Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry.

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from hip-worn triaxial-accelerometer (ActigraphGT3X+^TM) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart^TM); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled-water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR-model), a simple linear model (SLM), and equations provided by the activity-devices companion -software of used activity-devices (Freedson and Actiheart^TM models). AAR-model predictions were in closer agreement with selected references than those from other count-based models both for PAEE during the urban-circuit (RMSE=6.19 vs 7.90 for SLM and 9.62 kJ.min^-1 for Freedson) and for EE over the 14-day trial, reaching Actiheart^TM performances in the latter (PAEE: RMSE=0.93 vs 1.53 for SLM, 1.43 for Freedson, 0.91 MJ.day^-1 for Actiheart; TEE: RMSE=1.05 vs 1.57 for SLM, 1.70 for Freedson, 0.95 MJ.day^-1 for Actiheart^TM). Overall, the AAR-model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions.

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