Behavioral Neurocardiac Training in Hypertension
Bibliographic record
Abstract
It is not established whether behavioral interventions add benefit to pharmacological therapy for hypertension. We hypothesized that behavioral neurocardiac training (BNT) with heart rate variability biofeedback would reduce blood pressure further by modifying vagal heart rate modulation during reactivity and recovery from standardized cognitive tasks ("mental stress"). This randomized, controlled trial enrolled 65 patients with uncomplicated hypertension to BNT or active control (autogenic relaxation), with six 1-hour sessions over 2 months with home practice. Outcomes were analyzed with linear mixed models that adjusted for antihypertensive drugs. BNT reduced daytime and 24-hour systolic blood pressures (-2.4+/-0.9 mm Hg, P=0.009, and -2.1+/-0.9 mm Hg, P=0.03, respectively) and pulse pressures (-1.7+/-0.6 mm Hg, P=0.004, and -1.4+/-0.6 mm Hg, P=0.02, respectively). No effect was observed for controls (P>0.10 for all indices). BNT also increased RR-high-frequency power (0.15 to 0.40 Hz; P=0.01) and RR interval (P<0.001) during cognitive tasks. Among controls, high-frequency power was unchanged (P=0.29), and RR interval decreased (P=0.03). Neither intervention altered spontaneous baroreflex sensitivity (P>0.10). In contrast to relaxation therapy, BNT with heart rate variability biofeedback modestly lowers ambulatory blood pressure during wakefulness, and it augments tonic vagal heart rate modulation. It is unknown whether efficacy of this treatment can be improved with biofeedback of baroreflex gain. BNT, alone or as an adjunct to drug therapy, may represent a promising new intervention for hypertension.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".