Digital food content’s popularity is underscored by recent studies revealing its addictive nature and association with disordered eating. Notably, individuals with eating disorders exhibit a positive correlation between their digital food content consumption and disordered eating behaviors. Based on these findings, we introduce FoodCensor, an intervention designed to empower individuals with eating disorders to make informed, conscious, and health-oriented digital food content consumption decisions. FoodCensor (i) monitors and hides passively exposed food content on smartphones and personal computers, and (ii) prompts reflective questions for users when they spontaneously search for food content. We deployed FoodCensor to people with binge eating disorder or bulimia (n=22) for three weeks. Our user study reveals that FoodCensor fostered self-awareness and self-reflection about unconscious digital food content consumption habits, enabling them to adopt healthier behaviors consciously. Furthermore, we discuss design implications for promoting healthier digital content consumption practices for vulnerable populations to specific content types.
doi.org/10.1145/3613904.3641984
Large Language Models (LLMs) have the potential to contribute to the fields of nutrition and dietetics in generating food product explanations that facilitate informed food selections. However, the extent to which these models offer effective and accurate information remains unverified. In collaboration with registered dietitians (RDs), we evaluate the strengths and weaknesses of LLMs in providing accurate and personalized nutrition information. Through a mixed-methods approach, RDs validated GPT-4 outputs at various levels of prompt specificity, which led to the development of design guidelines used to prompt LLMs for nutrition information. We tested these guidelines by creating a GPT prototype, The Food Product Nutrition Assistant, tailored for food product explanations. This prototype was refined and evaluated in focus groups with RDs. We find that the implementation of these dietitian-reviewed template instructions enhance the generation of detailed food product descriptions and tailored nutrition information.
doi.org/10.1145/3613904.3641924
Understanding nutrition labels remains challenging for consumers; however, digital shopping environments offer opportunities to explore how interactive nutrition labels may be used to enhance comprehension. We conducted an A/B study with 24 participants, comparing their ability to interpret and apply nutrition information using conventional, static labels versus interactive labels. We evaluated interactive nutrition labels' impact through quantitative metrics and qualitative insights from interviews and think-aloud sessions. Our findings reveal a statistically significant improvement in assessing nutrient amounts and interpreting numerical information when users engage with interactive labels. These results underscore the potential interactivity has on promoting public understanding of nutritional content and highlight opportunities for refinement. Based on our findings, we propose new design directions and discuss technology's role in making nutrition labels more effective for decision-making and nutrition education.
doi.org/10.1145/3613904.3642672
Mobile health applications for weight maintenance offer self-monitoring as a tool to empower users to achieve health goals (e.g., losing weight); yet maintaining consistent self-monitoring over time proves challenging for users. These apps use push notifications to help increase users’ app engagement and reduce long-term attrition, but they are often ignored by users due to appearing at inopportune moments. Therefore, we analyzed whether delivering push notifications based on time alone or also considering user context (e.g., current activity) affected users’ engagement in a weight maintenance app, in a 4-week in-the-wild study with 30 participants. We found no difference in participants’ overall (across the day) self-monitoring frequency between the two conditions, but in the context-based condition, participants responded faster and more frequently to notifications, and logged their data more timely (as eating/exercising occurs). Our work informs the design of notifications in weight maintenance apps to improve their efficacy in promoting self-monitoring.
doi.org/10.1145/3613904.3641993
Digital health interventions (DHIs) offer promising solutions to the rising global challenges of noncommunicable diseases by promoting behavior change, improving health outcomes, and reducing healthcare costs. However, high churn rates are a concern with DHIs, with many users disengaging before achieving desired outcomes. Churn prediction can help DHI providers identify and retain at-risk users, enhancing the efficacy of DHIs. We analyzed churn prediction models for a weight loss app using various machine learning algorithms on data from 1,283 users and 310,845 event logs. The best-performing model, a random forest model that only used daily login counts, achieved an F1 score of 0.87 on day 7 and identified an average of 93% of churned users during the week-long trial. Notably, higher-dimensional models performed better at low false positive rate thresholds. Our findings suggest that user churn can be forecasted using engagement data, aiding in timely personalized strategies and better health results.
doi.org/10.1145/3613904.3642321