• Je něco špatně v tomto záznamu ?

Compositional data analysis for physical activity, sedentary time and sleep research

D. Dumuid, TE. Stanford, JA. Martin-Fernández, Ž. Pedišić, CA. Maher, LK. Lewis, K. Hron, PT. Katzmarzyk, JP. Chaput, M. Fogelholm, G. Hu, EV. Lambert, J. Maia, OL. Sarmiento, M. Standage, TV. Barreira, ST. Broyles, C. Tudor-Locke, MS. Tremblay, T. Olds,

. 2018 ; 27 (12) : 3726-3738. [pub] 20170530

Jazyk angličtina Země Velká Británie

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc19045611

The health effects of daily activity behaviours (physical activity, sedentary time and sleep) are widely studied. While previous research has largely examined activity behaviours in isolation, recent studies have adjusted for multiple behaviours. However, the inclusion of all activity behaviours in traditional multivariate analyses has not been possible due to the perfect multicollinearity of 24-h time budget data. The ensuing lack of adjustment for known effects on the outcome undermines the validity of study findings. We describe a statistical approach that enables the inclusion of all daily activity behaviours, based on the principles of compositional data analysis. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment, we demonstrate the application of compositional multiple linear regression to estimate adiposity from children's daily activity behaviours expressed as isometric log-ratio coordinates. We present a novel method for predicting change in a continuous outcome based on relative changes within a composition, and for calculating associated confidence intervals to allow for statistical inference. The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical methods in the field, and provides robust and reliable insights into the health effects of daily activity behaviours.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc19045611
003      
CZ-PrNML
005      
20200120083140.0
007      
ta
008      
200109s2018 xxk f 000 0|eng||
009      
AR
024    7_
$a 10.1177/0962280217710835 $2 doi
035    __
$a (PubMed)28555522
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a xxk
100    1_
$a Dumuid, Dorothea $u 1 School of Health Sciences, University of South Australia, Adelaide, Australia.
245    10
$a Compositional data analysis for physical activity, sedentary time and sleep research / $c D. Dumuid, TE. Stanford, JA. Martin-Fernández, Ž. Pedišić, CA. Maher, LK. Lewis, K. Hron, PT. Katzmarzyk, JP. Chaput, M. Fogelholm, G. Hu, EV. Lambert, J. Maia, OL. Sarmiento, M. Standage, TV. Barreira, ST. Broyles, C. Tudor-Locke, MS. Tremblay, T. Olds,
520    9_
$a The health effects of daily activity behaviours (physical activity, sedentary time and sleep) are widely studied. While previous research has largely examined activity behaviours in isolation, recent studies have adjusted for multiple behaviours. However, the inclusion of all activity behaviours in traditional multivariate analyses has not been possible due to the perfect multicollinearity of 24-h time budget data. The ensuing lack of adjustment for known effects on the outcome undermines the validity of study findings. We describe a statistical approach that enables the inclusion of all daily activity behaviours, based on the principles of compositional data analysis. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment, we demonstrate the application of compositional multiple linear regression to estimate adiposity from children's daily activity behaviours expressed as isometric log-ratio coordinates. We present a novel method for predicting change in a continuous outcome based on relative changes within a composition, and for calculating associated confidence intervals to allow for statistical inference. The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical methods in the field, and provides robust and reliable insights into the health effects of daily activity behaviours.
650    _2
$a dítě $7 D002648
650    12
$a interpretace statistických dat $7 D003627
650    12
$a cvičení $7 D015444
650    _2
$a lidé $7 D006801
650    12
$a obezita dětí a dospívajících $7 D063766
650    12
$a sedavý životní styl $7 D057185
650    12
$a spánek $7 D012890
655    _2
$a časopisecké články $7 D016428
655    _2
$a práce podpořená grantem $7 D013485
700    1_
$a Stanford, Tyman E $u 2 School of Mathematical Sciences, University of Adelaide, Adelaide, Australia.
700    1_
$a Martin-Fernández, Josep-Antoni $u 3 Dept. Informàtica, Matemàtica Aplicada i Estadística, Universitat de Girona, Girona, Spain.
700    1_
$a Pedišić, Željko $u 4 Institute of Sport, Exercise and Active Living, Victoria University, Melbourne, Australia.
700    1_
$a Maher, Carol A $u 1 School of Health Sciences, University of South Australia, Adelaide, Australia.
700    1_
$a Lewis, Lucy K $u 6 Department of Mathematical Analysis and Applications of Mathematics, Univerzita Palackeho, Olomouc, Czech Republic.
700    1_
$a Hron, Karel $u 6 Department of Mathematical Analysis and Applications of Mathematics, Univerzita Palackeho, Olomouc, Czech Republic.
700    1_
$a Katzmarzyk, Peter T $u 7 Pennington Biomedical Research Center, Baton Rouge, LA, USA.
700    1_
$a Chaput, Jean-Philippe $u 8 Healthy Active Living and Obesity Research, Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada.
700    1_
$a Fogelholm, Mikael $u 9 Department of Food and Environmental Sciences, Helsingin Yliopisto, Helsinki, Finland.
700    1_
$a Hu, Gang $u 7 Pennington Biomedical Research Center, Baton Rouge, LA, USA.
700    1_
$a Lambert, Estelle V $u 10 Department of Human Biology, University of Cape Town, Cape Town, South Africa.
700    1_
$a Maia, José $u 11 Faculdade de Desporto, Universidade do Porto, Porto, Portugal.
700    1_
$a Sarmiento, Olga L $u 12 Faculty of Medicine, Universidad de los Andes, Bogota, Colombia.
700    1_
$a Standage, Martyn $u 13 Department for Health, University of Bath, Bath, UK.
700    1_
$a Barreira, Tiago V $u 14 Department of Exercise Science, Syracuse University, Syracuse, NY, USA.
700    1_
$a Broyles, Stephanie T $u 7 Pennington Biomedical Research Center, Baton Rouge, LA, USA.
700    1_
$a Tudor-Locke, Catrine $u 15 Department of Kinesiology, University of Massachusetts, Amherst, MA, USA.
700    1_
$a Tremblay, Mark S $u 8 Healthy Active Living and Obesity Research, Children's Hospital of Eastern Ontario Research Institute, Ottawa, Canada.
700    1_
$a Olds, Timothy $u 1 School of Health Sciences, University of South Australia, Adelaide, Australia.
773    0_
$w MED00006126 $t Statistical methods in medical research $x 1477-0334 $g Roč. 27, č. 12 (2018), s. 3726-3738
856    41
$u https://pubmed.ncbi.nlm.nih.gov/28555522 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y a $z 0
990    __
$a 20200109 $b ABA008
991    __
$a 20200120083516 $b ABA008
999    __
$a ok $b bmc $g 1483879 $s 1084284
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2018 $b 27 $c 12 $d 3726-3738 $e 20170530 $i 1477-0334 $m Statistical methods in medical research $n Stat Methods Med Res $x MED00006126
LZP    __
$a Pubmed-20200109

Najít záznam

Citační ukazatele

Možnosti archivace