Association between objectively evaluated physical activity and sedentary behavior and screen time in primary school children
Tanaka et al. BMC Res Notes
Association between objectively evaluated physical activity and sedentary behavior and screen time in primary school children
Chiaki Tanaka 0
0 Division of Integrated Sciences, J. F. Oberlin University , 3758 Tokiwamachi, Machida, Tokyo 194-0294 , Japan
Background: Even when meeting guidelines for physical activity (PA), considerable sedentary time may be included. This study in primary school children investigated the relationships between objectively evaluated sedentary and PA times at different intensities using triaxial accelerometry that discriminated between ambulatory and non-ambulatory PA. The relationships between subjectively evaluated screen time (i.e. time spent viewing television and videos, playing electronic games, and using personal computers) and objectively evaluated sedentary and PA times were examined. Methods: Objectively evaluated sedentary and PA times were assessed for 7 consecutive days using a triaxial accelerometer (Active style Pro: HJA-350IT) in 426 first to sixth grade girls and boys. Metabolic equivalents [METs] were used to categorize the minutes of sedentary time (≤1.5 METs), light PA (LPA, 1.6-2.9 METs), moderate-to-vigorous PA (MVPA, ≥3.0 METs) and vigorous PA (VPA, ≥6.0 METs). The physical activity level (PAL) was calculated using the mean MET value. Subjectively evaluated screen time behaviors were self-reported by participants and parents acting together. The associations between PA and sedentary and screen time variables were examined using partial correlation analyses. Results: After adjustment for age, body weight and wearing time, objectively evaluated sedentary time correlated strongly with non-ambulatory and total LPA and PAL, moderately with ambulatory LPA, non-ambulatory or total MVPA, and weakly with ambulatory MVPA, ambulatory, non-ambulatory or total VPA. Subjectively evaluated screen time was not associated significantly with objectively evaluated sedentary and PA times or PAL. On average, each reduction of 30 min in daily sedentary time was associated with 6 or 23 min more of MVPA or LPA, respectively.
Exercise; Ambulatory activity; Non-ambulatory activity; Sitting; Accelerometer; Screen time
Conclusions: These findings show that higher daily sedentary time may be compensated mainly by lower LPA, while
the association between sedentary time and MVPA was moderate. Therefore, improving MVPA and reducing
sedentary time are important in primary school children.
The World Health Organization guidelines on physical
activity (PA) recommend at least 60 min every day of
moderate-to-vigorous physical activity (MVPA) in order
to benefit the health of children and adolescents .
However, considerable sedentary time can be
accumulated even when these PA guidelines are met. For
example, Marshall et al.  reported that clusters of UK and
USA adolescents of both genders had higher than
average levels of PA, but also increased levels of screen-based
sedentary behavior or sedentary socializing activities.
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Sedentary behavior is usually defined as behaviors
carried out in a seated or reclining position with an energy
cost of ≤1.5 METs . A previous systematic review of
the relationship between sedentary time or sedentary
behavior and health-related outcomes in children and
adolescences showed that long periods of sedentary
activity were associated with adverse health outcomes
[4–7]. Recently, guidelines for sedentary behavior in
children and adolescents have been published in several
Whether or not more time spent in MVPA is
associated with higher physical activity level (PAL) remains an
interesting question. One of the first studies to examine
this possibility was carried out in adults . A
multiple regression analysis of the proportion of time spent
on moderate or high intensity activities showed that
only moderate intensity activity was a significant
predictor of PAL (r2 = 0.51), while the proportions of low
and moderate intensity activities influenced total energy
expenditure. Recently, Pearson et al.  reviewed
several observational studies that examined the association
between PA and sedentary behavior or sedentary time in
young people (<18 years), and showed only a weak
negative association. However, only a small number of
studies have examined the associations between objectively
evaluated PA and sedentary time .
Previous studies proposed using prediction models of
metabolic equivalents (METs) for children with
accelerometers. The slope and intercept of ambulatory
activities in a predictive model such as walking and running
are different from those of non-ambulatory activities,
like playing games, cleaning, playing with blocks, tossing
a ball, and aerobic dance [14–17]. Based on the
variability in accelerometer counts, Hikihara et al.  showed
a discrimination between ambulatory activities, such
as continuous walking or jogging and non-ambulatory
activities, including lifestyle activity. Our previous study
in primary school children reported that ambulatory
light PA (LPA) and MVPA and non-ambulatory LPA were
lower in the summer vacation than during the school
year in both genders . We also observed that
nonambulatory MVPA in girls was significantly lower during
the summer vacation than in the school year. Another
study in preschool children showed that 25 m run speed,
skill-related physical fitness total Z-score, and total
physical fitness Z-score (health-related and skill-related
physical fitness total Z-score) correlated positively with
the time spent in ambulatory activity . Moreover, thin
preschool children spent significantly less time engaged
in ambulatory PA than normal-weight or overweight
children . Although the contribution of ambulatory
and non-ambulatory PAs to health outcomes in children
has not been well established [20, 21], physical fitness
and weight status in children correlated to the types of
PA, MVPA was shown to make up approximately 40–45%
of non-ambulatory activity in primary school boys and
girls in the school year . Therefore, ambulatory and
non-ambulatory PAs may have different effects on the
relationships between PAs and sedentary behavior (SB).
The objective of the present study was to examine the
association between PA intensities and sedentary time
in 6–12 year-old Japanese primary school children, using
objective accelerometer data for PA that discriminated
between non-ambulatory or ambulatory PA. We also
examined the contribution of reported screen time to
total sedentary and PA times.
Our convenience sample included Japanese primary
school children from 14 primary schools in urban areas
of Tokyo and Kyoto. The participants were invited to
participate in the study at their school using leaflets, such
as a newsletter. Informed consent was obtained from all
participants, and the Ethical Committee of J. F. Oberlin
University approved the study protocol (Receipt Number:
12023). All participations and their parents consented
to publication of the data. The data of anthropometric
measurements, sedentary time, and PA were collected
from June 2012 to January 2015 during the school year.
The initial sample comprised 569 participants with 143
children subsequently being withdrawn because of:
accelerometer data not conforming with the study
criteria (see below) [n = 95], revocation of the agreement
[n = 7], history of conditions affecting PA such as
respiratory disease or heart disease [n = 25], no questionnaire
data [n = 15], and belonging to a different ethnic group
[n = 1]. Data analysis was carried out on the remaining
426 children. These children, except those who revoked
the agreement [n = 7], participated in the study, but their
data were excluded from the analyses. There was no
significant difference in age, relative weight and gender
proportion between the study group and children withdrawn
from the analyses.
Objective measurement of sedentary time and physical
Habitual sedentary time and PA were measured using
a triaxial accelerometer (Active style Pro HJA-350IT,
Omron Healthcare, Kyoto; dimensions 74 × 46 × 34 mm
and weight 60 g including batteries). The device is
described in detail elsewhere . The participants wore
the accelerometer on the left side of the waist at school.
We calculated the synthetic acceleration in all three axes
using signals before and after high-pass filtering. The
ratio of unfiltered to filtered acceleration was then
calculated to identify non-ambulatory activities (e.g., playing
games, playing with blocks, tossing a ball, and cleaning
and clearing away) and ambulatory activities (e.g.,
walking and running). When the ratio of unfiltered to filtered
synthetic acceleration was 1.12, the rate of correct
discrimination between non-ambulatory and ambulatory
activities was excellent, with a mean of 99.1% . The
acceleration signals were calculated as the mean of the
absolute values of accelerometer output in each axis over
10 s epochs at the middle of each activity. The data were
expressed as variables of acceleration output. Because the
predictive equations used for the Active style Pro were
established in adults, the values of metabolic equivalent
(MET) values recorded by the accelerometer are
overestimated in primary school children . We therefore used
the following conversion equations for primary school
children, based on the results of Hikihara et al. .
0.6237 × MET value of Active style Pro + 0.2411
0.6145 × MET value of Active style Pro + 0.5573
Sedentary time and PA were monitored continuously
for 7 days. The participants were requested to wear the
devices at all times, except under special circumstances,
such as dressing and bathing. In fact, many participants
wore the accelerometer during sleep. Because sleep and
sedentary time cannot be discriminated we analyzed data
collected between 7:00 and 21:00 to exclude sleep time.
We included days in which >600 min (10 h) of wearing
time had accrued. Periods with >60 min of consecutive
‘‘non-wearing time’’ were as classified as non-wearing
time. Penpraze et al.  and Cliff et al.  suggested
at least 3 days were required for reliable PA monitoring
in young children. Participants with data from at least 2
weekdays and at least 1 weekend day were included in the
Time spent viewing television and videos, playing
electronic games and using a personal computer was assessed
by a questionnaire completed jointly by the children and
their parents. The children and parents were asked the
following two questions: “How many hours of
television and video movies (except at school) does the child
usually watch? (a) in a school day or (b) in a non-school
day—0, 30 min, 1, 2, 3, 4, or >5 h; and “How many hours
in a single day does the child usually use a personal
computer or play electronic games (including television,
personal computer, portable game machines such as mobile
phones at home and friends’ homes including arcade
games, etc.)? (a) in a school day or (b) a non-school day—
0, 30 min, 1, 1.5, 2, 2.5, or >3 h.
Body height and weight were measured without shoes,
but with clothing to the nearest 0.1 cm and 0.1 kg,
respectively. Net body weight was calculated as the weight of
clothing subtracted from the measured body weight. BMI
(body mass index) was calculated as weight in kilograms
divided by height in meters squared. Weight status was
classified as normal weight, overweight/obese, or thin
using the Japanese cut-offs for weight based on national
reference data for Japanese children .
Relative weight was calculated as [body weight
(kg) − standard weight for gender, age, and height (kg)]/
standard weight (kg) × 100 (%)
a and b are gender- and age-specific.
The cut-off values for weight categories were:
overweight/obesity combined, ≥ +20%; normal weight, −20
to +20%; thin, ≤ −20%.
The time spent in sedentary and each PA intensity each
day was calculated using METs for each individual:
average number of weekday and weekend minutes spent
in sedentary time (METs ≤1.5), LPA (METs 1.6–2.9),
MVPA (METs ≥3.0) and VPA (METs ≥6.0). The mean
weekly values were then calculated. Time spent
viewing TV and video games was calculated as (1) 0 min, (2)
30 min, (3) 1 h, (4) 2 h, (5) 3 h, (6) 4 h, (7) 5 h. Time using
a personal computer, or playing electronic games was
calculated as (1) 0 min, (2) 30 min, (3) 1 h, (4) 1.5 h, (5) 2 h,
(6) 2.5 h, or (7) 3 h. For the objective and subjective data,
the mean values were calculated by weighting for 5
weekdays and 2 weekend days [Weighted data = ((mean for
weekdays × 5) + (mean for weekend days × 2))/7]. PA
assessed by the accelerometer was presented as: (1) PA
states for ambulatory activity or non-ambulatory activity
in intensity-specific categories (LPA, MVPA, and VPA);
and (2) PAL, total energy expenditure (kcal/day) divided
by basal metabolic rate (kcal/day), calculated using the
mean value of METs.
The relationship between two variables was
examined using partial correlation analysis controlled for
gender, age, body weight, and wearing time. A simple
linear stepwise regression analysis was used to obtain
prediction equations. The dependent variables were
sedentary time, LPA, and MVPA and the
independent variables sedentary time, LPA, MVPA or VPA,
and gender, age, body weight, and wearing time. The
results are shown as mean ± standard deviation (SD).
The statistical analyses were performed using IBM
SPSS statistics 20.0 for Windows (IBM Co., Tokyo,
Japan). All statistical tests were regarded as significant
when p values were ≤0.05.
Characteristics of the study participants
Table 1 shows the characteristics of the study
participants and time spent at sedentary, different intensity
levels and total time for ambulatory and non-ambulatory
activity, PAL, and subjectively evaluated screen-based
sedentary behavior. Five percent of participants were
overweight/obese. The duration of accelerometry was
considerably greater than the minimum criteria specified
(at least 3 days and 10 h), with a mean of 6.3 days and
13.4 h, respectively. The percentage of children with >2 h/
day of screen time was 59.8%. The percentages of each
response category (1–7 corresponding to 0, 30 min, 1, 2,
3, 4, or >5 h, respectively) for television and video
viewing time in a school or non-school day were 6.9% (school
day) and 0.7% (non-school day) in category 1; 13.8 and
Table 1 Physical characteristics and determinants
at baseline of the participants
n = 426
LPA light physical activity, MVPA moderate-to-vigorous physical activity, VPA
vigorous physical activity
2.4% in category 2; 33.1 and 14.3% in category 3; 33.1
and 32.6% in category 4; 9.3 and 26.9% in category 5; 2.1
and 14.5% in category 6; and 1.7 and 8.6% in category 7.
The corresponding percentages in games and personal
computer time (1–7 corresponding to 0, 30 min, 1, 1.5,
2, 2.5, or >3 h, respectively) were 46.1% (school day) and
25.1% (non-school day) in category 1; 28.2 and 25.8% in
category 2; 15.5 and 22.9% in category 3; 6.0 and 6.9% in
category 4; 3.6 and 11.9% in category 5; 0.2 and 2.1% in
category 6; and 0.5 or 5.3% in category 7.
Table 2 shows the partial correlation between duration
of objectively evaluated sedentary time, each intensity
and type of PA, PAL, and screen time. After adjustment
for age, gender, body weight, and wearing time,
objectively evaluated sedentary time correlated strongly with
non-ambulatory and total LPA and PAL, moderately with
ambulatory LPA, non-ambulatory and total MVPA, and
weakly with ambulatory MVPA and ambulatory,
nonambulatory and total VPA. Screen time was not
associated significantly with objectively evaluated sedentary
time, PAs, or PAL.
The results of the stepwise linear regression
analysis (Table 3) showed that on average, each additional
10 min each day of MVPA or LPA was associated with 18
and 12 min less of objectively evaluated sedentary time,
respectively. In contrast, each 30 min reduction in daily
sedentary time was associated with 6 or 23 min more of
objectively evaluated MVPA or LPA.
This study examined the associations between sedentary
time and PAL or PAs classified as either ambulatory or
non-ambulatory using triaxial accelerometry in primary
school Japanese children. We showed significant
associations between objectively evaluated sedentary time
and PAL or PAs. In particular, non-ambulatory and total
LPA correlated strongly with sedentary time, with the
increase in sedentary time being compensated mainly
by decreased LPA. On the other hand, there was only a
moderate degree of correlation between MVPA and
sedentary time, while no association was found between
subjectively evaluated screen time and objectively
evaluated sedentary time, PAs or PAL. These findings indicate
that improvement in MVPA and decrease in sedentary
time may be independent of each other to some degree in
primary school children.
PAL for level II (moderate) and level III (high) in
Japanese individuals is categorized as 1.60 and 1.80 for
8–9 years and 1.65 and 1.85 for 10–11 years, respectively.
These Dietary Reference Intakes for Japanese—2015—
are based on the doubly labeled water method .
The mean value of PAL (1.74) in the sample of the
present study (9.3 ± 1.6 years) was categorized as level II
Table 2 Partial correlation coefficients between daily sedentary behavior and physical activity
Table 3 Prediction equations for sedentary behavior or moderate-to-vigorous physical activity
Adjusted R2 SEE p value
Sedentary behavior (min/day) = −64 (50) − 1.83 (0.12) * MVPA (min/day) + 0.606 (0.057) * wearing time (min/
day) − 19.7 (4.4) * sex + 6.88 (1.74) * age (year) + 1.06 (0.35) * body weight (kg)
Sedentary behavior (min/day) = −111 (19) − 1.16 (0.02) * LPA (min/day) + 1.04 (0.02) * wearing time (min/day) + 18.5
(1.6) * sex + 3.30 (0.55) * age (year)
Sedentary behavior (min/day) = −271 (59) − 3.75 (0.59) * VPA (min/day) + 0.60 (0.07) * wearing time (min/day) + 15.48
(2.05) * age (year) + 1.13 (0.44) * body weight (kg)
LPA (min/day) = −71 (16) − 0.78 (0.01) * sedentary behavior (min/day) + 0.85 (0.02) * wearing times (min/day) + 15.3
(1.3) * sex + 1.22 (0.47) * age (year)
LPA (min/day) = 56 (49) + 0.84 (0.11) * MVPA (min/day) + 0.38 (0.06) * wearing time (min/day) + 20.3 (4.3) * sex − 6.61
(1.71) * age (year) − 1.04 (0.35) * body weight (kg)
MVPA (min/day) = 67 (16) − 0.20 (0.01) * sedentary behavior (min/day) + 0.14 (0.02) * wearing time (min/day) − 14.9
(1.3) * sex − 1.16 (0.48) * age (year)
MVPA (min/day) = 78 (10) + 0.13 (0.02) * LPA (min/day) − 18.2 (1.6) * sex − 3.28 (0.55) * age (year)
SEE standard error of estimate, sex: boy: 1, girl: 2, LPA light physical activity, MVPA moderate-to-vigorous physical activity, VPA vigorous physical activity, the values
given in parentheses in prediction equations were SEE for each partial regression coefficient
(moderate). Screen time over 2 h/day was found in 59.8%
of children. Recently, Pearson et al.  reviewed several
observational studies that had examined the association
between PA and objectively evaluated sedentary behavior
or screen time in young people (<18 years), and showed
only a small, negative association. This suggested that
these behaviors did not directly displace one another.
Studies to date have assessed the association between
objectively measured PAL, PA intensities, and sedentary
time. Several studies [26–29] showed relatively strong
and negative correlations between sedentary time or
lowintensity PA and total PA such as PAL. On the other hand,
some studies reported a negative correlation between
sedentary time and MVPA [13, 28, 29], whereas others
reported no such association [26, 27]. These
discrepancies were probably due to differences in sample
characteristics, accelerometry, and cutoffs between sedentary
time, LPA, and MVPA. Our results are in agreement
with some of these previous studies in children and
adolescents. When interpreting the differences in results
between studies in children and adolescents, differences
in the definition or algorism of MVPA and differences in
epoch length between studies may have contributed to
the conflicting results [30–33]. In contrast to the
findings in adults, where PAL can be increased by increasing
the amount of time spent on moderate intensity
activities and reducing low-intensity activities , children
and adolescents are characterized by short, intermittent
bouts of VPA [34, 35]. The association between
sedentary time and MVPA observed in the present study was
not seen in previous studies. The associations between
sedentary time and MVPA were not significantly
different between boys and girls and lower grade and upper
grade individuals (1–3 grade boys, −0.64; 1–3 grade girls,
−0.58; 4–6 grade boys, −0.59; 4–6 grade girls, −0.68).
Therefore, all children were included in our analyses.
In the present study, time spent on screen-based
sedentary behavior (TV and video viewing time, PC and game
time, and total screen time) recorded in the
questionnaire did not correlate significantly with sedentary time
assessed by the accelerometer. Lubans et al.  reviewed
the validity of self- and proxy-report measures of
sedentary behavior estimates of screen time to assess the utility
of accelerometers to classify sedentary time in children
and adolescents and showed self-reported measures
remain largely untested . In the present study, PAs
and PAL were not associated with screen time. Herman
et al.  also showed that neither MVPA nor LPA were
associated with screen time in children. Self- and
proxyreport measures of sedentary behavior instead provided
information about the type of sedentary behavior or
context. Studies on the association between screen time and
PAs are therefore needed to further understand the
complex relationships between sedentary behavior and PAs.
The present study indicated that considerably more
time was spent in non-ambulatory LPA than ambulatory
LPA. On the other hand, non-ambulatory and
ambulatory MVPA minutes were comparable. Previous
studies did not, however, discriminate between ambulatory
and non-ambulatory PA. Objectively evaluated
sedentary time showed a stronger negative correlation with
non-ambulatory MVPA or LPA (r = −0.66 and −0.81,
respectively) than ambulatory MVPA or LPA (r = −0.41
and −0.58, respectively). Although there is only limited
evidence on the association between ambulatory and
non-ambulatory PAs and health outcome in children
[20, 21], physical fitness and weight status in children
seem to be related to the types of PA. MVPA
represents approximately 40–45% of non-ambulatory activity
in primary school boys and girls during the school year
. Evidence from the current study also indicates that
both ambulatory activity and non-ambulatory
activity are important factors in the PA lifestyle of children.
Therefore, public health strategies should target LPA and
non-ambulatory MVPA to decrease sedentary time and
improve overall PA and health in children. These data
may be particularly important for providing insights
into improving different intensities of PA in children. On
average, each additional 10 min of daily MVPA or LPA
was associated with 18 and 12 min less of objectively
evaluated sedentary time. Over the course of a week,
sedentary time was compensated mainly by higher daily
LPA, as indicated by the strong correlation and
partial regression coefficient being close to 1.0. Moreover,
although the correlation between MVPA and sedentary
time was slightly weaker, higher daily MVPA may lead to
lower sedentary time and higher total PA to some degree,
probably due to concomitant higher levels of LPA. In
contrast, each 30 min reduction in daily sedentary time
was associated with 6 or 23 min more of objectively
evaluated MVPA or LPA. Therefore, the discrepancy in the
corresponding durations was larger in the latter case (18
and 12 min vs. 6 and 23 min for MVPA and LPA,
respectively). The main reason for this observation may be that
reduced sedentary time was displaced mainly by LPA and
not MVPA, while higher MVPA accompanied LPA,
leading to sufficiently lower sedentary time.
There are several methodological points that need to
be considered when interpreting our results. Firstly, our
sample was not a representative sample of Japanese
children. Secondly, the accelerometer is a widely used tool to
measure PA, but it cannot assess all PA, such as
swimming and cycling. These two points were limitations of
the study. The strengths of our study include the use of
objective and quantitative measures of sedentary time,
classifying ambulatory and non-ambulatory PA, and the
use of a sample population of Japanese primary school
children from 14 different schools. It should be noted
that the data were recorded over a 10-sec epoch, which
should be sufficiently sensitive to pick up short bursts of
vigorous activity [31, 37]. Shorter epoch lengths could be
used to better reflect movement patterns of children.
In Japanese primary school children, objectively
evaluated sedentary time correlated strongly with
non-ambulatory and total LPA or PAL, moderately with ambulatory
LPA, non-ambulatory or total MVPA, and weakly with
ambulatory MVPA, ambulatory, non-ambulatory or
total VPA, after adjustment for age, gender, body weight
and wearing time. Screen time was not associated
significantly with objectively evaluated sedentary time,
PAs, or PAL. On average, each additional 10 min of daily
MVPA or LPA was associated with 18 or 12 min less of
objectively evaluated sedentary time. In contrast, each
30 min reduction in daily sedentary time was associated
with 6 or 23 min more of objectively evaluated MVPA or
LPA. These findings indicate that higher daily sedentary
time was compensated by lower LPA, with only a
moderate association between sedentary time and MVPA.
Taken together, these findings indicate that evaluation of
non-ambulatory activity or LPA is important in the
overall assessment of PA.
PA: physical activity; SB: sedentary behavior; METs: metabolic equivalents; LPA:
light intensity activity; MVPA: moderate-to-vigorous physical activity; VPA:
vigorous physical activity; PAL: physical activity level; BMI: body mass index; TV:
CT and ST designed the research. CT, MT, and MO coordinated data collection.
CT, ST, TA, and MT analyzed the data. CT, MO, SI, and ST discussed the analysis
and interpreted the results. CT wrote the paper and had primary responsibility
for the final content. All authors reviewed the manuscript critically. All authors
read and approved the final manuscript.
The authors would like to thank the participants for their cooperation in the
study. We also wish to thank the staff of the National Institute of Health and
Nutrition for their help with the experiments. This work was supported by a
Grant-in-Aid for Scientific Research (C) (24500832).
Dr. Shigeho Tanaka received consigned research funds from Omron
Healthcare Co., Ltd. The remaining authors declare no competing interests.
Availability of data and materials
The dataset supporting the conclusions of this article is currently not available
in the public domain but may be shared upon request. For further information
on the data and materials used in this study please contact the corresponding
Ethics approval and consent to participate
As described in the “Methods”, informed consent was obtained from all
participants, and the Ethical Committee of J. F. Oberlin University approved the
study protocol (Receipt Number: 12023). All participations and their parents
consented to publication of the data.
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