1
The dose-response association between V̇O2peak and self-reported physical activity in
children
Running head: The dose-response association between V̇O2peak and physical activity
Alan M. Nevill1, Michael J. Duncan2, Gavin Sandercock3
1. Faculty of Education, Health and Wellbeing, University of Wolverhampton, Walsall
Campus, Walsall, U.K.
2. Faculty of Health and Life Sciences, Coventry University, Coventry, U.K.
3. School of Biological Sciences, University of Essex, Colchester, U.K.
Address for correspondence:
Professor Alan M. Nevill, Ph.D.
University of Wolverhampton
Faculty of Education, Health and Wellbeing
Walsall Campus
Gorway Road
Walsall, WS1 3BD
Tel: +44 (0)1902 322838
Fax: +44 (0)1902 322894
Email:
a.m.nevill@wlv.ac.uk
2
Abstract
Background: Previous research into the association between aerobic fitness and physical
activity in children is equivocal. However, previous research has always assumed that such
an association was linear. This study sought to characterize the dose-response association
between physical activity and aerobic fitness and to assess whether this association is linear
or curvilinear and varies by sex, age and weight status.
Methods: Physical activity (assess using the Physical Activity Questionnaire), aerobic
fitness (20 m shuttle-run), BMI, screen-time and socio-demographic data were collected at
ages 12, 14 and 16-years in (n=1422) volunteers from 9 English schools. Multilevelregression modelling was used to analyse the longitudinal data.
Results: The analysis identified a significant inverted ‘u-shaped’ association between
VO2max and PAQ. This relationship remained havingcontrolling for the influences ofsex, age
and weight status. Daily screen time >4 hours and deprivation was also associated with
being less fit (P<0.01).
Conclusions: This longitudinal study suggests that the dose-response relationship between
PA and aerobic fitness in children is curvilinear. The health benefits of PA are greater in less
active children and that sedentary and less active children should be encouraged to engage
in PA rather than more active children to increase existing levels of PA.
3
Introduction
Cross-sectional studies in adults show physical activity (PA) and aerobic fitness share a
positive,
dose-responserelationship
(1,2).When
adjusted
for
anthropometric
and
sociodemographic factors, PA explainsas much as 20% of the variance in aerobic fitness
(3).Studies in youthare less consistent (4)showing only low-to-moderate associations (r=.10.45) between PA and fitness (5)Weight status explains a significant proportion of the
variance in fitness (1,6); moderating and mediating its association with PA (4,7,8).Onecrosssectional study by Nevill et al (9)has recently reported the association between children’s PA
and aerobic fitness. Nevill et al (9)found the association of PA with fitness to be
curvilinear(inverted u-shaped) withgreater/steeperincrements in fitnessper unit difference in
activity at lowerPA levels with the authors suggesting a dose-response relationship.
However, it could be argued that a true dose-response relationship can only be
determined using a longitudinal approachwith repeated assessments. Studies of this nature
are rare although,in the Amsterdam Growth and Health Study a 30% increase in PA over a
15-year period was associated with a 2-5% increase in V̇O2peak (10). The authors of the
Amsterdam Growth and Health Study concluded, ‘no clear relation can be proved between
PA and V̇O2peak in free living males and females’ (10). Likewise, in a longitudinal study of
over 200 children Armstrong et al. (11)used multilevel modelling to examine change in PA,
from the ages of 11–13 years. When adjusted for age, gender and maturitythere was no
evidence for an association with fitness (11). Other research using multilevel modelling of
longitudinal pooled data (12) confirmed the findings of cross-sectional studies showing
effects of body size and body composition on V̇O2peak but found no association withPA (12).
However, the lack of evidence that a dose-response association exists between aerobic
fitness and PA in previous studies (10,11,12) may well be due to the authors not allowing for
the strong possibility that the association between aerobic fitness and PA is curvilinear rather
than linear (see 9).The extant literature in regard to dose-response relationships between PA
and aerobic fitness in children remains equivocal but it is important to also note that
4
fewstudies examining the association between PA and aerobic fitness consider other
important confounders; in particular deprivation and correlates of low PA in childhood such
as screen-time (13). Deprivation in particular is a key confounder as more deprived children
in the UK are likely to have lower levels of aerobic fitness (14) and PA (15,16).
This study sought to address this gap in the literature by exploring the dose-response
rate (i.e., associations) between fitness and PA to be curvilinear rather than linear using a
longitudinal study design and accounting for known confounders (age, sex, and weight
status) as well as other possible influences such as deprivation and screen time.
Methods
Participants
We drew data from the East of England Healthy Hearts (EoEHHS) study, a large (n=8800)
school-based health and fitness survey of 11-16 year-olds (overall consent: 96%). The
longitudinal arm of the EoEHHS comprised a subsample of students from n=9 public schools
all of whom were in grade 7 at baseline (summer months of 2007; n=1503, 46% female).
Parents gave written informed consent prior to data collection. Trained researchers
conducted all measurements during regularly scheduled physical education classes. We
replicated two season-matched follow up assessments in grade 9 and grade 11. The present
longitudinal analysis includes n=1422 participants who had complete data for cardiorespiratory fitness, self-reported physical activity and screen time at all time points after
wave 1.
***Table 1 here***
Measures of Weight Status
5
Body mass and stature were measured (to the nearest 0.1 kg and 0.1 cm, respectively) with
light clothing (T-shirts and shorts) and without shoes. Body mass index (BMI) of each
participant was calculated in kilograms per square metre. BMI was categorized as
underweight, normal weight, overweight and obese according to the International Obesity
Task Force (IOTF) criteria (17).
Measures of Physical Activity
Each participant completed the Physical Activity Questionnaire (PAQ)(18)a self-administered
seven day recall instrument comprising 9-items scored on a 5 point Likert scale (1–5. A
sample item: ‘In the last 7 days, what did you do most of the time at break?’ the full scale is
freely available as is the scoring method (18). PAQ were anglicized (e.g., recess became
break; soccer became football) as described previously (19).
Measures of Deprivation
An area-level measure of deprivation was employed for each participant using their
individual home postcode. The English Index of Multiple Deprivation 2007 is measured
based on the small-area geographical units known as lower super output areas (LSOAs);
each LSOA containing 1,000-3,000 inhabitants (20). There are 32,482 LSOAs in England
and these are ranked from 1, the most deprived to 32,482, the least deprived based on
multicomponent score ranging from 0.4 (least deprived) to 85.5 (most deprived). A lower
score indicates low area-level deprivation, with a high score indicating higher deprivation.
Within the present data, this resulted in 32,394 IMD units/ranks ranging from low (very
deprived) to high (less deprived).
Screen time
6
Self-reported daily screen-time was assessedby asking: ‘How much time do you spend on
average each day watching television, watching DVDs or videos, using a computer or games
console’. Participants chose one of six responses: none, 0–30 min, 30–60 min, 1–2 hours,
2–4 hours and >4 hours. Responses were then collapsed to create groups based on
international recommendations(21) which represented low (<2 hours), high (2-4 hours), and
very high (>4 hours), screen-time.
Estimation of Aerobic Fitness
Fitness was assessed using the 20m shuttle-run test (20mSRT), an incremental running test
to maximal exertion (22). Testing was carried out by researchers accompanied by school
physical education staff. Testing was conducted in groups of up to 30 with a ratio of five
participants to one researcher. All participants had undertaken the 20mSRT before as part of
their school physical education. Recorded instructions on the informed participants to “run
for as long as possible” and a researcher at the beginning of each test reiterated this.
Researchers acted as “spotters” during the test and recorded the participants’ final shuttle
count at either the point of volitional exhaustion, or when they failed to maintain the given
pace for the second time. Running speedat final completed level wasused to
predictV̇O2peak(mlkg-1min-1) using the equation of Leger et al (22).
Statistical Methods
Descriptive statistics including the number of children (N) plus mean and standard
deviations (SD) of age, height, weight cardiorespiratory fitness (mlkg-1.min-1) and PAQ
scores by sex, visit occasions (wave) and year group are given in Table 1.To determine
whether analyses required adjustment for possible school-level clustering, an unconditional
multilevel model with school as a Level-3 predictor was fitted. As Level 3 (between-school)
variances for latent intercept and slope were non-significant subsequent models were not
7
adjusted by school.An appropriate way of analysing longitudinal (repeated-measures) data
can be multilevel modelling approach, using the program Multilevel Models Project MLwiN
(23). Multilevel modelling is an extension of ordinary multiple regression where the data
have a hierarchical or clustered structure. A hierarchy consist of units or measurements
grouped at different levels. One example is repeated measure data where individuals are
measured on more than one occasion. Here, the children, assumed to be a random sample,
represent the Level 2 units andtheir repeated measurements recorded at each visit
occasions were consideredLevel 1 units.
In the present study, multilevel regression analyses were performed using the MLwiN
software to identify those factors associated with the development of V̇O2peak, having
adjusting for differences in physical activity and age. The two levels of hierarchical or nested
observational units were the visit occasions at level 1 (within-individuals), and the sample of
children (between-individuals) at level 2. The model adopted is shown in Equation 1.
V̇O2peak (ml∙kg-1∙min-1) = ai + b · PAQ + c · PAQ2 + d · age + ij
(Eq. 1)
All parameters were fixed with the exception of the ‘constant’ parameter ‘ai’ that was allowed to
vary randomly from child to child (level 2), and ij, that was assumed to have a constant error
variance between visit occasions (level 1). The subscripts i and j are used to indicate random
variation at levels 2 and 1 respectively.
Potentialpredictors (sex, weight status and screen time)were incorporated into the
analysis by introducing them as indicator variables. Sex was introduced as a (boys=0,
girls=1) indicator variable, since, by doing so, the boys’ constant term would be incorporated
within a baseline parameter ai, from which the girls’ constant term would deviate. Similarly,
the weight statusgroup of “obese”was used as the baseline parameter (the constant ‘ai’) from
which other weight status groups (underweight, normal and overweight) were compared, i.e.
allowed to deviate from the constant baseline ‘ai’. To allow the rate (slope) of fitness to vary
with PA(either PAQ, PAQ2), age-by-groups(sex and weight status), the product between
8
age, PAQ and PAQ2and the group indicator variable was included in the analysis as
interaction terms.
Model 1 (Eq. 1) is the simplest model fitted that introduced sex as the sole categorical
predictor together with sex-by-PAQ and sex-by-age interactions. Model 2 incorporated
weight status and screen time as a further categorical variablesplus the Index of Multiple
Deprivation (recorded as ranks) as a further continuous covariate.
In order to provide a measure of goodness-of-fit “deviance” (-2 Log Likelihood) or more
specifically the change in deviancewas employed. The deviance statistic is a generalization
of the sum of squares of residualsused in ordinary least squaresbut where model-fitting is
achieved by maximum likelihood. It plays an important role in assessing the quality of fit in
generalized linear models especially multilevel analyses. The difference between the
deviances for competing models follows an approximate χ2 distribution with k-degrees of
freedom (24)
Results
The multilevel-regression analysis of aerobic fitness (V̇O2peakin mlkg-1min-1))
incorporating terms fromthe simplest Model 1(Eq. 1) revealed estimated parameters given in
Table
2a.
Theanalysis
identified
a
significant
quadratic
polynomial
association
betweenfitness andPA whereby the linear PAQ term was steeper for boys (b=3.83) than girls
(= 3.83-1.01=2.82) but the quadratic PAQ2 term (c) was found to be common for both sexes,
see Table 2a (Model 1). The PAQ values at which V̇O2peakpeaks can be estimated using
differential calculus, estimated for boys as 5.8 and for girls as 4.3. Figures 1 illustrates the
nature of the curvilinear associations between V̇O2peakand PAQ for boys and girls.
9
The regression analysisalso revealed significant declines in fitness with age in both
boys (d=-0.20) and girls, but with the age decline in girls being significantly steeper (d=-0.200.61=-0.81) (see Model 1).
***Table 2 Here***
*** Figure 1. Here ***
The multilevel-regression analysis of V̇O2peak(mlkg-1min-1) incorporating terms in
model 2 revealed the estimated parameters as reported in Table 2b. LikeModel 1, the more
complexModel 2 alsorevealed a quadratic polynomial association between aerobicfitness
and PA (both linear PAQ and PAQ2 terms, P<0.001). The slope of the decline in fitness with
age, which varied for boys and girls, also remained as in model 1.
Model 2 identified differences in fitnessby weight status. Comparedwith obese
participants, those who were underweight,normal weight and overweightwere all fitter:3.0,
2.9and 1.3 mlkg-1min-1respectively.
Model 2 also indicates that youth reporting >4 hours daily screen time were less fit (1.17mlkg-1min-1)than children who <2 hours per day. Note that children who spent between
2-4 hours screen time were marginally fitter than the baseline group who spent less than 2
hours on screen time per day.
Finally, deprivation was also found to be significantly associated with VO2max having
controlled
for
differences
in
PA,
sex
and
age.
The
rate
of
increase
in
VO2maxfitnesswaspredicted to be 0.8 (ml∙kg-1∙min-1) per 10,000 IMD ranks(P<0.001) (rank 1
being most deprived area and rank 32,844 being the least deprived area).
10
From Model 1 to 2, i.e., with increasing model complexity, the change in deviance (-2
Log Likelihood) went from 25445.4 (df=7) to 24717.7 (df=13). This change in deviance was
727.6 with df=6 (P<0.001).
Discussion
This is the first longitudinal study in English schoolchildren to identify the doseresponse relationshipbetweenaerobic fitness and PA to be curvilinear rather than linear
whilst at the same time, allowingfor potential confounding effects of weight status,
deprivation andscreen-time. No prior longitudinal study has allowed for the possibility that
the dose-response relationship is curvilinear, potentially explaining the lack of association in
some of the earlier studies examining this topic (10,11). The current study therefore presents
new knowledgeon the nature of the dose-response between aerobic fitness and PA in youth.
Only two cross-sectional studies and two longitudinal studies, reviewed by Rauner et
al (4). assessed the interactions among physical activity, fitness and weight status (25,26).
Like the present study both used the 20mSRT to provide estimates of aerobic fitness and
both used BMI to categorize weight status. The authors of these studies chose weight status
as the outcome measure in their analyses with fitness and PAused as predictorsof change in
BMI or weight status at follow-up.
The findings of the present studyalign withrecent cross-sectionalfindings in British
children (27). However, the approach employed in the current study, which is based on
longitudinal data, provides a more robust assessment as compared to studies using cross
sectional designs (25,26,27). Longitudinally assessment of children across three different
occasions (repeated measures) showed thatincreases in PA resulted in a positive doseresponse associationwith aerobic fitness.In agreement with recent cross-sectional findings
(9)the association of PA with fitness in the current studywas curvilinear rather than linearin
both boys and girls, see Table 2 (Model 1) and Figure 1.The nature of these curves suggest
that initially (for low levels of PA) these dose-response rates or slopes were steeper for
11
boys(β=3.8)*compared with girls (β=2.8) and that the curves peaked at PAQ score means of
5.8 for boys and 4.3 for girls when no further benefit in V̇O2peakwith increasing PAis
anticipated/predicted.(*ml∙kg-1∙min-1per unit of PAQ)
The fact that the boys’ slope parameter (β) is steeper than the girls’ (for the same level
of PA),can be explained simply by the fact that boys report more games activities (football,
rugby etc.) thought to be more “vigorous” PA when completing their PAQ questionnaire. In
contrast, girls report more activities such as walking and horse riding (thought to be more
moderate PA), but when the total PAQ score is summed, these activities are given equal
weights.
Fitness declined with age at an annual rate β=0.20 mlkg-1min-1in boys and β=-0.81 (.20-0.61=-0.81)in girls. Incorporating additional factors such as introducing weight status as
a categorical variable(Model 2) revealed better fitness in underweight,(+3.0 mlkgmin),normal-weight (+2.9 mlkg-1min) and overweight (+1.3 mlkg-1min) groups compared
1
withthe reference group ofobese children (all P<0.001). However, the effects (and their
estimated parameters) associated with sex, age and PA remained almost unchanged in the
revised Model 2.
Even after adjusting for sex, age, weight status and PA, children reporting>4 hours
daily screen time were -1.17(95CI; -0.71 to -1.63 mlkg-1min-1)lower thanthose reporting<2
hours per day. Screen time is a known correlate of children’s health generally and PA (7,24).
Systematic review evidence shows significant, but weak, inverse associations between
children’s daily screen time and aerobic fitness (14).The screen-time findings presented here
agree with cross-sectional research in English youth which found that ownership and access
to electronic devices to benegatively associated with aerobic fitness (27).
Deprivationwas a predictor of aerobic fitnessindependent of sex, age, PA and weight
status. The rate of increase in VO2peakper 10,000 ranks of IMD was estimated as 0.8 (mlkgmin-1). England comprised 32,000 lower super output areas (28);10,000 ranks represents
1
12
approximatelya third of the variance for deprivation. Consequently, Model 2predicts that
fitness of youth from the most deprived areas of England was 2.4 mlkg-1min-1lower than
thoseliving in the least deprived areas. Such a finding is not unexpected as low aerobic
fitness is more prevalent in youth from moredeprived areas (14,29); who tend to be less
physically active (30). Deprived children are also less likely at access facilities and sports
clubs for PA, a recognized barrier to physical activity for deprived youthand a predictor of low
fitness (14).
The Muscatine (31) and Amsterdam (10)longitudinal studies and pooled data from
the UK (6, 12) have previously reported associations between indices of aerobic fitness and
PA. However, the longitudinal design, repeated measurement points in a sample of this size
(n=1422) is astrength of the present study. To our knowledgeonly He et al (26). have
reportedlongitudinaldata of a comparable sample size (n=1795).Similarly, few studies
previously conducted that have examined the relationships between PA and aerobic fitness
variables have also considered key confounders of weight status, deprivation and screen
time, as is the case in the current study. The current study is also the first to present a true
examination of the dose-response between PA and aerobic fitness in children by virtue of
tracking data at three time points.
There are some limitations of the present study. The 20m shuttle-run test is widely
used estimate of aerobic fitness in pediatric populations. However, test familiarization and
day-to-day performance variation may influence results particularly when repeated
measurements are made. The use of the Leger prediction (22) is also a limitation. Leger’s
equation is valid it may underestimateV̇O2peak, and while this is a criticism leveled at other
similar prediction equations (32) this should be considered by researchers in future studies.
Likewise, age is a component within the Leger equation and was also considered in the
multilevel models used in the current study it is possible this could increase collinearity in the
data. In addition, during the 20m shuttle-run test motivation to perform and understating of
13
instruction might also differ because of age and maturity level, meaning aerobic fitness may
be underestimated (33).
PA wasself-reported and while the PAQ has acceptable convergent validity recall
error and biasareboth likely (18). The PAQ, is one of the most reliable and valid tools used to
assess PA in youth, however, the same review (4,34)also noted that children’s self-report of
activity does not provide the fidelity of PA measurement that objective methods, such as
accelerometry, may provide (34). The limitations are however consistent with other largescale epidemiological studies which necessitate such approaches. Direct measurement of
VO2max was not possible in the current study due to the time and labour intensive nature of
such assessment techniques in large numbers of participants. This is also the case for using
objective measures such as accelerometry to capture PA
In summary, the results of thislongitudinal study suggest the dose response between
aerobic fitness and PA in children aged 10-15 years is curvilinear rather than linear. The
nature of the dose response curve depends on a number of factors including sex and activity
level.In the current study the association betweenaerobic fitnessand PAis greater/steeper in
magnitude when children are less active demonstrating that the benefit of increasing PA on
aerobic fitness is greater in low active children, compared to high active children. The
steeper dose-response rate or slope found with boys compared with girls can be explained
by the fact that boys report more vigorous PA such as games. Girls, on the other hand,
report more moderate PA such as walking and horse riding, but allsuch activities are given
equal weight when summed in the final PAQ score.
Competing interests:The authors declare that they have no competing interests.
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14
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Figure Captions
Figure 1. The curvilinear association between VO2max and PAQ for boys and girls
Table Captions
Table 1. Number of children (N) plus mean ± standard deviation (SD) of age, height, weight
cardiorespiratory fitness (ml.kg-1.min-1) and PAQ scores by sex, visit occasions (wave) and
year group.
Tables 2a and 2b. The estimated parameters (means SE) from the multilevel regression
analyses of predicted VO2max (ml∙kg-1∙min-1) using Models 1 and 2.
19
67
62
VO2max (ml,kg-1.min-1)
57
52
47
Vo2max girls
42
Vo2max boys
37
Model girls
32
Model boys
27
22
17
1
1.5
2
2.5
3
3.5
4
4.5
5
PAQ
Figure 1. The curvilinear association between VO2max and PAQ for boys and girls
20
Table 1. Number of children (N) plus mean ± standard deviation (SD) of age, height, weight cardiorespiratory fitness (ml.kg-1.min-1) and PAQ
scores by sex, visit occasions (wave) and year group.
Male
Variables
Age (yrs)
Height (cm)
Weight (kg)
Fitness
(ml.kg1.min-1)
Year
Wave 1
Female
Wave 2
N
Mean
SD
11.00
419
11.6
12.00
352
12.4
N
Mean
Wave 3
SD
N
Mean
Wave 1
Wave 2
N
Mean
SD
.3
334
11.6
.3
.3
315
12.4
.2
2
13.4
.2
13.00
419
13.6
.3
1
13.4
14.00
352
14.4
.2
SD
Wave 3
N
Mean
SD
339
13.7
.3
N
Mean
SD
4
14.7
.4
310
14.3
.2
4
14.6
.3
15.00
459
15.6
.2
2
15.3
.2
351
15.6
.2
16.00
307
16.3
.2
296
16.3
.2
11.00
419
148.7
7.8
334
150.6
7.9
12.00
352
152.0
8.0
315
152.8
7.1
2
163.3
9.5
13.00
419
162.0
9.1
1
165.2
14.00
352
164.8
8.6
339
161.2
7.3
4
163.3
6.3
310
162.4
6.7
4
174.7
7.2
15.00
459
172.2
7.7
2
175.2
4.0
351
169.1
8.0
16.00
307
172.8
7.9
296
170.4
7.7
11.00
419
42.6
10.1
334
44.4
10.3
12.00
352
45.3
10.4
315
46.7
10.0
2
64.0
5.6
13.00
419
54.3
11.6
1
73.9
339
53.8
10.5
14.00
352
56.2
11.6
4
51.6
6.9
310
55.7
9.9
4
62.0
11.7
15.00
459
62.6
11.6
2
74.1
16.2
351
59.9
10.5
16.00
307
62.6
11.0
296
61.3
10.4
4
39.9
7.5
11.00
419
46.2
5.5
12.00
352
44.8
5.7
13.00
419
45.7
6.2
1
38.9
14.00
352
44.5
6.6
4
49.6
6.1
334
43.3
3.9
315
41.2
4.7
2
41.1
3.7
339
41.3
4.5
310
39.6
5.0
21
PAQ (scale
1 to 5)
15.00
459
44.2
7.1
16.00
307
43.9
7.2
2
11.00
419
3.0
.6
334
2.7
.6
12.00
352
3.0
.7
315
2.8
.5
2
2.6
.4
13.00
1.9
351
38.5
5.7
296
38.8
6.4
339
2.4
.5
.5
310
2.5
.6
4
2.1
.7
2.6
.6
2
3.1
1.4
351
2.4
.6
2.7
.6
296
2.5
.6
14.00
352
2.8
.6
4
2.9
15.00
771
2.8
.6
459
307
16.00
42.5
22
23
Tables 2a and 2b. The estimated parameters (means SE) from the multilevel regression analyses of predicted VO2max (ml∙kg-1∙min-1) using
Models 1 and 2.
Fixed effects
Fixed factors
2a Model 1
95%CI
2b Model 2
95%CI
Parameter
Estimate
SE
Lower
Upper
Estimate
SE
Lower
Upper
Constant (a)
36.91
1.19
34.58
39.24
32.69
1.28
30.18
35.20
Sex
Girls (a)
-1.38
0.76
-2.87
0.11
-1.59
0.76
-3.08
-0.10
PAQ
PAQ (b)
3.83
0.80
2.26
5.39
3.95
0.81
2.37
5.52
Girls*PAQ (b)
-1.01
0.27
-0.60
-0.06
-0.97
0.27
-0.62
-0.08
PAQ2 (c)
-0.33
0.14
-1.54
-0.48
-0.35
0.14
-1.50
-0.44
Age-13.96 years (d)
-0.20
0.05
-0.30
-0.11
-0.16
0.05
-0.27
-0.06
Girls*(Age-13.96) (d)
-0.61
0.07
-0.75
-0.47
-0.60
0.07
-0.75
-0.46
Underweight (a)
2.97
0.47
2.06
3.88
Normal (a)
2.92
0.31
2.31
3.52
Overweight (a)
1.33
0.32
0.71
1.94
2-4h (a)
0.26
0.16
-0.05
0.57
>4h (a)
-1.17
0.23
-1.63
-0.72
IMD rank
0.00008
0.00002
0.00004
0.00012
Age
Weight status
Screen Time
Deprivation
24
Deviance
- 2 Log Likelihood
25445.4
(df=7)
24717.7
(df=13)
Random variation
Variances
Level 2
(between participant)
18.39
0.86
15.41
0.76
Level 1
(within participant)
13.17
0.35
13.27
0.36
Values are means standard errors of estimate (SE) plus 95% Confidence Intervals (CI). VO 2max was expressed in ml.kg-1.min-1. The reference
or baseline group were boys (‘a’ in Model1), and obese boys who spend less than 2 hours per day on screen time (‘a’ in Model 2) and other groups
compared with it, indicated by (a). Age was centred about the mean age=13.96 years. IMD rank was incorporated into model 2 with rank 1 being
most deprived area and rank 32,844 being the least deprived area.
25