Intelligent Estimation of Exercise Induced Energy Expenditure Including Excess Post-Exercise Oxygen Consumption (EPOC) with Different Exercise Intensity

Moon, Junhyung; Oh, Minsuk; Kim, Soljee; Lee, Kyoungwoo; Lee, Junga; Song, Yoonkyung; Jeon, Justin Y.

doi:10.3390/s23229235

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Estimating the VO 2 associated with physical activity and exercise is possible using several methods [ 22 , 23 , 24 ]. The most rigorous methods involve assessing respiratory exchange during exercise, especially VO 2 , carbon dioxide consumption, and the respiratory quotient [ 1 ].…”

Section: Discussionmentioning

confidence: 99%

Net Heart Rate for Estimating Oxygen Consumption in Active Adults

Bragada,

Magalhães,

São-Pedro

et al. 2024

JFMK

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The aim of this study was to verify the accuracy of predicting oxygen consumption (O2) in predominantly aerobic activities based on net heart rate (netHR), sex, and body mass index (BMI) in active adults. NetHR is the value of the difference between the resting HR (HRrest) and the average HR value obtained during a given session or period of physical activity. These activities must be continuous, submaximal, and of a stabilized intensity. The magnitude of the netHR depends mainly on the intensity of the exercise. The HR is measured in beats per minute (bpm). A total of 156 participants, 52 women and 104 men, between the ages of 18 and 81, had their netHR and net oxygen intake (netVO2) assessed. There were 79 participants in group 1 (prediction sample) (52 males and 27 females). There were 77 people in group 2 (validation sample) (52 males and 25 females). The results of the multiple linear regression showed that netVO2 (R2 = 85.2%, SEE = 3.38) could be significantly predicted by sex (p < 0.001), netHR (p < 0.001), and BMI (p < 0.001). The Bland–Altman plots satisfied the agreement requirements, and the comparison of the measured and estimated netVO2 revealed non-significant differences with a trivial effect size. We calculated the formula NetVO2 (mL/(kg·min)) = 16 + 3.67 (sex) + 0.27 (netHR) − 0.57 (BMI) to predict netVO2, where netVO2 is the amount of oxygen uptake (mL/(kg·min)) above the resting value, netHR is the heart rate (beats per minute) above the resting value measured during exercise, sex is equal to zero for women and one for men, and BMI is the body mass index. In addition, based on the knowledge of VO2, it was possible to estimate the energy expenditure from a particular training session, and to determine or prescribe the exercise intensity in MET (metabolic equivalent of task).

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Section: Discussionmentioning

confidence: 99%

Net Heart Rate for Estimating Oxygen Consumption in Active Adults

Bragada,

Magalhães,

São-Pedro

et al. 2024

JFMK

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“…Research demonstrates a correlation between the percentage of fat energy supply after exercise and exercise intensity and duration. Exercise intensity affects the amount of excess post-exercise oxygen consumption, while exercise duration extends the time of excess post-exercise oxygen consumption ( 33 , 34 ). There is currently limited research on the recovery to resting state after high-intensity intermittent exercise, and changes in substrate levels can reflect changes in fat metabolism.…”

Section: Discussionmentioning

confidence: 99%

A comparative analysis of energy expenditure and substrate metabolism in male university students with overweight/obesity: Tabata vs HIIT and MICT

Wang,

Fan,

Cheng

et al. 2024

Front. Endocrinol.

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IntroductionExploring the energy expenditure and substrate metabolism data during exercise, 10-minute recovery, and 20-minute recovery phases in Tabata, HIIT(High-Intensity Interval Training), and MICT(Moderate-Intensity Continuous Training). This study explores the scientific aspects of weight reduction strategies, examining energy expenditure and substrate metabolism from various training perspectives. The aim is to establish a theoretical foundation for tailoring targeted exercise plans for individuals within the population with overweight/obesity.MethodsThis study used an experimental design with fifteen male university students with overweight/obesity. Participants underwent random testing with Tabata, HIIT, and MICT. Tabata involved eight sets of 20 seconds exercise and 10 seconds rest, totaling 4 minutes. HIIT included four sets of power cycling: 3 minutes at 80% VO2max intensity followed by 2 minutes at 20% VO2max. MICT comprised 30 minutes of exercise at 50% VO2max intensity. Gas metabolism indices were continuously measured. Subsequently, fat and glucose oxidation rates, along with energy expenditure, were calculated for each exercise type.ResultsDuring both the exercise and recovery phases, the Tabata group exhibited a significantly higher fat oxidation rate of (0.27 ± 0.03 g/min) compared to the HIIT group (0.20 ± 0.04 g/min, p<0.05) and the MICT group (0.20 ± 0.03g/min, p<0.001). No significant difference was observed between the HIIT and MICT groups (p=0.854). In terms of energy expenditure rate, the Tabata group maintained a substantially elevated level at 5.76 ± 0.74kcal/min compared to the HIIT group (4.81 ± 0.25kcal/min, p<0.01) and the MICT group (3.45 ± 0.25kcal/min, p<0.001). Additionally, the energy expenditure rate of the HIIT group surpassed that of the MICT group significantly (p<0.001).ConclusionThe study finds that male college students with overweight/obesity in both exercise and recovery, Tabata group has lower fat and glucose oxidation rates, and energy expenditure compared to HIIT and MICT groups. However, over the entire process, Tabata still exhibits significantly higher rates in these aspects than HIIT and MICT. Despite a shorter exercise duration, Tabata shows a noticeable “time-efficiency” advantage. Tabata can be used as an efficient short-term weight loss exercise program for male college students with overweight/obesity.

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“…However, there has been no approach to study the NE moment prediction using artificial intelligence (AI) techniques (“Challenge 2”). Recently, AI technology has been broadly employed in various digital healthcare applications, such as clinical deterioration prediction [ 26 ], infection detection [ 27 ], clinical decision support systems [ 28 ], energy expenditure estimation [ 29 ], and medical twins in drug delivery applications [ 30 ]. Since AI excels in the capture and analysis of nonlinear and complex patterns from high-dimensional data, it has significantly contributed to developing novel methods for diagnosis, treatment, and prevention in digital healthcare.…”

Section: Introductionmentioning

confidence: 99%

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

Lee,

Moon,

Lee

et al. 2024

Sensors

Self Cite

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Nocturnal enuresis (NE) is involuntary bedwetting during sleep, typically appearing in young children. Despite the potential benefits of the long-term home monitoring of NE patients for research and treatment enhancement, this area remains underexplored. To address this, we propose NEcare, an in-home monitoring system that utilizes wearable devices and machine learning techniques. NEcare collects sensor data from an electrocardiogram, body impedance (BI), a three-axis accelerometer, and a three-axis gyroscope to examine bladder volume (BV), heart rate (HR), and periodic limb movements in sleep (PLMS). Additionally, it analyzes the collected NE patient data and supports NE moment estimation using heuristic rules and deep learning techniques. To demonstrate the feasibility of in-home monitoring for NE patients using our wearable system, we used our datasets from 30 in-hospital patients and 4 in-home patients. The results show that NEcare captures expected trends associated with NE occurrences, including BV increase, HR increase, and PLMS appearance. In addition, we studied the machine learning-based NE moment estimation, which could help relieve the burdens of NE patients and their families. Finally, we address the limitations and outline future research directions for the development of wearable systems for NE patients

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Intelligent Estimation of Exercise Induced Energy Expenditure Including Excess Post-Exercise Oxygen Consumption (EPOC) with Different Exercise Intensity

Cited by 3 publications

References 31 publications

Net Heart Rate for Estimating Oxygen Consumption in Active Adults

Net Heart Rate for Estimating Oxygen Consumption in Active Adults

A comparative analysis of energy expenditure and substrate metabolism in male university students with overweight/obesity: Tabata vs HIIT and MICT

Wearable-Based Integrated System for In-Home Monitoring and Analysis of Nocturnal Enuresis

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