SAJSM vol 20 No. 1 2008 21
Introduction
Human movement and the concomitant increase in energy ex-
penditure are fundamental aspects of human existence. The
importance of movement-related energy expenditure has been
acknowledged since antiquity but has only relatively recently
seen substantial research activity.
7
Arguably, the dramatic global
increase in chronic diseases of lifestyle over the last century has
spurred the interest in exploring the importance of human energy
expenditure in relation to health
7
and has led to evidence-based
public health guidelines for health-enhancing physical activity.
13
A number of instruments are available for estimating human
energy expenditure and range from paper and pencil methods to
doubly-labelled water.
20,28
Irrespective of the method employed,
it is important that the sources and magnitude of the variability
of physical activity are quantified so that research activities in
physical activity and health are appropriately designed, analysed
and interpreted.
28
By partitioning physical activity variability
into discrete components, the number of periods of monitoring
required to reliably estimate physical activity volumes and
patterns of individuals in a population can be determined.
28
Importantly, the number of periods of monitoring will influence
aspects of study design such as sample size and statistical
power.
28
It should be noted that assessments of energy intake
(diet) and energy expenditure (physical activity) are susceptible
to the same types of measurement error.
16
Numerous studies in industrialised countries have
investigated the reliability of objectively monitored free-
living physical activity.
3,8-11,14-16,21,24-27
However, few papers
have reported sources of variation for either physical activity
questionnaires
14,17,18
or objectively monitored physical activity.
15
Within the South African context there is a dearth of reliability
studies for any form of physical activity assessment.
2,4,5,12
The
reliability of objectively monitored free-living physical activity in
South African samples has not been reported. Moreover, no data
have been reported regarding the sources of variation for any
type of physical activity measurement instrument in South African
samples. From a regional and international perspective, we are
not aware of any data from sub-Saharan Africa or any developing
origiNAl reSeArch ArTicle
Sources of variance and reliability of objectively
monitored physical activity in rural and urban Northern
Sotho-speaking blacks
Abstract
Objectives. We investigated the sources of variance and reli-
ability in an objective measure of physical activity for a 14-
hour and 4-day monitoring period.
Design. A convenience sample of rural (N=31) and urban
(N=30) adult, Northern Sotho-speaking blacks was recruited.
Physical activity was assessed for 8 consecutive days using
a uni-axial accelerometer. Physical activity indices were total
counts, average counts, inactivity (<500 counts) moderate-
1 activity (500 - 1 951 counts), moderate-2+vigorous activity
(≥1 952 counts), and were expressed per hour or per day as
required.
Results. Accelerometry data from 41 subjects (23 males,
18 females) complied with selection requirements and were
analysed for variance distribution and reliability (intraclass
correlation coefficients (ICCs)). For the 14-hour monitoring
period variance was distributed as follows: intra-individual (71
- 82%), inter-individual (3 - 18%) and hour-of-day (2 - 14%).
Attenuated ICCs ranged from 0.31 to 0.75 (median: 0.70).
Variance for the 4-day monitoring period differed from the
14-hour monitoring period: inter-individual (47 - 58%), intra-
individual (43 - 51%) and day-of-week (0 - 6.5%). Attenuated
ICCs ranged from 0.27 to 0.84 (median: 0.79). Irrespective
of the monitoring period, total counts, average counts and
moderate-2+vigorous activity tended to be the most reliable
measures requiring the fewest number of monitoring periods.
Conclusions. These findings provide an insight for under-
standing how variance is distributed in objectively measured
activity patterns of a South African sample and show that reli-
CORRespOnDenCe:
Ian Cook
Physical Activity Epidemiology Laboratory
University of Limpopo (Turfloop Campus)
PO Box 459
Fauna Park
0787 Polokwane
South Africa
Tel+fax: +27 15 268 2390
E-mail: ianc@ul.ac.za
ian cook (BA (Phys ed) hons, BSc (Med) hons)1
estelle v lambert (PhD)2
1
Physical Activity Epidemiology Laboratory, University of Limpopo (Turfloop Campus), South Africa
2
MRC/UCT Research Unit for Exercise Science and Sports Medicine, University of Cape Town Medical School, South Africa
able measures of adult physical activity behaviours require
18 - 128 hours and 3 - 44 days, depending on the monitoring
period, physical activity index, residence status and sex.
pg21-27.indd 21 4/23/08 11:32:03 AM
22 SAJSM vol 20 No. 1 2008
country that have addressed variance distribution and reliability
of objectively monitored free-living physical activity.
Reliability and variance distribution have been widely
investigated within nutritional epidemiology
22,23,29
but less so
in physical activity measures that are often used to estimate
physical activity patterns and energy expenditure.
28
This is
probably because of the relatively recent emergence of a new
branch of epidemiology, namely physical activity epidemiology.
7
Considering the heterogeneity of the South African population,
studies investigating the variance distribution and reliability of
physical activity assessments across sub-sections of the South
African population are required.
The objective of this paper was firstly to investigate the
sources and distribution of variance for objectively measured
physical activity over a number of hours and days in a sample
of rural and urban Northern Sotho-speaking blacks. The second
objective was to determine the number of hours and days
required to reliably measure 1 hour and 1 day of accelerometer-
derived indices of physical activity in this particular South African
sample.
Methods
study protocol
The data used in this analysis were collected during the va-
lidity trial of the International Physical Activity Questionnaire
(IPAQ) which has been reported elsewhere.
2,5
For this analy-
sis only the accelerometer data were considered. Briefly,
black Northern Sotho-speaking rural and urban participants
were recruited and contacted twice over an 8-day period.
On the first occasion, subjects were recruited, completed a
socio-demographic questionnaire and provided anthropometric
data. All interviews and anthropometric measures were con-
ducted by trained black male and female field workers. Anthro-
pometric measures included body mass (kg) and stature (cm)
allowing the calculation of body mass index (BMI, kg.m
-2
). Fi-
nally, subjects were instructed on the necessary procedures for
wearing the accelerometer. Eight days later the accelerometers
were collected. Subjects received a small honorarium on com-
pletion of the study. Signed informed consent was obtained from
all participants. The study was approved by the Ethics Commit-
tee of the University of Limpopo (Turfloop Campus).
subjects
Rural sample
A convenience sample of black employees, resident on farms
and villages, were recruited from the plantation section of a local
lumber mill situated in the Limpopo Province, South Africa (total
N=31, males N=18, females N=13). These workers performed a
variety of manual tasks and ensured that plantations were cre-
ated and maintained, and that raw timber was harvested, sized,
cleaned and stacked prior to transport to the saw mill for further
processing.
Urban sample
A convenience sample was recruited from black academic staff,
support staff and students of the University of the Limpopo
(Turfloop Campus), and black residents (office workers, teachers)
from the surrounding community (Mankweng) and nearby city
(Polokwane) (total N=30, males N=14, females N=16). For the
most part, these subjects performed tasks typical of office workers,
with long periods of sedentary activity (sitting, standing quietly).
physical activity counts and durations
To objectively quantify free-living physical activity of the subjects,
uni-axial accelerometers were worn for at least 8 days. The CSA
model 7164 (Computer Science Applications, Inc. Shalimar, FL),
now marketed as the MTI Actigraph (MTI Health Services, Fort
Walton Beach, FL), is small and unobtrusive (5.1 cm x 4.1 cm
x 1.5 cm, 42.6 g).
28
In this study, the epoch duration was set at
1 minute. The accelerometer was worn on the right waist, se-
curely attached to a nylon belt. The accelerometers could be
removed for sleeping and bathing purposes by unclipping the
nylon belt. The data were downloaded from the accelerometers
onto an IBM-compatible personal computer via an interface unit,
for further analysis using CSA-supplied software (DAYBYDAY.
XLS, Microsoft Excel©97 macro) and a customised data reduc-
tion programme (Microsoft Excel©97 macro). Physical activity
counts were defined as total counts (counts.day
-1
) and aver-
age counts (counts.min-1.day
-1
). Physical activity intensity pat-
terns or durations (min.day
-1
) of inactivity and moderate and
vigorous activity were created according to cut-points defined
by Matthews et al.
15
Inactivity (sitting, standing quietly) was de-
fined as less than 500 counts.min
-1
. For moderate activity (3-6
METs, 1 MET = 1 metabolic equivalent = 3.5 mlO2.kg
-1
.min
-1
= 1
kcal.kg
-1
.hr
-1
) a distinction was made between activities requir-
ing less ambulation (moderate-1: house work, yard work) and
predominantly ambulatory activities (moderate-2: walking). The
cut-points for moderate-1 and moderate-2 were defined as 500
- 1 591 counts.min
-1
and 1 592 - 5 724 counts.min
-1
, respec-
tively. Activities, such as running, which record more than 5 724
counts.min
-1
were defined as vigorous (>6 METs).
The first and last days of the 8-day monitoring period were
excluded. To evaluate the number of hours required to reliably
estimate 1 hour of objectively monitored physical activity, the
first weekday with at least 14 hours of registration (06h00 to
20h00) was selected. To evaluate the number of days required to
reliably estimate 1 day of objectively monitored physical activity,
accelerometer data for 4 days (3 weekdays and 1 weekend day)
were used. Only days with at least 10 hours.day
-1
(600 minutes.
day
-1
) of registration were included.
5
From the minute-by-minute
data, hourly and daily accelerometry indices were summed
(counts.hour
-1
, counts.day
-1
, minutes.hour
-1
, minutes.day
-1
).
Accelerometry data of 41 subjects (23 males, 18 females) which
constituted 67.2% of the original sample of 61, complied with all
the selection criteria.
statistical analysis
The descriptive analysis comprised residence-specific means
and standard deviations and percentages for continuous and
categorical variables, respectively. For skewed continuous accel-
erometry variables (≥2x standard deviation), residence-specific
medians and interquartile ranges were calculated. Differences
(rural v. urban) between two independent categorical variables
were tested for significance (with continuity correction for small
sample sizes).
1
To examine differences (rural v. urban) between
two independent continuous variables, an independent t-test
was used. Because the distributions of some variables were
neither normal nor lognormal a comparable non-parametric test
was used (Mann-Whitney U test).
pg21-27.indd 22 4/23/08 11:32:04 AM
SAJSM vol 20 No. 1 2008 23
Hourly (14 hours) and daily (4 day) accelerometry indices were
rank transformed because the distributions of several residence-
specific accelerometer indices were neither normal nor lognormal.
To evaluate the sources of variability in ranked accelerometer
data, variance components in mixed and random effects models
were estimated using restricted maximum likelihood methods.
15
Accelerometer indices were the dependant variables for these
analyses. Variance components were estimated for subject (inter-
individual) variance, trial (hour or day) variance, and residual
(intra-individual) variance. The variance components were also
expressed as a percentage of the total variance. Inter-individual
variance represents true variation between subjects while intra-
individual variance represents hour-to-hour or day-to-day variation
within subjects. The variance due to the hour or day effect was
nested within subjects. To identify variables that could affect the
inter-individual variance and thus the reliability we entered age,
body mass index, educational level, residence (rural/urban)
and sex (male/female) individually as fixed factors. From this
preliminary analysis (data not shown) we identified residence and
sex as having the most consistent and substantial impact on inter-
individual variance. The first analysis was conducted on the whole
sample such that variance components for subject, trial (day or
TAble I. Descriptive characteristics for rural and urban subjects
Residence
Rural Urban p
‡
Continuous variables (N = 21) (N = 20)
Age (years) 38.9 (10.4) 32.9 (6.7) 0.037
BMI (kg.m
-2
) 22.9 (3.9) 27.2 (5.3) 0.006
Accelerometer data (4-day average)
Activity counts (cts)
Total counts (cts.day
-1
) 644 102 (208 420) 409 341 (169 799) 0.001
Average counts (cts.min-1.day
-1
) 847 (267) 618 (248) 0.008
Duration (min.day
-1
)
Inactivity (0 - 499 cts) 1 078 (92) 1 236 (58) <0.001
Moderate 1 (500 - 1 951 cts) 265 (67) 141 (35) <0.001
Moderate 2 – vigorous (>1 951 cts) * 94 (55) 51 (65) 0.027
Categorical variables
†
Body mass index classification
Normal weight (<25 kg.m
-2
) 76.2 (16) 40.0 (8) 0.042
Overweight to obese (≥25 kg.m
-2
) 23.8 (5) 60.0 (12) 0.042
Female participants 47.6 (10) 40.0 (8) 0.860
Education (≥grade 12) 0 (0) 85.0 (17) <0.001
Ownership of motor vehicle (yes) 14.3 (3) 40.0 (8) 0.132
Electricity available inside house (yes) 19.0 (4) 85.0 (17) <0.001
Data are reported as mean (SD) for all continuous variables except * median (interquartile range) and categorical variables
†
% (n),
‡
p values evaluate rural v. urban differences.
TAble II. Crude and adjusted intra- to inter-subject variance ratios (σ2w /σ
2
b) by monitoring period
σ
2
w /σ
2
b ratio
Variables
*
Crude
†
Adjusted
‡
% Change
§
14-Hours (N = 41) Total counts 3.44 7.06 105.0
Average counts 3.80 7.29 91.6
Inactivity 3.09 9.07 193.2
Moderate-1 2.75 11.34 312.8
Moderate-2+vigorous 3.41 5.35 56.9
4-Days (N = 41) Total counts 0.66 1.01 53.6
Average counts 0.55 0.73 33.6
Inactivity 0.58 1.53 163.0
Moderate-1 0.67 2.49 269.9
Moderate-2+vigorous 0.77 1.04 35.6
* See Table Ι for variable units,
†
unadjusted for fixed effects of residence and sex,
‡
adjusted for fixed effects of residence and sex,
§
% change = [(Adjusted – Crude)/Crude] x 100.
pg21-27.indd 23 4/23/08 11:32:05 AM
24 SAJSM vol 20 No. 1 2008
hour) and residual were extracted with and without adjustment for
fixed effects of residence and sex. From the extracted variance
components, intra- to inter-subject variance ratios (σ2w /σ
2
B) were
calculated, where σ2B was the between or inter-individual variance
and σ2w was the within- or intra-individual variance. To examine
possible differences in the distribution of variance (inter-individual,
hour or day effect, residual) across residence status, the second
analysis was stratified by residence while treating sex as a fixed
factor.
Reliability coefficients were calculated from the variance
components extracted from the residence-stratified variance
component analysis, with sex treated as a fixed factor. Reliability
was calculated as an average measure (ICCM) and a single
measure (ICCS) intraclass correlation coefficient (ICC) using the
following equations, ICCM = σ
2
B / (σ
2
B + σ
2
w) and ICCS = σ
2
B /
(σ2B +σ
2
w / k), where σ
2
B was the inter-individual variance, σ
2
w
was the intra-individual variance and k was the number of days or
hours.
19
Because unbounded, ranked data were used to obtain an
ICC from a model meant for continuous data,
6
the corrected and
uncorrected ICCM from the mean squares of an ANOVA-based
variance component analysis were also calculated.
19
There was
no difference in ICCM after the correction (data not shown).
Deattenuated 4-day and 14-hour ICCm were calculated using
the formula, ICCtrue = ICCobs x (1 + [σ
2
w / σ
2
B] / k)
0.5
where ICCtrue
was the true correlation, ICCobs was the observed correlation,
σ2w was the intra-individual variance, σ
2
B was the inter-individual
variance and k the number of monitoring periods.
16,28
Because
random variation (intra-individual variance) reduces the ability to
identify significant effects, deattenuation is employed to adjust for
random variation such that a better estimate is obtained of the true
statistic. To estimate the number of hours and days required to
reliably predict 1 hour and 1 day of accelerometry, respectively, the
following equation was rearranged to solve for k, ICC = σ2B / (σ
2
B
+σ2w / k) were ICC = 0.80. Data were analysed using appropriate
statistical software (SPSS for Windows 11.0.1). Significance for all
inferential statistics was set at p<0.05.
Results
Subject characteristics are reported in Table I. Because of the
relatively low volume and highly skewed distribution of the record-
ed vigorous activity (rural: 3.7±6.7 min v. urban: 3.2±5.3 min), the
moderate-2 and vigorous variables were combined. Significant
differences were found between rural and urban groups for all
continuous and categorical variables, except for sex distribution
and vehicle ownership. Of note were the significantly lower levels
of obesity and inactivity, and greater levels of activity in the rural
group compared with the urban group.
Crude and adjusted variability ratios (σ2w /σ
2
B) for accelerometer
indices are reported in Table II. Both crude and adjusted variability
ratios were far higher for hourly accelerometer variables compared
with daily accelerometer variables. After adjustment for residence
and sex, the variability ratios increased for both 14-hour and 4-
day accelerometer variables by 34 - 313%, although the increases
were greater for the 14-hour period compared with the 4-day
period. Adjustment for residence and sex reduced the inter- or
between-subject variability (σ2B), thereby increasing the ratio. The
higher ratios mean that more periods of objective physical activity
monitoring would be required to reliably predict physical activity,
especially so for hour-by-hour accelerometer indices.
Total variance in each of the 14-hour accelerometer indices
was higher in the urban sample, suggesting that the distribution
of activity and inactivity levels in the urban sample was more
heterogeneous compared with the rural sample (Table III). For
both groups intra-individual variability was the largest source of
variance (71 - 82%). The distribution of inter-individual and hour of
day variability differed between the rural and urban group. In the
rural group, hour of day variability was the second highest source
of variance (15 - 18%), followed by inter-individual variability (3
- 14%). In contrast, for the urban group, inter-individual variability
was the second highest source of variance (14 - 18%), followed by
hour of day variability (2 - 7%).
Unlike the 14-hour accelerometer variability, total variance for
the 4-day period was not consistently higher in the rural or urban
group (Table IV). In the rural group, inter-individual variance (47
- 58%) tended to be slightly higher than intra-individual variance
(43 - 51%), while day of week variability was lowest (0 - 6.5%) of
all sources of variance. For accelerometer counts and moderate-
2+vigorous activity level, variance distribution in the urban group
mirrored that of the rural group; 49 - 57% inter-individual, 44 - 51%
intra-individual and 0 - 2% day of week. In contrast, the urban
group intra-individual variance for inactivity and moderate-1 levels
were high compared with inter-individual variance: 69 - 91% v. 8
- 31%, respectively.
Attenuated reliability coefficients for 14-hour accelerometer
indices were less than 0.8 and were lower in the rural group
compared with the urban group (Table V). Hourly moderate-
2+vigorous activity was the most reliable for both the rural and
urban groups. The most unreliable accelerometer indices were
the inactivity and moderate-1 levels in the rural group. Excluding
the two lowest reliabilities, the attenuated reliability coefficients
increased by 0.12 to 0.19 units after accounting for intra-individual
variation, while the reliability coefficients for the inactivity and
moderate-1 indices increased by ~0.23 units after deattenuation.
The difference between rural and urban reliability remained even
after deattenuation of all the reliability coefficients. To achieve a
reliability coefficient of 0.8 for hourly accelerometer variables in
the urban group would require approximately 2 periods of 12-hour
monitoring (24 hours). In contrast, approximately 4 - 11 periods
of 12-hour monitoring (48 - 130 hours) would be required in the
rural group. In both groups, moderate-2+vigorous activity required
fewer hours of monitoring to reliably predict 1 hour of activity (19
- 24 hours) compared with other accelerometer indices.
The reliability of 4-day accelerometer indices was generally
higher compared with the 14-hour accelerometer variables
(Table VI). Attenuated reliability coefficients in both the rural and
urban groups were nearly identical except for the low reliability
coefficients for inactivity and moderate-1 indices in the urban
group. The values of 8 of the 10 attenuated reliability coefficients
increased by 0.08 - 0.10 units after accounting for the intra-
individual variation. The effect of deattenuation was not greater
in the rural group (mean difference = 0.10 units) or the urban
group (mean difference = 0.09 units) for 8 of the 10 reliability
coefficients. Because of the higher intra-individual variation in the
inactivity and moderate-1 activity indices of the urban group, the
attenuated reliability coefficients increased by 0.16 to 0.48 units.
In the rural group, at least 5 days of monitoring would be required
to reliably predict one day of activity or inactivity. However, in the
urban group, to reliably predict one day of inactivity, moderate-1
activity and moderate-2+vigorous activity would require 9, 44 and
4 days of monitoring, respectively.
pg21-27.indd 24 4/23/08 11:32:07 AM
SAJSM vol 20 No. 1 2008 25
Discussion
This study is novel for two reasons. It is the first analysis that
has reported on the reliability of objectively monitored physical
activity in a South African setting. It is also the first analysis that
has investigated the distribution of variance for any physical ac-
tivity measure in a South African sample. The principal findings
of this analysis were firstly that the distribution of variance dif-
fered depending on the sampling period. For the 4-day sampling
period, between- or inter-subject variability, which represents
true differences in physical activity indices between subjects,
was at least as large as within- or intra-individual variability
(behavioural variability), while day of week accounted for little
of the variance (<7%). In contrast, for the 14-hour monitoring
period, intra-individual variation accounted for more than 70%
of the variance, while hour of day and inter-individual variation
accounted for the remaining variance. Secondly, irrespective of
the monitoring period (14-hour or 4-day), total counts, average
counts and moderate-2+vigorous activity tended to be the most
reliable measures requiring the fewest number of monitoring
periods. Thirdly, adjustment for basic demographic factors such
as residence and sex prevents the under-estimation of monitor-
ing days required so that reliable estimates of physical activity
volumes and patterns can be obtained.
The authors are not aware of any other analysis investigating
the reliability and variance distribution of accelerometry data
collected in adult populations over monitoring periods shorter
than a day. The results from the 14-hour monitoring period of the
present study show that a reversal in variance distribution occurs
in comparison to the 4-day period; intra-individual variance >
inter-individual variance. Moreover, the relative contributions of
the inter-individual variance and hour of day variance to the total
variance were contrasted in the two residence-defined groups.
This can be explained by the fact that the physical activity
patterns in the rural group show relatively large changes over
the course of the 14 hours, ranging from physical inactivity in
the morning to being physically active during the working day,
which is interspersed with breaks (tea, lunch), and back again to
physically inactive levels during the late afternoon and evenings.
This type of hourly activity pattern was quite homogenous
throughout the rural group such that inter-individual variance
was lower. In contrast, the activity patterns of the urban group
tended to remain relatively constant over the period of the 14
hours, although this could differ between individuals, which
explains the higher inter-individual variance in this group. It is
likely then that similar investigations of hourly physical activity
patterns in different samples will yield variance distributions
that are in accord with the particular activity demands required
of those samples. Importantly, the number of periods required
to reliably estimate physical activity volumes and patterns will
differ from sample to sample, particularly over shorter monitoring
periods where variance contrasts between groups can be large.
The greater intra-individual variance in the 14-hour monitoring
period, although in accord with the variance distribution in
questionnaire-based physical activity assessment cannot be
because of factors related to the imprecision of measurement
found in non-objective physical activity assessment.
17
Rather,
the greater intra-individual variance could be due to the natural
variation in physical activity behaviour from hour to hour.
The results for the 4-day monitoring period are generally in
agreement with data from North America in that inter-individual
variation accounted for most of the variation.
15
Matthews et al.
examined accelerometry data collected from 92 adults over a
period of 21 consecutive days.
15
They found inter-individual
variation contributed the most to overall variance (55 - 60%)
followed by intra-individual variance (30 - 45%) and day of the
week variance (1 - 8%). The number of days required to achieve
80% reliability for estimating activity counts and moderate-
2+vigorous activity was 3 - 4 day,
15
which is in agreement with
the present results of 4 - 5 days. Moreover, the North American
data also found that estimating physical inactivity was more
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(
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fix
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ff
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ct
s
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ce
.
pg21-27.indd 25 4/23/08 11:32:08 AM
26 SAJSM vol 20 No. 1 2008
unreliable requiring more days of monitoring compared to most
of the physical activity indices (7 days) and is in agreement with
our finding of 5 - 10 days in the present results. The difference
between inter-individual and intra-individual variance in the
present study was not as pronounced as found by Matthews et
al.
15
but is still quite different to the variance distribution found in
questionnaire-based physical activity assessment (50 - 60% intra-
individual, 20 - 30% inter-individual).
18
It has been suggested
that the differences in variance distribution between objective
and self-reported physical activity assessment may be due to
factors such as precision of objective measuring instruments,
the ability of objective measuring instruments to detect common,
light intensity activities and the level of variability present in self-
report instruments.
15
The results of the present investigation also accord with the
prediction of Matthews et al. that because of the differences
between study samples in terms of variance distribution, each
study sample would have different sampling requirements.
15
The present results have shown general agreement in that inter-
individual variance is at least as great as intra-individual variance.
Specific differences have also been shown in the present study, in
that the differences between inter-individual and intra-individual
variances are not as pronounced as those found by others.
15
Consequently, the number of days required to reliably estimate
the various physical activity and inactivity indices differ from
that proposed by others.
15
The predicted qualitative differences
between our results from those of others
15
would appear to add
further support the validity of the present analysis.
It would certainly be profitable to analyse the larger South
African accelerometry dataset that was part of the IPAQ
validation study, especially because of the heterogeneity of the
South African population. This dataset contains accelerometry
data from a relatively large sample of subjects (N>100) differing
in age, body composition, education level, ethnicity, fitness,
language, residence, sex, and socio-economic status. The
examination of the reliability and the variance distribution of this
dataset would provide valuable information for the South African
researcher. There is a lack of published information regarding
the number of days of objectively monitored physical activity
that would be required to reliably estimate objectively measured
physical activity levels and patterns in specific sub-sections of
the South African population.
The strength of the present study is firstly the uniqueness of
the analysis within a South African context, which will hopefully
provide further motivation and impetus for more analyses of this
kind. Secondly, this analysis provides reliability and variance
estimates for a South African sample of a particular ethnicity,
T
A
b
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I
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.
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R
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†
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d
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fix
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ff
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%
s
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rc
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ta
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f
to
ta
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ri
a
n
ce
.
TAble V. Intraclass correlation reliability analysis of
14-hour accelerometer output indices in rural and
urban subjects
Reliability Required number
(ICC) of hours to achieve
a reliability of 0.8
Variables
*
14 hours
†
1 hour
Rural (N = 21)
Total counts 0.55 (0.74) 0.08 45.4
Average counts 0.55 (0.74) 0.08 45.5
Inactivity 0.40 (0.63) 0.04 85.4
Moderate-1 0.31 (0.55)
‡
0.03 127.6
Moderate- 0.70 (0.84) 0.14 23.7
2+vigorous
Urban (N = 20)
Total counts 0.74 (0.86) 0.17 19.7
Average counts 0.73 (0.85) 0.16 20.8
Inactivity 0.74 (0.86) 0.17 20.1
Moderate-1 0.70 (0.84) 0.14 23.9
Moderate- 0.75 (0.87) 0.18 18.5
2+vigorous
*
see Table I for variable units, ICC = intraclass correlation coefficient,
†
deattenuated
ICCm appear in parenthesis, all ICC significant (p < 0.05) except
‡
(p=0.1036).
pg21-27.indd 26 4/23/08 11:32:09 AM
SAJSM vol 20 No. 1 2008 27
language and residence status. The weakness of this study is
the relatively low number of subjects. However, the fact that our
results concur generally and differ specifically, as expected, with
the results of a similar, larger analysis,
15
suggests that despite
the relatively small sample size the results of the present analysis
are valid. It should also be noted that some of the stratified
random effects analyses performed by Matthews et al. were
done on sample sizes as low as 14.
15
In conclusion, this analysis has provided quantitative
estimates of the reliability and distribution of variance of
objectively measured physical activity measures in a specific
ethnic and language group, over two monitoring periods (14-
hour and 4-day). Further analyses using larger sample sizes
and in different sub-sections of the South African population are
required for both questionnaire-based and objectively measured
physical activity indices.
Acknowledgements
We are indebted to the study participants and field workers
for their sustained and friendly co-operation, and to Mr Trevor
Phillips who kindly arranged access to the forestry and factory
staff at Steven’s Lumber Mills. The Research Development and
Administration Division of the University of Limpopo (Turfloop
Campus) and the Research Capacity Development Group of the
Medical Research Council of South Africa supported this study.
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TAble VI. Intraclass correlation reliability analysis of
4-day accelerometer output indices in rural and urban
subjects
Reliability Required number
(ICC) of days toachieve
a reliability of 0.8
Variables * 4 days
†
1 day
Rural (N = 21)
Total counts 0.80 (0.89) 0.50 4.0
Average counts 0.84 (0.92) 0.58 3.0
Inactivity 0.78 (0.88) 0.47 4.5
Moderate-1 0.76 (0.87) 0.45 5.0
Moderate- 0.79 (0.89) 0.49 4.2
2+vigorous
Urban (N = 20)
Total counts 0.79 (0.89) 0.49 4.2
Average counts 0.84 (0.92) 0.57 3.1
Inactivity 0.64 (0.80) 0.31 9.0
Moderate-1 0.27 (0.75) ‡ 0.08 43.8
Moderate- 0.79 (0.89) 0.49 4.2
2+vigorous
*See Table I for variable units, ICC = intraclass correlation coefficient,
†
deattenuated
ICCm appear in parenthesis, ICC significant (p<0.002) except
‡ (p=0.1799).
pg21-27.indd 27 4/23/08 11:32:10 AM