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Prediction for Retrospection: Integrating Algorithmic Stress Prediction into Personal Informatics Systems for College Students' Mental Health

Title
Prediction for Retrospection: Integrating Algorithmic Stress Prediction into Personal Informatics Systems for College Students' Mental Health
Author
한경식
Keywords
algorithm experience; explainability; mental wellbeing; personal informatics; stres management
Issue Date
2022-04
Publisher
Association for Computing Machinery
Citation
Conference on Human Factors in Computing Systems - Proceedings, article no. 279, Page. 1-20
Abstract
Reflecting on stress-related data is critical in addressing one's mental health. Personal Informatics (PI) systems augmented by algorithms and sensors have become popular ways to help users collect and reflect on data about stress. While prediction algorithms in the PI systems are mainly for diagnostic purposes, few studies examine how the explainability of algorithmic prediction can support user-driven self-insight. To this end, we developed MindScope, an algorithm-assisted stress management system that determines user stress levels and explains how the stress level was computed based on the user's everyday activities captured by a smartphone. In a 25-day field study conducted with 36 college students, the prediction and explanation supported self-reflection, a process to re-establish preconceptions about stress by identifying stress patterns and recalling past stress levels and patterns that led to coping planning. We discuss the implications of exploiting prediction algorithms that facilitate user-driven retrospection in PI systems.
URI
https://dl.acm.org/doi/10.1145/3491102.3517701https://repository.hanyang.ac.kr/handle/20.500.11754/177151
DOI
10.1145/3491102.3517701
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > INTELLIGENCE COMPUTING(데이터사이언스전공) > Articles
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