Validity of Prediction Equations of Maximal Heart Rate in Physically Active Female Adolescents and the Role of Maturation

Papadopoulou, Sophia; Papadopoulou, Sousana K.; Alipasali, Foteini; Hatzimanouil, Dimitrios; Rosemann, Thomas; Knechtle, Beat; Nikolaidis, Pantelis Theodoros

doi:10.3390/medicina55110735

Cited by 5 publications

(12 citation statements)

References 22 publications

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“…The finding might be because Tanaka s equation was derived from adult populations aged from 20 to 81, excluding those younger than 20 years of age. In addition, HR max predicted by Tanaka s equation showed significant differences from the actual HR max of females in all age groups, which indicated a discrepancy in the results of some existing literature [13,40]. Since the previous studies have investigated the validity of the existing formula on female athletes in America and/or Europe, and there were no studies that had developed the HR max formula for females, an explanation of this discrepancy might be the different samples' characteristics (i.e., general population and race).…”

Section: Discussionmentioning

confidence: 80%

Re-Visiting Maximal Heart Rate Prediction Using Cross-Validation in Population Aged 7–55 Years

Park

Jung

Jung³

et al. 2022

IJERPH

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The primary purpose of the present study was to re-visit HRmax prediction by two commonly used equations (i.e., Fox′s and Tanaka′s equation) compared to the direct measured HRmax using the large sample size of Asians. The second aim of the study was to focus on suggesting new equations for the Asian population by separating gender and specific age groups. A total of 672 participants aged from 7 to 55 years were recruited for the study (male: 280 and female: 392), and the maximal graded exercise test with Bruce protocol was used to measure HRmax. All data obtained from the study were analyzed by SPSS 25.0. Additionally, three statistical analysis methods (i.e., Mean Absolute Percent Errors (MAPE), Bland–Altman plots, and equivalence testing) were utilized to confirm the consistency between the measured HRmax and the two prediction equations. The main finding was that two equations showed significant differences in predicting the HRmax of Korean aged from 7 to 55 years. The outcome of children aged from 7 to 14 was a different fit in the agreement compared to other age groups. Fox′s equation had the best fit in the average of the difference closer to zero and completely included within the equivalence zone, but females over 15 years old revealed higher errors than males in the values calculated by the two equations compared to the direct measured HRmax. Consequently, the study demonstrated that both equations tended to overestimate the HRmax for males and females over 15 years old, and the two universal equations were not suitable to predict the HRmax of Koreans except for children aged from 7 to 14 years. The new HRmax prediction equations suggested in this study will more accurately predict the HRmax of Asians, and additional analyses should be examined the cross-validity of the developed HRmax equation by age and gender in the future study.

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Section: Discussionmentioning

confidence: 80%

Re-Visiting Maximal Heart Rate Prediction Using Cross-Validation in Population Aged 7–55 Years

Park

Jung

Jung³

et al. 2022

IJERPH

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“…The search resulted in three studies that developed new predictive equations: Mahon et al 12 classified with low methodological quality (50% score), Nikolaidis 23 with moderate quality (68%), and Gelbart et al 14 with high quality (82%). Ten studies performed external validation, of which only one was evaluated with low methodological quality (50%), 24 eight obtained scores between 50 and 70% attaining a moderate quality, [12][13][14][15]23,[25][26][27] and one scored 88%. 28 A single study was carried out to increase values to preexisting equations 29 and was classified as low quality (39%).…”

Section: Resultsmentioning

confidence: 99%

“…From 11 studies, 10 contained nonobese pediatric subjects [12][13][14][15][23][24][25][26][27]29 and 1 contained obese pediatric subjects; 28 of the 10 studies with nonobese sample, 7 included physically active young people. 14,15,23,24,26,27,29 Regarding the criterion to consider the HRmax, 8 of the 11 included studies used the peak HR. [13][14][15]23,26,28,29 Mahon et al 12 measured HRmax as the highest mean value obtained from two consecutive 15 s HR recordings.…”

Section: Resultsmentioning

confidence: 99%

“…10 Since there are physiological differences between children, adolescents, and adults, such as lower stroke volume and higher HRmax in pediatric population, 11 as a compensatory form for the smaller cardiac dimension, other variables, not just age, might influence HRmax prediction. [12][13][14][15] In relation to obese population, there is still no consensus on which predictive equations are more appropriate. Miller et al, 16 developed a predictive equation for obese adults, claiming to have a lower predictive error compared to that developed by Fox et al, 5 showing an association between body composition and HRmax.…”

Section: Introductionmentioning

confidence: 99%

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Prediction equations for maximal heart rate in obese and nonobese children and adolescents: a systematic review and meta-analysis

Carli

Junior

Menezes-Junior

et al. 2023

Rev. paul. pediatr.

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Objective: The aim of this study was to analyze which equation best estimates maximal heart rate (HRmax) for the pediatric population according to body mass. Data source: We performed a meta-analysis (PROSPERO No. CRD42020190196) of cross-sectional studies that aimed to validate or develop HRmax equations and that had children and adolescents as samples. The search was conducted in Scopus, Science Direct, Web of Science, PubMed, and Biblioteca Virtual em Saúde with the descriptors “prediction or equation,” “maximal heart rate,” “maximum heart rate,” “determination of heart rate,” children, and adolescent. The TRIPOD Statement tool was used to assess the methodological quality and the relevant data were extracted for analysis. The meta-analysis was conducted in the Comprehensive Meta-Analysis, adopting p<0.05 and a 95% confidence interval (CI). Data synthesis: In total, 11 studies were selected, of which 3 developed predictive equations, 10 performed external validity of the preexisting models, and 1 incremented values related to equations already developed. The results of the methodological quality analysis showed a moderate rating in most studies. The 164 + (0.270 × HRres) – (0.155 × body mass) + (1.1 × METs) + (0.258 × body fat percent) (r=0.500, 95%CI 0.426–0.567, p<0.001) and 166.7+ (0.46 × HRres) + (1.16 × maturation) (r=0.540, 95%CI 0.313–0.708, p<0.001) equations presented stronger correlations with measured HRmax in nonobese adolescents. The predictive model developed by 208 – (0.7 × age) showed a greater accuracy among the possible models for analysis (SDM=-0.183, 95%CI -0.787 to -0.422, p=0.554). No specific predictive equation was found for obese adolescents. Conclusions: Future research should explore new possibilities for developing predictive equations for this population as a tool to control exercise intensity in the therapeutic management of childhood and adolescent obesity.

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“…8 These formulas are simple to use but could underestimate or overestimate the measured HRmax. [8][9][10][11][12] The ACSM guidelines state that using directly measured HRmax is preferred to using estimated values for greater accuracy in determining exercise intensity.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of wrist-worn wearable devices for determining exercise intensity

Yang

2022

DIGITAL HEALTH

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Objective As an indicator of exercise intensity, heart rate can be measured in a timely manner using wrist-worn devices. No study has attempted to estimate a target exercise intensity using wearable devices. The objective of the study was to evaluate the validity of prescribing exercise intensity using wrist-worn devices. Methods Thirty healthy subjects completed a maximal cardiopulmonary exercise test. Their heart rates were recorded using an electrocardiogram and two devices—Apple Watch Series 6 and Garmin Forerunner 945. Exercise intensity with the target heart rate was defined as resting heart rate + (maximal heart rate − resting heart rate) * n% ( n%: 40–60% for moderate-intensity exercise and 60–89% for vigorous-intensity exercise). Heart rate was analyzed at the lower and upper limits of each exercise intensity (HR40, HR60, and HR89). The mean absolute percentage error and concordance correlation coefficient were calculated, and Bland–Altman plots and scatterplots were constructed. Results Both devices showed a low mean absolute error (1.16–1.48 bpm for Apple and 1.35–2.25 for Garmin) and mean absolute percentage error (<1% for Apple and 1.16–1.39% for Garmin) in all intensities. A substantial correlation with electrocardiogram-measured heart rate was observed for moderate to vigorous intensity with concordance correlation coefficient > 0.95 for both devices, except that Garmin showed moderate correlation at the upper limit of vigorous activity with concordance correlation coefficient = 0.936. Moreover, Bland–Altman plots and scatterplots demonstrated a strong correlation without systematic error when the values obtained via the two devices were compared with electrocardiogram measurements. Conclusions Our findings indicate the high validity of exercise prescriptions based on the heart rate measured by the two devices. Additional research should explore other populations to confirm these findings.

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Validity of Prediction Equations of Maximal Heart Rate in Physically Active Female Adolescents and the Role of Maturation

Cited by 5 publications

References 22 publications

Re-Visiting Maximal Heart Rate Prediction Using Cross-Validation in Population Aged 7–55 Years

Re-Visiting Maximal Heart Rate Prediction Using Cross-Validation in Population Aged 7–55 Years

Prediction equations for maximal heart rate in obese and nonobese children and adolescents: a systematic review and meta-analysis

Accuracy of wrist-worn wearable devices for determining exercise intensity

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