Evidence map›Paper›PMID 36080987›Full record

ArticleSensors (Basel, Switzerland)2022

How Reliable Are Ultra-Short-Term HRV Measurements during Cognitively Demanding Tasks?

André Bernardes, Ricardo Couceiro, Júlio Medeiros, Jorge Henriques, César Teixeira, Marco Simões, João Durães, Raul Barbosa, Henrique Madeira, Paulo Carvalho

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

10 authors.

André BernardesCentre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.ORCID 0000-0001-6408-9938
Ricardo CouceiroCentre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.
Júlio MedeirosCentre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.
Jorge HenriquesCentre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.
César TeixeiraCentre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.
Marco SimõesCentre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.ORCID 0000-0003-3713-2464
João DurãesCentre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.
Raul BarbosaCentre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.
Henrique MadeiraCentre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.
Paulo CarvalhoCentre for Informatics and Systems of the University of Coimbra (CISUC), 3030-290 Coimbra, Portugal.

Funding

Fundação para a Ciência e Tecnologia POCI - 01-0145 - FEDER- 031581
6 · The paper itself

Abstract

Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments. In this paper, we propose to evaluate these measurements' reliability under cognitively demanding tasks using a near real-life setting. For this purpose, we selected 31 HRV features, extracted from data collected from 21 programmers performing code comprehension, and compared them across 18 different time frames, ranging from 3 min to 10 s. Statistical significance and correlation tests were performed between the features extracted using the larger window (3 min) and the same features extracted with the other 17 time frames. We paired these analyses with Bland-Altman plots to inspect how the extraction window size affects the HRV features. The main results show 13 features that presented at least 50% correlation when using 60-second windows. The HF and mNN features achieved around 50% correlation using a 30-second window. The 30-second window was the smallest time frame considered to have reliable measurements. Furthermore, the mNN feature proved to be quite robust to the shortening of the time resolution.

Indexed as

ElectrocardiographyHeart RateReproducibility of Resultscode comprehensioncognitively demanding taskscorrelationstatistical significanceultra-short-term HRV features

Identifiers

PMID36080987
PMCPMC9460303

What OpenQuestion holds

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Registered trials

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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.