Biofeedback for Everyday Stress Management: A Systematic Review

Abstract

Background: Mainly due to an increase in stress-related health problems and driven by recent technological advances in biosensors, microelectronics, computing platform, and human-computer interaction, ubiquitous physiological information will potentially transform the role of biofeedback in clinical treatment. Such technology is also likely to provide a useful tool for stress management in everyday life. The aim of this systematic review is to: (1) Classify biofeedback systems for stress management, with a special focus on biosensing techniques, bio-data computing approaches, biofeedback protocol, and feedback modality. (2) Review ways of evaluating approaches to biofeedback applications in terms of their effectiveness in stress management. Method: A systematic literature search was conducted using keywords for "Biofeedback" and "Stress" within the following databases: PubMed, IEEE Xplore, ACM, and Scopus. Two independent reviewers were involved in selecting articles. Results: We identified 103 studies published between 1990 and 2016, 46 of which met our inclusion criteria and were further analyzed. Based on the evidence reviewed, HRV, multimodal biofeedback, RSP, HR, and GSR appear to be the most common techniques for alleviating stress. Traditional screen-based visual displays remain the most common devices used for biofeedback display. Biofeedback applications are usually assessed by making both physiological and psychological measurements. Conclusions: This review reveals several challenges related to biofeedback for everyday stress management, such as facilitating user's perception and interpretating the biofeedback information, the demand of ubiquitous biosensing and display technologies, and field evaluation in order to understand the use of biofeedback in everyday environments. We expect that various emerging HCI technologies could be used to address these challenges. New interaction designs as well as biofeedback paradigms can be further explored in order to improve the accessibility, usability, comfort, engagement with, and user experience of biofeedback in everyday use.

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

“…These effects are best achieved if the voluntary modulation of breathing is performed via respiratory biofeedback, which facilitates the maintenance of a given breathing pace or the execution of specific breathing exercises. f R is a fundamental variable for any respiratory biofeedback strategy, but other ventilatory variables may also prove useful, such as V T , inspiratory time, and expiratory time [274,275]. The primary role of f R in respiratory biofeedback is given by the marked effect of its change on different physiological systems, including the modulation of heart rate variability, which is particularly effective at f R values around 6 breaths/min [276].…”

“…It has even been suggested that the effect of respiratory modulation on heart rate variability is maximized when f R is set at the so-called individual resonant frequency, but more research is needed to further test this hypothesis [276]. Respiratory biofeedback is usually delivered by sound output and/or visual feedback, including attractive forms like music and biofeedback games [275,277,278]. It is a very useful technique to learn respiratory skills and exercises (e.g., diaphragmatic breathing) [271], especially for less compliant individuals like children and older adults.…”

Reducing the Noise of Reality

“…Biofeedback could be used to investigate well-established psychophysiological effects in environments that provide a higher degree of generalizability to everyday life than traditional experimental designs. A few examples could be studying (through training) the role of easily measurable markers like heart-rate variability for stress management (Yu, Funk, Hu, Wang, & Feijs, 2018) or anticipatory bradycardia for decision making (Roelofs, 2017). In these two specific examples, studying the contribution of well-established psychophysiological markers in generalizable contexts would pave the way for a wide range of affordable interventions aiming at changing, for example, the behavior of patients suffering from anxiety disorders.…”

Towards Classifying Cognitive Performance by Sensing Electrodermal Activity in Children With Specific Learning Disorders

“…Assessment is carried out during the interaction of children suffering from Specific Learning Disorders (SLD) with the HapHop-Physio system. Both physiological signals are good indicators of cognitive changes [13]- [15]. The final aim of this paper is going towards the validation of the relationship between cognition (as the class) and changes in these signals (as attributes), demonstrating the feasibility of a machine learning classification approach in a three-class classification task (low, medium, and high performance).…”