Heart Rate Variability Biofeedback in Adults with a Spinal Cord Injury: A Laboratory Framework and Case Series
Bibliographic record
Abstract
Heart rate variability biofeedback (HRV-F) is a neurocardiac self-regulation therapy that aims to regulate cardiac autonomic nervous system activity and improve cardiac balance. Despite benefits in various clinical populations, no study has reported the effects of HRV-F in adults with a spinal cord injury (SCI). This article provides an overview of a neuropsychophysiological laboratory framework and reports the impact of an HRV-F training program on two adults with chronic SCI (T1 AIS A and T3 AIS C) with different degrees of remaining cardiac autonomic function. The HRV-F intervention involved 10 weeks of face-to-face and telehealth sessions with daily HRV-F home practice. Physiological (HRV, blood pressure variability (BPV), baroreflex sensitivity (BRS)), and self-reported assessments (Fatigue Severity Scale, Generalised Anxiety Disorder Scale, Patient Health Questionnaire, Appraisal of Disability and Participation Scale, EuroQol Visual Analogue Scale) were conducted at baseline and 10 weeks. Participants also completed weekly diaries capturing mood, anxiety, pain, sleep quality, fatigue, and adverse events. Results showed some improvement in HRV, BPV, and BRS. Additionally, participants self-reported some improvements in mood, fatigue, pain, quality of life, and self-perception. A 10-week HRV-F intervention was feasible in two participants with chronic SCI, warranting further investigation into its autonomic and psychosocial effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".