Designing Multimodal Self-Tracking Technologies to Promote Data Capture and Self-Reflection

Self-Tracking is a powerful means to help individuals monitor and improve their behaviors. While numerous tracking technologies are available, it has been challenging to lower the tracking burden whilst promoting reflection. This is because low-burden tracking technologies utilizing automated sensors reduce people's awareness of their data; reflective tracking approaches, such as manual typing, often impose a high data capture burden. Motivated by speech input's fast and expressive nature, my dissertation examines how speech input complements traditional touch input in supporting self-Tracking. Taking a research-Through-design approach, I examine the use of speech input in exercise tracking and food journaling, and evaluate a flexible multimodal self-Tracking system incorporating touch and speech input in a domain-Agnostic context. I hope this work can inform the design of multimodal technologies to support low-burden, engaging, and reflective self-Tracking experiences.