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Theory in Behavioral Diabetes Research

Loyola College, Baltimore, Maryland.

A recent editorial regarding diabetes education discussed the difficulty of doing quality research in this area (1). Behavioral research is inherently difficult (behavioral researchers like to call it "hard science" as opposed to the "easy science" of other fields). However, I will not discuss methods per se, focusing instead on the role of theory. Below I address the ways that theory can inform research, and I offer some thoughts about the role of extra-scientific considerations in research. What I say is particularly addressed to behavioral researchers, but much of it is relevant to other researchers as well.

Theory testing.

The role of theory in behavioral diabetes research is poorly understood. Many see theory as a grandiose conceptual scheme with little relevance to research. But it has been noted that there is nothing so practical as a good theory. The problem is with how theory is used. For example, consider the use of theory in recent research on behavioral interventions in diabetes. A recent study tested a behavioral intervention based on the Transtheoretical Model (TTM), currently a popular theory, against standard care (i.e., standard care against standard care plus a behavioral intervention) (2). To the surprise of no one familiar with behavioral interventions, adding the behavioral intervention produced better outcomes. But did this "prove" TTM, or did it simply demonstrate that behavioral interventions work? And when Anderson et al. (3) found benefit from a behavioral intervention based on empowerment concepts, did this "prove" a theory, or only that another behavioral intervention worked? Clearly these are not tests of a theory; they merely demonstrate that, behaviorally speaking, something is better than nothing. This research approach "assumes" the theory by building it into the intervention. Similarly, I have seen several unpublished studies that have proposed elaborate formulations of "stages . . . [Full Text of this Article]

Causal modeling and statistical analysis.

Implications.

Footnotes

References

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