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: rodriguesari@hotmail.com

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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