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The Effect of Rapid and Slow Heat Acquisition on Heart Rate Variability

2019· article· en· W2955723516 on OpenAlexaff
Brandon Cotton, Cory Coehoorn, Lynneth Stuart-Hill

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2019
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSupine positionHeart rate variabilityHeart rateMedicineCardiologyHeat stressTreadmillCore temperatureCrossover studyInternal medicinePhysical therapyBlood pressureAtmospheric sciencesPhysics

Abstract

fetched live from OpenAlex

Autonomic tone (AT), measured by heart rate variability (HRV), has shown to be linked to the risk of cardiovascular and other diseases. Firefighters are chronically exposed to environments and tasks that put them under acute bouts of thermal and cardiovascular stress, acutely affecting AT. HRV has been shown to respond to both heat stress and heavy exercise though it is not known if rapid heat acquisition caused by the microclimate of personal protective equipment (PPE) affects tonal response magnitude during exercise. PURPOSE: The aim of this study was to determine if PPE-induced rapid heat acquisition affected HRV differently than standard heat acquisition. METHODS: 15 healthy male subjects (mean age, 31.3 ± 11.7 years) completed an incremental graded treadmill walking test until a core temperature of 39.5°C, volitional maximum, or a 2-hour time limit was obtained in both an experimental (PPE) and a control (CON) test in a random crossover design. Pre- and post-exercise, participants completed a 10-minute supine rest period, during which heart rate and R-R intervals were continuously collected. HRV data was filtered and analyzed in the frequency domain. Low (LF) and high frequencies (HF) were reported in normalized units (nu) along with the VLF (very-low frequency), LF, HF, and LF/HF ratio as a unit of power (ms2). RESULTS: Post-exercise LFnu was significantly increased in both CON (pre=73.3±3.5, post= 80.7±3.6, p<0.05) and PPE (pre=7.4±230.1, post=84.2±2.4, p<0.01 ) conditions while HFnu was significantly lower (CON; pre=26.7±3.5, post= 19.2±3.6, p<0.05 and PPE; pre= 31.6±4.5, post= 15.7±2.4, p<0.01). LF/HF ratios were also significantly different pre- to post-exercise in both conditions (CON: pre= 3.9±0.7 ms2, post= 7.3±1.2 ms2, p<0.05; PPE: pre= 3.9±0.6 ms2, post= 7.7±1.0 ms2 p<0.01). There was no difference between the two conditions either pre- or post-exercise for any of the variables measured in ms2 except for post-exercise VLF which was significantly higher in PPE compared to CON. CONCLUSION: Results from the current study suggest that regardless of the rate of thermal acquisition, HRV response is similar, however the shift of HRV into the VLF domain during the PPE condition may have masked the magnitude of sympathetic response by lowering the LF frequency domain.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.257
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2019
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