Perceived exertion and heart rate models for estimating metabolic workload in elite British soldiers performing a backpack load-carriage task
Bibliographic record
Abstract
Identifying field measures to estimate backpack load-carriage work intensity in elite soldiers is of interest to the military. This study developed rating of perceived exertion (RPE) and heart rate models to define metabolic workload for a backpack load-carriage task valid for a population of elite soldiers using serial data. Male soldiers (n = 18) from the British Parachute or Special Air Service Regiment completed an incremental treadmill walking and (or) running protocol while carrying a 20-kg backpack. Heart rate, RPE, and oxygen uptake were recorded at each incremental stage of the protocol. Linear mixed models were used to model the RPE and heart rate data in the metric of measured peak oxygen uptake. Workload was accurately estimated using RPE alone (SE = 6.03), percentage of estimated maximum heart rate (%E-MHR) (SE = 6.9), and percentage of measured maximum heart rate (%M-MHR) (SE = 4.9). Combining RPE and %E-MHR resulted in a field measure with an accuracy (SE = 4.9) equivalent to the %M-MHR model. We conclude that RPE, %E-MHR, and %M-MHR provide accurate field-based proxy measures of metabolic workload in elite British soldiers performing a backpack load-carriage task. The model is accurate for the metabolic range measured by these serial data for the backpack load-carriage task.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".