Validação de um aplicativo de smartphone para registro da variabilidade da frequência cardíaca e teste de sua sensibilidade para desgaste fisiológico provocado por treinamento desgastante

• Ano de defesa: 2019
• Autor(a) principal: Pereira, Reabias de Andrade
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Educação Física; Programa Associado de Pós Graduação em Educação Física (UPE/UFPB); UFPB
• Palavras-chave em Português: Variabilidade da frequência cardíaca; Aplicativo de smartphone; CardioMood; Sensibilidade; Microciclo regenerativo; Microciclo de choque; Desgaste fisiológico; Heart rate variability; Smartphone application; Overload microcycle; Regenerative microcycle; Sensitivity; Physiological damage; CNPQ::CIENCIAS DA SAUDE::EDUCACAO FISICA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/19069

Introduction: smartphones have made possible to offer several applications to evaluate heart rate variability (HRV). However, out of 25 applications available through 2019, only two were validated. In addition, HRV has been used to monitoring internal training loads, but studies have not determined the sensitivity of this tool to follow the physiological changes due to fluctuations in external training loads. Objective: to verify the accuracy of the HRV Expert by Cardiomood® smartphone application for the recording of RR intervals compared to ECG, and to test the sensitivity of HRV to the ondulation of external training loads during a mesocycle composed of ordinary, overload and regenerative microcycles in recreational runners athletes. Methods: thirty-one male recreational runners participated in the validation phase (36.1 ± 6.3 years). HRV was recorded during five minutes by CardioMood application and simultaneously by ECG, in both supine and sitting positions. Time domain (HR, MeanRR, SDNN, NN50, pNN50, rMSSD), frequency domain (LF, HF, LFnu, HFnu, LF / HF and VLF) and nonlinear indexes (SD1 and SD2) were compared by unpaired Test t, Pearson correlation, simple linear regression and Bland Altman to verify agreement between the devices. Thirteen athletes (37.8 ± 6.9 years) participated in the sensitivity test phase, being evaluated two times in each microcycle (Monday and Friday) during a mesocycle composed by ordinary 1, ordinary 2 (increase of 30% of the volume), overload (increase of 20% of intensity) and regenerative (reduction of external training loads) microcycles. In each evaluation, HRV was recorded during five minutes and the time, frequency domain and non-linear indexes that were used in the validation phase were used in this phase. Psychometric questionnaires were applied (RESTQ-Sport and POMS) and blood collection was performed for analysis of muscle damage markers (creatine kinase (CK) and lactate dehydrogenase (LDH)) and oxidative stress (malondialdehyde (MDA)). Results: in the validation phase, the results obtained by the instruments showed high similarity with p value ranged between 0.97 and 1.0 in both positions. Correlation coefficient of the HRV indexes was perfect (r = 1.0; p = 0.00) for all variables. The constant error, the standard error of estimation and the limits of agreement between ECG and APP data was considered small. Meanwhile, in the sensitivity test, increases in muscle damage (CK and LDH) and perceived stress (RESTQ-Sport) after increasing training loads and reductions after regenerative microcycle, proved the effectiveness of the training protocol. Parasympathetic indices rMSSD, pNN50, HF and SD1 followed the undulations of training loads with reduction after increasing training loads and increase after regenerative microcycle. Conclusion: smartphone application provides excellent concordance with the ECG, so that it can replace the ECG for any HRV analysis in athletes runners. In addition, parasympathetic reduction (rMSSD, pNN50, SD1, HF) suggest that these HRV vagal indexes may be sensitive markers for detecting and monitoring damage and recovery promoted by ondulations of external training loads in this population.