Abstract
The number of publications investigating heart rate variability (HRV) in psychiatry and the behavioral sciences has increased markedly in the last decade. In addition to the significant debates surrounding ideal methods to collect and interpret measures of HRV, standardized reporting of methodology in this field is lacking. Commonly cited recommendations were designed well before recent calls to improve research communication and reproducibility across disciplines. In an effort to standardize reporting, we propose the Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH), a checklist with four domains: participant selection, interbeat interval collection, data preparation and HRV calculation. This paper provides an overview of these four domains and why their standardized reporting is necessary to suitably evaluate HRV research in psychiatry and related disciplines. Adherence to these communication guidelines will help expedite the translation of HRV research into a potential psychiatric biomarker by improving interpretation, reproducibility and future meta-analyses.
Highlights
The number of publications investigating heart rate variability (HRV) in psychiatry and the behavioral sciences has increased markedly in the last decade
HRV has been shown to covary with a range of psychological phenomena that are impaired in psychiatric illnesses such as social cognition[19,20] and executive function.[21,22]
We hasten to emphasize that we are not attempting to advocate a standard for HRV research—the breadth of research questions and methods renders this impractical—providing this information will help improve the interpretation of HRV research in psychiatry and related disciplines
Summary
There are more than 70 published metrics for calculating HRV.[166,167] Methods may be time domain (such as the s.d. of the NN interval), frequency domain (such as the Fast Fourier Transform), time–frequency domain (such as the continuous wavelet transform) or ‘nonlinear’ methods, many of which are not strictly nonlinear Instead of relying on effect size comparison between studies with different methodologies for meta-analysis, pooling individual data points using mega-analysis would help adjust for within-subject differences in methodology.[180] Mega-analyses have already been applied to other areas of biobehavioral research.[181,182,183] The largest. HRV meta-analysis in psychiatry to date, which included 170 studies, suggested that only 20–25% of observed heterogeneity was because of sampling error.[14] This indicates that other factors, such as HRV calculation methodology, are contributing to heterogeneity in effect sizes. Mega-analyses can determine the degree to which differences in methodology contribute to heterogeneity compared with other heterogeneity sources.[184] given the complexities surrounding data sharing,[185] such as the precarious balance between openness and privacy, we feel that this cannot be included as a strict recommendation to publication. The availability of the precise details of statistical analysis (that is, analysis scripts) may improve reproducibility of research, as it offers the reader and reviewers the opportunity to closely examine how results were generated
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