Evidence map›Paper›PMID 33096844›Full record

ArticleSensors (Basel, Switzerland)2020

How to Use Heart Rate Variability: Quantification of Vagal Activity in Toddlers and Adults in Long-Term ECG.

Helmut Karl Lackner, Marina Tanja Waltraud Eglmaier, Sigrid Hackl-Wimmer, Manuela Paechter, Christian Rominger, Lars Eichen, Karoline Rettenbacher, Catherine Walter-Laager, Ilona Papousek

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.5field-weighted citation impact, top 15% of its field
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

14 citing papers in PubMed, 20 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Helmut Karl LacknerDivision of Physiology, Otto Loewi Research Center, Medical University of Graz, 8010 Graz, Austria.ORCID 0000-0002-0159-3720
Marina Tanja Waltraud EglmaierDivision of Physiology, Otto Loewi Research Center, Medical University of Graz, 8010 Graz, Austria.
Sigrid Hackl-WimmerDepartment of Psychology, Educational Psychology Unit, University of Graz, 8010 Graz, Austria.
Manuela PaechterDepartment of Psychology, Educational Psychology Unit, University of Graz, 8010 Graz, Austria.
Christian RomingerDivision of Physiology, Otto Loewi Research Center, Medical University of Graz, 8010 Graz, Austria.ORCID 0000-0003-3195-4555
Lars EichenDepartment of Early Childhood Education, University of Graz, 8010 Graz, Austria.ORCID 0000-0002-2375-8952
Karoline RettenbacherDepartment of Early Childhood Education, University of Graz, 8010 Graz, Austria.
Catherine Walter-LaagerDepartment of Early Childhood Education, University of Graz, 8010 Graz, Austria.
Ilona PapousekDepartment of Psychology, Biological Psychology Unit, University of Graz, 8010 Graz, Austria.ORCID 0000-0002-6620-0318
University of Graz · ATMedical University of Graz · AT

Funding

Zukunftsfonds Steiermark ABT08-103259/2018|PN:1018
6 · The paper itself

Abstract

Recent developments in noninvasive electrocardiogram (ECG) monitoring with small, wearable sensors open the opportunity to record high-quality ECG over many hours in an easy and non-burdening way. However, while their recording has been tremendously simplified, the interpretation of heart rate variability (HRV) data is a more delicate matter. The aim of this paper is to supply detailed methodological discussion and new data material in order to provide a helpful notice of HRV monitoring issues depending on recording conditions and study populations. Special consideration is given to the monitoring over long periods, across periods with different levels of activity, and in adults versus children. Specifically, the paper aims at making users aware of neglected methodological limitations and at providing substantiated recommendations for the selection of appropriate HRV variables and their interpretation. To this end, 30-h HRV data of 48 healthy adults (18-40 years) and 47 healthy toddlers (16-37 months) were analyzed in detail. Time-domain, frequency-domain, and nonlinear HRV variables were calculated after strict signal preprocessing, using six different high-frequency band definitions including frequency bands dynamically adjusted for the individual respiration rate. The major conclusion of the in-depth analyses is that for most applications that implicate long-term monitoring across varying circumstances and activity levels in healthy individuals, the time-domain variables are adequate to gain an impression of an individual's HRV and, thus, the dynamic adaptation of an organism's behavior in response to the ever-changing demands of daily life. The sound selection and interpretation of frequency-domain variables requires considerably more consideration of physiological and mathematical principles. For those who prefer using frequency-domain variables, the paper provides detailed guidance and recommendations for the definition of appropriate frequency bands in compliance with their specific recording conditions and study populations.

Indexed as

ElectrocardiographyHeart RateAdultChild, PreschoolData AnalysisHumansartifact detectionautonomic nervous systemdynamic adaptationECG derived respirationmethodological considerationsPoincaré plotshort-term variabilitysignal preprocessingwearable biomedical sensing

Identifiers

PMID33096844
PMCPMC7589813
OpenAlexW3094380109

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.