Abstract

Evidence-based practice is important for behavioral interventions but there is debate on how best to support real-world behavior change. The purpose of this paper is to define products and a preliminary process for efficiently and adaptively creating and curating a knowledge base for behavior change for real-world implementation. We look to evidence-based practice suggestions and draw parallels to software development. We argue to target three products: (1) the smallest, meaningful, self-contained, and repurposable behavior change modules of an intervention; (2) “computational models” that define the interaction between modules, individuals, and context; and (3) “personalization” algorithms, which are decision rules for intervention adaptation. The “agile science” process includes a generation phase whereby contender operational definitions and constructs of the three products are created and assessed for feasibility and an evaluation phase, whereby effect size estimates/casual inferences are created. The process emphasizes early-and-often sharing. If correct, agile science could enable a more robust knowledge base for behavior change.

Original languageEnglish (US)
Pages (from-to)317-328
Number of pages12
JournalTranslational Behavioral Medicine
Volume6
Issue number2
DOIs
StatePublished - Jun 1 2016

Keywords

  • Behavior change
  • Implementation science
  • Research methods

ASJC Scopus subject areas

  • Behavioral Neuroscience
  • Applied Psychology

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