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ArticleSports medicine (Auckland, N.Z.)2024

Are Wearable Photoplethysmogram-Based Heart Rate Variability Measures Equivalent to Electrocardiogram? A Simulation Study.

Hayden G Dewig, Jeremy N Cohen, Eric J Renaghan, Miriam E Leary, Brian K Leary, Jason S Au, Matthew S Tenan

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Article in Sports medicine (Auckland, N.Z.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hayden G DewigRockefeller Neuroscience Institute, West Virginia University, 33 Medical Center Dr, Morgantown, WV, 26505, USA.
Jeremy N CohenDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Eric J RenaghanDepartment of Athletics, University of Miami, Coral Gables, FL, USA.
Miriam E LearyDivision of Exercise Physiology, West Virginia University, Morgantown, WV, USA.
Brian K LearyDivision of Exercise Physiology, West Virginia University, Morgantown, WV, USA.
Jason S AuDepartment of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada.
Matthew S TenanRockefeller Neuroscience Institute, West Virginia University, 33 Medical Center Dr, Morgantown, WV, 26505, USA. matthew.tenan@hsc.wvu.edu.ORCID 0000-0003-2215-0846

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTraditional electrocardiography (ECG)-derived heart rate variability (HRV) and photoplethysmography (PPG)-derived "HRV" (termed PRV) have been reported interchangeably. Any potential dissociation between HRV and PRV could be due to the variability in pulse arrival time (PAT; time between heartbeat and peripheral pulse).

objectiveThis study examined if PRV is equivalent to ECG-derived HRV and if PRV's innate error makes it a high-quality measurement separate from HRV.

methodsECG data from 1084 subjects were obtained from the PhysioNet Autonomic Aging dataset, and individual PAT dispersions for both the wrist (n = 42) and finger (n = 49) were derived from Mol et al. (Exp Gerontol. 2020; 135: 110938). A Bayesian simulation was constructed whereby the individual arrival times of the PPG wave were calculated by placing a Gaussian prior on the individual QRS-wave timings of each ECG series. The standard deviation (σ) of the prior corresponds to the PAT dispersion from Mol et al. This was simulated 10,000 times for each PAT σ. The root mean square of successive differences (RMSSD) and standard deviation of N-N intervals (SDNN) were calculated for both HRV and PRV. The Region of Practical Equivalence bounds (ROPE) were set a priori at ± 0.2% of true HRV. The highest density interval (HDI) width, encompassing 95% of the posterior distribution, was calculated for each PAT σ.

resultsThe lowest PAT σ (2.0 SD) corresponded to 88.4% within ROPE for SDNN and 21.4% for RMSSD. As the σ of PAT increases, the equivalence of PRV and HRV decreases for both SDNN and RMSSD. The HDI interval width increases with increasing PAT σ, with the HDI width increasing at a higher rate for RMSSD than SDNN.

conclusionsFor individuals with greater PAT variability, PRV is not a surrogate for HRV. When considering PRV as a unique biometric measure, SDNN may have more favorable measurement properties than RMSSD, though both exhibit a non-uniform measurement error.

Indexed as

ElectrocardiographyHeart RatePhotoplethysmographyWearable Electronic DevicesAdultAgedBayes TheoremComputer SimulationFemaleFingersHumansMaleWrist

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.