Resource Type: Peer-Reviewed Publications

Background

The federal government has set measurable goals for reducing childhood obesity to 5% by 2010 (Healthy People 2010), and 10% lower than 2005-2008 levels by 2020 (Healthy People 2020). However, population-level estimates of the changes in daily energy balance needed to reach these goals are lacking.

Purpose

To estimate needed per capita reductions in youths’ daily “energy gap” (calories consumed over calories expended) to achieve Healthy People goals by 2020.

Methods

Analyses were conducted in 2010 to fit multivariate models using National Health and Nutrition Examination Surveys 1971-2008 (N=46,164) to extrapolate past trends in obesity prevalence, weight, and BMI among youth aged 2-19 years. Differences in average daily energy requirements between the extrapolated 2020 levels and Healthy People scenarios were estimated.

Results

During 1971-2008, mean BMI and weight among U.S. youth increased by 0.55 kg/m(2) and by 1.54 kg per decade, respectively. Extrapolating from these trends to 2020, the average weight among youth in 2020 would increase by ∼1.8 kg from 2007-2008 levels. Averting this increase will require an average reduction of 41 kcal/day in youth’s daily energy gap. An additional reduction of 120 kcal/day and 23 kcal/day would be needed to reach Healthy People 2010 and Healthy People 2020 goals, respectively. Larger reductions are needed among adolescents and racial/ethnic minority youth.

Conclusions

Aggressive efforts are needed to reverse the positive energy imbalance underlying the childhood obesity epidemic. The energy-gap metric provides a useful tool for goal setting, intervention planning, and charting progress.

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The global obesity epidemic has been escalating for four decades, yet sustained prevention efforts have barely begun. An emerging science that uses quantitative models has provided key insights into the dynamics of this epidemic, and enabled researchers to combine evidence and to calculate the effect of behaviours, interventions, and policies at several levels–from individual to population. Forecasts suggest that high rates of obesity will affect future population health and economics. Energy gap models have quantified the association of changes in energy intake and expenditure with weight change, and have documented the effect of higher intake on obesity prevalence. Empirical evidence that shows interventions are effective is limited but expanding. We identify several cost-effective policies that governments should prioritise for implementation. Systems science provides a framework for organising the complexity of forces driving the obesity epidemic and has important implications for policy makers. Many parties (such as governments, international organisations, the private sector, and civil society) need to contribute complementary actions in a coordinated approach. Priority actions include policies to improve the food and built environments, cross-cutting actions (such as leadership, healthy public policies, and monitoring), and much greater funding for prevention programmes. Increased investment in population obesity monitoring would improve the accuracy of forecasts and evaluations. The integration of actions within existing systems into both health and non-health sectors (trade, agriculture, transport, urban planning, and development) can greatly increase the influence and sustainability of policies. We call for a sustained worldwide effort to monitor, prevent, and control obesity.

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Obesity interventions can result in weight loss, but accurate prediction of the bodyweight time course requires properly accounting for dynamic energy imbalances. In this report, we describe a mathematical modelling approach to adult human metabolism that simulates energy expenditure adaptations during weight loss. We also present a web-based simulator for prediction of weight change dynamics. We show that the bodyweight response to a change of energy intake is slow, with half times of about 1 year. Furthermore, adults with greater adiposity have a larger expected weight loss for the same change of energy intake, and to reach their steady-state weight will take longer than it would for those with less initial body fat. Using a population-averaged model, we calculated the energy-balance dynamics corresponding to the development of the US adult obesity epidemic. A small persistent average daily energy imbalance gap between intake and expenditure of about 30 kJ per day underlies the observed average weight gain. However, energy intake must have risen to keep pace with increased expenditure associated with increased weight. The average increase of energy intake needed to sustain the increased weight (the maintenance energy gap) has amounted to about 0·9 MJ per day and quantifies the public health challenge to reverse the obesity epidemic.

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The simultaneous increases in obesity in almost all countries seem to be driven mainly by changes in the global food system, which is producing more processed, affordable, and effectively marketed food than ever before. This passive overconsumption of energy leading to obesity is a predictable outcome of market economies predicated on consumption-based growth. The global food system drivers interact with local environmental factors to create a wide variation in obesity prevalence between populations. Within populations, the interactions between environmental and individual factors, including genetic makeup, explain variability in body size between individuals. However, even with this individual variation, the epidemic has predictable patterns in subpopulations. In low-income countries, obesity mostly affects middle-aged adults (especially women) from wealthy, urban environments; whereas in high-income countries it affects both sexes and all ages, but is disproportionately greater in disadvantaged groups. Unlike other major causes of preventable death and disability, such as tobacco use, injuries, and infectious diseases, there are no exemplar populations in which the obesity epidemic has been reversed by public health measures. This absence increases the urgency for evidence-creating policy action, with a priority on reduction of the supply-side drivers.

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Simulation models (SMs) combine information from a variety of sources to provide a useful tool for examining how the effects of obesity unfold over time and impact population health. SMs can aid in the understanding of the complex interaction of the drivers of diet and activity and their relation to health outcomes. As emphasized in a recently released report of the Institute or Medicine, SMs can be especially useful for considering the potential impact of an array of policies that will be required to tackle the obesity problem. The purpose of this paper is to present an overview of existing SMs for obesity. First, a background section introduces the different types of models, explains how models are constructed, shows the utility of SMs and discusses their strengths and weaknesses. Using these typologies, we then briefly review extant obesity SMs. We categorize these models according to their focus: health and economic outcomes, trends in obesity as a function of past trends, physiologically based behavioural models, environmental contributors to obesity and policy interventions. Finally, we suggest directions for future research.

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Objective

Our goal was to quantify the magnitude of energy imbalance responsible for the increase in body weight among US children during the periods 1988-1994 and 1999-2002.

Methods

We adopted a counterfactual approach to estimate weight gains in excess of normal growth and the implicit “energy gap”–the daily imbalance between energy intake and expenditure. On the basis of Centers for Disease Control and Prevention growth charts, we constructed weight, height, and BMI percentile distributions for cohorts 2 to 4 and 5 to 7 years of age in the 1988-1994 National Health and Nutrition Examination Survey (N = 5000). Under the counterfactual “normal-growth-only” scenario, we assumed that these percentile distributions remained the same as the cohort aged 10 years. Under this assumption, we projected the weight and height distributions for this cohort at 12 to 14 and 15 to 17 years of age on the basis of their baseline weight-for-age and stature-for-age percentiles. We compared these distributions with those for corresponding age groups in the 1999-2002 National Health and Nutrition Examination Survey (N = 3091) approximately 10 years after the 1988-1994 National Health and Nutrition Examination Survey. We calculated differences between the counterfactual and observed weight distributions and translated this difference into the estimated average energy gap, adjusting for increased total energy expenditure attributable to weight gain. In addition, we estimated the average excess weight accumulated among overweight adolescents in the 1999-2002 National Health and Nutrition Examination Survey, validating our counterfactual assumptions by analyzing longitudinal data from the National Longitudinal Survey of Youth and Bogalusa Heart Study.

Results

Compared with the counterfactual scenario, boys and girls who were aged 2 to 7 in the 1988-1994 National Health and Nutrition Examination Survey gained, on average, an excess of 0.43 kg/year over the 10-year period. Assuming that 3500 kcal leads to an average of 1-lb weight gain as fat, our results suggest that a reduction in the energy gap of 110-165 kcal/day could have prevented this increase. Among overweight adolescents aged 12 to 17 in 1999-2002, results indicate an average energy imbalance ranging from 678 to 1017 kcal/day because of an excess of 26.5 kg accumulated over 10 years.

Conclusions

Quantifying the energy imbalance responsible for recent changes in weight distribution among children can provide salient targets for population intervention. Consistent behavioral changes averaging 110 to 165 kcal/day may be sufficient to counterbalance the energy gap. Changes in excess dietary intake (eg, eliminating one sugar-sweetened beverage at 150 kcal per can) may be easier to attain than increases in physical activity levels (eg, a 30-kg boy replacing sitting for 1.9 hours with 1.9 hours walking for an extra 150 kcal). Youth at higher levels of weight gain will likely need changes in multiple behaviors and environments to close the energy gap.

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