Stress Determined through Heart Rate Variability Predicts Immune Function
Bibliographic record
Abstract
BACKGROUND: Stress is a prevalent health problem in modern society. If experienced for long periods of time it can lead to immune dysfunctions. Thus, public health management practices must include the assessment of stress. In health management settings, electrocardiography (ECG) is routinely used to assess cardiovascular health and make inferences about stress using information from heart rate variability (HRV). However, it is unclear whether stress assessment based on HRV can also be used to index immune function. OBJECTIVES: To compare stress that was determined by a measure of HRV (pNN50) from ECG with immune function indices (neutrophil, monocyte, and lymphocyte percentages) obtained from blood samples. METHODS: A total of 184 healthy adults participated in the study, which took place in an examination room at the Health Management Center of The Affiliated Hospital of Hangzhou Normal University, China. Participants viewed a relaxing video while having a 2-min ECG recorded. They were then taken to have their blood drawn as part of their physical examination. Measures of stress (pNN50) were extracted from ECG, while measures of immune function (percentages of neutrophils, monocytes, and lymphocytes) were extracted from blood samples. RESULTS: Stress correlated positively with neutrophil percentages (r = 0.21) and negatively with monocyte (r = -0.16) and lymphocyte percentages (r = -0.18). CONCLUSIONS: These findings show HRV analysis to be a potentially viable noninvasive and inexpensive method not only for indexing stress, but also predicting immune function, thus managing the health risks associated with stress.
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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".