Data-driven Digital Wellbeing: Mobile Data Analytics and Context-Aware Intervention Design

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Digital wellbeing means that individuals, families, and organizations are capable of using digital technologies to help them to work productively, facilitate social relationships, and sustain healthy lives in a balanced way without experiencing negative side effects of digital technologies such as distraction, dependence, and health/safety/privacy threats. Despite the importance of digital wellbeing, user interface research so far neglected how to design user interfaces that can effectively address negative side effects, ranging from problematic technology use to negative health consequences. Instead, its focus has been minimizing the gulfs of execution and the gulfs of evaluation, by supporting easy and accurate formulation of the actions and intuitive interpretation of the presentation regarding the goals.

Recent advances in mobile user interfaces provide easy and convenient ways of accessing a large amount of online content and services (e.g., YouTube and Tiktok) and maintaining social relationships (e.g., Facebook and Instagram). Such access gives instant gratifications to users (e.g., interpersonal utility, pastimes, information seeking, and entertainment). This reinforces continuous usage of mobile services and may lower a user’s digital wellbeing, thereby leading to productivity loss, safety risks, and physical/mental health threats.

This project investigates “data-driven digital wellbeing” via mobile data analytics and context-aware intervention design. Modern smart devices allow us to continuously log users’ interactions along with passive sensor data (e.g., GPS locations and physical activities). Analyzing fine-grained interaction data allows us to identify harmful effects of problematic usage behaviors; e.g., how frequent notifications influence college students’ academic performance? It is possible to contextualize usage behaviors and identify their relationships with mental, physical, and social factors.

Deeper understanding of user behaviors and related factors offers novel insights for designing data-driven digital wellbeing intervention systems. We envision context-aware intelligent systems that can understand user needs and help to promote digital wellbeing by supporting various nudging techniques.

Publications

Frontiers Public Health
Editorial: Adverse Health Consequences of Excessive Smartphone Usage
Uichin Lee and Paul H. Lee
Volume II, Frontiers Public Health, 09 August 2022 Sec. Digital Public Health
IMWUT
Beneficial Neglect: Instant Message Notification Handling Behaviors and Academic Performance
Minhyung Kim, Inyeop Kim, and Uichin Lee ACM
Ubicomp 2021 / Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Volume 5, Issue 1, March 2021
CHI
GoldenTime: Exploring System-Driven Timeboxing and Micro-Financial Incentives for Self-Regulated Phone Use
Joonyoung Park, Hyunsoo Lee, Sangkeun Park, Kyong-Mee Chung, and Uichin Lee
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
IMWUT
Understanding User Contexts and Coping Strategies for Context-aware Phone Distraction Management System Design
Inyeop Kim, Hwarang Goh, Nematjon Narziev, Youngtae Noh, and Uichin Lee
ACM Ubicomp 2021/ Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) Volume 4, Issue 4, Dec. 2020

Participated Students

Inyeop Kim
Inyeop Kim
Post Doc
Woohyeok Choi
Woohyeok Choi
Post Doc
Joonyoung Park
Joonyoung Park
PhD Program