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Indian Pediatr 2009;46: 1051-1052 |
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Accelerometers for Measuring Physical Activity
Behavior in Children |
Brad Metcalf
Senior Research Statistician, The Early Bird Diabetes
Study, Department of Endocrinology and Metabolism, Peninsula Medical
School, Plymouth, UK.
Email: [email protected]
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Assessment of physical activity in children
was, until recently, somewhat of a challenge, with research relying on
subjective questionnaires and diaries. For the past 10 years or so, small
electronic motion sensors (i.e. pedometers and accelerometers) have
provided an objective means of measuring this lifestyle behaviour.
Accelerometers are fast becoming the objective method of choice as they
are capable of measuring the intensity and duration of the child’s
activity as well as the overall amount. Whilst there are several different
accelerometers available, the most common is the Actigraph (formerly MTI
and CSA) accelerometer. The Actigraph is small, unobtrusive, robust, and
does not have external buttons or a display screen, making it ideal for
use in often inquisitive and competitive children.
In this issue of Indian Pediatrics, Krishnaveni
and colleagues use Actigraphs to measure the physical activity of around
100 pre-pubertal children in India(1). The study is methodologically
equipped to address its primary objective - to describe the physical
activity level and pattern of the children. The authors comment that the
activity recorded by their children was lower than those of white European
children reported by others. They speculate that the factors responsible
for this difference could be environmental (e.g. reduced outdoor
spaces, fierce academic competition) or biological (e.g. the ‘low
muscle - high adipose’ body composition of the Indian child). In support
of a biological explanation, Owen, et al.(2) showed that among
children living in the UK, sharing many environmental factors (e.g.
the physical environ-ment, schooling, weather), those of South Asian
origin recorded 5-10% less activity than the white Europeans.
Accelerometers have proved to be of high technical
reliability(3), but it is important to note that this alone does not
guarantee a measure of high overall reliability. Children are rarely asked
to wear accelerometers for more than a single 4-7 day period, as in the
study by Krishnaveni, et al.(1), and this may not be representative
of their ‘usual’ habitual activity. My own research has shown that the
mean of four repeated 4-7 day samples provides 90% reliability in ordering
children from least to most active, compared to just 71% reliability from
a single 4-7 day sample(4). The true underlying associations between
physical activity and health may be subtle and difficult to detect if
studies continue to sample such short, one-off periods.
Another contentious issue regarding the use of
accelerometers is the lack of agreement between studies regarding
cut-points for defining intensity levels. For example, Krishnaveni and
colleagues established a cut-point of
³3000
counts/min for what they described as ‘vigorous’ activity, yet it was
within the range of cut-points typically used to define
‘moderate-and-vigorous’ activity (MVPA) -
³2000
to ³3600
counts/min. The wide range of cut-points being used to define MVPA makes
comparisons between studies very difficult. Accordingly, Actigraph
cut-points should be standardized to agreed levels of energy expenditure (e.g.
by METs) rather than to loose interpretations of the terms themselves.
Krishnaveni and colleagues attempted to validate the
Actigraph as a tool for characterising activity patterns by comparing its
data with diary-based estimates of energy expenditure collected during the
same week. The authors showed that there was poor agreement between the
two methods, at every intensity, especially vigorous activity. Whilst
these results are interesting, the findings are likely to reflect the
limitations of the diary rather than the Actigraph. Portable indirect
calorimetry would provide a more suitable method for this kind of
validation as they collect minute-to-minute measurements that could be
synchronised perfectly to the accelerometer recordings(5).
The article by Krishnaveni, et al.(1) reported
moderate inverse associations between physical activity and adiposity,
consistent with most other cross-sectional studies. The authors conclude
that "describing activity levels is a first step towards reducing
sedentary behaviour, and adiposity…’’. This leap of faith is intuitive
but in practice is proving somewhat paradoxical - attempts to improve
children’s body composition with extra activity have so far been
unsuccessful(6). Understanding why such attempts fail is crucial to the
success of future activity interventions, yet few studies offer reasons
beyond simple speculation. Accelerometer data is capable of showing that
the intervention failed to increase overall activity, and can also reveal
the reasons why. With data being recorded against clock time it is
possible to see if children had off-set any session-specific increases by
being less active at other times, or if intervention-sessions themselves
had been insufficient. Future intervention studies should consider
utilising the time-resolved nature of accelerometer data.
As we aim to understand which children are inactive and
why, it is encouraging to see that accelerometers are measuring children’s
activity in so many countries across different continents. Westernised
lifestyles are often blamed, in part, for today’s so called ‘inactive’
child and India provides a perfect setting to test this with many parts of
the country currently experiencing the transition to westernisation.
Funding: None.
Competing interests: None stated.
References
1. Krishnaveni GV, Mills IC, Veena SR, Wootton SA,
Wills AK, Coakley PJ. Accelerometers for measuring physical activity
behavior in Indian children. Indian Pediatr 2009; 46: 1055-1062.
2. Owen CG, Nightingale CM, Rudnicka AR, Cook DG,
Ekelund U, Whincup PH. Ethnic and gender differences in physical activity
levels among 9-10-year-old children of white European, South Asian and
African-Caribbean origin: the Child Heart Health Study in England (CHASE
Study). Int J Epidemiol 2009; 38: 1082-1093.
3. Metcalf BS, Curnow JS, Evans C, Voss LD, Wilkin TJ.
Technical reliability of the CSA activity monitor: The EarlyBird Study.
Med Sci Sports Exerc 2002; 34: 1533-1537.
4. Metcalf BS, Voss LD, Hosking J, Jeffery AN, Wilkin
TJ. Physical activity at the government-recommended level and
obesity-related health outcomes: a longitudinal study (Early Bird 37).
Arch Dis Child 2008; 93: 772-777.
5. Schmitz KH, Treuth M, Hannan P, McMurray R, Ring KB,
Catellier D, et al. Predicting energy expenditure from
accelerometry counts in adolescent girls. Med Sci Sports Exerc 2005; 37:
155-161.
6. Harris KC, Kuramoto LK, Schulzer M, Retallack JE. Effect of
school-based physical activity interventions on body mass index in
children: a meta-analysis. CMAJ 2009; 180: 719-726.
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