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 conduct 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 leverage large language models (LLMs) to enable the automatic and dynamic generation of effective persuasion content. We develop MindShift, a novel LLM-powered problematic smartphone use intervention technique. MindShift takes users' in-the-moment physical contexts, mental states, app usage behaviors, users' goals & habits as input, and generates high-quality and flexible persuasive content with appropriate persuasion strategies. We conduct a 5-week field experiment (N=25) to compare MindShift with baseline techniques. The results show that MindShift significantly improves intervention acceptance rates by 17.8-22.5% and reduces smartphone use frequency by 12.1-14.4%. Moreover, users have a significant drop in smartphone addiction scale scores and a rise in self-efficacy. Our study sheds light on the potential of leveraging LLMs for context-aware persuasion in other behavior change domains.

翻译：问题性智能手机使用会对身心健康产生负面影响。尽管已有大量研究，但现有说服技术在根据用户物理环境和心理状态提供动态说服内容方面仍缺乏灵活性。我们首先通过"巫师之奥兹"研究（N=12）和访谈研究（N=10）归纳出问题性智能手机使用背后的心理状态：无聊、压力和惯性。基于此，我们设计了四种说服策略：理解、安抚、唤起和搭建习惯。我们利用大语言模型（LLMs）实现说服内容的自动化与动态生成。我们开发了MindShift——一种基于大语言模型的新型问题性智能手机使用干预技术。MindShift以用户当下的物理环境、心理状态、应用使用行为、用户目标与习惯为输入，生成采用适当说服策略的高质量、灵活的说服内容。我们进行了为期5周的现场实验（N=25），将MindShift与基线技术进行比较。结果表明，MindShift使干预接受率显著提升17.8-22.5%，智能手机使用频率降低12.1-14.4%。此外，用户智能手机成瘾量表评分显著下降，自我效能感显著提升。本研究揭示了在大语言模型辅助下，将情境感知说服应用于其他行为改变领域的潜力。