Agreement in activity energy expenditure assessed by accelerometer
and self-report in adolescents: Variation by sex, age, and weight status
ARISTIDES M. MACHADO-RODRIGUES1,4, MANUEL J. COELHO-E-SILVA1,
JORGE MOTA2, EDILSON CYRINO3, SEAN P. CUMMING4, CHRIS RIDDOCH4,
GASTON BEUNEN5, & ROBERT M. MALINA6
1University of Coimbra, Coimbra, Portugal,
2Research Centre in Physical Activity, Health and Leisure, Faculty of Sport,
University of Porto, Porto, Portugal,3State University of Londrina, Parana, Brazil,
4School for Health, University of Bath,
Bath, UK,5Katholieke Universiteit Leuven, Leuven, Belgium, and
6Department of Kinesiology and Health Education,
University of Texas at Austin, Austin, Texas, USA
(Accepted 30 May 2011)
Abstract
The agreement between self-reported and objective estimates of activity energy expenditure was evaluated in adolescents byage, sex, and weight status. Altogether, 403 participants (217 females, 186 males) aged 13–16 years completed a 3-dayphysical activity diary and wore a GT1M accelerometer on the same days. Partial correlations (controlling for body mass)were used to determine associations between estimated activity energy expenditure (kcal � min71) from the diary andaccelerometry. Differences in the magnitude of the correlations were examined using Fisher’s r to z transformations. Bland–Altman procedures were used to determine concordance between the self-reported and objective estimates. Partialcorrelations between assessments of activity energy expenditure (kcal � min71) did not differ significantly by age (13–14years: r¼ 0.41; 15–16 years: r¼ 0.42) or weight status (normal weight: r¼ 0.42; overweight: r¼ 0.39). The magnitude of theassociation was significantly affected by sex (Dr¼ 0.11; P5 0.05). The agreement was significantly higher in males than infemales. The relationship between activity energy expenditure assessed by the objective method and the 3-day diary wasmoderate (controlling for weight, correlations ranged between 0.33 and 0.44). However, the 3-day diary revealed lessagreement in specific group analyses; it markedly underestimated activity energy expenditure in overweight/obese and olderadolescents. The assessment of activity energy expenditure is complex and may require a combination of methods.
Keywords: Physical activity, 3-day diary, accelerometry, weight status, adolescence
Introduction
The beneficial effects of physical activity on indica-
tors of health, fitness, and behaviour appear to
differentiate between ‘‘healthy’’ and ‘‘unhealthy’’
youth (Cumming & Riddoch, 2008; Strong et al.,
2005). Among ‘‘healthy’’ children and adolescents
(normal weight, normotensive blood pressure), the
evidence for beneficial effects of physical activity is
strongest for skeletal health, aerobic fitness, and
muscular strength and endurance, whereas effects
are relatively small for lipids, adiposity, and blood
pressure (Strong et al., 2005). The beneficial effects
of systematic physical activity are generally more
apparent among ‘‘unhealthy’’ youth (the obese,
hypertensive, and those with features of the meta-
bolic syndrome) (Flynn et al., 2006; Hallal, Victora,
Azevedo, & Wells, 2006). Among ‘‘unhealthy
youth’’, interventional physical activity programmes
have a beneficial effect on adiposity in the obese,
blood pressure in the hypertensive, and insulin,
triglycerides, and adiposity in obese youth with the
metabolic syndrome (McMurray & Andersen,
2010). There is also evidence for the beneficial
effects of physical activity programmes in conjunc-
tion with cognitive-behavioural modification on
anxiety and depression (Strong et al., 2005).
Adequate and comprehensive physical activity
assessment techniques are needed to evaluate re-
lationships between physical activity and indicators
of health status, fitness, and behaviour (Rush,
Valencia, & Plank, 2008; Schutz, Weinsier, &
Hunter, 2001; Trost, 2001; Wickel, Welk, &
Eisenmann, 2006). Moreover, methods of physical
Correspondence: A. M. Machado-Rodrigues, Universidade de Coimbra, Estadio Universitario Coimbra Pavilhao-III, 3040–156 Coimbra.
E-mail: [email protected]
Journal of Sports Sciences, November 2011; 29(14): 1503–1514
ISSN 0264-0414 print/ISSN 1466-447X online � 2011 Taylor & Francis
http://dx.doi.org/10.1080/02640414.2011.593185
activity assessment should be socially acceptable,
should not burden the participant with cumbersome
equipment, and should minimally influence normal
physical activity patterns (Armstrong & Welsman,
2006). It is also generally accepted that no single
measurement accurately reflects all dimensions of
physical activity, i.e. type, frequency, intensity, and
duration (Armstrong & Welsman, 2006; Trost,
2001). Although commonly used objective measures
of physical activity provide valid and reliable in-
formation about frequency, intensity, and duration
(Freedson, Melanson, & Sirard, 1998; Janz, Witt, &
Mahoney, 1995; Montoye, 1996), they provide
limited information about the context and type of
physical activity, and are less practical for use with
large samples. Self-report physical activity protocols,
such as questionnaires and diaries, provide a low-
cost alternative to objective assessments, but rely on
the ability to recall and/or record information and
may be influenced by social desirability. Commonly
used questionnaires for assessing physical activity in
large samples include the Leisure-Time Exercise
Questionnaire (Godin & Shephard, 1985a, 1985b)
and Physical Activity Questionnaires for children
(Crocker, Bailey, Faulkner, Kowalski, & McGrath,
1997) and adolescents (Martinez-Gomez et al.,
2011). In contrast to questionnaires, several studies
have derived estimates of daily energy expenditure
and activity energy expenditure from a 3-day diary
(Huang & Malina, 1996; Huang & Malina, 2002;
Katzmarzyk & Malina, 1998; Katzmarzyk, Malina, &
Bouchard, 1999; Katzmarzyk, Malina, Song, &
Bouchard, 1998a, 1998b). Current recommenda-
tions for the assessment of physical activity call for a
combination of methods (Welk, 2002).
Physical activity tends to decrease, on average,
with age (Malina & Katzmarzyk, 2006). This decline
is more apparent at high intensities of physical
activity. The decline in physical activity with age
may be influenced by differences between younger
and older adolescents to recall information. Older
adolescents are more capable of processing, retain-
ing, and recalling information than are younger
adolescents (Baranowski, 1988). This may predis-
pose older adolescents and young adults to provide
more accurate information on physical activity. In
addition, evidence for adults suggests that weight
status of individuals influences the accuracy of self-
reported physical activity (Irwin, Ainsworth, &
Conway, 2001; Lichtman et al., 1992; Walsh,
Hunter, Sirikul, & Gower, 2004). Overweight and
obese adults tend to overestimate involvement in
physical activity compared with adults of normal
weight, although not all studies have noted such
differences (Lee, Cook, & Henneckens, 1993).
Differences between actual and reported involve-
ment in physical activity in normal weight and obese/
overweight youth are less well documented, although
McMurray and colleagues (2008) reported that
overweight and obese girls aged 11–14 years were
also more likely to overestimate involvement in
moderate-to-vigorous physical activity.
The agreement between self-reported and objec-
tive assessment protocols may thus be affected by
age, weight status, and gender. In addition, there is a
clear need for valid assessments of physical activity in
adolescents (Slootmaker, Schuit, Chinapaw, Seidell,
& van Mechelen, 2009). Therefore, the purpose of
this study was to evaluate the concordance between
self-reported and objective estimates of activity
energy expenditure among adolescents by gender,
age, and weight status. Relationships between
estimated activity energy expenditure derived from
a 3-day diary and accelerometry were separately
evaluated in male and female adolescents, in two age
groups (13–14 and 15–16 years), and by weight
status (normal weight and overweight/obese).
Methods
Study design and sampling
The study was school-based and was part of a cross-
sectional research project with Portuguese adoles-
cents (Midlands Adolescents Lifestyle Study –
MALS). Participants were volunteers from public
schools in the Portuguese Midlands. The sample
included 492 students (265 girls and 227 boys) aged
12.5–16.4 years. The project received approval from
the Scientific Committee of the University of
Coimbra, and the Portuguese Commission for Data
Protection (Process #3132006), which requires
anonymity and non-transmissibility of data. In-
formed consent was provided by parents and pupils.
Anthropometry
Height and weight were measured using a portable
stadiometer (Harpenden model 98.603, Holtain
Ltd., Crosswell, UK) and a portable scale (Seca
model 770, Hanover, MD, USA) to the nearest
0.1 cm and 0.1 kg, respectively, with participants in
T-shirt and shorts, and without shoes. Technical
errors of measurement (se) and reliability (R) based
on replicate measurements of 23 individuals indi-
cated acceptable quality for both measurements:
se¼ 0.34 cm, R¼ 1.00 for height; se¼ 0.58 kg,
R¼ 1.00 for weight. The body mass index (BMI,
kg � m72) was calculated; age- and gender-specific
International Obesity Task Force cut-offs (Cole,
Bellizzi, Flegal, & Dietz, 2000) were used to classify
youth as normal weight, overweight or obese.
Adolescents were grouped into two weight-status
groups: normal weight and overweight/obese.
1504 A. M. Machado-Rodrigues et al.
Three-day diary
The diary protocol (Bouchard et al., 1983) divided
each day into 96 periods of 15 min and required the
participant to record activities over three complete
days (Thursday, Friday, and Saturday). Participants
were required to rate the intensity of the primary
activity performed in each 15-min period using a
numeric code ranging from 1 to 9 (see Table I).
Energy expenditure was subsequently estimated
from equivalents for each.
Total daily energy expenditure was estimated for
each of the 3 days. Intensity categories 1–3 (52.8
METs) and 6–9 (4.8–7.8 METs) represented seden-
tary behaviours and moderate-to-vigorous physical
activities, respectively (Bouchard et al., 1983).
Twenty-three individuals completed the 3-day diary
protocol twice (one week apart). The technical error
and reliability coefficient was 146.3 kcal (0.10
kcal � min71) and 0.91, respectively. Intra-individual
correlations were 0.93 (P5 0.01) for weekdays and
0.70 (P5 0.01) for weekend days. Further details
are available elsewhere (Machado-Rodrigues et al.,
2010b).
Accelerometry
The GT1M Actigraph accelerometer was used. It is a
uniaxial accelerometer designed to detect vertical
accelerations of 0.05–2.00g with a frequency of
response of 0.25–2.50 Hz that allows assessment of
normal human motion. The filtered acceleration
signal is digitized and the magnitude is summed over
a user-specified period of time (an epoch interval).
The sampling period was set at one minute as in
other studies of adolescents (Anderson, Hagstromer,
& Yngve, 2005; Bringolf-Isler et al., 2009; Mota
et al., 2002; Riddoch et al., 2004; Troiano et al.,
2008; Trost, 2001).
Procedures and data reduction
The first visit to schools occurred on Wednesdays
and was used to explain the objectives of the study
and to collect anthropometric data. Adolescents were
instructed on application of the accelerometer and to
remove the monitor while showering or participating
in water activities. The accelerometer was placed
over the hip for three consecutive days (Thursdays,
Fridays, and Saturdays), the same days that the diary
protocol was completed. Accelerometer data were
electronically downloaded using ActiLife software.
The MAHUffe program (www.mrc-epid.cam.
ac.uk) was used to reduce the data in a file
containing minute-by-minute movement counts.
The output also included the total time that the
accelerometer was worn (minutes) each day. The
total amount of daily physical activity was ex-
pressed as total counts divided by registered time
(counts � min71). Participants who did not com-
plete a minimum of 600 min of accelerometer data
per day (after removing sequences of 20 or more
consecutive zero counts; Anderson et al., 2005;
Bringolf-Isler et al., 2009) were excluded from
subsequent analyses. Data processing and inclusion
criteria were the same as in the European Youth
Heart Study (Riddoch et al., 2004), the Avon
Longitudinal Study of Parents and Children
(Riddoch et al., 2007), and the National Health
and Nutrition Examination (Troiano et al., 2008).
Data for 403 youth (82% of the initial sample) met
the criteria for inclusion and were used for
subsequent analyses. of the other 89 students
failed to achieve 10 h of registered time. There
were no significant differences in the distributions
of included and excluded participants by sex
(w2ð1Þ ¼ 1.49; P¼ 0.22), age (w2ð1Þ ¼ 2.35; P¼ 0.13) or
weight status (w2ð1Þ ¼ 0.38; P¼ 0.54).
An inclusion record for the 3-day diary corre-
sponds to the completion of all ninety-six 15-min
episodes per day with a categorical value from 1 to 9.
The original version of the protocol (Bouchard et al.,
1983) assumes a standard energy equivalent for all
activity codes, including code 1, which represents
sleeping or resting activities (resting energy expen-
diture). Activity energy expenditure was calculated
by subtracting resting energy expenditure (0.26
kcal � kg71 � 15 min71 or approximately 25 kcal �kg71 � day71) from daily energy expenditure. For
accelerometry, average counts per minute were
converted to activity energy expenditure using the
equation proposed by Trost et al. (1998). This
equation was developed in 20 individuals aged 10–14
years during treadmill walking and running at 3, 4,
and 6 mph. Activity counts were strongly correlated
with indirect calorimetry energy expenditure
(r¼ 0.86).
Table I. Numeric codes for primary activities performed in each
15-min period.
Numeric
code
Primary
activity
Energy expenditure
(kcal � kg71)
1 Sleeping or resting in bed 0.26
2 Sitting 0.38
3 Light activity standing 0.57
4 Slow walking ~4 km �h71 0.69
5 Light manual tasks 0.84
6 Leisure and recreational sports 1.20
7 Manual tasks at a moderate pace 1.40
8 Leisure and sport activities of
higher intensity – not competitive
1.50
9 Very intensive activities –
competitive sports
2.00
Activity energy expenditure by self-report and accelerometry 1505
Statistical analysis
Descriptive statistics were calculated for age, height,
weight, body mass index, activity energy expendi-
ture, and accelerometer counts by gender, age group,
and weight status. One-way analysis of variance
(ANOVA) was performed to test the effect of gender
on body size, counts of physical activity, and activity
energy expenditure. The effects of age and weight
status were subsequently tested using ANOVA
separately for males and females. Statistical signifi-
cance was set at P5 0.05. Partial eta-squared was
used to determine the magnitude of the differences
between groups. F-values of 0.10, 0.25, and 0.40
were interpreted as a small, medium, and large
effect, respectively (Cohen, 1988). Translated into
partial eta-squared, values of 0.01, 0.06, and 0.14
were considered a small, moderate, and large effect,
respectively.
Agreement between estimated activity energy
expenditure derived from the diary and
accelerometry was assessed using the Bland–
Aldman procedure (Bland & Altman, 1986).
Partial correlations between methods controlling
for body mass were calculated by gender, age, and
weight status. Fisher’s r to z transformation
procedure (Ramseyer, 1979) was used to deter-
mine the variability in magnitude of correlations by
gender, age group, and weight status. According to
Nevill and Atkinson (1997), and assuming a
relation exists between the measurement differ-
ences (errors) and the mean, an analysis was
conducted to determine the ratio limits of agree-
ment using natural log transformed measurements.
These ‘‘limits of agreement’’ were expressed as a
dimensionless ratio, multiplied or divided by the
second ratio as a measure of agreement having
already taken antilogs (Nevill & Atkinson, 1997).
SPSS v.15.0 (SPSS Inc., Chicago, IL) was used for
all the analyses.
Results
Descriptive statistics for chronological age, stature,
weight, body mass index, activity energy expendi-
ture, and daily physical activity (counts � min71) are
summarized in Table II. On average, boys were
1.5 kg heavier (F¼ 5.45, P5 0.05) and 6.1 cm taller
(F¼ 205.87, P5 0.01) than girls. Based on the
estimates of activity energy expenditure, males
expended more kcal per minute than females (diary:
sex difference¼ 0.19, F¼ 23.26, P5 0.01; accelero-
metry: sex difference¼ 0.20, F¼ 15.06, P5 0.01).
The magnitude of the significant effects was small.
As expected, boys aged 15–16 years were taller
(F¼ 202.30, P5 0.01), heavier (F¼ 178.84,
P5 0.01), and had a higher BMI (F¼ 53.72,
P5 0.01) than boys aged 13–14 years (Table III).
Older girls were taller (F¼ 13.59, P5 0.01) and
heavier (F¼ 6.86, P5 0.01) than girls aged 13–14
years, but the age groups did not differ in BMI.
Activity energy expenditure was higher in older than
younger boys (diary: difference¼ 0.40 kcal � min71,
F¼ 36.13, P5 0.01; accelerometry: difference¼0.90 kcal � min71, F¼ 160.99, P5 0.01). The cor-
responding age group comparisons for girls were
significant only for activity energy expenditure
estimated by accelerometry; older girls spent 0.17
kcal � min71 more than younger female participants
(F¼ 6.12, P5 0.01). The magnitude of the effects
ranged from small to moderate, except for differ-
ences in activity energy expenditure between younger
and older boys by accelerometry (large effects: partial
eta-squared¼ 0.21–0.24).
Results by weight status are summarized in Table
IV. Weight status was a significant source of variation
in activity energy expenditure by both the diary
(boys: F¼ 94.31, P5 0.01; girls: F¼ 172.50,
P5 0.01) and accelerometry (boys: F¼ 460.80,
P5 0.01; girls: F¼ 667.96, P5 0.01). Overweight
and obese adolescents presented high values of
Table II. Results of ANOVA testing the effect of sex on chronological age, body size, daily activity energy expenditure, and accelerometer
counts per minute (mean + s).
Males
(n¼186)
Females
(n¼217) F P Z2
Anthropometry
Age (years) 14.2+1.1 14.2+1.0 0.51 0.48 0.00
Height (cm) 164.5+9.6 158.4+8.5 205.87 0.00 0.15
Weight (kg) 55.1+11.6 53.6+10.2 5.45 0.02 0.01
BMI (kg �m72) 20.19+3.15 21.29+3.59 33.48 0.00 0.03
Diary
AEE (kcal �min71) 2.34+0.81 2.13+0.69 23.26 0.00 0.02
Accelerometer
AEE (kcal �min71) 2.58+0.93 2.38+0.84 15.06 0.00 0.01
Counts per minute 504.3+255.2 408.3+183.2 58.68 0.00 0.05
Note: BMI¼body mass index; AEE¼ activity energy expenditure.
1506 A. M. Machado-Rodrigues et al.
activity energy expenditure. When daily physical
activity was expressed in counts per minute, the
effect of weight status was still significant for girls
(normal weight: 398 counts � min71; overweight:
441 counts � min71; F¼ 6.00, P5 0.01), but not for
boys. The magnitude of the significant effects ranged
from moderate to large.
Partial correlations between the diary and accel-
erometry, controlling for body mass, ranged between
0.33 and 0.44 (Table V). The magnitude of the
relationship was significantly higher in males than in
females (Dr¼ 0.11, P5 0.05). Coefficients did not
differ by age group or weight status.
Concordance between self-reported and objective
methods was assessed according to the 95% limits of
agreement determined by the procedures of Bland–
Altman (Bland & Altman, 1986). The upper and
lower bounds of the 95% confidence intervals
represent the largest error that could be expected
for the diary method (Table V).
Agreement between the diary and accelerometry
estimates of activity energy expenditure is illustrated
in Figures 1–6. The 3-day diary revealed less
agreement in the specific group analyses. The diary
markedly underestimated activity energy expenditure
in overweight/obese and older adolescents of both
sexes. The bias tended to be stronger at high
intensities of physical activity, especially when the
data were plotted by gender and age group. A trend
line (association between the difference and the
average of the two protocols) is apparent in both
sexes (males: r¼70.18, P5 0.01; females: r¼70.28, P5 0.01) and also in the two age groups
(13–14 years: r¼ –0.21, P5 0.01; 15–16 years: r¼70.13; P5 0.01), but is not apparent for weight
status groups.
Table VI summarizes the mean of the log
transformed measurement, and their mean differ-
ences, together with ‘‘limits of agreement’’. Table VI
also gives the correlation between the absolute
Table III. Descriptive statistics by sex and age group and results of ANOVA testing the effect of age on chronological age, body size, daily
activity energy expenditure, and accelerometer counts per minute (mean+ s).
Males (n¼ 186) Females (n¼ 217)
13–14 years
(n¼112)
15–16 years
(n¼74) F P Z2
13–14 years
(n¼130)
15–16 years
(n¼ 87) F P Z2
Anthropometry
Age (years) 13.5+0.6 15.3+0.6 1214.75 0.00 0.69 13.5+ 0.58 15.2+ 0.53 1455.18 0.00 0.69
Height (cm) 160.5+8.9 170.6+7.1 202.30 0.00 0.27 157.7+ 6.4 159.5+ 5.8 13.59 0.00 0.02
Weight (kg) 50.4+10.4 62.1+9.7 178.84 0.00 0.24 52.7+ 10.8 54.8+ 9.1 6.86 0.01 0.01
BMI (kg �m72) 19.44+2.92 21.34+3.13 53.72 0.00 0.09 21.13+ 3.82 21.53+ 3.21 1.98 0.16 0.00
Diary
AEE (kcal �min71) 2.18+0.71 2.58+0.88 36.13 0.00 0.06 2.12+ 0.76 2.14+ 0.57 0.06 0.81 0.00
Accelerometer
AEE (kcal �min71) 2.22+0.83 3.12+0.80 160.99 0.00 0.23 2.31+ 0.89 2.48+ 0.76 6.12 0.01 0.01
Counts per minute 522.2+262.1 477.2+242.3 4.17 0.04 0.01 410.6+ 167.1 404.8+ 205.2 0.15 0.70 0.00
Note: BMI¼body mass index; AEE¼ activity energy expenditure.
Table IV. Descriptive statistics for normal-weight and overweight/obese adolescents and results of ANOVA testing the effect of weight status
on chronological age, body size, daily activity energy expenditure, and accelerometer counts per minute (mean+ s).
Males Females
Normal-weight
(n¼151)
Overweight/obese
(n¼35) F P Z2
Normal-weight
(n¼165)
Overweight/obese
(n¼52) F P Z2
Anthropometry
Age (years) 14.2+1.1 14.2+1.0 0.00 0.97 0.00 14.1+1.0 14.0+1.0 14.56 0.00 0.02
Height (cm) 164.2+9.5 165.7+9.8 3.27 0.07 0.01 158.3+6.4 158.7+5.4 2.29 0.13 0.00
Weight (kg) 51.8+9.2 69.1+10.8 497.62 0.00 0.47 49.6+6.3 66.1+10.1 689.09 0.00 0.52
BMI (kg �m72) 19.06+1.89 25.07+2.83 877.06 0.00 0.61 19.76+1.92 26.17+3.27 996.98 0.00 0.61
Diary
AEE (kcal �min71) 2.19+0.68 2.95+0.99 94.31 0.00 0.15 1.95+0.52 2.68+0.83 172.50 0.00 0.21
Accelerometer
AEE (kcal �min71) 2.32+0.73 3.69+0.88 460.80 0.00 0.45 2.06+0.52 3.41+0.83 667.96 0.00 0.51
Counts per minute 505.4+254.1 499.6+260.7 0.05 0.83 0.00 397.8+160.5 441.4+239.1 6.00 0.02 0.01
Note: BMI¼body mass index; AEE¼ activity energy expenditure.
Activity energy expenditure by self-report and accelerometry 1507
Table V. Partial correlations (controlling for body mass) between estimates of activity energy expenditure by diary and accelerometry, bias
and its limits of agreement between two estimates of activity energy expenditure by sex, age group, and weight status.
No. observed days
Partial
Bias
Limits of agreementa
Trendr P Lower Upper
Sex
Males 558 0.44 50.01 70.24 71.75 1.26 70.18**
Females 651 0.33 50.01 70.25 71.46 0.95 70.28**
Dr¼ 0.11 50.05
Age group
13–14 years 726 0.41 50.01 70.12 7.42 1.17 70.21**
15–16 years 483 0.42 50.01 70.43 71.79 0.92 70.13**
Dr¼ 0.01 N.S.
Weight status
Normal-weight 948 0.42 50.01 70.11 71.25 1.02 70.05
Overweight/obese 261 0.39 50.01 70.74 72.36 0.89 0.07
Dr¼ 0.03 N.S.
aDiary – Accelerometry. **P5 0.01.
Figure 1. Bland–Altman plot for activity energy expenditure (AEE) of male adolescents showing the difference between the diary and
accelerometer estimates plotted against the mean of the two estimates.
Figure 2. Bland–Altman plot for activity energy expenditure (AEE) of female adolescents showing the difference between the diary and
accelerometer estimates plotted against the mean of the two estimates.
1508 A. M. Machado-Rodrigues et al.
Figure 3. Bland-Altman plot for activity energy expenditure (AEE) of adolescents aged 13–14 years showing the difference between the diary
and accelerometer estimated plotted against the mean of the two estimates.
Figure 4. Bland-Altman plot for activity energy expenditure (AEE) of adolescents aged 15–16 years showing the difference between the diary
and accelerometer estimates plotted against the mean of the two estimates.
Figure 5. Bland-Altman plot for activity energy expenditure (AEE) of normal-weight adolescents showing the difference between the diary
and accelerometer estimates plotted against the mean of the estimates.
Activity energy expenditure by self-report and accelerometry 1509
differences and the mean (using the log transformed
data). By observing the agreement ratios, the sub-
group of older adolescents shows the greatest
agreement, with a bias of 0.84 and an agreement
ratio of */7 1.61. The worst agreement was found
for male adolescents, where two methods of measur-
ing activity energy expenditure were compared.
Although the bias ratio is not great (i.e. 0.90), the
agreement ratio (*/7 1.87) implies that 95% of ratios
will lie between 187% of the mean bias (Table VI).
Discussion
In this study, we examined the concordance of
activity energy expenditure derived from a 3-day
diary and accelerometry relative to age, gender, and
weight status in adolescents aged 13–16 years. Partial
correlations between protocols were significantly
higher in males than in females and did not vary
with age group. Bias ranged between 70.12 and
70.74. The diary underestimated activity energy
expenditure, especially among overweight/obese
adolescents (70.74 kcal � min71) and participants
aged 15–16 years (70.43 kcal � min71). Since self-
reported physical activity is prone to misreporting
and the correlations between the 3-day diary and
accelerometer were no more than moderate, results
should be interpreted with caution.
A decline in daily physical activity and activity
energy expenditure is often assumed across the
adolescent years, although not all studies support
this trend. For example, in a large sample of
Portuguese youth aged 10–18 years, school- and
sport-related activities declined in females while both
school- and sport-related physical activities increased
with age in males (Teixeira e Seabra et al., 2008). In
the present study, physical activity objectively
assessed (counts per minute) did not differ among
younger and older girls, but younger boys showed
significantly higher levels of physical activity than
Figure 6. Bland-Altman plot for activity energy expenditure (AEE) of overweight/obese adolescents showing the difference between the diary
and accelerometer estimates plotted against the mean of the two estimates.
Table VI. The log transformed (ln) measurement means and differences, the ‘‘ratio limits of agreement’’, together with the correlation
between the absolute differences and the mean (log transformed) by sex, age group, and weight status.
Log transformed
measurements
Mean 1 Mean 2
Difference (S.D.) Ratio limits
Correlation
No. observed days Diary Accelerometry (abs(diff) vs. mean)
Sex 558 0.798 0.934 70.107 (0.318) 0.90 (*/7 1.87) 70.23**
Males 651 0.698 0.820 70.102 (0.258) 0.90 (*/7 1.66) 70.30**
Females
Age group 726 0.698 0.779 70.058 (0.301) 0.94 (*/7 1.80) 70.24**
13–14 years 483 0.779 0.971 70.169 (0.243) 0.84 (*/7 1.61) 70.16**
15–16 years
Weight status 948 0.673 0.772 70.065 (0.280) 0.94 (*/7 1.73) 70.11**
Normal-weight 261 0.975 1.230 70.259 (0.253) 0.77 (*/7 1.64) 0.03
**P5 0.01.
1510 A. M. Machado-Rodrigues et al.
their older counterparts. Note, however, discussions
of the adolescent decline in physical activity consider
a broader age range that spans early through late
adolescence, while the present study compared two-
year age groups across a relatively narrower age span.
The relationship between the 3-day diary and
objective estimates of activity energy expenditure
varied by gender but not by age group. Leisure-time
physical activity, in particular sports, is a more salient
feature of daily life in males than in females (Chase &
Dummer, 1992), and given the trend for a stronger
association between instruments in males, it is
perhaps likely that males face fewer choices in
selecting categorical values for each 15-min segment
of the diary, particularly for more structured
activities. In addition, and contrary to expectation,
the present study showed less concordance between
protocols for older adolescents. From a cognitive
perspective, one would expect that older adolescents
were more able to process and recall information
pertaining to daily involvement in physical activity
(Baranowski, 1988; Montoye, 1996). However,
changes in the nature of physical activity, as a result
of increasing social autonomy, independence from
family-based routines, and probably sport engage-
ment or dropout, are potential contributors to the
increased bias in the agreement between protocols
among adolescents aged 15–16 years.
The Bland-Altman plots point out that the
observed bias between the 3-day diary and the
accelerometer is systemic by gender and age groups,
and may be related to the amount of activity that is
reported. In the present study, adolescents who
reported lower amounts of physical activity had
estimated activity energy expenditure values that
were closer to those obtained from the objective
measure. However, participants who reported high
amounts of activity had estimated activity energy
expenditure values that were much lower than those
from the accelerometer, especially among the older
age group. The bias could result from the inaccuracy
in the diary entries or from the accelerometer
limitations previously mentioned. However, it should
be clearly emphasized that the accuracy of the 3-day
diary depends on adolescents’ willingness to adhere
to the instruction as well as their ability to correctly
identify the appropriate activity code, as previously
noted in adults (Wickel et al., 2006).
Concordance between the 3-day diary and accel-
erometry estimates of activity energy expenditure
varied by weight status. At higher intensities of
activity energy expenditure, overweight/obese ado-
lescents tended to overestimate physical activity,
while the opposite tendency was observed among
normal weight youth (Table V and Figures 5 and 6).
Previous research reported that overweight and
obese adolescents provided less accurate self-assess-
ments of physical activity (McMurray et al., 2008;
Slootmaker et al., 2009). American girls classified as
overweight and obese, for example, had 17.7% and
19.4% fewer minutes of moderate-to-vigorous phy-
sical activity based on a self-instrument compared
with normal weight girls (McMurray et al., 2008). In
contrast, European adolescents aged 12–18 years
self-reported more time in moderate and vigorous
physical activity compared with objective accelero-
metry data, and results differed significantly in all
subgroups (i.e. gender, weight status, and education)
(Slootmaker et al., 2009). The current study suggests
that the overweight/obese participants underesti-
mated their activity energy expenditure. Accordingly,
further research is needed to examine the agreement
between self-reported and objective measures at high
intensities of physical activity.
Social and cultural factors not assessed in the
current study may contribute to the validity of self-
reported physical activity. Over-reporting physical
activity may be related to social desirability issues
(Klesges et al., 2004). Adolescent girls and boys from
families with greater parental education over-report
time spent in moderate and vigorous physical
activity, respectively, relative to accelerometer regis-
tered time (Slootmaker et al., 2009). Another factor
that may affect the relationships between self-report
and objective measures of physical activity and
activity energy expenditure assessment might be
socio-geographical location. Perceptions of occupa-
tional activities (helping the family at home or in
agriculture) may differ from ratings and duration of
unstructured leisure activities and games. Variation
in biological maturity status among adolescents is an
additional concern, since it relates to physical activity
and sedentary behaviour (Machado-Rodrigues et al.,
2010a) and probably mediates the perceived tired-
ness of intense activities. However, findings are
inconsistent among studies that have examined the
relationship between biological maturity and physical
activity, and associations, when noted, are generally
low (Sherar, Cumming, Eisenmann, Baxter-Jones, &
Malina, 2010).
Although accelerometry is often mentioned as the
reference in studies of validation (Welk, 2002), it
does not capture all types of physical activity (e.g.
sports/activities that involve water or when wearing
the accelerometer presents physical risk). On the
other hand, self-report protocols may be affected by
the nature of the behaviour recalled. Most daily
activities are intermittent and may involve substantial
periods of rest, which may lead to significant
overestimation of time spent on daily activities
(Slootmaker et al., 2009; Trost et al., 2002). The
use of physical activity diaries in children and
adolescents presents additional challenges. Com-
pared with adults, youth are less time-conscious, are
Activity energy expenditure by self-report and accelerometry 1511
less able to process and recall information, and are
more likely to engage in sporadic activities (Bailey
et al., 1995; Baquet, Stratton, Van Praagh, &
Berthoin, 2007). Such intermittent activities are
probably more difficult to define or quantify than
occupational activities or structured exercises
(Armstrong & Welsman, 2006). Since the acceler-
ometer is not a ‘‘gold standard’’ for assessment of
activity energy expenditure, the disagreement be-
tween instruments is a result of weaknesses of both
instruments. Thus, a multi-method design to provide
quantitative and qualitative physical activity data is
recommended in research with young people.
In the present study, the compliance for wearing
the accelerometer was 82% (three consecutive days).
Corresponding rates in previous research were 71%
(days with valid accelerometer recording and corre-
sponding diary entries) in a study of European
adolescents (Bringolf-Isler et al., 2009) and 62–
75% in US youth (Anderson et al., 2005; Sallis et al.,
1998). The results of the current study are limited to
Portuguese youth aged 13–16 years living in the
Portuguese Midlands. Cultural and social factors
specific to this region of Portugal were not con-
sidered (e.g. parental education of participants,
perceptions of physical activities, rearing style, spatial
incentives for physical activity and sports). It is also
possible that relationships between estimates of
activity energy expenditure may vary with season of
the year. The current data were collected in the
spring when youth were likely to be engaged in
outdoor activities. The present study adopted an
epoch of 60 s, which tends to underestimate
moderate, vigorous, and very vigorous physical
activities, especially in children (Nilsson, Ekelund,
Yngve, & Sjostrom, 2002; Rowlands, Powell,
Humphries, & Eston, 2006; Stone, Rowlands, &
Eston, 2009). However, the time spent in activities of
different intensities was not considered in the present
study. Finally, Trost’s equation (Trost et al., 1998)
has revealed a systematic error in physical activity
energy expenditure estimates (Corder et al., 2007)
that might also influence the agreement between the
3-day diary and accelerometry in the present study.
Nevertheless, Corder et al. (2007) also demonstrated
that the equation was the most accurate for physical
activity energy expenditure estimates when com-
pared with others, such as the equations of Corder
and colleagues (Corder, Brage, Wareham, &
Ekelund, 2005) and Puyau and colleagues (Puyau,
Adolph, Vohra, & Butte, 2002).
Conclusion
The results of the present study show a moderate
relationship between activity energy expenditure
assessed by accelerometry and a 3-day diary.
However, the diary protocol revealed less agreement
in specific group analyses. The diary markedly
underestimated activity energy expenditure in over-
weight/obese and older adolescent. Accordingly,
those working to improve the physical activity of
youth should be aware of these and other factors to
better develop future educational and clinical inter-
ventions/strategies for health promotion.
Acknowledgements
This research was partially supported by Fundacao
para a Ciencia e a Tecnologia – Ministerio da Ciencia,
Tecnologia e Ensino Superior (SRFH/BD/38988/
2007, PTDC/DES/110158/2009). The authors also
acknowledge the support provided by the Portuguese
Ministry of Education.
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