Comparative validity of energy expenditure prediction algorithms using wearable devices for people with spinal cord injury.

STUDY DESIGN

Cross-sectional validation study.

OBJECTIVES

To conduct a literature search for existing energy expenditure (EE) predictive algorithms using ActiGraph activity monitors for manual wheelchairs users (MWUs) with spinal cord injury (SCI), and evaluate their validity using an out-of-sample dataset.

SETTING

Research institution in Pittsburgh, USA.

METHODS

A literature search resulted in five articles containing five sets of predictive equations using an ActiGraph activity monitor for MWUs with SCI. Out-of-sample data were collected from 29 MWUs with chronic SCI who were asked to follow an activity protocol while wearing an ActiGraph GT9X Link on the dominant wrist. They also wore a portable metabolic cart which provided the criterion measure for EE. The out-of-sample dataset was used to evaluate the validity of the five sets of EE predictive equations.

RESULTS

None of the five sets of predictive equations demonstrated equivalence within 20% of the criterion measure based on an equivalence test. The mean absolute error for the five sets of predictive equations ranged from 0.87 to 6.41 kilocalories per minute (kcal min) when compared with the criterion measure, and the intraclass correlation estimates ranged from 0.06 to 0.59. The range between the Bland-Altman upper and lower limits of agreement was from 4.70 kcal min to 25.09 kcal min.

CONCLUSIONS

The existing EE predictive equations based on ActiGraph monitors for MWUs with SCI showed varied performance when compared with the criterion measure. Their accuracies may not be sufficient to support future clinical and research use. More work is needed to develop more accurate EE predictive equations for this population.