Journal Article

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

Alshurafa, Nabil, Rooper, Isabel R, Massion, Thomas, Ortega, Adrian, Camino, Macarena Kruger, Lipman, Lindsay D, Lakhtakia, Tanvi, Tack, Emily, Graham, Andrea K, Parsons, Leah M, Azubuike, Chidibiere, Kay, Matthew

2025

Overview

Abstract Background 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. Purpose 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. Methods 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. Results 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. Conclusions Predictions should be presented in efficient, organized layouts and with encouragement. Future studies should empirically validate findings in practice. Clinical Trial information The Clinical Trials Registration #: NCT06349460. Individuals with binge eating and obesity are interested in using predictions from an app to help them lower their binge eating but want these predictions to be encouraging and presented in a way they can understand easily. Lay Summary Digital interventions, like health apps, are promising tools to help people improve their health because they can use data from previous users to make predictions about what strategy someone should try that would help them the most. Showing users these predictions could improve their decision-making and make the app more engaging and useful. However, like any prediction—these could be wrong. Therefore, showing predictions to users can be confusing, decrease their trust in the app, and make them feel bad if they do not meet their predicted level of success. It is also unclear the best way to present the predictions to users in a way that makes sense to most people. We interviewed users who would be interested in using an app to lower their binge eating and manage their weight about using predictions to help them. We showed them how we could present predictions to them and asked for their feedback. We learned that most users were interested in using predictions to help them pick the best strategy to work on. Some were worried that they would feel discouraged and upset if they did not meet their prediction. Users wanted predictions presented in a simple and easy-to-understand formats.

• Publisher: Oxford University Press
• Subject: Binge eating / Care and treatment / Compulsive eating / Design / Digital health / Telehealth & mHealth