Research Study Abstract

Predicting energy expenditure from a wrist-worn ActiGraph GT3X+ accelerometer

  • Presented on May 21, 2014

Purpose: To examine the validity of a wrist-worn accelerometer for predicting energy expenditure in adults.

Methods: Thirty-eight adults (Mage: 35.7 ± 12.2; 54% male) participated in up to 6 prescribed activities of daily living (e.g. washing dishes, sweeping), and walking (slow and brisk), while wearing an ActiGraph GT3X+ accelerometer on the non-dominant wrist. Concurrent assessment of oxygen consumption was performed using a Cosmed K4b indirect calorimeter. Each activity was performed for 6 minutes and minutes 3-5 were used in the analysis to represent steady-state activity. METS were predicted by the three movement planes and vector magnitude (VM) in counts per minute using linear mixed-effects models. A 10-fold cross validation technique was used to calculate the error rate. Activity intensity classification in the predicted model was compared to classification using Freedson-1998, Freedson-VM and Troiano-2008 hip-based cut points on wrist-worn data.

Results: The final prediction model used the vertical axis (A1) and anterior-posterior axis (A3) (rMSE = 1.63, Bias = -0.31) defining the equation as: METS = e^((1.2514+ 0.000050A1- 0.000031A3)). The predicted model has a sensitivity of 59.8% and a specificity of 59.1% for activity intensity classification and outperformed hip-cut-point-based activity intensity classification.

Conclusions: A wrist-based accelerometer has a low sensitivity and specificity for activity intensity classification. However, when hip-based cut-points are used to classify activity intensity from a wrist-worn device, validity is even poorer. Due to the complexity of arm movement during daily living activities, it may be beneficial to use machine learning to improve classification accuracy.

Presented at

ISBNPA 2014 Annual Conference