Maximal Heart Rate Prediction in Adults that Are Overweight or Obese
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An accurate predictor of maximal heart rate (MHR) is necessary to prescribe safe and effective exercise in those considered overweight and obese when actual measurement of MHR is unavailable or contraindicated. To date, accuracy of MHR prediction equations in individuals that are overweight or obese has not been well established. The purpose of this study was to examine the accuracy of 3 equations for predicting MHR in adults that are overweight or obese. One hundred seventy-three sedentary adults that were overweight or obese enrolled in weight-loss study and performed a VO₂peak treadmill test before the start of the weight loss treatment. A total of 132 of the 173 participants met conditions for achieving maximal exercise testing criteria and were included in this study. Maximal heart rate values determined from VO₂peak treadmill tests were compared across gender, age, and weight status with the following prediction equations: (a) 220 - age, (b) 208 - 0.7 × age, and (c) 200 - 0.48 × age. Among 20- to 40-year-old participants, actual MHR averaged 180 ± 9 b·min⁻¹ and was overestimated (p < 0.001) at 186 ± 5 b·min⁻¹ with the 220 - age equation. Weight status did not affect predictive accuracy of any of the 3 equations. For all participants, the equation, 200-0.48 × age estimated MHR to be 178 ± 4 b·min⁻¹, which was greater than the actual value (175 ± 12, p = 0.005). Prediction equations showed close agreement to actual MHR, with 208 - 0.7 × age being the most accurate.
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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 | 0.004 | 0.001 |
| 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 | 0.001 |
| 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 it