Han Xiao 肖寒

Smartphone overuse is related to a variety of issues such as lack of sleep and anxiety. We explore the application of Self-Affirmation Theory on smartphone overuse intervention in a just-in-time manner. We present TypeOut, a just-in-time intervention technique that integrates two components: an in-situ typing-based unlock process to improve user engagement, and self-affirmation-based typing content to enhance effectiveness. We hypothesize that the integration of typing and self-affirmation content can better reduce smartphone overuse. We conducted a 10-week within-subject field experiment (N=54) and compared TypeOut against two baselines: one only showing the self-affirmation content (a common notification-based intervention), and one only requiring typing non-semantic content (a state-of-the-art method). TypeOut reduces app usage by over 50%, and both app opening frequency and usage duration by over 25%, all significantly outperforming baselines. TypeOut can potentially be used in other domains where an intervention may benefit from integrating self-affirmation exercises with an engaging just-in-time mechanism.

Video lectures are increasingly being used by learners in a ubiquitous manner. However, existing video designs are not optimised for ubiquitous use, creating the need to adapt the style of these videos to meet the constraints of the learning platform and context of use. Our formative study with experienced video editing users, however, found that performing these adaptations using traditional video editors can be a challenging and time-consuming task. We developed VidAdapter, a tool that facilitates lecture video adaptation by allowing direct manipulation of the video content. For this, VidAdapter automatically extracts meaningful elements from the video, enables spatial and temporal reorganisation of the elements, and streamlines the modification of an element's visual appearance. We demonstrate the capabilities and specific use cases of VidAdapter within the domain of adapting existing blackboard lecture videos for on-the-go learning on Optical Head-Mounted Displays. Our evaluation of the tool with experienced video editing users revealed that VidAdapter was strongly preferred over traditional approaches and can improve the efficiency of the adaptation process by over 53% on average.

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.