No Cover Image

Journal article 1652 views 250 downloads

Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers

ALEXANDER H. K. MONTOYE, M. BENJAMIN NELSON, JOSHUA M. BOCK, MARY T. IMBODEN, LEONARD A. KAMINSKY, Kelly Mackintosh Orcid Logo, Melitta McNarry Orcid Logo, KARIN A. PFEIFFER

Medicine & Science in Sports & Exercise, Volume: 50, Issue: 5, Pages: 1103 - 1112

Swansea University Authors: Kelly Mackintosh Orcid Logo, Melitta McNarry Orcid Logo

Check full text

DOI (Published version): 10.1249/MSS.0000000000001534

Abstract

PURPOSE: To enable inter- and intra-study comparisons it is important to ascertain comparability among accelerometer models. This study compared raw and count data between hip-worn ActiGraph GT3X+ and GT9X Link accelerometers. METHODS: Adults (n=26 [n=15 women]; aged 49.1±20.0 years) wore GT3X+ and...

Full description

Published in: Medicine & Science in Sports & Exercise
ISSN: 0195-9131
Published: 2018
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa37798
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2018-01-02T20:07:44Z
last_indexed 2020-06-18T18:52:00Z
id cronfa37798
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2020-06-18T17:28:50.6510299</datestamp><bib-version>v2</bib-version><id>37798</id><entry>2018-01-02</entry><title>Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers</title><swanseaauthors><author><sid>bdb20e3f31bcccf95c7bc116070c4214</sid><ORCID>0000-0003-0355-6357</ORCID><firstname>Kelly</firstname><surname>Mackintosh</surname><name>Kelly Mackintosh</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>062f5697ff59f004bc8c713955988398</sid><ORCID>0000-0003-0813-7477</ORCID><firstname>Melitta</firstname><surname>McNarry</surname><name>Melitta McNarry</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2018-01-02</date><deptcode>STSC</deptcode><abstract>PURPOSE: To enable inter- and intra-study comparisons it is important to ascertain comparability among accelerometer models. This study compared raw and count data between hip-worn ActiGraph GT3X+ and GT9X Link accelerometers. METHODS: Adults (n=26 [n=15 women]; aged 49.1&#xB1;20.0 years) wore GT3X+ and Link accelerometers over the right hip for an 80-min protocol involving 12-21 sedentary, household, and ambulatory/exercise activities lasting 2-15 min each. For each accelerometer, mean and variance of the raw (60 Hz) data for each axis and vector magnitude (VM) were extracted in 30-s epochs. A machine learning model (Montoye 2015) was used to predict energy expenditure in METs from the raw data. Raw data were also processed into activity counts in 30-s epochs for each axis and VM, with Freedson 1998 and 2011 count-based regression models used to predict METs. Time spent in sedentary, light, moderate, and vigorous intensities were derived from predicted METs from each model. Correlations were calculated to compare raw and count data between accelerometers, and percent (%) agreement was used to compare epoch-by-epoch activity intensity. RESULTS: For raw data, correlations for mean acceleration were 0.96&#xB1;0.05, 0.89&#xB1;0.16, 0.71&#xB1;0.33, and 0.80&#xB1;0.28 and for variance 0.98&#xB1;0.02, 0.98&#xB1;0.03, 0.91&#xB1;0.06, and 1.00&#xB1;0.00 in the X, Y, and Z axes and VM, respectively. For count data, corresponding correlations were 1.00&#xB1;0.01, 0.98&#xB1;0.02, 0.96&#xB1;0.04, and 1.00&#xB1;0.00, respectively. Freedson 1998 and 2011 count-based models had significantly higher %agreement for activity intensity (95.1&#xB1;5.6% and 95.5&#xB1;4.0%) than the Montoye 2015 raw data model (61.5&#xB1;27.6%; p&amp;#60;0.001). CONCLUSIONS: Count data were more highly comparable than raw data between accelerometers. Data filtering and/or more robust raw data models are needed to improve raw data comparability between ActiGraph GT3X+ and Link accelerometers.</abstract><type>Journal Article</type><journal>Medicine &amp; Science in Sports &amp; Exercise</journal><volume>50</volume><journalNumber>5</journalNumber><paginationStart>1103</paginationStart><paginationEnd>1112</paginationEnd><publisher/><issnPrint>0195-9131</issnPrint><keywords>reliability, activity monitor, agreement, physical activity, energy expenditure</keywords><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2018</publishedYear><publishedDate>2018-12-31</publishedDate><doi>10.1249/MSS.0000000000001534</doi><url/><notes/><college>COLLEGE NANME</college><department>Sport and Exercise Sciences</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>STSC</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2020-06-18T17:28:50.6510299</lastEdited><Created>2018-01-02T15:58:51.4162562</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences</level></path><authors><author><firstname>ALEXANDER H. K.</firstname><surname>MONTOYE</surname><order>1</order></author><author><firstname>M. BENJAMIN</firstname><surname>NELSON</surname><order>2</order></author><author><firstname>JOSHUA M.</firstname><surname>BOCK</surname><order>3</order></author><author><firstname>MARY T.</firstname><surname>IMBODEN</surname><order>4</order></author><author><firstname>LEONARD A.</firstname><surname>KAMINSKY</surname><order>5</order></author><author><firstname>Kelly</firstname><surname>Mackintosh</surname><orcid>0000-0003-0355-6357</orcid><order>6</order></author><author><firstname>Melitta</firstname><surname>McNarry</surname><orcid>0000-0003-0813-7477</orcid><order>7</order></author><author><firstname>KARIN A.</firstname><surname>PFEIFFER</surname><order>8</order></author></authors><documents><document><filename>0037798-02012018160045.pdf</filename><originalFilename>montoye2017.pdf</originalFilename><uploaded>2018-01-02T16:00:45.6570000</uploaded><type>Output</type><contentLength>1716756</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2018-12-28T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2020-06-18T17:28:50.6510299 v2 37798 2018-01-02 Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers bdb20e3f31bcccf95c7bc116070c4214 0000-0003-0355-6357 Kelly Mackintosh Kelly Mackintosh true false 062f5697ff59f004bc8c713955988398 0000-0003-0813-7477 Melitta McNarry Melitta McNarry true false 2018-01-02 STSC PURPOSE: To enable inter- and intra-study comparisons it is important to ascertain comparability among accelerometer models. This study compared raw and count data between hip-worn ActiGraph GT3X+ and GT9X Link accelerometers. METHODS: Adults (n=26 [n=15 women]; aged 49.1±20.0 years) wore GT3X+ and Link accelerometers over the right hip for an 80-min protocol involving 12-21 sedentary, household, and ambulatory/exercise activities lasting 2-15 min each. For each accelerometer, mean and variance of the raw (60 Hz) data for each axis and vector magnitude (VM) were extracted in 30-s epochs. A machine learning model (Montoye 2015) was used to predict energy expenditure in METs from the raw data. Raw data were also processed into activity counts in 30-s epochs for each axis and VM, with Freedson 1998 and 2011 count-based regression models used to predict METs. Time spent in sedentary, light, moderate, and vigorous intensities were derived from predicted METs from each model. Correlations were calculated to compare raw and count data between accelerometers, and percent (%) agreement was used to compare epoch-by-epoch activity intensity. RESULTS: For raw data, correlations for mean acceleration were 0.96±0.05, 0.89±0.16, 0.71±0.33, and 0.80±0.28 and for variance 0.98±0.02, 0.98±0.03, 0.91±0.06, and 1.00±0.00 in the X, Y, and Z axes and VM, respectively. For count data, corresponding correlations were 1.00±0.01, 0.98±0.02, 0.96±0.04, and 1.00±0.00, respectively. Freedson 1998 and 2011 count-based models had significantly higher %agreement for activity intensity (95.1±5.6% and 95.5±4.0%) than the Montoye 2015 raw data model (61.5±27.6%; p&#60;0.001). CONCLUSIONS: Count data were more highly comparable than raw data between accelerometers. Data filtering and/or more robust raw data models are needed to improve raw data comparability between ActiGraph GT3X+ and Link accelerometers. Journal Article Medicine & Science in Sports & Exercise 50 5 1103 1112 0195-9131 reliability, activity monitor, agreement, physical activity, energy expenditure 31 12 2018 2018-12-31 10.1249/MSS.0000000000001534 COLLEGE NANME Sport and Exercise Sciences COLLEGE CODE STSC Swansea University 2020-06-18T17:28:50.6510299 2018-01-02T15:58:51.4162562 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences ALEXANDER H. K. MONTOYE 1 M. BENJAMIN NELSON 2 JOSHUA M. BOCK 3 MARY T. IMBODEN 4 LEONARD A. KAMINSKY 5 Kelly Mackintosh 0000-0003-0355-6357 6 Melitta McNarry 0000-0003-0813-7477 7 KARIN A. PFEIFFER 8 0037798-02012018160045.pdf montoye2017.pdf 2018-01-02T16:00:45.6570000 Output 1716756 application/pdf Accepted Manuscript true 2018-12-28T00:00:00.0000000 true eng
title Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers
spellingShingle Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers
Kelly Mackintosh
Melitta McNarry
title_short Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers
title_full Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers
title_fullStr Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers
title_full_unstemmed Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers
title_sort Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers
author_id_str_mv bdb20e3f31bcccf95c7bc116070c4214
062f5697ff59f004bc8c713955988398
author_id_fullname_str_mv bdb20e3f31bcccf95c7bc116070c4214_***_Kelly Mackintosh
062f5697ff59f004bc8c713955988398_***_Melitta McNarry
author Kelly Mackintosh
Melitta McNarry
author2 ALEXANDER H. K. MONTOYE
M. BENJAMIN NELSON
JOSHUA M. BOCK
MARY T. IMBODEN
LEONARD A. KAMINSKY
Kelly Mackintosh
Melitta McNarry
KARIN A. PFEIFFER
format Journal article
container_title Medicine & Science in Sports & Exercise
container_volume 50
container_issue 5
container_start_page 1103
publishDate 2018
institution Swansea University
issn 0195-9131
doi_str_mv 10.1249/MSS.0000000000001534
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Sport and Exercise Sciences
document_store_str 1
active_str 0
description PURPOSE: To enable inter- and intra-study comparisons it is important to ascertain comparability among accelerometer models. This study compared raw and count data between hip-worn ActiGraph GT3X+ and GT9X Link accelerometers. METHODS: Adults (n=26 [n=15 women]; aged 49.1±20.0 years) wore GT3X+ and Link accelerometers over the right hip for an 80-min protocol involving 12-21 sedentary, household, and ambulatory/exercise activities lasting 2-15 min each. For each accelerometer, mean and variance of the raw (60 Hz) data for each axis and vector magnitude (VM) were extracted in 30-s epochs. A machine learning model (Montoye 2015) was used to predict energy expenditure in METs from the raw data. Raw data were also processed into activity counts in 30-s epochs for each axis and VM, with Freedson 1998 and 2011 count-based regression models used to predict METs. Time spent in sedentary, light, moderate, and vigorous intensities were derived from predicted METs from each model. Correlations were calculated to compare raw and count data between accelerometers, and percent (%) agreement was used to compare epoch-by-epoch activity intensity. RESULTS: For raw data, correlations for mean acceleration were 0.96±0.05, 0.89±0.16, 0.71±0.33, and 0.80±0.28 and for variance 0.98±0.02, 0.98±0.03, 0.91±0.06, and 1.00±0.00 in the X, Y, and Z axes and VM, respectively. For count data, corresponding correlations were 1.00±0.01, 0.98±0.02, 0.96±0.04, and 1.00±0.00, respectively. Freedson 1998 and 2011 count-based models had significantly higher %agreement for activity intensity (95.1±5.6% and 95.5±4.0%) than the Montoye 2015 raw data model (61.5±27.6%; p&#60;0.001). CONCLUSIONS: Count data were more highly comparable than raw data between accelerometers. Data filtering and/or more robust raw data models are needed to improve raw data comparability between ActiGraph GT3X+ and Link accelerometers.
published_date 2018-12-31T03:47:39Z
_version_ 1763752290054832128
score 11.037581

Similar Items