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The relation between dietary change and rising US obesity


OBJECTIVE: To determine if the source from which food is obtained has contributed to the increased obesity of the US population, while controlling for demographic, lifestyle and regional factors.

METHODS: Multiple regression was used to estimate the effect of food source on body mass index (BMI) while accounting for other factors which have been shown to affect obesity in a nationally representative sample of the US population.

SAMPLE: This study used secondary data from the 1994–1996 Continuing Survey of Food Intake by Individuals (CSFII). The CSFII is a nationally representative sample of 16,103 individuals, obtaining for each respondent 24 h recalls of all food intake on two nonconsecutive days as well as demographics and information on lifestyle choices.

RESULTS: For a large number of demographic and lifestyle factors, our results support those which have previously been found to contribute to increased overweight. Our contribution is to examine whether the source from which food is obtained also contributes to increased overweight. Our evidence suggests that this is the case. The average height for males in our sample was 1.77 m. For two such males, one who ate food away from home (FAFH) during the previous 24 h period and the other who did not, results suggest that the first will be about 1 kg heavier, all other factors being equal. For two females of average height (1.63 m) the same is true for those who ate fast food, but not at restaurants. In all cases, except females who ate at restaurants, the effects are significant in the regression (P<0.05).

CONCLUSION: The trends in both increased US obesity and in increased consumption of FAFH are unlikely to be coincidental. FAFH, and particularly fast food consumption, are likely to be contributing factors to increased obesity.


Despite growing awareness of the health problems associated with excess weight, and the wide availability of low calorie, ‘lite’, and low or no-fat foods, Americans are gaining weight. It has been well documented that, starting as early as 1960, there has been a consistent year-to-year increase in the percentage of adults who are overweight.1,2,3 The trend appears to have accelerated. Data from the Behavioral Risk Factor Surveillance System (BRFSS) show that in the 10 y period 1985–1995 the percentage overweight (defined as body mass index (BMI) between 25 and 30) rose from 29.2 to 34.4 and the percentage of obese individuals (defined as BMI greater than 30) rose from 8.6 to 15.2.4 The association between overweight and many chronic diseases and adverse health outcomes makes this a cause for public health concern.1 Obesity is also a factor in the nation's burgeoning health care bill: it is estimated that for 1990 direct and indirect costs of excess weight in the US were $46 billion and $23 billion, respectively.5

The causes of increased overweight are not readily apparent. It has long been held that weight depends on an energy balance: the relation between energy intake and energy expenditure. This suggests that average energy consumption has risen and/or activity levels have declined. The Third Report on Nutrition Monitoring notes that ‘surveys have indicated an excess of energy over consumption, probably because of low levels of physical activity.’1 However, although a large percentage of adults report sedentary lifestyles, and many children spend little time in vigorous physical activity, there is no direct evidence of major recent changes in these measures. The median percentages of the US population reporting ‘no activity or a physical activity or pair of activities that were done for 20 minutes or less, fewer than three times a week’, for 1990–1994 are: 58.09, 56.51, 56.52, 59.84 and 57.79, respectively.6

There is some evidence that energy intake may have increased. US data on food availability suggests that per capita energy availability rose from 3300 to 3800 calories per day between 1977 and 1994 suggesting a 15% increase in energy available in the US food supply.7 Due to waste, this is not all consumed, but unless waste is increasing, which is possible given more processing and rising FAFH, it suggests a significant increase. A less bleak picture emerges from a recent USDA report.8 Lin, Frazão and Guthrie examine self-reported consumption from the 1977–1978 and 1987–1988 Nationwide Food Consumption Surveys and the 1989–1991 and 1994–1995 Continuing Survey of Food Intake by Individuals for nutrient intakes. Their findings show claimed consumption of 1876 kcal per person in 1977–1978 and 2006 in 1994. Past studies have found that self-reported intakes are biased downward, but the bias is consistently about 20%.9 This suggests a more modest increase in energy of about 7% over the period. They do find a shift in the sources of energy with 18% coming from FAFH in 1977–1978 and 34% in 1995. Other USDA nutrition surveys have found little evidence of differences in energy intake between the overweight and the general population.10 Furthermore, some contend that there are differences between the macronutrients such that fat energy may promote obesity more than energy from other sources.11 Again, evidence from Lin, Frazão and Guthrie suggests the overall percentage of energy from fat has decreased from 41.2% in 1977–1978 to 33.6% in 1995. However, the pictures are dramatically different for at-home and away-from-home food consumption. Both were comparable to the average of 4l% in 1977–1978, but while at-home-food energy from fat dropped to 31.5% in 1995, away-from-home food dropped by a much smaller amount to 37.6%. For saturated fats the trends were somewhat less pronounced, but qualitatively similar. This suggests the possibility that, for a given energy intake, changes in the composition of the US diet are a factor in rising weight in the population.

Indeed, if total per capita energy consumption is in fact increasing, an unchanged diet would imply that Americans are simply eating more. This does not seem likely. One plausible explanation is the increased FAFH consumption just noted, especially fast food, a favorite culprit in the popular press.12 Certainly there has been a shift from food consumed at home to food consumed away from home.13,14,15,16,17,18,19,20 The importance of the growth in FAFH is illustrated in Table 1. Between 1980 and 1995, growth in both table service restaurants and fast food has been impressive, but in real terms the fast food segment has grown twice as fast. The parallel between trends in FAFH and obesity provides ecological-level evidence of an association.3 The purpose of this study is to investigate the sensitivity of respondent's BMI to source of food consumed.

Table 1 Growth in expenditures on FAFH (billions of dollars)

Data and methodology

Study design

Data used in this study is from the Continuing Survey of Food Intake by Individuals 1994–1996 (CSFII).21 The CSFII is a survey conducted for USDA by Westat, Incorporated of Rockville, Maryland. Its design was implemented with the goal of obtaining a nationally representative sample of noninstitutionalized individuals. Sample selection is based on a stratified, multistage area probability sample.22 Ultimately, this resulted in the in-person interview of 16,103 individuals, obtaining for most 24 h recalls of all food intake on two nonconsecutive days. The sample is stratified by sex, 10 age categories and income, with the low income strata being over-sampled. While the CSFII contains information on individuals of all ages, attention here is limited to those 18 y old and above.


Relevant to the current study, each survey respondent was asked their height and weight, from which their body mass index (BMI=weight in kg/(height in m2)) was calculated. Also of primary importance to this study, each food item's source was determined, that is: was the food item purchased at the grocery store, a restaurant, a fast food outlet, or some place else? A number of variables to characterize diet, demographics, lifestyle and region were obtained as well. All of the variables are defined in Table 2. Descriptive statistics by gender and expected effects for the variables are given in Table 3.

Table 2 Variables used in the BMI regressions
Table 3 Descriptive statistics of variables used in BMI regressions

The appropriate way to measure food obtained from the different sources is a matter of concern. Below, restaurant and fast food are measured as the percentage of total grams of food obtained from each source. This was done to differentiate those who, for example, stopped at a food outlet for a beverage from those who had a complete fast food meal, and similarly for restaurants. While percentage of energy from each source was considered as an alternative variable definition, interpretation would have been difficult, since total energy is included in the model. This would allow energy from FAFH to affect BMI differently than do energy from other sources, which we do not believe. Rather it is the different mix of foods obtained from these sources which we wish to examine as a potential contributor to increased BMI. Other definitions for the food source variables that were considered were proportion of items consumed from each source and dummy variables which were one if any food was obtained from a source and zero otherwise. Results for each of these definitions were qualitatively similar to those presented, below.

Statistical analysis

Linear regression is used in the analysis, so that confounding factors, such as diet, demographics and lifestyles, can be controlled for while the effect of food source on BMI is determined. Implicitly, in relating current BMI to current behavior, demographics and lifestyle variables, we are assuming that these variables will be highly positively correlated with their past values, which are the determinants of current BMI. Again, expected effects are given in Table 3. A discussion of the rationale for these expectations is given in the Appendix.

Virtually all previous studies of obesity have found differences in the effects of factors between men and women. We examined this by using a Chow test to see if the men and women in the CSFII sample could be pooled.23 The hypothesis that males and females could be modeled together was rejected (F=7.35 with 24 and 7225 degrees of freedom, P<0.0001). Thus, results will be presented for gender-specific regressions.

As regression results make comparisons of the relative importance of the determinants of BMI difficult, results for key variables will also be presented in terms of the change in weight for the appropriate change in the determinant. That is, the effects of changes in BMI determinants are presented in terms of the number of kilograms of weight (more or less) which would result from an appropriate change in the determinants for males and females of average height (1.77 and 1.63 m, respectively). The appropriate changes to determinants are defined as follows. Restaurant and Fast Food are measured as the proportion of total grams obtained from each source. Over half the sample obtained no food from either of these sources (which is why the mean of each for the whole sample is about 10%). If sample means are calculated using the sub-sample who consumed food from each source, they are higher (Table 4). For example, the average proportion of grams of food obtained from restaurants by males is 0.10 and 0.31 in the whole sample of males and the sub-sample of males who ate some food from a restaurant, respectively. Effects for key variables are presented in terms of the expected added weight (ceteris paribus) resulting from a change from zero to these sub-sample mean figures. The results for Percentage Fat measure the impact of a change from the current percentage of energy from fat down to the recommended maximum of 30%. College and Vigorous Exercise are dummy variables, so effects are with vs without. TV is the impact of an extra hour per day of television viewing.

Table 4 Descriptive statistics for those who had some food from a source


Results of gender-specific regressions are reported in Table 5. The diet variables have expected effects and are significant except for total energy for men. Most demographic variables have the expected signs and are significant, the exception being African American men and income for men. With the exception of employment status, all lifestyle variables are significant (P<0.01) and have expected effects. Unemployment significantly decreases mens’ BMI (P<0.05), but not women's. Region and urbanization has no effect on BMIs of women and is only significant for rural men.

Table 5 Determinants of overweight

Food sources are significant determinants of BMIs even when controlling for other determinants. Both eating out at restaurants and at fast food outlets significantly increased males BMI, while for females only the fast food source was a significant BMI determinant.

As indicated earlier, results in Table 5 make comparisons of the relative importance of BMI determinants difficult. To facilitate such comparisons, results for effects of some BMI determinants are given in terms of change of weight for individuals of average height in Table 6 along with 95% confidence intervals. So, for example, a female who is 1.63 m tall and consumes food at a restaurant weighs 0.2 kg more than those who do not on average (95% CI: (−0.8, 1.2)). Similarly, a male who is 1.77 m tall and ate at a restaurant was 0.9 kg heavier than those that did not (95% CI: (0.1, 1.7)). Those in the sample who consumed fast food were 0.8 kg heavier if they were men (95% CI: (0.1, l.5)) and 1.0 kg heavier if they were women (95% CI: (0.1, 1.8)). Reduction of percentage of energy from fat to the maximum recommended had a statistically significant, but relatively minor effect on both males (95% CI: (0.0, 0.3)) and females (95% CI: (0.0, 0.2)). An extra hour a day of television watching adds about 0.6 kg to body weight for both men (95% CI: (0.4, 0.8)) and women (95% CI: (0.3, 0.8)). Vigorous exercise at least twice a week results in a decrease in weight of 1.2 kg for males (95% CI: (−2.0, −0.3)) and 1.6 kg for females (95% CI: (−2.6, −0.6)). Males with a college education are 1.6 kg. lighter (95% CI: (−2.5, −0.6)) and females weigh 2.0 kg less (95% CI: (−3.1, −0.9)).

Table 6 Change in kilograms resulting from an increase in the determinants of BMI for the sample average height male (1.77 m) and female (1.63 m)


Results for the demographic, lifestyle and regional variables are generally quite plausible. Consider the case of age, for which we examined two effects. People tend to gain weight as they age, but obesity decreases life expectancy. The data supports the significance of both. Age has the expected positive and then negative effect with the implied age of maximum weight being 51 y for men and 52 y for women. This should not be taken to mean that any particular individual will lose weight in the later years of life, which may or may not be the case. Rather it means that those who are heavier are less likely to be sampled, due to their decreased life expectancy. In this light, the aging of the US population will have countervailing effects on the level of obesity.

The racial variables all have the expected signs. Results for African Americans and Hispanics agree with previous findings.2,3,24 Asian Americans are significantly lighter than Whites. This agrees with the findings of Cairney and Ostbye and is in agreement with an Asian diet, which is generally lower in fat and cholesterol.28 Education has a negative effect on obesity.28 Lifestyle variables all have significant, expected effects for both men and women. After accounting for all the other factors, regional effects are not significant. Urban/suburban/rural distinction only has a significant impact for rural males.

The two regressions differ in terms of the impacts of income and unemployment. For males, income has a positive but insignificant effect, while unemployment is negative and significant. For females, the results are the opposite. Income has a negative and significant effect, while unemployment has a positive and insignificant effect. The effect of income on female overweight is in agreement with previous work.25,28,29,30 It suggests that higher income women are more concerned with their appearance. The negative association between unemployed males and their BMI may reflect the increasingly sedentary nature of most jobs held by the employed or an increased time on the part of the unemployed to devote to diet and exercise. Such interpretations agree with previous studies where increased hours worked has been found to have a positive effect on overweight.24,25

The effects of the source of food variables are as expected: Restaurant is significant for males and Fast Food is significant for both males and females. The Fast Food results agree with the findings of Jeffery and French for women, but not for men.23 Jeffery and French found eating at fast food outlets had no effect on men. Men studied by Jeffery and French were overwhelmingly White (97%) and had at least some college (96%) and were an average age of 35.3 y old, whereas the men in the CSFII were selected to be nationally representative, so 80% were White, 43% had some college, and the average age was 47.9 y old. In terms of TV watched per day the men in the CSFII sample are more like low income women than the men in Jeffery and French's sample. In their most complete regression results, Jeffery and French controlled for age, education, smoking, total energy, percentage of fat and physical activities. We also control for these confounders, and in addition we control for restaurants, age-squared, race, household income, employment status, special diet, self-evaluated health, vegetarianism, region, and whether the household is urban, suburban or rural. We tested the joint significance of our extra confounders and found their inclusion in the model to be statistically significant (F=43.3 with 9 and 3755 degrees of freedom (P<0.0001) for males and F=40.5 with 9 and 3470 degrees of freedom (P<0.0001) for females). Thus, at least a portion of the differences in our findings for fast food and television can be attributed to the differences in the samples and specification of the regression relationship.

Statistical significance does not always imply any practical significance. Our comparison across variables for males and females who are the average height of those in our sample suggests that for males, food source appears to be more important than Percentage Fat, while for females Fast Food is the important BMI determinant. For both sexes, education (College) and exercise (Vigorous Exercise and TV) are also significant practically as well as statistically.

Results of this study support current policy efforts aimed at increasing public awareness of the benefits of a proper diet, in particular, one lower in fat, as well as the value of and need for exercise. Concerning the specific issue of food away from home, in the US there is support for imposing nutritional labeling similar to that required for processed foods on restaurant menus. While this can only increase consumer awareness it should be noted that several major fast food chains already post in plain view the nutritional content of their products. Furthermore, low-fat versions of their offerings have met with little success. It may be that more emphasis on awareness of the consequences of obesity is needed, for until consumers have the determination to make required lifestyle adjustments, knowledge of the causes is likely to be of little avail.

To conclude, we feel our main contribution is in shedding light on the impact that source of food has on BMI. There is evidence of a significant, positive relationship between a respondent's BMI and whether they consumed food away from home in the past 24 h period. While clearly food consumed yesterday has little effect on current BMI, on average we expect that people who consumed FAFH yesterday are more likely to do so regularly than those who did not. Thus, assuming this, evidence presented here suggests that trends in increased consumption of food away from home appear to have contributed to this increase in overweight in the United States. Of perhaps equal importance is our finding—certainly not surprising—of a significant impact of physical activity on respondents’ BMI. If they engaged in vigorous exercise at least twice a week, they were significantly lighter than those who did not, ceteris paribus. If they watched an extra hour of television, they were significantly heavier. In terms of the relative impacts of increased FAFH food consumption vs increased exercise in the determination of BMI, they seem roughly on a par in terms of importance for men, but activity level seems more important for women. We would argue that it is the make-up of the diet that is significantly different when dining out and that this is something which should be examined in future studies.


Support from Purdue University's Agricultural Research Program is gratefully acknowledged.


Expected effects of the independent variables in the regressions are as follows: the hypothesis that growth in FAFH has contributed to increased overweight translates to an expectation of positive effects for the fast food and restaurant variables, especially for the former.27

Diet variables included in the model are total energy, percentage fat (percentage of energy from fat), special diet (whether the respondent was on a weight-loss diet), and vegetarian (whether they claimed to be a vegetarian). The effect of total energy, percentage fat, and special diet on BMI should be positive. Vegetarians tend to be lighter, so its effect should be negative.

Two effects are expected for age. As people age, they gain weight. Therefore over the lower range of age we expect the effect of increased age to be positive. However, obesity significantly increases chances of morbidity, reducing life expectancy. Thus, over the higher range of age, we expect the effect to be negative. This is incorporated by controlling for both age and age squared. Cairney and Wade and Flegal et al found a similar pattern in the response of BMI to age.28,29 The ethnicity of the respondent is expected to be related to overweight for cultural reasons. Both African Americans and Hispanics have been found to be more prone to be overweight, especially in the case of women.2,3,30 Evidence on the prevalence of overweight among Asian Americans is scant. Pomer1eau et al found that South Asian women's BMI fell between that of European and Afro-Caribbean women.31 Cairney and Ostbye found that male Asian immigrants are significantly less likely to be overweight than were Canadian-born men.32 Education and income are additional demographic factors which have been found to be associated with overweight in past studies. Education, associated with greater health and nutrition awareness, should have a negative effect on overweight.3,33 Income has been found to be negatively related to obesity, again primarily for women.29,34,35 Although the reason for this is a matter for debate, data from the Minnesota Pound of Prevention Survey suggest the negative relationship between income and obesity is associated with higher energy intake rather than reduced activity in lower income households.36

Several lifestyle variables are included in the model: employment status, exercise, television viewing, smoking, and whether respondents claim their health is excellent or very good. A respondent was deemed unemployed if they claimed to be not employed in the previous week. If they lead less active lives than those who are employed, then the effect of unemployment should be positive. But employment imposes time constraints, which can lead to improper eating habits. Furthermore, the work force is increasingly sedentary, and people may well be more active when not working. Hence, being unemployed can have a negative effect relative to being employed. Exercise and television watching are measures of active vs sedentary lifestyles. As such we would expect the effects to be negative for exercise and positive for TV, although TVs value as a measure of sedentary lifestyle has been questioned.2,27,37,38 Decreased smoking is associated with increased eating, so its effect should be negative.2,39

Finally, we include regional dummy variables and whether the household is located in an urban or rural environment. Because this survey is a national one, we include regional dummy variables based on the anecdotal evidence that there are differences between and similarities within certain regions of the country. We have no a priori expectations for the regional variables. Urban lifestyles tend to be more hectic, rural lifestyles less so. Therefore, Urban is expected to have a negative effect on overweight and Rural is expected to be positive.


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Binkley, J., Eales, J. & Jekanowski, M. The relation between dietary change and rising US obesity. Int J Obes 24, 1032–1039 (2000).

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  • obesity
  • food away from home
  • fast food
  • diet
  • exercise

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