Stress Watch: The Use of Heart Rate and Heart Rate Variability to Detect Stress: A Pilot Study Using Smart Watch Wearables

Abstract

Stress is an inherent part of the normal human experience. Although, for the most part, this stress response is advantageous, chronic, heightened, or inappropriate stress responses can have deleterious effects on the human body. It has been suggested that individuals who experience repeated or prolonged stress exhibit blunted biological stress responses when compared to the general population. Thus, when assessing whether a ubiquitous stress response exists, it is important to stratify based on resting levels in the absence of stress. Research has shown that stress that causes symptomatic responses requires early intervention in order to mitigate possible associated mental health decline and personal risks. Given this, real-time monitoring of stress may provide immediate biofeedback to the individual and allow for early self-intervention. This study aimed to determine if the change in heart rate variability could predict, in two different cohorts, the quality of response to acute stress when exposed to an acute stressor and, in turn, contribute to the development of a physiological algorithm for stress which could be utilized in future smartwatch technologies. This study also aimed to assess whether baseline stress levels may affect the changes seen in heart rate variability at baseline and following stress tasks. A total of 30 student doctor participants and 30 participants from the general population were recruited for the study. The Trier Stress Test was utilized to induce stress, with resting and stress phase ECGs recorded, as well as inter-second heart rate (recorded using a FitBit). Although the present study failed to identify ubiquitous patterns of HRV and HR changes during stress, it did identify novel changes in these parameters between resting and stress states. This study has shown that the utilization of HRV as a measure of stress should be calculated with consideration of resting (baseline) anxiety and stress states in order to ensure an accurate measure of the effects of additive acute stress.

BECOME: A Modular Recommender System for Coaching and Promoting Empowerment in Healthcare

“…These automatically obtained data provide a way to capture unique digital behavioral markers while also lessening the strain on patients that is generally associated with active data collection. Wearables, mobile, and ambient sensors may be used to (a) measure anthropometric, musculoskeletal characteristics, and balance with, for example, a smart scale; (b) monitor physical activity through commercial smart bracelets such as Fitbit that have a proven high accuracy for measuring physical activity (number of steps, heart rate, and distance) [11]; (c) monitor sleep through ballistocardiographic systems [12] or smart bracelets; (d) monitor heart rate variability [13] and electrodermal activity [14] to assess psychophysiological stress via smart bracelets to complement the subjective assessment of stress captured via questionnaires, surveys, and user inputs; and (e) track social behavior through Bluetooth Low Energy (BLE) beacons to measure social interactions [15].…”

Heart Rate Variability Control Using a Biofeedback and Wearable System

“…The HRV is used in several areas of knowledge as an indicator of metabolic function and stress levels, as shown in [ 5 , 21 ]. This parameter was shown to be correlated with insomnia using polysomnographic studies that correlated changes in HRV with this pathology [ 6 ].…”

“…The use of HRV is widespread and is used in areas of medicine for the detection and recognition of stress [ 3 , 4 ]; cardiovascular health [ 5 ]; insomnia [ 6 ], and autoimmune diseases, such as ulcerative colitis [ 7 ].…”

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

“…Researchers in this field detect depression by recognizing changes in body shape such as a patient's gait [13,14], head position [15], and thoracic kyphosis [16]. The method of detecting depression using the sensors is again divided into obtrusive and unobtrusive; the former has the problem of inconvenience and the latter suffers from low accuracy [5,17]. The other one detects depression early by analyzing behavior in cyberspace using various AI techniques [18].…”