Assessment of Maximum Heart Rate Prediction Equations in Adults at Low and High Risk of Cardiovascular Disease
Bibliographic record
Abstract
PURPOSE: Maximum heart rate (HRmax) is commonly used to estimate exercise intensity. Since direct measurement of HRmax is not always practical, prediction equations were developed. However, most equations have not been properly validated in adults at low and high risk of cardiovascular disease (CVD). We sought to: 1) assess the accuracy of commonly used equations to predict HRmax among adults at low and high CVD risk and, 2) determine if SuperLearner (SL) modeling combining base machine algorithms could improve HRmax prediction. METHODS: A total of 1208 participants (61.6 ± 7.3 yr; 62.7% male) were included. HRmax was measured during a maximal cardiorespiratory exercise test. Predicted HRmax was estimated using the following published equations: Fox, Åstrand, Tanaka, Gelish and Gulati, and a SL model. Bland-Altman analyses as well as performance indicators such as root mean squared error (RMSE) and Lin's Concordance Correlation Coefficient were performed. RESULTS: All predicted HRmax-derived equations were positively associated with measured HRmax (women: r = 0.31; men: r = 0.46, P ≤ 0.001) but to a greater extent using a SL model (women: r = 0.47; men: r = 0.59, P ≤ 0.001). Overall, all equations tended to overestimate measured HRmax, with a RMSE which varied between 10.4 and 12.3 bpm. Although the SL model outperformed other equations, with no significant difference between measured and predicted HRmax, RMSE remained high (11.3 bpm). Lack of accuracy was mainly observed among adults with low aerobic fitness and with CVD risk factors, such as obesity, diabetes, and hypertension. CONCLUSIONS: We showed that commonly used equations and the SL model have insufficient accuracy to predict HRmax among adults. The performance of the prediction equations varied considerably according to the population clinical characteristics such as the presence of CVD risk factors or a low aerobic fitness.
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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 | 0.005 | 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 | 0.000 |
| 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 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".