Vol.1, No.2, 13- 19 (2011)
doi:10.4236/ojpm.2011.12003
Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/OJPM/
Open Journal of Preventive Medicine
Demograph ic variations in discrepancies between
objective and subjective measures of physical activity
Lisa M. Mackay *, Melody Oliver, Grant M. Schofield
Centre for Physical Activity and Nutrition, Auckland University of Technology, Auckland, New Zealand;
*Corresponding author:
Received 17 May 2011; revised 29 June 2011; accepted 29 July 2011.
AB S TRAC T
Demographic effects (sex and parenthood status)
on the level of association between self-re-
ported and accelerometer assessed physical
activity were examined among a large diverse
sample of adults. Participant s (N = 1249, aged 20
- 65 years) wore accelerometers (Actical) for 7
days and completed an interviewer-adminis-
tered physical activity recall questionnaire (IP A Q-
LF) for the same period. Mean daily minutes of
moderate physical activity (MPA) and moderate
to vigorous physical activity (MVPA) were used
in analyses. Linearity between methods was
explored by regressing mean minutes of activity
and Pearson’s correlations were performed. A
weak association between IPAQ-LF and Actical
minutes of MPA and MVPA per day was shown
for the whole sample (rs = 0.216 - 0.260). The
magnitud e of association v aried between males
(rs = 0.265 - 0.366) and females (rs = 0.124 -
0.167), although no obvious variations in asso-
ciations were evident for parenting status. The
IPAQ-LF produced substantially greater varia-
tions in estimates of physical activity than that
recorded by the Actical a ccel ero mete r an d la rg e
discrepancies between methods were observed
at an individual level. Self-report tools provide a
poor proxy of overall human movement, par-
ticularly among females. Inferences made at an
individual level from self-reported data, such as
intervention efficacy or health outcomes, may
have substantial error.
Keywords: Sex; Parent; IPAQ; Accelerometer
1. INTRODUCTION
Physical activity is an essential behavioral element in
maintaining good health, preventing disease, and pro-
longing longevity [1]. The epidemiology of physical
activity considers the association physical activity and
inactivity have with chronic diseases, and the mecha-
nisms to prevent and control these diseases. Accurate
monitoring of physical activity engagement in free-liv-
ing populations is central to correctly determining the
direction and magnitude of these associations and me-
chanisms. Conventionally emphasis has been placed on
measuring moderate and vigorous physical activities
performed in leisure domains, although more recently
non-leisure domains (e.g., occupational, transport, house-
hold) have featured. In response to growing evidence for
the poor health outcomes associated with sedentary be-
haviors [2,3] and the positive health effects of low-level
activity [4,5], a call has been made to incorporate these
behaviors in measures of physical activity so that they
may be tracked and health outcomes determined [6,7].
In the absence of an agreed-upon criterion for quanti-
fying physical activity many types of measures are ap-
plied [8]. Self-report tools (e.g., questionnaires, diaries)
are the most widely-used measure of physical activity at
a population level. Inherently, these tools rely on par-
ticipants’ ability to accurately recall, quantify, and cate-
gorize their physical activity behaviors according to the
framework of the self-report tool. Conversely, motion
sensors (e.g., accelerometers, some pedometers) can be
used to provide an objective assessment of the accumu-
lation of activity movement (commonly lower body
movements) throughout a period of time. Accelerome-
ters (e.g., Actigraph, Actical, Caltrac) use piezoelectric-
ity to register acceleration, recording detailed temporal
data across the spectrum of activity intensity, including
sedentary behaviors and low-level activity.
Many epidemiological studies using self-report meas-
ures have shown women to be less active than men [9].
Moreover, some sub-groups of women, such as women
with young children (WYC), are thought to be even less
active [10]. In part, this may be due differences in the
way physical activity is performed and thus measured.

L. M. Mac kay et al. / Open Journal of Preventive Medicine 1 (2011) 13-19
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14
Firstly, planned activities and those performed at vigor-
ous-intensity are most memorable and are therefore
more likely to be accurately recalled. Whereas, WYC are
known to spend longer durations in total work (paid and
unpaid) each day than men with young children (MYC)
and women without young children (WNYC), poten-
tially leaving less time for planned leisure [11]. This may
also be the case for MYC compared to those without
children (MNYC). Additionally, activities of WYC are
often sporadic and performed simultaneously with other
tasks, for example, carrying a child whilst vacuuming.
These types of activities are regularly interrupted with
needs of young children that need tending, and difficult
to categorize and quantify through self-report [12]. Yet,
accelerometers have the ability to record movement re-
gardless of duration, intensity or purpose. It is possible
therefore, that systematic differences in the validity of
self-report tools may be present across major demo-
graphics.
A variety of self-report tools exist, and many studies
have examined the validity of self-report tools using
accelerometry as the criterion, or objective, measure of
physical activity. Previously, discrepancies between ob-
jective and subjective measures of physical activity have
been shown within adult populations [13,14]; however it
is unknown whether these discrepancies vary by certain
demographic variables. If this were the case, it would
have significant bearing on the selection of measurement
tools dependent upon the population being studied. The
International Physical Activity Questionnaire (IPAQ)
was developed following an international collaboration
to develop a standardized self-report measure of physical
activity suitable for population-wide assessments of
physical activity [15]. The IPAQ long form (IPAQ-LF)
requires recall of physical activity engagement at mod-
erate and vigorous intensities for occupational, transport,
household, and leisure domains for a 7-day period (ei-
ther usual or previous).
This study examines the association between activity
derived from the IPAQ-LF and concurrent accelerometer
derived activity, and, the potential methodological im-
pact on mis-measurement imposed by differences in sex
and adults’ parenting status. The aims of this study were
therefore to: 1) examine the level of association between
self-reported and accelerometer assessed physical activ-
ity engagement in a large sample of adults, and 2) de-
termine differences in the level of association between
measures among men and women with and without
young children (aged 0 - 4 years).
2. METHODS
Participants were part of the Understanding the Rela-
tionship between Activity and Neighborhoods (URBAN)
Study, a multi-centered, stratified, cross-sectional study
of associations between physical activity, health, and the
built environment in adults and children residing in New
Zealand [16]. Objective and self-reported physical activ-
ity engagement, neighborhood perceptions, demograph-
ics, and body size measures were collected, along with
built environment variables. The study contributes to a
larger, international collaborative project where similar
procedures are utilized across eight countries (www.
ipenproject.org).
2.1. Participants
Adults aged 20 to 65 years were recruited randomly
from 48 neighborhoods (stratified by high/low walkabil-
ity, high/low Māori population) across four New Zealand
cities during 2008-2010. Trained interviews followed
pre-determined walk paths for each neighborhood and
approached every nth house, according to the neighbor-
hood household sampling rate. One adult from each
household was invited to participate. Further details of
the neighborhood selection, recruitment methods, and
sample power calculations have been described else-
where [16].
2.2. Data Collection
Trained interviewers gained written informed consent
and delivered accelerometers and travel/compliance logs
during the first home visit. Eight days later, the inter-
viewer visited the home a second time to collect the ac-
celerometer and travel/compliance log, measure partici-
pants’ height, weight, waist, and hip circumferences, and
to administer the study questionnaire.
2.3. Measures
A range of measures were utilized in the URBAN
study. Those relevant to the current study are outlined
below:
2.3.1. Objectively Assessed Physical Activity
Hip-mounted Actical accelerometers (Mini-Mitter,
Sunriver, OR) were used to objectively measure partici-
pants’ physical activity. The units have been shown to be
a reliable and valid measure of physical activity in adult
populations [17,18]. Accelerometers were prepared to
record physical activity and step counts in 30-second
epochs. Participants were instructed to wear the unit for
all waking hours (excluding water-based activities) for
seven consecutive days. Participants self-completed a
compliance log of wear-time and activities the partici-
pant engaged in whilst not wearing units, for the dura-
tion of accelerometer data collection period. The infor-
mation derived from the log was checked and matched

L. M. Mac kay et al. / Open Journal of Preventive Medicine 1 (2011) 13-19
Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/OJPM/
15 15
against accelerometer data.
2.3.2. Self-Reported Physical Activity
The International Physical Activity Questionnaire in
long form (IPAQ-LF) was administered via interview at
the second home visit to capture adults’ self-reported
physical activity for the previous seven days (the period
when the accelerometer was worn). The IPAQ-LF as-
sesses frequency (days), duration (minutes), and inten-
sity (walking, moderate, vigorous) of physical activity
engagement across four domains: occupation, transpor-
tation, household, and leisure. Moderate physical activ-
ity was defined as “those activities that take moderate
physical effort and make you breathe somewhat harder
than usual”; vigorous physical activity as “those activi-
ties that take hard physical effort and make you breathe
much harder than normal” [19]. Evidence for the reli-
ability and validity of this tool has been provided for 744
adults across 12 countries [15].
2.3.3. Demographics
Participants completed a demographic survey that in-
cluded: gender, age, ethnicity, marital status, household
income, academic qualifications, occupation, dwelling
type, and the number and ages of children living in the
dwelling.
2.4. Data Treatment
2.4.1. Self-Reported Physical Activity
According to the IPAQ scoring protocol (www.ip aq . k i.
se/), minutes of physical activity engagement from the
IPAQ-LF were summed across activity domains for each
level of intensity (walking, moderate, and vigorous).
Mean daily minutes of moderate (sum of walking and
moderate, MPA), vigorous (VPA), and sum of MPA and
VPA (MVPA) activity engagement were calculated to
minimize the effect of missing days of accelerometer
data.
2.4.2. Objectively Assessed Physical Activity
Accelerometer data were downloaded using Actical®
version 2.04 (Mini-Mitter Co., Inc., Bend, OR, USA).
Thresholds for MPA and VPA were generated by the
Actical software and were based on MET-value based
cutpoints. Data were prepared for analysis using SAS
(version 9.1, SAS Institute Inc., Cary, NC, USA) and
Microsoft Excel. Bouts of 60 or more consecutive min-
utes of zero counts were considered non-wear-time and
extracted prior to analysis [20]. Wear-time criteria for
inclusion were defined as having five or more days of 10
or more hours of wear-time per day. Mean daily minutes
of MPA, VPA, and MVPA activity were used in all ana-
lyses to ensure comparability across the sample. Mean
values for individuals were calculated using the number
of days of accelerometer wear that met wear- time crite-
ria.
2.5. Statistical An alyses
All analyses were undertaken using SPSS (version 18)
and statistical significance was set at α = 0.05. Shapiro-
Wilk’s test of normality was conducted for both physical
activity measures, and non-normal distributions were log
transformed to achieve normality. Means and standard
deviations were used to describe both methods of meas-
urement for the whole sample and each demographic
(WYC, women with young children [children aged 0 - 4
years]; MYC, men with young children; WNYC, women
with no young children; MNYC, men with no young
children).
Commonly, correlation coefficients are used as a sin-
gle score of validity between measures however it is
appropriate to explore associations with a broader view.
Firstly, a test of the differences between measurement
means allows quantitative assessment of whether meth-
ods are significantly different from each other. Secondly,
a scatter plot of the two measures with the line of iden-
tity provides visual assessment of linearity and system-
atic or random bias in the relationship between measures.
The correlation coefficient can then be calculated as a
summary of the overall scatter between measures, indi-
cating the strength of the linear relationship [21]. There-
fore, paired t tests were used to compare means between
methods, and linearity was explored by regressing mean
minutes of activity at each intensity derived using the
IPAQ-LF against mean minutes of Actical. Evaluation of
linear relationships between Actical and IPAQ-LF using
Pearson’s correlation were performed. Results are pre-
sented for the whole sample and comparisons made be-
tween demographic groups (WYC, MYC, WNYC,
MNYC).
3. RESULTS
3.1. Participants
A total of 2013 adults aged 20 to 65 years participated
in the URBAN study between April 2008 and September
2010. Participants with missing demographic (n = 4) or
IPAQ-LF (n = 5) data were excluded, as were partici-
pants who did not meet criteria for accelerometer
wear-time (n = 731). Outliers in IPAQ-LF data were
calculated using interquartile range (IQR) computation,
where any value more than 3 IQR above the third quar-
tile were considered a problematic outlier (n = 24).
Therefore data from 1249 participants were included in

L. M. Mac kay et al. / Open Journal of Preventive Medicine 1 (2011) 13-19
Copyright © 2011 SciRes. http://www.scirp.org/journal/OJPM/
16
these analyses (Table 1).
3.2. Moderate Physical Activity
Descriptive statistics, presented in Table 1, indicate
that the IPAQ-LF reported significantly higher means of
MPA engagement than the Actical (t = 2.104, p = 0.036).
Additionally, standard deviations of the means were
substantially greater for the IPAQ-LF indicating greater
variance in self-reported activity levels. The scatter plot
demonstrated a weak relationship between measures, as
can be observed in Figure 1; whilst the regression line
passes the line of identity (logny = lognx) near the
means for both measures, the scatter indicates significant
random bias. Further, evaluation of the linear relation-
ship between methods indicated a weak association (r =
0.216, p = 0.000).
Openly accessible at
All demographic groups reported higher mean esti-
mates of MPA by IPAQ-LF than measured by Actical,
however these differences were only significant in
MNYC (t = 2.680, p = 0.008). Weak associations be-
tween methods were found for both WYC and WNYC (r
= 0.124, p = 0.164 and r = 0.130, p = 0.002 respectively),
while for MYC and MNYC a linear (albeit weak-mod-
erate) relationship was found between measures (r =
0.265, p = 0.015 and r = 0.331, p < 0.001, respectively).
3.3. Moderate-to-Vigorous Physical Activity
Actical derived MVPA increased marginally from
MPA whereas IPAQ-LF MVPA values increased dispro-
portionately indicating much greater vigorous activity
via self-report compared with objective measurement
(Ta b l e 1 ). As was observed with MPA, paired t test re-
sults revealed significant differences between method
means (t = –3.385, p = 0.001) and weak association be-
tween methods (r = 0.260, p = 0.000). Scatter plots con-
tinued to show significant random bias in self-report
(Figure 2).
All demographic groups self-reported more MVPA
than recorded by accelerometer, and both male groups
self-reported substantially greater VPA than either fe-
male group. Significant differences were observed be-
tween methods for MVPA means for MYC, and WNYC,
but not for WYC or MNYC. Methods were moderately
correlated for MVPA for both male groups (r = 0.337, p
= 0.002 and r = 0.366, p < 0.001, respectively) yet both
female groups showed similarly weak associations (r =
Figure 1. Association between self-report and ac-
celerometer derived MPA for the whole sample.
Note: dashed line represents line of identity; solid
line represents linear regression line.
Table 1. Participant characteristics and descriptive statistics of IPAQ-LF and Actical measures.
8 Total sample
N = 1249
WYC
N = 128
MYC
N = 84
WNYC
N = 588
MNYC
N = 449
Age
18 - 25 121 10 8 50 53
26 - 35 254 52 32 93 77
36 - 45 353 55 34 161 103
46 - 55 301 6 9 175 111
56 - 65 213 4 1 106 102
Missing 7 1 0 3 3
Height cm* 169.32 ± 9.83 164.80 ± 6.96 177.99 ± 6.12 163.55 ± 7.70 176.53 ± 7.41
Weight kg* 76.51 ± 17.24 72.31 ± 18.42 85.67 ± 13.53 70.16 ± 15.62 84.15 ± 15.68
BMI kg/m2* 26.70 ± 8.26 26.49 ± 5.85 26.90 ± 3.81 26.52 ± 10.98 26.95 ± 4.51
Moderate physical activity
Actical (min·day–1)* 106.51 ± 57.61 104.79 ± 56.79 114.31 ± 56.79 101.31 ± 53.60 112.36 ± 62.33
IPAQ-LF (min·day–1)* 134.91 ± 128.35 132.05 ± 111.40 180.65 ± 185.37 125.36 ± 137.90 139.68 ± 137.90
Correlation R = 0.216, p < 0.001 R = 0.124, p = 0.164R = 0.265, p = 0.015R = 0.130, p = 0.002 R = 0.331, p < 0.001
T-test t = 2.104, p = 0.036 t = –0.205, p = 0.838t = –0.111, p = 0.912t = 0.893, p = 0.372 t = 2.680, p = 0.008
Moderate-vigorous physical
activity
Actical (min·day–1)* 106.75 ± 57.91 104.93 ± 56.86 114.52 ± 57.04 101.48 ± 53.77 112.72 ± 62.85
IPAQ-LF (min·day–1)* 160.66 ± 157.98 140.21 ± 115.72 227.24 ± 234.39 140.56 ± 122.96 180.36 ± 184.03
Correlation R = 0.260, p = 0.000 R = 0.161, p = 0.069R = 0.337, p = 0.002R = 0.167, p = 0.000 R = 0.366, p = 0.000
T-test t = –3.385, p = 0.001 t = –1.144, p = 0.255t = –2.533, p = 0.013t = –2.060, p = 0.040 t = –1.596, p = 0.111
*Values are presented as Mean ± Standard Deviation.

L. M. Mac kay et al. / Open Journal of Preventive Medicine 1 (2011) 13-19
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17 17
Figure 2. Association between self-report and
accelerometer derived MVPA for the whole sam-
ple. Note: dashed line represents line of identity;
solid line represents linear regression line.
0.161, p = 0.069 and r = 0.167, p < 0.001, respectively).
4. DISCUSSION
This study investigated the demographic effects of sex
and parenthood status on associations between a self-
report tool (IPAQ-LF) and objective method (Actical
accelerometry) for describing MPA and MVPA in adults.
A weak linear relationship between IPAQ-LF and Actical
minutes of MPA and MVPA per day was shown and the
magnitude of association varied between men and
women; no obvious variations in associations were evi-
dent for parenting status.
It is evident from the regression plots that the IPAQ-
LF produces greater variation in estimates of physical
activity variables than recorded by the Actical acceler-
ometer, which is reflected by the weak associations
found between measures across all demographic groups.
Importantly, at a population level the method means
were similar however substantial discrepancies between
measures occurred at an individual level. This indicates
that inferences made at an individual level, such as in-
tervention efficacy or health outcomes may have sub-
stantial error; the utility of self-report tools for such a
purpose is therefore questionable for all demographic
groups, however this may be more so among women.
Previous IPAQ-LF validation studies have reported
moderate associations with accelerometry (r = 0.30 -
0.33) [15,22] and doubly labeled water (r = 0.31) [23]
for describing MVPA in adults. None of these studies
reported demographic-specific associations, although
similar associations were reported across 12 countries,
including developing countries [15]. In the validation
study of a self-report tool developed for use among
WYC incorporating a variety of domains, comparably
weak associations of self-reported MVPA with acceler-
ometer-derived MVPA were reported (rs = 0.13) [24].
The same study reported improved associations when
considering MVPA from planned and transport domains
of activity only (rs = 0.28).
Moderate associations between self-reported and ac-
celerometer-derived physical activity are typical [13-25].
Because of their widespread availability, low cost, and
ease of use, self-report tools continue to be employed
despite their well-documented shortcomings [15]. In
particular, self-report tools are cognitively challenging;
they require participants to recall, estimate, and classify
physical activity engagement, usually over a 7 day pe-
riod. A study that probed respondents for clarification of
self-reported responses found 74% over-reported, 10%
under-reported, and 16% reported total activity accu-
rately [26]. Social desirability bias may also lead to
over-inflated estimates of physical activity behavior.
Further, self-report tools are biased to certain patterns of
activity; planned activities and those of vigorous-inten-
sity are more accurately and reliably recalled than low-
level intermittent behaviors [15,26]. The latter meth-
odological flaw potentially misses vast quantities of
health-enhancing activity, especially in those who are
unable or lack opportunity to participate in vigorous-
intensity activity. Efforts have been made to rectify this
somewhat by attempting to capture time spent in occu-
pation and domestic physical activities. Arguably how-
ever, physical activity performed in these domains is
often low-level and intermittent, therefore difficult to
recall accurately. A common feature of self-report tools
is also to exclude activity bouts of <10 minutes. Whilst
this may improve the reliability in recalling behavior it
systematically excludes activities regularly promoted as
health enhancing, such as using stairs instead of the ele-
vator, parking further away and walking the extra dis-
tance, and many domestic and yard activities of moder-
ate-intensity. Algorithms to extract minimum bouts of
activity recorded by accelerometers (e.g., ≥10 minutes,
allowing 1 - 2 minutes interruption within each bout)
have been promoted and utilized in some studies in order
to provide comparability with many self-report tools
[27-29]. Whilst this method may produce greater con-
vergence between measures, this may simply be case of
biasing the objective measure to systematically miss
more actual activity. Such methods were not used in this
study because of its inherent limitations. Firstly, activity
which most health professionals would regard as “health-
related” is often not conducted in one continuous bout,
even after allowing for a 1 - 2 minute interruption, and
may fluctuate between light-intensity and vigorous in-
tensity, as is common place in many sports and recrea-
tional activities. Further, this approach may exclude sig-
nificant contributions that short bouts of activity make to

L. M. Mac kay et al. / Open Journal of Preventive Medicine 1 (2011) 13-19
Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/OJPM/
18
overall daily physical activity.
It is likely that patterns of activity may contribute to
the sex effects observed in this study. Particularly,
women (both with and without young children) may be
more likely to perform low-level intermittent activity
than engage in planned bouts of vigorous-intensity activ-
ity, potentially producing greater variability in reporting.
It appears that sex has a greater effect on self-report ac-
curacy than parent status; this may be due to the pres-
ence of confounding factors such as parental employ-
ment, presence of older children, socioeconomic status,
and marital status. The IPAQ battery of questionnaires
were developed in an attempt to provide a standardized
self-report tool suitable for population estimates of the
prevalence of physical activity so that comparisons be-
tween countries may be made [15]. Whilst there is some
merit in this purpose at a population level, the applica-
tion of self-report tools to determine health outcomes of
physical activity behavior is inappropriate. The present
study has concurred with previous research demonstrat-
ing a weak-to-moderate association between self-re-
ported and accelerometer derived behavior [13,14]. Im-
portantly however, this research further shows that the
ability of self-report tools to capture overall activity is
particularly weak among women, probably due to lower
levels of planned moderate- to vigorous-intensity activity.
With greater emphasis now being placed on measuring
physical activity across the spectrum it is important that
measurement tools are capable of doing so accurately.
The strengths of this study include its large heteroge-
neous sample with a spread of demographics providing
adequate variations in physical activity for exploring
associations across a full range of activity levels. It ap-
pears that the sample in this study were highly active,
although this is an acknowledged outcome of the IPAQ-
LF given the number of domains considered [15]. A li-
mitation of the study is that it was not methodologically
designed for measurement tool validation, reflected by
the high proportion of participants that were excluded
due to inadequate accelerometer wear-time. This study
demonstrates substantial discrepancies between self-
report tools (specifically the IPAQ-LF) and accelerome-
ter derived physical activity. Findings indicate that self-
report tools provide a poor proxy of human movement,
especially among women. Careful consideration must be
given to the patterns of activity in the intended study
population and the purpose of measurement.
5. ACKNOWLEDGEMENTS
The URBAN study was supported by a three-year research grant
from the Health Research Council of New Zealand. At the time of
writing, MO was supported by a National Heart Foundation of New
Zealand Research Fellowship.
REFERENCES
[1] Warburton, D., Charlesworth, S., Ivey, A., et al. (2010) A
systematic review of the evidence for Canada’s Physical
Activity Guidelines for Adults. International Journal of
Behavioral Nutrition and Physical Activity, 7, 39.
doi:10.1186/1479-5868-7-39
[2] Hamilton, M.T., Hamilton, D.G. and Zderic, T.W. (2007)
Role of low energy expenditure and sitting in obesity,
metabolic syndrome, type 2 diabetes, and cardiovascular
disease. Diabetes, 56, 2655-2667.
do i:1 0. 23 37 / db 07 - 08 82
[3] Katzmarzyk, P.T., Church, T.S., Craig, C.L., et al. (2009)
Sitting time and mortality from all causes, cardiovascular
disease, and cancer. Medicine and Science in Sports and
Exercise, 41, 998-1005.
doi:10.1249/MSS.0b013e3181930355
[4] Levine, J.A., McCrady, S.K., Lanningham-Foster, L.M.,
et al. (2008) The role of free-living daily walking in hu-
man weight gain and obesity. Diabetes, 57, 548-554.
do i:1 0. 23 37 / db 07 - 08 15
[5] Levine, J.A., Schleusner, S.J. and Jensen, M.D. (2000)
Energy expenditure of nonexercise activity. American
Journal of Clinical Nutrition, 72, 1451-1454.
[6] Brown, W.J., Bauman, A.E. and Owen, N. (2009) Stand
up, sit down, keep moving: Turning circles in physical
activity research? British Journal of Sports Medicine, 43,
86-88. doi:10.1136/bjsm.2008.055285
[7] Hallal, P. and Ekelund, U. (2010) CRF, MVPA, NEAT,
PAEE, and now sedentary time: Will the pendulum swing
back again? Journal of Physical Activity and Health, 7,
569-570.
[8] Welk, G.J. (2002) Physical activity assessments for health-
related research. Human Kinetics, Champaign.
[9] Bauman, A., Bull, F., Chey, T., et al. (2009) The interna-
tional prevalence study on physical activity: Results from
20 countries. International Journal of Behavioral Nutri-
tion and Physical Activity, 6, 21.
doi:10.1186/1479-5868-6-21
[10] Bellows-Riecken, K.H. and Rhodes, R.E. (2008) A birth
of inactivity? A review of physical activity and parent-
hood. Preventive Medicine, 46, 99-110.
doi:10.1016/j.ypmed.2007.08.003
[11] Craig, L. and Bittman, M. (2008) The incremental time
costs of children: An analysis of children’s impact on
adult time use in Australia. Feminist Economics, 14,
59-88. doi:10.1080/13545700701880999
[12] Collins, B.S., Marshall, A.L. and Miller, Y.D. (2007)
Physical activity in women with young children: How
can we assess “anything that’s not sitting”? Women and
Health, 45, 95-116. doi:10.1300/J013v45n02_06
[13] Prince, S.A., Adamo, K.B., Hamel, M.E., et al. (2008) A
comparison of direct versus self-report measures for as-
sessing physical activity in adults: A systematic review.
International Journal of Behavioral Nutrition and Phys-
ical Activity, 5, 56. doi:10.1186/1479-5868-5-56
[14] Mackay, L.M., Schofield, G.S. and Schluter, P.J. (2007)
Validation of self-report measures of physical activity: A
case study using the New Zealand Physical Activity

L. M. Mac kay et al. / Open Journal of Preventive Medicine 1 (2011) 13-19
Copyright © 2011 SciRes. http://www.scirp.org/journal/OJPM/Openly accessible at
19 19
Questionnaire. Research Quarterly for Exercise and
Sport, 78, 189-196
[15] Craig, C.L., Marshall, A.L., Sjostrom, M., et al. (2003)
International physical activity questionnaire: 12-country
reliability and validity. Medicine and Science in Sports
and Exercise, 35, 1381-1395.
doi:10.1249/01.MSS.0000078924.61453.FB
[16] Badland, H., Schofield, G., Witten, K., et al. (2009) Under-
standing the Relationship between Activity and Neigh-
bourhoods (URBAN) Study: Research design and meth-
odology. BMC Public Health, 9, 224.
doi:10.1186/1471-2458-9-224
[17] Heil, D.P. (2006) Predicting activity energy expenditure
using the Actical activity monitor. Research Quarterly
for Exercise and Sport, 77, 64-80.
[18] Welk, G.J. (2002) Use of accelerometry-based activity
monitors to assess physical activity. In: Welk, G.J. Ed.,
Use of accelerometry-based activity monitors to assess
physical activity, Human Kinetics, Champaign, 125-142.
[19] IPAQ Group (2002) International Physical Activity
Questionnaire: Long last 7 days telephone format.
www.ipaq.ki.se
[20] Oliver, M., Schofield, G.M., Badland, H., et al. (in press)
Identification of non-wear time and sedentary behavior
using accelerometry. Research Quarterly for Exercise
and Sport.
[21] Hopkins, W.G. (2010) A Socratic dialogue on comparison
of measures. Sport Science, 14, 15.
[22] Boon, R.M., Hamlin, M.J., Steel, G.D., et al. (2010) Va-
lidation of the New Zealand Physical Activity Question-
naire (NZPAQ-LF) and the International Physical Activ-
ity Questionnaire (IPAQ-LF) with accelerometry. British
Journal of Sports Medicine, 44, 741-746.
doi:10.1136/bjsm.2008.052167
[23] Maddison, R., Ni M., C., Jiang, Y., et al. (2007) Interna-
tional Physical Activity Questionnaire (IPAQ) and New
Zealand Physical Activity Questionnaire (NZPAQ): A
doubly labelled water validation. International Journal of
Behavioral Nutrition and Physical Activity, 4, 62.
doi:10.1186/1479-5868-4-62
[24] Fjeldsoe, B.S., Marshall, A.L. and Miller, Y.D. (2009)
Measurement properties of the Australian Women’s Ac-
tivity Survey. Medicine and Science in Sports and Exer-
cise, 41, 1020-1033.
doi:10.1249/MSS.0b013e31819461c2
[25] Sallis, J.F. and Saelens, B.E. (2000) Assessment of phys-
ical activity by self-report: Status, limitations, and future
directions. Research Quarterly for Exercise and Sport, 71,
1-14.
[26] Rzewnicki, R., Vanden Auweele, Y. and De Bourdeaud-
huij, I. (2003) Addressing overreporting on the Interna-
tional Physical Activity Questionnaire (IPAQ) telephone
survey with a population sample. Public Health Nutrition,
6, 299-305. doi:10.1079/PHN2002427
[27] Masse, L., Fuemmeler, B.F., Anderson, C., et al. (2005)
Accelerometer data reduction: A comparison of four re-
duction algorithms on select outcome variables. Medicine
and Science in Sports and Exercise, 37, S544-S554.
doi:10.1249/01.mss.0000185674.09066.8a
[28] Troiano, R.P., Berrigan, D., Dodd, K.W., et al. (2008)
Physical activity in the United States measured by accel-
erometer. Medicine and Science in Sports and Exercise,
40, 181-188.
[29] Ward, D.S., Evenson, K.R., Vaughn, A., et al. (2005)
Accelerometer use in physical activity: Best practices
and research recommendations. Medicine and Science in
Sports and Exercise, 37, S582-S588.
doi:10.1249/01.mss.0000185292.71933.91