Abstract
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.
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Data availability
The datasets supporting this study are available on reasonable request from the corresponding author.
Change history
17 August 2020
An amendment to this article has been published and can be accessed via a link at the top of the article.
18 August 2020
A Correction to this paper has been published: https://doi.org/10.1038/s41393-020-00534-z
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Acknowledgements
We would like to acknowledge and thank Steven Knezevic, Dr Eunkyoung Hong, and Dr Ann Spungen at the James J. Peters VA Medical Center for their assistance in collecting a portion of the out-of-sample datasets.
Funding
This study was funded by the VA Rehabilitation Research & Development under Grant #1I01RX000971-01A. The content is solely the responsibility of the authors and does not represent the official views of the VA.
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YJS was responsible for conducting data analysis, obtaining results, and writing the paper in its entirety. ALV was responsible for designing the protocol, screening potential participants, and performing data collection. She also contributed to data analysis and interpretation of results. ZH was responsible for developing the search criteria used to obtain publications of existing energy expenditure equations as mentioned in the methods. He also contributed to data analysis and interpretation of results. DD provided guidance throughout the study including formulating the paper plan, supervising the literature search, data collection, data analysis, and result interpretation, as well as editing the paper.
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This study was approved by the US Department of Veterans Affairs (VA) Central Institutional Review Board and the local Institutional Review Boards at the VA Pittsburgh Healthcare System and the James J. Peters VA Medical Center, respectively. We certify that all applicable institutional and governmental regulations concerning the ethical use of human volunteers were followed during the course of this research.
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Shwetar, Y.J., Veerubhotla, A.L., Huang, Z. et al. Comparative validity of energy expenditure prediction algorithms using wearable devices for people with spinal cord injury. Spinal Cord 58, 821–830 (2020). https://doi.org/10.1038/s41393-020-0427-5
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DOI: https://doi.org/10.1038/s41393-020-0427-5
