalence in different countries are unclear but may relate to cultural variations in parental feeding practices for boys and girls and societal ideals of body size 24 . In 2016, obesity in the age group 5–19 years was more prevalent in girls than in boys in sub-Saharan Africa, Oceania and some middle-income countries in other regions, whereas it was more prevalent in boys than in girls in all HICs, and in East and South-East Asia 21 . Ethnic and racial differences in obesity prevalence within countries are often assumed to mirror variations in social deprivation and other social determinants of obesity. However, an independent effect of ethnicity even after adjustment for socioeconomic status has been documented in the UK, with Black and Asian boys in primary school having higher prevalence of obesity than white boys 25 .

Among individuals with obesity, very high BMI values have become more common in the past 15 years. The prevalence of severe obesity (BMI ≥120% of the 95th percentile (CDC definition), or ≥35 kg/m 2 at any age 26 , 27 ) has increased in many HICs, accounting for one-quarter to one-third of those with obesity 28 , 29 . Future health risks of paediatric obesity in adulthood are well documented. For example, in a data linkage prospective study in Israel with 2.3 million participants who had BMI measured at age 17 years, those with obesity (≥95th percentile BMI for age) had a much higher risk of death from coronary heart disease (HR 4.9, 95% CI 3.9–6.1), stroke (HR 2.6, 95% CI 1.7–4.1) and sudden death (HR 2.1, 95% CI 1.5–2.9) compared with those whose BMI fell between the 5th and 24th percentiles 30 .

Causes and risk factors

Early life is a critical period for childhood obesity development 9 , 31 , 32 , 33 . According to the Developmental Origins of Health and Disease framework, the early life environment may affect organ structure and function and influence health in later life 34 , 35 . Meta-analyses have shown that preconception and prenatal environmental exposures, including high maternal pre-pregnancy BMI and, to a lesser extent, gestational weight gain, as well as gestational diabetes and maternal smoking, are associated with childhood obesity, potentially through effects on the in utero environment 33 , 36 , 37 , 38 . Paternal obesity is also associated with childhood obesity 33 . Birthweight, reflecting fetal growth, is a proxy for in utero exposures. Both low and high birthweights are associated with later adiposity, with high birthweight linked to increased BMI and low birthweight to central obesity 33 , 39 .

Growth trajectories in early life are important determinants of later adiposity. Rapid weight gain in early childhood is associated with obesity in adolescence 32 . Also, later age and higher BMI at adiposity peak (the usual peak in BMI around 9 months of age), as well as earlier age at adiposity rebound (the lowest BMI reached between 4 and 7 years of age), are associated with increased adolescent and adult BMI 40 , 41 . Specific early life nutritional factors, including a lower protein content in formula food, are consistently associated with a lower risk of childhood obesity 42 , 43 . These also include longer breastfeeding duration, which is generally associated with a lower risk of childhood obesity 42 . However, some controversy exists, as these effects are affected by multiple sociodemographic confounding factors and their underlying mechanisms remain uncertain 44 . Some studies comparing higher and lower infant formula protein content have reported that the higher protein group have a greater risk of subsequent obesity, especially in early childhood 41 , 42 ; however, one study with a follow-up period until age 11 years found no significant difference in the risk of obesity, but an increased risk of overweight in the high protein group was still observed 42 , 43 , 45 . A high intake of sugar-sweetened beverages is associated with childhood obesity 33 , 46 .

Many other behavioural factors are associated with an increased risk of childhood obesity, including increased screen time, short sleep duration and poor sleep quality 33 , 47 , reductions in physical activity 48 and increased intake of energy-dense micronutrient-poor foods 49 . These have been influenced by multiple changes in the past few decades in the broader social, economic, political and physical environments, including the widespread marketing of food and beverages to children, the loss of walkable green spaces in many urban environments, the rise in motorized transport, rapid changes in the use of technology, and the move away from traditional foods to ultraprocessed foods.

Obesity prevalence is inextricably linked to relative social inequality, with data suggesting a shift in prevalence over time towards those living with socioeconomic disadvantage, and thus contributes to social inequalities. In HICs, being in lower social strata is associated with a higher risk of obesity, even in infants and young children 50 , whereas the opposite relationship occurs in middle-income countries 51 . In low-income countries, the relationship is variable, and the obesity burden seems to be across socioeconomic groups 52 , 53 .

Overall, many environmental, lifestyle, behavioural and social factors in early life are associated with childhood obesity. These factors cannot be seen in isolation but are part of a complex interplay of exposures that jointly contribute to increased obesity risk. In addition to multiple prenatal and postnatal environmental factors, genetic variants also have a role in the development of childhood obesity (see section Mechanisms/pathophysiology).

Comorbidities and complications

Childhood obesity is associated with a wide range of short-term comorbidities (Fig.  2 ). In addition, childhood obesity tracks into adolescence and adulthood and is associated with complications across the life course 32 , 41 , 54 , 55 .

Obesity in children and adolescents can be accompanied by various other pathologies. In addition, childhood obesity is associated with complications and disorders that manifest in adulthood (red box).

Increased BMI, especially in adolescence, is linked to a higher risk of many health outcomes, including metabolic disorders, such as raised fasting glucose, impaired glucose tolerance, type 2 diabetes mellitus (T2DM), metabolic syndrome and fatty liver disease 56 , 57 , 58 , 59 . Other well-recognized obesity-associated complications include coronary heart disease, asthma, obstructive sleep apnoea syndrome (itself associated with metabolic dysfunction and inflammation) 60 , orthopaedic complications and a range of mental health outcomes including depression and low self-esteem 27 , 55 , 57 , 61 , 62 , 63 .

A 2019 systematic review showed that children and adolescents with obesity are 1.4 times more likely to have prediabetes, 1.7 times more likely to have asthma, 4.4 times more likely to have high blood pressure and 26.1 times more likely to have fatty liver disease than those with a healthy weight 64 . In 2016, it was estimated that, at a global level by 2025, childhood obesity would lead to 12 million children aged 5–17 years with glucose intolerance, 4 million with T2DM, 27 million with hypertension and 38 million with fatty liver disease 65 . These high prevalence rates have implications for both paediatric and adult health services.

Mechanisms/pathophysiology

Body weight regulation.

Body weight is regulated within narrow limits by homeostatic and cognitive–emotional processes and a multifactorial interplay of hormones and messenger substances in complex regulatory circuits (Fig.  3 ). When these regulatory circuits are disturbed, an imbalance between energy intake and expenditure leads to obesity or to poor weight gain. As weight loss is much harder to achieve than weight gain in the long term due to the regulation circuits discussed below, the development of obesity is encouraged by modern living conditions, which enable underlying predispositions for obesity to become manifest 8 , 66 .

Body weight is predominantly regulated by two systems: energy homeostasis and cognitive–emotional control. Both homeostatic and non-homeostatic signals are processed in the brain, involving multiple hormone and receptor cascades 217 , 218 , 219 . This overview depicts the best-known regulatory pathways. The homeostatic system, which is mainly regulated by brain centres in the hypothalamus and brainstem, operates on an unconscious level. Both long-term signals from the energy store in adipose tissue (for example, leptin) and short-term hunger and satiety signals from the gastrointestinal tract signal the current nutrient status. During gastric distension or after the release of gastrointestinal hormones (multiple receptors are involved) and insulin, a temporary feeling of fullness is induced. The non-homeostatic or hedonic system is regulated by higher-level brain centres and operates at the conscious level. After integration in the thalamus, homeostatic signals are combined with stimuli from the environment, experiences and emotions; emotional and cognitive impulses are then induced to control food intake. Regulation of energy homeostasis in the hypothalamus involves two neuron types of the arcuate nucleus: neurons producing neuropeptide Y (NPY) and agouti-related peptide (AgRP) and neurons producing pro-opiomelanocortin (POMC). Leptin stimulates these neurons via specific leptin receptors (LEPR) inducing anabolic effects in case of decreasing leptin levels and catabolic effects in case of increasing leptin levels. Leptin inhibits the production of NPY and AgRP, whereas low leptin levels stimulate AgRP and NPY production resulting in the feeling of hunger. Leptin directly stimulates POMC production in POMC neurons. POMC is cleaved into different hormone polypeptides including α-melanocyte-stimulating hormone which in turn activates melanocortin 4 receptors (MC4R) of cells in the nucleus paraventricularis of the hypothalamus, leading to the feeling of satiety. CART, cocaine and amphetamine responsive transcript; IR, insulin receptor.

In principle, there are two main systems in the brain which regulate body weight 8 , 66 (Fig.  3 ): energy homeostasis and cognitive–emotional control. Energy homeostasis is predominantly regulated by brain centres in the hypothalamus and brainstem and operates at an unconscious level. Both long-term signals from the adipose tissue energy stores and short-term hunger and satiety signals from the gastrointestinal tract signal the current nutrient status 8 , 66 . For example, negative energy balance leading to reduced fat mass results in reduced leptin levels, a permanently reduced urge to exercise and an increased feeling of hunger. During gastric distension or after the release of gastrointestinal hormones and insulin, a temporary feeling of fullness is induced 8 , 66 . Cognitive–emotional control is regulated by higher brain centres and operates at a conscious level. Here, the homeostatic signals are combined with stimuli from the environment (sight, smell and taste of food), experiences and emotions 8 , 66 . Disorders at the level of cognitive–emotional control mechanisms include emotional eating as well as eating disorders. For example, the reward areas in the brain of people with overweight are more strongly activated by high-calorie foods than those in the brain of people with normal weight 67 . Both systems interact with each other, and the cognitive–emotional system is strongly influenced by the homeostatic control circuits.

Disturbances in the regulatory circuits of energy homeostasis can be genetically determined, can result from disease or injury to the regulatory centres involved, or can be caused by prenatal programming 8 , 66 . If the target value of body weight has been shifted, the organism tries by all means (hunger, drive) to reach the desired higher weight. These disturbed signals of the homeostatic system can have an imperative, irresistible character, so that a conscious influence on food intake is no longer effectively possible 8 , 66 . The most important disturbances of energy homeostasis are listed in Table  1 .

The leptin pathway

The peptide hormone leptin is primarily produced by fat cells. Its production depends on the amount of adipose tissue and the energy balance. A negative energy balance during fasting results in a reduction of circulating leptin levels by 50% after 24 h (ref. 68 ). In a state of weight loss, leptin production is reduced 69 . In the brain, leptin stimulates two neuron types of the arcuate nucleus in the hypothalamus via specific leptin receptors: neurons producing neuropeptide Y (NPY) and agouti-related peptide (AgRP) and neurons producing pro-opiomelanocortin (POMC). High leptin levels inhibit the production of NPY and AgRP, whereas low leptin levels stimulate AgRP and NPY production. By contrast, leptin directly stimulates POMC production in POMC neurons (Fig.  3 ). POMC is a hormone precursor that is cleaved into different hormone polypeptides by specific enzymes, such as prohormone convertase 1 (PCSK1). This releases α-melanocyte-stimulating hormone (α-MSH) which in turn activates melanocortin 4 receptors (MC4R) of cells in the nucleus paraventricularis of the hypothalamus, leading to the feeling of satiety. Rare, functionally relevant mutations in the genes for leptin and leptin receptor, POMC , PCSK1/3 or MC4R lead to extreme obesity in early childhood. These forms of obesity are potential indications for specific pharmacological treatments, for example setmelanotide 70 , 71 . MC4R mutations are the most common cause of monogenic obesity, as heterozygous mutations can be symptomatic depending on the functional impairment and with variable penetrance and expression. Other genes have been identified, in which rare heterozygous pathological variants are also associated with early onset obesity (Table  1 ).

Pathological changes in adipose tissue

Adipose tissue can be classified into two types, white and brown adipose tissue. White adipose tissue comprises unilocular fat cells and brown adipose tissue contains multilocular fat cells, which are rich in mitochondria 72 . A third type of adipocyte, beige adipocytes, within the white adipose tissue are induced by prolonged exposure to cold or adrenergic signalling, and show a brown adipocyte-like morphology 72 . White adipose tissue has a large potential to change its volume to store energy and meet the metabolic demands of the body. The storage capacity and metabolic function of adipose tissue depend on the anatomical location of the adipose tissue depot. Predominant enlargement of white adipose tissue in the visceral, intra-abdominal area (central obesity) is associated with insulin resistance and an increased risk of metabolic disease development before puberty. Accumulation of adipose tissue in the hips and flanks has no adverse effect and may be protective against metabolic syndrome. In those with obesity, adipose tissue is characterized by an increased number of adipocytes (hyperplasia), which originate from tissue-resident mesenchymal stem cells, and by enlarged adipocytes (hypertrophy) 73 . Adipocytes with a very large diameter reach the limit of the maximal oxygen diffusion distance, resulting in hypoxia, the development of an inflammatory expression profile (characterized by, for example, leptin, TNF and IL-6) and adipocyte necrosis, triggering the recruitment of leukocytes. Resident macrophages switch from the anti-inflammatory M2 phenotype to a pro-inflammatory M1 phenotype, which is associated with insulin resistance, further promoting local sterile inflammation and the development of fibrotic adipose tissue. This process limits the expandability of the adipose tissue for further storage of triglycerides. In the patient, the increase in fat mass in obesity is associated with insulin resistance and systemic low-grade inflammation characterized by elevated serum levels of C-reactive protein and pro-inflammatory cytokines. The limitation of adipose tissue expandability results in storage of triglycerides in other organs, such as the liver, muscle and pancreas 74 .

Genetics and epigenetics in the general population

Twin studies have found heritability estimates for BMI of up to 70% 75 , 76 . In contrast to rare monogenic forms of obesity, which are often caused by a single genetic defect with a large effect, the genetic background of childhood obesity in the general population is shaped by the joint effects of many common genetic variants, each of which individually makes a small contribution to the phenotype. For adult BMI, genome-wide association studies, which examine associations of millions of such variants across the genome at the same time, have identified around 1,000 genetic loci 77 . The largest genome-wide association studies in children, which include much smaller sample sizes of up to 60,000 children, have identified 25 genetic loci for childhood BMI and 18 for childhood obesity, the majority of which overlap 78 , 79 . There is also a clear overlap with genetic loci identified in adults, for example for FTO , MC4R and TMEM18 , but this overlap is not complete, some loci are specific to early life BMI, or have a relatively larger contribution in childhood 78 , 79 , 80 . These findings suggest that biological mechanisms underlying obesity in childhood are mostly similar to those in adulthood, but the relative influence of these mechanisms may differ at different phases of life.

The role of epigenetic processes in childhood and adolescent obesity has gained increasing attention. In children, several studies found associations between DNA methylation and BMI 81 , 82 , 83 , 84 , but a meta-analysis including data from >4,000 children identified only minimal associations 85 . Most studies support the hypothesis that DNA methylation changes are predominantly a consequence rather than a cause of obesity, which may explain the lower number of identified (up to 12) associations in children, in whom duration of exposure to a higher BMI is shorter than in adults, in whom associations with DNA methylation at hundreds of sites have been identified 85 , 86 , 87 . In addition to DNA methylation, some specific circulating microRNAs have been found to be associated with obesity in childhood 84 .

The field of epigenetic studies in childhood obesity is relatively young and evolving quickly. Future studies will need to focus on defining robust associations in blood as well as other tissues and on identifying cause-and-effect relationships. In addition, other omics, such as metabolomics and proteomics, are promising areas that may contribute to an improved aetiological understanding or may provide biological signatures that can be used as predictive or prognostic markers of childhood obesity and its comorbidities.

Parental obesity and childhood obesity

There is an established link between increased parental BMI and increased childhood BMI 88 , 89 . This link may be due to shared genetics, shared environment, a direct intrauterine effect of maternal BMI or a combination of these factors. In the case of shared genetics, the child inherits BMI-increasing genetic variants from one or both parents. Shared environmental factors, such as diet or lifestyle, may also contribute to an increased BMI in both parents and child. In addition, maternal obesity might create an intrauterine environment that programmes metabolic processes in the fetus, which increases the risk of childhood obesity. Some studies show larger effects of maternal than paternal BMI, indicating a potential causal intrauterine mechanism of maternal obesity, but evidence showing similar maternal and paternal effects is increasing. The data may indicate that there is only a limited direct intrauterine effect of maternal obesity on childhood obesity; rather, genetic effects inherited from the mother or father, or both, and/or shared environmental factors may contribute to childhood obesity risk 90 , 91 , 92 , 93 , 94 , 95 .

Diagnosis, screening and prevention

Diagnostic work-up.

The extent of overweight in clinical practice is estimated using BMI based on national charts 96 , 97 , 98 , 99 , 100 . Of note, the clinical classification of overweight or obesity differ depending on the BMI charts used and national recommendations; hence, local guidelines should be referred to. For example, the US CDC Growth Charts and several others use the 85th and 95th centile cut-points to denote overweight and obesity, respectively 19 . The WHO Growth Reference for children aged 5–19 years defines cut-points for overweight and obesity as a BMI-for-age greater than +1 and +2 SDs for BMI for age, respectively 18 . For children <5 years of age, overweight and obesity are defined as weight-for-height greater than +2 and +3 SDs, respectively, above the WHO Child Growth Standards median 17 . The IOTF and many countries in Europe use cut-points of 85th, 90th and 97th to define overweight, obesity and extreme obesity 26 .

BMI as an indirect measurement of body fat has some limitations; for example, pronounced muscle tissue leads to an increase in BMI, and BMI is not independent of height. In addition, people of different ethnicities may have different cut-points for obesity risk; for example, cardiometabolic risk occurs at lower BMI values in individuals with south Asian than in those with European ancestry 101 . Thus, BMI is best seen as a convenient screening tool that is supplemented by clinical assessment and investigations.

Other measures of body fat may help differentiate between fat mass and other tissues. Some of these tools are prone to low reliability, such as body impedance analyses (high day-to-day variation and dependent on level of fluid consumption) or skinfold thickness (high inter-observer variation), or are more expensive or invasive, such as MRI, CT or dual-energy X-ray absorptiometry, than simpler measures of body composition or BMI assessment.

Primary diseases rarely cause obesity in children and adolescents (<2%) 102 . However, treatable diseases should be excluded in those with obesity. A suggested diagnostic work-up is summarized in Fig.  4 . Routine measurement of thyroid-stimulating hormone (TSH) is not recommended 96 . Moderately elevated TSH levels (usually <10 IU/l) are frequently observed in obesity and are a consequence, and not a cause, of obesity 103 . In a growing child with normal height velocity, a normal BMI at the age of 2 years and normal cognitive development, no further diagnostic steps are necessary to exclude primary diseases 96 , 104 .

Concerning findings from a detailed medical history and physical examination will lead to further examinations. In individuals with early onset, extreme obesity (before age 3 years) and signs of hyperphagia, serum leptin level should be measured to rule out the extremely rare condition of congenital leptin deficiency. In individuals with normal or high leptin levels, genetic testing is indicated to search for monogenetic obesity. In individuals with intellectual disability, a syndromic disease may be present. Signs of impaired growth velocity or the history of central nervous system trauma or surgery will result in deeper endocrine evaluation and/or brain MRI. BDNF , brain-derived neurotropic factor; FT4, free thyroxin; KSR2 , kinase suppressor of ras 2; MC4R , melanocortin 4 receptor; POMC , pro-opiomelanocortin; SH2B1 , Src-homology 2 (SH2) B adapter protein 1; SIM1 , single-minded homologue 1; TSH, thyroid-stimulating hormone.

Clinical findings which need no further examination include pseudogynaecomastia (adipose tissue mimicking breast development; differentiated from breast tissue by ultrasonography), striae (caused by rapid weight increase) and a hidden penis in suprapubic adipose tissue (differentiated from micropenis by measurement of stretched penis length while pressing down on the suprapubic adipose tissue) 96 , 105 . Girls with obesity tend to have an earlier puberty onset (usually at around 8–9 years of age) and boys with severe obesity may have a delayed puberty onset (usually at around 13–14 years of age) 106 . Thus, if pubertal onset is slightly premature in girls or slightly delayed in boys, no further endocrine assessment is necessary.

Assessment of obesity-associated comorbidities

A waist to height ratio of >0.5 is a simple tool to identify central obesity 107 , 108 . Screening for cardiometabolic risk factors and fatty liver disease is recommended, especially in adolescents, and in those with more severe obesity or central adiposity, a strong family history of T2DM or premature heart disease, or relevant clinical symptoms, such as high blood pressure or acanthosis nigricans 96 , 97 , 98 , 99 , 109 . Investigations generally include fasting glucose levels, lipid profile, liver function and glycated haemoglobin, and might include an oral glucose tolerance test, polysomnography, and additional endocrine tests for polycystic ovary syndrome 96 , 97 , 98 , 99 .

T2DM in children and adolescents often occurs in the presence of a strong family history and may not be related to obesity severity 110 . T2DM onset usually occurs during puberty, a physiological state associated with increased insulin resistance 111 and, therefore, screening for T2DM should be considered in children and adolescents with obesity and at least one risk factor (family history of T2DM or features of metabolic syndrome) starting at pubertal onset 112 . As maturity-onset diabetes of the young (MODY) type II and type III are more frequent than T2DM in children and adolescents in many ethnicities, genetic screening for MODY may be appropriate 112 . Furthermore, type 1 diabetes mellitus (T1DM) should be excluded by measurement of autoantibodies in any individual with suspected diabetes with obesity. The differentiation of T2DM from MODY and T1DM is important as the diabetes treatment approaches differ 112 .

Several comorbidities of obesity should be considered if specific symptoms occur 96 , 109 . For polycystic ovary syndrome in hirsute adolescent girls with oligomenorrhoea or amenorrhoea, moderately increased testosterone levels and decreased sex hormone binding globulin levels are typical laboratory findings 113 . Obstructive sleep apnoea can occur in those with more severe obesity and who snore, have daytime somnolence or witnessed apnoeas. Diagnosis is made by polysomnography 114 . Minor orthopaedic disorders, such as flat feet and genu valgum, are frequent in children and adolescents with obesity and may cause pain. Major orthopaedic complications include slipped capital femoral epiphyses (acute and chronic), which manifest with hip and knee pain in young adolescents and are characterized by reduced range of hip rotation and waddling gait; and Blount disease (tibia vara), typically occurring in children aged 2–5 years 105 , 115 . In addition, children and adolescents with extreme obesity frequently have increased dyspnoea and decreased exercise capacity. A heightened demand for ventilation, elevated work of breathing, respiratory muscle inefficiency and diminished respiratory compliance are caused by increased truncal fat mass. This may result in a decreased functional residual capacity and expiratory reserve volume, ventilation to perfusion ratio abnormalities and hypoxaemia, especially when supine. However, conventional respiratory function tests are only mildly affected by obesity except in extreme cases 116 . Furthermore, gallstones should be suspected in the context of abdominal pain after rapid weight loss, which can be readily diagnosed via abdominal ultrasonography 105 . Finally, pseudotumor cerebri may present with chronic headache, and depression may present with flat affect, chronic fatigue and sleep problems 105 .

Obesity in adolescents can also be associated with disordered eating, eating disorders and other psychological disorders 117 , 118 . If suspected, assessment by a mental health professional is recommended.

A comprehensive approach

The 2016 report of the WHO Commission on Ending Childhood Obesity stated that progress in tackling childhood obesity has been slow and inconsistent, with obesity prevention requiring a whole-of-government approach in which policies across all sectors systematically take health into account, avoiding harmful health impacts and, therefore, improving population health and health equity 13 , 119 . The focus in developing and implementing interventions to prevent obesity in children should be on interventions that are feasible, effective and likely to reduce health inequalities 14 . Importantly, the voices of children and adolescents living with social disadvantage and those from minority groups must be heard if such interventions are to be effective and reduce inequalities 120 .

Figure  5 presents a system for the prevention of childhood obesity within different domains of the socioecological model 121 and highlights opportunities for interventions. These domains can be described on a continuum, from (most downstream) individual and interpersonal (including parents, peers and wider family) through to organizational (including health care and schools), community (including food, activity and environment), society (including media and finally cultural norms) and (most upstream) public policy (from local to national level). Interventions to prevent childhood obesity can be classified on the Nuffield intervention ladder 122 . This framework was proposed by the Nuffield Council on Bioethics in 2007 (ref. 122 ) and distributes interventions on the ladder steps depending on the degree of agency required by the individual to make the behavioural changes that are the aim of the intervention. The bottom step of the ladder includes interventions that provide information, which requires the highest agency and relies on a child, adolescent and/or family choosing (and their ability to choose) to act on that information and change behaviour. The next steps of the ladder are interventions that enable choice, guide choice through changing the default policy, guide choice through incentives, guide choice through disincentives, or restrict choice. On the top-most step of the ladder (lowest agency required) are interventions that eliminate choice.

This schematic integrates interventions that were included in a Cochrane review 127 of 153 randomized controlled trials of interventions to prevent obesity in children and are high on the Nuffield intervention ladder 122 . The Nuffield intervention ladder distributes interventions depending on the degree of agency required for the behavioural changes that are the aim of the intervention. The socioecological model 121 comprises different domains (or levels) from the individual up to public policy. Interventions targeting the individual and interpersonal domains can be described as downstream interventions, and interventions within public policy can be described as the highest level of upstream interventions. Within each of these domains, arrow symbols with colours corresponding to the Nuffield intervention ladder category are used to show interventions that were both included in the Cochrane review 127 and that guide, restrict or eliminate choice as defined by the Nuffield intervention ladder 122 . Upstream interventions, and interventions on the top steps of the Nuffield ladder, are more likely to reduce inequalities. NGO, non-governmental organization.

Downstream and high-agency interventions (on the bottom steps of the Nuffield ladder) are more likely to result in intervention-generated inequalities 123 . This has been elegantly described and evidenced, with examples from the obesity prevention literature 124 , 125 . A particularly strong example is a systematic review of 38 interventions to promote healthy eating that showed that food price (an upstream and low-agency intervention) seemed to decrease inequalities, all interventions that combined taxes and subsidies consistently decreased inequalities, and downstream high-agency interventions, especially dietary counselling, seemed to increase inequalities 126 .

Effectiveness of prevention interventions

A 2019 Cochrane review of interventions to prevent obesity in children 127 included 153 randomized controlled trials (RCTs), mainly in HICs (12% were from middle-income countries). Of these RCTs, 56% tested interventions in children aged 6–12 years, 24% in children aged 0–5 years, and 20% in adolescents aged 13–18 years. The review showed that diet-only interventions to prevent obesity in children were generally ineffective across all ages. Interventions combining diet and physical activity resulted in modest benefits in children aged 0–12 years but not in adolescents. However, physical activity-only interventions to prevent obesity were effective in school-age children (aged 5–18 years). Whether the interventions were likely to work equitably in all children was investigated in 13 RCTs. These RCTs did not indicate that the strategies increased inequalities, although most of the 13 RCTs included relatively homogeneous groups of children from disadvantaged backgrounds.

The potential for negative unintended consequences of obesity prevention interventions has received much attention 128 . The Cochrane review 127 investigated whether children were harmed by any of the strategies; for example, by having injuries, losing too much weight or developing damaging views about themselves and their weight. Of the few RCTs that did monitor these outcomes, none found any harms in participants.

Intervention levels

Most interventions (58%) of RCTs in the Cochrane review aimed to change individual lifestyle factors via education-based approaches (that is, simply provide information) 129 . In relation to the socioecological model, only 11 RCTs were set in the food and physical activity environment domain, and child care, preschools and schools were the most common targets for interventions. Of note, no RCTs were conducted in a faith-based setting 130 . Table  2 highlights examples of upstream interventions that involve more than simply providing information and their classification on the Nuffield intervention ladder.

Different settings for interventions to prevent childhood obesity, including preschools and schools, primary health care, community settings and national policy, offer different opportunities for reach and effectiveness, and a reduction in inequalities.

Preschools and schools are key settings for public policy interventions for childhood obesity prevention, and mandatory and voluntary food standards and guidance on physical education are in place in many countries. Individual schools are tasked with translating and implementing these standards and guidance for their local context. Successful implementation of a whole-school approach, such as that used in the WHO Nutrition-Friendly Schools Initiative 131 , is a key factor in the effectiveness of interventions. Careful consideration should be given to how school culture can, and needs to, be shifted by working with schools to tailor the approach and manage possible staff capacity issues, and by building relationships within and outside the school gates to enhance sustainability 132 , 133 .

Primary health care offers opportunities for guidance for obesity prevention, especially from early childhood to puberty. Parent-targeted interventions conducted by clinicians in health-care or community settings have the strongest level of evidence for their effectiveness in reducing BMI z -score at age 2 years 134 . These interventions include group programmes, clinic nurse consultations, mobile phone text support or nurse home visiting, and focusing on healthy infant feeding, healthy childhood feeding behaviours and screen time.

A prospective individual participant data meta-analysis of four RCTs involving 2,196 mother–baby dyads, and involving nurse home visiting or group programmes, resulted in a small but significant reduction in BMI in infants in the intervention groups compared with control infants at age 18–24 months 134 . Improvements were also seen in television viewing time, breastfeeding duration and feeding practices. Interventions were more effective in settings with limited provision of maternal and child health services in the community. However, effectiveness diminished by age 5 years without further intervention, highlighting the need for ongoing interventions at each life stage 135 . Evidence exists that short-duration interventions targeting sleep in very early childhood may be more effective than nutrition-targeted interventions in influencing child BMI at age 5 years 136 .

Primary care clinicians can provide anticipatory guidance, as a form of primary prevention, to older children, adolescents and their families, aiming to support healthy weight and weight-related behaviours. Clinical guidelines recommend that clinicians monitor growth regularly, and provide guidance on healthy eating patterns, physical activity, sedentary behaviours and sleep patterns 97 , 100 . Very few paediatric trials have investigated whether this opportunistic screening and advice is effective in obesity prevention 100 . A 2021 review of registered RCTs for the prevention of obesity in infancy found 29 trials 137 , of which most were delivered, or were planned to be delivered, in community health-care settings, such as nurse-led clinics. At the time of publication, 11 trials had reported child weight-related outcomes, two of which showed a small but significant beneficial effect on BMI at age 2 years, and one found significant improvements in the prevalence of obesity but not BMI. Many of the trials showed improvements in practices, such as breastfeeding and screen time.

At the community level, local public policy should be mindful of the geography of the area (such as urban or rural) and population demographics. Adolescents usually have more freedom in food and beverage choices made outside the home than younger children. In addition, physical activity levels usually decline and sedentary behaviours rise during adolescence, particularly in girls 138 , 139 . These behavioural changes offer both opportunities and barriers for those developing community interventions. On a national societal level, public policies for interventions to prevent obesity in children include the control of advertising of foods and beverages high in fat, sugar and/or salt in some countries. Industry and the media, including social media, can have a considerable influence on the food and physical activity behaviours of children 13 , 119 .

Public policy may target interventions at all domains from the individual to the societal level. The main focus of interventions in most national public policies relies on the ability of individuals to make the behavioural changes that are the aim of the intervention (high-agency interventions) at the individual level (downstream interventions). An equal focus on low-agency and upstream interventions is required if a step change in tackling childhood obesity is to be realized 140 , 141 .

COVID-19 and obesity

Early indications in several countries show rising levels of childhood obesity, and an increase in inequalities in childhood obesity during the COVID-19 pandemic 142 . The substantial disruptions in nutrition and lifestyle habits of children during and since the pandemic include social isolation and addiction to screens 143 . Under-nutrition is expected to worsen in poor countries, but obesity rates could increase in middle-income countries and HICs, especially among vulnerable groups, widening the gap in health and social inequalities 143 . Public health approaches at national, regional and local levels should include strategies that not only prevent obesity and under-nutrition, but also reduce health inequalities.

In summary, although most trials of obesity prevention have occurred at the level of the individual, the immediate family, school or community, effective prevention of obesity will require greater investment in upstream, low-agency interventions.

Treatment goals

Treatment should be centred on the individual and stigma-free (Box  1 ) and may aim for a reduction in overweight and improvement in associated comorbidities and health behaviours. Clinical considerations when determining a treatment approach should include age, severity of overweight and the presence of associated complications 144 , 145 .

Box 1 Strategies for minimizing weight stigma in health care 220 , 221 , 222

Minimizing weight bias in the education of health-care professionals

Improved education of health professionals:

pay attention to the implicit and explicit communication of social norms

include coverage of the broader determinants of obesity

include discussion of harms caused by social and cultural norms and messages concerning body weight

provide opportunities to practise non-stigmatizing care throughout education

Provide causal information focusing on the genetic and/or socioenvironmental determinants of weight.

Provide empathy-invoking interventions, emphasizing size acceptance, respect and human dignity.

Provide a weight-inclusive approach, by emphasizing that all individuals, regardless of size, have the right to equal health care.

Addressing health facility infrastructure and processes

Provide appropriately sized chairs, blood pressure cuffs, weight scales, beds, toilets, showers and gowns.

Use non-stigmatizing language in signage, descriptions of clinical services and other documentation.

Providing clinical leadership and using appropriate language within health-care settings

Senior clinicians and managers should role-model supportive and non-biased behaviours towards people with obesity and indicate that they do not tolerate weight-based discrimination in any form.

Staff should identify the language that individuals prefer in referring to obesity.

Use person-first language, for example a ‘person with obesity’ rather than ‘an obese person’.

Treatment guidelines

Clinical guidelines advise that first-line management incorporates a family-based multicomponent approach that addresses dietary, physical activity, sedentary and sleep behaviours 97 , 99 , 109 , 146 . This approach is foundational, with adjunctive therapies, especially pharmacotherapy and bariatric surgery, indicated under specific circumstances, usually in adolescents with more severe obesity 144 , 145 . Guideline recommendations vary greatly among countries and are influenced by current evidence, and functionality and resourcing of local health systems. Hence, availability and feasibility of therapies differs internationally. In usual clinical practice, interventions may have poorer outcomes than is observed in original studies or anticipated in evidence-based guidelines 147 because implementation of guidelines is more challenging in resource-constrained environments 148 . In addition, clinical trials are less likely to include patients with specialized needs, such as children from culturally diverse populations, those living with social disadvantage, children with complex health problems, and those with severe obesity 149 , 150 .

Behavioural interventions

There are marked differences in individual responses to behavioural interventions, and overall weight change outcomes are often modest. In children aged 6–11 years, a 2017 Cochrane review 150 found that mean BMI z -scores were reduced in those involved in behaviour-changing interventions compared with those receiving usual care or no treatment by only 0.06 units (37 trials; 4,019 participants; low-quality evidence) at the latest follow-up (median 10 months after the end of active intervention). In adolescents aged 12–17 years, another 2017 Cochrane review 149 found that multicomponent behavioural interventions resulted in a mean reduction in weight of 3.67 kg (20 trials; 1,993 participants) and reduction in BMI of 1.18 kg/m 2 (28 trials; 2,774 participants). These effects were maintained at the 24-month follow-up. A 2012 systematic review found significant improvements in LDL cholesterol triglycerides and blood pressure up to 1 year from baseline following lifestyle interventions in children and adolescents 151 .

Family-based behavioural interventions are recommended in national level clinical practice guidelines 97 , 100 , 146 , 152 . They are an important element of intensive health behaviour and lifestyle treatments (IHBLTs) 109 . Family-based approaches use behavioural techniques, such as goal setting, parental monitoring or modelling, taught in family sessions or in individual sessions separately to children and care givers, depending on the child’s developmental level. The priority is to encourage the whole family to engage in healthier behaviours that result in dietary improvement, greater physical activity, and less sedentariness. This includes making changes to the family food environment and requires parental monitoring.

Family-based interventions differ in philosophy and implementation from those based on family systems theory and therapy 153 . All are intensive interventions that require multiple contact hours (26 or more) with trained specialists delivered over an extended period of time (6–12 months) 10 . Changing family lifestyle habits is challenging and expensive, and the therapeutic expertise is not widely available. Moving interventions to primary care settings, delivered by trained health coaches, and supplemented by remote contact (for example by phone), will improve access and equity 154 .

Very few interventions use single psychological approaches. Most effective IHBLTs are multicomponent and intensive (many sessions), and include face-to-face contact. There has been interest in motivational interviewing as an approach to delivery 155 . As client-centred counselling, this places the young person at the centre of their behaviour change. Fundamental to motivational interviewing is the practitioner partnership that helps the young person and/or parents to explore ambivalence to change, consolidate commitment to change, and develop a plan based on their own insights and expertise. Evidence reviews generally support the view that motivational interviewing reduces BMI. Longer interventions (>4 months), those that assess and report on intervention fidelity, and those that target both diet and physical activity are most effective 155 , 156 .

More intensive dietary interventions

Some individuals benefit from more intensive interventions 98 , 144 , 157 , 158 , which include very low-energy diets, very low-carbohydrate diets and intermittent energy restriction 159 . These interventions usually aim for weight loss and are only recommended for adolescents who have reached their final height. These diets are not recommended for long periods of time due to challenges in achieving nutritional adequacy 158 , 160 , and lack of long-term safety data 158 , 161 . However, intensive dietary interventions may be considered when conventional treatment is unsuccessful, or when adolescents with comorbidities or severe obesity require rapid or substantial weight loss 98 . A 2019 systematic review of very low-energy diets in children and adolescents found a mean reduction in body weight of −5.3 kg (seven studies) at the latest follow‐up, ranging from 5 to 14.5 months from baseline 161 .

Pharmacological treatment

Until the early 2020s the only drug approved in many jurisdictions for the treatment of obesity in adolescents was orlistat, a gastrointestinal lipase inhibitor resulting in reduced uptake of lipids and, thereby, a reduced total energy intake 162 . However, the modest effect on weight in combination with gastrointestinal adverse effects limit its usefulness overall 163 .

A new generation of drugs has been developed for the treatment of both T2DM and obesity. These drugs are based on gastrointestinal peptides with effects both locally and in the central nervous system. GLP1 is an incretin that reduces appetite and slows gastric motility. The GLP1 receptor agonist liraglutide is approved for the treatment of obesity in those aged 12 years and older both in the USA and Europe 164 , 165 . Liraglutide, delivered subcutaneously daily at a higher dose than used for T2DM resulted in a 5% better BMI reduction than placebo after 12 months 166 . A 2022 trial of semaglutide, another GLP1 receptor agonist, delivered subcutaneously weekly in adolescents demonstrated 16% weight loss after 68 weeks of treatment, with modest adverse events and a low drop-out rate 12 . Tirzepatide, an agonist of both GLP1 and glucose-dependent insulinotropic polypeptide (GIP), is approved by the FDA for the treatment of T2DM in adults 167 . Subcutaneous tirzepatide weekly in adults with obesity resulted in ~20% weight loss over 72 weeks 168 . Of note, GIP alone increases appetite, but the complex receptor–agonist interaction results in downregulation of the GIP receptors 169 , illustrating why slightly modified agonists exert different effects. A study of the use of tirzepatide in adolescents with T2DM has been initiated but results are not expected before 2027 (ref. 170 ). No trials of tirzepatide are currently underway in adolescents with obesity but without T2DM.

Hypothalamic obesity is difficult to treat. Setmelanotide is a MC4R agonist that reduces weight and improves quality of life in most people with LEPR and POMC mutations 71 . In trials of setmelanotide, 8 of 10 participants with POMC deficiency and 5 of 11 with LEPR deficiency had weight loss of at least 10% at ~1 year. The mean percentage change in most hunger score from baseline was −27.1% and −43.7% in those with POMC deficiency and leptin receptor deficiency, respectively 71 .

In the near future, effective new drugs with, hopefully, an acceptable safety profile will be available that will change the way we treat and set goals for paediatric obesity treatment 171 .

Bariatric surgery

Bariatric surgery is the most potent treatment for obesity in adolescents with severe obesity. The types of surgery most frequently used are sleeve gastrectomy and gastric bypass, both of which reduce appetite 172 . Mechanisms of action are complex, involving changes in gastrointestinal hormones, neural signalling, bile acid metabolism and gut microbiota 173 . Sleeve gastrectomy is a more straightforward procedure and the need for vitamin supplementation is lower than with gastric bypass. However, long-term weight loss may be greater after gastric bypass surgery 174 .

Prospective long-term studies demonstrate beneficial effects of both sleeve gastrectomy and gastric bypass on weight loss and comorbidities in adolescents with severe obesity 175 , 176 . In a 5-year follow-up period, in 161 participants in the US TEEN-LABS study who underwent gastric bypass, mean BMI declined from 50 to 37 kg/m 2 (ref. 11 ). In a Swedish prospective study in 81 adolescents who underwent gastric bypass, the mean decrease in BMI at 5 years was 13.1 kg/m 2 (baseline BMI 45.5 kg/m 2 ) compared with a BMI increase of 3.1 kg/m 2 in the control group 176 . Both studies showed marked inter-individual variations. Negative adverse effects, including gastrointestinal problems, vitamin deficits and reduction in lean body mass, are similar in adults and adolescents. Most surgical complications following bariatric surgery in the paediatric population are minor, occurring in the early postoperative time frame, but 8% of patients may have major perioperative complications 177 . Up to one-quarter of patients may require subsequent related procedures within 5 years 109 . However, many adolescents with severe obesity also have social and psychological problems, highlighting the need for routine and long-term monitoring 109 , 178 .

Recommendations for bariatric surgery in adolescents differ considerably among countries, with information on long-term outcomes emerging rapidly. In many countries, bariatric surgery is recommended only from Tanner pubertal stage 3–4 and beyond, and only in children with severe obesity and cardiometabolic comorbidities 177 . The 2023 American Academy of Pediatrics clinical practice guidelines recommend that bariatric surgery be considered in adolescents ≥13 years of age with a BMI of ≥35 kg/m 2 or 120% of the 95th percentile for age and sex, whichever is lower, as well as clinically significant disease, such as T2DM, non-alcoholic fatty liver disease, major orthopaedic complications, obstructive sleep apnoea, the presence of cardiometabolic risk, or depressed quality of life 109 . For those with a BMI of ≥40 kg/m 2 or 140% of the 95th percentile for age and sex, bariatric surgery is indicated regardless of the presence of comorbidities. Potential contraindications to surgery include correctable causes of obesity, pregnancy and ongoing substance use disorder. The guidelines comment that further evaluation, undertaken by multidisciplinary centres that offer bariatric surgery for adolescents, should determine the capacity of the patient and family to understand the risks and benefits of surgery and to adhere to the required lifestyle changes before and after surgery.

Long-term weight outcomes

Few paediatric studies have investigated long-term weight maintenance after the initial, more intensive, weight loss phase. A 2018 systematic review of 11 studies in children and adolescents showed that a diverse range of maintenance interventions, including support via face-to-face psychobehavioural therapies, individual physician consultations, or adjunctive therapeutic contact via newsletters, mobile phone text or e-mail, led to stabilization of BMI z -score compared with control participants, who had increases in BMI z -score 179 . Interventions that are web-based or use mobile devices may be particularly useful in young people 180 .

One concern is weight regain which occurs after bariatric surgery in general 181 but may be more prevalent in adolescents 176 . For example, in a Swedish prospective study, after 5 years, 25–30% of participants fulfilled the definitions of low surgical treatment effectiveness, which was associated with poorer metabolic outcomes 176 . As with adults, prevention of weight regain for most at-risk individuals might be possible with the combination of lifestyle support and pharmacological treatment 182 . Further weight maintenance strategies and long-term outcomes are discussed in the 2023 American Academy of Pediatrics clinical practice guidelines 109 . The appropriate role and timing of other therapies for long-term weight loss maintenance, such as anti-obesity medications, more intensive dietary interventions and bariatric surgery, are areas for future research.

In summary, management of obesity in childhood and adolescence requires intensive interventions. Emerging pharmacological therapies demonstrate greater short-term effectiveness than behavioural interventions; however, long-term outcomes at ≥2 years remain an important area for future research.

Quality of life

Weight bias describes the negative attitudes to, beliefs about and behaviour towards people with obesity 183 . It can lead to stigma causing exclusion, and discrimination in work, school and health care, and contributes to the inequities common in people with obesity 184 . Weight bias also affects social engagement and psychological well-being of children.

Children and adolescents with obesity score lower overall on health-related quality of life (HRQoL) 4 , 5 . In measures that assess domains of functioning, most score lower in physical functioning, physical/general health and psychosocial areas, such as appearance, and social acceptance and functioning. HRQoL is lowest in treatment-seeking children and in those with more extreme obesity 185 . Weight loss interventions generally increase HRQoL independent of the extent of weight loss 186 , especially in the domains most affected. However, changes in weight and HRQoL are often not strongly correlated. This may reflect a lag in the physical and/or psychosocial benefit from weight change, or the extent of change that is needed to drive change in a child’s self-perception.

Similar observations apply to the literature on self-esteem. Global self-worth is reduced in children and adolescents with obesity, as is satisfaction with physical appearance, athletic competence and social acceptance 187 . Data from intensive interventions suggest the psychological benefit of weight loss may be as dependent on some feature of the treatment environment or supportive social network as the weight loss itself 188 . This may include the daily company of others with obesity, making new friendships, and experienced improvements in newly prioritized competences.

There is a bidirectional relationship between HRQoL and obesity 189 , something also accepted in the relationship with mood disorder. Obesity increases the risk of depression and vice versa, albeit over a longer period of time and which may only become apparent in adulthood 190 . Obesity also presents an increased risk of anxiety 191 .

Structured and professionally delivered weight management interventions ameliorate mood disorder symptoms 192 and improve self-esteem 193 . Regular and extended support are important components beyond losing weight. Such interventions do not increase the risk of eating disorders 194 . This is despite a recognition that binge eating disorder is present in up to 5% of adolescents with overweight or obesity 195 . They are five times more likely to have binge eating symptoms than those with average weight. Importantly, adolescents who do not have access to professionally delivered weight management may be more likely to engage in self-directed dieting, which is implicated in eating disorder development 196 .

The literature linking childhood obesity with either attention deficit hyperactivity disorder or autism spectrum disorder is complex and the relationship is uncertain. The association seems to be clearer in adults but the mechanisms and their causal directions remain unclear 109 , 197 . Young children with obesity, especially boys, are more likely to be parent-rated as having behavioural problems 198 . This may be a response to the behaviour of others rather than reflect clinical diagnoses such as attention deficit hyperactivity disorder or autism spectrum disorder. Conduct and peer relationship problems co-occur in children, regardless of their weight.

Children with obesity experience more social rejection. They receive fewer friendship nominations and more peer rejections, most pronounced in those with severe obesity 199 . This continues through adolescence and beyond. Children with obesity are more likely to report being victimized 200 . Younger children may respond by being perpetrators themselves. While it is assumed that children are victimized because of their weight, very few studies have looked at the nature or reason behind victimization. A substantial proportion of children with obesity fail to identify themselves as being fat-teased 187 . Although the stigma associated with obesity should be anticipated in children, especially in those most overweight, it would be inappropriate to see all as victims. A better understanding of children’s resilience is needed.

Many gaps remain in basic, translational and clinical research in child and adolescent obesity. The mechanisms (genetic, epigenetic, environmental and social) behind the overwhelming association between parental obesity and child and adolescent obesity are still unclear given the paradoxically weak association in BMI between adopted children and their parents in combination with the modest effect size of known genetic loci associated with obesity 201 .

Early manifestation of extreme obesity in childhood suggests a strong biological basis for disturbances of homeostatic weight regulation. Deep genotyping (including next-generation sequencing) and epigenetic analyses in these patients will reveal new genetic causes and causal pathways as a basis for the development of mechanism-based treatments. Future work aiming to understand the mechanisms underlying the development of childhood obesity should consider the complex biopsychosocial interactions and take a systems approach to understanding causal pathways leading to childhood obesity to contribute to evidence-based prevention and treatment strategies.

Long-term outcome data to better determine the risks of eating disorders are required. Although symptoms improve during obesity treatment in most adolescents, screening and monitoring for disordered eating is recommended in those presenting for treatment 202 and effective tools for use in clinical practice are required. A limited number of tools are validated to identify binge eating disorder in youth with obesity 203 but further research is needed to screen appropriately for the full spectrum of eating disorder diagnoses in obesity treatment seeking youth 203 . Recent reviews provide additional detail regarding eating disorder risk in child and adolescent obesity 117 , 202 , 204 .

Most studies of paediatric obesity treatment have been undertaken in HICs and predominantly middle-class populations. However, research is needed to determine which strategies are best suited for those in LMICs and low-resource settings, for priority population groups including indigenous peoples, migrant populations and those living with social disadvantage, and for children with neurobehavioural and psychiatric disorders. We currently have a limited understanding of how best to target treatment pathways for different levels of genetic risk, age, developmental level, obesity severity, and cardiometabolic and psychological risk. Current outcomes for behavioural interventions are relatively modest and improved treatment outcomes are needed to address the potentially severe long-term health outcomes of paediatric obesity. Studies also need to include longer follow-up periods after an intervention, record all adverse events, incorporate cost-effectiveness analyses and have improved process evaluation.

Other areas in need of research include the role of new anti-obesity medications especially in adolescents, long-term outcomes following bariatric surgery and implementation of digital support systems to optimize outcomes and reduce costs of behavioural change interventions 205 . We must also better understand and tackle the barriers to implementation of treatment in real-life clinical settings, including the role of training of health professionals. Importantly, treatment studies of all kinds must engage people with lived experience — adolescents, parents and families — to understand what outcomes and elements of treatment are most valued.

Obesity prevention is challenging because it requires a multilevel, multisectoral approach that addresses inequity, involves many stakeholders and addresses both the upstream and the downstream factors influencing obesity risk. Some evidence exists of effectiveness of prevention interventions operating at the level of the child, family and school, but the very poor progress overall in modifying obesity prevalence globally highlights many areas in need of research and evidence implementation. Studies are needed especially in LMICs, particularly in the context of the nutrition transition and the double burden of malnutrition. A focus on intergenerational research, rather than the age-based focus of current work, is also needed. Systems research approaches should be used, addressing the broader food and physical activity environments, and links to climate change 206 . In all studies, strategies are needed that enable co-production with relevant communities, long-term follow-up, process evaluation and cost-effectiveness analyses. In the next few years, research and practice priorities must include a focus on intervention strategies in the earliest phases of life, including during pregnancy. The effects of COVID-19 and cost of living crises in many countries are leading to widening health inequalities 207 and this will further challenge obesity prevention interventions. Available resourcing for prevention interventions may become further constrained, requiring innovative solutions across agendas, with clear identification of co-benefits. For example, public health interventions for other diseases, such as dental caries or depression, or other societal concerns, such as urban congestion or climate change, may also act as obesity prevention strategies. Ultimately, to implement obesity prevention, societal changes are needed in terms of urban planning, social structures and health-care access.

Future high-quality paediatric obesity research can be enabled through strategies that support data sharing, which avoids research waste and bias, and enables new research questions to be addressed. Such approaches require leadership, careful engagement of multiple research teams, and resourcing. Four national or regional level paediatric weight registries exist 208 , 209 , 210 , 211 , which are all based in North America or Europe. Such registries should be established in other countries, especially in low-resource settings, even if challenging 208 . Another data-sharing approach is through individual participant data meta-analyses of intervention trials, which can include prospectively collected data 212 and are quite distinct from systematic reviews of aggregate data. Two recent examples are the Transforming Obesity Prevention in Childhood (TOPCHILD) Collaboration, which includes early interventions to prevent obesity in the first 2 years of life 213 , and the Eating Disorders in Weight-Related Therapy (EDIT) Collaboration, which aims to identify characteristics of individuals or trials that increase or protect against eating disorder risk following obesity treatment 214 . Formal data linkage studies, especially those joining up routine administrative datasets, enable longer-term and broader outcome measures to be assessed than is possible with standard clinical or public health intervention studies.

Collaborative research will also be enhanced through the use of agreed core outcome sets, supporting data harmonization. The Edmonton Obesity Staging System – Paediatric 215 is one option for paediatric obesity treatment. A core outcome set for early intervention trials to prevent obesity in childhood (COS-EPOCH) has been recently established 216 . These efforts incorporate a balance between wanting and needing to share data and adhering to privacy protection regulations. Objective end points are ideal, including directly measured physical activity and body composition.

Collaborative efforts and a systems approach are paramount to understand, prevent and manage child and adolescent obesity. Research funding and health policies should focus on feasible, effective and equitable interventions.

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Children’s Hospital Westmead Clinical School, Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia

Natalie B. Lister & Louise A. Baur

Institute of Endocrinology and Diabetes, The Children’s Hospital at Westmead, Sydney, New South Wales, Australia

Natalie B. Lister

Sydney School of Public Health, The University of Sydney, Sydney, New South Wales, Australia

Louise A. Baur

Weight Management Services, The Children’s Hospital at Westmead, Sydney, New South Wales, Australia

The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands

Janine F. Felix

Department of Paediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands

Institute of Health Sciences, School of Medicine, University of Leeds, Leeds, UK

Andrew J. Hill

Division of Paediatrics, Department of Clinical Science Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden

Claude Marcus

Vestische Hospital for Children and Adolescents Datteln, University of Witten/Herdecke, Datteln, Germany

Thomas Reinehr

Department of Sport and Exercise Sciences, Durham University, Durham, UK

• Carolyn Summerbell

Division of Paediatric Endocrinology and Diabetes, Department of Paediatrics and Adolescent Medicine, Ulm University Medical Centre, Ulm, Germany

Martin Wabitsch

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Introduction (L.A.B., J.F.F. and N.B.L.); Epidemiology (L.A.B. and J.F.F.); Mechanisms/pathophysiology (L.A.B., J.F.F., T.R. and M.W.); Diagnosis, screening and prevention (L.A.B., N.B.L., T.R., C.S. and M.W.); Management (L.A.B., N.B.L., A.J.H., C.M. and T.R.); Quality of life (L.A.B., N.B.L. and A.J.H.); Outlook (L.A.B., N.B.L., J.F.F., A.J.H., C.M., T.R., C.S. and M.W.); Overview of the Primer (L.A.B. and N.B.L.).

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A.J.H. reports receiving payment for consultancy advice for Slimming World (UK). L.A.B. reports receiving honoraria for speaking in forums organized by Novo Nordisk in relation to management of adolescent obesity and the ACTION-Teens study, which is sponsored by Novo Nordisk. L.A.B. is the Australian lead of the study. T.R. received funding from the German Federal Ministry of Education and Research (BMBF; 01GI1120A/B) as part of the German Competence Network Obesity (Consortium ‘Youth with Extreme Obesity’). T.R. receives payment for consultancy advice related to pharmacological treatment of obesity from Novo Nordisk and Lilly, as well as honoraria for lectures in symposia organized by Novo Nordisk, Novartis and Merck. C.M. receives payments for consultancy advice and advisory board participation from Novo Nordisk, Oriflame Wellness, DeFaire AB and Itrim AB. C.M. also receives honoraria for speaking at meetings organized by Novo Nordisk and Astra Zeneca. C.M. is a shareholder and founder of Evira AB, a company that develops and sells systems for digital support for weight loss, and receives grants from Novo Nordisk for epidemiological studies of the effects of weight loss on future heath. M.W. received funding from the German Federal Ministry of Education and Research (BMBF; 01GI1120A/B) as part of the German Competence Network Obesity (Consortium ‘Youth with Extreme Obesity’). M.W. receives payment for consultancy advice related to pharmacological treatment of obesity from Novo Nordisk, Regeneron, Boehringer Ingelheim and LG Chem, as well as honoraria for speaking in symposia organized by Novo Nordisk, Rhythm Pharmaceuticals and Infectopharm. M.W. is principal investigator in phase II and phase III studies of setmelanotide sponsored by Rhythm Pharmaceuticals. N.B.L., J.F.F. and C.S. declare no competing interests.

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Lister, N.B., Baur, L.A., Felix, J.F. et al. Child and adolescent obesity. Nat Rev Dis Primers 9 , 24 (2023). https://doi.org/10.1038/s41572-023-00435-4

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Childhood obesity: A review of current and future management options

Affiliations.

• 1 Department of Paediatric Endocrinology, Alder Hey Children's Hospital, Liverpool, UK.
• 2 Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine, Queen Mary University of London, London, UK.
• 3 Department of Paediatric Dietetics, Alder Hey Children's Hospital, Liverpool, UK.
• 4 Department of Paediatric Clinical Psychology, Alder Hey Children's Hospital, Liverpool, UK.
• PMID: 34750858
• DOI: 10.1111/cen.14625

Obesity is becoming increasingly prevalent in paediatric populations worldwide. In addition to increasing prevalence, the severity of obesity is also continuing to rise. Taken together, these findings demonstrate a worrying trend and highlight one of the most significant challenges to public health. Childhood obesity affects multiple organs in the body and is associated with both significant morbidity and ultimately premature mortality. The prevalence of complications associated with obesity, including dyslipidaemia, hypertension, fatty liver disease and psychosocial complications are becoming increasingly prevalent within the paediatric populations. Treatment guidelines currently focus on intervention with lifestyle and behavioural modifications, with pharmacotherapy and surgery reserved for patients who are refractory to such treatment. Research into adult obesity has established pharmacological novel therapies, which have been approved and established in clinical practice; however, the research and implementation of such therapies in paediatric populations have been lagging behind. Despite the relative lack of widespread research in comparison to the adult population, newer therapies are being trialled, which should allow a greater availability of treatment options for childhood obesity in the future. This review summarizes the current evidence for the management of obesity in terms of medical and surgical options. Both future therapeutic agents and those which cause weight loss but have an alternative indication are also included and discussed as part of the review. The review summarizes the most recent research for each intervention and demonstrates the potential efficacy and limitations of each treatment option.

Keywords: BMI; childhood obesity; lifestyle interventions; paediatrics; pharmacotherapy.

© 2021 John Wiley & Sons Ltd.

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Home — Essay Samples — Nursing & Health — Childhood Obesity — Child Obesity Essay Outline

Child Obesity Essay Outline

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

Justin d. smith.

1 Department 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

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

Marissa Kobayashi

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

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.

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 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 ≥ 95 th 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 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 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, (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.

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.

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.

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 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 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 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 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 ( 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 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, 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 ).

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 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/m 2 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 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 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 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 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 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-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.

SUMMARY POINTS

• Childhood obesity is a complex, multidetermined, preventable chronic disease that increases risk for premature death and psychological problems.
• Evidence-based interventions for obesity are available for all stages of development from birth to 18 years.
• 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.
• Associated co-occurring medical and psychological conditions of childhood obesity present an opportunity for clinical and health psychology researchers and practitioners.

FUTURE ISSUES

• Future research ought to focus on translational considerations from the start and ways to scale up delivery of effective interventions.
• Research is needed on interventions and their implementation to more effectively reach minority and underserved populations at greatest risk for obesity.
• Increasing engagement and retention in childhood obesity interventions is a promising focus for future research.

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.

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.

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