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

Abstract

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.

Stress at work on drilling rigs in Poland in a 20-year research perspective: time as a determinant of the stress-at-work dynamics

“…Supplying additional, contextual information to this data allows the client and health professional to discover the patterns and to adjust their interventions to actual needs. It is likely that the combination of this contemporary trend with studies of subjective stress will help to determine the actual nature and intensity of stress at work [30].…”

Empirical stress prediction among drivers utilizing wearable sensors and psychometric signals: Towards smart health

“…After artifact removals, data is successfully used to discriminate stress and non-stress mind conditions by using machine learning algorithms. A similar Care-wear project is developed in [14] that performs the data collection through the process of self-tracking. This project aims to help people understand their behavioral changes and provide them a guided therapy by mental health professionals.…”

“…It can collect data from various sensors for prevention and can provide feedback regarding the physiological signals received [13]. However, it is very important to meticulously select the set of physiological parametric signals for stress detection, as the detection process and decision-making rely completely on the values collected by them [14]. As stress involves the interplay of various physiological responses received from the sensors that involve physiological and behavioral signals.…”

Dropping the E: The potential for integrating e-mental health in psychotherapy

“…The evidence-base for the added value of wearables in clinical practice is, however, currently still very limited. An increasing range of applications are nevertheless emerging, with the potential to extend and ameliorate treatment, for example, in sleep monitoring or for the detection of stressors in everyday life [26,27].…”