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Background Designing evidence-based interventions to address socioeconomic disparities in health and health behaviours requires a better understanding of the specific explanatory mechanisms. We aimed to investigate a comprehensive range of potential theoretical mediators of physical activity (PA) and screen time in different socioeconomic status (SES) groups: a high SES group of high school students, and a low SES group of vocational school students. The COM-B system, including the Theoretical Domains Framework (TDF), was used as a heuristic framework to synthesise different theoretical determinants in this exploratory study. Methods Finnish vocational and high school students (N = 659) aged 16–19, responded to a survey assessing psychological, social and environmental determinants of activity (PA and screen time). These determinants are mappable into the COM-B domains: capability, opportunity and motivation. The outcome measures were validated self-report measures for PA and screen time. The statistical analyses included a bootstrapping-based mediation procedure. ResultsRegarding PA, there were SES differences in all of the COM-B domains. For example, vocational school students reported using less self-monitoring of PA, weaker injunctive norms to engage in regular PA, and fewer intentions than high school students. Mediation analyses identified potential mediators of the SES-PA relationship in all of three domains: The most important candidates included self-monitoring (CI95 for b: 0.19–0.47), identity (0.04–0.25) and material resources available (0.01–0.16). However, SES was not related to most determinants of screentime, where there were mainly gender differences. Most determinants were similarly related with both behaviours in both SES groups, indicating no major moderation effect of SES on these relationships. Conclusions This study revealed that already in the first years of educational differentiation, levels of key PA determinants differ, contributing to socioeconomic differences in PA. The analyses identified the strongest mediators of the SES-PA association, but additional investigation utilising longitudinal and experimental designs are needed. This study demonstrates the usefulness of combining constructs from various theoretical approaches to better understand the role of distinct mechanisms that underpin socioeconomic health behaviour disparities.
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R E S E A R C H A R T I C L E Open Access
What explains the socioeconomic status
gap in activity? Educational differences in
determinants of physical activity and
screentime
Nelli Hankonen
1,2*
, Matti T. J. Heino
1
, Emilia Kujala
1
, Sini-Tuuli Hynynen
1
, Pilvikki Absetz
3
, Vera Araújo-Soares
4
,
Katja Borodulin
5
and Ari Haukkala
1
Abstract
Background: Designing evidence-based interventions to address socioeconomic disparities in health and health
behaviours requires a better understanding of the specific explanatory mechanisms. We aimed to investigate a
comprehensive range of potential theoretical mediators of physical activity (PA) and screen time in different
socioeconomic status (SES) groups: a high SES group of high school students, and a low SES group of vocational
school students. The COM-B system, including the Theoretical Domains Framework (TDF), was used as a heuristic
framework to synthesise different theoretical determinants in this exploratory study.
Methods: Finnish vocational and high school students (N= 659) aged 1619, responded to a survey assessing
psychological, social and environmental determinants of activity (PA and screen time). These determinants are
mappable into the COM-B domains: capability, opportunity and motivation. The outcome measures were
validated self-report measures for PA and screen time. The statistical analyses included a bootstrapping-based
mediation procedure.
Results: Regarding PA, there were SES differences in all of the COM-B domains. For example, vocational school
students reported using less self-monitoring of PA, weaker injunctive norms to engage in regular PA, and fewer
intentions than high school students. Mediation analyses identified potential mediators of the SES-PA relationship
in all of three domains: The most important candidates included self-monitoring (CI95 for b: 0.190.47), identity
(0.040.25) and material resources available (0.010.16). However, SES was not related to most determinants of
screentime, where there were mainly gender differences. Most determinants were similarly related with both
behaviours in both SES groups, indicating no major moderation effect of SES on these relationships.
Conclusions: This study revealed that already in the first years of educational differentiation, levels of key PA
determinants differ, contributing to socioeconomic differences in PA. The analyses identified the strongest
mediators of the SES-PA association, but additional investigation utilising longitudinal and experimental designs
are needed. This study demonstrates the usefulness of combining constructs from various theoretical approaches
to better understand the role of distinct mechanisms that underpin socioeconomic health behaviour disparities.
Keywords: Socioeconomic status, Adolescents, Physical activity, Screen time, Sedentary behaviour, Theoretical
determinants, Theoretical domains framework
* Correspondence: nelli.hankonen@staff.uta.fi
1
Department of Social Research, University of Helsinki, Helsinki, Finland
2
School of Social Sciences and Humanities, University of Tampere, Tampere,
Finland
Full list of author information is available at the end of the article
© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Hankonen et al. BMC Public Health (2017) 17:144
DOI 10.1186/s12889-016-3880-5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Background
Lack of physical activity (PA) is a major public health
problem. Globally, four-fifths of adolescents do not
reach recommended levels of PA, i.e. 6090 minutes a
day [1]. Adolescents also engage in unhealthy amounts
of sedentary behaviours (SB), especially screen time such
as sitting in front of TV, computers and console games,
linked to adverse health outcomes independent of PA
[2, 3]. Consequently, many national PA guidelines for
children and youth additionally include a recommenda-
tion of a maximum of two hours of screen time per
day, also in Finland [4].
Socioeconomic status (SES) refers to socioeconomic
standing in society, measured by educational level, occu-
pation, or income [5]. Educational level is the most fre-
quently used measure of SES in Finland [6], and among
adolescents, this means those enrolled in a vocational
school (lower SES) versus those in high school (higher
SES). High SES is linked with higher levels of PA [7],
and physical inactivity is one of the most important be-
haviours explaining higher mortality in lower SES popu-
lation [8, 9]. SES differences in PA appear already in
youth [10], and worldwide, this difference has increased
over the last decade [6, 11].
To address socioeconomic health disparities [12], it is
necessary to move beyond description, to increase know-
ledge on the potential modifiable factors explaining the
SESPA relationship. The known correlates or determi-
nants of PA in youth are such potential mediators.
Determinants of adolescent physical activity and
screen time
Several reviews [1316] have identified psychosocial and
environmental determinants of adolescent PA. As studies
tend to refer to different theories and use a multitude of
theoretical constructs although often strongly overlap-
ping a useful framework for classifying the various
determinants is provided by the COM-B model [17]. The
COM-B assumes three essential categories of necessary
factors for the performance of a specific behaviour, these
are: 1) capability, an individuals psychological and physi-
cal capacity to engage in a specific behaviour or sets of
behaviours, 2) opportunity, defined as factors outside an
individual that make the behaviour possible or prompt it,
and 3) motivation to engage in the behaviour [17]. In line
with dual process models in psychology, the COM-B dis-
tinguishes reflective motivation (e.g., intention) and auto-
matic motivation (e.g., automaticity) as key influences on
behaviour, with capability and opportunity also influen-
cing motivation. Other determinants may have differen-
tial impacts on motivation, not only on behaviour, thus
it is important to investigate indicators of motivation as
outcomes.
The COM-B can further be specified with sub-constructs
mapped onto the Theoretical Domains Framework (TDF).
The TDF was developed based on 128 unique theoretical
constructs from 33 different theories, these unique
constructs were then aggregated into 14 theoretical
domains [18].
Evidence on determinants of PA and of screentime will
next be presented, organised under the COM-B domains
and the TDF [18, 19] See Appendix 1 for the COM-B
categories, Theoretical Domains and the determinants
measured in this study.
Determinants of physical activity
A subfacet of capability, the psychological ability to
regulate ones behaviour meaning anything aimed at
managing or changing objectively observed or measured
actions[18] is important for both initiation and main-
tenance of behaviour change. A recent review [20] has
identified a relationship between behaviour planning and
PA among adolescents.
Environmental opportunity, i.e. favourable context
and sufficient resources are important facilitators of be-
haviour. Perceived access to PA facilities [13], as well as
opportunities for PA in the community [15], and school
[16] are positively correlated with PA among adoles-
cents. Social environment plays an important role, too,
as parental support for PA and support from significant
others, such as siblings and peers, are related with ado-
lescent PA [14, 16]. Support from teachers and coaches,
however, do not seem to be as important as that from
parents and peers [15].
The motivation category contains several determinants
underlying these processes directing and energising be-
haviour. Self-efficacy, i.e. individuals confidence in his/
her ability to be physically active in specific situations, is
positively correlated with adolescent PA [13, 16, 21, 22],
as is higher perceived behavioural control, i.e. perceived
ease of being physically active [13, 21] (correlation
among adolescents from a meta-analysis r= 0.32)[23].
Beliefs about positive and negative consequences of a be-
haviour, i.e. outcome expectancies (e.g., [24]), have been
linked to changes in adolescent PA, but the evidence is
inconclusive [22]. This also applies to evidence for atti-
tudes as determinants of PA [16, 20, 21] (correlation
among adolescents from a meta-analysis r= 0.36 [23]).
Although intention to engage in PA partially determines
adolescent PA [15, 25] (correlation among adolescents
from a meta-analysis r= 0.46 [23]), there is a well-known
gap between intentions and behaviour [26, 27]. Habit
strength (automaticity) and identity relevance of a be-
haviour are factors related to motivation, which have
recently gained increasing attention in research on
energy-balance behaviours. Already in children, high
habit strength is associated with more PA [28]. Those
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adolescents who identify with the concept of being a
physically active person are more likely to engage in
regular PA than those who do not [29].
Determinants of screen time
Very few high quality studies have investigated determi-
nants of adolescent SBs [16, 20, 30]. Furthermore, screen
time consists of different behaviours, which may also
have different behavioural determinants. Thus far, TV-
viewing has been studied much more than other forms
of screen time. Determinants may also vary between
target population [31].
Opportunity Among children, parental rules and limita-
tions on screen time have been associated with less screen
time, and availability of devices (e.g. TV or a computer in
the bedroom) with greater amount of screen time [32, 33].
Various social demographical correlates (e.g. single-parent
family as well as low parental income and education) are
related with more screen time [16, 30].
Motivation Perceived benefits of SBs (e.g.,enjoyment
and the opportunity to unwind) have been linked with
resistance to change sedentary habits [34]. Self-efficacy
and habit strength also play a role in SBs: youth with
higher confidence in their ability to reduce SB are less
sedentary [34], while strong TV-viewing habits are re-
lated to exceeding the recommended levels of TV-
viewing [35].
Capability to use the technological equipment re-
quired for screentime and TV viewing is easily acquired
by all of us, as the technological design of these products
relies on cognitive abilities that all humans are capable
of developing. Hence, we expect that variables associated
with capability for screen time behaviour will not be as
impactful.
Which determinants mediate the influence of SES on
activity?
What then could explain the well-documented SES-
differences in PA? Socioeconomic differences may be
evidenced as different levels in key theoretical determi-
nants, accountable for differing levels of activity (i.e.,
mediation).
Previous studies are sparse. Among adults, self-efficacy,
social support [36, 37], and availability of and access to PA
facilities [3739] are potential candidates. Also, favourable
environment for PA may not be equally accessible for
those with lower SES [40]. People with higher SES may
have greater sense of control over their PA and their
health and higher levels of social support (e.g. [4143]).
Higher education may also enhance individualsability to
use self-regulatory skills [44].
Socio-structural factors such as SES are often excluded
from health behaviour change models [45], a limitation
recently acknowledged (e.g., [45, 46]). Several health be-
haviour theories assume the SES to be a distal influence
[24], but this assumption is rarely tested. This study aims
at filling the gaps in literature by investigating this as-
sumption explicitly across several potential determinants.
SES differences may arise also from moderation effects.
For example, intention had a weaker relationship to pro-
spective behaviour among those with lower SES compared
to their high SES counterparts [46], suggesting that those
with lower SES may have difficulties in translating healthy
intentions into action [46], although findings on this are
mixed [47, 48].
Aims
The present study will comprehensively investigate the-
oretical constructs that may explain SES differences in
activity behaviours, i.e., moderate-to-vigorous PA (MVPA)
and screen time. We make comparisons between a broad
range of determinants based on relevant behavioural theo-
ries, roviding by the COM-B model and TDF, in a repre-
sentative sample of vocational and high school students,
representing low and high SES youth respectively. First,
we investigate whether SES is associated both with the de-
terminants and the behaviours (RQ1a). We also investi-
gate which determinants might mediate the relationship
between SES and behaviour (RQ1b). We analyse whether
there are differences in determinants by gender, given the
expected differences from previous literature both on PA
and SB. Secondly, we investigate whether SES moderates
the relationships between specific determinants and be-
haviour (RQ2). See representation of the research ques-
tions (RQ) in Fig. 1.
Methods
Data were collected via an electronic survey among
Finnish vocational and high school students during
March-April 2013. Data collection took place in schools
under teachers supervision. Altogether the 13 largest
vocational schools from five different areas in Finland
were invited to participate in the survey [49]. The largest
municipal education and training consortia include the
Fig. 1 Research questions
Hankonen et al. BMC Public Health (2017) 17:144 Page 3 of 15
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highest number in educational tracks (tourism industry,
beauty care, catering, metalwork and machinery), com-
pared to smaller school units, and thus improved the
variability of educational tracks in our sample. To enable
comparison between the low and high SES groups, six
high-schools in the areas of the participating vocational
schools were also invited to participate. We aimed for
better comparability by inviting the high schools from
geographically matching areas. 765 students responded
to the questionnaire. 507 students from eight vocational
schools (62% of the schools invited agreed to participate)
and 152 students from three high schools (50% of the
schools invited agreed) fit the age criterion of 1619
years, and were thus included in the analysis.
Measures
Behavioural determinants
To develop the assessment tool, the determinants were
selected utilising previous reviews on the determinants
of adolescent PA and sedentary behaviour to map on all
relevant domains of the COM-B [17]. Determinants
were measured according to specific recommendations
[24, 50] and in line with earlier research (e.g., [51]).
Table 1 shows the items. Cronbachs alphas ranged from
0.42 (material resources) to 0.96 (Physical Education
(PE) teacher autonomy support and action planning),
with most scales at satisfactory levels (see Table 1).
Other measured variables included self-assessed health
and physical condition (both measured on a scale from
1 =very good to 5 = very poor), as well as injuries (yes/no).
Behaviours
Self-reported MVPA was assessed with a question: Dur-
ing the last seven days, on how many days were you
physically active so that the activity intensity was moder-
ate or vigorous and you were active at least 30 minutes
per one day (scale 07 days). The validity of this ques-
tion was tested against objectively measured PA in a
sub-sample (n= 44) of adolescents, using a triaxial accel-
erometer (Hookie Meter v2.0, Hookie Technologies Ltd,
Espoo, Finland). The activity data was registered as raw
data at a 100 Hz sample rate in a 2GB internal flash
memory. Accelerometers were worn to monitor PA for
seven consecutive days. After the week, participants
responded to the questionnaire that included the self-
reported MVPA question (see above). The correlation
coefficient between the Hookie-measured average
daily MVPA (approximately above four METs) and the
self-reported MVPA was adequate, r=.38 (p<.02).
Self-reported screen time was reported separately for
weekday and weekend andassessed with the following
questions: How many hours a day during the last
4 weeks have you watched TV on a normal weekday/
weekend?and How many hours a day during the last
4 weeks have you played console games or used a com-
puter for your free time activities on a normal weekday/
weekend?.The response alternatives were: not at all,
0.5 hours per day,one hour per day,2hoursperday,
2.5 hours per day,3hoursperday,3.5 hours per day,
and 4 hours or more per day.
Statistical analyses
To analyse groups differences (SES) in the assessed
theory-driven determinants of PA and screen time, t-
tests as well as analyses of variance and covariance were
conducted. The interrelationships between the determi-
nants, PA and screen time were analysed using pairwise
bivariate correlations. Comparisons of proportions be-
tween students estimating the national recommendations
correctly and incorrectly were conducted using chi-square
tests.
For the parallel multiple mediation analyses, SPSS
Statistics 23.0 was used with Hayess PROCESS macro
(Version 2.15) [52] model 4 (see [53] for full documen-
tation). This OLS regression-based conditional process
analysis allows for a maximum of 10 mediators in one
test, hence, not all of the mediators were entered in
oneanalysis.LedbytheCOM-Bmodel,wetestedthree
models, one for each of the COM category, to first
identify the most important mediators in each of the
categories (Capability, Opportunity, and Motivation).
Finally, we tested additional models for sensitivity, in
which we included the supported mediators from the
first three models and gender as a covariate. Bias cor-
rected bootstrap confidence intervals were created by
using 1000 bootstrap samples. This means repeatedly
sampling from the original data with replacement and
adjusting the interval, based on the skew of the distri-
bution of bootstrap estimates [53]. Calculations for the
test of the difference between two independent correl-
ation coefficients [54] were conducted with computer
software available at http://quantpsy.org [55]. Model
assumptions were tested and fulfilled.
Results
Participant characteristics are shown in Table 2. Age
ranged between 1619 years (M = 17.8, SD = 0.73). Self-
reported health was on average high (M=2.1, SD. = .83
for vocational students; M=2.0, SD = .68 for high school
students). Vocational school students reported poorer
physical condition than the high school students (M=2.5,
SD = .91 for vocational students; M=2.3, SD = .88 for high
school students). No differences between schools were
found in illnesses or injuries limiting PA.
Determinants of PA: SES differences
Compared to high school students, vocational school
students reported lower weekly frequency of MVPA
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Table 1 Measures for determinants of physical activity and screen time
Physical activity Screen time
COM-B domain Measure/example items Scale (α) COM-B domain Measure/example items Scale (α)
Capability Capability
Self-monitoring Sniehotta et al., 2005 17 (.92)
Action planning Sniehotta et al., 2005 14 (.96)
Coping planning Sniehotta et al., 2005 14 (.93)
Knowledge of
physical activity
recommendations
How much brisk physical
activity do you think is
recommended for
adolescents aged 1518?
16 Knowledge of screen
time recommendations
How much screen time - sitting in
front of a computer,watching TV
or playing video games do you think
is recommended for adolescents
aged 1518?
16
Opportunity Opportunity
Access to facilities There are good paths for cycling and
jogging in my environment.
I have plenty of good exercising
facilities (e.g. sports centres and
halls,gyms,fitness centres)in
my neighbourhood.
There are good public transport and
travel connections to
exercising facilities.
I have a lot to do in terms of
school,hobbies and friends.
17 (.78) TV, play console and
computer in room
I have a TV,play console and/or
computer in my room.
17
Material resources I have enough money to be
physically active.
I dont have the equipment
I need for PA.
17(.42)
Injunctive norm My parents would like me to
exercise regularly
a
17 (.75) Injunctive norm My parents would approve
of me engaging in screen
time more than two hours
per day in my free time
a
17 (.82)
Descriptive norm Most of my friends exercise regularly
a
17 (.63) Descriptive norm Most of my friends engage
in screen time more than
two hours per day on their
free time
a
17 (.67)
Parental support My parents encourage me to
be physically active in my
free time.
I feel that my parents give me
choices,options and
opportunities to be physically active.
17 (.88) Parents restrict
screen time
No screen time rules
at home
My parents restrict my
screen time.
There are no rules about
the length of screen time in
my home.
17
17
PE Autonomy support Hagger et al., (2009) 17 (.96)
Motivation Motivation
Positive outcome
expectancy
It would put me in a good mood
a
17 (.87) Positive outcome
expectancy
I would be informed about
what is happening in the
world
a
17 (.83)
Negative outcome
expectancy
It would take take too much time from
other important things in my life
a
17 (.74) Negative outcome
expectancy
My neck and upper back muscles
would get stiff or sore
a
17 (.82)
Instrumental attitude Engaging in MVPA three
times per week for at least 30 minutes
at a time would be useful
useless
a
17 (.92) Instrumental attitude Watching TV,playing
console games and using a
computer more than 2 hours
per day would be a
good bad thing
a
17 (.93)
Affective attitude Engaging in MVPA three
times per week for at least 30 minutes
at a time would feel pleasant
unpleasant
a
17 (.55) Affective attitude Watching TV,playing console
games and using a computer
more
than 2 hours per day would
feel pleasant unpleasant
a
17 (.40)
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(Table 3) (Cohensd= -.33). Capability. Self-
regulatory behaviours were lower among vocational
school students than among high school students,
with statistically significant differences in mean levels
of self-monitoring (d = -.43) and action planning (d
= -.25), but not in coping planning (d=-.06). The
current recommendation for PA among 1518-year-
olds was correctly estimated by 14.4%, underestimated
by 72.7%, and overestimated by 13.0% of the vocational
students in comparison to 26.6%, 62.9% and 10.5% of
high school students, respectively (x
2
= 11.3, df = 2, p
=.004). This means that a larger proportion of high
school students had the correct knowledge regarding the
national recommendation, and that, compared to high
school students, more vocational students estimated the
national recommendation to endorse less PA.
Opportunity
Vocational students reported less material resources (e.g.
money, equipment) for PA than high school students
(d = -.30), but differences were not detected regarding
access to PA facilities (d = -.10). The social environment
was less supportive of PA among vocational students:
subjective norms, both injunctive (d = -.43) and
descriptive (d = -.23), as well as parental support for PA
(d = -.23) were lower than among high school students.
No differences were detected in the amount of auton-
omy support the groups reported getting from their
current PE teacher (.00 <d<.01).
Motivation
Vocational students had more negative outcome expect-
ancies (d = .35), and less favourable instrumental (d =-.27)
and affective attitudes (d = -.35) towards PA. Their inten-
tions to be physically active were lower as were their rat-
ings of their PA identity (d= -.39), self-efficacy and
perceived behavioural control (d= -.28). No significant
SES differences were found in positive outcome expectan-
cies (d= -.20) and automaticity (d = -.11).
Gender differences and interactions
Boys reported having more material resources for PA
than girls did (p= .009), but girls displayed more positive
outcome expectancies and attitudes than boys (p< .01).
Self-efficacy was highest among high school boys, and
lowest among vocational school boys (p=.039).
Determinants of screen time: SES differences
Compared to high school students, vocational students
reported more leisure screen time on weekdays but not
on weekend (Table 4).
Capability Altogether 36.6% of the vocational school
and 47.2% of high school students correctly estimated the
screen time recommendation. It was under-estimated by
56.1% of the vocational and by 45.8% of the high school
students (x
2
=5.2, df = 2, p =.075).
Table 1 Measures for determinants of physical activity and screen time (Continued)
Intention I intend to do active sports and/or
vigorous exercise,for at least
30 minutes,3 days per week during
my free time,over the next 4 weeks
a
17 (.95) Intention I intend to watch TV,play
console games or spend my
time on a computer more than
two hours a day on weekdays
over the next four weeks/on
weekend over the next 4 weeks
a
17
PA identity 3 items describing identity from
SRHI (Verplanken & Orbell 2003)
17 (.66) ST identity 3 items describing identity
from SRHI (Verplanken &
Orbell 2003)
17 (.67)
Self-efficacy
and Perceived
behavioural control
If I wanted to,I could do active
sports and/or vigorous exercise
three times per week
a
I feel in complete control over
whether I will do active sports
and/or vigorous exercise three
times a week
a
17 (.88) Self-efficacy and Perceived
behavioural control
If I wanted to,I could watch
TV,play console games and
spend time on
computer more than two hours
per day on my free time
a
I feel in complete control over
whether I will watch TV,play
console games or spend time
on computer more than
two hours per day in my
free time
a
17 (.80)
Habit strength
(automaticity)
SRBAI (Gardner et al., 2012) 17 (.93) Habit strength
(automaticity)
SRBAI (Gardner et al., 2012) 17 (.92)
a
Measure based on Theory of Planned behavior, Fishbein & Ajzen, 2010 and Francis et al., 2004. The target behaviors were defined in the questionnaire as follows:
PA: With physical activity, we mean leisure-time PA that increases your heart rate and makes your breathing get faster. Such PA can be e.g. cycling to school, ball
games, running, brisk walking, roller skating, skateboarding, snowboarding, downhill skiing, weight training, aerobics or other group exercise classes, and dancing
ST: With screen time, we mean watching TV, playing console games and spending time on a computer while sitting during leisure time. Spending time on a
computer may include e.g. surfing the internet, using social media, chatting with friends via the internet and watching TV and movies and listening to music on
the computer. In this survey, time used for homework is not considered screen time
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Opportunity There were no statistically significant
differences between vocational school and high school
students in the variables used to measure opportunities
to engage in screen time.
Motivation The only difference in motivational corre-
lates of screen time between vocational school and high
school students was found regarding outcome expectan-
cies: high school students had more positive outcome
expectancies towards screen time than vocational school
students (d = -.25). No significant SES differences were
detected in screen time automaticity (d = -.12).
Gender differences and interactions
In both vocational and high schools, boys reported more
screen time than girls both on weekdays and weekend,
and also better material resources and higher motivation
for screen time than girls did (see Table 4). The pattern
of results in determinants was in line with this finding:
compared to boys, girls reported lower availability of
screens, more negative outcome expectancies, as well
as less positive instrumental and affective attitudes (all
p< .001). High school boys reported stronger screen time
automaticity than vocational school boys (p=.028).
Mediation analyses
We constructed three models to investigate how the
effect of SES might be mediated on PA, one for each of
the Capability, Opportunity, and Motivation dimensions
(see Appendix 2 for the individual path coefficients). For
screen time, no mediation analyses were carried out
because no SES differences were detected.
For the capability-model, self-monitoring accounted
for most of the effect of SES on PA. The total indirect
effect of SES on PA via self-monitoring was b= .33, with
a 95% bias corrected and accelerated confidence interval
(BCa CI) of [.19, .51]. Direct effect of SES on PA was
.23, 95% BCa CI [.04, .50]. Thus, we cannot rule out
self-monitoring as a mediator.
For the opportunity-model, four mediators were not ex-
cluded: material resources (b= .07, 95% Bca CI [.02, .16]),
injunctive norms (b= .06, 95% BCa CI [.00, .15]),
Table 2 Descriptives, vocational and high school students
Vocational school High School
N (%) N (%) p
Age 507
M = 17.77
152
M = 17.78
0.88
Gender 506 152 0.07
boy 232 (45.8) 51 (33.6)
girl 274 (54.2) 101 (66.4)
Study year 506 152 < .001
1st 228 (45.1) 47 (30.9)
2nd 167 (33.0) 75 (49.3)
3rd 107 (21.1) 24 (15.8)
4th 5 (3.3)
Self-reported health 473 144 .039
very good 103 (21.8) 33 (22.9)
good 243 (51.4) 84 (58.3)
average 104 (22.0) 25 (17.4)
poor 16 (3.4) 2 (1.4)
very poor 7 (1.5) 0.0
Self-reported physical condition 473 144 .024
very good 65 (13.7) 26 (18.1)
good 180 (38.1) 66 (45.8)
average 172 (36.4) 37 (25.7)
poor 48 (10.1) 15 (10.4)
very poor 8 (1.7) 0.0
Illness or injury limiting PA 474 144 .630
no 373 (78.7) 116 (80.6)
yes 101 (21.3) 28 (19.4)
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descriptive norms (b= .04, 95% BCa CI [.00, .11] and par-
ental support (b= .03, 95% BCa CI [.00, .09]). Direct effect
of SES on PA in this model was .38, 95% BCa CI [.07, .69].
For the motivation-model, intention (b= .20, 95%
BCa CI [.11, .33]) and PA identity (b= .13, 95% BCa CI
[.05, .25]) were not excluded as mediators. Direct effect
of SES on PA was .30, 95% BCa CI [.02, .57].
In summary, self-monitoring (Capability), material re-
sources, injunctive and descriptive norms, parental sup-
port (Opportunity), as well as intention and identity
(Motivation) were found to potentially mediate the rela-
tionship between SES and physical activity.
An additional cross-dimensional sensitivity analysis was
carried out. In this model, we included the mediators
which were supported by the individual-domain medi-
ation analyses presented above. In this model, only Self
monitoring (b= .17, 95% BCa CI [.08, .27]), Intention
(b= .12, 95% BCa CI [.03, .23]) and PA identity (b=.11,
95% BCa CI [.04, .21]) had CIs that excluded zero.
Adding gender as a covariate did not affect results in
any of the models. All Variance Inflation Factors (VIFs)
were under 4, not indicating strong multicollinearity
problems.
Differences in the associations
As a second research question, we investigated whether
the strength of correlations varied across vocational and
high scool students. For PA, all of the measured deter-
minants except the knowledge of PA recommendation
correlated with weekly PA frequency (see Table 5). The
highest correlations to PA were by self-monitoring (r = .52,
p<.01), intention (r =.49, p<.001) and PA identity (r = .48,
p<.001). On the whole, the correlations were similar
among high-school and vocational school students, except
for one variable: The self-reported environment and access
to PA facilities correlated significantly with PA among
vocational school students (r = .20,p<.01) but not among
high school students (r = -.02, p=.776).
Weekday and weekend screen time were highly inter-
correlated among both SES groups (r=.72,p<.01)
(Table 6). The highest correlations to screen time were by
intention (weekday r=.41, <.01;weekendr=.46,p<.01),
Table 3 Mean values of PA
a
and determinants of PA (N= 656)
Vocational school
Mean (sd)
High school
Mean (sd)
Boys
(N= 231)
Girls
(N= 273)
Total
(N= 504)
Boys
(N= 51)
Girls
(N= 101)
Total
(N= 152)
School
p
School
η
2
Gender
p
School x Gender
interaction
p
Physical activity
b
4.0 (1.8) 3.7 (1.6) 3.8 (1.7) 4.5 (1.6) 4.4 (1.8) 4.4 (1.7) .001 .018 .380 .674
Capability
Self-monitoring 4.6 (1.7) 4.8 (1.5) 4.7 (1.6) 5.3 (1.2) 5.3 (1.4) 5.3 (1.4) .000 .030 .383 .493
Action planning 2.8 (.98) 2.7 (.99) 2.7 (.99) 3.2 (.82) 2.9 (.98) 3.0 (.94) .002 .014 .046 .223
Coping planning 2.6 (.94) 2.5 (.91) 2.5 (.92) 3.0 (.83) 2.4 (1.0) 2.6 (1.0) .081 .005 .000 .005
Opportunity
Access to facilities 5.1 (1.6) 5.1 (1.5) 5.1 (1.5) 5.4 (1.4) 5.2 (1.4) 5.2 (1.4) .219 .002 .545 .523
Material resources
(money,equipment)
5.1 (1.5) 4.8 (1.5) 4.9 (1.5) 5.7 (1.5) 5.2 (1.4) 5.4 (1.4) .000 .019 .009 .546
Injunctive norm 4.3 (1.6) 4.2 (1.5) 4.3 (1.6) 5.1 (1.3) 4.8 (1.4) 4.9 (1.4) .000 .034 .163 .450
Descriptive norm 4.4 (1.3) 4.3 (1.1) 4.4 (1.3) 4.7 (1.2) 4.6 (1.4) 4.6 (1.3) .016 .009 .558 1.0
Parental support 5.0 (1.7) 4.9 (1.7) 5.0 (1.7) 5.4 (1.3) 5.3 (1.7) 5.3 (1.6) .014 .009 .422 .978
PE Autonomy support 5.1 (1.5) 5.1 (1.5) 5.1 (1.5) 5.1 (1.5) 5.1 (1.4) 5.1 (1.4) .996 .000 .745 .966
Motivation
Positive OE 5.0 (1.4) 5.4 (1.4) 5.2 (1.2) 5.3 (1.2) 5.6 (1.1) 5.5 (1.2) .077 .005 .001 .560
Negative OE 3.4 (1.3) 3.3 (1.3) 3.3 (1.3) 2.9 (1.2) 2.9 (1.2) 2.9 (1.2) .000 .020 .322 .591
Instrumental attitude 6.1 (1.3) 6.6 (.86) 6.3 (1.1) 6.4 (.86) 6.7 (.57) 6.6 (.69) .010 .010 .000 .361
Affective attitude 5.0 (1.4) 5.4 (1.4) 5.2 (1.4) 5.4 (1.2) 5.9 (1.4) 5.7 (1.3) .002 .015 .002 .532
Intention 5.0 (1.9) 5.4 (1.6) 5.2 (1.8) 6.0 (1.2) 5.8 (1.7) 5.9 (1.6) .000 .029 .579 .085
PA identity 4.7 (1.4) 4.5 (1.3) 4.6 (1.3) 5.0 (1.5) 5.0 (1.6) 5.0 (1.5) .001 .017 .390 .432
SE & PBC 5.8 (1.5) 6.0 (1.4) 5.9 (1.4) 6.5 (.71) 6.2 (1.3) 6.3 (1.1) .001 .017 .744 .039
Automaticity 4.5 (1.6) 4.3 (1.6) 4.4 (1.6) 4.8 (1.5) 4.5 (1.8) 4.6 (1.7) .150 .003 .106 .830
a
PA physical activity, OE outcome expectancy, PE physical education, SE self-efficacy, PBC perceived behavioural control
b
Days per week with > 30 min MVPA
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automaticity (weekday r=.30,p< .01; weekend r=.35,
p<.01), and instrumental attitude (weekday r= .39, p<.01;
weekend r=.32, p< .01). The correlation coefficients
among vocational and high school students were again
largely similar. Positive outcome expectancy had a sig-
nificantly larger correlation with weekend screen time
among high school students (r = .42,p< .01) than voca-
tional school students (r = .24,p<.01).
Discussion
We explored socioeconomic differences in a wide range of
determinants of adolescent physical activity and screen
time, using the Capability-Opportunity-Motivation-Behav-
iour model as a heuristic framework (COM-B; [17]), and in-
vestigated potential mediators for PA. We also examined
whether there are SES differences in the strengths of the as-
sociations between these determinants and the respective
behaviours. Regarding PA, SES differences were found in all
of the COM-B domains, in 13 determinants out of 17 mea-
sured. Vocational students reported, for example, less self-
monitoring, lower injunctive norms and intentions than
high school students. Regarding screen time, however, there
were only two modestly statistically significant differences
attributable to SES; there were substantially more gender
differences in the levels of screen time determinants, than
SES differences. The mediation analysis pointed to im-
portance of self-monitoring in explaining the link be-
tween SES and PA. Also resources, norms as well as
intention and identity emerged as statistically significant
mediators of the effect. In the final mediation analyses,
PA identity, intention and self-monitoring remained
significant mediators of the SES-PA-relationship. We
found no evidence of significant moderating effects,
which implies that the determinants are equally rele-
vant in both SES groups.
Previous research suggest that SES differences in
adult PA are explained by availability of and access to
PA facilities [3739], social support and self-efficacy
[36, 37]. Our results are partially in line with these. Re-
garding capability, high SES adolescents engaged in
more self-monitoring and action planningthe boys also
in coping planningof their PA, with the findings sup-
porting earlier suggestions of a link between education
and self-regulation [44]. Self-monitoring has earlier
been shown to be a key behaviour change technique
characterising effective interventions to change PA (e.g.
[56], and ours is among the first studies to demonstrate
its role in explaining socioeoconomic gap in activity.
Table 4 Mean values of screen time and determinants of screen time
Vocational school
Mean (sd)
High school
Mean (sd)
Boys
(N= 201)
Girls
(N= 256)
Total
(N= 457)
Boys
(N= 48)
Girls
(N= 96)
Total
(N= 144)
School
p
School
η
2
Gender
p
School x Gender
interaction
p
Screen time
Weekday 3.5 (2.2) 2.8 (1.8) 3.1 (2.0) 3.2 (1.6) 2.3 (1.6) 2.6 (1.6) .039 .007 .000 .708
Weekend 3.7 (2.2) 3.3 (2.0) 3.5 (2.1) 3.8 (1.7) 3.4 (1.8) 3.5 (1.8) .707 .000 .040 .907
Opportunity
Injunctive norm 4.7 (1.7) 4.5 (1.8) 4.6 (1.7) 4.9 (1.6) 4.6 (1.8) 4.7 (1.8) .293 .002 .189 .754
Descriptive norm 4.4 (1.4) 4.2 (1.7) 4.3 (1.6) 4.6 (1.4) 4.2 (1.7) 4.4 (1.6) .473 .001 .056 .551
Parents restrict
screen time
3.0 (1.7) 2.5 (1.8) 2.7 (1.8) 3.3 (1.7) 2.7 (1.9) 2.9 (1.8) .118 .004 .003 .649
No screen time rules at home 4.7 (2.0) 4.9 (2.2) 4.8 (2.1) 4.6 (1.9) 4.9 (2.1) 4.8 (2.0) .888 .000 .205 .620
TV,play console and computer
in room
4.9 (2.1) 4.5 (2.3) 4.7 (2.2) 5.2 (2.1) 3.9 (2.3) 4.4 (2.3) .599 .000 .000 .044
Motivation
Positive OE
a
4.7 (1.4) 4.7 (1.4) 4.7 (1.4) 5.3 (1.2) 4.9 (1.2) 5.1 (1.2) .003 .014 .087 .136
Negative OE 4.3 (1.4) 4.9 (1.5) 4.6 (1.5) 4.1 (1.5) 5.0 (1.4) 4.7 (1.5) .976 .000 .000 .400
Instrumental attitude 3.5 (1.6) 2.9 (1.6) 3.2 (1.6) 3.3 (1.4) 2.6 (1.5) 2.9 (1.5) .123 .004 .000 .820
Affective attitude 4.0 (1.3) 3.4 (1.3) 3.7 (1.3) 4.0 (1.4) 3.3 (1.5) 3.6 (1.5) .964 .000 .000 .908
Intention 4.5 (1.9) 3.9 (2.0) 4.1 (1.9) 4.6 (1.9) 4.0 (2.2) 4.2 (2.1) .523 .001 .003 .954
ST identity 4.3 (1.3) 4.2 (1.3) 4.2 (1.3) 4.6 (1.1) 4.3 (1.6) 4.4 (1.5) .103 .004 .150 .369
SE & PBC 5.4 (1.6) 5.6 (1.6) 5.5 (1.6) 5.8 (1.5) 5.7 (1.7) 5.8 (1.6) .122 .004 .766 .479
Automaticity 4.1 (1.5) 4.4 (1.7) 4.2 (1.6) 4.7 (1.2) 4.3 (1.9) 4.4 (1.7) .081 .005 .696 .028
a
OE = outcome expectancy, ST = screen time, SE = self-efficacy, PBC = perceived behavioural control
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Those with lower SES may typically experience higher
levels of stress from various sources, mainly related to
economic factors. This might lead to a more short-term
approach to life (time perspective, e.g. [57]) or limita-
tion in cognitive function [58]. These aspects were not
directly measured here, but SES differences in self-
regulatory constructs such as planning and self-monitoring
may also reflect such socioeconomic discrepancies.
High SES adolescents, who had better opportunities
for PA, i.e.greater material resources and supportive so-
cial environment, also exercised more, again in line with
previous research [36]. It has been suggested that indi-
viduals with a low socio-economic background have
poorer access to and availability of PA facilities, reducing
their PA [38, 39]. The mediation analyses pointed to four
mediators of the SES-PA effect in the Opportunity
category: material resources for PA, descriptive and
injunctive norms, and parental support. Such material
and cultural environmental barriers have emerged also
in the literature.
Regarding motivation, self-efficacy seems to play an
important role in explaining SES differences in adoles-
cent PA, as among adults [36, 37]. However, the medi-
ation analyses did not point to self-efficacy as a key
mediator. In the domain of motivation, intention (which
is, on the other hand, hypothesised to be influenced by
self-efficacy in many theories, e.g. [24]) and identity were
found to be potential mediators of on PA.
This study revealed that already in the first years of edu-
cational differentiation, levels of key PA determinants differ
(as adolescentsSES was based on their own educational
path rather than defined based on their parentsSES). Inter-
estingly, no SES differences were detected in screen time
determinants, contrary to some evidence [59]. Screen time
may be better explained by gender than SES boys have
beenshowntoengageinmorescreentimethangirls[16].
Despite some evidence of SES moderating e.g. intention-
behaviour relationship [46], these results were in line with
the more recent meta-analysis [48], suggesting that PA in-
tentions lead to behaviour similarly regardless of educa-
tional background. Also, another Finnish study [42] has
showed that self-efficacy, action planning, coping planning,
and social support had similar effects on behaviour among
both high and low educated adults. Only three correlations
differed between the SES groups, suggesting that interven-
ing on PA and screen time determinants may have similar
effects irrespective of SES. Our study also showed an
absence of relationship between correct knowledge of
national PA recommendations and PA behaviour.
This study has several implications for practice and pol-
icy. Considering the wide-echoed political concern about
socioeconomic inequalities in health, our study, if replicated
in more robust designs, may inform policy to reduce the
SES gap in PA and consequently of future health outcomes.
This may include acknowledging the heightened needs vo-
cational school students have regarding, especially, self-
regulatory skills to plan and monitor their PA. Secondly,
lower SES adolescents may currently not be provided with
as much social and material support to be physically active
as higher SES youth. Lack of environmental resources may
be reflected in psychological determinants such as intention
and identity. Experimental designs could thus further test
whether providing more opportunities and prompting so-
cial acceptance for PA, as well as financial support for PA
equipment is effective in SES-targeted interventions.
One explanatory mechanism for the lower PA in youth
with lower education were the lower ratings of PA-related
identity. This is in line with, for example, self-
determination theory and evidence on the key role of inte-
grated and identified motivational regulations for PA [60].
It should be noted that PA behaviour may also be influen-
cing the motivation mediators, in a cyclical fashion. Thus, it
is unlikely that simply by targeting for example identity and
self-monitoring, the socioeconomic difference in PA would
Table 5 Pairwise correlations between PA, intention, automaticity
and other determinants of PA
PA Intention Automaticity
PA 1.0
Intention .49** 1.0
Automaticity .43** .55** 1.0
Capability
Knowledge of recommendations .04 .05 .11**
Action planning .42** .56** .54**
Coping planning .34** .41** .52**
Self-monitoring .52** .70** .61**
Opportunity
Access to facilities .16**
a
.36** .28**
Material resources .25** .31** .33**
Injunctive norm .25** .36** .35**
Descriptive norm .26** .38** .46**
Parental support .26** .40** .42**
PE autonomy support .20** .38** .34**
Motivation
Positive outcome expectancy .24** .44** .42**
Negative outcome expectancy .21** .40** .29**
Instrumental attitude .17** .41** .24**
Affective attitude .34** .54** .46**
PA identity .48** .58** .64**
Self-efficacy & perceived behavioural
control
.30** .55** .43**
**Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant
at the 0.05 level (2-tailed).
a
the test of the difference between two independent correlation coefficients
for vocational school- and high school is significant (p< .05).
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disappear rather, it is likely that factors such as cultural
conceptions and childhood PA behaviour manifest them-
selves in perceived self-identity. However, it is beyond the
scope of the present study examine how long-term, societal
level processes are causing SES differences in determinants
of PA and PA itself.
From the standpoint of developing an intervention,
the investigation at hand represents an important first
step toward understanding the target behaviour (e.g.,
PA) and the behavioural determinants in the target
population (adolescents) [61]. Table 5 demonstrates a
multitude of correlates of both PA behaviour as well as
motivation (intention and automaticity). Creating an
intervention requires additionally identifying the best
intervention methods or techniques to influence these
determinants (see e.g [62]). For this purpose, experimen-
tal studies and meta-analyses of interventions provide
further evidence (e.g. [63]).
The current study informed the development of an
intervention for vocational school students [64]. In such
development work, the levels of the most important PA
determinants among the high-school students could be
used as benchmarksto identify potential intermediate
targets that are relevant, yet potentially changeable, also
among the vocational school youth. Yet, such bench-
marking should not override a key intervention design
principle of understanding the behaviour, needs and re-
sources of the target group in context.
Limitations includes the use of self-report measures of be-
haviour, skills and environment, thus subject to bias. How-
ever, self-report measures are a feasible and a cost-effective
waytogatherdatainalargegroup,andthedatafromthe
subsample assessed with the concomitant use of accelerom-
eters showed that the correlation between the self-reported
measure of PA and accelerometer was moderate. Secondly,
with multiple tests, the possibility of chance findings and
Type 1 errors exist [65]. Thus, although our findings gener-
ally are in line with both theory and earlier evidence, these
results should be interpreted with caution. Only 62% of the
vocational schools and 50% of the high schools invited to
participate in the survey finally participated. It may be that
the teachers advocating and already promoting a physically
activelifestylemoreeasilyofferedtheirstudentstheoppor-
tunity to respond to the survey as participation was volun-
tary and took place during school hours. This may have
affected the results in a way that the differences between
the two SES groups were slightly smaller than in national
surveys [66]. Fourth, the mediation tests were not optimal
in that they were conducted within each of the categories.
However, models containing all of the variables in the same
multivariate model would not have been feasible. Also, the
tests were guided by the theoretical domains framework
Table 6 Pairwise correlations between screen time, intention, automaticity and other determinants of screen time
Weekday screen time Weekend screen time Intention Automaticity
Weekday screen time 1.0
Weekend screen time .72** 1.0
Intention .41** .46** 1.0
Automaticity .30** .35** .50** 1.0
Capability
Knowledge of recommendations .09*
a
.13** .13** .04
Opportunity
Parents restrict screen time .04 .05 .12** .04
No screen time rules at home .01 .01 .11** .13**
TV, play console and/or computer in room .14** .13** .14** .15**
Injunctive norm .20** .28** .42** .44**
Descriptive norm .24** .25** .42** .42**
Motivation
Positive outcome expectancy .21** .27**
a
.47** .40**
Negative outcome expectancy .22** .16** .24** .04
Instrumental attitude .39** .32** .45** .24**
Affective attitude .31** .32** .49** .30**
Screen time identity .26** .32** .49** .51**
Self-efficacy & perceived behavioural control .09* .20** .28** .32**
**Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed).
a
the test of the difference between two independent correlation coefficients is significant (p< .05).
The differences between correlation coefficients are calculated with Preacher, K. J. (2002, May). Calculation for the test of the difference between two independent
correlation coefficients [Computer software]. Available from http://quantpsy.org
Hankonen et al. BMC Public Health (2017) 17:144 Page 11 of 15
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(TDF) and the COM-B: these are among the first tests that
examine a wide variety of theoretical predictors in an inte-
grated way, a trend toward which psychological science is
currently progressing. We believe that such exploratory,
comprehensive studies are important in helping advance
theorising about socioeconomic health disparities. A further
limitation resides in the cross-sectional and observational
design (see also [67, 68]). Given this, no conclusions regard-
ing the causal relationship between the measured theory-
based constructs and behaviours can be made. Correlational
findings are consistent with the identified variables serving
as mediators of the causal relationships between SES and
physical activity. This warrants additional investigation uti-
lising longitudinal and experimental designs. Finally, these
results may not be generalisable to other populations, e.g.
age groups, and further studies are warranted in various
subgroups, countries and cultures.
Thestrengthsofthisstudyincludecoverageofawide
range of determinants for two distinct forms of activity be-
haviours, enabling a comprehensive investigation of poten-
tial explanations for SES differences. Previous investigations
have focused on a limited set of determinants, and thus
used a narrow conceptualizationoftherangeofinfluences
on behaviour. We also examined a wide range of determi-
nants of screen time, an understudied topic [20]. The sam-
plesizewaslargeenoughtodetectstatisticallysignificant
differences in the determinants that are also meaningful in
practice. However, effect sizes were not very large, indi-
cating to a wide heterogeneity within both groups.
Similar studies are emerging to build the evidence base
for designing interventions sensitive to PA and SB deter-
minants critical to low-SES individuals (e.g., [69]), but
more are needed. Future studies should investigate SES
differences in determinants in different ages to under-
stand whether and how their role changes over the life
course. We also recommend including measures other
than self-report, e.g., computerised measurements of im-
plicit attitudes and motivations. Preliminary evidence indi-
cates that interventions may induce differential effects in
PA motivation for low and high SES youth [70], hence, we
encourage such intervention process evaluations sensitive
to SES, to identify mechanisms responsible for possibly
different outcomes for low and high SES participants.
Conclusions
SES differences emerged in the domains of capability, op-
portunity and motivation for PA, but screen time behav-
iour determinants are better explained by gender than
SES. To our knowledge, this was the first study to system-
atically examine SES differences in a range of known de-
terminants of adolescent PA and screen time as well as on
the behaviours. Investigating the SES differences in not
only behaviours but also in behavioural determinants
makes a crucial contribution in the efforts to better
understand the origins of social inequalities in health.
Such analysis enables identifying and targeting the most
important determinants in interventions to reduce
health inequalities.
Table 7 Theoretical determinants of this study classified by COM-B categories and theoretical domains.
COM-B domain Theoretical domain Physical activity determinant measure Screen time determinant measure
Physical CAPABILITY Physical skill - -
Psychological CAPABILITY Knowledge
Cognitive and int.skills
Memory, attention, decision
processes
Behavioural Regulation
Knowledge of recommendation
-
-
Action & coping planning, self-monitoring
Knowledge of recommendation
-
-
-
Automatic MOTIVATION Reinforcement
Emotion
-
Affective attitude
Automaticity
-
Affective attitude
Automaticity
Reflective MOTIVATION Identity
Beliefs about capabilities
Optimism
Intention
Goals
Beliefs about consequences
Identity
Self-efficacy, PBC
-
Intention
-
Outcome expectations, instrumental att.
Identity
Self-efficacy, PBC
-
Intention
-
Outcome expectations,
instrumental att.
Social OPPORTUNITY Social influences Injunctive & descriptive norms (peers, parents)
Parental support
PE teacher autonomy support
Injunctive & descriptive norms
(peers, parents)
Parental restriction & rules
PE teacher autonomy support
Physical OPPORTUNITY Environmental context and resources Access to PA facilities
Material resources
TV, play console and computer
in own room
Note. PBC =Perceived Behavioural Control, PE =Physical Education
Appendix 1
Hankonen et al. BMC Public Health (2017) 17:144 Page 12 of 15
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Abbreviations
PA: Physical activity; SES: Socioeconomic status; TDF: Theoretical Domains
Framework
Acknowledgements
The authors would like to thank Professor Taru Lintunen, Ms. Anna Aistrich,
and Mr. Mikko Annala for their contributions.
Funding
The study was supported by the Ministry of Education and Culture, funding
number 34/626/2012 (years 201214), and funding number OKM/81/626/
2014, (years 201517), the Ministry of Social Affairs and Health, funding
number 201310238 (years 201315). The first author was supported by the
Academy of Finland.
Availability of data and materials
The dataset will be stored in the Finnish Social Science Data Archive (FSD)
and will be available from there.
Authorscontributions
NH conceived of the original research idea, participated in the data
collection and design of the study, and was responsible for writing the final
version of the article. MH conducted the mediation analyses and contributed
to writing the article. EK participated in the data collection and analysis and
was responsible of drafting the first version of the article. SH participated in the
data collection and conducted the preliminary statistical analyses. AH
participated in the design of the study and the data analysis. PA, KB, and V A-S
were contributed to project proposal, planning the data collection and the ana-
lyses, and to writing the article. All authors read and approved the final
manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Participation in the survey was voluntary and all participants gave their
written informed consent to participate online. The survey questionnaire and
the study protocol were reviewed by the ethics committee of the Hospital
District of Helsinki and Uusimaa, Ethics Committee for Gynaecology and
Obstetrics, Pediatrics and Psychiatry (decision number 249/13/03/03/2011).
Appendix 2
Table 8 Individual path coefficients for mediation analysis.
Total effect on PA Variable - > PA SES - > Variable
βCI95 βCI95 βCI95
Model 1: Capability
(R^2 = 0.29)
SES 0.59 [0.28, 0.90] 0.23 [0.04, 0.50]
Action planning 0.12 [0.10, 0.34] 0.22 [0.04, 0.40]
Coping planning 0.10 [0.09, 0.30] 0.05 [0.12, 0.22]
Self-monitoring 0.48 [0.38, 0.58] 0.68 [0.40, 0.97]
Model 2: Opportunity
(R^2 = 0.13)
SES 0.58 [0.26, 0.89] 0.38 [0.07, 0.69]
Access to facilities 0.00 [0.10, 0.09] 0.14 [0.14, 0.42]
Material resources 0.15 [0.05, 0.24] 0.48 [0.19, 0.76]
Injunctive norm 0.10 [0.00, 0.20] 0.64 [0.36, 0.92]
Descriptive norm 0.14 [0.01, 0.27] 0.26 [0.02, 0.49]
Parental support 0.08 [0.02, 0.18] 0.33 [0.02, 0.64]
PE Autonomy support 0.11 [0.02, 0.20] 0.02 [0.27, 0.30]
Model 3: Motivation
(R^2 = 0.32)
SES 0.60 [0.29, 0.92] 0.30 [0.02, 0.57]
Affective attitude 0.03 [0.09, 0.14] 0.48 [0.23, 0.74]
Instrumental attitude 0.07 [0.21, 0.07] 0.26 [0.07, 0.44]
Habit 0.15 [0.05, 0.25] 0.18 [0.12, 0.48]
Intention 0.30 [0.21, 0.39] 0.67 [0.35, 0.99]
Negative OE 0.07 [0.03, 0.17] 0.45 [0.69,0.22]
PA identity 0.29 [0.17, 0.40] 0.46 [0.19, 0.72]
Positive OE 0.04 [0.14, 0.06] 0.26 [0.01, 0.51]
SE & PBC 0.01 [0.12, 0.09] 0.39 [0.14, 0.64]
Hankonen et al. BMC Public Health (2017) 17:144 Page 13 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Author details
1
Department of Social Research, University of Helsinki, Helsinki, Finland.
2
School of Social Sciences and Humanities, University of Tampere, Tampere,
Finland.
3
School of Health Sciences, University of Tampere, Tampere, Finland.
4
Institute of Health and Society, Faculty of Medical Sciences, Newcastle
University, Newcastle, UK.
5
National Institute for Health and Welfare, Helsinki,
Finland.
Received: 17 May 2016 Accepted: 25 November 2016
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... En la Estrategia Nacional sobre Adicciones diseñada en España para el período 2017-2024 (Ministerio de Sanidad, 2017), se señala que el uso excesivo de Internet, pantallas digitales y otras TIC podría derivar en comportamientos que presentan analogías con la adicción a sustancias. En el presente estudio, se ha constatado que el tiempo de pantallas en los niños andaluces de ocho años se sitúa en niveles moderados, pues uno de cada cuatro no llega a usarlas más de una hora al día y solo el 13.9% sobrepasa las dos horas, que constituyen el tiempo máximo recomendado en las directrices dadas por los sistemas de salud pública de diversos países (Hankonen et al., 2017;Tremblay et al., 2016). Aunque el exceso de tiempo de pantallas afecta a un grupo relativamente reducido de niños andaluces al finalizar la primera infancia, la acción preventiva con este colectivo es decisiva para evitar desde etapas tempranas los perjuicios asociados. ...
... Por último, otra importante variable en la explicación del tiempo de pantallas es el nivel socioeconómico y cultural de las familias. Un mayor nivel se asocia a un menor tiempo de exposición a pantallas, en consonancia con la denominada brecha socioeconómica y cultural en la actividad física y tiempo de pantallas que la literatura previa ha señalado de manera consistente (Hankonen et al., 2017;Oswald et al., 2020). ...
... In the National Strategy concerning Addictions designed in Spain for the period 2017-2024 (Ministerio de Sanidad, 2017), it is pointed out that the excessive use of the Internet, digital screens and other ICTs could lead to behaviors which present analogies with substance addiction. In the present study, it has been noted that the screen time of eight-year-old Andalusian children is at moderate levels, as one out of four do not use them more than one hour per day and only 13.9% surpass two hours, which is the maximum time recommended in the guidelines given by the public health system of diverse countries (Hankonen et al., 2017;Tremblay et al., 2016). Although the excess of screen time affects a relatively small group of Andalusian children at the end of early childhood, preventive action for this group is decisive to avoid, from early on, the damage related with it. ...
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El actual desarrollo de las TIC ha generalizado el uso de pantallas. Los riesgos de un tiempo excesivo de exposición a pantallas son especialmente relevantes para los niños, desde edades tempranas. Este trabajo analiza la relación entre tiempo de pantallas y otras variables personales y contextuales en la infancia. Participan 94092 niños de aproximadamente 8 años, escolarizados en centros educativos de Andalucía (España). Los datos fueron generados en una evaluación a gran escala promovida por la Administración educativa regional, que supuso la aplicación de pruebas para medir aprendizajes y de cuestionarios de contexto familiar. Los análisis se basan en las pruebas t y chi-cuadrado, para comprobar las diferencias entre los grupos, y en la construcción de un modelo de regresión logística binaria para valorar el peso de las variables en la explicación del tiempo de pantallas. Los resultados indican que un elevado tiempo de pantallas se asocia a menor rendimiento en comunicación lingüística y razonamiento matemático. El género, la hora de acostarse y el nivel socioeconómico familiar son variables que contribuyen a explicar el tiempo de pantallas. A partir de estos resultados se formulan recomendaciones de cara a la intervención preventiva por parte de las familias y otros agentes educativos.
... Furthermore, Finnish vocational education students meet these recommendations more rarely than Finnish high school students (15.4% vs.17.5%) (Finnish Institute of Health and Welfare 2021). Other research has reported that high school students are also more physically active than vocational school students (Hankonen et al. 2017;Heradstveit et al. 2020). According to WHO recommendations, adults should have at least 150 min of moderate-intensity or 75 min of vigorous-intensity physical activity each week (WHO 2020a, b). ...
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Digital gaming is considered to be a major sedentary lifestyle among youth. The time spent on digital gaming may also affect the physical behavior of young adults. Objective This study aimed to investigate the associations between various characteristics of digital gaming behavior (i.e., gaming time, device, and game type) and participation in physical activity among Finnish vocational students. Materials and methods The research employed a cross-sectional survey design. The analyzed sample consisted of 773 students (455 males, 318 females) from eight vocational school units in Northern Finland who regularly played digital games. Data were collected via an online self-reported questionnaire, which included questions concerning average weekly time spent on digital gaming, preferred device, favorite types of games, and physical activity. Results The students spent an average of two hours each day playing digital games. Males preferred to play using personal computers (PCs), whereas mobile gaming was more popular among females. Shooter (42.4%) and entertainment (64.2%) games were the most popular game types among males and females, respectively. The results revealed that male gender and PC gaming were both positively related to physical inactivity among vocational school students. A preference toward sport games was inversely related with physical inactivity. Conclusion The presented findings can be utilized to develop interventions that target the prevention of sedentary behavior among vocational students. Further longitudinal studies will be required to reliably assess the relationship between digital gaming and physical activity.
... Explanations along this line of selective human behaviour are quite complex as the behaviours are multifactorially determined and, to simplify things, most researchers, rightly perhaps, choose one or another of the possible and relevant variables that determine physical activity or inactivity as the topic of study. Therefore, there are studies that generally address the following variables: age [3][4][5][6][7][8][9][10][11], gender identity [1, 10,[12][13][14][15][16][17][18][19][20][21][22], socioeconomic and education level [23][24][25][26][27][28][29][30][31], personality characteristics/motivation [12,15,32], social environment/family and friends [12,26,31,33], as well as environmental setting [4,[34][35][36], all of which can influence the level of physical activity as a means of maintaining and/or improving health. Some of the variables listed above are not changeable (such as age, gender), but the others can be altered to a certain extent (level of education, subjects' motivations, social and proximal environment in which each individual lives). ...
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... Good collective integration is an important guarantee for teenagers to adapt to school life. Appropriate physical exercise can improve the school adaptability of teenagers [42], which is conducive to physical and mental health development. The effect of physical exercise on school adjustment was better among boys and only children, which may be related to physiological differences and the home environment. ...
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Background: With the development of educational philosophy, physical exercise is considered to be an important factor in the development of individual cognitive abilities in adolescents. However, the effects of physical exercise on non-cognitive abilities in adolescents remain inadequate. Objective: This study examined the effect of physical exercise on non-cognitive ability and further examined the differences in this effect in different groups. Methods: Survey data on non-cognitive ability, physical exercise and covariates were collected in the China Education Panel Survey (2014-2015) from a nationally representative sample of adolescents (n = 7904) in the eighth (follow-up) grade. Results: The results show that, after controlling for the influence of other factors, physical exercise has a promoting effect on six non-cognitive abilities-social behavior (p < 0.001), communication ability (p < 0.01), perseverance (p < 0.05), school adaptation (p < 0.001), educational expectation (p < 0.01) and creative thinking (p < 0.01)-and there is no significant effect on emotional control (p > 0.05). Furthermore, the development of non-cognitive ability in physical exercise shows certain group differences. Conclusions: There are gender differences in the performance of non-cognitive ability. Girls perform better than boys in non-cognitive ability. Physical exercise is an important means to develop non-cognitive ability, which indicates that non-cognitive ability has plasticity in adolescence. Future intervention may improve the non-cognitive ability of Chinese adolescents by increasing physical exercise.
... However, Kristensen et al. (47) in a study with European children showed that SES was unrelated to PA in 8-to 10-yrold children. The discrepancies between studies may arise from putative mediational effects of other variables on SES (48). Therefore, further research is needed to better comprehend of the precise link between SES and MVPA. ...
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Purpose To describe longitudinal trends in children’s moderate-to-vigorous physical activity (MVPA), and to investigate associations with individual, familial and school characteristics. Methods A sample of 341 5-10 years old Portuguese children (173 girls) from 6 age-cohorts was followed over three years using a mixed-longitudinal design. Physical activity, body mass index (BMI), gross motor coordination and musculoskeletal fitness were assessed annually. Information on socioeconomic status (SES) and school characteristics was collected and analyzed with mixed-models. Results MVPA shows a similar declining trend in both sexes, but on average boys exceeded the WHO recommendations of 60 min·d-1. The best model showed that boys spend, on average, more time in MVPA than girls. Children with lower BMI are less prone to the decline in MVPA, whereas higher levels of musculoskeletal fitness were associated with lower declines in MVPA. Of all school characteristics, only playground dimension was related to MVPA decreasing trajectories. Conclusions MVPA systematically declines from 5 to 10 years of age in both boys and girls, but boys remained more active than girls across the age range. The best predictors of MVPA decline are sex, BMI, musculoskeletal fitness and school playground dimension.
... Pemantauan penggunaan waktu layar pada anak tidak selalu memungkinkan bagi orang tua yang bekerja dikarenakan jadwal kerja orang tua sehingga mempersulit pemantauan pada penggunaaan layar pada anak (Neshteruk et al., 2021). Selain status sosial ekonomi, gender anak memengaruhi penggunaan waktu layar yang berlebih pada anak (Hankonen et al., 2017). Dalam penelitian Oliveira et al., (2021) menambahkan bahwa sosial ekonomi orang tua yang kurang, pola asuh orang tua yang otoriter, tingkat pendidikan rendah, mendapatkan hasil skor tertinggi pada masalah perilaku anak, kecanduan game dan gangguan tidur pada anak. ...
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This study aims to determine the factors influencing parents to give children gadgets. This research method is a systematic review using several online databases, ProQuest, EBSCOhost, Scopus, and Google scholar. The results showed that 11 articles were reviewed and found three main themes: socio-economic status, knowledge, and parenting patterns related to giving gadgets to children. In conclusion, this article shows that giving gadgets to children is influenced by the family's socio-economic status, parenting patterns with permissive and authoritarian parenting styles, and parents' knowledge of gadget use. Keywords: Children, Gadgets, Knowledge, Parenting, Socio-Economic Family
... Aaltonen et al.[24] particularly highlighted the effect of genetic influence and an even greater influence of environmental factors. Similarly, Hankonen et al.[45] emphasized the effect of socioeconomic and other factors. ...
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Interventions aimed at motivation for physical activity (PA) are mostly beneficial, but the effects on preventing the decrease in PA are not entirely clear, especially in girls. The main aim of this study was to identify the differences and associations between PA motives and types of PA in boys and girls and between low and high motivated boys and girls. Another aim is to identify the types of motivation and PA that increase the likelihood of achieving PA recommendations and to propose ways of increasing PA among low motivated adolescents. The research carried out before the COVID-19 pandemic (2010–2019) and involved 2,149 Czech and 1,927 Polish adolescents aged 15–19 years. The International Physical Activity Questionnaire-Long Form was used to identify the level of PA types, while PA motivation was examined through the Motives for Physical Activities Measure-Revised. During the ten years, a decline was observed in enjoyment, fitness, and social motives. An increase in appearance motives was observed in girls, while no significant changes were seen in boys. Boys showed a higher motivation for PA than girls in enjoyment, competence, fitness, and social motives, while girls were high motivated in appearance motives. The greatest statistically significant differences between low and high motivated individuals were found in the associations between recreation/vigorous PA and between all types of motivation in boys and girls in both countries. The strongest associations in both genders were observed between enjoyment/competence motives and recreation/vigorous PA. Respecting and using the associations between the types of PA motives and types of PA in low and high motivated boys and girls can support feelings of PA enjoyment, increase PA, support the achievement of PA recommendations, and positively affect adolescents’ healthy lifestyles.
... Our results showed decreases in student-reported PA and play and increased time spent in SB during remote learning. The co-occurrence of PA declines and increased SB comes with important health implications, particularly for children in lower-socioeconomic areas who are more likely to be overweight or obese and reside in environments more conducive to SB and less favorable to PA. 7 In-person schooling is essential for children's health as schools provide opportunities for movement and access to nearly all children. 8 The lower levels of PA during recess reported in 2021 were likely because students were learning remotely and may have not perceived breaks as typical recess. ...
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Purpose The lack of in-person schooling and participation in structured recreation activities during the COVID-19 pandemic may have altered children’s movement behaviors. This study assessed changes in children’s self-reported in school and out of school physical activity, sedentary behavior, and play before and during the pandemic. Design A repeated cross-sectional online survey was administered in February 2020 (pre-pandemic, in-person) and 2021 (during pandemic, remote). Setting Children attended an urban public school district in Phoenix (AZ) serving a low-income population. Subjects Students in grades 4–8 completed the survey in 2020 ( n = 253, 62% response rate) and 2021 ( n = 261, 77% response rate). Measures The survey included items from the Youth Activity Profile and three additional questions about play. Analysis Differences in mean scores and mean scores by gender were analyzed using one-way and two-way ANOVAs. Results Students reported less physical activity during remote recess in 2021 (M = 3.42, SD = .80 v. M = 2.99, SD = .86, p < .05). Physical activity outside of school decreased during the pandemic (M = 2.76, SD = 1.26 v. M = 2.53, SD = 1.18, p < .05). Most students (55%) reported playing less during the pandemic, but playing in new ways (67%). Conclusion Children may benefit from interventions to counter reduced movement experienced during the pandemic, particularly in under-resourced areas.
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Objectives Although socioeconomic status (SES) has been shown to be an important determinant of physical activity (PA) in adults, the association in children and adolescents remains less consistent based on evidence from western developed countries. The purpose of this study, therefore, is to investigate associations between SES and PA among Chinese children and adolescents. Methods A cross-sectional study was conducted with a self-reported questionnaire in China. The multi-stage stratified cluster sampling method was used, and 2,955 children and adolescents (8–17 years old, 53.4% girls) were enrolled in this study. SES was assessed by measuring parental education levels, perceived family wealth, and Family Affluence Scale II (FAS II). PA was assessed by reliable and valid items from the Health Behavior School-aged Children Surveillance questionnaires. Descriptive statistics were used to report sample characteristics, including demographic characteristics, levels of PA, and SES. The Chi-square test was applied to analyze the differences of PA in gender and school period. Binary logistic regression was used to analyze the association between SES and PA among children and adolescents. Results Overall, 10.4% of children and adolescents accumulated moderate-to-vigorous physical activity (MVPA) at least 60 min per day. Boys were more physically active than girls (14.1% vs. 7.2%, p < 0.001). Higher socioeconomic status was positively associated with higher physical activity levels among children and adolescents, especially using FAS and mother education level as SES measures. The association between SES and PA varied by gender and grade group, and the effects of SES are also different on weekdays and weekends. Conclusion This study found socioeconomic disparities in PA among Chinese children and adolescents, and showed the importance of targeting children and adolescents with low SES intervention priority. Based on these research findings, it is suggested that SES, especially for FAS and mother education level, should be considered when designing and implementing the promotion of regular PA in children and adolescents. Health policymakers may use this information to develop interventions to reduce health inequalities among children and adolescents in the future.
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Background Physical activity (PA) has been shown to decline during adolescence, and those with lower education have lower levels of activity already at this age, calling for targeted efforts for them. No previous study has demonstrated lasting effects of school-based PA interventions among older adolescents. Furthermore, these interventions have rarely targeted sedentary behaviour (SB) despite its relevance to health. The Let’s Move It trial aims to evaluate the effectiveness and the cost-effectiveness of a school-based, multi-level intervention, on PA and SB, among vocational school students. We hypothesise that the intervention is effective in increasing moderate-to-vigorous-intensity physical activity (MVPA), particularly among those with low or moderate baseline levels, and decreasing SB among all students. Methods The design is a cluster-randomised parallel group trial with an internal pilot study. The trial is conducted in six vocational schools in the Helsinki Metropolitan area, Finland. The intervention is carried out in 30 intervention classes, and 27 control classes retain the standard curriculum. The randomisation occurs at school-level to avoid contamination and to aid delivery.Three of the six schools, randomly allocated, receive the ‘Let’s Move It’ intervention which consists of 1) group sessions and poster campaign targeting students’ autonomous PA motivation and self-regulation skills, 2) sitting reduction in classrooms via alterations in choice architecture and teacher behaviour, and 3) enhancement of PA opportunities in school, home and community environments. At baseline, student participants are blind to group allocation. The trial is carried out in six batches in 2015–2017, with main measurements at pre-intervention baseline, and 2-month and 14-month follow-ups. Primary outcomes are for PA, MVPA measured by accelerometry and self-report, and for SB, sedentary time and breaks in sedentary time (accelerometry).Key secondary outcomes include measured body composition, self-reported well-being, and psychological variables. Process variables include measures of psychosocial determinants of PA (e.g. autonomous motivation) and use of behaviour change techniques. Process evaluation also includes qualitative interviews. Intervention fidelity is monitored. DiscussionThe study will establish whether the Let’s Move It intervention is effective in increasing PA and reducing SB in vocational school students, and identify key processes explaining the results. Trial registrationISRCTN10979479. Registered: 31.12.2015
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This study tested the predictive validity of a multi-theory process model in which the effect of autonomous motivation from self-determination theory on physical activity participation is mediated by the adoption of self-regulatory techniques based on control theory. Finnish adolescents (N=411, aged 17-19) completed a prospective survey including validated measures of the predictors and physical activity, at baseline and after one month (N=177). A subsample used an accelerometer to objectively measure physical activity and further validate the physical activity self-report assessment tool (n=44). Autonomous motivation statistically significantly predicted action planning, coping planning and self-monitoring. Coping planning and self-monitoring mediated the effect of autonomous motivation on physical activity, although self-monitoring was the most prominent. Controlled motivation had no effect on self-regulation techniques or physical activity. Developing interventions that support autonomous motivation for physical activity may foster increased engagement in self-regulation techniques and positively affect physical activity behavior.
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Lack of physical activity (PA) and high levels of sedentary behaviour (SB) have been associated with health problems. This systematic review evaluates the effectiveness of school-based interventions to increase PA and decrease SB among 15–19-year-old adolescents, and examines whether intervention characteristics (intervention length, delivery mode and intervention provider) and intervention content (i.e. behaviour change techniques, BCTs) are related to intervention effectiveness. A systematic search of randomised or cluster randomised controlled trials with outcome measures of PA and/or SB rendered 10 results. Risk of bias was assessed using the Cochrane risk of bias tool. Intervention content was coded using Behaviour Change Technique Taxonomy v1. Seven out of 10 studies reported significant increases in PA. Effects were generally small and short-term (Cohen's d ranged from 0.132 to 0.659). Two out of four studies that measured SB reported significant reductions in SB. Interventions that increased PA included a higher number of BCTs, specific BCTs (e.g., goal setting, action planning and self-monitoring), and were delivered by research staff. Intervention length and mode of delivery were unrelated to effectiveness. More studies are needed that evaluate long-term intervention effectiveness and target SBs among older adolescents.
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In this paper, we introduce the IM taxonomy of behavior change methods and its potential to be developed into a coding taxonomy. That is, although IM and its taxonomy of behavior change methods are not in fact new, because IM was originally developed as a tool for intervention development, this potential was not immediately apparent. Second, in explaining the IM taxonomy and defining the relevant constructs, we call attention to the existence of parameters for effectiveness of methods, and explicate the related distinction between theory-based methods and practical applications and the probability that poor translation of methods may lead to erroneous conclusions as to method-effectiveness. Third, we recommend a minimal set of intervention characteristics that may be reported when intervention descriptions and evaluations are published. Specifying these characteristics can greatly enhance the quality of our meta-analyses and other literature syntheses. In conclusion, the dynamics of behavior change are such that any taxonomy of methods of behavior change needs to acknowledge the importance of, and provide instruments for dealing with, three conditions for effectiveness for behavior change methods. For a behavior change method to be effective: 1) it must target a determinant that predicts behavior; 2) it must be able to change that determinant; 3) it must be translated into a practical application in a way that preserves the parameters for effectiveness and fits with the target population, culture, and context. Thus, taxonomies of methods of behavior change must distinguish the specific determinants that are targeted, practical, specific applications, and the theory-based methods they embody. In addition, taxonomies should acknowledge that the lists of behavior change methods will be used by, and should be used by, intervention developers. Ideally, the taxonomy should be readily usable for this goal; but alternatively, it should be clear how the information in the taxonomy can be used in practice. The IM taxonomy satisfies these requirements, and it would be beneficial if other taxonomies would be extended to also meet these needs. IM_Taxonomy_-_Tables_and_Figure.pdf.
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Objectives Unhealthy behaviour is more common amongst the deprived, thereby contributing to health inequalities. The evidence that the gap between intention and behaviour is greater amongst the more deprived is limited and inconsistent. We tested this hypothesis using objective and self-report measures of three behaviours, both individual- and area-level indices of socio-economic status, and pooling data from five studies. DesignSecondary data analysis. Methods Multiple linear regressions and meta-analyses of data on physical activity,diet, and medication adherence in smoking cessation from 2,511 participants. ResultsAcross five studies, we found no evidence for an interaction between deprivation and intention in predicting objective or self-report measures of behaviour. Using objectively measured behaviour and area-level deprivation, meta-analyses suggested that the gap between self-efficacy and behaviour was greater amongst the more deprived (B=.17 [95% CI=0.02, 0.31]). Conclusions We find no compelling evidence to support the hypothesis that the intention-behaviour gap is greater amongst the more deprived.
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