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

ABSTRACT: 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.

SUBMITTER: Ho CS

PROVIDER: S-EPMC6836751 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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

Ho Chin-Shan CS Chang Chun-Hao CH Lin Kuo-Chuan KC Huang Chi-Chang CC Hsu Yi-Ju YJ

PeerJ 20191104

<h4>Background</h4>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 imp ...[more]

PMID: 31720110

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Project description:Background and Aims:The unique demographic and dietary characteristics of modern Arabic population require development of a new predictive equation for the estimation of resting energy expenditure (REE). This study presented new equations characteristic to Saudi population. Methods:A set of predictive equations for REE was derived for 427 healthy male and female subjects (aged 18-57?±?14 years). REE was measured (REEm) by indirect calorimetry (IC) and predicted (REEp) using nine equations. REEp was compared with REEm to determine the predictive accuracy of these equations. Using IC and anthropometrics for stepwise linear regression analysis, a new set of equations to predict REE of men and women was developed. Accuracy of the new main equations was further tested in an external sample of 48 subjects (men?=?50%). Results:Using a number of parameters (bias, underprediction, overprediction, and % accurate prediction), our results suggested that almost all (9/9 in men and 7/9 in women) equations either underpredicted or overpredicted (2/9) REE. None of the already existing equations showed an acceptable REEp/REEm difference as low as 5% and an accurate prediction (?55%) at the individual level. Based on these findings, a new prediction equation (hereafter referred to as the Almajwal-Abulmeaty (AA) equation) was developed using this study's data, after a rigorous stepwise regression analysis using the following formula: REE?=?3832.955?+?AdjWt (kg)?×?48.037?-?Ht (cm)?×?30.642?+?gender?×?141.268?-?age (years)?×?4.525 [AdjWt is Adjusted body weight?=?(Wt?-?IBW)/4?+?IBW. IBW is Ideal body weight; for men IBW?=?(Ht(cm)?-?152.4)?×?1.0714)?+?45.36 and for women IBW?=?(Ht(cm)-152.4)?×?0.8928)?+?45.36]. The regression model accounted for approximately 70% of the variance in REEm (R 2?=?0.702). Conclusion:Previous equations likely over- or underpredicted REE. Therefore, the new predictive AA equations developed in this study are recommended for the estimation of REE in young to middle-aged Saudi men and women with different body mass indexes. Future research is also required for further clinical and cross-validation of these new equations.

Dataset Information

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

Publications

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

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