Activity‐Responsive Pacing Produces Long‐Term Heart Rate Variability
Bibliographic record
Abstract
INTRODUCTION: Long-term heart rate variability (HRV) measures, including the standard deviation of means of successive 5-minute epochs of R-R interval intervals (SDANN) and the power law slope (beta), are important prognostic measures, yet their physiologic basis is unknown. We tested the hypothesis that long-term HRV arises from physical activity in a randomized cross-over study in patients with rate-responsive pacemakers. METHODS AND RESULTS: Ten patients with complete heart block and dual-chamber pacemakers underwent 24-hour periods of ambulatory ECG in each of three pacing modes: atrially tracked, fixed-rate, and rate-responsive pacing. SDANN, ultra low frequency (ULF; frequencies <0.0033 Hz), and beta slope were calculated; and high-frequency power and root mean square of consecutive normal R-R intervals (rMSSD) were calculated as measures of short-term HRV, which have autonomic origins. Long-term HRV measures were similar with atrially tracked and rate-responsive pacing and were much greater than in fixed-rate pacing (SDANN P = 0.0001; ULF P = 0.0001; beta slope P = 0.0002). Short-term HRV measures were similarly low in fixed-rate and rate-responsive pacing (P = NS) and were significantly lower than with atrially tracked pacing (P = 0.0034). CONCLUSION: Rate-responsive pacing reproduces long-term, but not short-term, measures of HRV, suggesting that they may be markers of heart rate responses to patient activity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".