Large language models (LLMs) offer novel opportunities to support health behavior change, yet existing work has narrowly focused on text-only interactions. Building on decades of HCI research on effective behavior change interactions, we present Bloom, an application for physical activity promotion that integrates an LLM-based health coaching chatbot with existing design strategies and UI elements. As part of Bloom’s development, we conducted a redteaming evaluation and contribute a safety benchmark dataset. In a four-week randomized field study (N=54) comparing Bloom to a no-LLM control, we observed important shifts in psychological outcomes: participants in the LLM condition reported stronger beliefs that activity was beneficial, greater enjoyment, and more self-compassion. Both conditions significantly increased physical activity levels, doubling the proportion of participants meeting recommended weekly guidelines, though descriptively, we observed no advantage for the LLM condition in short-term physical activity levels. Instead, our findings suggest that LLMs may be more effective at shifting mindsets that precede longer-term behavior change.

Optical see-through head-mounted displays (OHMDs) can provide just-in-time digital assistance to users while they are engaged in ongoing tasks. However, given users’ limited attentional resources when multitasking, there is a need to concisely and accurately present information in OHMDs. Existing approaches for digital information presentation involve using either text or pictograms. While pictograms have enabled rapid recognition and easier use in warning messages and traffic signs, most studies using pictograms for digital notifications have exhibited unfavorable results. We thus conducted a series of four iterative studies to understand how we can support effective notification presentation on OHMDs during multitasking scenarios. We find that while icon-augmented notifications can outperform text-only notifications, their effectiveness depends on icon familiarity, encoding density, and environmental brightness. We reveal design implications when using icon-augmented notifications in OHMDs and present plausible reasons for the observed disparity in literature.

In order to satisfy users’ information needs while incurring minimum interference to their ongoing activities, previous studies have proposed using Optical Head-mounted Displays (OHMDs) with different input techniques. However, it is unclear how these techniques compare against one another in terms of being comfortable and non-intrusive to a user’s everyday tasks. Through a wizard-of-oz study, we thus compared four subtle interaction techniques (feet, arms, thumb-index-fingers, and teeth) in three daily hands-busy tasks under different settings (giving a presentation–sitting, carrying bags–walking, and folding clothes–standing). We found that while each interaction technique has its niche, thumb-index-finger interaction has the best overall balance and is most preferred as a cross-scenario subtle interaction technique for smart glasses. We provide further evaluation of thumb-index-finger interaction with an in-the-wild study with 8 users. Our results contribute to an enhanced understanding of user preferences for subtle interaction techniques with smart glasses for everyday use.