Exploring Data-Driven Approaches to Stress Management: A Systematic Review of Stress Tracking, Intervention, and System Evaluation Methods

要旨

Advances in ubiquitous and wearable sensing and HCI research have made stress monitoring increasingly accessible, enabling the development of personalized stress management technologies. Yet, stress is a subjective and contextual experience, making effective intervention design challenging. Prior studies often isolate stress detection or intervention, without providing an integrated view of how these components connect and are evaluated in real-world use. To address this gap, we conducted a systematic review of 2,152 papers and selected 52 empirical studies where stress tracking informed interventions. Using a framework based on three stress constructs (subjective stress, psycho-physiological stress, and exposure stress), we analyzed how definitions of stress shape detection indicators, intervention design and timing, and evaluation methods. We show that stress conceptualization strongly influences system design, and we propose a conceptual framework linking detection, intervention, and evaluation to guide future user-centered stress management technologies.

著者
Youngji Koh
KAIST, Daejeon, Korea, Republic of
Jeonghyun Kim
KAIST, Daejeon, -Select-, Korea, Republic of
Kwangyoung Lee
KAIST, Daejeon, Korea, Republic of
Yugyeong Jung
KAIST, Daejeon, Korea, Republic of
Hwajung Hong
KAIST, Deajeon, Korea, Republic of
Uichin Lee
KAIST, Daejeon, Korea, Republic of

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Stress Management and Emotional Regulation

P1 - Room 124
7 件の発表
2026-04-16 20:15:00
2026-04-16 21:45:00