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

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Authors: Youngji Koh, Jeonghyun Kim, Kwangyoung Lee, Yugyeong Jung, Hwajung Hong, Uichin Lee
Cited by: 1
References: 106

Abstract

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.

Fields of Study & Subjects

Emotion and Mood Recognition Digital Mental Health Interventions Innovative Human-Technology Interaction

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–25
Publication date
13 April 2026