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Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts

BACKGROUND: Accelerometers are designed to measure plausible human activity, however extremely high count values (EHCV) have been recorded in large-scale studies. Using population data, we develop methodological principles for establishing an EHCV threshold, propose a threshold to define EHCV in the...

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Autores principales: Rich, Carly, Geraci, Marco, Griffiths, Lucy, Sera, Francesco, Dezateux, Carol, Cortina-Borja, Mario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3890298/
https://www.ncbi.nlm.nih.gov/pubmed/24454804
http://dx.doi.org/10.1371/journal.pone.0085134
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author Rich, Carly
Geraci, Marco
Griffiths, Lucy
Sera, Francesco
Dezateux, Carol
Cortina-Borja, Mario
author_facet Rich, Carly
Geraci, Marco
Griffiths, Lucy
Sera, Francesco
Dezateux, Carol
Cortina-Borja, Mario
author_sort Rich, Carly
collection PubMed
description BACKGROUND: Accelerometers are designed to measure plausible human activity, however extremely high count values (EHCV) have been recorded in large-scale studies. Using population data, we develop methodological principles for establishing an EHCV threshold, propose a threshold to define EHCV in the ActiGraph GT1M, determine occurrences of EHCV in a large-scale study, identify device-specific error values, and investigate the influence of varying EHCV thresholds on daily vigorous PA (VPA). METHODS: We estimated quantiles to analyse the distribution of all accelerometer positive count values obtained from 9005 seven-year old children participating in the UK Millennium Cohort Study. A threshold to identify EHCV was derived by differentiating the quantile function. Data were screened for device-specific error count values and EHCV, and a sensitivity analysis conducted to compare daily VPA estimates using three approaches to accounting for EHCV. RESULTS: Using our proposed threshold of ≥ 11,715 counts/minute to identify EHCV, we found that only 0.7% of all non-zero counts measured in MCS children were EHCV; in 99.7% of these children, EHCV comprised < 1% of total non-zero counts. Only 11 MCS children (0.12% of sample) returned accelerometers that contained negative counts; out of 237 such values, 211 counts were equal to −32,768 in one child. The medians of daily minutes spent in VPA obtained without excluding EHCV, and when using a higher threshold (≥19,442 counts/minute) were, respectively, 6.2% and 4.6% higher than when using our threshold (6.5 minutes; p<0.0001). CONCLUSIONS: Quality control processes should be undertaken during accelerometer fieldwork and prior to analysing data to identify monitors recording error values and EHCV. The proposed threshold will improve the validity of VPA estimates in children’s studies using the ActiGraph GT1M by ensuring only plausible data are analysed. These methods can be applied to define appropriate EHCV thresholds for different accelerometer models.
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spelling pubmed-38902982014-01-21 Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts Rich, Carly Geraci, Marco Griffiths, Lucy Sera, Francesco Dezateux, Carol Cortina-Borja, Mario PLoS One Research Article BACKGROUND: Accelerometers are designed to measure plausible human activity, however extremely high count values (EHCV) have been recorded in large-scale studies. Using population data, we develop methodological principles for establishing an EHCV threshold, propose a threshold to define EHCV in the ActiGraph GT1M, determine occurrences of EHCV in a large-scale study, identify device-specific error values, and investigate the influence of varying EHCV thresholds on daily vigorous PA (VPA). METHODS: We estimated quantiles to analyse the distribution of all accelerometer positive count values obtained from 9005 seven-year old children participating in the UK Millennium Cohort Study. A threshold to identify EHCV was derived by differentiating the quantile function. Data were screened for device-specific error count values and EHCV, and a sensitivity analysis conducted to compare daily VPA estimates using three approaches to accounting for EHCV. RESULTS: Using our proposed threshold of ≥ 11,715 counts/minute to identify EHCV, we found that only 0.7% of all non-zero counts measured in MCS children were EHCV; in 99.7% of these children, EHCV comprised < 1% of total non-zero counts. Only 11 MCS children (0.12% of sample) returned accelerometers that contained negative counts; out of 237 such values, 211 counts were equal to −32,768 in one child. The medians of daily minutes spent in VPA obtained without excluding EHCV, and when using a higher threshold (≥19,442 counts/minute) were, respectively, 6.2% and 4.6% higher than when using our threshold (6.5 minutes; p<0.0001). CONCLUSIONS: Quality control processes should be undertaken during accelerometer fieldwork and prior to analysing data to identify monitors recording error values and EHCV. The proposed threshold will improve the validity of VPA estimates in children’s studies using the ActiGraph GT1M by ensuring only plausible data are analysed. These methods can be applied to define appropriate EHCV thresholds for different accelerometer models. Public Library of Science 2014-01-13 /pmc/articles/PMC3890298/ /pubmed/24454804 http://dx.doi.org/10.1371/journal.pone.0085134 Text en © 2014 Rich et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Rich, Carly
Geraci, Marco
Griffiths, Lucy
Sera, Francesco
Dezateux, Carol
Cortina-Borja, Mario
Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts
title Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts
title_full Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts
title_fullStr Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts
title_full_unstemmed Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts
title_short Quality Control Methods in Accelerometer Data Processing: Identifying Extreme Counts
title_sort quality control methods in accelerometer data processing: identifying extreme counts
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3890298/
https://www.ncbi.nlm.nih.gov/pubmed/24454804
http://dx.doi.org/10.1371/journal.pone.0085134
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