Heart rate fragmentation: A novel analytic approach to early allostatic load detection among healthy adults
Bibliographic record
Abstract
The current study explores an emerging cardiac metric, heart rate fragmentation (HRF), as a novel biomarker for allostatic load (AL). HRF may better address the limitations of existing cardiac biomarkers (e.g., confounds and interpretation consistency) in applied research settings, with nonclinical samples. The study’s objectives were: 1) can HRF represent response to psychological stress and 2) can resting HRF be used as a measure of predicting subclinical mental health symptoms. One hundred and fifty-six (n = 156; 75% female) undergraduate students were fitted with a chest band to monitor cardiovascular activity, and completed online demographic and psychosocial surveys in which they were grouped as healthy or displaying probable mental health symptoms (pMH; n = 94, 60.25%) based on respective inventory thresholds for depression, anxiety, and posttraumatic stress disorder. Cardiovascular activity was measured capturing the three R’s of cardiac vagal control: a resting baseline, a reactive acute stressor task, and a paced breathing recovery. Results supported the first hypothesis, in that that HRF significantly differentiated between each RRR condition (p < 0.001). While healthy and pMH individuals did not significantly differ within individual conditions, exploratory analyses revealed healthy individuals displayed significantly larger change in HRF reactivity between conditions (p’s < 0.001) in comparison to pMH, which displayed a more blunted pattern. Overall, this study establishes associations between HRF and mental health, and serves as a promising new biomarker that may identify AL in samples that may be otherwise considered “healthy”, while addressing the limitations of prior biomarkers in non-clinical studies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".