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


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.

Taewan Kim
KAIST, Daejeon, Korea, Republic of
Haesoo Kim
KAIST, Daejeon, Korea, Republic of
Ha Yeon Lee
Seoul National University, Seoul, Korea, Republic of
Hwarang Goh
Inha University, Incheon, Korea, Republic of
Shakhboz Abdigapporov
Inha University, Michuhol-gu, Incheon, Korea, Republic of
Mingon Jeong
Hanyang University, Seoul, Korea, Republic of
Hyunsung Cho
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Kyungsik Han
Hanyang University, Seoul, Korea, Republic of
Youngtae Noh
KENTECH, Naju-si, Jeollanam-do, Korea, Republic of
Sung-Ju Lee
KAIST, Daejeon, Korea, Republic of
Hwajung Hong
KAIST, Deajeon, Korea, Republic of


会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Health Informatics and Visualization

4 件の発表
2022-05-02 23:15:00
2022-05-03 00:30:00