Innovative methods for observing and changing complex health behaviors: Four propositions

Abstract

Background and purpose: Precision health initiatives aim to progressively move from traditional, group-level approaches to health diagnostics and treatments toward ones that are individualized, contextualized and timely. This article aims to provide an overview of key methods and approaches that can help facilitate this transition in the health behavior change domain. Methods: Narrative review of the methods used to observe and change complex health behaviors. Results: Based on available literature, we argue that health behavior change researchers should progressively transition from (i) low-to high-resolution behavioral assessments,(ii) group only to group-and individual-level statistical inference,(iii) narrative theoretical models to dynamic computational models, and (iv) static to adaptive and continuous tuning interventions. Rather than providing an exhaustive and technical presentation of each method and approach, this article articulates why and how researchers interested in health behavior change can apply these innovative methods. Practical examples contributing to these efforts are presented. Conclusion: If successfully adopted and implemented, the four propositions made in this article have the potential to greatly improve our public health and behavior change practices in the near future.

Publication
Translational Behavioral Medicine