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Associations of Vigorous-Intensity Physical Activity with Biomarkers in Youth


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Medicine & Science in Sports & Exercise: July 2017 - Volume 49 - Issue 7 - p 1366-1374
doi: 10.1249/MSS.0000000000001249


Emerging research using international samples (7,17) has indicated that many children globally are spending an insufficient amount of time engaging in physical activity (PA) and an excessive amount of time engaging in sedentary behaviors. Engaging in international guideline recommendations (38) of 60 min·d−1 of moderate-to-vigorous PA (MVPA) is inversely associated with biomarkers of cardiometabolic health (13,25), including lower rates of obesity (17), independent of time spent sedentary. Although the benefits of MVPA are well established cross-sectionally (7) and longitudinally (6), few studies of PA in youth have examined the contribution of specific intensities to the association, despite a growing body of literature that suggests that vigorous-intensity PA (VPA) may be more important for the prevention and amelioration of cardiometabolic risk factors (13,39). A small number of studies have used an objective measure of PA to examine associations with cardiometabolic biomarkers. These studies suggest that VPA is independently associated with cardiorespiratory fitness (positive) (23), body mass index (negative) (17), adiposity (negative) (32), HDL cholesterol (positive) (22), fasting glucose (negative) (31), and fasting insulin (negative) (1). However, an extensive examination of the literature suggests that the relationship between VPA and cardiometabolic biomarkers is inconsistent, potentially because of small samples, VPA definition, and other methodological limitations (11).

Complicating examinations of the relations between VPA and cardiometabolic biomarkers are some of the issues of VPA measurement, or more specifically, the threshold for which VPA occurs. Although imperfect, accelerometers are still considered one of the best objective measures available for epidemiological studies of PA (5,28), but the processing of data generated by accelerometers (e.g., “counts”) lacks uniformity or consistency across studies (18), which can lead to misclassification of exercise intensity (12) and/or lack of comparability across studies (3,4). Because the choice of cut point is a de facto selection of an intensity threshold with all other sources of variability held constant (e.g., monitor brand, epoch), and no standard exists for the VPA cut point, it is imperative to consider a range of accelerometer cut points for VPA if the relationship between VPA and cardiometabolic biomarkers is to be studied.

The benefits of MVPA in youth are well established, but little research has been conducted to examine the contribution of PA intensity in cardiometabolic health in youth. Therefore, the objective of the present investigation was to determine (a) the associations between VPA and cardiometabolic biomarkers independent of moderate-intensity PA (MPA) and sedentary time and (b) the accelerometer cut point that best represents the threshold for health-promoting VPA in a diverse sample of youth.


Study Design

Data were taken from the International Children’s Accelerometry Database (ICAD,, which was established to pool data on PA from studies in youth worldwide. A comprehensive description of the ICAD can be found elsewhere (34). Briefly, in 2008, 19 studies were identified from a PubMed search that used an ActiGraph (ActiGraph, LLC, Pensacola, FL) accelerometer and included a minimum of 400 participants 3 to 18 yr of age. Six additional studies were identified through professional colleagues, with 21 studies ultimately contributing data to the final database (7,34). For the current study, 11 studies were included (7,34), which are presented in brief with the variables each contributed in Table 1 (details of the Avon Longitudinal Study of Parents and Children are available at and including the data that are available via a fully searchable data dictionary; Ethical approval for the present study was attained from participating institutions, and data-sharing agreements were established before contribution of data.

List of studies contributing data with cardiometabolic biomarkers present in analytical data set.


Data from 11,588 youth (4–18 yr), representing 11 studies from Brazil, Europe, and the United States from the ICAD, were analyzed. Data from studies conducted between 1998 and 2009 were included in the present analyses if the data set contained PA, age, sex, and at least one biomarker of a cardiometabolic risk (defined as “A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” [2]).



A comprehensive description of the measurement of PA has been published previously (34). ICAD data were reanalyzed to allow for comparability across studies by aggregating data to a 60-s epoch. The criterion of 60 min of consecutive zeros was used to designate nonwear time, with a tolerance for 2 min of nonzero epochs (35). Participants with three or more days with 600 min of valid wear time were included in analyses. VPA was defined by cut points from Pate et al. (26), Evenson et al. (8), and the ICAD workgroup (7,34). These cut points were selected because they represent the most generous, lowest threshold defining VPA (Pate ≥ 3365 counts per minute), a medium threshold (Evenson ≥ 4012 counts per minute), to the most stringent, highest threshold for VPA (ICAD ≥ 6000 counts per minute).

Cardiometabolic biomarkers

Eight cardiometabolic biomarkers reflecting a diverse array of health indices were collected, including the following: waist circumference (as a proxy for adiposity [30]); systolic and diastolic blood pressure (hemodynamics); HDL cholesterol, LDL cholesterol, and fasting triglycerides (lipid metabolism); and fasting glucose and fasting insulin (glucose metabolism). Details of data collection procedures can be found elsewhere (7,34). Waist circumference was assessed midway between the lower rib margin and the iliac crest using a metal tape (10), except in the National Health and Nutrition Examination Survey where waist circumference was measured just above the iliac crest at the mid-axillary line using similar equipment (36). Resting blood pressure was measured using standard procedures, as reported previously (7). Markers of lipid and glucose metabolism were assessed using standard clinical procedures described in detail elsewhere (36).

Statistical Analysis

Descriptive analyses of accelerometer-derived estimates of minutes per day spent in sedentary, MPA, and VPA were computed across all studies using three sets of cut points to define PA intensities. To evaluate the cross-sectional association of cardiometabolic biomarkers and time spent in VPA, a series of isotemporal substitution quantile regression models were estimated for each set of cut points separately (20,21,40). Quantile regression models were used because biomarkers are often nonnormal in their distribution, and quantile regression models are not influenced by normality and are free from distributional assumptions (19). Individual models for each biomarker as the dependent variable were estimated. Time spent in VPA, defined by one of the three sets of cut points, separately, served as the primary independent variable. Because of its nonnormal distribution, minutes per day spent in VPA was placed into four categories, i.e., none (0 min·d−1—reference category), low (lower 33%), middle (middle 33%), and high (upper 33%), based on the distribution of VPA for each of the three sets of cut points. The relationship between cardiometabolic biomarkers and four categories of VPA minutes per day (none, 0 min·d−1—reference category; low, 7.2Pate, 4.0Evenson, 1.5ICAD min·d−1; medium, 18.6Pate, 11.0Evenson, 3.5ICAD min·d−1; and high, 42.7Pate, 28.9Evenson, 11.9ICAD min·d−1) estimated via three sets of cut points (Pate: sedentary = 0–152 counts per minute, MPA = 1677–3364, VPA ≥ 3365 counts per minute; Evenson: sedentary = 0–100 counts per minute, MPA = 2296–4011, VPA ≥ 4012 counts per minute; and ICAD: sedentary = 0–100 counts per minute, MPA = 3000–6000, VPA ≥ 6001 counts per minute) were examined using isotemporal substitution quantile regression modeled at the 10th, 25th, 50th, 75th, and 90th percentiles of the distribution of each biomarker. Included in each model were age (yr), sex, average total daily wear time, and minutes per day in sedentary and MPA distilled using the corresponding cut point for VPA. Because light PA (LPA) was the only intensity excluded from the models, all estimates are interpreted as substituting x amount of LPA with VPA. Separate models were estimated for each study and for each set of cut points used to define VPA within each study. An example of the modeling approach is: insulin serving as the dependent variable, with three separate models using VPA levels (i.e., low, middle, and high, with no VPA as the referent group) reduced with each of the sets of cut points for each study, run separately. Statistical significance was set at P = 0.05.

Meta-analytical techniques were used to combine the quantile regression model coefficients and SE for each biomarker across the 11 studies for each of the three sets of cut points, separately. Random effects inverse variance weighting was used to pool effects across studies and within study for each set of cut points. The study served as the unit of analysis for each quantile and category of VPA. For instance, the VPA estimates representing the lowest 33rd of the distribution of VPA regressed on the 10th quantile of insulin were combined across all studies for a given biomarker. All quantile regression analyses were conducted in 2015 using Stata (version 13.0; StataCorp, College Station, TX), and all meta-analytic analyses were conducted using Comprehensive Meta-Analysis (version 2.2; Biostat, Englewood, NJ).


Descriptive information for each study is presented in Table 2. The average amount of VPA minutes per day for each set of cut points (highest to lowest) by tertile ranged from 1.5 to 7.2 min·d−1 for the lowest tertile, the medium tertile 3.5 to 18.6 min·d−1, and the highest tertile 11.9 to 42.7 min·d−1. The results of the pooled meta-analytic effects for each quantile and level of VPA across each cardiometabolic biomarker are presented in the supplemental table (see Table, Supplemental Digital Content 1, Results of meta-analytical combination of quantile regression model coefficients and SE for each risk factor across the 11 studies for each of the three sets of accelerometer cut points,

Descriptive statistics for demographic, PA, and cardiometabolic biomarkers variables by study.

Relationship of volume of VPA with cardiometabolic biomarkers

Substituting LPA with VPA was inconsistently related to systolic/diastolic blood pressure, fasting triglycerides, HDL, or LDL after controlling for time sedentary and MPA at all tertiles of VPA volume, with only 32 of a possible 360 associations statistically significant (P < 0.05). Independent of minutes per day spent sedentary and in MPA, substituting LPA with VPA was associated with a smaller waist circumference of 0.67 to 7.30 cm at the 50th to 90th percentiles. Relationships were observed for all three tertiles of VPA, but relationships at the lowest tertile of VPA volume were significant at only the highest cut point value (i.e., ICAD). Substituting LPA with VPA was associated with 12.6 to 27.0 pmol·L−1 lower insulin values at the 75th to 90th percentiles. Relationships were observed for all three tertiles of VPA, but relationships at the lowest tertile of VPA were significant at only the highest tertiles of VPA volume for the highest cut point value (i.e., ICAD).

Influence of cut point

Independent of minutes per day spent sedentary and in MPA, substituting LPA with the high volume of VPA defined via Pate cut points was associated with a smaller waist circumference only at the 90th percentile. For VPA determined via Evenson cut points, substituting LPA for medium and high VPA levels were associated with a smaller waist circumference at the 25th to 90th centiles. Substituting LPA with the lowest, medium, and highest volumes of VPA reduced via ICAD cut points was associated with a smaller waist circumference at the 50th to 90th, the 75th and 90th, and the 25th to 90th, respectively. Across all other biomarkers (i.e., SBP, DBP, HDL, LDL, glucose, and triglycerides), no consistent associations or patterns were observed, with only nine significant associations observed from a possible 270 tested (<5%; see Fig. 1).

Combination of quantile regression model coefficients and SE for each risk factor across the 11 studies for each of the three sets of accelerometer cut points. Risk factors include, diastolic and systolic blood pressure, high-density and low-density lipoprotein cholesterol, glucose, insulin, triglycerides, and waist circumference.
Combination of quantile regression model coefficients and SE for each risk factor across the 11 studies for each of the three sets of accelerometer cut points. Risk factors include, diastolic and systolic blood pressure, high-density and low-density lipoprotein cholesterol, glucose, insulin, triglycerides, and waist circumference.


The present study is the first of this scope (e.g., sample size and diversity of national origin) to examine the relationship between VPA and cardiometabolic biomarkers in youth. The results are consistent with previous studies using more homogeneous samples, such as Carson et al. (6), where no association was found between diastolic blood pressure and VPA, but a significant negative association was reported between waist circumference and VPA in children of the 2nd and 3rd quartiles (relative to the 1st). The more nuanced analyses presented here, taken with those of Carson et al. (6), provide additional insight into the complex relationship between VPA and cardiometabolic biomarkers (11,25). The results suggest that substituting modest amounts of LPA for VPA may have cardiometabolic benefits above and beyond those conveyed by MPA and avoidance of sedentary behavior (24). Of potentially greater importance, the current results suggest that these health supportive associations are most pronounced in those who have undesirable levels of these biomarkers, specifically those with relatively large waist circumference or fasting insulin levels. If these relationships are found to be robust in longitudinal and experimental studies, then a specific frequency and duration of VPA could be incorporated as a distinct component of a PA “prescription” for youth (24). However, it must be noted that VPA was independently associated with only two of the markers examined. Therefore, while VPA may relay meaningful health benefits, the number of markers exhibiting those benefits may be few relative to less intense movement.

These results, taken with a growing body of literature demonstrating the independent health benefits of VPA for youth (6,11,14,16,17,23,24,37), support the assertion that this intensity should be considered when setting policy recommendations for PA of youth. For example, it has been shown previously that as little as 9 (15) to 14 min (17) of VPA per day is associated with less adiposity in Canadian (15) and multinational samples of youth (17). These previous findings, derived from independent samples, are consistent with the present findings showing an association of substituting 11.9 to 42.7 min·d−1 of LPA for VPA. Although this is a considerable range, with the top end (42.7 min·d−1) potentially impractical, consistent benefits were seen for VPA defined by the ICAD cut points, which even in the high volume category represented 11.9 min·d−1 of VPA, is potentially achievable for most youth (39). Therefore, the present findings suggest a modest duration (e.g., approximately 10 min) of high-intensity PA may be related to health benefits in youth who exhibit undesirable levels of insulin or waist circumference.

Although the present study has several strengths, including an objective measure of PA, a large sample size, a diverse and international sample, and an advanced analytical approach, the present results should be considered in light of several limitations. First, all data were cross sectional in nature; therefore, causality cannot be assumed. For example, it is possible that children with smaller waist circumference are more vigorously active because it is less cumbersome for them to do so. However, the nature of our analyses, which examined the relationship of VPA and waist circumference at different quantiles of waist circumference, is less supportive of this possibility. Second, although these cross-sectional results are supportive of VPA specific PA recommendations for youth, it is unknown if changes in youth VPA levels will result in meaningful changes in diastolic blood pressure, HDL, cholesterol, insulin, or adiposity. Although a recent study is supportive of the latter three (29), the literature is mixed on the relationship between increased VPA and blood pressure (9,11,27,33), and very few studies have examined the responsiveness of insulin or other markers of glucose metabolism (11,13). Third, the database we used lacks standardized dietary data or genetic data that might confound the observed relationships. For example, children with higher levels of VPA may consume fewer calories, or possess a genetic makeup supportive of a positive biomarker profile. This possibility cannot be ruled out using the currently available data. Despite these limitations, this study represents one of the largest to date that examined VPA in relation to cardiometabolic biomarkers in youth.

In summary, the present results suggest few significant or clinically meaningful associations between VPA and most cardiometabolic biomarkers studied in youth, but health-promoting associations were observed between VPA and select cardiometabolic biomarkers (i.e., insulin and waist circumference), with the associations observed at higher levels of the biomarkers and higher volumes of VPA. As such, VPA may have unique metabolic health benefits beyond those conveyed by MPA or minimizing time spent sedentary. The present results also suggest that higher VPA cut points represent an intensity that is associated with healthier insulin levels and waist circumference. Future longitudinal and intervention studies are needed to determine the temporal relationship between these variables, the modifiability of VPA, and the effect of increased VPA on biomarkers in youth. If these results are indeed robust, then a less time consuming, more intense dose of PA may be a viable option for youth seeking to achieve or maintain cardiovascular health.

The pooling of the data was funded through a grant from the National Prevention Research Initiative (grant no. G0701877) ( The funding partners relevant to this award are the following: British Heart Foundation, Cancer Research UK, Department of Health, Diabetes UK, Economic and Social Research Council, Medical Research Council, Research and Development Office for the Northern Ireland Health and Social Services, Chief Scientist Office, Scottish Executive Health Department, The Stroke Association, and Welsh Assembly Government and World Cancer Research Fund. This work was additionally supported by the Medical Research Council (MC_UU_12015/3; MC_UU_12015/7), Bristol University, Loughborough University, and Norwegian School of Sport Sciences.

The authors have no financial relationships relevant to this article and no conflicts of interest to disclose.

The ICAD Collaborators include the following: Professor L. B. Andersen, University of Southern Denmark, Odense, Denmark (Copenhagen School Child Intervention Study); Professor S. Anderssen, Norwegian School for Sport Science, Oslo, Norway (European Youth Heart Study [EYHS], Norway); Professor G. Cardon, Department of Movement and Sports Sciences, Ghent University, Belgium (Belgium Pre-School Study); Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville, MD (National Health and Nutrition Examination Survey); Professor A. Cooper, Centre for Exercise, Nutrition and Health Sciences, University of Bristol, UK (Personal and Environmental Associations with Children's Health); Dr. R. Davey, Centre for Research and Action in Public Health, University of Canberra, Australia (Children's Health and Activity Monitoring for Schools [CHAMPS]); Professor U. Ekelund, Norwegian School of Sport Sciences, Oslo, Norway, and MRC Epidemiology Unit, University of Cambridge, UK; Dr. D. W. Esliger, School of Exercise and Health Sciences, Loughborough University, UK; Dr. K. Froberg, University of Southern Denmark, Odense, Denmark (EYHS, Denmark); Dr. P. Hallal, Postgraduate Program in Epidemiology, Federal University of Pelotas, Brazil (1993 Pelotas Birth Cohort); Professor K. F. Janz, Department of Health and Human Physiology, Department of Epidemiology, University of Iowa, Iowa City, US (Iowa Bone Development Study); Dr. K. Kordas, School of Social and Community Medicine, University of Bristol, UK (Avon Longitudinal Study of Parents and Children); Dr. S. Kriemler, Institute of Social and Preventive Medicine, University of Zürich, Switzerland (Kinder-Sportstudie [KISS]); Dr. A. Page, Centre for Exercise, Nutrition and Health Sciences, University of Bristol, UK; Professor R. Pate, Department of Exercise Science, University of South Carolina, Columbia, US (Physical Activity in Pre-school Children [CHAMPS-US] and Project Trial of Activity for Adolescent Girls [Project TAAG]); Dr. J. J. Puder, Service of Endocrinology, Diabetes and Metabolism, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Switzerland (Ballabeina Study); Professor J. Reilly, Physical Activity for Health Group, School of Psychological Sciences and Health, University of Strathclyde, Glasgow, UK (Movement and Activity Glasgow Intervention in Children); Professor J. Salmon, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia (Children Living in Active Neigbourhoods); Professor L. B. Sardinha, Exercise and Health Laboratory, Faculty of Human Movement, Technical University of Lisbon, Portugal (EYHS, Portugal); Dr. L. B. Sherar, School of Sports, Exercise and Health Sciences, Loughborough University, UK; Dr. A. Timperio, Centre for Physical Activity and Nutrition Research, Deakin University Melbourne, Australia (Healthy Eating and Play Study); Dr. EMF van Sluijs, MRC Epidemiology Unit, University of Cambridge, UK (Sport, Physical Activity and Eating Behaviour: Environmental Determinants in Young People).

The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation, and the results of the present study do not constitute endorsement by the American College of Sports Medicine. The authors thank all participants and funders of the original studies that contributed data to ICAD. They also gratefully acknowledge the contribution of Professor Chris Riddoch, Professor Ken Judge, and Dr. Pippa Griew to the development of ICAD.


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