Making Wearable Technology Available for Mental Healthcare through an Online Platform with Stress Detection Algorithms: The Carewear Project

Abstract EN

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.