Heart Rate Variability to Assess Combat Readiness
Bibliographic record
Abstract
Chronic fatigue/physical exhaustion (FPE) impacts combat readiness but is difficult to identify. We tested the hypothesis that resting heart rate variability (HRV), including both time- and frequency-domain assessments, would correlate with hydration status and aerobic capacity in military recruit-age men and women with varying fitness levels. Cardiac interbeat intervals were recorded using a heart R-R monitor during 20 minutes of quiet, supine rest with paced breathing (0.25 Hz). HRV metrics included average R-R interval (RRIavg), R-R interval standard deviation (RRISD), the percentage of adjacent R-R intervals varying by > or = 50 ms (pNN50), and integrated areas of R-R interval spectral power at the high (0.15-0.4 Hz) (RRIHF) and low (0.04-0.15 Hz) (RRILF) frequencies. Treadmill maximal oxygen uptake (VO2 max), segmental bioimpedance estimates of total body water (TBW), and urine specific gravity (USG) were also assessed. All dependent variables of interest were within expected ranges, although absolute ranges of individual values were considerable. RRI correlated with VO2 max (r = 0.49; p < 0.001), with TBW (r = 0.38; p < 0.001), and inversely with USG (r = -0.23; p = 0.02). RRISD correlated with VO2 max (r = 0.21; p = 0.03), but not with TBW or USG. pNN50 correlated inversely with USG (r = -0.21; p = 0.03) but not with VO2 max or TBW. R-R interval spectral power at the high and low frequencies did not correlate with VO2 max, TBW, or USG. We have demonstrated that fitness level and hydration status may affect cardiac function via changes in autonomic tone, highlighting the potential of field-based assessment of heart rate variability metrics to identify FPE and other aspects of combat readiness.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".