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Effects of exercise on heart rate variability by time-domain, frequency-domain and non-linear analyses in equine athletes

2019· preprint· en· W2913398450 on OpenAlexaff
Ka Hou Christien Li, Rachel Wing Chuen Lai, Yimei Du, Vivian Ly, David Chun Yin Li, Michael Huen Sum Lam, Leonardo Roever, Sophia Fitzgerald-Smith, Guangping Li, Tong Liu, Gary Tse, Mei Dong

Bibliographic record

VenueF1000Research · 2019
Typepreprint
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsMount Royal University
FundersCroucher Foundation
KeywordsHeart rate variabilityHeart rateCardiologyInternal medicineMedicineBlood pressure

Abstract

fetched live from OpenAlex

Background: Heart rate variability (HRV) is an intrinsic property that reflects autonomic balance and has been shown to be predictive of all-cause and cardiovascular mortality. It can be altered by physiological states such as exercise or pathological conditions. However, there are only a handful of studies on HRV in horses. The aim of this study is to compare HRV parameters before and during exercise in horses. Methods: Time-domain, frequency-domain and non-linear analyses were applied to quantify time series data on RR intervals before and during exercise in horses (n=7). Results: Exercise increased heart rate from 44±8 to 113±17 bpm (ANOVA, P <0.05) and decreased standard deviation (SD) from 7±2 to 4±2 bpm, coefficient of variation (CoV) from 16±4% to 3±2% and root mean square of successive RR interval differences (RMSSD) from 89.4±91.5 to 6.5±3.7 ms. Contrastingly, no difference in low-frequency (0.10±0.03 vs. 0.09±0.03 Hz) and high-frequency (0.19±0.03 vs. 0.18±0.03 Hz) peaks, nor in their percentage powers (2±1 vs. 4±5%; 59±9 vs. 64±20%; 39±10 vs. 32±19%) were observed but very low-frequency, low-frequency, and high-frequency powers (ms 2 ) were reduced from 29±17 to 2±5, 1138±372 to 22±22 and 860±564 to 9±6, respectively, as was total power (in logarithms) (7.52±0.52 to 3.25±0.73). Poincaré plots of RR n+1 against RR n revealed similar ellipsoid shapes before and after exercise. The SD along the line-of-identity (SD2) and SD perpendicular to the line-of-identity (SD1) were decreased by exercise (62±17 vs. 9±5 and 63±65 vs. 5±3), corresponding to increased SD2/SD1 ratio from 1.33±0.45 to 2.19±0.72. No change in approximate and sample entropy was detected (0.97±0.23 vs. 0.82±0.22 and 1.14±0.43 vs. 1.37±0.49). Detrended fluctuation analysis revealed unaltered short-term fluctuation slopes (0.76±0.27 vs. 1.18±0.55) but increased long-term fluctuation slopes (0.16±0.11 vs. 0.50±0.16) after exercise. Conclusion: Exercise leads to a decrease in HRV but did not affect signal entropy in horses.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.027
GPT teacher head0.346
Teacher spread0.319 · 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".

Quick stats

Citations11
Published2019
Admission routes1
Has abstractyes

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