Key result
Wearable stress-detection algorithms yield substantial false positives despite favorable user reception in real-life testing.
Why the study?
Mental healthcare professionals lack tools and knowledge to properly implement and use wearable technology data in clinical practice.
Does the Carewear online platform with Empatica E4 stress detection algorithms accurately detect real-life acute stress moments and provide usable clinical data in healthy participants?
Observational (n=5)
No
Does the Carewear online platform with Empatica E4 stress detection algorithms accurately detect real-life acute stress moments and provide usable clinical data in healthy participants?
Although integrating wearable physiological signals (HR, HRV, EDA, and activity) with contextual self-reports on a collaborative mental health platform is viewed positively by clinicians and users, real-life acute stress detection algorithms currently suffer from substantial false positives and usability hurdles.
Answers are grounded in this paper and cite their sources.
High false-positive rates limit wearable stress detection reliability; leaves open algorithm refinement before clinical validation.
Over the past years, mobile health (mHealth) applications and specifically wearables have become able and available to collect data of increasing quality of relevance for mental health. Despite the large potential of wearable technology, mental healthcare professionals are currently lacking tools and knowledge to properly implement and make use of this technology in practice. The Carewear project is aimed at developing and evaluating an online platform, allowing healthcare professionals to use data from wearables in their clinical practice. Carewear implements data collection through self-tracking, which is aimed at helping people in their behavioral change process, as a component of a broader intervention or therapy guided by a mental healthcare professional. The Empatica E4 wearables are used to collect accelerometer data, electrodermal activity (EDA), and blood volume pulse (BVP) in real life. This data is uploaded to the Carewear platform where algorithms calculate moments of acute stress, average resting heart rate (HR), HR variability (HRV), step count, active periods, and total active minutes. The detected moments of acute stress can be annotated to indicate whether they are associated with a negative feeling of stress. Also, the mood of the day can be elaborated on. The online platform presents this information in a structured way to both the client and their mental healthcare professional. The goal of the current study was a first assessment of the accuracy of the algorithms in real life through comparisons with comprehensive annotated data in a small sample of five healthy participants without known stress-related complaints. Additionally, we assessed the usability of the application through user reports concerning their experiences with the wearable and online platform. While the current study shows that a substantial amount of false positives are detected in a healthy sample and that usability could be improved, the concept of a user-friendly platform to combine physiological data with self-report to inform on stress and mental health is viewed positively in our pilots.
No takes yet. Share an insight, caveat, or question.
Debard et al. (2020) conducted an observational in Healthy participants without known stress-related complaints (mental health stress detection) (n=5). Carewear online platform and Empatica E4 wearable wristband with stress detection algorithms vs. Comprehensive self-reported annotated activity and stress journal (ground truth) was evaluated on Accuracy of acute stress detection algorithms in real life compared with annotated self-report data, and usability of the wearable and online platform. The Carewear platform and Empatica E4 stress detection algorithms showed a substantial amount of false positives in 5 healthy participants over 1 week, though the concept was viewed positively.
Synapse has enriched 10 related papers. Consider them for comparative context: