Impact of Acute Dietary and Exercise Manipulation on Next-Day RMR Measurements and DXA Body Composition Estimates
Bibliographic record
Abstract
PURPOSE: The objective of this study is to investigate the effects of acute diet and exercise manipulation on resting metabolic rate (RMR) measurement variability and dual-energy x-ray absorptiometry (DXA) body composition estimates. METHODS: Ten male and 10 female endurance athletes (12 cyclists, 5 triathletes, 4 runners) of tier 2 ( n = 18) to tier 3 ( n = 2) caliber underwent five conditions using a Latin square counterbalance design. For 24 h, athletes consumed a diet providing excessive energy availability (EA) (75 kcal⋅kg fat-free mass (FFM) -1 ) without exercise (GEA rest ), high-EA (45 kcal⋅kg FFM -1 ) without (HEA rest ) or with exercise (HEA ex ), or low-EA (15 kcal⋅kg FFM -1 ) without (LEA rest ) or with exercise (LEA ex ). Exercise involved two bouts of cycling (morning bout: 149 ± 34 min at 55% of maximal aerobic capacity (V̇O 2max ); afternoon bout: 60 min at 65% of V̇O 2max ) that resulted in a cumulative exercise energy expenditure of 30 kcal⋅kg FFM -1 . The following day, RMR and DXA measurements occurred after a 10-h fast and 12-h postexercise. RESULTS: There were neither sex differences in relative RMR ( P = 0.158) nor effects of any of the five conditions on RMR ( P = 0.358). For both male and female athletes, FFM estimates were decreased following the LEA rest (-0.84 ± 0.66 kg; P = 0.001) and LEA ex (-0.65 ± 0.86 kg; P = 0.016) conditions compared with the GEA rest condition and following the LEA rest (-0.73 ± 0.51 kg; P = 0.001) and LEA ex (-0.54 ± 0.79 kg; P = 0.024) conditions compared with the HEA ex condition. There was no effect of condition on fat mass estimates ( P = 0.819). CONCLUSIONS: Acute periods of diet and exercise manipulation did not create artifacts in next-day RMR measurements. However, as changes in estimates of FFM were seen, diet and exercise should be controlled in the 24-h before DXA scans.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".