Validating the Use of Heart Rate Variability for Estimating Energy Expenditure
Bibliographic record
Abstract
The ability to measure free-living and activity-specific energy expenditure (EE) is useful for a variety of purposes. Heart rate variability (HRV) monitoring is emerging as a means for estimating EE and other physiological measures. The purpose of this study was to assess the accuracy of HRV-derived EE across a range of physical intensities and during free-living. Participants (n=30) completed two treadmill tests (walk and VO2max) measuring EE via Indirect Calorimetry (IC) and with the FirstBeat Bodyguard HRV monitor. Participants also wore the HRV monitors continuously for four consecutive days under free-living conditions. During the walk test, HRV-EE estimates across analysis conditions correlated moderately with IC estimates of EE (r=0.60-0.75; p<0.05). During VO2max testing, HRV-EE estimates across analysis conditions correlated strongly with IC estimates of EE with (r=0.85-0.98; p<0.05). During free-living conditions, daily average and 4-day total HRV-EE estimates across all analysis conditions correlated strongly (r=0.75-0.98; p<0.05). HRV-EE estimation improves as activity-intensity increases. HRV-EE estimates improve further with the addition of IC-measured HRmax and VO2max, particularly at low intensities; however, meaningful differences were not seen between values when considering group means. HRV-EE estimates are sufficiently accurate to indicate this method possesses practical utility and may be used for individual EE monitoring.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".