Evidence map›Paper›PMID 33922202›Full record

ArticleLife (Basel, Switzerland)2021

Impact of Water Temperature on Heart Rate Variability during Bathing.

Jianbo Xu, Wenxi Chen

Abstract read
In one paragraph

Article in Life (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

2 citing papers in PubMed.

  1. Article
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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

2 authors.

Jianbo XuBiomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan.ORCID 0000-0003-3533-2796
Wenxi ChenBiomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan.ORCID 0000-0002-7938-9033

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeart rate variability (HRV) is affected by many factors. This paper aims to explore the impact of water temperature (WT) on HRV during bathing.

methodsThe bathtub WT was preset at three conditions: i.e., low WT (36-38 °C), medium WT (38-40 °C), and high WT (40-42 °C), respectively. Ten subjects participated in the data collection. Each subject collected five electrocardiogram (ECG) recordings at each preset bathtub WT condition. Each recording was 18 min long with a sampling rate of 200 Hz. In total, 150 ECG recordings and 150 WT recordings were collected. Twenty HRV features were calculated using 1-min ECG segments each time. The k-means clustering analysis method was used to analyze the rough trends based on the preset WT. Analyses of the significant differences were performed using the multivariate analysis of variance of

resultsThe statistics show that with increasing WT, 11 HRV features are significantly (

conclusionThe WT has an important impact on HRV during bathing. The findings in the present work reveal an important physiological factor that affects the dynamic changes of HRV and contribute to better quantitative analyses of HRV in future research works.

Indexed as

bathingECGheart rate variabilityquantitative analysist-testwater temperature

Identifiers

PMID33922202
PMCPMC8145520

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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.