A new age-based equation for predicting maximum heart rate in endurance-trained runners

Machado, Fabiana Andrade; Kravchychyn, Ana Claudia Pelissari; Peserico, Cecília Segabinazi; Silva, Danilo Fernandes da; Mezzaroba, Orides

doi:10.1016/j.rbce.2018.01.003

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…While the talk test setup we used has several limitations—such as errors because of head wind and no control over whether participants regularly reperformed the talk test to ensure an appropriate training prescription 27 —we purposely choose the talk test over other methods to prescribe training intensity (eg, percentage of estimated maximum heart rate or ), as the error with these methods might be even larger on an individual level. 18,38,48,57 A third consideration is related to issues or crashing of the wearable application for several participants. This could have led to an underestimation of the distance run in some instances, and thus the distance and time at risk.…”

Section: Discussionmentioning

confidence: 99%

The Effect of Wearable-Based Real-Time Feedback on Running Injuries and Running Performance: A Randomized Controlled Trial

Van Hooren,

Plasqui,

Meijer

2024

Am J Sports Med

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Background: Running technique and running speed are considered important risk factors for running injuries. Real-time feedback on running technique and running speed by wearables may help reduce injury risk. Purpose: To investigate whether real-time feedback on spatiotemporal metrics and relative speed by commercially available pressure-sensitive insoles would reduce running injuries and improve running performance compared with no real-time feedback. Study Design: Randomized controlled trial; Level of evidence, 1. Methods: A total of 220 recreational runners were randomly assigned into the intervention and control groups. Both groups received pressure-sensitive insoles, but only the intervention group received real-time feedback on spatiotemporal metrics and relative speed. The feedback aimed to reduce loading on the joint/segment estimated to exhibit the highest load. Injury rates were compared between the groups using Cox regressions. Secondary outcomes compared included injury severity, the proportion of runners with multiple injuries, changes in self-reported personal best times and motivation (Behavioral Regulation in Exercise Questionnaire–2), and interest in continuing wearable use after study completion. Results: A total of 160 participants (73%) were included in analyses of the primary outcome. Intention-to-treat analysis showed no significant difference in injury rate between the groups (Hazard ratio [HR], 1.11; P = .70). This was expected, as 53 of 160 (33%) participants ended up in the unassigned group because they used incorrect wearable settings, nullifying any interventional effects. As-treated analysis showed a significantly lower injury rate among participants receiving real-time feedback (HR, 0.53; P = .03). Similarly, the first-time injury severity was significantly lower (–0.43; P = .042). Per-protocol analysis showed no significant differences in injury rates, but the direction favored the intervention group (HR, 0.67; P = .30). There were no significant differences in the proportion of patients with multiple injuries (HR, 0.82; P = .40) or changes in running performance (3.07%; P = .26) and motivation. Also, ~60% of the participants who completed the study showed interest in continuing wearable use. Conclusion: Real-time feedback on spatiotemporal metrics and relative speed provided by commercially available instrumented insoles may reduce the rate and severity of injuries in recreational runners. Feedback did not influence running performance and exercise motivation. Registration: NL8472 (Dutch Trial Register).

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Section: Discussionmentioning

confidence: 99%

The Effect of Wearable-Based Real-Time Feedback on Running Injuries and Running Performance: A Randomized Controlled Trial

Van Hooren,

Plasqui,

Meijer

2024

Am J Sports Med

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Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists

Kasiak

Wiecha

Cieśliński

et al. 2023

JCM

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Maximal heart rate (HRmax) is a widely used measure of cardiorespiratory fitness. Prediction of HRmax is an alternative to cardiopulmonary exercise testing (CPET), but its accuracy among endurance athletes (EA) requires evaluation. This study aimed to externally validate HRmax prediction models in the EA independently for running and cycling CPET. A total of 4043 runners (age = 33.6 (8.1) years; 83.5% males; BMI = 23.7 (2.5) kg·m−2) and 1026 cyclists (age = 36.9 (9.0) years; 89.7% males; BMI = 24.0 (2.7) kg·m−2) underwent maximum CPET. Student t-test, mean absolute percentage error (MAPE), and root mean square error (RMSE) were applied to validate eight running and five cycling HRmax equations externally. HRmax was 184.6 (9.8) beats·min−1 and 182.7 (10.3) beats·min−1, respectively, for running and cycling, p = 0.001. Measured and predicted HRmax differed significantly (p = 0.001) for 9 of 13 (69.2%) models. HRmax was overestimated by eight (61.5%) and underestimated by five (38.5%) formulae. Overestimated HRmax amounted to 4.9 beats·min−1 and underestimated HRmax was in the range up to 4.9 beats·min−1. RMSE was 9.1–10.5. MAPE ranged to 4.7%. Prediction models allow for limited precision of HRmax estimation and present inaccuracies. HRmax was more often underestimated than overestimated. Predicted HRmax can be implemented for EA as a supplemental method, but CPET is the preferable method.

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A new age-based equation for predicting maximum heart rate in endurance-trained runners

Cited by 2 publications

References 25 publications

The Effect of Wearable-Based Real-Time Feedback on Running Injuries and Running Performance: A Randomized Controlled Trial

The Effect of Wearable-Based Real-Time Feedback on Running Injuries and Running Performance: A Randomized Controlled Trial

Validity of the Maximal Heart Rate Prediction Models among Runners and Cyclists

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