Walking with PACE - Personalized and Automated Coaching Engine

We design and implement a personalized and automated physical activity coaching engine, PACE, which uses the Fogg’s behavioral model (FBM) to engage users in mini-conversation based coaching sessions. It is a chat-based nudge assistant that can boost (encourage) and sense (ask) the motivation, ability and propensity of users to walk and help them in achieving their step count targets, similar to a human coach. We demonstrate the feasibility, effectiveness and acceptability of PACE by directly comparing to human coaches in a Wizard-of-Oz deployment study with 33 participants over 21 days. We tracked coach-participant conversations, step counts and qualitative survey feedback. Our findings indicate that the PACE framework strongly emulated human coaching with no significant differences in the overall number of active days, step count and engagement patterns. The qualitative user feedback suggests that PACE cultivated a coach-like experience, offering barrier resolution via motivational and educational support. We use traditional human-computer interaction approaches, to interrogate the conversational data and report positive PACE-participant interaction patterns with respect to addressal, disclosure, collaborative target settings, and reflexivity. As a post-hoc analysis, we annotated the conversation logs from the human coaching arm and trained machine learning (ML) models on these data sets to predict the next boost (AUC 0.73 ± 0.02) and sense (AUC 0.83 ± 0.01) action. In future, such ML-based models could be made increasingly personalized and adaptive based on user behaviors.