Patterns of Objectively Measured Physical Activity in
Normal Weight, Overweight, and Obese Individuals (20–
85 Years): A Cross-Sectional Study
Bjørge Herman Hansen*, Ingar Holme, Sigmund Alfred Anderssen, Elin Kolle
Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway
Background: The magnitude of the association between physical activity (PA) and obesity has been difficult to establish
using questionnaires. The aim of the study was to evaluate patterns of PA across BMI-defined weight categories and to
examine the independent contribution of PA on weight status, using accelerometers.
Methods: The study was a cross-sectional population-based study of 3,867 adults and older people aged 20–85 years, living
in Norway. PA was assessed for seven consecutive days using the ActiGraph GT1M accelerometer. Anthropometrical data
was self-reported and overweight and obesity was defined as having a body mass index (BMI) of 25–,30 and $30 kg/m2,
Results: Overweight and obese participants performed less overall PA and PA of at least moderate intensity and took fewer
steps, compared to normal weight participants. Although overall PA did not differ between weekdays and weekends, an
interaction between BMI category and type of day was present, indicating a larger difference in overall PA between BMI
categories on weekends compared to weekdays. Obese participants displayed 19% and 25% lower overall physical activity
compared to normal weight participants, on weekdays and weekends, respectively. Participants in the most active quintile
of overall PA had a 53% lower risk (OR 0.47, 95% CI: 0.37 to 0.60) for having a BMI above or below 25 kg/m2, and a 71%
lower risk (OR: 0.29, 95% CI: 0.20 to 0.44) for having a BMI above or below 30 kg/m2.
Conclusions: Overweight and obese participants engaged in less overall PA and moderate and vigorous PA compared with
normal weight individuals. The weight related differences in overall PA were most pronounced on the weekend and the risk
of being overweight or obese decreases across quintiles of PA.
Citation: Hansen BH, Holme I, Anderssen SA, Kolle E (2013) Patterns of Objectively Measured Physical Activity in Normal Weight, Overweight, and Obese
Individuals (20–85 Years): A Cross-Sectional Study. PLoS ONE 8(1): e53044. doi:10.1371/journal.pone.0053044
Editor: Noel Christopher Barengo, Fundacio ´n para la Prevencio ´n y el Control de las Enfermedades Cro ´nicas No Transmisibles en Ame ´rica Latina (FunPRECAL),
Received July 3, 2012; Accepted November 23, 2012; Published January 7, 2013
Copyright: ? 2013 Hansen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The study was funded by the Norwegian Directorate of Health and the Norwegian School of Sport Sciences. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
The adverse effects of overweight and obesity on health are well
documented . The prevalence of overweight and obesity has
reached epidemic proportions worldwide , and Norwegian data
indicate that 44% of women and 65% of men (aged 40–42 years)
are either overweight or obese . Although obesity is a complex
disorder, a secular decrease in energy expenditure is believed to be
an important contributor to both the development and mainte-
nance of obesity [4,5].
Nutrition surveys conducted in Norway in the past decades
show that energy intake has not increased substantially ,
whereas average weight and prevalence of overweight and obesity
have increased, during the same time period . Although the
composition of available foods may have changed, the increase in
weight can be explained at least partly by a gradual decrease in
habitual physical activity (PA), most notably by the apparent
transition in occupational PA demands  and by increased car
use and time spent at screen-based entertainment [8,9].
Although the extent to which PA affects body composition has
been evaluated comprehensively and there is generally an inverse
relationship between PA and body weight [8,10–16], the true
magnitude of the association might be attenuated by a lack of
precision in the measurement of PA and body composition [17–
20]. Objective assessment of PA using activity monitors such as
accelerometers can overcome many of the challenges related to
self-reported measures of PA because they are unobtrusive and
capable of accurately documenting the degree, nature, and pattern
of PA [21,22].
Accelerometers have been applied in large population-based
studies of adults and older people and showed that overall PA,
intensity-specific PA and time spent being sedentary differed
according to body mass index (BMI) [23–26]. However, no studies
of objectively assessed PA in a nationally representative Norwegian
sample of adults and older people exist. The study will extent
PLOS ONE | www.plosone.org1 January 2013 | Volume 8 | Issue 1 | e53044
current knowledge by including analyses regarding differences in
activity patterns between BMI-categories and the individual
contribution of PA on the risk of being overweight or obese.
Detailed information on the differences across BMI-categories in
the amount of overall PA, intensity-specific PA, sedentary
behaviour, as well as the patterns of PA is vital for developing
our understanding of the aetiology of obesity, and will be useful for
planning interventions to prevent weight gain and to increase PA
in the general population.
The aim of the present study was to examine the relationship
between PA and BMI by; 1) describing overall PA and intensity-
specific PA across BMI categories; 2) evaluating the hourly
patterns of overall PA stratified by BMI category across weekdays
and weekend days 3) determining the independent contribution of
overall PA and MVPA on weight status.
All participants provided written informed consent and the
study was approved by the Regional Committee for Medical
Ethics and the Norwegian Social Science Data Services AS.
Study Design and Sample
The study was a cross-sectional multicentre study involving 10
test centres throughout Norway. Representative samples of 11,515
invitees (20–85 years) from the areas surrounding each test centre
were randomly sampled from the Norwegian population registry.
The study information and informed consent form were distrib-
uted via mail to the representative sample; 267 invitations were
returned because of an unknown address, resulting in an eligible
sample of 11,248 individuals. Written informed consent was
obtained from a total of 3,867 individuals (34%). A total of 382 did
not return any data. Because this study focused on BMI-defined
weight categories, we excluded six women who self-reported
pregnancy, giving a final sample of 3,479 (53% women)
individuals. Of the final sample, 86 individuals did not wear the
accelerometer, 14 had a defective monitor, 118 were excluded for
providing fewer than 4 days of valid accelerometer data, and 171
reported no height and/or weight. A total of 3,090 (89% of the
final sample) individuals were included in the association analysis.
Assessment of PA
We used the ActiGraph GT1M accelerometer (ActiGraph,
LLC, Pensacola, FL, USA) to assess each participant’s PA level.
This micro-electro-mechanical system accelerometer is lightweight
(27 g) and small (3.8 cm63.7 cm61.8 cm) and comprises a solid
state monolithic accelerometer that uses microprocessor digital
filtering. The accelerometer registers vertical acceleration as the
number of counts per user-defined sampling interval (epoch),
providing the researcher with a measure of overall PA (mean
counts per minute; CPM) and intensity specific PA (number of
time units with a mean count per time unit below or above a given
threshold). Steps taken per day (steps/day) are also reported as
a function of the ‘‘threshold crossing mode’’ embedded in the
accelerometer, which counts the number of times the acceleration-
generated signal crosses through the baseline reference each epoch
and, according to the manufacturer, is representative of the
number of steps taken.
Each participant received pre-programmed accelerometer and
questionnaire by mail. Standardized instructions included in-
formation about wearing the accelerometer in an adjustable cotton
fabric belt over the right hip for seven consecutive days, and
removing it for water activities such as showering and swimming.
After registration, the participants returned the accelerometer and
questionnaire by mail to their respective test centre.
Accelerometer Data Handling
Accelerometers were initialized and downloaded using software
provided by the manufacturer (ActiLife, ActiGraph). Data were
collected in 10-s epochs. The 10-s epochs were collapsed into 60-s
epochs for comparison with other studies. The data were reduced
to derivative variables with customized SAS-based macros (SAS
Institute Inc., Cary, NC, USA), and included if the participant had
accumulated at least 10 h of valid activity recordings per day for at
least 4 days. Time periods of at least 60 consecutive minutes with
zero counts, with allowance for 1 minute with counts above zero,
was defined as non-wear time and thus, wear time was defined by
subtracting non-wear time from 18 hours (all data between 00:00
and 06:00 were excluded to avoid the potential bias of participants
wearing the monitor while sleeping). In addition to overall PA and
steps/day, all time awake was categorised by intensity according to
the specific activity CPM values. In particular, light intensity PA
was defined by counts between 100 and 2,019 CPM, moderate
intensity PA as counts between 2,020 and 5,999 CPM and 6,000
CPM represents the lower threshold for vigorous intensity
activities . Time spent at ,100 CPM (not counting non-wear
time) was classified as sedentary behaviour. Bouts of moderate-to-
vigorous PA (MVPA) was calculated by summing all activity
$2020 counts per minute that occurred in sustained bouts of at
least 10 min (with allowance for one or two interruptions). To
establish patterns of overall PA, minute-by-minute activity counts
were summed for each hour of measurement for weekdays and
weekend days, respectively.
Height and weight were self-reported by questionnaire and BMI
was computed as weight (kg) divided by meters squared (m2). BMI
was categorized according to the guidelines set forward by the
World Health Organization, with overweight and obesity defined
as a BMI of 25–,30 and $30 kg/m2, respectively . Because of
the small sample size, underweight participants (n=35) were
included in the normal weight category; this did not cause any
significant change in overall PA for the normal weight partici-
pants. To assess health status, participants were asked to rate their
perceived health status as very good, good, either, poor, or very
poor. Because of the low prevalence of poor health (n=104, 3.0%)
and very poor health (n=3, 0.1%), the answers were grouped into
two categories for the analysis; very good/good and either/poor/
very poor. Educational attainment was categorized into four
groups: less than high school, high school, less than 4 years of
university, and university for 4 years or more. Smoking habits
were reported and dichotomized before the variable was entered
into the analysis (smoking vs. not smoking). In order to register the
amount of certain activities poorly registered by the acceler-
ometers, participants also answered a 1-page questionnaire
assessing the amount of cycling, swimming and muscular strength
training performed during the 7-day registration period.
The descriptive data are presented according to sex specific
BMI categories as percentage, mean, and standard deviation (SD)
or standard error of the mean (SE), and 95% confidence interval
(CI) where appropriate. Student’s t-test for independent groups
was used to identify differences in anthropometric data between
sexes. Chi-square tests were used to test for differences in self-
reported health and level of education between weight categories.
One-way analyses of covariance adjusting for age and test centre,
Physical Activity and Overweight
PLOS ONE | www.plosone.org2January 2013 | Volume 8 | Issue 1 | e53044
with the Bonferroni post hoc tests, were performed to identify
within-sex differences in PA between BMI categories.
A one-way repeated measurement analysis was conducted to
explore whether the impact of type of day (weekday or weekend)
differed across BMI category (normal weight, overweight and
obese). Type of day was defined as the repeated factor in the
analysis, with weight category as the between-subject factor, and
age, sex and test centre as covariates. A Wilks ` Lambda with
a significance level of p,0.05 indicated a significant interaction
effect between BMI category and type of day.
Logistic regression was performed to assess the impact of
a number of factors on the likelihood that participants were either
overweight or obese (classified as having a BMI $25 kg/m2) or
obese (BMI$30 kg/m2). The independent variables included in
the model were age, sex, level of education, self-reported health,
smoking, and quintiles of either CPM or MVPA. These variables
were included because of their known association to body weight.
For the logistic regression, CPM and MVPA was categorized into
quintiles and assigned ascending values where 1 was the least
active group and 5 the most active group. A significant interaction
was found between self-reported health and quintile of PA
(p=0.016). However, stratifying by health status did not change
the direction of the relationship or the magnitude substantially and
for sake of simplicity, the variable was included in the model and
treated as a potential confounder. A total of 4 regression analyses
were performed (quintiles of CPM and risk of BMI $25 kg/m2,
quintiles of CPM and risk of BMI $30 kg/m2, quintiles of MVPA
and risk of BMI $25 kg/m2, and quintiles of MVPA and risk of
BMI$30 kg/m2). The resulting odds ratios are displayed graph-
ically as reduction in relative odds (%). All statistical analyses were
performed using PASW Statistics 18 for Windows (IBM Corpo-
ration, Route, Somers, NY, USA) and a two-tailed alpha level of
0.05 was used for statistical significance.
The physical characteristics of the participants with complete
anthropometric data are presented in Table 1. The prevalence of
overweight and obesity was 30% and 11% for women, and 47%
and 13% for men. Health status differed according to weight
status. Although 82% of normal weight individuals reported
having at least good health, the corresponding percentages were
75% for overweight and 58% for obese individuals.
The number of valid days of activity recordings (6.8 days, data
not shown) and daily wearing time (880 min, data not shown) did
not differ between the weight categories. The measures of PA
stratified by BMI category are presented in Table 2. Normal
weight women had a higher overall PA level and steps/day
compared with both overweight and obese women. The mean
difference between normal weight and obese women was 76 CPM
(95% CI: 51, 101) and 1,971 steps/day (95% CI: 1,412, 2,529).
Overall PA and steps per day displayed a similar pattern for men,
although only reaching statistical significance for overall PA. The
mean difference in overall PA between normal weight and obese
men was 78 CPM (95% CI: 50, 106).
Normal weight women and men spent an average of 8.8 and
9.2 h per day, respectively, being sedentary. The amount of time
spent being did not differ between normal weight and overweight
participants, but obese women and men spent an average of
17 min (95% CI: 3, 32) and 22 min (95% CI: 7, 37) more,
respectively, pursuing sedentary behaviours. The amount of light
PA did not differ between BMI categories, but PA of at least
moderate intensity decreased significantly with increasing BMI.
Overall PA decreased across BMI categories at both weekdays
and weekends. However, a significant interaction (Wilks ` Lambda
0.998, p=0.042) was observed between type of day and weight
category, indicating that the impact of type of day on overall PA
differed between the BMI categories. Overall, differences in PA
were larger between the BMI categories on weekends compared to
weekdays. Compared to normal weight participants, obese
participants displayed a 19.2% (355 CPM vs. 287 CPM) lower
overall PA on weekdays, while similar difference on weekends
24.6% (370 CPM vs. 279 CPM). As displayed in Figure 1–2, these
differences were particularly visible at around midday and early
Logistic regression was performed to assess the impact of
a number of factors on the likelihood that individuals would be
either overweight or obese (Figure 3). The models containing all
predictors were significant (p,0.001), indicating the ability to
distinguish between normal weight, overweight and obese
individuals. The model including quintiles of CPM explained
between 8% (Cox and Snell R-squared) and 11% (Nagelkerke R-
squared) of the variance in weight status. The models showed an
increased odds ratio (OR) for being overweight or obese between
quintiles of PA and the dose-response relationship was about linear
(Figure 3). Participants in the most active quintile of overall PA
had a 53% lower risk (OR: 0.47, 95% CI: 0.37 to 0.60) for having
a BMI of 25 kg/m2or above, and a 71% lower risk (OR: 0.29,
95% CI: 0.20 to 0.44) for having a BMI of 30 kg/m2or above.
Similar findings were observed for quintiles of MVPA.
The present study shows a consistent decrease in PA level with
increasing BMI. Overweight and obese participants had a lower
overall PA level, took fewer steps each day, and performed less
daily moderate and vigorous PA and MVPA performed in bouts of
$10 minutes than did normal weight participants. Obese
participants also accumulated more sedentary time, compared
with normal weight participants.
The results of the present study are consistent with those of
studies that used accelerometers to measure PA in large
populations of adults and older people. Tudor-Locke et al.
(2010) showed that, among Americans, overall PA decreased
consistently with increasing BMI and that men had a higher
overall PA than women, within each BMI category. The gradient
between BMI categories was similar in the present study,
indicating that the decrease in overall PA with increasing BMI is
a consistent finding. However, only negligible sex differences
within each BMI category were observed in our study. Norwegian
women are consistently more active than American women,
whereas Norwegian men are consistently less active than
American males across all BMI categories, independent of age
. This finding also agrees with Swedish data showing a similar
decrease in overall PA with increasing BMI but no apparent sex
difference within each BMI category .
The relative differences in PA between BMI categories in the
present study were larger for intensity-specific PA than for the
indicators of overall PA. Normal weight women performed twice
as much MVPA in bouts as obese women. Similar relative
differences between intensity-specific PA stratified by BMI have
been reported by others [24,25,28]. The larger relative difference
in intensity-specific PA between BMI categories than in overall PA
may be explained partly by thermodynamics. Because of the
greater body mass, resting energy expenditure is higher in obese
compared to normal weight individuals; the greater body mass is
associated with a higher metabolic cost of PA for heavier
Physical Activity and Overweight
PLOS ONE | www.plosone.org3January 2013 | Volume 8 | Issue 1 | e53044
individuals. An accelerometer calibration study showed that the
true MVPA intensity threshold is substantially lower for obese
compared with normal weight individuals . Although the
metabolic cost of exercise increases with body mass, we are
confident that the differences in PA between BMI categories are
real and are important to public health, although care must be
taken when interpreting the results for intensity-specific PA. It
should also be recognized that BMI category related differences in
PA might be underestimated in the present study. A study of PA
using pedometers showed that a larger percentage of obese
individuals increased their PA compared to those who decreased
their behaviour, when monitored over 1 year . If a collective
behaviour of increased PA among overweight and obese in order
to affect weight is picked up in the present study, this might
moderate the gradient in the relationship between PA and weight
According to the recommendation for PA and public health set
forward by the Nordic Councils of Ministers, those who are
physically inactive may achieve the greatest health gains of
increasing their regular PA, independent of age . Although
cross-sectional, the linear reduction in relative odds for being
overweight or obese observed with higher levels of physical activity
indicates the importance of PA to weight management. The odds
of being overweight or obese differed by 53–71% between the least
and most active quintile of PA and the relationship between PA
and risk reductions associated with higher quintiles of PA appears
to be about linear.
To our knowledge, BMI related differences in hourly activity
patterns of overall PA (counts per minute) across weekdays and
Table 1. Descriptive data for participants (SD) by weight category.
Normal weight Overweight Obesity
WomenMen WomenMenWomen Men
n (%)1046(60) 638(41)519(30)707(47)190(11) 206(13)
Age (years)47.5 (15.5)49.6 (16.4)50.5 (14.1)51.0(14.2)48.5(13.6)49.0 (13.3)
Weight (kg)62.2(6.5)75.2(7.6)75.3(6.4)88.0(7.4)91.8 (12.4)105.1 (11.4)
General health (%)
Less than high school11.312.815.913.713.217.2
University ,4 years28.221.620.222.820.519.1
University $4 years28.330.221.722.323.712.3
Table 2. Measures of PA and sedentary behaviour (95% Confidence Intervals) stratified by BMI category.
Normal weight OverweightObesity
WomenMen Women MenWomen Men
Overall PA (CPM) 352(344, 360) 368(357, 379)324(313, 336)** 331(320, 314)** 276(257, 295)** 290(270, 310)**
Steps per day8554 (8374,
9196 (8177, 10.214) 7789(7532, 8046)** 8621(7654, 9587)6583 (6163, 7003)** 6980(5179, 8780)
528(524, 533) 552(546, 558)529(523, 534)558(552, 564) 546(535, 557)*574(5.64, 585)**
Light PA (min)304(300, 309) 284(278, 289)310 (304, 317)284 (278, 289)301(291, 312)273 (263, 283)
Moderate PA (min) 33.3(32.0, 34.6) 35.6(33.9, 37.3)28.4(26.6, 30.2)**32.2(30.5, 33.8)* 21.7(18.8, 24.7)*27.0(23.9, 30.0)**
Vigorous PA (min) 2.6(2.3, 2.9)4.0(3.5, 4.6)1.4(1.1, 1.9)**1.6(1.1, 2.1)*0.7(0.0, 1.4)**1.1(0.2, 2.1)**
Bouts of MVPA
21.0(19.9, 22.2) 19.3(17.7, 20.8)15.7(14.1, 17.3)**15.4 (14.0, 16.9)**10.4(7.8, 13.1)**13.2(10.4, 15.9)**
All values are adjusted for test centre and age, and indicators of intensity-specific PA were additionally adjusted for mean daily wear time.
*p,0.05, compared with normal weight, within sex.
**P#0.001, compared with normal weight, within sex.
Physical Activity and Overweight
PLOS ONE | www.plosone.org4January 2013 | Volume 8 | Issue 1 | e53044
weekends have not been examined in large and randomly selected
samples of adults and older people. A study of 108 participants by
Cooper et al. (2000) showed that although the obese participants
were consistently less active than non-obese participants, no
significant differences were observed while participants were at
work . Although time at work could not be identified in the
present study, the patterns of overall PA suggest that differences
were least pronounced between the hours of 09:00 and 16:00 on
weekdays and largest around midday and early afternoon on
weekends. Further, compared to normal weight participants, obese
participants displayed 19% lower overall PA on weekdays and
a 25% lower overall PA on weekends. As the majority of the
analysed sample reports working either full time (59%) or part time
(11%), the observed larger relative difference in overall PA
between obese and normal weight participants on weekends
compared to weekdays implies that overweight and obese
participants are more likely to pursuit sedentary behaviours when
not constrained by work.
The findings of this study must be interpreted in light of the
following limitations. We acknowledge the limitations of a cross-
sectional design in establishing a causal relationship between level
of activity and weight status. However, it clearly shows quantita-
tive differences in amount of PA performed as well as differences in
patterns of activity. Further, although BMI is the most commonly
used measure to identify and grade overweight and obesity in
populations, the method’s reliability had been questioned in
individuals at the extremes of age, muscle mass, and height
[33,34]. BMI accurately predicts obesity-related morbidity and
mortality in epidemiological studies , and it provides a reliable
and robust estimate of height-independent body fatness. Another
limitation is that height and weight were self-reported, which
might introduce bias because of the suspected underestimating
that occurs when participants self-report body weight . In
order to control this source of error, trained test personnel
measured the weight and height of a randomly selected sub sample
of the initial participants (n=904), in a laboratory. The largest
discrepancy between the self-reported and objectively measured
anthropometrical data was observed for overweight women who
on average underestimated their weight by 1.4 kg, indicating that
a bias as a result of self-reported weight is not a threat to the
validity of the present study. Among men, a small, but significant,
underestimation of weight was only observed in the normal weight
category (0.44 kg).
We acknowledge that accelerometers are unable to register
water activities such as swimming and to accurately assess
movement associated with non-ambulatory activity such as cycling
. To try to account for this potential source of error,
participants reported the frequency and duration of cycling and
swimming performed during the week of assessment. No
significant differences in the total time spent performing such
activities were observed between the participants in the different
weight categories (data not shown) indicating that the omission of
these activities from the accelerometer counts did not affect the
Another limitation of the present study is the relatively low
participation rate. Given the declining response rates in Norway,
and in other countries [38,39], and the risk for selection bias, it is
important to describe the non-responders in studies that attempt to
examine samples that are representative of the general population
[40,41]; however, such analysis is rarely available . Analysis of
Figure 1. Hourly distribution of overall PA level (CPM) for normal weight, overweight and obese individuals on weekdays.
Physical Activity and Overweight
PLOS ONE | www.plosone.org5January 2013 | Volume 8 | Issue 1 | e53044
Figure 2. Hourly distribution of overall PA level (CPM) for normal weight, overweight and obese individuals on weekend days.
Figure 3. The reduction in relative odds for being overweight or obese associated with increased overall PA and MVPA (the models
are adjusted for age, sex, level of education, smoking and self-reported health).
Physical Activity and Overweight
PLOS ONE | www.plosone.org6 January 2013 | Volume 8 | Issue 1 | e53044
the non-responders in our study by the use of registry linkage
showed that they were more likely to be either at the younger or
older end of the age spectrum, unmarried and not of Norwegian
origin and had lower educational and income levels, compared to
the responders . This has also been observed in most
population-based surveys [38,43]. Further, the sample included
participants from throughout Norway, and the prevalence of
overweight or obesity and other non-communicable diseases such
as type 2 diabetes was similar to other national estimates. This
indicates that the results from the present study have a general
validity corresponding to similar studies and that the study sample
was fairly representative of the general population in Norway. The
study is the first epidemiological study to objectively show
differences in activity patterns across weight categories and to
demonstrate the contribution of PA to the prevalence of over-
weight and obesity in Norway.
The worldwide obesity epidemic shows no signs of abating, and,
given the health risks and costs of the condition, it is crucial to
understand as much as possible about the relationship between PA
and weight status. Although we acknowledge that multiple factors
other than PA, such as the energy intake, consummation of
specific foods and beverages, alcohol use, and television watching.
, play vital roles in the development of overweight or obesity,
we believe that the findings of the present study provides
additional information on the relationship between PA and BMI
and suggests that there might be a particular scope for targeting
the weekend as a source of increased PA among overweight and
Both indicators of overall PA and intensity-specific PA differ
between BMI categories and the risk of being overweight or obese
increased with decreasing PA level. The BMI category related
difference in overall PA is largest on weekends, with obese
participants displaying an overall PA level 25% lower than the
normal-weight participants. These findings indicate the need for
planned interventions to increase the overall level of PA in the
population to counteract the environmental forces that are
producing a gradual weight gain in the population. The
continuing use of accelerometers to monitor longitudinally the
level of activity in the general population is vital for identifying the
dose response relationship between PA and the prevention and
treatment of overweight and obesity.
We thank the following test personnel at the ten institutions involved in the
study, for their invaluable work during the data collection: Sigurd Beldo
(Finnmark University College), Jon Egil Jakobsen (Hedmark University
College), Nils Petter Aspvik (NTNU Social Research AS), Ane Solbraa and
Einar Ylvisa ˚ker (Sogn og Fjordane University College), Hilde Lohne-Seiler
(University of Agder), Thomas Dillern and Freddy Pedersen (University of
Nordland), Sindre Mikal Dyrstad (University of Stavanger), Eva Maria
Støa (Telemark University College), Catherine Lorentzen, Signe Vallum-
rød og Anne Rabe (Vestfold University College), and Ingeborg Barth-
Vedøy, Anita Stokkeland, Johanne Støren Stokke and Karoline Steinbek-
ken (Norwegian School of Sport Sciences).
Conceived and designed the experiments: BHH IH SAA EK. Performed
the experiments: BHH IH SAA EK. Analyzed the data: BHH IH SAA EK.
Wrote the paper: BHH IH SAA EK.
1. Berrington de GA, Hartge P, Cerhan JR, Flint AJ, Hannan L, et al. (2010) Body-
mass index and mortality among 1.46 million white adults. N Engl J Med 363:
2. World Health Organization (2000) Obesity: preventing and managing the global
epidemic. Report of a WHO consultation.
3. Meyer HE, Tverdal A (2005) Development of body weight in the Norwegian
population. Prostaglandins Leukot Essent Fatty Acids 73: 3–7.
4. Haapanen N, Miilunpalo S, Pasanen M, Oja P, Vuori I (1997) Association
between leisure time physical activity and 10-year body mass change among
working-aged men and women. Int J Obes Relat Metab Disord 21: 288–296.
5. Weinsier RL, Hunter GR, Heini AF, Goran MI, Sell SM (1998) The etiology of
obesity: relative contribution of metabolic factors, diet, and physical activity.
Am J Med 105: 145–150.
6. Norum KR, Bjørnebo GE, Oshaug A, Botten G, Johansson L (2005) Nutrition
and food policy in Norway. In: Bendich A, Dencelbaum RJ, editors. Preventitive
nutrition: the comprehensive guide for health professionals. Totowa: Humana
7. Katzmarzyk PT, Mason C (2009) The physical activity transition. J Phys Act
Health 6: 269–280.
8. Ball K, Owen N, Salmon J, Bauman A, Gore CJ (2001) Associations of physical
activity with body weight and fat in men and women. Int J Obes Relat Metab
Disord 25: 914–919.
9. Prentice AM, Jebb SA (1995) Obesity in Britain: gluttony or sloth? BMJ 311:
10. Anderssen SA, Engeland A, Sogaard AJ, Nystad W, Graff-Iversen S, et al. (2008)
Changes in physical activity behavior and the development of body mass index
during the last 30 years in Norway. Scand J Med Sci Sports 18: 309–317.
11. Bernstein MS, Costanza MC, Morabia A (2004) Association of physical activity
intensity levels with overweight and obesity in a population-based sample of
adults. Prev Med 38: 94–104.
12. Davis JN, Hodges VA, Gillham MB (2006) Physical activity compliance:
differences between overweight/obese and normal-weight adults. Obesity (Silver
Spring) 14: 2259–2265.
13. Thompson DL, Rakow J, Perdue SM (2004) Relationship between accumulated
walking and body composition in middle-aged women. Med Sci Sports Exerc
14. Tucker LA, Peterson TR (2003) Objectively measured intensity of physical
activity and adiposity in middle-aged women. Obes Res 11: 1581–1587.
15. Wyatt HR, Peters JC, Reed GW, Barry M, Hill JO (2005) A Colorado statewide
survey of walking and its relation to excessive weight. Med Sci Sports Exerc 37:
16. Droyvold WB, Holmen J, Midthjell K, Lydersen S (2004) BMI change and
leisure time physical activity (LTPA): an 11-y follow-up study in apparently
healthy men aged 20–69 y with normal weight at baseline. Int J Obes Relat
Metab Disord 28: 410–417.
17. Corder K, van Sluijs EM (2010) Invited commentary: comparing physical
activity across countries–current strengths and weaknesses. Am J Epidemiol 171:
18. Forbes GB (1993) Diet and exercise in obese subjects: self-report versus
controlled measurements. Nutr Rev 51: 296–300.
19. Sallis JF, Saelens BE (2000) Assessment of physical activity by self-report: status,
limitations, and future directions. Res Q Exerc Sport 71: S1–S14.
20. Wareham NJ, Rennie KL (1998) The assessment of physical activity in
individuals and populations: why try to be more precise about how physical
activity is assessed? Int J Obes Relat Metab Disord 22 Suppl 2: S30–S38.
21. Prince SA, Adamo KB, Hamel ME, Hardt J, Gorber SC, et al. (2008) A
comparison of direct versus self-report measures for assessing physical activity in
adults: a systematic review. Int J Behav Nutr Phys Act 5: 56.
22. Westerterp KR (2009) Assessment of physical activity: a critical appraisal.
Eur J Appl Physiol 105: 823–828.
23. Strath SJ, Holleman RG, Ronis DL, Swartz AM, Richardson CR (2008)
Objective physical activity accumulation in bouts and nonbouts and relation to
markers of obesity in US adults. Prev Chronic Dis 5: A131.
24. Yoshioka M, Ayabe M, Yahiro T, Higuchi H, Higaki Y, et al. (2005) Long-
period accelerometer monitoring shows the role of physical activity in
overweight and obesity. Int J Obes (Lond) 29: 502–508.
25. Tudor-Locke C, Brashear MM, Johnson WD, Katzmarzyk PT (2010)
Accelerometer profiles of physical activity and inactivity in normal weight,
overweight, and obese U.S. men and women. Int J Behav Nutr Phys Act 7: 60.
26. Hagstromer M, Troiano RP, Sjostrom M, Berrigan D (2010) Levels and patterns
of objectively assessed physical activity–a comparison between Sweden and the
United States. Am J Epidemiol 171: 1055–1064.
27. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, et al. (2008) Physical
activity in the United States measured by accelerometer. Med Sci Sports Exerc
Physical Activity and Overweight
PLOS ONE | www.plosone.org7 January 2013 | Volume 8 | Issue 1 | e53044
28. Davis JN, Hodges VA, Gillham MB (2006) Physical activity compliance: Download full-text
differences between overweight/obese and normal-weight adults. Obesity (Silver
Spring) 14: 2259–2265.
29. Lopes VP, Magalhaes P, Bragada J, Vasques C (2009) Actigraph calibration in
obese/overweight and type 2 diabetes mellitus middle-aged to old adult patients.
J Phys Act Health 6 Suppl 1: S133–S140.
30. Tudor-Locke C, Giles-Corti B, Knuiman M, McCormack G (2008) Tracking of
pedometer-determined physical activity in adults who relocate: results from
RESIDE. Int J Behav Nutr Phys Act 5: 39.
31. Anderssen SA (2004) Physical activity. In: Becker, W, Pedersen, A, Lyhne, N,
Aro, A, Anderssen, SA, Fogelholm, M, Pedersen, J, I, Torsdottir, I, Meltzer, H,
and Alexander, J, Nordic Nutrition Recommendations 2004. Copenhagen.
32. Cooper AR, Page A, Fox KR, Misson J (2000) Physical activity patterns in
normal, overweight and obese individuals using minute-by-minute accelerome-
try. Eur J Clin Nutr 54: 887–894.
33. James PT (2004) Obesity: the worldwide epidemic. Clin Dermatol 22: 276–280.
34. Frankenfield DC, Rowe WA, Cooney RN, Smith JS, Becker D (2001) Limits of
body mass index to detect obesity and predict body composition. Nutrition 17:
35. Must A, Spadano J, Coakley EH, Field AE, Colditz G, et al. (1999) The disease
burden associated with overweight and obesity. JAMA 282: 1523–1529.
36. Nyholm M, Gullberg B, Merlo J, Lundqvist-Persson C, Rastam L, et al. (2007)
The validity of obesity based on self-reported weight and height: Implications for
population studies. Obesity (Silver Spring) 15: 197–208.
37. Corder K, Brage S, Ekelund U (2007) Accelerometers and pedometers:
methodology and clinical application. Curr Opin Clin Nutr Metab Care 10:
38. Sogaard AJ, Selmer R, Bjertness E, Thelle D (2004) The Oslo Health Study:
The impact of self-selection in a large, population-based survey. Int J Equity
Health 3: 3.
39. Van Loon AJ, Tijhuis M, Picavet HS, Surtees PG, Ormel J (2003) Survey non-
response in the Netherlands: effects on prevalence estimates and associations.
Ann Epidemiol 13: 105–110.
40. Sonne-Holm S, Sorensen TI, Jensen G, Schnohr P (1989) Influence of fatness,
intelligence, education and sociodemographic factors on response rate in a health
survey. J Epidemiol Community Health 43: 369–374.
41. Jacobsen BK, Thelle DS (1988) The Tromso Heart Study: responders and non-
responders to a health questionnaire, do they differ? Scand J Soc Med 16: 101–
42. Hansen BH, Kolle E, Dyrstad SM, Holme I, Anderssen SA (2012)
Accelerometer-Determined Physical Activity in Adults and Older People. Med
Sci Sports Exerc 44: 266–272.
43. Strandhagen E, Berg C, Lissner L, Nunez L, Rosengren A, et al. (2010) Selection
bias in a population survey with registry linkage: potential effect on
socioeconomic gradient in cardiovascular risk. Eur J Epidemiol 25: 163–172.
44. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB (2011) Changes in diet
and lifestyle and long-term weight gain in women and men. N Engl J Med 364:
Physical Activity and Overweight
PLOS ONE | www.plosone.org8 January 2013 | Volume 8 | Issue 1 | e53044