Many people struggle with efforts to make healthy behavior changes, such as healthy eating. Several existing approaches promote healthy eating, but present high barriers and yield limited engagement. As a lightweight alternative approach to promoting mindful eating, we introduce and examine crumbs: daily food challenges completed by consuming one food that meets the challenge. We examine crumbs through developing and deploying the iPhone application Food4Thought. In a 3-week field study with 61 participants, crumbs supported engagement and mindfulness while offering opportunities to learn about food. Our 2×2 study compared nutrition versus non-nutrition crumbs coupled with social versus non-social features. Nutrition crumbs often felt more purposeful to participants, but non-nutrition crumbs increased mindfulness more than nutrition crumbs. Social features helped sustain engagement and were important for engagement with non-nutrition crumbs. Social features also enabled learning about the variety of foods other people use to meet a challenge.

Although food journaling is understood to be both important and difficult, little work has empirically documented the specific challenges people experience with food journals. We identify key challenges in a qualitative study combining a survey of 141 current and lapsed food journalers with analysis of 5,526 posts in community forums for three mobile food journals. Analyzing themes in this data, we find and discuss barriers to reliable food entry, negative nudges caused by current techniques, and challenges with social features. Our results motivate research exploring a wider range of approaches to food journal design and technology.

The authors present an approach for designing self-monitoring technology called 'semi-automated tracking,' which combines both manual and automated data collection methods. Through this approach, they aim to lower the capture burdens, collect data that is typically hard to track automatically, and promote awareness to help people achieve their self-monitoring goals. They first specify three design considerations for semi-automated tracking: data capture feasibility, the purpose of self-monitoring, and the motivation level. They then provide examples of semi-automated tracking applications in the domains of sleep, mood, and food tracking to demonstrate strategies they developed to find the right balance between manual tracking and automated tracking, combining each of their benefits while minimizing their associated limitations.