Carli MEC, Moraes Junior FBD, Menezes-Junior FJD, Tadiotto MC, Mota J, Leite N. Prediction equations for maximal heart rate in obese and nonobese children and adolescents: a systematic review and meta-analysis.
Rev Paul Pediatr 2023;
41:e2021397. [PMID:
36888751 PMCID:
PMC9984157 DOI:
10.1590/1984-0462/2023/41/2021397]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 05/06/2022] [Indexed: 03/08/2023]
Abstract
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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