Accuracy of the energy expenditure during uphill exercise measured by the Waist-worn ActiGraph

Chang, Chun-Hao; Lin, Kuo-Chuan; Ho, Chen Hsun; Huang, Chi‐Chang

doi:10.1016/j.jesf.2019.01.003

Cited by 9 publications

(23 citation statements)

References 40 publications

(51 reference statements)

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“…(2018) integrated HR and accelerator parameters, demonstrating that exercise-related changes in HR (△HR) improved the accuracy of the EE predictive equation during walking uphill of participants. Chang et al. (2019) also corrected the accelerator-based traditional empirical EE formulas by using HR and HRR as compensation factors while participants were walking/running uphill and revealed that the HRR outperformed HR in the adjustment of physical intensity.…”

Section: Discussionmentioning

confidence: 99%

“…However, it, on the other hand, introduces biases to estimate EE because HR is susceptible to physical fitness and psychological factors, such as excitement and nervousness ( Patrik Johansson et al., 2006 ). Previously, we demonstrated the heart rate reserve (HRR) to be a preferable parameter that considers exercise intensity while standardizing individual variations ( Chang et al., 2019 ). In light of this, this study included HRR to be an important indicator for calibrating the physical activity levels among different groups to improve the accuracy of EE estimation.…”

Section: Introductionmentioning

confidence: 99%

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Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults

Chang

Hsu

et al. 2020

PeerJ

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Background Inertial sensors, such as accelerometers, serve as convenient devices to predict the energy expenditures (EEs) during physical activities by a predictive equation. Although the accuracy of estimate EEs especially matter to athletes receive physical training, most EE predictive equations adopted in accelerometers are based on the general population, not athletes. This study included the heart rate reserve (HRR) as a compensatory parameter for physical intensity and derived new equations customized for sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults. Methods With indirect calorimetry as the criterion measure (CM), the EEs of participants on a treadmill were measured, and vector magnitudes (VM), as well as HRR, were simultaneously recorded by a waist-worn accelerometer with a heart rate monitor. Participants comprised a sedentary group (SG), an exercise-habit group (EHG), a non-endurance group (NEG), and an endurance group (EG), with 30 adults in each group. Results EE predictive equations were revised using linear regression with cross-validation on VM, HRR, and body mass (BM). The modified model demonstrates valid and reliable predictions across four populations (Pearson correlation coefficient, r: 0.922 to 0.932; intraclass correlation coefficient, ICC: 0.919 to 0.930). Conclusion Using accelerometers with a heart rate monitorcan accurately predict EEs of athletes and non-athletes with an optimized predictive equation integrating the VM, HRR, and BM parameters.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults

Chang

Hsu

et al. 2020

PeerJ

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“…If wrist-mounted monitoring is adopted, it will affect not only EE estimates, but it potentially could also affect health and/or fitness outcomes for the user. According to our previous study, HRR parameters can be used to calibrate differences in physical fitness and standardize an individual's physical fitness level (Chang et al 2019). In this study, HRR parameters were also added to the traditional estimation equation (Freedson's VM3 Combination equation, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…It is also important to note that accelerometers can cause overestimation or underestimation of EE based on different exercise types or intensities (i.e. cycling, uphill exercise, etc) (Schneller et al 2015;Tarp, Andersen & Østergaard, 2015;Yang et al, 2018;Kuo et al, 2018;Chang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #2 of "Correction of estimation bias of predictive equations of energy expenditure based on wrist/waist-mounted accelerometers (v0.1)"

2019

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Using wearable inertial sensors to accurately estimate energy expenditure (EE) during an athletic training process is important. Due to the characteristics of inertial sensors, however, the positions in which they are worn can produce signals of different natures. To understand and solve this issue, this study used the heart rate reserve (HRR) as a compensation factor to modify the traditional empirical equation of the accelerometer EE sensor and examine the possibility of improving the estimation of energy expenditure for sensors worn in different positions. Methods: Indirect calorimetry was used as the criterion measure (CM) to measure the EE of 90 healthy adults on a treadmill (5 speeds: 4.8, 6.4, 8.0, 9.7, and 11.3 km/h). The measurement was simultaneously performed with the ActiGraph GT9X-Link (placed on the wrist and waist) with the Polar H10 Heart Rate Monitor. Results: At the same exercise intensity, the EE measurements of the GT9X on the wrist and waist had significant differences from those of the CM (p < 0.05). By using multiple regression analysis-utilizing values from vector magnitudes (VM), body weight (BW) and HRR parameters-accuracy of EE estimation was greatly improved compared to traditional equation. Modified models explained a greater proportion of variance (R 2

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“…It is also important to note that accelerometers can cause overestimation or underestimation of EE based on different exercise types or intensities (i.e., cycling, uphill exercise, etc.) (Schneller et al., 2015; Tarp, Andersen & Ostergaard, 2015; Yang et al., 2018; Kuo et al., 2018; Chang et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Correction of estimation bias of predictive equations of energy expenditure based on wrist/waist-mounted accelerometers

Chang

Lin

et al. 2019

PeerJ

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Background Using wearable inertial sensors to accurately estimate energy expenditure (EE) during an athletic training process is important. Due to the characteristics of inertial sensors, however, the positions in which they are worn can produce signals of different natures. To understand and solve this issue, this study used the heart rate reserve (HRR) as a compensation factor to modify the traditional empirical equation of the accelerometer EE sensor and examine the possibility of improving the estimation of energy expenditure for sensors worn in different positions. Methods Indirect calorimetry was used as the criterion measure (CM) to measure the EE of 90 healthy adults on a treadmill (five speeds: 4.8, 6.4, 8.0, 9.7, and 11.3 km/h). The measurement was simultaneously performed with the ActiGraph GT9X-Link (placed on the wrist and waist) with the Polar H10 Heart Rate Monitor. Results At the same exercise intensity, the EE measurements of the GT9X on the wrist and waist had significant differences from those of the CM (p < 0.05). By using multiple regression analysis—utilizing values from vector magnitudes (VM), body weight (BW) and HRR parameters—accuracy of EE estimation was greatly improved compared to traditional equation. Modified models explained a greater proportion of variance (R2) (wrist: 0.802; waist: 0.805) and demonstrated a good ICC (wrist: 0.863, waist: 0.889) compared to Freedson’s VM3 Combination equation (R2: wrist: 0.384, waist: 0.783; ICC: wrist: 0.073, waist: 0.868). Conclusions The EE estimation equation combining the VM of accelerometer measurements, BW and HRR greatly enhanced the accuracy of EE estimation based on data from accelerometers worn in different positions, particularly from those on the wrist.

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Accuracy of the energy expenditure during uphill exercise measured by the Waist-worn ActiGraph

Cited by 9 publications

References 40 publications

Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults

Reliability and validity of the physical activity monitor for assessing energy expenditures in sedentary, regularly exercising, non-endurance athlete, and endurance athlete adults

Peer Review #2 of "Correction of estimation bias of predictive equations of energy expenditure based on wrist/waist-mounted accelerometers (v0.1)"

Correction of estimation bias of predictive equations of energy expenditure based on wrist/waist-mounted accelerometers

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