Key result
An artificial neural network estimates maximal heart rate with ~9% error, outperforming traditional age-based models.
Why the study?
Most studies have focused on age-based equations to estimate maximal heart rate, neglecting multiple factors that affect prediction accuracy.
Does a multivariate regression model and artificial neural network improve the accuracy of maximal heart rate estimation compared to a traditional age-based model in middle-aged adults?
Cross-Sectional (n=121)
No
Does a multivariate regression model and artificial neural network improve the accuracy of maximal heart rate estimation compared to a traditional age-based model in middle-aged adults?
Absolute Event Rate: 9.42% vs 10.31%
Multivariate regression models and artificial neural networks incorporating age, resting heart rate, and second-order heart rate provide more accurate estimates of maximal heart rate than traditional univariate age-based models.
May refine maximal heart rate estimates in middle-aged adults; leaves open whether it improves exercise prescription or outcomes.
Purpose : Heart rate is the most commonly used indicator in clinical medicine to assess the functionality of the cardiovascular system. Most studies have focused on age-based equations to estimate the maximal heart rate, neglecting multiple factors that affect the accuracy of the prediction. Methods : We studied 121 middle-aged adults at an average age of 57.2years with an average body mass index (BMI) of 25.9. The participants performed on a power bike with a starting wattage of 0W that was increased by 25W every 3min until the experiment terminated. Ambulatory blood pressure and electrocardiography were monitored through gas metabolic analyzers for safety concerns. Six descriptive characteristics of participants were observed, which were further analyzed using a multivariate regression model and an artificial neural network (ANN). Results : The input variables for the multivariate regression model and ANN were selected by correlation for the reduction of dimension. The accuracy of estimation by multivariate regression model and ANN was 9.74 and 9.42%, respectively, which outperformed the traditional age-based model (with an accuracy of 10.31%). Conclusion : This study provides comprehensive approaches to estimate the maximal heart rate using multiple indicators, revealing that both the multivariate regression model and ANN incorporated with age, resting heart rate (RHR), and second-order heart rate (SOHR) are more accurate than univariate models.
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Tao et al. (2021) conducted a cross-sectional in Healthy middle-aged adults (n=121). Artificial Neural Network (ANN) vs. Univariate age-based regression model was evaluated on Accuracy of maximal heart rate estimation (error rate). An artificial neural network incorporating age, resting heart rate, and second-order heart rate estimated maximal heart rate with an error rate of 9.42%, outperforming the traditional age-based model.
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