MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention
Name
3613904.3642790.pdf
Size
3.61 MB
Format
Adobe PDF
Checksum (MD5)
96558010ad94d8a7aabb4d0ef98ee2ed
Author(s)
Wu, Ruolan
Yu, Chun
Pan, Xiaole
Liu, Yujia
Zhang, Ningning
Fu, Yue
Wang, Yuhan
Zheng, Zhi
Chen, Li
Jiang, Qiaolei
Date Issued
May 11, 2024
Publisher
ACM
Citation
Wu, Ruolan, Yu, Chun, Pan, Xiaole, Liu, Yujia, Zhang, Ningning et al. 2024. "MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention."
Version
Final published version
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.
Description
CHI '24: Proceedings of the CHI Conference on Human Factors in Computing Systems May 11–16, 2024, Honolulu, HI, USA
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
10.1145/3613904.3642790
