Understanding the relative importance of overconsumption and physical inactivity to excess weight gain among children and adolescents can contribute to the development and evaluation of interventions and policies to reduce childhood obesity. However, whether energy intake or expenditure is the dominant contributor to childhood obesity is a subject of debate. To date, no study has systematically reviewed the literature on this subject.
We searched PubMed and Ovid Medline (January 1970 to January 2010) for potentially relevant English-language abstracts and obtained full-text articles for the abstracts, which passed the initial inclusion–exclusion criteria. Reference lists of full-length articles were hand searched to identify additional studies potentially relevant for inclusion. Relevant studies were characterized into one of the following three categories: cross-sectional studies with a nationally representative sample, cross-sectional studies among population subgroups and longitudinal studies.
This review identified 26 studies examining factors related to energy intake, energy expenditure and obesity among children and adolescents. Cross-sectional and longitudinal studies suggest that the primary determinant of energy imbalance at both the population and the individual levels is not definitive. Our findings further suggest that there is wide variation in data quality between studies. Future research in this area should aim to improve the accuracy of measures of energy intake, expenditure and their net balance over time; capitalize on under-utilized, non-traditional data sources, which have not been widely used; use modeling techniques to synthesize studies of shorter follow-up period and different outcome measures; and examine the unique determinants of energy imbalance among demographic groups at higher risk for obesity.
On the basis of the current evidence, there is no consensus on the main driver of secular trends on weight gain among US children and adolescents. More research and better methods are needed to identify the relative contribution of energy intake and energy expenditure to obesity in the pediatric population.
Childhood obesity—defined as a body mass index (BMI) at or above the 95th percentile of the sex-specific Centers for Disease Prevention and Control BMI-for-age growth charts—affects one in five children and adolescents in the United States, disproportionately impacting girls, minorities and low socioeconomic status groups.1 Since 1980, rates of obesity tripled for children ages 2–5 years old and adolescents ages 12–19 years old and quadrupled for children ages 6–11 years old.2 Obesity in childhood significantly increases several health risks in youth and in adulthood. Obese children and adolescents are more likely to develop cardiovascular risk factors (for example, hyperinsulinemia, high blood pressure and high cholesterol)3, 4 during childhood and in the future. They are also more likely to remain obese as adults.4, 5 In addition, childhood obesity has been shown to negatively affect quality of life6 and school performance7 as well as increase the risk for depression8 and low self-esteem.9
Although the behavioral, social and environmental factors driving the US obesity epidemic are complex, the root cause is simple—on average, energy intake is higher than energy expenditure, resulting in a positive energy balance cumulating over a long period of time.10 Reducing childhood obesity is likely going to require a multitude of behavioral changes to decrease caloric intake and increase activity.11 The surplus of energy intake over energy expenditure required for normal growth, activity and daily function (or the ‘energy gap’) responsible for the increase in body weight among US children and adolescents from 1988–1994 to 1999–2002, is estimated to be 110–165 calories per day. For children who became obese adolescents, the energy imbalance accumulated over a 10-year period could average as high as 600–1100 kcal per day.12 It is to be noted that as individuals gain weight, even if one's physical activity level (PAL) remains the same, there will be an increase in basal metabolic rate and as a result, greater energy expenditure, which eventually brings the body weight to a new steady state. In order to fuel a continued increase in body weight and/or maintain a higher body weight, the ‘energy gap’ would accumulate and become larger as more weight is gained over time.
Although it is clear that the accumulation of excess calories resulting from today's ‘obesogenic’ environment shapes children's tendency to consume too much and exercise too little, there exists disagreement about the relative role of overconsumption and physical inactivity to the obesity epidemic in children. Considerable debate is ongoing in a diverse number of disciplines including economics, epidemiology and health services research. Some researchers have argued that the population trend in caloric intake has remained fairly constant and that changes in energy expenditure account for most of the increase in obesity prevalence.13, 14, 15, 16 Other researchers have argued the opposite.17, 18, 19, 20
To date, no study has systematically reviewed the published literature related to the relative contribution of energy intake and energy expenditure to the US childhood obesity trend. The goal of this paper is to synthesize that evidence base for the United States. For this review, we consider three types of evidence: cross-sectional studies with population-representative samples, cross-sectional studies limited to specific subgroups and longitudinal studies. Within cross-sectional studies, those using serial cross-sectional studies (multiple samples of the same population but not the same individuals) are informative for understanding secular changes in population-level energy imbalance and shifts in weight distributions. Those using cross-sectional data from only one point in time are useful for suggesting possible correlations between behavioral patterns and between-individual or between-group variability in BMI, but fall short in making inferences about changes over time. In comparison, longitudinal studies are superior in their ability to inform age- and/or growth-related changes in energy regulation and body weight as well as between-individual variability in weight change in response to energy imbalance. This review focuses on the magnitude of energy intake and energy expenditure rather than the contribution of a wide range of more upstream behavioral, psychosocial or environmental factors, although their importance in forming interventions and policies should not be discounted. Specifically, we aimed to address the following research questions:
What is the relative contribution of increased energy intake compared with decreased physical activity in explaining patterns or secular changes in childhood obesity at the population level?
What is the relative contribution of increased energy intake compared with decreased physical activity in explaining differences in weight status and weight change among individual children and adolescents?
The importance of understanding the primary determinant of energy imbalance
Understanding the relative contributions of the determinants of energy imbalance is critical for two reasons. First, the annual medical costs of obesity are estimated to be $147 billion (roughly 10% of all medical spending),21 and such trends are primarily driven by increased prevalence (rather than by increased cost per obese person).21 Therefore, understanding the relative contribution of energy intake and energy expenditure in childhood is key for improving the effectiveness of anti-obesity efforts to reduce future burden of obesity given that obese children are more likely to become obese adults.22 Second, identifying the relative importance of behaviors driving energy intake and expenditure can guide the design, implementation, and monitoring of various prevention or treatment programs. It may also accelerate the identification and application of helpful countermeasures in the public and private sectors, which are already underway. For example, in the public sector, the Centers for Disease Prevention and Control, National Institute of Health (NIH) and Robert Wood Johnson Foundation have recently joined forces to form a National Collaborative on Childhood Obesity Research with the aim of accelerating the discovery and applications of effective policy and environmental initiatives to reverse childhood obesity. In the private sector, the Healthy Weight Commitment Foundation—a collaboration of >40 members of the food and beverage industry manufacturers and retailers, fitness companies and diverse non-governmental organizations—was recently created to promote solutions to promote energy balance (www.healthyweightcommit.org). The Healthy Weight Commitment Foundation activities will be independently evaluated by the Robert Wood Johnson Foundation.
Materials and methods
We searched PubMed and Ovid Medline (January 1970 to January 2010) for potentially relevant abstracts based on the inclusion–exclusion criteria described below. Databases were searched for English-language articles using combinations of the following key words ‘children’, ‘childhood’, ‘adolescent’, ‘caloric intake’, ‘caloric expenditure’, ‘energy intake’, ‘energy expenditure’, ‘physical activity’, ‘obesity’, ‘BMI’, ‘energy balance’, ‘energy imbalance’ and ‘energy gap’. We then obtained full-text articles for the abstracts, which passed the initial inclusion–exclusion criteria and for the abstracts in which the full article was required to make a determination. Reference lists of full-length articles were hand searched to identify additional studies potentially relevant for inclusion. A summary of the search terms for the literature review (including the number of hits and number of relevant hits) can be found in Appendix A.
Inclusion and exclusion criteria
We included English-language studies examining the association between energy intake, energy expenditure and energy imbalance among children and adolescents. We included studies that used BMI, obesity or change in body weight (measured or self-reported) as their outcome. We excluded studies that focused only on body composition (for example, percent body fat, skinfold thickness) without measures of body weight or BMI because of the mixed evidence on the correlation between body composition and obesity, particularly among older age groups, the morbidly obese, and individuals with above-average lean muscle mass (for example, athletes).23, 24, 25, 26 We also excluded studies in infants.
Relevant studies were characterized into one of following three categories: cross-sectional studies with a nationally representative sample, cross-sectional studies among population subgroups and longitudinal studies. Studies were considered ‘cross-sectional’ if the study data were collected at one point in time or a series of time points but with different individuals sampled at each time. Studies using data from a single cross-section offer a snapshot of the population and can be used for associating differences in body weight status between individuals or demographic subgroups with differences in energy intake or PALs. In comparison, however, serial cross-sectional studies provide opportunities to observe changes in energy intake and expenditure over time, albeit usually at the aggregate level. Drivers of differential weight trajectories over time between populations (for example, by age group, race-ethnicity or socioeconomic status) can therefore be examined. Even better at assessing drivers of energy balance within individual body weight changes are longitudinal studies, wherein a cohort is followed over time with at least two measurements at different time points. Generally speaking, we used cross-sectional studies to inform our first research question—increased energy intake compared with decreased physical activity in explaining patterns or secular changes in childhood obesity at the population level—and longitudinal studies to inform our second research question—the relative contribution of increased energy intake compared with decreased physical activity in explaining differences in weight status and weight change among individual children and adolescents. Owing to the substantial methodological heterogeneity between studies, we did not attempt a quantitative synthesis (for example, meta-analysis). A table detailing the primary strengths and weaknesses of common methodologies used to measure the drivers of energy imbalance can be found in Appendix B.
Trends in caloric intake among children and adolescents
Data on trends in food intake come from two main sources: the National Health Examination and Nutrition Survey (NHANES) and the United States Department of Agriculture's (USDA) Continuing Survey of Food Intake by Individuals/National Food Consumption Survey (described in more detail in Appendix C). These data do not report consistent trends in caloric intake among children. From the period 1971–1974 to 1999–2006, the NHANES did not detect an increase in caloric intake among children with the exception of adolescent girls ages 12–19 (Briefel and Johnson27; Troiano et al.28; author calculations of changes in overall caloric intake from NHANES 1988–1994 to 1999–2006). In contrast, the Continuing Survey of Food Intake by Individuals/National Food Consumption Survey data suggest that children increased their energy intake by almost 200 calories per day from the period 1989–1991 to 1994–1996.29, 30 Over the last century, the major contributors to increased energy intake appear to be oils, shortening, meat, cheese, frozen desserts and—more recently—added sweeteners.31 Of note, food intake data are highly subject to reporting error, particularly among children, which may explain the mixed findings related to total caloric intake.32 We discuss the limitations of dietary intake data in more detail in later sections of the review.
Although the secular trends in energy intake among children and adolescents are somewhat ambiguous, the trends in sugar-sweetened beverage (SSB) consumption are clear. Consumption of SSB has been linked to weight gain among the pediatric population, particularly among black and Hispanic youth.33, 34, 35 In the past 20 years, US children and adolescents are increasingly consuming more SSB (primarily at home followed by at school and at restaurants/fast food outlets).36 Per-capita daily caloric contribution from SSBs increased from 204 kcal per day in 1988–1994 to 224 kcal per day in 1999–2004. Children and adolescents derive 10–15% of total calories from SSBs and 100% fruit juice. Of note, the increasing calorie contribution from beverages, primarily driven by SSBs, has been partially offset by a decline in milk drinkers among children37 and adults.38
PAL among US children and adolescents
Evidence from the 2005 Youth Risk Behavior Surveillance System, a national school-based survey of 9–12th graders, indicates that less than half of boys (43.8%) and less than a third of girls (27.8%) were physically active for a total of 60 min or more per day (on 5 or more of the past 7 days).39 From 1991 to 2005, Youth Risk Behavior Surveillance System data indicate that the percentage of adolescents attending daily physical education classes fell from 41.6 to 33.0%, suggesting that the average amount of physical activity during school time is declining.40 In another study of nearly 4500 children aged 9–13 years, two-thirds (61.5%) did not participate in an organized physical activity during their non-school hours, and nearly a quarter (22.6%) did not engage in any leisure-time physical activity.41
A recent nationally representative study analyzing data collected using accelerometers (a small device that measures and records movement intensity and duration) found that physical activity declines dramatically across age groups between childhood and adolescence.42 In particular, 42% of children ages 6–11 years obtained the 60 min a day of PAL (recommended by the Centers for Disease Control and Prevention and American College of Sports Medicine) compared with only 8% of adolescents. Although the overall pattern based on existing single cross-sectional studies suggests that most children and adolescents are sedentary, there is a lack of longitudinal or serial cross-sectional studies that objectively measure PALs overtime, making it difficult to make inferences about population-level secular trends.
The initial search identified 6674 articles. Of those, 6656 did not meet inclusion–exclusion criteria, leaving 18 articles. Nine additional articles were identified for inclusion following hand searches of reference lists from the 18 original articles. Overall, we identified 27 relevant articles. A flowchart summarizing the process of identifying relevant studies and applying inclusion–exclusion criteria is presented in Figure 1.
Details of the 26 articles can be found in Tables 1, 2, 3. Table 1 includes the cross-sectional studies with a nationally representative population, Table 2 includes the cross-sectional studies among population subgroups and Table 3 includes longitudinal studies. Each of the tables is further divided into three sections: articles supporting energy intake as the primary determinant of obesity, including those focused on energy expenditure and obesity, which found no association (labeled ‘not expenditure’), articles supporting energy expenditure as the primary determinant of obesity and articles wherein the evidence was ambiguous (that is, supported both energy intake and energy expenditure, supported neither or unclear). Hereby, we used the term ‘determinant’ to include all influencing elements or factors of energy balance, which may or may not be time dependent. The results are sorted chronologically (most recent first) and alphabetically within each year. For those studies conducted outside of the United States, the country is indicated in column one under the first authors name. The last column identifies the conclusion of the study. Study results were classified into one of six possible categories: intake (listed as ‘intake’ or ‘not expenditure’), expenditure, intake and expenditure, neither, both, unclear.
Of the 27 studies, 4 were cross-sectional with a nationally representative population, 16 were cross-sectional among population subgroups and 7 were longitudinal. Of the cross-sectional studies, 15 were single cross-sectional studies and 5 were serial cross-sectional studies. Twenty-three of the studies used measured height and body weight (rather than self-report). Eleven of the studies used objective measures of energy expenditure. Only one study used an objective measure of energy intake.
Cross-sectional studies with a nationally representative sample
Of the four cross-sectional studies with a nationally representative population identified in this review, three supported energy intake as the primary determinant of childhood obesity43, 44, 45 and one supported both energy intake and energy expenditure.46 We identified no cross-sectional studies with a nationally representative population that supported energy expenditure as the primary determinant of obesity.
Two of the studies supporting energy intake—both of which looked at trends in adolescent physical activity using data from the Youth Risk Behavior Surveillance System—found no clear evidence of decreased physical activity43 or small changes in overall activity45 and concluded that physical inactivity was not the primary determinant of adolescent obesity. Swinburn et al.70 combined National Health and Nutrition Examination Survey data from 1970 to 1976 and 1999 to 2002 with US food supply data (adjusted for spoilage and wastage) and predicted changes in body weight from changes in estimated energy intake. The investigators found that the measured weight gain and predicted weight gain for the increased energy intake were identical, suggesting that increased energy intake over the period was sufficient to explain weight gain. In the Young Hearts Study in Northern Ireland, which suggests that both increased energy intake and decreased physical activity are important to childhood obesity, Watkins et al.46 examined trends in body fatness with trends in intake and physical activity. The investigators found that mean energy intake increased in girls but not in boys while mean physical activity decreased only in 12-year-old girls.
Cross-sectional studies among population subgroups
Of the 16 cross-sectional studies among population subgroups, 5 provided evidence supporting energy intake as the primary determinant of childhood obesity,47, 48, 49, 50, 51 8 provided evidence for energy expenditure,52, 53, 54, 55, 56, 57, 58, 59 2 provided evidence for both energy intake and expenditure60, 61 and 1 was unclear.62
Studies that provided evidence supporting energy intake found that increased total energy intake was positively associated with BMI,47, 48 that obese children consumed more than non-obese children,49, 51 and that Pima Indian children, although they had higher body weight, had similar levels of total energy expenditure and physical activity-related energy expenditure as white children.50 With the exception of the Pima Indian study,50 all of the studies supporting energy intake included measures of both energy intake and energy expenditure. In contrast, studies supporting physical inactivity as the primary determinant of obesity found that children with a higher BMI were less physically active than children with a lower BMI53, 55, 56, 58 and that exercise was negatively associated with higher BMI.59 Two of these studies found no relationship between total energy intake and BMI.52, 54 Studies that provided evidence for both energy intake and energy expenditure, found that children with a higher BMI had greater energy intake and lower physical activity than children with a lower BMI.60, 61 Finally, one study found that neither intake nor expenditure significantly differed between obese and non-obese children.62
Of the seven longitudinal studies, two supported energy intake as primary determinant,63, 64 three supported energy expenditure65, 66, 67 and two supported both energy intake and energy expenditure being important.11, 69
Ekelund et al.63 examined the relationship between changes in weight, fat mass and physical activity among obese and normal weight adolescents in Sweden. They found that both the normal weight and obese groups gained weight and fat mass over the 4-year follow-up period. They also found that overall change in physical activity was associated with a significant reduction in fat mass in the normal weight group but not in the obese group. Bandini et al.64 compared baseline energy expenditure, change in fat mass and change in relative weight among premenarchal girls in Massachusetts and found no relationship between baseline energy expenditure and change in fat mass over 4 years.
Data from Project Heartbeat!,65 the Baton Rouge Children's Study67 and the National Growth and Health Study66 argued for physical inactivity as the dominant driver of energy imbalance. In a 4-year longitudinal study, Fulton et al.65 found that moderate-to-vigorous physical activity at baseline was inversely related to changes in BMI. In a 10-year multicenter study among girls, Kimm et al.,66 found that changes in activity level significantly affected changes in BMI; in particular, declines in physical activity, but not total energy intake, were significantly associated with increases in BMI. DeLany et al.67 followed up lean and obese children for 2 years and found that obese children tended to have lower activity-related energy expenditure than lean children at follow-up, even after adjustment for the 18 kg of additional body weight observed in the obese children, suggesting the role of physical inactivity in the maintenance of excess weight.
The longitudinal study of preadolescent girls by Berkey et al.69 suggested that both energy intake and energy expenditure were important in explaining the incidence of obesity. The investigators examined the role of physical activity, sedentary behaviors and dietary patterns on annual body weight changes. They found that for both boys and girls, a 1-year increase in BMI was larger among those children who reported more time spent on TV, videos/VCR or video/computer games and among those children who reported more increased caloric intake during the previous year. Larger year-to-year increases in BMI were also seen among girls who reported higher caloric intakes and less physical activity.
Butte et al.11 modified an adult-based mathematical model and applied the total energy cost of weight gain, underlying increase in energy intake and decrease in PAL on 488 non-overweight children (5–19 years of age) in the 1-year follow-up of the VIVA LA FAMILIA study. Changes in fat-free mass and fat mass were obtained by dual-energy X-ray absorptiometry, and basal metabolic rate was measured by calorimetry. The model incorporated these objective measurements and empirically estimated parameters, by sex, age and Tanner stage, related to energy conversion, storage and deposition to fat mass versus fat-free mass. With the model, they not only estimated the amount of energy imbalance required to produce the observed weight gain, but also partitioned the energy cost of weight gain to physical activity and caloric intake. For instance, when holding physical activity constant, energy intake would have been higher to support weight gain and higher basal and activity-related energy expenditure. In comparison, when holding energy intake constant, the continuous changes in PAL would be larger than the initial energy imbalance. They concluded that because the plasticity of dietary intake is larger than physical activity, and because most children today are at the physiological minimum of PAL, excess dietary intake is likely to be the culprit of recent trend in obesity.
The purpose of this review was to help us better understand the literature related to the relative importance of energy intake and energy expenditure to the energy imbalance among children and adolescents. We examined two specific research questions: the relative contribution of energy intake and energy expenditure to secular changes in childhood obesity at the population-level using cross-sectional evidence and the relative contribution of energy intake and energy expenditure to weight gain among individual children and adolescents using longitudinal evidence. The key distinction between the two types of evidence is that cross-sectional studies, especially those with only single cross-sectional survey, are limited to measuring associations while longitudinal studies can better inform causal inferences. This review identified a greater number of cross-sectional studies (20) than longitudinal studies (7); 15 of the 20 cross-sectional studies included only single cross-sections, revealing significant gap in providing a fuller picture on the main drivers of childhood obesity epidemic in the United States.
Multiple modes of data collection for body weight, energy intake and energy expenditure distinguishes the quality of the studies but also complicates the comparison between articles. In all, 23 of the 27 studies reviewed used objectively measured height and body weight rather than self-report. This distinction is one key feature of higher quality studies given the well-established self-report bias on height and body weight.71, 72 The method of data acquisition for the energy intake and energy expenditure was mixed. Objective measures of energy expenditure (for example, accelerometer or doubly labeled water, a technique, which uses the naturally occurring stable isotopes of water to assess energy expenditure) or energy intake (for example, direct observation) are more accurate and less subject to reporting bias as compared with self-reports based on participant recall.73, 74 Only 1 study used an objective measure of energy intake51 while 11 studies used objective measures of energy expenditure. Nevertheless, there is a paucity of studies using objective measures across multiple nationally representative samples, especially for physical activity.
Of the cross-sectional studies among nationally representative populations, most of the evidence pointed toward energy intake. Of the cross-sectional studies among population subgroups and the longitudinal studies, however, the evidence was evenly divided among support for energy intake, support for energy expenditure and support for both energy intake and expenditure. These findings suggest that both reduced caloric intake and increased energy expenditure are comparable in explaining differences in weight gain between groups and between individuals. Owing to large variations in methodology and target populations between the studies included in this review, there is insufficient evidence to provide agreement about the primary determinant of obesity among children and adolescents. What is clear is that more research and better data are needed to improve the literature related to the primary driver of energy imbalance and childhood obesity at the population level.
Recommendations for future energy balance research
On the basis of the results from this literature review, we suggest possible directions for future research to improve the evidence base related to energy imbalance among children and adolescents. Specific recommendations are described in detail below and summarized in Table 4.
In an attempt to fill the existing knowledge gaps, future data collection efforts should focus on improving the accuracy of measures of energy intake, expenditure and their net balance over time. This is particularly true for energy expenditure data. The incorporation of accelerometer data into the 2003–2006 NHANES survey instrument was an important step in this direction, particularly because self-reported activity measures have been shown to overestimate moderate and vigorous activity among youth.75 Prudent trend analyses based on 4 years of accelerometer data, adequately addressing the potential sample size issues, could be informative. However, caloric intake in NHANES remains constrained by potential under-reporting in dietary recall surveys. Despite less-than-perfect measures of energy intake and expenditure, a ‘change predicts change’ framework (using regression techniques such as fixed-effect models) to relate secular changes in caloric intake and in PAL with changes in population BMI over time may produce reasonable estimates on the relative contribution of overconsumption versus inactivity in driving the obesity trend.
To improve the evidence base related to energy imbalance with respect to both primary and distal determinants, researchers should also capitalize on under-utilized, non-traditional data sources, which have not been widely used by obesity researchers. This includes data from the commercial (for example, beverage and food industries), agricultural, finance and urban planning/transportation industries. For example, there is a wealth of relevant trend data in the food and beverage industries about the volume or quantity of calories available for consumption. Analyses that incorporate supply chain information or sales–purchase information could be very useful for understanding the social–commercial determinants of energy imbalance and for forming empirically driven policy alternatives. To provide the best evidence, it will be important to have a good match between the data and the specific research question being addressed.
Model-based studies offer a potential opportunity, at both the population and the individual levels, to synthesize studies of shorter follow-up period and different outcome measures to form a policy relevant perspective. At the population level, for example, Bleich et al.17 and Swinburn et al.44 used energy balance equations to leverage food supply or dietary intake and national BMI data to explain the observed differences between populations and trends over time. At the individual level, Butte et al.11 and Swinburn et al.70 simulated physiological processes of tissue deposition to understand the shifts in body size by changes in energy intake and expenditure. Drawing inferences from theory and empirical observations have advanced the scientific knowledge base on the likely determinants of energy imbalance, but more importantly, these models can be extremely informative for policy contexts. Specifically, these evidence-based models can simulate ‘what-if’ scenarios and serve as a ‘laboratory’ for childhood obesity prevention strategies. Although it remains top priority to invest in higher quality follow-up data, building modeling capacity and incorporating relevant industry data for addressing important questions related to energy imbalance may be fruitful and time-efficient.
Given the disparities in obesity among sub-populations,1 examining the unique determinants of energy imbalance (for example, genetic, environmental, sociobehavioral factors) among demographic groups at higher risk for obesity (such as Hispanics and Blacks) should be a research priority. However, the existing literature is limited in identifying behavioral determinants of energy imbalance among subgroups under heightened risk for obesity, largely because of the lack of available long-term data with sufficient sample size. To date, the majority of the research addressing disparities relies on single cross-sectional studies. As disease risks associated with excess adiposity differ substantially by race-ethnicity and by specific gene–environment interaction, cohort studies from specific race-ethnic subgroups across multiple geographical areas will be extremely informative. For example, studies of Pima Indians have shown that a low metabolic rate may contribute to an aggregation of obesity in families.76 Other research has identified elevated disease risk at a lower BMI values in Asians.77 More knowledge is needed on how different diet and activity environments, both visible and contextual, contribute to energy imbalance and possibly facilitate the development of more effective preventive strategies.
A final important area of research relates to the issue of compensation—the reduction in caloric intake from one energy source in response to calories ingested from another energy source or the change in energy intake in response to a change in energy expenditure. It is widely recognized that individuals do not compensate well for calories consumed in liquid form. For example, people do not sufficiently reduce energy intake in response to calories ingested from SSBs.78, 79 Also well known is the biological relationship underpinning energy balance—a change in one side of the equation will result in a response on the other side of the equation—as the body works to maintain a steady state. Yet, there remain many unanswered questions in this area. Key among them is: (1) the compensation between energy intake and energy expenditure, (2) the relationship between compensation and location of consumption or time of consumption and (3) the possible unintended consequences of compensation. One example is increasing the number of playgrounds. On one hand, a greater volume of playgrounds may increase energy expenditure, but if it also leads to increased energy intake, the impact of playgrounds on energy balance may be null or positive. Another example is the consumption of sports drinks while exercising. If the calories consumed from sports drinks are equal to or exceed the calories burned from exercise, the effect will be a null or a net positive energy balance. A third example is the removal of soda from schools. If vending machines selling SSBs are removed from schools, do children drink more soda and SSBs at home? Similarly, if children drink less soda during the week, do they then drink more soda during the weekend? The current evidence base does not allow us to predict these and other related outcomes reliably. Therefore, a better understanding in this area will be critical for informing the development of effective policy and predicting the impact of unintended consequences as they relate to issues of compensation.
There are several limitations to this review worth noting. First, because of the substantial methodological heterogeneity between the studies, it was not possible to conduct a quantitative synthesis of the evidence. Second, there may be publication bias—the tendency for researchers to submit or for journals to accept studies for publication based on the direction or strength of the study findings—and this review is restricted to the published literature. Therefore, the 27 articles included in this review may not represent an exhaustive list of all relevant studies. Third, although we did our best to include all relevant articles from the published literature, it is possible that some were accidentally omitted, which could bias the interpretation of results. Fourth, some studies had small sample size, which may provide insufficient power to fully address the research questions in this study. Fifth, several studies in support of energy expenditure measured physical activity but not energy expenditure. Given that physical activity makes up only a portion of total energy expenditure (∼30% of daily energy expenditure) while the rest comprises the basal metabolic rate, energy associated with keeping the body alive (∼60% of daily energy expenditure) and the thermic effect of food, energy associated with processing food (∼10% of daily energy expenditure), the compensatory increase in basal metabolic rate after weight gain and the time course of reaching a new steady state may not be fully described. This is a specific constraint for single cross-sectional studies. Sixth, in cross-sectional studies, reverse causation may bias the association between obesity, energy intake and energy expenditure. For instance, obese children and teens intending to lose weight may cut back on calories or exercise more. One recent study found that physical inactivity in children is the result of weight gain rather than its cause.80 This caveat can be partly addressed by incorporating information on the subjects’ intention to lose weight to better understand single cross-sectional studies. Finally, cross-sectional studies, particularly those looking at nationally representative populations are also subject to ecological fallacy81 wherein average characteristics of a group are assumed to accurately represent the individual members of a group.
In summary, this review identified and catalogued a wide range of studies with various designs, measurements and target populations aimed to understand the relative contributions of increased energy intake versus decreased energy expenditure on childhood obesity and weight gain at the individual and at the population level. Overall, single cross-sectional studies were informative in identifying determinants of differences between subgroups and between individuals, while serial cross-sectional population surveys provided better insight on secular trends. In addition, longitudinal studies, while time and resource intensive in many cases, presented unique opportunities to investigate, in more detail, the individual-level changes in energy balance and weight change. Finally, a growing number of modeling studies can potentially shed light on the debate and inform obesity-prevention strategies and policies by producing ‘what-if’ scenarios. On the basis of the current evidence, there is no consensus on the main driver of secular trends on weight gain among US children and adolescents.
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We acknowledge support for this review from the Robert Wood Johnson Foundation and thank Dr Mary Story and Dr Tracy Orleans for helpful comments on earlier versions of this paper.
The authors declare no conflict of interest.
Summary of common data sources used to estimate energy balance
The National Health and Nutrition Examination Survey (NHANES) combines both in-person interviews and physical exams (including measured height and weight) to determine the health and nutrition status of non-institutionalized adults and children in the United States. Data sets were collected from the following time periods: 1959–1962, 1971–1975, 1976–1980 and 1988–1994. In 1999, NHANES became a continuous program with an annual sample size of approximately 5000 persons.82
The Behavioral Risk Factor Surveillance System (BRFSS) is an on-going telephone survey that has been conducted annually since 1984 to track health conditions and risk behaviors in the United States. The program targets non-institutionalized adults aged 18 and older in all 50 states, Puerto Rico, United States, Virgin Islands and Guam. More than 350 000 adults are interviewed each year. Height and weight are self-reported.
Similar to the BRFSS is the Youth Risk Behavior Surveillance System (YRBSS), a nationally representative school-based survey conducted biennially since 1991 that targets young adults in grades 9–12 with annual sample sizes consistently above 10 000. The YRBSS monitors six main categories: behaviors that contribute to unintentional injuries and violence, tobacco use, alcohol and other drug use, sexual behaviors, unhealthy dietary behavior and physical inactivity. The surveys are administered by teachers in the classrooms, and height and weight are self-reported.
Food balance sheets (FBSs) from the Food and Agricultural Organization provide information on the per capita national supply and use of food. Although these offer the most comprehensive estimate of a nation's food consumption, they do not reflect actual consumption or household waste and spoilage. Thus, data from FBS tend to overestimate the energy intake.
The Continuing Survey of Food Intake by Individuals (CSFII) was a food recall study targeting non-institutionalized persons of all ages from all 50 states conducted in 1977–1978 and 1994–1996. Over 16 000 persons were interviewed in 1994–1996. Respondents provided 24-h dietary recall for two non-consecutive days during in-person interviews. Given that people may not recall everything that they eat, the CSFII tends to underestimate the energy intake.
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Bleich, S., Ku, R. & Wang, Y. Relative contribution of energy intake and energy expenditure to childhood obesity: a review of the literature and directions for future research. Int J Obes 35, 1–15 (2011). https://doi.org/10.1038/ijo.2010.252
- childhood obesity
- energy imbalance
- energy intake
- energy expenditure
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