Fast Food, Soft Drink, And Candy Intake Is Unrelated To BMI For 95% Of American Adults

David Just

Cornell University – Dyson School of Applied Economics and Management

Brian Wansink

Cornell University

October 1, 2015


Objective: Excessive intake of fast food, soft drinks, and candy are considered major factors leading to overweight and obesity. This article examines whether the epidemiological relationship between frequency of intake of these foods and Body Mass Index (BMI) is driven by the extreme tails ( /- 2 SDs). If so, a clinical recommendation to reduce intake frequency may have little relevance to 95% of the population.

Methods: Using 2007-2008 Centers for Disease Control’s National Health and Nutrition Examination Survey, the consumption incidence of targeted foods on two non-continuous days was examined across discrete ranges of BMI. Data were analyzed in 2011.

Results: After excluding the clinically underweight and morbidly obese, consumption of fast food, soft drinks or candy was not positively correlated with measures of BMI. This was true for sweet snacks (r = .005, p= <.001) and salty snacks (r = .001, p= .040). No significant variation was found between BMI subcategories in weekly consumption frequency of fast food meals.

Conclusion: For 95% of this study’s sample, the association between the intake frequency of fast food, soft drinks, and candy and BMI was negative. This result suggests that a strategy that focuses solely on these problem foods may be ineffective in reducing weight. Reducing the total calories of food eaten at home and the frequency of snacking may be more successful dieting advice for the majority of individuals.

Fast Food, Soft Drink, And Candy Intake Is Unrelated To BMI For 95% Of American Adults – Introduction

Overweight patients are often advised to reduce their intake of fast food, soft drinks, and candy (1). Part of the reason for the recommendation is that these indulgences are primary contributors to obesity and Body Mass Index (BMI) (2). These foods have also been linked to chronic diseases such as diabetes (3). While this seems reasonable, the epidemiological relationship between the incidence of intake of these indulgent foods and BMI may be driven by the extreme tails (+/- 2 SDs).(1) As a result, a clinical recommendation to reduce the intake frequency of certain foods may have little relevance to 95% of the population.

Past analyses of “junk food” intake and BMI may have spuriously capitalized on the extreme tails of the BMI distribution, which are associated with eating disorders. (4-6) On one extreme, there are the clinically underweight (BMI<18.5); on the other extreme, there are those clinically classified as morbidly obese (BMI> 40). The eating habits of both groups are atypically extreme.(5) Extreme behavior at these endpoints could cloud any generalization made for the 95% of the population with more moderate weight problems.

This research examines how consumption frequency of seemingly unhealthy foods relates to the BMI of the 95% of Americans who are neither extremely underweight nor extremely overweight.(7) This has direct application for providing efficacious clinical advice that will not be discarded as irrelevant by most people.


A representative sample of the non-institutionalized civilian population of the United States was selected for the 2007-2008 Centers for Disease Control’s National Health and Nutrition Examination Survey (NHANES). (9) The NHANES consists of approximately 5000 in-person surveys, with a complex multi-stage probability sample design used to ensure that results are representative of the U.S. population. We restrict our sample to adults, defined as age 18 or older, who completed two 24-hour dietary recall surveys. Participants were given a broad health survey which included general food intake questions. On two separate occasions participants were administered 24-hour dietary recalls. These recalls are administered in face-to-face interviews using a five step process designed to encourage accurate reporting of all eating episodes and foods consumed as well as where the foods were purchased and consumed. These foods are then coded into narrow categories using the hierarchical USDA Food Coding Scheme.1 We used location data to classify food as being eaten away from home, or fast food. The USDA Food Codes were used to classify foods by food type (e.g., fruits, vegetables, desserts). Both 24-hour dietary recalls are summed within subjects for a non-continuous two day consumption profile. Foods analyzed were chosen on an ad hoc basis to represent foods often targeted by both policy and interventions.

Anthropometric body measurements including height and weight were taken by trained professionals, while participants were wearing identical gowns and slippers. Body mass index (BMI) is calculated as weight in kilograms divided by height in meters squared. Participants were divided into 8 groups for analysis based upon their BMI. Consistent with the World Health Organization classifications(10), those with BMIs less than 18.5 are classified underweight, 18.5 to 24.9 as normal, 25 to 29.9 as overweight, 30 to 39.9 as obese, and over 40 as morbidly obese. The morbidly obese were further classified as morbidly obese 1 (BMI of 40 to 44.8) and morbidly obese 2 (BMI above 44.9). These values ensure that each of the 8 groups had sufficient observations for analysis while corresponding with commonly used classifications.

Data were analyzed in 2011. All analyses were performed using STATA statistical software (version 11.0, StataCorp LP, College Station, TX). A P value < .05 was considered statistically significant. We compare average eating episodes within food and across BMI categories. We focus on eating episode rather than amount eaten because the authors believe it is less subject to recall bias. We do not analyse total quantity of these foods, one limitation of this study. Mean instances of food intake are reported for various food categories, by BMI classifications and sub-classifications in Table 1. Differences in sub-group consumption frequency patterns are tested using standard ANOVA F-tests for differences between subgroups. This analysis is conducted both with and without those who are clinically underweight (1.8 percent of the sample) and most morbidly obese (2.5 percent of the sample) to demonstrate the impact of the extremes on the statistical results. Missing data were omitted from the analysis leaving a sample of n = 4895.


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