TY - JOUR
T1 - Using compositional data analysis to explore accumulation of sedentary behavior, physical activity and youth health
AU - Verswijveren, Simone J.J.M.
AU - Lamb, Karen E.
AU - Martín-Fernández, Josep A.
AU - Winkler, Elisabeth
AU - Leech, Rebecca M.
AU - Timperio, Anna F
AU - Salmon, Jo
AU - Daly, Robin M.
AU - Cerin, Ester
AU - Dunstan, David W.
AU - Telford, Rohan M.
AU - Telford, Richard D.
AU - Olive, Lisa S.
AU - Ridgers, Nicola D.
N1 - Funding Information:
The authors acknowledge the assistance of Eoin O'Connell, who developed the customized Excel macro used in this study. We would also like to thank the staff and participants in the LOOK and Transform-Us! projects and the participants? parents and schools for their participation in this research. In particular, we would like to thank the chief investigators in the original Transform-Us! and LOOK trials. Finally, we would like to thank Dr Lauren Arundell. SJJMV led the project, including running the statistical analyses and drafting the manuscript, this included critically revising it for important intellectual content, based on co-author feedback; KEL, JAMF, EW, RML, AT, JS, and NDR contributed equally in designing the study and writing the manuscript; JS, RMD, EC, DWD, RMT, RDT, and LSO were involved in the original study design of the LOOK and Transform-Us! trials. All authors helped draft the manuscript. All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors. This research involves secondary data analysis. The LOOK study received funding from the Commonwealth Education Trust and the Canberra Hospital Clinical Trials Unit. Transform-Us! was supported by a National Health and Medical Research Council (NHMRC) Project Grant (ID: 533815) and a Diabetes Australia Research Trust grant. This research was also supported by the NHMRC Centre of Research Excellence (APP1057608). SJJMV holds a Deakin University PhD Scholarship. NDR is supported by a Future Leader Fellowship from the National Heart Foundation (NHF) of Australia (101895). JAMF is supported by the Spanish Ministry of Science, Innovation and Universities under the project CODAMET (RTI2018-095518-B-C21, 2019?2021). JS is supported by an NHMRC Leadership Level 2 Fellowship (APP1176885). DWD is supported by an NHMRC Senior Research Fellowship (APP1078360) and the Victorian Government's Operational Infrastructure Support Program. RML is supported by an NHF of Australia Postdoctoral Research Fellowship (102109) and an NHMRC Emerging Leadership Fellowship (APP1175250). LSO is supported by an NHMRC/NHF Postgraduate Fellowship (APP1056551) and an NHMRC Early Career Fellowship (APP1158487). All the support had no involvement in the study design and writing of the manuscript or the decision to submit it for publication. The authors declare that they have no other competing interests.
Funding Information:
This research involves secondary data analysis. The LOOK study received funding from the Commonwealth Education Trust and the Canberra Hospital Clinical Trials Unit. Transform-Us! was supported by a National Health and Medical Research Council (NHMRC) Project Grant (ID: 533815) and a Diabetes Australia Research Trust grant. This research was also supported by the NHMRC Centre of Research Excellence (APP1057608). SJJMV holds a Deakin University PhD Scholarship. NDR is supported by a Future Leader Fellowship from the National Heart Foundation (NHF) of Australia (101895). JAMF is supported by the Spanish Ministry of Science, Innovation and Universities under the project CODAMET (RTI2018-095518-B-C21, 2019‒2021). JS is supported by an NHMRC Leadership Level 2 Fellowship (APP1176885). DWD is supported by an NHMRC Senior Research Fellowship (APP1078360) and the Victorian Government's Operational Infrastructure Support Program. RML is supported by an NHF of Australia Postdoctoral Research Fellowship (102109) and an NHMRC Emerging Leadership Fellowship (APP1175250). LSO is supported by an NHMRC/NHF Postgraduate Fellowship (APP1056551) and an NHMRC Early Career Fellowship (APP1158487). All the support had no involvement in the study design and writing of the manuscript or the decision to submit it for publication. The authors declare that they have no other competing interests.
Publisher Copyright:
© 2021
PY - 2022/3
Y1 - 2022/3
N2 - Purpose: To describe youth time-use compositions, focusing on time spent in shorter and longer bouts of sedentary behavior and physical activity (PA), and to examine associations of these time-use compositions with cardiometabolic biomarkers. Methods: Accelerometer and cardiometabolic biomarker data from 2 Australian studies involving youths 7–13 years old were pooled (complete cases with accelerometry and adiposity marker data, n = 782). A 9-component time-use composition was formed using compositional data analysis: time in shorter and longer bouts of sedentary behavior; time in shorter and longer bouts of light-, moderate-, or vigorous-intensity PA; and “other time” (i.e., non-wear/sleep). Shorter and longer bouts of sedentary time were defined as <5 min and ≥5 min, respectively. Shorter bouts of light-, moderate-, and vigorous-intensity PA were defined as <1 min; longer bouts were defined as ≥1 min. Regression models examined associations between overall time-use composition and cardiometabolic biomarkers. Then, associations were derived between ratios of longer activity patterns relative to shorter activity patterns, and of each intensity level relative to the other intensity levels and “other time”, and cardiometabolic biomarkers. Results: Confounder-adjusted models showed that the overall time-use composition was associated with adiposity, blood pressure, lipids, and the summary score. Specifically, more time in longer bouts of light-intensity PA relative to shorter bouts of light-intensity PA was significantly associated with greater body mass index z-score (zBMI) (β = 1.79, SE = 0.68) and waist circumference (β = 18.35, SE = 4.78). When each activity intensity was considered relative to all higher intensities and “other time”, more time in light- and vigorous-intensity PA, and less time in sedentary behavior and moderate-intensity PA, were associated with lower waist circumference. Conclusion: Accumulating PA, particularly light-intensity PA, in frequent short bursts may be more beneficial for limiting adiposity compared to accumulating the same amount of PA at these intensities in longer bouts.
AB - Purpose: To describe youth time-use compositions, focusing on time spent in shorter and longer bouts of sedentary behavior and physical activity (PA), and to examine associations of these time-use compositions with cardiometabolic biomarkers. Methods: Accelerometer and cardiometabolic biomarker data from 2 Australian studies involving youths 7–13 years old were pooled (complete cases with accelerometry and adiposity marker data, n = 782). A 9-component time-use composition was formed using compositional data analysis: time in shorter and longer bouts of sedentary behavior; time in shorter and longer bouts of light-, moderate-, or vigorous-intensity PA; and “other time” (i.e., non-wear/sleep). Shorter and longer bouts of sedentary time were defined as <5 min and ≥5 min, respectively. Shorter bouts of light-, moderate-, and vigorous-intensity PA were defined as <1 min; longer bouts were defined as ≥1 min. Regression models examined associations between overall time-use composition and cardiometabolic biomarkers. Then, associations were derived between ratios of longer activity patterns relative to shorter activity patterns, and of each intensity level relative to the other intensity levels and “other time”, and cardiometabolic biomarkers. Results: Confounder-adjusted models showed that the overall time-use composition was associated with adiposity, blood pressure, lipids, and the summary score. Specifically, more time in longer bouts of light-intensity PA relative to shorter bouts of light-intensity PA was significantly associated with greater body mass index z-score (zBMI) (β = 1.79, SE = 0.68) and waist circumference (β = 18.35, SE = 4.78). When each activity intensity was considered relative to all higher intensities and “other time”, more time in light- and vigorous-intensity PA, and less time in sedentary behavior and moderate-intensity PA, were associated with lower waist circumference. Conclusion: Accumulating PA, particularly light-intensity PA, in frequent short bursts may be more beneficial for limiting adiposity compared to accumulating the same amount of PA at these intensities in longer bouts.
KW - Accumulation patterns
KW - Cardiometabolic health
KW - Children
KW - Compositional data analysis
KW - Time-use
UR - http://www.scopus.com/inward/record.url?scp=85106222634&partnerID=8YFLogxK
U2 - 10.1016/j.jshs.2021.03.004
DO - 10.1016/j.jshs.2021.03.004
M3 - Article
C2 - 33737239
AN - SCOPUS:85106222634
SN - 2095-2546
VL - 11
SP - 234
EP - 243
JO - Journal of Sport and Health Science
JF - Journal of Sport and Health Science
IS - 2
ER -