Mean Normalized Gain: A New Method for the Assessment of the Aerobic System Temporal Dynamics during Randomly Varying Exercise in Humans

Abstract

The temporal dynamics of the oxygen uptake (trueV˙O2) during moderate exercise has classically been related to physical fitness and a slower trueV˙O2 dynamics was associated with deterioration of physical health. However, methods that better characterize the aerobic system temporal dynamics remain challenging. The purpose of this study was to develop a new method (named mean normalized gain, MNG) to systematically characterize the trueV˙O2 temporal dynamics. Eight healthy, young adults (28 ± 6 years old, 175 ± 7 cm and 79 ± 13 kg) performed multiple pseudorandom binary sequence cycling protocols on different days and time of the day. The MNG was calculated as the normalized amplitude of the trueV˙O2 signal in frequency-domain. The MNG was validated considering the time constant τ obtained from time-domain analysis as reference. The intra-subject consistency of the MNG was checked by testing the same participant on different days and times of the day. The MNG and τ were strongly negatively correlated (r = −0.86 and p = 0.005). The MNG measured on different days and periods of the day was similar between conditions. Calculations for the MNG have inherent filtering characteristics enhancing reliability for the evaluation of the aerobic system temporal dynamics. In conclusion, the present study successfully validated the use of the MNG for aerobic system analysis and as a potential complementary tool to assess changes in physical fitness.

Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities

“…WRs systematically alternated in the three PRBS exercise test between 25 W and 90% of the VT (L-M), 25 W and Δ50% (L-H), or VT and Δ50% (VT-H). The time series for the changes in WR for PRBS protocols were generated by a digital shift register with an adder module feedback 37 , 40 – 42 . This process pseudo-randomized the changes in WR and ensured that there would be sufficient signal amplitude while performing non-constant load exercise.…”

Associations Between Heart Rate Recovery Dynamics With Estradiol Levels in 20 to 60 Year-Old Sedentary Women

“…Exponential data modeling was performed using a specific routine developed in OriginPro 8.0 software (OriginLab, Northampton, MA, United States). This routine fitted the HRR data into an exponential model using the Levenberg-Marquardt algorithm that finds the lowest sum of the square error to select the best equation parameters ( Motulsky and Ransnas, 1987 ; Bearden and Moffatt, 2001 ; Beltrame and Hughson, 2017 ). Although the use of multiparameter exponential equations may present smaller fitting residuals ( Motulsky and Ransnas, 1987 ; Bell et al, 2001 ), too many parameters increase the model degree of freedom and more exercise repetitions may be necessary (not applicable in a real clinical environment).…”

Peningkatan Kompetensi Guru SD No.1 Kesiut Tabanan Melalui Pelatihan Media Pembelajaran

“…Ada beberapa metode dalam menganalisis hasil dari pre-test -post-test metode diantaranya menggunakan anova (O'Connell, 2017). Analisis untuk penilaian pada pelatihan ini berdasarkan Mean Normalized Gain untuk setiap peserta (Beltrame, 2017).…”

“…Untuk mengukur peningkatan kemampuan peserta didik, maka cara yang paling umum digunakan adalah dengan menggunakan metode Mean Normalised Gain. Metode ini banyak digunakan untuk mencari peningkatan kemampuan (gain) peserta didik seperti dalam beberapa riset yang telah dilakukan oleh (Beltrame, 2017), (Bonney, 2015) dan (Scalise, 2011). Pada metode ini, nilai pre-test dan post-test digunakan sebagai bagian dari perhitungan gain di mana nilai gain ( ) normalisasi dapat dinyatakan sebagai berikut: vol.…”