- 18 Mar 2008
- Working Paper
Modeling Expert Opinions on Food Healthiness: A Nutrition Metric
Executive Summary — Despite an increased standard of living in the United States and other developed countries, health problems attributable to poor nutrition persist in part due to consumers' inability to translate the dietary advice of nutrition experts into anything actionable. Citing the improvement of public health as a primary objective, numerous studies have highlighted the need for a nutritional scoring system that is both comprehensive in its coverage of food products and easily understood by consumers. In this paper the researchers advance this objective by proposing a nutrition metric that is based on the current views of leading experts in the field. The metric can be used to score any food or beverage for which several component nutrient quantities are known. Key concepts include:
- This model encompasses the factors that matter most to the professional judgment of nutrition experts.
- Previous models focusing solely on either positive or negative nutrients have omitted critical information that experts take into account when assessing a food's healthiness.
- This model could be used to generate healthiness ratings that are displayed on or near food and beverage labels, allowing consumers to make more informed choices about which products to purchase and consume.
Background: Research over the last several decades indicates the failure of existing nutritional labels to substantially improve the healthiness of consumers' food and beverage choices. The obstacle for policy-makers is to encapsulate a wide body of scientific knowledge into a labeling scheme that is also comprehensible to the average shopper. Here, we describe our method of developing a nutrition metric to fill this void. Methods: We asked leading nutrition experts to rate the healthiness of 205 sample foods and beverages, and after verifying the similarity of their responses, generated a model that calculates the expected average healthiness rating that experts would give to any other product based on its nutrient content. Results: The form of the model is a linear regression that places weights on 12 nutritional components (total fat, saturated fat, cholesterol, sodium, total carbohydrate, dietary fiber, sugars, protein, vitamin A, vitamin C, calcium, and iron) to predict the average healthiness rating that experts would give to any food or beverage. We provide sample predictions for other items in our database. Conclusions: Major benefits of the model include its basis in expert judgment, its straightforward application, the flexibility of transforming its output ratings to any linear scale, and its ease of interpretation. This metric serves the purpose of distilling expert knowledge into a form usable by consumers so that they are empowered to make healthier decisions.