  Problematic smartphone use negatively affects physical and mental health.
Despite the wide range of prior research, existing persuasive techniques are
not flexible enough to provide dynamic persuasion content based on users'
physical contexts and mental states. We first conducted a Wizard-of-Oz study
(N=12) and an interview study (N=10) to summarize the mental states behind
problematic smartphone use: boredom, stress, and inertia. This informs our
design of four persuasion strategies: understanding, comforting, evoking, and
scaffolding habits. We leveraged large language models (LLMs) to enable the
automatic and dynamic generation of effective persuasion content. We developed
MindShift, a novel LLM-powered problematic smartphone use intervention
technique. MindShift takes users' in-the-moment app usage behaviors, physical
contexts, mental states, goals \& habits as input, and generates personalized
and dynamic persuasive content with appropriate persuasion strategies. We
conducted a 5-week field experiment (N=25) to compare MindShift with its
simplified version (remove mental states) and baseline techniques (fixed
reminder). The results show that MindShift improves intervention acceptance
rates by 4.7-22.5% and reduces smartphone usage duration by 7.4-9.8%. Moreover,
users have a significant drop in smartphone addiction scale scores and a rise
in self-efficacy scale scores. Our study sheds light on the potential of
leveraging LLMs for context-aware persuasion in other behavior change domains.
