Theories of Behavior Change

Over the last week of engagement with the behavior change theories of Prochaska, Duhigg and Fogg, I’ve come to a better understanding of the opportunities and challenges posed by the formalization of habit formation. Proschaska’s Transtheoretical Model is the theory with which I have the most experience. As a student of Public Health with a background in the development and evaluation of digital health tools, I frequently call upon the Transtheoretical Model for insight into the design of interventions for individuals and populations at different stages of technology acceptance, adoption and use. The six stages of behavior change and the ~14 subconstructs that underlie them provide a useful framework with which to formulate and evaluate interventions. The construction of a stage-based framework that recognizes the ability of subjects to skip steps or move non-linearly through the proposed progression ensures that the model is sufficiently adaptive to describe and perhaps predict behaviors as we observe them, without unnecessary fitting into a rigid or prescriptive framework.  Most importantly, Prochaska is explicit in the recognition that, “no single theory can account for all complexities of behavior change,” and that, “the majority of at-risk populations are not prepared for action and will not be served effectively by traditional action-oriented behavior change programs.”¹ The recognition that at-risk and unwilling populations may be highly resilient to behavior change interventions is echoed in the work of Duhigg and Fogg. Duhigg focuses his theory of Cues, Routines and Rewards on changes within our own habits, and in doing so, reduces the subjects of intervention to an N of 1 preselected for motivation and engagement. Fogg also offers strategies for self-influence of habit formation, retains a focus on those subjects whose motivations are already high or accessibly ‘matchable’ in his theory of interpersonal behavior change. In discussions of his b=mat formulation, Fogg advocates for a focus on ease-of-use over motivation as a successful strategy for behavior change. In his Tiny Habits process, Fogg’s advice for habit formation focuses on establishing triggers and creating rewards, rather than providing information on the benefits of the target behavior.   When he does focus on motivation, Fogg suggests that ‘matching’ target behaviors to pre-existing, compelling motivations is more effective than trying to enhance low-level motivations or generating new ones.  The implication of this approach is that making somebody ‘want’ something is a fraught and fragile endeavor, and that we would do well to concentrate on framing our products as resources that provide an already-recognized value, and paring them with behaviors that serve those same ends. An interesting interrelation between Fogg’s theory and Prochaska’s emerges in Fogg’s Behavior Grid, wherein he describes a framework for understanding how to approach subjects with different historical relationships to target behaviors, and with different goals for the frequency or duration of those behaviors. Using this grid, product/behavior designers can track a subject through different stages of habit development, using the grid to understand what strategies might be most effective at each step. Prochaska and Fogg’s theories have provided the basis for several new theories of behavior change and technology adoption in recent years. Nir Eyal (a student and collaborator of Fogg’s) has developed a stage-based model of behavior adoption in his recent book titled ‘hooked: how to build habit-forming products’. Eyal’s Hook Model describes how to, “connect the user’s problem to your solution,” and is comprised of a four-step process of Triggers, Rewards, Actions and Investments. Using this model, Eyal provides a readily applicable toolset for designers, and offers a cogent analysis of the practical and ethical implications of designing habit-forming products. Another interesting offshoot of Fogg and Prochaska’s work is the Stage-Based Model of Personal Informatics Systems proposed by Ian Li. This model puts forth a theory of technology adoption focused on personal informatics and comprised of a five-stage process of PreparationàCollectionàIntegrationàReflectionàActon. An interesting element of Li’s model is its focus on the principle of ‘cascading barriers’, which postulates that, “problems in earlier stages affect the later stages.”² This simple statement has significant implications for understanding products and processes, and is particularly useful for its recognition of the fact that barriers in early stages of technology adoption may not halt the process, but are almost assured to influence the perceptions and behaviors of users are later stages. This allows for an analysis of the interrelation between interventions at different stages, and moves towards a model that might formalize our understanding of the cumulative experiences and outcomes of stage-based interventions for behavior change.