Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Apr 29, 2025
Open Peer Review Period: Apr 28, 2025 - Jun 23, 2025
Date Accepted: May 22, 2026
(closed for review but you can still tweet)
Machine Learning Frameworks for Wearable-Based Stress Modeling in Naturalistic Settings: A Scoping Review
ABSTRACT
Background:
Stress is not just commonly discussed, but an integral part of modern life, which significantly affects mental and physical health. While significant advancements have been made in measuring physical fitness through wearable devices, the detection and measurement of mental stress remains in its early stages.
Objective:
The objective of this paper is to review recent studies of wearable-based stress detection in naturalistic settings, with a specific focus on characterizing machine learning frameworks inspired by the model card approach.
Methods:
This review was conducted using the PRISMA-SCR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. A total of 319 articles were identified through searches in databases such as PubMed, MEDLINE, ScienceDirect, IEEE, ACM, and Web of Science. Studies were considered eligible if they collected data from healthy adults in naturalistic settings using wearable devices and employed machine learning models for stress detection.
Results:
A total of 34 articles met the eligibility criteria, including 11 conference papers, 22 journal articles, and 1 preprint published between 2017 and 2024. From these studies, we analyzed key machine learning modeling decisions such as problem formulation, ground truth determination, and machine learning algorithms. Additionally, we examined the major contributions of each study, focusing on the challenges they addressed and the solutions they proposed.
Conclusions:
This scoping review highlights recent trends in machine learning models for stress detection and measurement using wearable signals. It underscores the need for improved standardization in datasets, problem formulation, and reporting practices, as well as the importance of addressing critical challenges associated with data collection in real-world settings. We hope this review will support and strengthen ongoing research efforts, promote knowledge sharing, and promote collaboration among researchers—ultimately advancing the field as a community. Clinical Trial: NA
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.