Comparing Heart Rate Variability in Canadian Armed Forces Patients to Control Participants without Chronic Pain/Mental Health Issues
Bibliographic record
Abstract
Abstract Background The autonomic nervous system is subserved by the sympathetic and parasympathetic which regulate vital involuntary physiological functions like heart rate. Parasympathetic activity can be measured from the high-frequency component of heart rate variability (HRV), measured via the amplitude of RSA, as a possible predictor for mental health and chronic pain disorders. Therefore, investigators looked to correlate HRV with chronic pain when compared healthy controls. Methods As part of a larger ongoing study, patients complete pre-defined questionnaires on their pain condition, potential risk factors, and function. For patients and controls investigators collected performance and cardiac measures (RSA, LF-HRV, heart period) while at rest, walking and lifting tests. This analysis focused on differences in heart rate variability measures between 100 patients and 48 controls. Results Preliminary analysis revealed demographic and anthropometric variables varied significantly between groups. When comparing HRV measures, respiratory sinus arrhythmia (RSA) during lying and sitting were significantly decreased in patients compared to controls while heart period lying and walking were significantly increased in patients. Correlation analysis revealed significant positive correlation between RSA during lying and sitting when looking at age, gender, and weight. Heart periods during lying and walking were negatively correlated with gender and weight. Discussion To our knowledge it is the first study to look at chronic pain and HRV in the Canadian Forces, while also collecting data on patient reported outcomes, and during various resting and activities. Many potential limitations exist for this study including challenges with respect to controlling for known confounders of heart variability. Conclusion By establishing heart rate variability as a correlate of chronic pain, the outcome of this project could potentially improve quality of care for patients with these conditions. Further work controlling for confounders and relating HRV to pain severity, subtypes, patient reported outcomes and functional abilities will be required to determine the exact value for clinical decision making.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".