Accuracy of optimized branched algorithms to assess activity-specific physical activity energy expenditure

Abstract

Purpose: To assess the activity-specific accuracy achievable by branched algorithm (BA) analysis of simulated daily living physical activity energy expenditure (PAEE) within a sedentary population.

Methods: Sedentary men (n = 8) and women (n = 8) first performed a treadmill calibration protocol, during which HR, accelerometry (ACC), and PAEE were measured in 1-min epochs. From these data, HR-PAEE and ACC-PAEE regressions were constructed and used in each of six analytic models to predict PAEE from ACC and HR data collected during a subsequent simulated daily living protocol. Criterion PAEE was measured during both protocols via indirect calorimetry. The accuracy achieved by each model was assessed by the root mean square of the difference between model-predicted daily living PAEE and the criterion daily living PAEE (expressed here as percent of mean daily living PAEE).

Results: Across the range of activities, an unconstrained post hoc-optimized BA best predicted criterion PAEE. Estimates using individual calibration were generally more accurate than those using group calibration (14% vs 16% error, respectively). These analyses also performed well within each of the six daily living activities, but systematic errors appeared for several of those activities, which may be explained by an inability of the algorithm to simultaneously accommodate a heterogeneous range of activities. Analyses between mean square error by subject and activity suggest that optimization involving minimization of root mean square for total daily living PAEE is associated with decreased error between subjects but increased error between activities.

Conclusions: The performance of post hoc-optimized BA may be limited by heterogeneity in the daily living activities being performed.

Figure 1

Total daily-living PAEE (kJ.kg⁻¹) as estimated under individual (open bars) and group (filled bars) calibration, and by accelerometry alone (ACC), heart rate alone (HR), multiple linear regression (MLR), an a priori branched algorithm (APBA), constrained post hoc optimized branched algorithm (C-PHBA), and unconstrained post hoc optimized branched algorithm (U-PHBA). Data are presented as mean PAEE, + RMS, and − SEM. * p < 0.05 both calibration methods vs. criterion PAEE, † p < 0.05 group calibration vs. individual calibration, # p < 0.05 group calibration vs. criterion PAEE

Figure 2

Mean estimation error expressed as a fraction of criterion PAEE for all daily-living activities and estimation models (accelerometry alone, ACC; heart rate alone, HR; multiple linear regression, MLR; an a priori branched algorithm, APBA; constrained post hoc optimized branched algorithm, C-PHBA; and unconstrained post hoc optimized branched algorithm, U-PHBA). Panel A provides data for individual calibration, and Panel B for group calibration.

Figure 2

Mean estimation error expressed as a fraction of criterion PAEE for all daily-living activities and estimation models (accelerometry alone, ACC; heart rate alone, HR; multiple linear regression, MLR; an a priori branched algorithm, APBA; constrained post hoc optimized branched algorithm, C-PHBA; and unconstrained post hoc optimized branched algorithm, U-PHBA). Panel A provides data for individual calibration, and Panel B for group calibration.

Figure 3

Root mean square error (kJ.kg⁻¹) for estimation of total daily-living PAEE as a function of between mean square error by subject (filled symbols), and activity (open symbols), among the four optimized branched algorithm models. Both relationships were significant at p < 0.05.