Exercise Intensity Prescription in Obese Individuals
Bibliographic record
Abstract
The main purpose of this study was to evaluate the relationship between different methods proposed by the American College of Sports Medicine (ACSM) to prescribe exercise intensity using heart rate (HR) and oxygen uptake (VO2) in obese individuals. Sixty-eight overweight to severely obese adults were divided into three groups (tertile) based on their BMI. The groups were T1 group (BMI = 30.5 +/- 1.5, n = 23), T2 group (BMI = 34.3 +/- 1.0, n = 23), and T3 group (BMI = 40.2 +/- 3.7, n = 22). All subjects performed a graded exercise test using a ramp protocol on a treadmill. Individual linear regressions between %HR reserve (%HRR) and %VO2 reserve (%VO2R), %HRR and %VO2 peak (%VO2peak), %maximal HR (%HRmax) and %VO2R, and %HRmax and %VO2peak were calculated. When all the subjects were grouped together, the %HRR-%VO2R mean regression was partially related to the line of identity, while the %HRR-%VO2peak, %HRmax-%VO2R, and %HRmax-%VO2peak mean regressions were all significantly different than the line of identity (P < 0.001). The degree of obesity accounted for approximately 15% of the variation for both %HRR-%VO2R and %HRR-%VO2peak mean regressions. The %HRmax-%VO2R and %HRmax-%VO2peak mean regressions were not affected by the degree of obesity but resting HR accounted for 28-37% of the variation. The relationship between the exercise intensity determined by the %HRR-%VO2R and the %HRR-%VO2peak mean regression seems to be influenced by the degree of obesity. The degree of obesity does not affect the relationship between exercise intensity generated by the %HRmax-%VO2R or %HRmax-%VO2peak equations but the resting HR does.
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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 | 0.001 | 0.004 |
| 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) | 0.000 | 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 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".