Validity of the Elite HRV Smartphone Application for Examining Heart Rate Variability in a Field-Based Setting
Bibliographic record
Abstract
Perrotta, AS, Jeklin, AT, Hives, BA, Meanwell, LE, and Warburton, DER. Validity of the elite HRV smartphone application for examining heart rate variability in a field-based setting. J Strength Cond Res 31(8): 2296-2302, 2017-The introduction of smartphone applications has allowed athletes and practitioners to record and store R-R intervals on smartphones for immediate heart rate variability (HRV) analysis. This user-friendly option should be validated in the effort to provide practitioners confidence when monitoring their athletes before implementing such equipment. The objective of this investigation was to examine the relationship and validity between a vagal-related HRV index, rMSSD, when derived from a smartphone application accessible with most operating systems against a frequently used computer software program, Kubios HRV 2.2. R-R intervals were recorded immediately upon awakening over 14 consecutive days using the Elite HRV smartphone application. R-R recordings were then exported into Kubios HRV 2.2 for analysis. The relationship and levels of agreement between rMSSDln derived from Elite HRV and Kubios HRV 2.2 was examined using a Pearson product-moment correlation and a Bland-Altman Plot. An extremely large relationship was identified (r = 0.92; p < 0.0001; confidence interval [CI] 95% = 0.90-0.93). A total of 6.4% of the residuals fell outside the 1.96 ± SD (CI 95% = -12.0 to 7.0%) limits of agreement. A negative bias was observed (mean: -2.7%; CI 95% = -3.10 to -2.30%), whose CI 95% failed to fall within the line of equality. Our observations demonstrated differences between the two sources of HRV analysis. However, further research is warranted, as this smartphone HRV application may offer a reliable platform when assessing parasympathetic modulation.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".