MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention

Abstract

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.

arXiv commentPublished at ACM CHI'24

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• Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention
• Exploring Large-Scale Language Models to Evaluate EEG-Based Multimodal Data for Mental Health
• InteractOut: Leveraging Interaction Proxies as Input Manipulation Strategies for Reducing Smartphone Overuse
• The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting
• Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation
• What Social Media Use Do People Regret? An Analysis of 34K Smartphone Screenshots with Multimodal LLM
• AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI
• Remini: Leveraging Chatbot-Mediated Mutual Reminiscence for Promoting Positive Affect and Feeling of Connectedness among Loved Ones
• Seeing Your Mindless Face: How Viewing One's Live Self Interrupts Mindless Short-Form Video Scrolling