international children accelerometer database
TRANSCRIPT
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International Children
Accelerometer Database
Ulf Ekelund, PhD
Department of Sports Medicine
Norwegian School of Sport Sciences
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Children’s Games, Bruegel Pieter the Elder, 1560
Nowadays…
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Reanalysed data from
66 LMIC and 38 European
Countries (GSHS and HBSC)
Overall
World 80.3%
Inactive
(Hallal et al, Lancet 2012)
Measuring Physical Activity in
Youth – A Challenge
• Physical Activity is a complex behaviour
– Type
– Domain
– Frequency
– Duration
– Intensity
– Volume
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Measuring Physical Activity in
Youth – A Challenge
• Physical Activity in highly variable
0
1
2
3
4
5
6
Boys Girls Total
Perc
en
tag
e (
%)
(N=2662)
(N=2933)
(N=5595)
Cut-point = 3600 cpmMVPA = 4 METs
(Riddoch et al, Arch Dis Child, 2008)
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• 7 to 8 year old UK children
(N=6497)
• PA measured for 10 h for ≥ 2 days
• MVPA > 2241 cpm
• SED < 100 cpm
• Median MVPA time 60 min/d
• Median SED time 6.4 h/d
• 38% of girls and 63% of boys were
active according to PA
recommendations
(Griffiths et al, BMJ Open 2013)
0.4% - 97.4%
(Ekelund et al, BJSM 2011)
Interpretation of objective
activity data – also a challenge
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ICAD – A pooling Project
The Opportunity
1. Accelerometry – increased precision of
PA measurement
2. Accelerometers used in large children’s
studies
3. An ‘instrument of choice’ has emerged
4. Relatively standardised protocols
5. A ‘pooling’ project is therefore feasible
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Advantages of pooling data
• Cost effective
• Increased analytical power - ‘meta-analysis using
individual data’
• Socio-cultural diversity – a more heterogeneous
and potentially more representative sample
• Standardize and optimize the analytical methods
used in the generation of PA variables
• Open resource for the scientific community
The Process
• ‘Pragmatic’ search for studies with accelerometry
• Approached 24 large studies (N>400)
• 20 agreed to participate
• Obtained individual .dat files through secure FTP drop site
• Telephone interviews
• Reduced to PA variables (www.kinesoft.org)
• Obtained broad range of accompanying variables
– Phenotypic, Socio-demographic and environmental variables
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Inclusion Criteria
• Age <18 years
• Individual .dat files (7164, 71256, GT1M)
• Sex, age, height and weight
Studies – United Kingdom
Movement and Activity
Glasgow Intervention in
Children (MAGIC)
Sport, Physical activity
and Eating behaviour:
Environmental
Determinants in Young
people (SPEEDY)Avon Longitudinal Study of
Parents and Children
(ALSPAC)
Personal and Environmental
Associations with Children’s
Health (PEACH)
Children’s Health and Activity
Monitoring Programme in Schools
(CHAMPS (UK))
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Studies - Europe
Studies - US
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Studies - Brazil
Studies - Australia
Children Living in Active Neighbourhoods (CLAN)
Healthy Eating and Play Study (HEAPS)
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Sample size
Age distribution (repeated
measures included)
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Evidence based data reduction
decisions
The ICAD database
PA and Sedentary exposures
Counts/min
Sedentary
Light
Moderate
Vigorous
Bouts
Hourly
Travel
TV
Computer
Confounders/mediators
Age
Gender
Ethnicity
SEP
Puberty
Birthweight
Parental characteristics
Education
SeasonHealth outcomes
Body Composition
CVD risk factors
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Outputs
Outputs
(Ekelund et al, JAMA 2012)
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Outputs
(Hildebrand et al, AJCN 2015)
Outputs
• Hildebrand M, et al. Association between birth weight and objectively measured sedentary
time is mediated by central adiposity: data in 10,793 youth from the International Children’s
Accelerometry Database. Am J Clin Nutr, 2015;101:983-90
• Brazendale K, et, al. Equating accelerometer estimates among youth: the Rosetta Stone 2. J
Sci Med Sports, 2015, Published Online Feb 2015 [full text]
• Goodman A, et al. Daylight saving time as a potential public health intervention: an
observational study of evening daylight and objectively-measured physical activity among
23,000 children from 9 countries. Int J Behav Nutr Phys Act, 2014, Oct 23;11:84.[full text]
• Atkin AJ, et al. Prevalence and correlates of screen time in youth: an international
perspective. Am J Prev Med, 2014 Sep 16 [full text]
• Kwon S et al. Tracking of accelerometry-measured physical activity during childhood: ICAD
pooled analysis. Int J Behav Nutr Phys Act, 2012; 9:68 [full text]
• Ekelund U, et al. Moderate to Vigorous Physical Activity and Sedentary Time and
Cardiometabolic Risk Factors in Children and Adolescents. JAMA, 2012; 307: 704-712 [full
text]
• Sherar L, et al. International children’s accelerometry database (ICAD): Design and methods.
BMC Public Health, 2011 [full text]
• 4-5 additional manuscripts under review
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ICAD – Ongoing
• Existing partners currently contributing additional data (e.g. prospective)
• New partners identified and will be invited during 2015
• Exploring the possibility to combine data from studies using different accelerometer brands (placements?)
http://www.mrc-epid.cam.ac.uk/research/studies/icad/
Take home message
• Pooling objectively measured physical activity
from large scale observational studies i youth
is feasible and have the opportunity to
generate new knowledge about population
levels and determinants of sedentary time and
physical activity and how these behaviours are
associated with health outcomes
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Acknowledgements
- Funding from the National Prevention Research Initiative:
- Study participants from the contributing studies:
ALSPAC Project TAAG KISS
Ballabeina Study CLAN MAGIC
Belgium Pre-school Study HEAPS NHANES
CHAMPS-UK CoSCIS PEACH
CHAMPS US 1993 Pelotas SPEEDY
EYHS Denmark/Norway/Estonia/Portugal