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Unobtrusive Estimation of Cardiorespiratory Fitness with Daily Activity in Healthy Young Men

Despite the importance of cardiorespiratory fitness, no practical method exists to estimate maximal oxygen consumption (VO(2)max) without a specific exercise protocol. We developed an estimation model of VO(2)max, using maximal activity energy expenditure (aEEmax) as a new feature to represent the l...

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Detalles Bibliográficos
Autores principales: Ahn, Joong Woo, Hwang, Se Hee, Yoon, Chiyul, Lee, Joonnyong, Kim, Hee Chan, Yoon, Hyung-Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Academy of Medical Sciences 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5680492/
https://www.ncbi.nlm.nih.gov/pubmed/29115075
http://dx.doi.org/10.3346/jkms.2017.32.12.1947
Descripción
Sumario:Despite the importance of cardiorespiratory fitness, no practical method exists to estimate maximal oxygen consumption (VO(2)max) without a specific exercise protocol. We developed an estimation model of VO(2)max, using maximal activity energy expenditure (aEEmax) as a new feature to represent the level of physical activity. Electrocardiogram (ECG) and acceleration data were recorded for 4 days in 24 healthy young men, and reference VO(2)max levels were measured using the maximal exercise test. aEE was calculated using the measured acceleration data and body weight, while heart rate (HR) was extracted from the ECG signal. aEEmax was obtained using linear regression, with aEE and HR as input parameters. The VO(2)max was estimated from the aEEmax using multiple linear regression modeling in the training group (n = 16) and was verified in the test group (n = 8). High correlations between the estimated VO(2)max and the measured VO(2)max were identified in both groups, with a 15-hour recording being sufficient to produce a highly accurate VO(2)max estimate. Additional recording time did not significantly improve the accuracy of the estimation. Our VO(2)max estimation method provides a robust alternative to traditional approaches while only requiring minimal data acquisition time in daily life.