| Food Composition Data: A User's Perspective (1987) |
|Experiences with food composition data: the context|
|Data: the user context|
The link between the user and the data
Most users of good composition data have to balance three major, different types of information - answers to three different questions: what is in foods?; what do people eat?; and what do people need?
While these can be written as distinct questions they are closely interrelated. Discussion of any one of them without consideration of the others would be possible; however, it would be, for the most part, academic. Since this conference is directed towards the relationship between the user and the single question of what is in foods, it is important to realize that there must be at least implicit realization of the importance of the other two aspects - what people eat and what they need. The strict inseparability of these questions becomes more apparent if we examine more closely the relationships between foods and users, following just the two questions of what is in foods and what people eat. Figure 1 shows the four major components of this linkage: food, data, the individual, and the user. These are connected by four types of activity: analysis, data usage, consumption, and diet evaluation. It is obvious that, for the user to be able to do anything meaningful, the two paths from food to user must be both compatible and consistent.
The linking of data and user, and how this link can be improved, is of special interest. It must be appreciated, however, just how constrained this link is by its following the analysis of the foods - for example, often we cannot get all the data we may think we need. Moreover, while we often feel that we really want to know precisely what is in foods, that information is really only useful if it can be correlated with what we can find out about what people really are eating.
Two additional points need to be made in conjunction with considering the data-user link in the context of our three questions. These are complementary and follow from the above. The first is that good, readily available food composition data, however we define good and readily available, will not solve all the users' problems. This leads to the second point that data are not of interest in and of themselves; they are of value only if they permit some generalizations to be made, some predictions or decisions.