Lab-based validation of different data processing methods for wrist-worn ActiGraph accelerometers in young adults

The wrist is increasingly being used as the preferred site for objectively assessing physical activity but the relative accuracy of processing methods for wrist data has not been determined.

This study evaluates the validity of four processing methods for wrist-worn ActiGraph (AG) data against energy expenditure (EE) measured using a portable metabolic analyzer (OM; Oxycon mobile) and the Compendium of physical activity.

Fifty-one adults (ages 18-40) completed 15 activities ranging from sedentary to vigorous in a laboratory setting while wearing an AG and the OM. Estimates of EE and categorization of activity intensity were obtained from the AG using a linear method based on Hildebrand cutpoints (HLM), a non-linear modification of this method (HNLM), and two methods developed by Staudenmayer based on a Linear Model (SLM) and using random forest (SRF). Estimated EE and classification accuracy were compared to the OM and Compendium using Bland-Altman plots, equivalence testing, mean absolute percent error (MAPE), and Kappa statistics.

Overall, classification agreement with the Compendium was similar across methods ranging from a Kappa of 0.46 (HLM) to 0.54 (HNLM). However, specificity and sensitivity varied by method and intensity, ranging from a sensitivity of 0% (HLM for sedentary) to a specificity of ~99% for all methods for vigorous. None of the methods was significantly equivalent to the OM (p  >  0.05).

Across activities, none of the methods evaluated had a high level of agreement with criterion measures. Additional research is needed to further refine the accuracy of processing wrist-worn accelerometer data.