Cold Acclimation in Humans: Effects of Changes in Brown Fat on the Recruitment and Shivering Pattern of Superficial Muscles
Bibliographic record
Abstract
During cold exposure, humans rely primarily on shivering thermogenesis (ST) and to a lesser extent on nonshivering thermogenic (NST) processes in an attempt to counteract increases in heat loss. Recently, cold acclimation has been shown to increase the volume and activity of brown adipose tissue (BAT). The purpose of this study was to quantify changes in ST intensity, muscle recruitment and shivering pattern (i.e. continuous vs burst shivering) following 4 weeks of cold acclimation in young men. Nine participants were exposed to a cold condition for 180 min using a liquid‐conditioned suit (LCS) perfused with 4°C water before and after cold acclimation (2hr/day at 10°C using an LCS, 5 days/week, for 4 weeks). Changes in ST were monitored by surface electromyography (sEMG) in 12 superficial muscles of torso, arms and legs. For all these muscles, results showed that ST intensity, their relative contribution to total ST and shivering pattern (total number of bursts, burst rate and burst intensity) remained unchanged following this level of cold acclimation. In previously published work, we showed that these same subjects increased BAT volume by ~45% and metabolic activity increased ~120% following cold acclimation (Blondin et al. , JCEM 2014). While this suggested an increase role of NST following acclimation, the present study shows that this increase in BAT activity does not alter shivering intensity and/or recruitment pattern of muscles specifically measured in this study. Additional work will be needed to determine whether the activity of deeper muscles is affected by changes in cold acclimation.
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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 | 0.000 | 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) | 0.002 | 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".