Personalized Feedback Visualizations for Just-In-Time Adaptive Interventions

Smoking remains the leading cause of preventable disease and death, particularly impacting individuals with substance use disorders (SUDs) and those living with HIV. Although mobile health (mHealth) technologies provide easy access to smoking cessation treatments, user engagement remains suboptimal. Personalized feedback visualizations represent a promising strategy to boost initial and sustained engagement with mobile interventions.

Personalized feedback visualizations could improve engagement in mobile smoking cessation interventions, addressing a key barrier to successful quitting.

Fast Fact

Smoking disproportionately affects vulnerable groups, such as individuals with substance use disorders and those living with HIV. Although most smokers wish to quit, fewer than 6% successfully do so. Mobile health interventions offer convenient, accessible support, yet user engagement remains a critical hurdle. Innovative strategies, such as personalized feedback visualizations, are urgently needed to improve engagement and outcomes for smoking cessation efforts.

Mobile apps have great potential for delivering timely smoking cessation interventions, yet engagement often remains limited. The Mobile Assistance for Regulating Smoking (MARS) study demonstrated increased engagement through prompted self-regulatory activities, but overall participation still showed significant variability. To further enhance engagement, this pilot project specifically focuses on personalized feedback visualizations as a novel approach.

Guided by the multiphase optimization strategy for intervention development (MOST), this project will conduct preparation and optimization phase research by pursuing two specific aims:

Aim 1: Develop prototype feedback visualizations informed by existing research in personal informatics, behavior change, commercial visualization designs, and emotion science principles to elicit intrinsic interest.

Aim 2: Conduct user experience testing through semi-structured interviews with adults who have successfully quit smoking (n=10) and those currently interested in quitting (n=10). Participants will provide feedback on prototype visualizations through a think-aloud process, highlighting interpretation issues, usability, and suggestions for refinement.

This pilot will produce foundational data on personalized feedback visualizations, directly informing future intervention development efforts.

Principal Investigator

Lizbeth ‘Libby’ Benson, PhD
Assistant Professor
Institute for Social Research
University of Michigan

Key Collaborators

Mark Newman, University of Michigan

Funding Source

d3center Pilot Grant Program

Focus Area

Advancing mobile health interventions