A fitness tracker can be used interchangeably with a reference method for underwater single-lead electrocardiography but not heart rate variability analysis in swimming horses
Bibliographic record
Abstract
Objective: To validate a single-lead ECG fitness tracker device (FT) against the reference multilead ECG recording device (RM) for heart rate (HR) and HR variability (HRV) monitoring of horses during routine swimming. Methods: 40 race-fit Thoroughbred racehorses were used for 5 days in May 2024. Surface ECG recordings were obtained simultaneously from the FT and RM devices in horses swimming 63.65 m across a pool. Electrocardiograms were reviewed and artifacts corrected when necessary, and correlations were analyzed between the 2 devices for HR and HRV parameters. Descriptive statistics and Bland-Altman tests were used to determine the agreement between data generated from the 2 devices. Results: A negligible bias was observed for HR (r = 0.99) between the 2 devices. The root mean squared error calculated for HR between them was 0.28 beats/min. The time domain and nonlinear HRV parameters, except triangular index, had additional variability and bias with the FT compared to the RM. This suggests that additional testing is needed to validate the FT for assessment of HRV parameters. Conclusions: The FT and the RM can be used interchangeably for HR monitoring in swimming horses. The FT is not recommended for comprehensive HRV analysis during swimming before bias and variability are corrected. Clinical Relevance: Our findings confirm that the FT is a practical tool to reliably monitor HR in swimming horses, which has applications for fitness and workload monitoring during training.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".