HCMar 4, 2016

Designing for Different Stages in Behavior Change

arXiv:1603.01369v16 citations
Originality Synthesis-oriented
AI Analysis

This addresses the problem of low adoption in behavior change technologies for users by proposing stage-tailored designs, but it is incremental as it builds on existing stage models.

The paper identifies that individuals' readiness for behavior change strongly predicts adoption of a mobile activity tracking app, with adoption rates of 56% for those in contemplation/preparation stages versus 20% for others, and argues for technologies tailored to different stages of behavior change.

The behavior change process is a dynamic journey with different informational and motivational needs across its different stages; yet current technologies for behavior change are static. In our recent deployment of Habito, an activity tracking mobile app, we found individuals "readiness" to behavior change (or the stage of behavior change they were in) to be a strong predictor of adoption. Individuals in the contemplation and preparation stages had an adoption rate of 56%, whereas individuals in precontemplation, action or maintenance stages had an adoption rate of only 20%. In this position paper we argue for behavior change technologies that are tailored to the different stages of behavior change.

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