Objectively measured physical activity and sedentary time in youth: the International children’s accelerometry database (ICAD)
- Ashley R. Cooper1, 2Email author,
- Anna Goodman3,
- Angie S. Page1,
- Lauren B. Sherar4,
- Dale W. Esliger4,
- Esther MF van Sluijs5,
- Lars Bo Andersen6,
- Sigmund Anderssen7,
- Greet Cardon8,
- Rachel Davey9,
- Karsten Froberg6,
- Pedro Hallal10,
- Kathleen F. Janz11,
- Katarzyna Kordas12,
- Susi Kreimler13,
- Russ R. Pate14,
- Jardena J. Puder15,
- John J. Reilly16,
- Jo Salmon17,
- Luis B. Sardinha18,
- Anna Timperio17 and
- Ulf Ekelund7
© Cooper et al. 2015
Received: 17 June 2015
Accepted: 4 September 2015
Published: 17 September 2015
Physical activity and sedentary behaviour in youth have been reported to vary by sex, age, weight status and country. However, supporting data are often self-reported and/or do not encompass a wide range of ages or geographical locations. This study aimed to describe objectively-measured physical activity and sedentary time patterns in youth.
The International Children’s Accelerometry Database (ICAD) consists of ActiGraph accelerometer data from 20 studies in ten countries, processed using common data reduction procedures. Analyses were conducted on 27,637 participants (2.8–18.4 years) who provided at least three days of valid accelerometer data. Linear regression was used to examine associations between age, sex, weight status, country and physical activity outcomes.
Boys were less sedentary and more active than girls at all ages. After 5 years of age there was an average cross-sectional decrease of 4.2 % in total physical activity with each additional year of age, due mainly to lower levels of light-intensity physical activity and greater time spent sedentary. Physical activity did not differ by weight status in the youngest children, but from age seven onwards, overweight/obese participants were less active than their normal weight counterparts. Physical activity varied between samples from different countries, with a 15–20 % difference between the highest and lowest countries at age 9–10 and a 26–28 % difference at age 12–13.
Physical activity differed between samples from different countries, but the associations between demographic characteristics and physical activity were consistently observed. Further research is needed to explore environmental and sociocultural explanations for these differences.
KeywordsAccelerometer Physical activity Sedentary Children Adolescents
Physical activity in youth is associated with many health benefits [1, 2] and physical activity behaviours established in youth are likely to be carried through into adulthood . Widely accepted public health recommendations are that young people should accumulate at least 60 min of moderate to vigorous intensity physical activity (MVPA) daily ; however, data suggest that the majority of youth do not meet these guidelines, with approximately 80 % of 13–15 year olds worldwide insufficiently physically active .
Consequently, there is interest in describing how physical activity in youth varies by factors such as sex, age and weight status between samples from different countries in order to identify potential opportunities to intervene to increase physical activity. Studies using both self-report and objective measurement of physical activity have consistently shown that boys are more active than girls [6–9] and that physical activity declines and time spent sedentary increases throughout adolescence [10–12]. Physical activity is also frequently reported to be lower in overweight and/or obese children [13, 14], although the direction of causation is uncertain [15, 16]. Between-country differences in physical activity have also been described. Self-report data from the Health Behaviour in School-Aged Children study (HBSC) and the World Health Organisation (WHO) Global School-based Student Health Survey, show that the prevalence of youth aged 11–15 years meeting physical activity guidelines varies widely by country [17, 18]. In addition, two previous studies using accelerometers have shown differences in total physical activity and MVPA between Denmark, Portugal, Estonia and Norway in participants aged nine and 15 years  and in total physical activity, MVPA and sedentary time in children aged 11 years from Belgium, Greece, Hungary, the Netherlands and Switzerland , with differences in total physical activity between the highest and lowest countries generally in the range of 15–25 %.
Although objective measurement of physical activity is desirable for between-country analyses of physical activity, differences in data reduction methodology can make comparisons problematic. For example, a recent review of European studies that objectively assessed physical activity with accelerometers reported that the prevalence of children meeting guidelines for sufficiently active youth ranged between 3 and 100 % depending on the accelerometer intensity thresholds used . Standardisation of accelerometer data reduction methodology is required for comparisons to be useful, and this study therefore aims to use pooled accelerometer data from the International Children’s Accelerometry Database (ICAD) to: 1) characterise variation in children and adolescents physical activity and sedentary time by age, sex and weight status; and 2) to examine to what extent the levels and patterns of children’s physical activity differ between samples from different countries located in the northern and southern hemispheres. No child and adolescent physical activity data pooling studies have incorporated samples from such a diverse range of countries using consistent measures and methodology. Exploring associations between demographic characteristics and young people’s physical activity among samples from different countries can inform the potential importance of environmental and sociocultural factors in the development of effective strategies for promoting physical activity in this age group.
The International Children’s Accelerometry Database (http://www.mrc-epid.cam.ac.uk/research/studies/icad) pools data on objectively measured physical activity from international studies using the Actigraph accelerometer in youth, using a standard data reduction procedure .
The ICAD contains accelerometer data from twenty studies conducted in ten countries (Additional file 1: Table S1). Some of these studies include measurements taken at multiple time points for the same individual. The present analyses included baseline measurements from all 20 studies and follow-up measurements from the seven longitudinal studies and one experimental study. In addition, follow-up measurements from the control group of one of the four randomised controlled trials were also included, since for this one study it was possible to distinguish intervention and control groups. Analyses were restricted to 27,637 participants (92 % of 29,967 potential participants) who provided at least 3 days of valid accelerometer data (mean 5.3, range 3–7) from at least one time point. These 27,637 participants were aged 2.8 to 18.4 years, and between them provided 188,416 days of valid data across 35,360 time points. In analyses comparing activity levels between countries, we restricted our analyses to children 9–10 or 12–13, resulting in a sample of 10,741 participants from ten studies in eight countries.
Physical activity and sedentary time
A detailed description of the accelerometer data reduction methods used in ICAD is available elsewhere . Briefly, all accelerometer data files were reintegrated to a 60 s epoch and then processed using commercially available software (KineSoft v3.3.20, Loughborough, UK) to provide physical activity outcome variables that could be directly compared across studies. Non-wear time was defined as 60 min of consecutive zeros allowing for 2 min of non-zero interruptions . A valid day was defined as recording at least 500 min of measured wear time between 07:00 and 22:59 (19 % days excluded as invalid).
The primary measure of physical activity was the participants’ average accelerometer counts per minute (cpm). This was computed by calculating, for each day, the total accelerometer counts recorded divided by minutes of recording, and then averaging these daily averages across all valid days. Standard cut-points were used to define the mean daily percentage of time spent at various intensities: sedentary (≤100 cpm), light (101–2295 cpm), and moderate to vigorous (≥2296 cpm) . The proportion of youth meeting physical activity guidelines is presented in two ways. Firstly, in strict agreement with current World Health Organisation physical activity guidelines  we present the proportion of youth who recorded at least 60 min of MVPA on every valid day measured. Secondly, we also calculated a more liberal interpretation of these guidelines, being the percentage of valid days where ≥60 min of MVPA were accumulated.
Height and body weight were measured using standardised procedures across studies. Body mass index (BMI) was calculated as weight (kg)/height (m)2 and participants were categorised into normal weight, overweight and obese groups according to age and sex-specific cut points . For each participant, we also computed BMI standardised by sex and by age (in 1-year age bands) in order to examine associations with a continuous measure of weight status.
Linear regression was used to examine cross-sectional associations between age, sex, weight status and physical activity outcomes. All analyses were adjusted for the ICAD study from which the data was drawn and also for time of year (May-October vs. November-April, with the months inverted for the three studies from the Southern hemisphere). In order to increase statistical power we treated repeated measures in the same participants (i.e. the same child measured at different ages) as adding to the samples for cross-sectional analyses at different ages. When performing cross-sectional analyses, robust standard errors were used to adjust for the clustering of measurement waves within participants. Two-way interaction terms between age and sex were initially entered in order to characterise the physical activity of every age-sex combination relative to boys aged 5–6 years. We then fitted three-way interactions between age, sex and whether the child was normal weight versus overweight/obese.
Analyses comparing activity levels between countries were restricted to participants aged 9–15 as most measurement time points (82 %) were of participants between these ages. Country-level analyses were also restricted to ten studies from eight countries (eight studies from seven countries age 9–10, and five studies from four countries age 12–13; for both age groups, we pooled two separate studies from Melbourne, Australia). These studies were selected because they were large and/or nationally- or regionally-representative and covered a diverse range of regions from around the world. We first compared countries in terms of their absolute average activity levels, stratified by age and sex, and then compared the countries in terms of the relative within-country effect of sex, age and weight status. To make these comparisons we first standardised our activity outcome within each country in order to account for between-country differences in absolute activity levels when comparing the relative within-country effects. Separate regression models were then run in each country, and the coefficients from that regression model entered into a random-effects meta-analysis. All analyses used Stata 12.
Descriptive characteristics of study participants
N (%) participants
N (%) measured time points
27,637 (100 %)
35,360 (100 %)
11,199 (41 %)
14,633 (41 %)
16,438 (59 %)
20,727 (59 %)
1044 (4 %)
1044 (3 %)
2379 (9 %)
2379 (7 %)
1168 (4 %)
1654 (5 %)
6054 (22 %)
6910 (20 %)
9981 (36 %)
11,186 (32 %)
4706 (17 %)
9196 (26 %)
1747 (6 %)
2342 (7 %)
558 (2 %)
649 (2 %)
20,387 (74 %)
26,222 (75 %)
4939 (18 %)
6238 (18 %)
2048 (7 %)
2450 (7 %)
Australia [N = 2]
2395 (9 %)
3531 (10 %)
Belgium [N = 1]
257 (1 %)
257 (1 %)
Brazil [N = 1]
420 (2 %)
420 (1 %)
Denmark [N = 2]
1905 (7 %)
2563 (7 %)
Estonia [N = 1]
643 (2 %)
643 (2 %)
Norway [N = 1]
364 (1 %)
364 (1 %)
Portugal [N = 1]
1070 (4 %)
1174 (3 %)
Switzerland [N = 2]
742 (3 %)
742 (2 %)
UK [N = 5]
10,301 (37 %)
14,424 (41 %)
USA [N = 4]
9540 (35 %)
11,242 (32 %)
Physical activity by sex, age and weight status
Cross-country comparisons of physical activity
This paper describes objectively measured physical activity and sedentary time in 27,637 young people from ten countries. Physical activity was consistently lower in girls than boys, was lower in overweight/obese youth, and decreased cross-sectionally each year after age five, with a corresponding increase in time spent sedentary. Substantial differences in physical activity between countries were seen for both sexes, including in the proportion meeting physical activity guidelines. Nevertheless, all countries were alike in showing the same differences in physical activity by sex, by age and (almost always) by weight status.
Sex differences in physical activity have been consistently reported in the literature and the present study confirms these findings at all ages. Previous longitudinal studies have also reported that physical activity declines through adolescence but were limited by having relatively few measurement time points and by self-reported physical activity data. A systematic review and pooled analysis  concluded that physical activity declined by approximately 7 % per year after the age of ten. The present study found a difference of 4.2 % per annum in overall physical activity when calculated with reference to the activity level at age five. When calculated relative to the average activity level at age 12, the annual difference was 5.8 % (4.7 % in boys, 6.8 % in girls; data not shown).
When specific physical activity intensities were examined, the lower levels of overall physical activity with increasing age were reflected by a progressive increase in the volume of sedentary time and an almost equivalent ‘displacement’ of light-intensity physical activity. An increase in accelerometer-measured sedentary time with age has been reported in other studies [24, 25], though the health consequences of this are unclear. Limited evidence supports an association between objectively measured sedentary time and metabolic health in youth in observational studies [24, 26], though prospective associations between accelerometer-measured sedentary time and the development of obesity have been reported . Further studies are required to explore the consequences of displacing light-intensity physical activity with sedentary time, since engagement in even light-intensity physical activity has been shown to be beneficially associated with cardiometabolic health in adolescents .
The generally low levels of MVPA in this study were reflected in the small proportion of participants (9 % of boys and 2 % of girls) who met the physical activity guidelines requiring participants to accumulate ≥60 min MVPA on all measured days. In comparison, ≥60 min of MVPA were accumulated on 46 % of days for boys and 22 % for girls. This difference in outcomes highlights the difficulties in comparing studies utilising different measurement criteria. For example, a review of European studies using Actigraph accelerometers reported that 3–5 % of children met guidelines using an accelerometer threshold of >3000 cpm increasing to up to 87 % meeting guidelines using a threshold of >2000 cpm . The ICAD begins to address this issue by providing accelerometer data from a substantial sample of youth reduced using a standard methodology. Adoption of this methodology by other international studies, and further extension of the ICAD, will allow a consistent picture of physical activity levels to be obtained.
Overweight/obese youth have frequently been reported to be less active than their normal weight counterparts, and again this analysis of the ICAD adds to the literature by showing that differences in physical activity by weight status are seen from 6 years of age. The direction of causation in the association between weight status and physical activity was not investigated in this analysis, but recent studies [26, 29] have suggested that lower levels of physical activity are a consequence of increased adiposity, and thus early prevention of overweight/obesity is important in maintaining physical activity levels. These studies do not, however, rule out that lower levels of physical activity may also be a contributor to increased adiposity (i.e. the relationship between physical activity and overweight is bi-directional).
Substantial differences in overall physical activity between countries were identified in both age groups investigated. There are many possible individual, social and environmental explanations for these differences that were not explored in this study. For example, active travel to school or to non-school places can be substantial contributors to overall physical activity, as are active play and independent mobility [30–33], and equally the availability of opportunities to be active or differences in the built environment may be influential on physical activity. The consistent differences by age and sex persist even though the countries differ in total physical activity levels, suggesting that these differences may to an extent be biological. However, comparison of data from Norway and the US shows that Norwegian 9–10 year old girls were, on average, as active as American boys (658 vs. 655 cpm). This suggests that the tendency of boys to be more active than girls in relative terms does not imply that the physical activity levels of girls need be ‘low’ in absolute terms, and indicates the need to identify both within- and between-population determinants of physical activity.
Strengths of this study include a substantial sample of young people across a wide age range, a consistent measurement instrument and data reduction procedures, and physical activity data from a wide variety of countries. However, although this is a substantial sample, the number of participants in some countries did not allow analysis by country, and overall the sample is skewed towards two main age groups. In addition, the countries included are not globally representative, with the US, UK and Northern Europe over-represented, few data from other geographical regions, and a dearth of data from low and middle-income countries. In addition, most samples are not nationally representative, although studies have generally sampled to be at least regionally representative. Accelerometers also have a number of limitations. When worn on the waist, as in the studies comprising the ICAD, they poorly record upper body physical activity and physical activity when cycling . Thus, in countries with a high prevalence of cycling for transportation (e.g. Denmark), physical activity may be under-estimated. In addition, waist-worn accelerometers mis-classify time spent in motionless-standing as sedentary, which is not considered to be a sedentary behaviour (defined as “any waking behaviour characterised by an energy expenditure ≤1.5 metabolic equivalents (METs) while in a sitting or reclining posture” ), potentially leading to over estimates of the true volume of sedentary time. A further limitation is in the accelerometer thresholds used to define MVPA. Whilst the thresholds used have been shown to provide valid estimates of physical activity intensity for youth aged 5 years and above , more recently they have been shown to perform poorly for MVPA in children aged 4–6 years  and have not been validated in children under 4 years. Estimates of MVPA in these younger age groups should thus be viewed with an element of caution.
The ICAD provides a large volume of accelerometer data reduced using a common protocol, which allows comparison of study samples from a number of countries. Analyses show consistent differences in physical activity by age, sex and weight status and also identify between-country differences in activity levels. Further detailed studies of the determinants of these between country differences in physical activity are required to explain the differences observed in this study. Such studies are warranted since between-country differences may show different determinants than within-population differences and give new insights into preventive mechanisms.
We would like to thank all participants of the original studies that contributed data to ICAD.
We also gratefully acknowledge the contribution of Professor Chris Riddoch, Professor Ken Judge and Dr Pippa Griew to the development of ICAD.
This work was supported by the UK Medical Research Council National Prevention Research Initiative (NPRI) [grant number G0701877]. Funding partners are: 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; Welsh Assembly Government and World Cancer Research Fund. AC is supported by the National Institute for Health Research Bristol Nutrition Biomedical Research Unit based at University Hospitals Bristol NHS Foundation Trust and the University of Bristol. AG is funded by a post-doctoral fellowship from the National Institute of Health Research (NIHR). The views expressed in this paper are those of the author(s) and not necessarily those of the study funders, NHS, the NIHR or the Department of Health. JS is supported by a Principal Research Fellowship from the National Health & Medical Research Council of Australia (APP1026216). AT is supported by a Future Leader Fellowship from the National Heart Foundation of Australia (Award ID 100046).
Contributors of data to the International Children's Accelerometry Database (ICAD) are:
Dr. K. Kordas, Avon Longitudinal Study of Parents and Children (ALSPAC) (Additional file 4: Avon Longitudinal Study of Parents and Children (ALSPAC). (firstname.lastname@example.org)
Dr. J.J. Puder, Ballabeina Study (email@example.com)
Dr. G. Cardon, Belgium Pre-School Study (firstname.lastname@example.org)
Professor. R. Davey, Children’s Health and Activity Monitoring Programme for Schools (CHAMPS UK) (email@example.com)
Prof. R.R. Pate, Physical Activity in Pre-school Children; Trial of Activity for Adolescent Girls (TAAG) (firstname.lastname@example.org)
Prof. J. Salmon, Children Living in Active Neighbourhoods (CLAN) (email@example.com)
Prof. L. B. Andersen, Copenhagen School Child Intervention Study (CSCIS) (firstname.lastname@example.org)
Dr. K. Froberg, European Youth Heart Study (EYHS), Denmark, Estonia (email@example.com)
Prof. L. B. Sardinha, EYHS, Portugal (firstname.lastname@example.org)
Prof. S. Anderssen, EYHS, Norway (email@example.com)
Dr. A. Timperio, Healthy Eating and Play Study (HEAPS) (firstname.lastname@example.org)
Prof. Kathleen F. Janz, Iowa Bone Development Study (email@example.com)
Dr. S. Kreimler, Kinder-Sportstudie (KISS) (firstname.lastname@example.org)
Prof. J.J. Reilly, Movement and Activity Glasgow Intervention in Children (MAGIC) (email@example.com)
Centers for Disease Control and Prevention (CDC), National Health and Nutrition Examination Survey (NHANES; 03/04, 05/06)
Prof. A.R. Cooper, Personal and Environmental Associations with Children’s Health (PEACH) (firstname.lastname@example.org)
Dr. P. Hallal, 1993 Pelotas Birth Cohort (email@example.com)
Dr E. van Sluijs, Sport, Physical activity and Eating behaviour: Environmental Determinants in Young people (SPEEDY) (firstname.lastname@example.org)
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Strong WB, Malina RM, Blimke CJR, Daniels SR, Dishman RK, Gutin B, et al. Evidence based physical activity for school-age youth. J Pediatr. 2005;146:732–7.View ArticleGoogle Scholar
- Janssen I, Leblanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phys Act. 2010;7:40.View ArticleGoogle Scholar
- Telama R, Yang X, Viikari J, Valimaki I, Wanne O, Raitakari O. Physical activity from childhood to adulthood: a 21-year tracking study. Am J Prev Med. 2005;28:267–73.View ArticleGoogle Scholar
- WHO. Global recommendations on physical activity for health. Geneva: World Health Organisation; 2010.Google Scholar
- Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9828):247–57.View ArticleGoogle Scholar
- Brodersen NH, Steptoe A, Boniface DR, Wardle J. Trends in physical activity and sedentary behaviour in adolescence: ethnic and socioeconomic differences. Br J Sports Med. 2007;41:140–4.View ArticleGoogle Scholar
- Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical Activity in the United States Measured by Accelerometer. Med Sci Sports Exerc. 2008;40(1):181–8.View ArticleGoogle Scholar
- Verloigne M, Van Lippevelde W, Maes L, Yildirim M, Chinapaw M, Manios Y, et al. Levels of physical activity and sedentary time among 10- to 12-year-old boys and girls across 5 European countries using accelerometers: an observational study within the ENERGY-project. Int J Behav Nutr Phys Act. 2012;9:34.View ArticleGoogle Scholar
- Nader PR, Bradley RH, Houts RM, McRitchie SL, O’Brien M. Moderate-to-vigorous physical activity from ages 9 to 15 years. JAMA. 2008;300(3):295–305.View ArticleGoogle Scholar
- Dumith SC, Gigante DP, Domingues MR, HW K l. Physical activity change during adolescence: a systematic review and pooled analysis. Int J Epidemiol. 2011;40:685–98.View ArticleGoogle Scholar
- Ortega FB, Konstabel K, Pasquali E, Ruiz JR, Hurtig-Wennlof A, Maestu J, et al. Objectively Measured Physical Activity and Sedentary Time during Childhood, Adolescence and Young Adulthood: A Cohort Study. PLoS One. 2013;8(4):e60871.View ArticleGoogle Scholar
- Telford RM, Telford RD, Cunningham RB, Cochrane T, Davey R, Waddington G. Longitudinal patterns of physical activity in children aged 8 to 12 years: the LOOK study. Int J Behav Nutr Phys Act. 2013;10:81.View ArticleGoogle Scholar
- Jiminez-Pavon D, Fernandez-Vazquez A, Alexy U, Cuenca-Garcia M, Polito A, Vanhelst J, et al. Association of objectively measured physical activity with body components in European adolescents. BMC Public Health. 2013;13:667.View ArticleGoogle Scholar
- Mitchell JA, Pate RR, Beets MW, Nader PR. Time spent in sedentary behavior and changes in childhood BMI: a longitudinal study from ages 9 to 15 years. Int J Obes. 2013;37(1):54–60. doi:10.1038/ijo.2012.41. Epub Mar 20.View ArticleGoogle Scholar
- Hallal PC, Reichert FF, Ekelund U, Dumith SC, Menezes AM, Victora CG, et al. Bidirectional cross-sectional and prospective associations between physical activity and body composition in adolescence: Birth cohort study. J Sports Sci. 2012;30(2):185–92.View ArticleGoogle Scholar
- Metcalf BS, Hosking J, Jeffery AN, Voss LD, Henley W, Wilkin TJ. Fatness leads to inactivity, but inactivity does not lead to fatness: a longitudinal study in children (EarlyBird 45). Arch Dis Childhood doi:10.1136/adc.2009.175927.
- Currie C, Zanotti C, Morgan A, Currie D, de Looze M, Roberst C, et al. Social determinants of health and well-being among young people. Health Behaviour in School-aged Children (HBSC) study: international report from the 2009/2010 survey. Copenhagen: WHO Regional Office for Europe; 2012. Health Policy for Children and Adolescents, No. 6.Google Scholar
- Guthold R, Cowan MJ, Autenrieth CS, Kann L, Riley LM. Physical activity and sedentary behavior among schoolchildren: A 34-country comparison. J Pediatr. 2010;157(1):43–9.View ArticleGoogle Scholar
- Riddoch CJ, Andersen LB, Wedderkopp N, Harro M, Klasson-Heggebo L, Sardinha LB, et al. Physical activity levels and patterns of 9- and 15-yr-old European children. Med Sci Sports Exerc. 2004;36(1):86–92.View ArticleGoogle Scholar
- Guinhouya BC, Samouda H, de Beaufort C. Level of physical activity among children and adolescents in Europe: a review of physical activity assessed objectively by accelerometry. Public Health. 2013;127(4):301–11.View ArticleGoogle Scholar
- Sherar LB, Griew P, Esliger DW, Cooper AR, Ekelund U, Judge K, et al. International children’s accelerometry database (ICAD): design and methods. BMC Public Health. 2011;11:485.View ArticleGoogle Scholar
- Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26(14):1557–65.View ArticleGoogle Scholar
- Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320:1240–3.View ArticleGoogle Scholar
- Colley RC, Garriguet D, Janssen I, Wong SL, Saunders TJ, Carson V, et al. The association between accelerometer-measured patterns of sedentary time and health risk in children and youth: results from the Canadian Health Measures Survey. BMC Public Health. 2013;13:200.View ArticleGoogle Scholar
- Spittaels H, Van Cauwenberghe E, Verbestel V, De Meester F, Van Dyck D, Verloigne M, et al. Objectively measured sedentary time and physical activity time across the lifespan: a cross-sectional study in four age groups. Int J Behav Nutr Phys Act. 2012;9:149.View ArticleGoogle Scholar
- Ekelund U, Luan J, Sherar LB, Esliger DW, Griew P, Cooper AR. Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents. JAMA. 2012;307(7):704–12.View ArticleGoogle Scholar
- Mitchell JA, Pate RR, Dowda M, Mattocks C, Riddoch C, Ness AR, et al. A prospective study of sedentary behavior in a large cohort of youth. Med Sci Sports Exerc. 2012;44(6):1081.View ArticleGoogle Scholar
- Carson V, Ridgers ND, Howard BJ, Winkler EAH, Healy GN, Owen N, et al. Light-intensity physical activity and cardiometabolic biomarkers in US adolescents. PLoS One. 2013;8(8):e71417.View ArticleGoogle Scholar
- Richmond RC, Davey Smith G, Ness AR, den Hoed M, McMahon G, Timpson NJ. Assessing causality in the association between child adiposity and physical activity levels: A Mendelian randomization analysis. PLoS Med. 2014;11(3):e1001618. 10.1371/journal.pmed.1001618.View ArticleGoogle Scholar
- Southward EF, Page AS, Wheeler BW, Cooper AR. Quantifying the contribution of the journey to school to daily physical activity in secondary school age children: the PEACH project. Am J Prev Med. 2012;43(2):201–4.View ArticleGoogle Scholar
- Page AS, Cooper AR, Griew P, Davis L, Hillsdon M. Independent mobility in relation to weekday and weekend physical activity in children aged 10–11 years: The PEACH Project. Int J Behav Nutr Phys Act. 2009;7(6):2.Google Scholar
- Page AS, Cooper AR, Griew P, Jago R. Independent mobility, perceptions of the built environment and children’s participation in play, active travel and structured exercise and sport: The PEACH Project. Int J Behav Nutr Phys Act. 2010;7:17.View ArticleGoogle Scholar
- Goodman A, Mackett R, Paskins J. Activity compensation and activity synergy in British 8–13 year olds. Prev Med. 2011;53:293–8.View ArticleGoogle Scholar
- Corder K, Brage S, Ekelund U. Accelerometers and pedometers: methodology and clinical application. Curr Opin Clin Nutr Metab Care. 2007;10:597–603.View ArticleGoogle Scholar
- Network SBR. Letter to the Editor: Standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab. 2012;37:540–2.View ArticleGoogle Scholar
- Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc. 2011;43(7):1360–8.View ArticleGoogle Scholar
- Janssen X, Cliff DP, Reilly JJ, Hinkley T, Jones RA, Batterham M, et al. Predictive validity and classification accuracy of Actigraph energy expenditure equations and cut-points in young children. PLoS ONE. 2013;8(11):e79124. 10.1371/journal.pone.0079124.View ArticleGoogle Scholar