research-article

Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention

Authors:

Adiba Orzikulova,

Marzyeh Ghassemi,

Xuhai XuAuthors Info & Claims

CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

Article No.: 250, Pages 1 - 20

https://doi.org/10.1145/3613904.3642747

Published: 11 May 2024 Publication History

Abstract

Despite a rich history of investigating smartphone overuse intervention techniques, AI-based just-in-time adaptive intervention (JITAI) methods for overuse reduction are lacking. We develop Time2Stop, an intelligent, adaptive, and explainable JITAI system that leverages machine learning to identify optimal intervention timings, introduces interventions with transparent AI explanations, and collects user feedback to establish a human-AI loop and adapt the intervention model over time. We conducted an 8-week field experiment (N=71) to evaluate the effectiveness of both the adaptation and explanation aspects of Time2Stop. Our results indicate that our adaptive models significantly outperform the baseline methods on intervention accuracy (>32.8% relatively) and receptivity (>8.0%). In addition, incorporating explanations further enhances the effectiveness by 53.8% and 11.4% on accuracy and receptivity, respectively. Moreover, Time2Stop significantly reduces overuse, decreasing app visit frequency by 7.0 ∼ 8.9%. Our subjective data also echoed these quantitative measures. Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust. We envision our work can inspire future research on JITAI systems with a human-AI loop to evolve with users.

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Cited By

Jeon HJin DChoi SCho HLee KKoo Y(2024)AI-assisted Work Services Utilizing human-AI Collaboration Levels : The Case of Ward NursesArchives of Design Research10.15187/adr.2024.11.37.5.35337:5(353-383)Online publication date: 30-Nov-2024
https://doi.org/10.15187/adr.2024.11.37.5.353
Deters HDroste JHess AKlös VSchneider KSpeith TVogelsang A(2024)The X Factor: On the Relationship between User eXperience and eXplainabilityProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685352(1-12)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685352
Wu RYu CPan XLiu YZhang NFu YWang YZheng ZChen LJiang QXu XShi Y(2024)MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use InterventionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642790(1-24)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642790
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Index Terms

Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention
1. Human-centered computing

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cover image ACM Conferences

CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

May 2024

18961 pages

ISBN:9798400703300

DOI:10.1145/3613904

Editors:
Florian Floyd Mueller
Monash University
,
Penny Kyburz
The Australian National University
,
Julie R. Williamson
University of Glasgow
,
Corina Sas
Lancaster University
,
Max L. Wilson
University of Nottingham
,
Phoebe Toups Dugas
Monash University/New Mexico State University
,
Irina Shklovski
University of Copenhagen

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Published: 11 May 2024

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May 11 - 16, 2024

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Cited By

Jeon HJin DChoi SCho HLee KKoo Y(2024)AI-assisted Work Services Utilizing human-AI Collaboration Levels : The Case of Ward NursesArchives of Design Research10.15187/adr.2024.11.37.5.35337:5(353-383)Online publication date: 30-Nov-2024
https://doi.org/10.15187/adr.2024.11.37.5.353
Deters HDroste JHess AKlös VSchneider KSpeith TVogelsang A(2024)The X Factor: On the Relationship between User eXperience and eXplainabilityProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685352(1-12)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3679318.3685352
Wu RYu CPan XLiu YZhang NFu YWang YZheng ZChen LJiang QXu XShi Y(2024)MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use InterventionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642790(1-24)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642790
Lu TZheng HZhang TXu XGuo A(2024)InteractOut: Leveraging Interaction Proxies as Input Manipulation Strategies for Reducing Smartphone OveruseProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642317(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642317
Meegahapola L(2024)Xuhai “Orson” Xu: “Toward Building Computational Well-Being Ecosystems”IEEE Pervasive Computing10.1109/MPRV.2024.338395623:2(62-64)Online publication date: 28-Jun-2024
https://dl.acm.org/doi/10.1109/MPRV.2024.3383956

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