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This is the published version ( version of record) of:
Hilland, Toni A., Ridgers, Nicola D., Stratton, Gareth and Fairclough, Stuart J. 2011-02,
Associations between selected demographic, biological, school environmental and physical
education based correlates, and adolescent physical activity, Pediatric exercise science, vol.
23, no. 1, pp. 61-71.
Available from Deakin Research Online:
Reproduced with kind permission of the copyright owner
Copyright : 2011, Human Kinetics, Inc.
Pediatric Exercise Science, 2011, 23, 61-71
© 2011 Human Kinetics, Inc.
Hilland and Fairclough are with the REACH Group, Faculty of Education, Community, and Leisure,
Liverpool John Moores University, Liverpool, UK. Ridgers and Stratton are with the REACH Group,
Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK
Associations Between Selected
Demographic, Biological, School
Environmental and Physical Education
Based Correlates, and Adolescent
Toni A. Hilland, Nicola D. Ridgers, Gareth Stratton,
and Stuart J. Fairclough
Liverpool John Moores University
The study investigated associations between selected physical activity correlates
among 299 adolescents (90 boys, age 12–14 years) from 3 English schools. Physi-
cal activity was assessed by self-report and accelerometry. Correlates represented
biological, predisposing, and demographic factors as described in the Youth Physi-
cal Activity Promotion Model. Boys engaged in more self-reported (p < .01) and
accelerometer assessed physical activity than girls (p = .02). Positive associations
between sex (male), BMI, Perceived PE Ability, Perceived PE Worth, number of
enrolled students, and physical activity outcomes were evident (p < .05). School-
based physical activity promotion should emphasize sex-specific enhancement of
students’ perceived PE competence and enjoyment.
Regular physical activity participation is an important contributor to healthy
lifestyles for children and adolescents (22). However, it is a pervasive finding that
levels of physical activity decline with age, especially through adolescence (33).
Physical activity guidelines have been developed to encourage participation, with
the main recommendation being that children and adolescents engage in at least 60
min of moderate to vigorous physical activity (MVPA) every day (27,39). Though
the prevalence of youth physical activity varies depending upon assessment method
employed (6), current evidence suggests that many young people are not meeting
the recommended guideline and that sedentary lifestyles remain a problem (25). The
correlates of youth physical activity are multidimensional and affect participation
(22). To address declining activity levels there is a need therefore for researchers
and practitioners to better understand these correlates.
62 Hilland et al.
The Youth Physical Activity Promotion Model (YPAPM; 40) was developed
to facilitate the application of youth physical activity correlates to physical activity
promotion. Within the YPAPM physical activity participation is predicted through
interactions of four categories of correlates termed predisposing, reinforcing,
enabling, and personal demographic factors. Though the YPAPM provides a broad
perspective on the factors that influence habitual physical activity, it may also be
applicable to specific physical activity contexts, such as school Physical Educa-
tion (PE; 40). PE has been identified as an important setting to help accumulate
physical activity as it provides many children with their only regular opportunity to
engage in MVPA (37). However, the contribution of PE to youth physical activity
is constrained by the limited frequency and duration of classes, and a lack of PE
during school holidays (34). Enabling and predisposing correlates such as the PE
environment, perceptions of PE competence, PE self-efficacy, PE enjoyment, and
PE attitudes may affect youth physical activity most strongly when the YPAPM
is applied to PE. In particular, Welk suggests that PE can play a primary role in
influencing students’ predisposing correlates relating to perceptions of ability
and attitudes toward participating (40). It has been frequently reported that self-
efficacy and perceived competence are positively associated with physical activity
(2). Moreover, if children experience fun and enjoyment, they are more likely to
participate, persist, exert effort and be committed to that activity (35). Enabling
factors include environmental variables such as the school physical environment,
which may also influence physical activity, as the majority of youth spend around
40% of their waking hours there (13) and most of their week day physical activity
is accumulated at school (16).
As PE is a central aspect of school-based physical activity promotion one of
its fundamental goals is to encourage young people to be physically active (26).
However, there is little evidence available to evaluate whether this goal is being
met. Therefore, the aim of this study was to investigate the association between
selected demographic, biological, school environmental and PE-based correlates,
and adolescent physical activity.
Participants and Settings
After obtaining institutional ethical approval and receiving written parental and
student informed consent, data were gathered from 299 Year 8 and 9 students (U.S.
grades 7 & 8; 90 boys, age 12–14 years) from three schools in the North West of
England. Year 8 and 9 students were invited to participate in the research as physical
activity levels decline most during early adolescence (33). Two of the three schools
were coeducational community schools and the other school was an independent
girls’ school. Each school followed the English PE National Curriculum.
Anthropometry. Stature, sitting stature and body mass were measured following
standardized procedures. Body mass index (BMI) was calculated as (weight (kg)
/ height (m) 2).
Adolescent Physical Activity Correlates 63
Maturity Status. Somatic maturity was determined by estimating years from
attainment of peak height velocity (PHV; 20), which reflects the age at maximum
growth rate in stature during adolescence. Years from attainment of PHV for each
student were predicted using sex-specific regression equations that include stature,
sitting height, leg length, chronological age and their interactions (24). This method
has demonstrated acceptable agreement when correlated against skeletal age (r =
Socioeconomic Status (SES). Socioeconomic status was represented by
deprivation scores derived from participants’ home postcodes using the National
Statistics Postcode Directory database. SES was calculated from seven domains
of deprivation, which include income deprivation, employment deprivation, health
deprivation and disability, education, skills and training deprivation, barriers to
housing and services, crime and the living environment deprivation domains (10).
Motivational Predispositions to Physical Education. Motivational
predispositions were assessed using the Physical Education Predispositions Scale
(PEPS; 18), which consists of 11 items, measured on a 5-point Likert scale.
Perceived PE Worth is calculated from the mean of six items representing attitude
affective and attitude cognitive. Perceived PE Ability is derived from the mean
of the remaining 5 items which are indicative of perceptions of competence and
self-efficacy in PE. The PEPS has previously demonstrated acceptable construct
validity, internal consistency (Perceived PE Worth: a = .91; Perceived PE Ability:
a = .89), and test-retest reliability with adolescent boys and girls (18).
Out of School Physical Activity Impact and Awareness. To assess students’
perceptions of the role of PE in relation to their physical activity participation
outside of school students were asked to indicate how much they agreed with two
statements; (i) What we learn in PE can have an impact on the types of physical
activities, exercise and sports we take part in outside of school, and, (ii) PE lessons
help make us aware of opportunities and places close to where we live, where we
can take part in physical activities, exercise and sports. These statements were
scored on a 5-point Likert scale.
School Environment. A PE environment survey was completed by one PE
teacher in each school to assess environmental enabling factors of physical activity.
The main outcome variables of interest were number of students enrolled in the
school, the percentage of students eligible for free school meals (FSM), number of
indoor spaces for physical activity, number of outdoor spaces for physical activity,
permanent resources per student, which were defined as facilities or equipment that
are fixed and therefore not portable (e.g., basketball court markings, soccer goals),
and curricular and extracurricular PE time (minutes). Further details of the survey
are available from the authors.
Self-Reported Physical Activity. Habitual physical activity was assessed using
the PAQ-C (7), which comprises nine items to derive an overall activity score. Each
statement is scored on a 5-point Likert scale ranging from low (1) to very high
(5) levels of activity, with overall the PAQ-C score calculated as the mean of the
nine items. The PAQ-C has demonstrated validity and reliability as a measure of
general physical activity (7). The PAQ-C, PEPS, and out of school physical activity
64 Hilland et al.
questionnaires were included in one packet and were administered together before
PE classes commenced. Students were asked to answer all questions as honestly
as possible, not to confer with others, and to ask if they were unsure about any of
Objectively Assessed Physical Activity. Physical activity was objectively
assessed every 5 s for seven consecutive days using ActiGraph accelerometers
(Model GT1M, ActiGraph LLC, Pensacola, FL). Sustained 20 min periods of zero
counts were deemed to indicate that the ActiGraph had been removed, and total
“missing” counts for those periods represented the duration that monitors were
not worn (5). Inclusion criteria were defined as minimum wearing times of ³ 670
min and ³ 555 min on each week day and weekend day, respectively. These figures
represent “non-missing” counts for at least 80% of a standard measurement day,
which was defined as the length of time that at least 70% of the sample wore the
monitor (5). Data from students with at least 3 valid measurement days (including
at least 1 weekend day) were retained. Forty-eight students (27 boys) did not meet
the minimum wear time criteria and so were excluded from analysis, leaving a
final sample size of 113 (30 boys). Minutes of MVPA were calculated using age
and gender-specific cut-points (14).
Exploratory independent t tests were conducted using SPSS v. 15 (SPSS Inc,
Chicago, IL) to assess sex differences in descriptive characteristics. ANCOVAs
assessed sex differences between predictor and physical activity outcome variables,
while controlling for any of the descriptive characteristics that were significantly
different. For the main analyses multilevel modeling (MLM) was conducted,
which is considered to be the most appropriate technique for nested data (38). A
two-level data structure was used, where children were defined as the first level
unit and school as the second level unit (38). Data were analyzed using MLwiN
1.10 software (Institute of Education, University of London, UK). An association
model was used to assess the effects of the predictor variables on physical activity
outcomes (minutes of MVPA, and PAQ-C score). Two analyses were conducted
for each outcome variable, the first determined the effect of sex (Model 1), whist
the second (Model 2) determined the effect of all other student and school level
predictor variables. The effect of the predictor variables on each outcome variable
was assessed for significance by comparing the—2 log likelihood (2*LL) for each
model on the Chi-square distribution with 2 degrees of freedom and the Wald
statistic (38). Alpha was set at p < .05 for all analyses.
The descriptive characteristics of the students are presented in Table 1. Girls were
younger and heavier than boys, and had significantly higher maturity offset scores
(t (293) = -22.48, p < .01). School level characteristics are shown in Table 2. Enroll-
ment in the three schools ranged from 512 to 1650 students. Schools had between
4 and 9 instructional spaces for PE and physical activity and between 42 and 71
permanent resources were reported.
Adolescent Physical Activity Correlates 65
Exploratory Results: Predictor Variables
Estimated years from age at PHV was covaried into all analyses of sex differences.
Students’ responses to the statements about PE’s influence on out of school physical
activity impact and awareness are presented in Table 3. Boys reported higher values
on schools’ impact and awareness of out of school physical activity compared with
girls, though differences were not significant. Boys and girls scored 3.94 (± 0.77)
and 3.67 (± 0.59) respectively, on Perceived PE Worth and 4.14 (± 0.60) and 3.78
(± 0.58), on Perceived PE Ability (F 1, 285 = 5.00, p = .03, d = 0.61).
Exploratory Results: Outcome Variables
Mean physical activity counts • min, minutes in MVPA, and PAQ-C scores are
presented in Table 3. ANCOVAs revealed that boys engaged in significantly more
MVPA (F 1, 110 = 5.53, p = .02, d = 0.95) and reported significantly higher PAQ-C
scores than girls (F 1, 285 = 9.24, p < .01, d = 0.57).
Multilevel Analyses Results
Minutes of MVPA were most strongly associated with sex [-15.82 (4.58)], Per-
ceived PE Ability [9.08 (3.06)] and number of students enrolled in the school [0.01
Table 1 Descriptive Characteristics of the Sample
(n = 295; mean ± SD)
Age (years) 13.16 ± 0.58 13.06 ± 0.59.20
Body Mass (kg) 52.43 ± 12.4653.28 ± 13.17.61
Stature (m) 1.59 ± 0.09 1.59 ± 0.07.77
BMI (kg • m2) 20.60 ± 3.7820.88 ± 4.35.60
Estimated years from PHV -1.17 ± 0.88 0.92 ± 0.66<.01
Deprivation score34.42 ± 21.76 37.39 ± 21.49.28
Table 2 School Level Characteristics
School A School BSchool C
NOR 512912 1650
% FSM eligibility 112612
No. permanent resources427170
Curricular PE time (mins • week)90120120
Extracurricular PE time (mins • week)660 6001200
66 Hilland et al.
(0.00); Table 4]. BMI and deprivation score were retained in the model as they
significantly improved the fit. As boys were the reference group in the model, the
significant negative outcome for sex describes how boys engaged in 15.82 min more
of MVPA, compared with girls, and that 9.08 min of MVPA were accumulated
for every 1 unit on the Perceived PE Ability scale. In addition, 0.01 min of MVPA
were accrued for every 1 student on roll at school.
Table 5 demonstrates that PAQ-C scores were best predicted by sex [-0.22
(0.07)], BMI [0.01 (0.01)], Perceived PE Ability [0.01 (0.01)], and Perceived PE
Worth [0.13 (0.06)]. Deprivation score and number of students enrolled in the school
were retained as they significantly improved the model fit. Boys’ PAQ-C scores
were 4.4% higher than girls’ and a there was a 0.2% increase in PAQ-C score for
Table 3 Mean (± SD) Scores of PE Predictor and Outcome Variables
(PAQ-C Scores, Counts • Min, and MVPA) by Sex
PE’s impact upon out of
school physical activity
4.03 ± 0.80 3.88 ± 0.63.37 0.21
PE’s influence of awareness
of physical activity oppor-
tunities out of school
3.87 ± 0.82 3.69 ± 0.75.63 0.23
PAQ-C 2.81 ± 0.58 2.50 ± 0.51<.010.57
Counts • min 455.52 ± 157.99329.29 ± 102.12<.01 0.95
MVPA (min • day)76.56 ± 27.61 54.36 ± 18.12 .020.95
Table 4 Results of MLM Analysis of Predictor Variables on Daily
Model 1 Model 2
Constant75.08 (3.85)**67.53–82.63 31.89 (18.78) -4.96–68.70
Sex -20.73 (4.49)**-29.53 to -11.93 -15.82 (4.58)** -24.80 to -6.84
Number of stu-
0.01 (0.00)* -0.01–0.01
BMI -0.37 (0.55)-1.45–0.71
Note: The reference category for sex was boys.
*p < .05
**p < .01
Adolescent Physical Activity Correlates 67
every one unit increase in BMI. The findings also suggest a 5.8% increase in PAQ-C
score for every one unit on the Perceived PE Ability scale, and a 2.6% increase in
PAQ-C score for every one unit on the Perceived PE Worth scale.
The study purpose was to investigate the association between selected demographic,
biological, school environmental and PE based correlates, and adolescent physical
The results concur with other studies by highlighting the significant predic-
tive nature of sex on self-reported and objectively assessed physical activity, with
boys engaging in more activity than girls. The sex difference in PAQ-C scores
(0.31) was similar to those reported previously (7) with differences ranging from
0.20 to 0.48. The sex differences in accelerometer counts • min also followed a
similar pattern to other studies (30). Comparing sex differences in MVPA with
other studies can be problematic where different accelerometer cut-points have
been used. However, results were comparable to other studies utilizing Freedson
et al.’s (14) regression equations (36). Despite the consistent sex differences in
MVPA, these studies generally reported higher volumes of activity than in the
current study. Our data were collected during November and December when
reduced daylight hours limit opportunities for outdoor physical activity. It is well
established that children’s physical activity is lowest during winter months (31)
and greatest in the spring (19). This may explain why the physical activity levels
in our sample were somewhat lower than those described in other studies, which
often negated the confounding effects of seasonality by measuring physical activ-
ity over the full year (30).
Table 5 Results of MLM Analysis of Predictor Variables on PAQ-C
Model 1 Model 2
Constant2.77 (0.06)** 2.65–2.89 0.99 (0.30)** 0.40–1.58
Sex -0.27 (0.07)**-0.41 to -0.13 -0.22 (0.07)** -0.36 to -0.08
BMI 0.01 (0.01)* -0.01–0.03
Perceived PE Ability0.29 (0.06)** 0.17–0.41
Perceived PE Worth 0.13 (0.06)*0.01–0.25
Deprivation score0.00 (0.00)0.00–0.00
Number of students
Note: The reference category for sex is boys.
*p < .05
**p < .01
68 Hilland et al.
The sex differences in physical activity may be attributed to a combination of
factors (33). One of these is maturity status which may exert an influence on adoles-
cents’ physical activity (20). One of the consequences of biological maturation among
girls is an increase in adiposity from approximately 15–22% body fat (20), which
leads to changes in body shape and size that are generally opposed to competence
in athletic events and physical activities (29). In contrast, biological maturation for
boys involves an increase in muscle mass leading to enhanced speed, strength, power
and performance on motor tasks and in physical activity (20). Some girls’ responses
to the physical changes associated with biological maturity include reductions in
self-esteem, self-perceptions and poor body image which may contribute to negative
feelings about their physical activity competencies (8). It has recently been reported
that when the effect of biological maturation was controlled, the influence of sex
on early adolescents’ MVPA and physical self-perceptions diminished, suggesting
that maturation may be a significant confounder when comparing physical activity
of boys and girls matched by chronological-age (12). Differential treatment of boys
and girls from their parents and teachers may also influence sex differences in physi-
cal activity (28). Evidence from the USA suggests that boys receive more parental
support, parental facilitation, and parental encouragement to be physically active
than girls (41). It has also been found in Canada and the UK that compared with
girls, boys receive more feedback and attention, particularly praise, criticism, and
technical information from their teachers (11,15). This enhanced feedback may lead
to the advanced development of motor performance which has been found to heighten
students’ perceptions of competence, effort and enjoyment of PE (28). Though no
data were collected to investigate the impact of feedback on physical activity par-
ticipation in the current study, this should be an area for continued future research
to enhance our understanding of teachers’ influences on youth physical activity.
Perceived PE Ability was significantly associated with self-reported and
objectively assessed physical activity, suggesting that adolescents’ judgments
about their abilities in PE may influence their habitual physical activity participa-
tion. These findings align with Competence Motivation Theory (17) and Cogni-
tive Evaluation Theory (9), which contend that an individual’s motivation varies
according to changes in perceptions of their competence, autonomy, enjoyment,
optimal challenge, and choice. The significant influence of Perceived PE Ability
concurs with previous observations that children with high perceived PE compe-
tence participated in significantly more physical activity outside of school than
peers with lower competence perceptions (3). These findings are consistent with
the YPAPM, suggesting that a dynamic relationship exists between Perceived PE
Ability and physical activity (40). Number of students enrolled in the school was
also significantly associated with MVPA, indicating that more physical activity
may be accumulated in schools with greater student numbers. Carron concluded
that consequences of larger group sizes included greater availability and range of
resources to enable participation in physical activity and sport (4). This is supported
by our data (Table 2), which indicated that schools with higher student numbers
had more permanent resources.
Other predictor variables that had a significant influence on PAQ-C scores
included BMI and Perceived PE Worth. Students with higher BMI values reported
greater physical activity levels, which contrast with previous research suggest-
ing an inverse association between BMI and physical activity (1). Rowlands and
colleagues suggest that controversy surrounds the relationship between physical
Adolescent Physical Activity Correlates 69
activity and levels of fatness, as this area is plagued with measurement problems
(32). Discrepancy between studies may in part be attributable to small sample sizes,
differences in the definition of obesity where weight status has been classified in
this way, and disparities between the various methods used to assess and quantify
physical activity (32). For example, it was observed that children with a BMI ³ 85th
percentile increased their PAQ-C scores over time, compared with normal weight
peers (1). In addition, another study reported that overweight adolescents tended
to over-report physical activity levels (23). These findings are comparable to the
current study in which analyses showed that the difference in standardized (z)
scores between PAQ-C and MVPA for normal weight children was 0.03, compared
with 0.10 for overweight/obese children, suggesting that normal weight children’s
self-reported physical activity better reflected their objectively measured MVPA.
In agreement with previous studies, the results indicate that young people with
higher BMIs may over report their physical activity levels possibly due to socially
desirable responses, and perceptions that the physical activity is more intense than
it actually is when assessed objectively (1,23). In the current study, a combination
of over-reporting of physical activity and the acknowledged limitations of using
BMI as a measure of body composition (21) may have resulted in the positive
association between the two variables.
Perceived PE Worth also had a significant impact upon PAQ-C scores, which
highlights the positive consequences of students perceiving PE as enjoyable and
stimulating. While the possible affect of social desirability bias on both question-
naire datasets cannot be ignored, the observed association is consistent with Cog-
nitive Evaluation and Self-Determination Theories (9). Findings also concur with
previous research concluding that if children experience fun and enjoyment they are
more likely to participate, persist, exert effort and be committed to that particular
activity (3,35). Moreover, it has been reported that enjoyment in PE contributes to
the quality (frequency and intensity) of activity participated in outside of school
(3). These findings are consistent with YPAPM model (40) and emphasize the role
of Perceived PE Worth in promoting active lifestyles outside school.
Strengths of this study were that it was based on the YPAPM (40) as a con-
ceptual framework, it used a combination of physical activity assessment methods,
and the multilevel data analysis allowed for the effects of individual and school
level correlates to be considered simultaneously. Limitations include the possibility
of sampling bias, the imbalance in the number of boys and girls and that a small
number of schools were recruited to the study, which may have affected the statisti-
cal power. Therefore, the generalizability of the findings beyond the locale where
the study occurred is likely limited. Furthermore, the study was cross-sectional
and so causality cannot be inferred from the reported associations. The study took
place during winter months thus seasonal effects may have influenced the physi-
cal activity data in particular. Finally, although a selection of correlates of youth
physical activity was measured other potentially significant factors described in
the YPAPM (40) were not included due to resource constraints.
This novel study supports the application of the YPAPM (40) to the school
and PE context and suggests that sex, BMI, Perceived PE Ability, Perceived PE
Worth and student numbers are most strongly associated with adolescent physically
activity. It is recommended that PE teachers maximize opportunities to enhance
students’ enjoyment and perceptions of competence in PE, which are differentiated
to the particular needs of girls and boys.
70 Hilland et al.
1. Ball, G.D., J.D. Marshall, and L.J. McCargar. Physical activity, aerobic fitness, self-
perception, and dietary intake in at risk of overweight and normal weight children.
Can. J. Diet. Pract. Res. 66(3):162–169, 2005.
2. Barr-Anderson, D.J., D.R. Young, J.F. Sallis, et al. Structured physical activity and
psychosocial correlates in middle-school girls. Sports Med. 44(5):404–409, 2007.
3. Carroll, B., and J. Loumidis. Children’s perceived competence and enjoyment in physi-
cal education and physical activity outside school. Eur. Phys. Educ. Rev. 7(1):24–43,
4. Carron, A.V. Group size in sport and physical activity: social psychological and per-
formance consequences. Int. J. Sport Psychol. 21(4):286–304, 1990.
5. Catellier, D.J., P.J. Hannan, D.M. Murray, C.L. Addy, and T.L. Conway. S., Yang, and J.
C. Rice. Imputation of missing data when measuring physical activity by accelerometry.
Med. Sci. Sports Exerc. 37(11):S555–S562, 2005.
6. Corder, K., U. Ekelund, R.M. Steele, N.J. Wareham, and S. Brage. Assessment of
physical activity in youth. J. Appl. Physiol. 105(3):977–987, 2008.
7. Crocker, P.R., D.A. Bailey, R.A. Faulkner, K.C. Kowalski, and R. McGrath. Measur-
ing general levels of physical activity: preliminary evidence for the Physical Activity
Questionnaire for Older Children. Med. Sci. Sports Exerc. 29(10):1344–1349, 1997.
8. Davison, K.K., J.L. Werder, S.G. Trost, B.L. Baker, and L.L. Birch. Why are early matur-
ing girls less active? Links between pubertal development, psychological well-being,
and physical activity among girls at ages 11 and 13. Soc. Sci. Med. 64(12):2391–2404,
9. Deci, E.L., and R.M. Ryan. Intrinsic motivation and self-determination in human
behavior. New York: Plenum Press, 1985.
10. Department for Communities and Local Government. The English Indices of Depriva-
tion 2007. Wetherby: Communities and Local Government Publications, 2008.
11. Dunbar, R.R., and M.M. O’Sullivan. Effects of intervention on differential treatment of
boys and girls in elementary physical education lessons. J Teach Phys Educ. 5:166–175,
12. Fairclough, S.J., and N.D. Ridgers. Relationships between maturity status, physi-
cal activity, and physical self-perceptions in primary school children. J Sports Sci.
13. Fox, K.R., A. Cooper, and J. McKenna. The School and Promotion of Children’s
Health-Enhancing Physical Activity: Perspectives from the United Kingdom. J Teach
Phys Educ. 23:338–358, 2004.
14. Freedson, P.S., J. Sirard, E. Debold, et al. Calibration of the Computer Science and
Applications, Inc. (CSA) Accelerometer 256. Med. Sci. Sports Exerc. 29(5):S45, 1997.
15. French, J., and P. French. Gender imbalance in the primary classroom: an interactional
account. Educ. Res. 26(2):122–136, 1984.
16. Fridlund - Dunton. G., C. K. Whalen, L. D. Jamner, and J. N. Floro. Mapping the Social
and Physical Contexts of Physical Activity Across Adolescence Using Ecological
Momentary Assessment. Ann. Behav. Med. 34(2):144–153, 2007.
17. Harter, S. The perceived competence scale for children. Child Dev. 53(1):87–97, 1982.
18. Hilland, T.A., G. Stratton, N.D. Ridgers, and S. Fairclough. The Physical Educa-
tion Predisposition Scale: Preliminary development and validation. J Sports Sci.
19. Kolle, E., J. Steene-Johannessen, L.B. Andersen, and S.A. Anderssen. Objectively
assessed physical activity and aerobic fitness in a population-based sample of Norwegian
9- and 15-year-olds. Scand J Med Sci Sports. 20:41–47, 2009.
20. Malina, R.M., C. Bouchard, and O. Bar-Or. Growth, Maturation, and Physical Activity.
Champaign, IL; Leeds: Human Kinetics, 2004.
Adolescent Physical Activity Correlates 71
21. McCarthy, H.D., T.J. Cole, T. Fry, S.A. Jebb, and A.M. Prentice. Body fat reference
curves for children. Int. J. Obes. 30:598–602, 2006.
22. McKenzie, T.L., S.J. Marshall, J.F. Sallis, and T.L. Conway. Leisure-time physical
activity in school environments: an observational study using SOPLAY. Prev. Med.
23. McMurray, R.G., D.S. Ward, J.P. Elder, et al. Do overweight girls overreport physical
activity? Am. J. Health Behav. 32(5):538–546, 2008.
24. Mirwald, R.L., A.D. Baxter-Jones, D.A. Bailey, and G.P. Beunen. An assessment of
maturity from anthropometric measurements. Med. Sci. Sports Exerc. 34(4):689–694,
25. Muller-Riemenschneider, F., T. Reinhold, M. Nocon, and S.N. Willich. Long-term
effectiveness of interventions promoting physical activity: A systematic review. Prev.
Med. 47:354–368, 2008.
26. National Association for Sport and Physical Education. Moving into the Future: National
Standards for Physical Education. Reston, VA: McGraw Hill Companies, 2004.
27. National Institute for Health and Clinical Excellence. Promoting Physical Activity for
Children and Young People. London: NICE, 2009.
28. Nicaise, V., G. Cogerino, S. Fairclough, J. Bois, and K. Davis. Teacher feedback and
interactions in physical education: Effects of student gender and physical activities.
Eur. Phys. Educ. Rev. 13(3):319–337, 2007.
29. Niven, A.G., S.G. Fawkner, A.M. Knowles, and C. Stephenson. Maturational differ-
ences in physical self- perceptions and the relationship with physical activity in early
adolescent girls. Pediatr. Exerc. Sci. 19(4):472–480, 2007.
30. Riddoch, C.J., L. Bo Andersen, N. Wedderkopp, et al. Physical activity levels and
patterns of 9- and 15-yr-old European children. Med. Sci. Sports Exerc. 36(1):86–92,
31. Riddoch, C., C. Mattocks, K. Deere, et al. Objective measurement of levels and patterns
of physical activity. Arch. Dis. Child. 92:963–969, 2007.
32. Rowlands, A.V., R.G. Eston, and D.K. Inglewdew. Relationship between activity
levels, aerobic fitness, and body fat on 8- to 10-yr-old children. J. Appl. Physiol.
33. Sallis, J.F. Age-related decline in physical activity: a synthesis of human and animal
studies. Med. Sci. Sports Exerc. 32(9):1598–1600, 2000.
34. Stratton, G., S.J. Fairclough, and N.D. Ridgers. Physical activity levels during the
school day. In: Youth Physical Activity and Sedentary Behavior. Challenges and Solu-
tions, A. L. Smith, and S. J. H. Biddle (Eds). Champaign, IL: Human Kinetics, 2008,
35. Stucky-Ropp, R.C., and T.M. DiLorenzo. Determinants of exercise in children. Prev.
Med. 22(6):880–889, 1993.
36. Trost, S.G., R.R. Pate, J.F. Sallis, et al. Age and gender differences in objectively
measured physical activity in youth. Med. Sci. Sports Exerc. 34(2):350–355, 2002.
37. Trudeau, F., and R.J. Shephard. Contribution of school programmes to physical activity
levels and attitudes in children and adults. Sports Med. 35(2):89–105, 2005.
38. Twisk, J.W.R. Applied Multilevel Analysis. Cambridge: University Press, 2006.
39. United States Department of Health and Human Services. Physical Activity Guidelines
for Americans. US Department of Health and Human Services, Office of Disease
Prevention and Health Promotion, 2008.
40. Welk, G.J. The Youth Physical Activity Promotion Model: A conceptual bridge between
theory and practice. Quest. 51:5–23, 1999.
41. Welk, G.J., K. Wood, and G. Morss. Parental influences on physical activity in children:
An exploration of potential mechanisms. Pediatr. Exerc. Sci. 15:19–33, 2003.
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