Effects of functional training and 2 interdisciplinary interventions on maximal oxygen uptake and weight loss of women with obesity: a randomized clinical trial
Bibliographic record
Abstract
Our aim was to analyze and compare functional training, interdisciplinary therapy, and interdisciplinary education on cardiorespiratory fitness (CF) and anthropometric characteristics of women with obesity. Forty-four women (age = 39.7 ± 5.9 years, body mass index (BMI) = 35.5 ± 2.8 kg/m2) completed 30 weeks of intervention randomly assigned to 3 groups: functional training (FT) (n = 14), interdisciplinary therapy (IT) (n = 19), and interdisciplinary education (IE) (n = 11). The FT group participated in the training program (3/week), the IT group received the same training intervention plus nutrition (1/week) and psychology advice (1/week) and physical therapy (1/week). The IE group participated in interdisciplinary lectures on topics related to health promotion (1/month). CF (ergospirometry), anthropometry, and body composition (electrical bioimpedance) were measured pre-intervention (Pre) and post-intervention (Post). CF increased (p ≤ 0.05) significantly (Pre vs. Post) in the FT (7.5%) and IT (10.8%) groups, but not in the IE group (1.8%). Body mass (BM), BMI, relative fat mass, and waist circumference significantly (p ≤ 0.05) decreased (Pre vs. Post) in IT (−4.4%, −4.4%, −2.3%, and −5.1%, respectively). The IE group showed a significant decrease in BM (−3.7%), BMI (−3.7%), and waist circumference (−3.5%), whereas the FT group promoted significant decrease in waist circumference (−3.4%). In conclusion, functional training increased CF but only interdisciplinary interventions improved the anthropometric profile of women with obesity. Novelty Interdisciplinary therapy provided more comprehensive adaptations in women with obesity, including morphological variables and CF. Functional training increased CF but reduced only abdominal obesity. Interdisciplinary education provided benefits on morphological variables, but it does not increase CF.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".