Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation

Matthew N Ahmadi, Alok Chowdhury, Toby Pavey and Stewart G Trost

PLOS ONE, 2020, vol. 15, issue 5, 1-14

Abstract: Purpose: To evaluate the accuracy of LAB EE prediction models in preschool children completing a free-living active play session. Performance was benchmarked against EE prediction models trained on free living (FL) data. Methods: 25 children (mean age = 4.1±1.0 y) completed a 20-minute active play session while wearing a portable indirect calorimeter and ActiGraph GT3X+ accelerometers on their right hip and non-dominant wrist. EE was predicted using LAB models which included Random Forest (RF) and Support Vector Machine (SVM) models for the wrist, and RF and Artificial Neural Network (ANN) models for the hip. Two variations of the LAB models were evaluated; 1) an “off the shelf” model without additional training; 2) models retrained on free-living data, replicating the methodology used in the original calibration study (retrained LAB). Prediction errors were evaluated in a hold-out sample of 10 children. Results: Root mean square error (RMSE) for the FL and retrained LAB models ranged from 0.63–0.67 kcals/min. In the hold out sample, RMSE’s for the hip LAB (0.62–0.71), retrained LAB (0.58–0.62) and FL models (0.61–0.65) were similar. For the wrist placement, FL SVM had a significantly higher RMSE (0.73 ± 0.29 kcals/min) than the retrained LAB SVM (0.63 ± 0.30 kcals/min) and LAB SVM (0.64 ± 0.18 kcals/min). The LAB (0.64 ± 0.28), retrained LAB (0.64 ± 0.25), and FL (0.62 ± 0.26) RF exhibited comparable accuracy. Conclusion: Machine learning EE prediction models trained on LAB and FL data had similar accuracy under free-living conditions.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0233229

DOI: 10.1371/journal.pone.0233229

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