Literature DB >> 24870577

Comparability of measured acceleration from accelerometry-based activity monitors.

Alex V Rowlands1, FranÇois Fraysse, Mike Catt, Victoria H Stiles, Rebecca M Stanley, Roger G Eston, Tim S Olds.   

Abstract

BACKGROUND: Accelerometers that provide triaxial measured acceleration data are now available. However, equivalence of output between brands cannot be assumed and testing is necessary to determine whether features of the acceleration signal are interchangeable.
PURPOSE: This study aimed to establish the equivalence of output between two brands of monitor in a laboratory and in a free-living environment.
METHODS: For part 1, 38 adults performed nine laboratory-based activities while wearing an ActiGraph GT3X+ and GENEActiv (Gravity Estimator of Normal Everyday Activity) at the hip. For part 2, 58 children age 10-12 yr wore a GT3X+ and GENEActiv at the hip for 7 d in a free-living setting.
RESULTS: For part 1, the magnitude of time domain features from the GENEActiv was greater than that from the GT3X+. However, frequency domain features compared well, with perfect agreement of the dominant frequency for 97%-100% of participants for most activities. For part 2, mean daily acceleration measured by the two brands was correlated (r = 0.93, P < 0.001, respectively) but the magnitude was approximately 15% lower for the GT3X+ than that for the GENEActiv at the hip.
CONCLUSIONS: Frequency domain-based classification algorithms should be transferable between monitors, and it should be possible to apply time domain-based classification algorithms developed for one device to the other by applying an affine conversion on the measured acceleration values. The strong relation between accelerations measured by the two brands suggests that habitual activity level and activity patterns assessed by the GENE and GT3X+ may compare well if analyzed appropriately.

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Mesh:

Year:  2015        PMID: 24870577     DOI: 10.1249/MSS.0000000000000394

Source DB:  PubMed          Journal:  Med Sci Sports Exerc        ISSN: 0195-9131            Impact factor:   5.411


  15 in total

1.  Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging.

Authors:  Michelle W Voss; Timothy B Weng; Agnieszka Z Burzynska; Chelsea N Wong; Gillian E Cooke; Rachel Clark; Jason Fanning; Elizabeth Awick; Neha P Gothe; Erin A Olson; Edward McAuley; Arthur F Kramer
Journal:  Neuroimage       Date:  2015-10-19       Impact factor: 6.556

2.  Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches.

Authors:  Kishan Bakrania; Thomas Yates; Alex V Rowlands; Dale W Esliger; Sarah Bunnewell; James Sanders; Melanie Davies; Kamlesh Khunti; Charlotte L Edwardson
Journal:  PLoS One       Date:  2016-10-05       Impact factor: 3.240

3.  Fitness, fatness and the reallocation of time between children's daily movement behaviours: an analysis of compositional data.

Authors:  Stuart J Fairclough; Dorothea Dumuid; Sarah Taylor; Whitney Curry; Bronagh McGrane; Gareth Stratton; Carol Maher; Timothy Olds
Journal:  Int J Behav Nutr Phys Act       Date:  2017-05-10       Impact factor: 6.457

4.  Measuring Physical Activity in Free-Living Conditions-Comparison of Three Accelerometry-Based Methods.

Authors:  Anna-Maiju Leinonen; Riikka Ahola; Janne Kulmala; Harto Hakonen; Henri Vähä-Ypyä; Karl-Heinz Herzig; Juha Auvinen; Sirkka Keinänen-Kiukaanniemi; Harri Sievänen; Tuija H Tammelin; Raija Korpelainen; Timo Jämsä
Journal:  Front Physiol       Date:  2017-01-10       Impact factor: 4.566

5.  Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation.

Authors:  Matthew N Ahmadi; Alok Chowdhury; Toby Pavey; Stewart G Trost
Journal:  PLoS One       Date:  2020-05-20       Impact factor: 3.240

6.  Simple and rationale-providing SMS reminders to promote accelerometer use: a within-trial randomised trial comparing persuasive messages.

Authors:  Matti T J Heino; Keegan Knittle; Ari Haukkala; Tommi Vasankari; Nelli Hankonen
Journal:  BMC Public Health       Date:  2018-12-07       Impact factor: 3.295

7.  Accelerometric outcomes of motor function related to clinical evaluations and muscle involvement in dystrophic dogs.

Authors:  Mutsuki Kuraoka; Yuko Nitahara-Kasahara; Hisateru Tachimori; Naohiro Kato; Hiroyuki Shibasaki; Akihiko Shin; Yoshitsugu Aoki; En Kimura; Shin'ichi Takeda
Journal:  PLoS One       Date:  2018-12-11       Impact factor: 3.240

8.  Associations of physical activity and sedentary time with body composition in Brazilian young adults.

Authors:  Bruna Gonçalves C da Silva; Inácio Crochemore M da Silva; Ulf Ekelund; Soren Brage; Ken K Ong; Emanuella De Lucia Rolfe; Natália Peixoto Lima; Shana Ginar da Silva; Giovanny V Araújo de França; Bernardo Lessa Horta
Journal:  Sci Rep       Date:  2019-04-01       Impact factor: 4.379

9.  A cluster randomised controlled trial to evaluate the effectiveness and cost-effectiveness of the GoActive intervention to increase physical activity among adolescents aged 13-14 years.

Authors:  Helen Elizabeth Brown; Fiona Whittle; Stephanie T Jong; Caroline Croxson; Stephen J Sharp; Paul Wilkinson; Edward Cf Wilson; Esther Mf van Sluijs; Anna Vignoles; Kirsten Corder
Journal:  BMJ Open       Date:  2017-09-27       Impact factor: 2.692

10.  Towards a Portable Model to Discriminate Activity Clusters from Accelerometer Data.

Authors:  Petra Jones; Evgeny M Mirkes; Tom Yates; Charlotte L Edwardson; Mike Catt; Melanie J Davies; Kamlesh Khunti; Alex V Rowlands
Journal:  Sensors (Basel)       Date:  2019-10-17       Impact factor: 3.576

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