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

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.

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).

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.

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