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Record W4412040968 · doi:10.14814/phy2.70449

Regular postexercise sauna bathing does not improve heart rate variability: A multi‐arm randomized controlled trial

2025· article· en· W4412040968 on OpenAlexaff
Earric Lee, Sascha Ketelhut, Petri Wiklund, Joel Kostensalo, Iiris Kolunsarka, Hans Hägglund, Juha P. Ahtiainen

Bibliographic record

VenuePhysiological Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersSuomen Kulttuurirahasto
KeywordsHeart rate variabilityMedicineHeart ratePhysical therapyBathingCardiologyRandomized controlled trialInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

Abstract Regular exercise has been shown to increase heart rate variability (HRV) for different populations. Acute and short‐term studies using heat therapy and sauna bathing have also shown HRV improvements. However, long‐term adaptations in HRV to regular exercise and sauna bathing remain unexplored. In this 1:1:1 multi‐arm trial, sedentary participants ( n = 38) aged 49 ± 9 years with at least one CVD risk factor were randomly assigned to regular exercise and 15‐min postexercise sauna (EXS), regular exercise only (EXE), or control (CON) group, for an 8‐week intervention. Indices of HRV (RR interval, RMSSD, SDNN, resting heart rate [HR], HRMAX–HRMIN, high frequency power [HFP], and low frequency power [LFP]) were measured before (PRE) and after (POST) the trial. Compared to CON, EXE increased the time‐domain measure of HR MAX –HR MIN ( p = 0.003), and elicited significantly smaller decreases in the frequency‐domain measure of LFP ( p = 0.022). There were no statistically significant differences between EXS and EXE for any of the HRV indices measured. Eight weeks of regular exercise conferred positive changes in both time‐ and frequency‐domain measures of HRV. However, adding regular sauna bathing postexercise offered no additional benefits to HRV over regular exercise alone.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.292
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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