Associations of Vigorous-Intensity Physical Activity with Biomarkers in Youth.
- Moore JB 1Department of Family and Community Medicine, Wake Forest School of Medicine, Winston-Salem, NC; 2Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC; 3Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC; 4Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, Odense, DENMARK; 5Department of Sport Medicine, Norwegian School of Sport Science, Oslo, NORWAY; 6Federal University of Pelotas, Pelotas, BRAZIL; 7School of Social and Community Medicine, University of Bristol, Bristol, UNITED KINGDOM; 8Epidemiology, Biostatistics and Public Health Institute, University of Zürich, Zürich, SWITZERLAND; 9Physical Activity for Health Group, School of Psychological Sciences and Health, University of Strathclyde, Glasgow, UNITED KINGDOM; and 10Exercise and Health Laboratory, CIPER, Faculty of Human Kinetics, University of Lisbon, Cruz-Quebrada, PORTUGAL.
- Beets MW
- Brazendale K
- Blair SN
- Pate RR
- Andersen LB
- Anderssen SA
- Grøntved A
- Hallal PC
- Kordas K
- Kriemler S
- Reilly JJ
- Sardinha LB
- 2017-03-10
Published in:
- Medicine and science in sports and exercise. - 2017
Accelerometry
Adolescent
Biomarkers
Blood Pressure
Child
Child, Preschool
Cholesterol
Exercise
Female
Humans
Insulin
Male
Physical Exertion
Sedentary Behavior
Triglycerides
Waist Circumference
English
INTRODUCTION
Physical activity (PA) conveys known cardiometabolic benefits to youth, but the contribution of vigorous-intensity PA (VPA) to these benefits is unknown. Therefore, we sought to determine (a) the associations between VPA and cardiometabolic biomarkers independent of moderate-intensity PA (MPA) and time sedentary and (b) the accelerometer cut point that best represents the threshold for health-promoting VPA in youth.
METHODS
Data from the International Children's Accelerometry Database (ICAD) were analyzed in 2015. The relationship between cardiometabolic biomarkers and four categories of VPA estimated via three sets of cut points were examined using isotemporal substitution quantile regression modeling at the 10th, 25th, 50th, 75th, and 90th percentile of the distribution of each biomarker, separately. Age, sex, accelerometer wear time, sedentary time, and MPA were controlled for while allowing substitution for light-intensity PA. Data from 11,588 youth (4-18 yr) from 11 ICAD studies (collected 1998-2009) were analyzed.
RESULTS
Only 32 of 360 significant associations were observed. Significant, negative relationships were observed for VPA with waist circumference and insulin. Replacing light-intensity PA with VPA (corresponding to at the 25th to 90th percentiles of VPA) was associated with 0.67 (-1.33 to -0.01; P = 0.048) to 7.30 cm (-11.01 to -3.58; P < 0.001) lower waist circumference using Evenson and ICAD cut points (i.e., higher counts per minute). VPA levels were associated with 12.60 (-21.28 to -3.92; P = 0.004) to 27.03 pmol·L (-45.03 to -9.03; P = 0.003) lower insulin levels at the 75th to 90th percentiles using Evenson and ICAD cut points when substituted for light PA.
CONCLUSIONS
Substituting light PA with VPA was inversely associated with waist circumference and insulin. However, VPA was inconsistently related to the remaining biomarkers after controlling for time sedentary and MPA.
Physical activity (PA) conveys known cardiometabolic benefits to youth, but the contribution of vigorous-intensity PA (VPA) to these benefits is unknown. Therefore, we sought to determine (a) the associations between VPA and cardiometabolic biomarkers independent of moderate-intensity PA (MPA) and time sedentary and (b) the accelerometer cut point that best represents the threshold for health-promoting VPA in youth.
METHODS
Data from the International Children's Accelerometry Database (ICAD) were analyzed in 2015. The relationship between cardiometabolic biomarkers and four categories of VPA estimated via three sets of cut points were examined using isotemporal substitution quantile regression modeling at the 10th, 25th, 50th, 75th, and 90th percentile of the distribution of each biomarker, separately. Age, sex, accelerometer wear time, sedentary time, and MPA were controlled for while allowing substitution for light-intensity PA. Data from 11,588 youth (4-18 yr) from 11 ICAD studies (collected 1998-2009) were analyzed.
RESULTS
Only 32 of 360 significant associations were observed. Significant, negative relationships were observed for VPA with waist circumference and insulin. Replacing light-intensity PA with VPA (corresponding to at the 25th to 90th percentiles of VPA) was associated with 0.67 (-1.33 to -0.01; P = 0.048) to 7.30 cm (-11.01 to -3.58; P < 0.001) lower waist circumference using Evenson and ICAD cut points (i.e., higher counts per minute). VPA levels were associated with 12.60 (-21.28 to -3.92; P = 0.004) to 27.03 pmol·L (-45.03 to -9.03; P = 0.003) lower insulin levels at the 75th to 90th percentiles using Evenson and ICAD cut points when substituted for light PA.
CONCLUSIONS
Substituting light PA with VPA was inversely associated with waist circumference and insulin. However, VPA was inconsistently related to the remaining biomarkers after controlling for time sedentary and MPA.
- Language
-
- English
- Open access status
- green
- Identifiers
-
- DOI 10.1249/MSS.0000000000001249
- PMID 28277404
- Persistent URL
- https://sonar.ch/global/documents/47497
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