Effects of winter military training on energy balance, whole-body protein balance, muscle damage, soreness, and physical performance
Bibliographic record
Abstract
Physiological consequences of winter military operations are not well described. This study examined Norwegian soldiers (n = 21 males) participating in a physically demanding winter training program to evaluate whether short-term military training alters energy and whole-body protein balance, muscle damage, soreness, and performance. Energy expenditure (D2(18)O) and intake were measured daily, and postabsorptive whole-body protein turnover ([(15)N]-glycine), muscle damage, soreness, and performance (vertical jump) were assessed at baseline, following a 4-day, military task training phase (MTT) and after a 3-day, 54-km ski march (SKI). Energy intake (kcal·day(-1)) increased (P < 0.01) from (mean ± SD (95% confidence interval)) 3098 ± 236 (2985, 3212) during MTT to 3461 ± 586 (3178, 3743) during SKI, while protein (g·kg(-1)·day(-1)) intake remained constant (MTT, 1.59 ± 0.33 (1.51, 1.66); and SKI, 1.71 ± 0.55 (1.58, 1.85)). Energy expenditure increased (P < 0.05) during SKI (6851 ± 562 (6580, 7122)) compared with MTT (5480 ± 389 (5293, 5668)) and exceeded energy intake. Protein flux, synthesis, and breakdown were all increased (P < 0.05) 24%, 18%, and 27%, respectively, during SKI compared with baseline and MTT. Whole-body protein balance was lower (P < 0.05) during SKI (-1.41 ± 1.11 (-1.98, -0.84) g·kg(-1)·10 h) than MTT and baseline. Muscle damage and soreness increased and performance decreased progressively (P < 0.05). The physiological consequences observed during short-term winter military training provide the basis for future studies to evaluate nutritional strategies that attenuate protein loss and sustain performance during severe energy deficits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".