A New Equation to Estimate Energy Expenditure Using Heart Rate in Children

Abstract

The first aim of this study was to develop equations to predict physical activity energy expenditure (PAEE) for children utilizing heart rate monitors (HRM) and vector magnitudes (VM) from accelerometers. The second aim was to cross-validate the developed PAEE prediction equations and compare the equations to the pre-existing accelerometer-based PAEE equation (i.e., Trost). Seventy-five students in elementary school (from 10 to 13 years old) were classified into an equation calibration group (N = 50, 33 boys and 17 girls) and a cross-validation group (N = 25, 20 boys and 5 girls). Participants simultaneously wore a portable indirect calorimeter (Cosmed’s K4b2), a heart rate monitor on the chest, and an accelerometer on the right side of the waist. Then, the participants performed a series of various intensity activities. The energy expenditure (EE) measured by K4b2 was set as the dependent variable. Multiple regression analysis was performed to derive the heart rate and accelerometer-based equations. The heart-rate-based EE equation had an explanatory power of adj. R2 = 0.814 and the accelerometer-based EE equation had an explanatory power of adj. R2 = 0.802. The VM-based EE indicated high mean absolute percent errors (MAPE) at light, moderate, and vigorous intensity. The heart-rate-based EE was included in the range of equivalence limit in all activities, but the VM and pre-existing equation showed some overestimation beyond the equivalence range. The agreement errors between the criterion EE and the estimated EE were lower in the heart-rate-based equation than the accelerometer-based equations (i.e., VM and Trost). The approach with the heart-rate-based EE equation demonstrated higher accuracy than the accelerometer-based EE equations. The results of the current study indicate that the heart-rate-based PAEE equation can be a potential method for estimating children’s PAEE.

Ergonomics Design and Assistance Strategy of A-Suit

“…A subject performs each experiment by walking on the treadmill for 10 min, as shown in Figure 8 a. The four subjects were informed that they could not eat and not exercise 3 h before the tests to prevent further variability in energy consumption that could affect the experimental results [ 41 , 42 ]. A group of experiments were carried out each day and all evaluations were completed within 6 days.…”

“…The gas detection equipment, especially the portable ones, are generally expensive. Heart rate, which directly reflects the changes of metabolic level [ 41 , 43 , 44 ], can be measured and displayed in real-time. Heart rate detection is a mature technology, and the specialized equipment to measure it is cheap.…”

Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model

“…EE increased with increasing exercise intensity from drawing (4.2 ± 0.9 kJ·min −1 ) to regular walking (8.2 ± 2.4 kJ·min −1 ) and tricycling (13.4 ± 3.8 kJ·min −1 ). Lee et al [ 48 ] measured EE, using indirect calorimetry (Cosmed’s K 4 b 2 ), in children aged 10- to 13-years-old, who performed a series of PAs of different intensities. The EE values measured in boys and girls gradually increased from resting (boys: 1.12 ± 0.38 kcal/min; girls: 1.06 ± 0.27 kcal/min) to running at 4 km/h (boys: 3.66 ± 1.08 kcal/min; girls: 3.68 ± 1.14 kcal/min) and 8 km/h (boys: 7.46 ± 1.80 kcal/min; girls: 7.99 ± 2.04 kcal/min).…”

“…Our results ( Table 2 ) in adults (R 2 = 0.984, RMSE = 0.739 kcal/min) thus seem to agree with those of previous studies, such as those of Farrahi et al [ 56 ], which show that the use of a DL model improves the accuracy of EE estimation. Using an accelerometer and a CNN model, Chowdhury et al [ 48 ], estimated EE in eight preschool children (5.23 ± 0.75 years old) during 10 simulated free-living activities ranging in intensity from sedentary to vigorous. Their results showed that the CNN models provide efficient EE prediction (RMSE = 0.54 kcal/min).…”