on their HDI classification to cover costs associated with the Global Matrix 3.0 initiative. Three different tiers of registration fees ($500 USD for the low HDI countries, $750 USD for the medium HDI countries, $1000 USD for the high HDI countries, and $1500 USD for the very high HDI countries) were
Salomé Aubert, Joel D. Barnes, Chalchisa Abdeta, Patrick Abi Nader, Ade F. Adeniyi, Nicolas Aguilar-Farias, Dolores S. Andrade Tenesaca, Jasmin Bhawra, Javier Brazo-Sayavera, Greet Cardon, Chen-Kang Chang, Christine Delisle Nyström, Yolanda Demetriou, Catherine E. Draper, Lowri Edwards, Arunas Emeljanovas, Aleš Gába, Karla I. Galaviz, Silvia A. González, Marianella Herrera-Cuenca, Wendy Y. Huang, Izzeldin A.E. Ibrahim, Jaak Jürimäe, Katariina Kämppi, Tarun R. Katapally, Piyawat Katewongsa, Peter T. Katzmarzyk, Asaduzzaman Khan, Agata Korcz, Yeon Soo Kim, Estelle Lambert, Eun-Young Lee, Marie Löf, Tom Loney, Juan López-Taylor, Yang Liu, Daga Makaza, Taru Manyanga, Bilyana Mileva, Shawnda A. Morrison, Jorge Mota, Vida K. Nyawornota, Reginald Ocansey, John J. Reilly, Blanca Roman-Viñas, Diego Augusto Santos Silva, Pairoj Saonuam, John Scriven, Jan Seghers, Natasha Schranz, Thomas Skovgaard, Melody Smith, Martyn Standage, Gregor Starc, Gareth Stratton, Narayan Subedi, Tim Takken, Tuija Tammelin, Chiaki Tanaka, David Thivel, Dawn Tladi, Richard Tyler, Riaz Uddin, Alun Williams, Stephen H.S. Wong, Ching-Lin Wu, Paweł Zembura and Mark S. Tremblay
Christiana M.T. van Loo, Anthony D. Okely, Marijka Batterham, Tina Hinkley, Ulf Ekelund, Soren Brage, John J. Reilly, Gregory E. Peoples, Rachel Jones, Xanne Janssen and Dylan P. Cliff
To validate the activPAL3 algorithm for predicting metabolic equivalents (TAMETs) and classifying MVPA in 5- to 12-year-old children.
Fifty-seven children (9.2 ± 2.3y, 49.1% boys) completed 14 activities including sedentary behaviors (SB), light (LPA) and moderate-to-vigorous physical activities (MVPA). Indirect calorimetry (IC) was used as the criterion measure. Analyses included equivalence testing, Bland-Altman procedures and area under the receiver operating curve (ROC-AUC).
At the group level, TAMETs were significantly equivalent to IC for handheld e-game, writing/coloring, and standing class activity (P < .05). Overall, TAMETs were overestimated for SB (7.9 ± 6.7%) and LPA (1.9 ± 20.2%) and underestimated for MVPA (27.7 ± 26.6%); however, classification accuracy of MVPA was good (ROC-AUC = 0.86). Limits of agreement were wide for all activities, indicating large individual error (SB: −27.6% to 44.7%; LPA: −47.1% to 51.0%; MVPA: −88.8% to 33.9%).
TAMETs were accurate for some SB and standing, but were overestimated for overall SB and LPA, and underestimated for MVPA. Accuracy for classifying MVPA was, however, acceptable.
Jairo H. Migueles, Alex V. Rowlands, Florian Huber, Séverine Sabia and Vincent T. van Hees
://open.geneactiv.org ), is designed for the accelerometer hardware developed by the same company. GENEAclassify is an open source R package primarily aimed at facilitating the segmentation and classification of accelerometer data produced by the GENEAactiv accelerometer ( Campbell, Gott, Langford, & Sweetland, 2018 ). OMGUI
sedentary time ( Rowlands et al., 2014 ). The challenge is to make these approaches as accessible to researchers as cut-point approaches, thus facilitating more accurate classification of time spent in sedentary behaviors in large-scale surveys. The authors have generated a valuable dataset that is
Association, 2013 ) and the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10; World Health Organization, 2016 ) are lacking ( Casey & Bailey, 2011 ). Nonetheless, symptoms of low mood, sadness, worry, anxiety, insomnia, and poor concentration in
Inácio Crochemore M. da Silva, Grégore I. Mielke, Andréa D. Bertoldi, Paulo Sergio Dourado Arrais, Vera Lucia Luiza, Sotero Serrate Mengue and Pedro C. Hallal
Health Survey in Brazil, 2013 . Epidemiol Serv Saúde . 2015 ; 24 ( 2 ): 197 – 206 . 16. Brazilian Association of Research Companies . CCEB 2010 adoption—Brazilian Economic Classification Criteria . São Paulo, Brazil : Brazilian Association of Research Companies ; 2010 . www.abep.org . 17
Meera Sreedhara, Karin Valentine Goins, Christine Frisard, Milagros C. Rosal and Stephenie C. Lemon
regional) 4.57 (1.09–19.13) a 1.44 (0.23–9.05) State and LHD governance classification Centralized, shared, or mixed Ref. Ref. Decentralized 0.42 (0.17–1.05) 0.31 (0.09–1.13) Public Health Accreditation Board accreditation status Achieved accreditation 4.81 (1.72–13.49) b 3.67 (1.11–12.05) c In
Silvia A. González, Joel D. Barnes, Patrick Abi Nader, Dolores Susana Andrade Tenesaca, Javier Brazo-Sayavera, Karla I. Galaviz, Marianella Herrera-Cuenca, Piyawat Katewongsa, Juan López-Taylor, Yang Liu, Bilyana Mileva, Angélica María Ochoa Avilés, Diego Augusto Santos Silva, Pairoj Saonuam and Mark S. Tremblay
Venezuela. Methods Global Matrix Involvement Countries registered for the Global Matrix 3.0 project between April 2017 and January 2018 and paid a registration fee according to their HDI classification. Eleven high-HDI countries registered and 10 fully participated. According to the mentorship model
Maria-Christina Kosteli, Jennifer Cumming and Sarah E. Williams
active individuals to 3,000 MET-min/week and above representing high active individuals. However, the classification of individuals in categories depends on the combination of intensity and frequency of PA. For instance, individuals engaging in vigorous-intensity activity on at least 3 days/week and
Levi Frehlich, Christine Friedenreich, Alberto Nettel-Aguirre, Jasper Schipperijn and Gavin R. McCormack
.S. ( 2011 ). Validation of accelerometer wear and nonwear time classification algorithm . Medicine and Science in Sports and Exercise, 43 ( 2 ), 357 – 364 . PubMed ID: 20581716 doi:10.1249/MSS.0b013e3181ed61a3 10.1249/MSS.0b013e3181ed61a3 Chomistek , A.K. , Yuan , C. , Matthews , C.E. , Troiano