Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
102 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention (2403.05584v1)

Published 3 Mar 2024 in cs.HC and cs.AI

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$\sim$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.

PDF HTML Abstract
Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (94)
  1. A systemic smartphone usage pattern analysis: focusing on smartphone addiction issue. Int J Multimed Ubiquitous Eng 9, 6 (2014), 9–14.
  2. StayFree Apps. 2017. StayFree. https://play.google.com/store/apps/details?id=com.burockgames.timeclocker.
  3. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion 58 (June 2020), 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
  4. Yoshua Bengio. 2012. Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML workshop on unsupervised and transfer learning. JMLR Workshop and Conference Proceedings, 17–36.
  5. Negative emotional and cognitive responses to being unfriended on Facebook: An exploratory study. Computers in Human Behavior 28, 4 (2012), 1458–1464. https://doi.org/10.1016/j.chb.2012.03.008
  6. Explainable machine learning in deployment. In Proceedings of the 2020 conference on fairness, accountability, and transparency. 648–657.
  7. Virginia Braun and Victoria Clarke. 2012. Thematic analysis. American Psychological Association.
  8. Sparks of Artificial General Intelligence: Early experiments with GPT-4. http://arxiv.org/abs/2303.12712
  9. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16 (2002), 321–357.
  10. Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (Oct. 2021), 1–36. https://doi.org/10.1145/3479600
  11. Headache in smartphone users: a cross-sectional study. J Neurol Psychol 4, 1 (2016), 5.
  12. Android Developers. 2021. Android Accessibility Service. https://developer.android.com/guide/topics/ui/accessibility.
  13. Allen L Edwards. 1951. Balanced latin-square designs in psychological research. The American journal of psychology 64, 4 (1951), 598–603.
  14. Bringing Transparency Design into Practice. In 23rd International Conference on Intelligent User Interfaces. ACM, Tokyo Japan, 211–223. https://doi.org/10.1145/3172944.3172961
  15. AWARE: mobile context instrumentation framework. Frontiers in ICT 2 (2015), 6.
  16. Risk factors for problematic smartphone use in children and adolescents: a review of existing literature. neuropsychiatrie 33, 4 (2019), 179.
  17. Raymond Fok and Daniel S Weld. 2023. In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making. arXiv preprint arXiv:2305.07722 (2023).
  18. Zoubin Ghahramani. 2003. Unsupervised learning. In Summer school on machine learning. Springer, 72–112.
  19. Mobile social media usage and academic performance. Computers in Human Behavior 82 (2018), 177–185. https://doi.org/10.1016/j.chb.2017.12.041
  20. NOTE: Robust continual test-time adaptation against temporal correlation. Advances in Neural Information Processing Systems 35 (2022), 27253–27266.
  21. SoTTA: Robust Test-Time Adaptation on Noisy Data Streams. In Thirty-seventh Conference on Neural Information Processing Systems. https://openreview.net/forum?id=3bdXag2rUd
  22. DAPPER: Label-Free Performance Estimation after Personalization for Heterogeneous Mobile Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7, 2, Article 55 (jun 2023), 27 pages. https://doi.org/10.1145/3596256
  23. MetaSense: Few-shot adaptation to untrained conditions in deep mobile sensing. SenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems (2019), 110–123. https://doi.org/10.1145/3356250.3360020
  24. The privacy–personalization paradox in mHealth services acceptance of different age groups. Electronic Commerce Research and Applications 16 (2016), 55–65.
  25. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA psychiatry 71, 5 (2014), 566–572.
  26. Andree Hartanto and Hwajin Yang. 2016. Is the smartphone a smart choice? The effect of smartphone separation on executive functions. Computers in human behavior 64 (2016), 329–336.
  27. MyTime: Designing and Evaluating an Intervention for Smartphone Non-Use. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 4746–4757. https://doi.org/10.1145/2858036.2858403
  28. Joyce Ho and Stephen S Intille. 2005. Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. In Proceedings of the SIGCHI conference on Human factors in computing systems. 909–918.
  29. Metrics for explainable AI: Challenges and prospects. arXiv preprint arXiv:1812.04608 (2018).
  30. Impulse and Self-Control From a Dual-Systems Perspective. Perspectives on Psychological Science 4, 2 (March 2009), 162–176. https://doi.org/10.1111/j.1745-6924.2009.01116.x
  31. Using Machine Learning Explainability Methods to Personalize Interventions for Students. International Educational Data Mining Society (2022).
  32. Shamsi T Iqbal and Brian P Bailey. 2010. Oasis: A framework for linking notification delivery to the perceptual structure of goal-directed tasks. ACM Transactions on Computer-Human Interaction (TOCHI) 17, 4 (2010), 1–28.
  33. Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1–14. https://doi.org/10.1145/3313831.3376219
  34. Technology supported behavior restriction for mitigating self-interruptions in multi-device environments. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 1–21.
  35. LocknType: Lockout Task Intervention for Discouraging Smartphone App Use. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3290605.3300927
  36. Prediction for Retrospection: Integrating Algorithmic Stress Prediction into Personal Informatics Systems for College Students’ Mental Health. In CHI Conference on Human Factors in Computing Systems. ACM, New Orleans LA USA, 1–20. https://doi.org/10.1145/3491102.3517701
  37. Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychology 34, S (2015), 1220.
  38. Lock n’ LoL: Group-Based Limiting Assistance App to Mitigate Smartphone Distractions in Group Activities. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 998–1010. https://doi.org/10.1145/2858036.2858568
  39. NUGU: A Group-Based Intervention App for Improving Self-Regulation of Limiting Smartphone Use. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (Vancouver, BC, Canada) (CSCW ’15). Association for Computing Machinery, New York, NY, USA, 1235–1245. https://doi.org/10.1145/2675133.2675244
  40. NUGU: a group-based intervention app for improving self-regulation of limiting smartphone use. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing. 1235–1245.
  41. Geza Kovacs. 2019. HabitLab: In-the-wild Behavior Change Experiments at Scale. Stanford University.
  42. Rotating online behavior change interventions increases effectiveness but also increases attrition. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 1–25.
  43. Exploring the state-of-receptivity for mhealth interventions. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 4 (2019), 1–27.
  44. Development and validation of a smartphone addiction scale (SAS). PloS one 8, 2 (2013), e56936.
  45. Selective explanations: Leveraging human input to align explainable ai. arXiv preprint arXiv:2301.09656 (2023).
  46. Is Smartphone Usage Truly Smart? A Qualitative Investigation of IT Addictive Behaviors. In 2013 46th Hawaii International Conference on System Sciences. 1063–1072. https://doi.org/10.1109/HICSS.2013.367
  47. Explanations from large language models make small reasoners better. arXiv preprint arXiv:2210.06726 (2022).
  48. Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine 37, 3 (May 2020), 50–60. https://doi.org/10.1109/MSP.2020.2975749
  49. Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (March 2020), 1–22. https://doi.org/10.1145/3381007
  50. Analyzing the training processes of deep generative models. IEEE transactions on visualization and computer graphics 24, 1 (2017), 77–87.
  51. AppDetox: Helping Users with Mobile App Addiction. In Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia (Luleå, Sweden) (MUM ’13). Association for Computing Machinery, New York, NY, USA, Article 43, 2 pages. https://doi.org/10.1145/2541831.2541870
  52. InteractOut: Leveraging Interaction Proxies as Input Manipulation Strategies for Reducing Smartphone Overuse. In Proceedings of the 2024 CHI conference on human factors in computing systems. Association for Computing Machinery, New York, NY, USA, 1–18.
  53. SwitchTube: A Proof-of-Concept System Introducing “Adaptable Commitment Interfaces” as a Tool for Digital Wellbeing. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 197, 22 pages. https://doi.org/10.1145/3544548.3580703
  54. How the Design of YouTube Influences User Sense of Agency. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 368, 17 pages. https://doi.org/10.1145/3411764.3445467
  55. Scott M. Lundberg and Su In Lee. 2017. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 2017-Decem, Section 2 (2017), 4766–4775.
  56. The Goldilocks level of support: Using user reviews, ratings, and installation numbers to investigate digital self-control tools. International journal of human-computer studies 166 (2022), 102869.
  57. Self-control in cyberspace: Applying dual systems theory to a review of digital self-control tools. In proceedings of the 2019 CHI conference on human factors in computing systems. 1–18.
  58. LLM-Powered Conversational Voice Assistants: Interaction Patterns, Opportunities, Challenges, and Design Guidelines. arXiv preprint arXiv:2309.13879 (2023).
  59. Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 4 (2023), 1–32.
  60. Detecting receptivity for mHealth interventions in the natural environment. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 5, 2 (2021), 1–24.
  61. Detecting Receptivity for mHealth Interventions in the Natural Environment. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 2 (June 2021), 1–24. https://doi.org/10.1145/3463492
  62. Alberto Monge Roffarello and Luigi De Russis. 2019. The Race Towards Digital Wellbeing: Issues and Opportunities. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3290605.3300616
  63. Translating Strategies for Promoting Engagement in Mobile Health: A Proof-of-Concept Micro-Randomized Trial. Health psychology : official journal of the Division of Health Psychology, American Psychological Association 40, 12 (Dec. 2021), 974–987. https://doi.org/10.1037/hea0001101
  64. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Annals of Behavioral Medicine 52, 6 (May 2018), 446–462. https://doi.org/10.1007/s12160-016-9830-8
  65. Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine 52, 6 (2018), 446–462.
  66. Urbandroid (Petr Nálevka). 2015. Digital Detox: Focus & Live. https://play.google.com/store/apps/details?id=com.urbandroid.ddc.
  67. FinerMe: Examining App-level and Feature-level Interventions to Regulate Mobile Social Media Use. Proc. ACM Hum.-Comput. Interact. 7, CSCW2, Article 274 (oct 2023). https://doi.org/10.1145/3610065
  68. Smartphone usage and increased risk of mobile phone addiction: A concurrent study. International journal of pharmaceutical investigation 7, 3 (2017), 125.
  69. Don’t bother me. I’m socializing! A breakpoint-based smartphone notification system. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. 541–554.
  70. Shaokan Pi. 2015. Forest. https://www.forestapp.cc/.
  71. MyBehavior: Automatic Personalized Health Feedback from User Behaviors and Preferences using Smartphones. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing September (2015), 707–718. https://doi.org/10.1145/2750858.2805840 ISBN: 9781450317702.
  72. ”Why should i trust you?” Explaining the predictions of any classifier. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 13-17-Augu (2016), 1135–1144. https://doi.org/10.1145/2939672.2939778
  73. Internet and mobile phone text messaging intervention for college smokers. Journal of American College Health 57, 2 (2008), 245–248.
  74. Contextualization and individualization for just-in-time adaptive interventions to reduce sedentary behavior. In Proceedings of the conference on health, inference, and learning. 246–256.
  75. Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4 (2008), 1–32.
  76. Prototypical networks for few-shot learning. Advances in neural information processing systems 30 (2017).
  77. Robert Thomson and Jordan Richard Schoenherr. 2020. Knowledge-to-information translation training (kitt): An adaptive approach to explainable artificial intelligence. In Adaptive Instructional Systems: Second International Conference, AIS 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings 22. Springer, 187–204.
  78. Screen Time. 2020. Screen Time. https://support.apple.com/en-us/HT208982.
  79. Blackberry addiction: Symptoms and outcomes. AMCIS 2008 Proceedings (2008), 73.
  80. Explanations can reduce overreliance on ai systems during decision-making. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1–38.
  81. Generalizing from a few examples: A survey on few-shot learning. ACM computing surveys (csur) 53, 3 (2020), 1–34.
  82. Digital Wellbeing. 2018. Digital Wellbeing. https://www.android.com/digital-wellbeing/.
  83. Exploring Understandable Algorithms to Suggest Fitness Tracker Goals That Foster Commitment. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society (Tallinn, Estonia) (NordiCHI ’20). Association for Computing Machinery, New York, NY, USA, Article 35, 12 pages. https://doi.org/10.1145/3419249.3420131
  84. MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention. In Proceedings of the 2024 CHI conference on human factors in computing systems. Association for Computing Machinery, New York, NY, USA, 1–18. https://doi.org/10.1145/3613904.3642790
  85. Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (Sept. 2019), 1–33. https://doi.org/10.1145/3351274
  86. Enabling Hand Gesture Customization on Wrist-Worn Devices. In Proceedings of the ACM Conference on Human Factors in Computing Systems. ACM, New Orleans LA USA, 1–19. https://doi.org/10.1145/3491102.3501904
  87. GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 4 (2023), 32.
  88. Leveraging large language models for mental health prediction via online text data. arXiv preprint arXiv:2307.14385 (2023).
  89. GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. 18.
  90. TypeOut: Leveraging Just-in-Time Self-Affirmation for Smartphone Overuse Reduction. In Proceedings of the ACM Conference on Human Factors in Computing Systems. ACM, New Orleans LA USA, 1–17. https://doi.org/10.1145/3491102.3517476
  91. Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model. arXiv preprint arXiv:2309.09357 (2023).
  92. Digital Danger in Our Pockets: Effect of Smartphone Overuse on Mental Fatigue and Cognitive Flexibility. The Journal of Nervous and Mental Disease (2022), 10–1097.
  93. Effect of ai explanations on human perceptions of patient-facing ai-powered healthcare systems. Journal of Medical Systems 45, 6 (2021), 64.
  94. Association between excessive smartphone use and cervical disc degeneration in young patients suffering from chronic neck pain. Journal of Orthopaedic Science 26, 1 (2021), 110–115.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (10)
  1. Adiba Orzikulova (4 papers)
  2. Han Xiao (104 papers)
  3. Zhipeng Li (42 papers)
  4. Yukang Yan (15 papers)
  5. Yuntao Wang (75 papers)
  6. Yuanchun Shi (51 papers)
  7. Marzyeh Ghassemi (96 papers)
  8. Sung-Ju Lee (22 papers)
  9. Xuhai "Orson" Xu (4 papers)
  10. Anind K Dey (1 paper)
Citations (9)
View on Semantic Scholar
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube