Linear Mixed-Effects Modeling of the Relationship Between Heart Rate Variability and Fatigue Arising from Sleep Deprivation
Bibliographic record
Abstract
INTRODUCTION: Fatigue degrades cognitive performance, yet there is no universally accepted objective measure of fatigue. We tested whether fatigue arising from sleep deprivation can be quantified objectively using heart rate variability (HRV). METHODS: There were 35 male subjects (mean +/- SD; age = 21.4 +/- 2.6 yr) who were assigned to one of two experimental groups: (1) control (N = 16), or (2) 48-h sleep-deprived (N=19). Using 3-h sampling intervals, we simultaneously tracked fatigue level, cognitive performance, and HRV. Linear mixed-effects (LME) models were used to evaluate linear relationships between fatigue level and cognitive performance, as well as between fatigue level and HRV. RESULTS: Significant negative slopes were observed in LME models of cognitive performance and fatigue level. Of the several HRV parameters examined during standing and supine rest, the ratio of low-frequency to high-frequency R-R interval in the supine position had the clearest significant relationship when modeled against fatigue level. DISCUSSION: In summary, our results suggest that HRV tracks fatigue arising from sleep deprivation. This noninvasive, objective tool can quantify fatigue in real time.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".