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Calibration and validation of accelerometry to measure physical activity in adult clinical groups: A systematic review
Preventive Medicine Reports, Volume: 16, Start page: 101001
Swansea University Authors: Melitta McNarry , Kelly Mackintosh
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DOI (Published version): 10.1016/j.pmedr.2019.101001
A growing body of research calibrating and validating accelerometers to classify physical activity intensities has led to a range of cut-points. However, the applicability of current calibration protocols to clinical populations remains to be addressed. The aim of this review was to evaluate the acc...
|Published in:||Preventive Medicine Reports|
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A growing body of research calibrating and validating accelerometers to classify physical activity intensities has led to a range of cut-points. However, the applicability of current calibration protocols to clinical populations remains to be addressed. The aim of this review was to evaluate the accuracy of the methods for calibrating and validating of accelerometers to estimate physical activity intensity thresholds for clinical populations. Six databases were searched between March and July to 2017 using text words and subject headings. Studies developing moderate-to-vigorous intensity physical activity cut-points for adult clinical populations were included. The risk of bias was assessed using the health measurement instruments and a specific checklist for calibration studies. A total of 543,741 titles were found and 323 articles were selected for full-text assessment, with 11 meeting the inclusion criteria. Twenty-three different methods for calibration were identified using different models of ActiGraph and Actical accelerometers. Disease-specific cut-points ranged from 591 to 2717 counts·min−1 and were identified for two main groups of clinical conditions: neuromusculoskeletal disorders and metabolic diseases. The heterogeneity in the available clinical protocols hinders the applicability and comparison of the developed cut-points. As such, a mixed protocol containing a controlled laboratory exercise test and activities of daily-life is suggested. It is recommended that this be combined with a statistical approach that allows for adjustments according to disease severity or the use of machine learning models. Finally, this review highlights the generalisation of cut-points developed on healthy populations to clinical populations is inappropriate.
ActiGraph, Disease-specific, Cut-points, Medical conditions, Motion
Faculty of Science and Engineering
This review summarises independent research funded by the Cystic Fibrosis Trust UK under its programme grant for Strategic Research Centres (grant reference No. RP-PG-0108-10011). MSB is a funded PhD student by the Cystic Fibrosis Trust. The funder had no role in the conduct of the study, the writing of the manuscript, or the decision to submit it for publication.