Life stress and background anxiety are not associated with resting metabolic rate in healthy adults
Bibliographic record
Abstract
This study examined associations between anxiety, stress, and resting metabolic rate (RMR). Thirty women and 23 men had RMR measured at two visits. Participants also had body composition assessed and completed several questionnaires: State–Trait Inventory for Cognitive and Somatic Anxiety (STICSA), Anxiety Sensitivity Index (ASI)-3, and Perceived Stress Scale (PSS)-14. The state version of the STICSA was completed at both visits, while the other questionnaires were completed at visit one. RMR was expressed in kilocalories per day and relative to lean mass (RMRrelative). Participants were divided into low-, medium-, and high-anxiety groups based on STICSA trait scores, and RMR was compared among groups using one-way ANOVA. Changes between visits were evaluated using paired t tests and Wilcoxon signed-rank tests. RMR did not change from visit one to visit two (1589 to 1586 kcal/day, p = 0.86) even though STICSA state scores slightly declined (Z-statistic = –2.39, p = 0.017). RMRrelative values were 30.3 ± 3.7, 29.0 ± 1.9, and 29.9 ± 3.6 kcal/kg of lean mass among low, medium, and high trait anxiety groups, respectively (F = 0.70, p = 0.50). No RMR variable significantly correlated with PSS-14, ASI-3, or STICSA scores. This study provides evidence that trait anxiety and life stress do not impact RMR. Whether an association between these factors exists in anxiety disorders remains to be evaluated. Novelty Contrary to previous research, this study found no associations between anxiety and RMR. It is doubtful whether practitioners need to account for healthy subjects’ trait anxiety and stress when analyzing RMR data.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".