Co-designing prediction data visualizations for a digital binge eating intervention

Digital interventions can leverage user data to predict their health behavior, which can improve users' ability to make behavioral changes. Presenting predictions (e.g. how much a user might improve on an outcome) can be nuanced considering their uncertainty. Incorporating predictions raises design-related questions, such as how to present prediction data in a concise and actionable manner.

We conducted co-design sessions with end-users of a digital binge-eating intervention to learn how users would engage with prediction data and inform how to present these data visually. We additionally sought to understand how prediction intervals would help users understand uncertainty in these predictions and how users would perceive their actual progress relative to their prediction.

We conducted interviews with 22 adults with recurrent binge eating and obesity. We showed prototypes of hypothetical prediction displays for 5 evidence-based behavior change strategies, with the predicted success of each strategy for reducing binge eating in the week ahead (e.g. selecting to work on self-image this week might lead to 4 fewer binges while mood might lead to 1 fewer). We used thematic analysis to analyze data and generate themes.

Users welcomed using prediction data, but wanted to maintain their autonomy and minimize negative feelings if they do not achieve their predictions. Although preferences varied, users generally preferred designs that were simple and helped them quickly compare prediction data across strategies.

Predictions should be presented in efficient, organized layouts and with encouragement. Future studies should empirically validate findings in practice.

The Clinical Trials Registration #: NCT06349460.