Autonomic Nervous System Changes In Individuals With Chronic Pain: A Systematic Review Of The Literature
Bibliographic record
Abstract
PURPOSE: The aim of this review was to investigate the relationship between heart rate (HR) or blood pressure (BP) and chronic pain conditions.\nBACKGROUND: Treatment of chronic pain has placed enormous economic burden on the healthcare system. Autonomic nervous system (ANS) dysregulation is correlated to chronic pain. One measure of ANS dysregulation is heart rate variability (HRV), and decreased HRV can predict adverse future prognosis in a variety of conditions. While HRV validly measures ANS dysregulation, inexpensive and quicker measurements of HR and BP have been less investigated.\nMETHODS: Searches in PubMed, Ovid, Google Scholar, and CINAHL were performed using combinations of the following search terms: “HR”, “BP”, “chronic pain”, “persistent pain”. Inclusion criteria was preregistered though PROSPERO.\nRESULTS: Review of 47 articles found differences in HR and BP measurements between individuals with and without chronic pain conditions. These differences varied in their significance and were found in various states including at rest, during physical activity, during psychological or physical stress tests, or before and after specific interventions.\nCONCLUSIONS: Differences in HR and BP exist in individuals with chronic pain conditions compared to healthy, age-matched controls, which may be indicative of ANS dysregulation. Further research is needed to determine if HR and BP are valid measures of ANS dysregulation in individuals with chronic pain. HR and BP are quicker and more cost-effective measurements compared to HRV, and if they validly measure ANS dysregulation, may provide insight into the future prognosis of individuals with chronic pain conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".