Initial Dietary Protein Intake Influence Muscle Function Adaptations in Older Men and Women Following High-Intensity Interval Training Combined with Citrulline
Bibliographic record
Abstract
Background: This study evaluates whether the initial amount of dietary protein intake could influence the combined effect of high-intensity interval training (HIIT) and citrulline (CIT), or HIIT alone, on body composition, muscle strength, and functional capacities in obese older adults. Methods: Seventy-three sedentary obese older men and women who completed a 12-week elliptical HIIT program with double-blinded randomized supplementation of CIT or placebo (PLA) were divided into four groups according to their initial protein intake (CIT–PROT+: n = 21; CIT–PROT−: n = 19; PLA–PROT+: n = 19; PLA–PROT−: n = 14). Body composition (fat and fat-free masses), handgrip (HSr) strength, knee extensor (KESr) strength, muscle power, and functional capacities were measured pre-intervention and post-intervention. Results: Following the intervention, the four groups improved significantly regarding all the parameters measured. For the same initial amount of protein intake, the CIT–PROT− group decreased more gynoid fat mass (p = 0.04) than the PLA–PROT− group. The CIT–PROT+ group increased more KESr (p = 0.04) than the PLA–PROT+ group. In addition, the CIT–PROT− group decreased more gynoid FM (p = 0.02) and improved more leg FFM (p = 0.02) and HSr (p = 0.02) than the CIT–PROT+ group. Conclusion: HIIT combined with CIT induced greater positive changes than in the PLA groups. The combination seems more beneficial in participants consuming less than 1 g/kg/d of protein, since greater improvements on body composition and muscle strength were observed.
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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) | 0.001 | 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".