Predicting physical activity intensity using raw accelerometer signals in manual wheelchair users with spinal cord injury.

STUDY DESIGN: Cross-sectional validation study. OBJECTIVES: The performance of previously published physical activity (PA) intensity cutoff thresholds based on proprietary ActiGraph counts for manual wheelchair users (MWUs) with spinal cord injury (SCI) was initially evaluated using an out-of-sample dataset of 60 individuals with SCI. Two types of PA intensity classification models based on raw accelerometer signals were developed and evaluated. SETTING: Research institutions in Pittsburgh PA, Birmingham AL, and Bronx NY. METHODS: Data were collected from 60 MWUs with SCI who followed a structured activity protocol while wearing an ActiGraph activity monitor on their dominant wrist and portable metabolic cart which measured criterion PA intensity. Data was used to assess published models as well as develop and assess custom models using recall, specificity, precision, as well as normalized Mathew's correlation coefficient (nMCC). RESULTS: All the models performed well for predicting sedentary vs non-sedentary activity, yielding an nMCC of 0.87-0.90. However, all models demonstrated inadequate performance for predicting moderate to vigorous PA (MVPA) with an nMCC of 0.76-0.82. CONCLUSIONS: The mean absolute deviation (MAD) cutoff threshold yielded the best performance for predicting sedentary vs non-sedentary PA and may be used for tracking daily sedentary activity. None of the models displayed strong performance for MVPA vs non-MVPA. Future studies should investigate combining physiological measures with accelerometry to yield better prediction accuracies for MVPA.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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-1) 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-1 to 25.09 kcal min-1. 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.