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The effect of heart rate variability biofeedback training on stress and anxiety: a meta-analysis

Published online by Cambridge University Press:  08 May 2017

V. C. Goessl ,
J. E. Curtiss  and
S. G. Hofmann
V. C. Goessl
Affiliation:
Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
J. E. Curtiss
Affiliation:
Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
S. G. Hofmann*
Affiliation:
Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
*
*Address for correspondence: S. G. Hofmann, Ph.D., Department of Psychological and Brain Sciences, Boston University, 648 Beacon St., 6th floor, Boston, MA 02215, USA. (Email: shofmann@bu.edu)
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Abstract

Background

Some evidence suggests that heart rate variability (HRV) biofeedback might be an effective way to treat anxiety and stress symptoms. To examine the effect of HRV biofeedback on symptoms of anxiety and stress, we conducted a meta-analysis of studies extracted from PubMed, PsycINFO and the Cochrane Library.

Methods

The search identified 24 studies totaling 484 participants who received HRV biofeedback training for stress and anxiety. We conducted a random-effects meta-analysis.

Results

The pre-post within-group effect size (Hedges' g) was 0.81. The between-groups analysis comparing biofeedback to a control condition yielded Hedges' g = 0.83. Moderator analyses revealed that treatment efficacy was not moderated by study year, risk of study bias, percentage of females, number of sessions, or presence of an anxiety disorder.

Conclusions

HRV biofeedback training is associated with a large reduction in self-reported stress and anxiety. Although more well-controlled studies are needed, this intervention offers a promising approach for treating stress and anxiety with wearable devices.

Keywords

AnxietybiofeedbackHRVmeta-analysisstress
Type
Review Article
Information
Psychological Medicine , Volume 47 , Issue 15 , November 2017 , pp. 2578 - 2586
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

†

Both authors claim first-authorship as they have both contributed equally to the current project.

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