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      Exploring the Effects of a Brief Biofeedback Breathing Session Delivered Through the BioBase App in Facilitating Employee Stress Recovery: Randomized Experimental Study

      research-article
      , BSc, MSc, PhD 1 , , BA, MSc, PhD 2 , 3 , 4 , , BSc, MSc, PhD 4 , , BSc, MSc, PhD 4 , 5 , , BSc, PhD 6 ,
      (Reviewer), (Reviewer), (Reviewer)
      JMIR mHealth and uHealth
      JMIR Publications
      breathing, biofeedback, smartphone, heart rate variability, recovery, mindfulness, stress, mobile phone

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          Abstract

          Background

          Recovery from stress is a predictive factor for cardiovascular health, and heart rate variability (HRV) is suggested to be an index of how well people physiologically recover from stress. Biofeedback and mindfulness interventions that include guided breathing have been shown to be effective in increasing HRV and facilitating stress recovery.

          Objective

          This study aims to assess the effectiveness of a brief app-based breathing intervention (BioBase) in enhancing physiological recovery among employees who were induced to cognitive and emotional stress.

          Methods

          In total, we recruited 75 full-time employees. Interbeat (RR) intervals were recorded continuously for 5 min at baseline and during cognitive and emotional stress induction. The session ended with a 5-min recovery period during which participants were randomly allocated into 3 conditions: app-based breathing (BioBase), mindfulness body scan, or control. Subjective tension was assessed at the end of each period.

          Results

          Subjective tension significantly increased following stress induction. HRV significantly decreased following the stress period. In the recovery phase, the root mean square of successive RR interval differences ( P=.002), the percentage of successive RR intervals that differed by >50 ms ( P=.008), and high frequency ( P=.01) were significantly higher in the BioBase breathing condition than in the mindfulness body scan and the control groups.

          Conclusions

          Biofeedback breathing interventions digitally delivered through a commercially available app can be effective in facilitating stress recovery among employees. These findings contribute to the mobile health literature on the beneficial effects of brief app-based breathing interventions on employees’ cardiovascular health.

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          Most cited references75

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          Autonomic nervous system activity in emotion: A review

          Autonomic nervous system (ANS) activity is viewed as a major component of the emotion response in many recent theories of emotion. Positions on the degree of specificity of ANS activation in emotion, however, greatly diverge, ranging from undifferentiated arousal, over acknowledgment of strong response idiosyncrasies, to highly specific predictions of autonomic response patterns for certain emotions. A review of 134 publications that report experimental investigations of emotional effects on peripheral physiological responding in healthy individuals suggests considerable ANS response specificity in emotion when considering subtypes of distinct emotions. The importance of sound terminology of investigated affective states as well as of choice of physiological measures in assessing ANS reactivity is discussed. Copyright © 2010 Elsevier B.V. All rights reserved.
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            Mindfulness-Based Interventions in Context: Past, Present, and Future

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              Heart rate variability: a review.

              Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random-during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                October 2020
                15 October 2020
                : 8
                : 10
                : e19412
                Affiliations
                [1 ] Evolution, Behaviour and Environment School of Life Sciences University of Sussex Brighton United Kingdom
                [2 ] Initiative in the Digital Economy Department of Science, Innovation, Technology, and Entrepreneurship University of Exeter Exeter United Kingdom
                [3 ] Social Cognition Lab Department of Experimental Psychology University of Oxford Oxford United Kingdom
                [4 ] BioBeats Group Ltd London United Kingdom
                [5 ] Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford United Kingdom
                [6 ] Faculty of Health and Medical Sciences School of Psychology University of Surrey Guildford United Kingdom
                Author notes
                Corresponding Author: Mark Cropley mark.cropley@ 123456surrey.ac.uk
                Author information
                https://orcid.org/0000-0002-0259-3546
                https://orcid.org/0000-0002-0476-3342
                https://orcid.org/0000-0002-6754-5078
                https://orcid.org/0000-0002-1041-6793
                https://orcid.org/0000-0002-8483-1797
                Article
                v8i10e19412
                10.2196/19412
                7596654
                33055072
                98564ee4-98c3-423a-b1d0-3dccc27ca4ec
                ©Olga Chelidoni, David Plans, Sonia Ponzo, Davide Morelli, Mark Cropley. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 15.10.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 16 April 2020
                : 12 June 2020
                : 20 July 2020
                : 26 July 2020
                Categories
                Original Paper
                Original Paper

                breathing,biofeedback,smartphone,heart rate variability,recovery,mindfulness,stress,mobile phone

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