Jump starting skeletal health: A 4-year longitudinal study assessing the effects of jumping on skeletal development in pre and circum pubertal children

Article (PDF Available)inBone 42(4):710-8 · May 2008with86 Reads
DOI: 10.1016/j.bone.2008.01.002 · Source: PubMed
Abstract
Evidence suggests bone mineral increases attributable to exercise training prior to puberty may confer a significant advantage into adulthood. However, there is a dearth of supportive prospective longitudinal data. The purpose of this study was to assess bone mineral content (BMC) of the whole body (WB), total hip (TH), femoral neck (FN) and lumbar spine (LS) over four years in pre-pubertal boys and girls following a 7-month jumping intervention. The study population included 107 girls and 98 boys aged 8.6+/-0.88 years at baseline. Participating schools were randomly assigned as either intervention or control school. Children at the intervention school (n=101) participated in a jumping intervention embedded within the standard PE curriculum. The control school children (n=104) had similar exposure to PE without the jumping intervention. BMC was assessed by DXA at baseline, at 7-month post intervention, and annually thereafter for three years totaling 5 measurement opportunities. Multi-level random effects models were constructed and used to predict change from study entry in BMC parameters at each measurement occasion. A significant intervention effect was found at all bone sites. The effect was greatest immediately following the intervention (at 7 months) but still significant three years after the intervention. At 7 months, intervention participants had BMC values that were 7.9%, 8.4%, 7.7% and 7.3% greater than the controls at the LS, TH, FN and WB, respectively (p<0.05), when the confounders of age, maturity and tissue mass were controlled. Three years after the intervention had concluded the intervention group had 2.3%, 3.2%, 4.4% and 2.9% greater BMC than controls at the LS, TH, FN and WB respectively (p<0.05), when the confounders of age, maturity and tissue mass were controlled. This provides evidence that short-term high impact exercise in pre-puberty has a persistent effect over and above the effects of normal growth and development. If the benefits are sustained until BMC plateaus in early adulthood, this could have substantial effects on fracture risk.

Figures

Figure
Jump starting skeletal health: A 4-year longitudinal study assessing the
effects of jumping on skeletal development in pre and
circum pubertal children
Katherine Gunter
a,
, Adam D.G. Baxter-Jones
b
, Robert L. Mirwald
b
, Hawley Almstedt
c
,
Arwen Fuller
a
, Shantel Durski
a
, Christine Snow
a
a
Oregon State University, Bone Research Laboratory, Department of Nutrition and Exercise Sciences, USA
b
University of Saskatchewan, College of Kinesiology, Canada
c
Loyola Marymount University, Department of Natural Science, USA
Received 24 July 2007; revised 19 December 2007; accepted 5 January 2008
Available online 18 January 2008
Abstract
Introduction: Evidence suggests bone mineral increases attributable to exercise training prior to puberty may confer a significant advantage into
adulthood. However, there is a dearth of supportive prospective longitudinal data. The purpose of this study was to assess bone mineral content
(BMC) of the whole body (WB), total hip (TH), femoral neck (FN) and lumbar spine (LS) over four years in pre-pubertal boys and girls following
a 7-month jumping intervention.
Methods: The study population included 107 girls and 98 boys aged 8.6 ± 0.88 years at baseline. Participating schools were randomly assigned as
either intervention or control school. Children at the intervention school ( n = 101) participated in a jumping intervention embedded within the
standard PE curriculum. The control school children (n =104) had similar exposure to PE without the jumping intervention. BMC was assessed by
DXA at baseline, at 7-month post intervention, and annually thereafter for three years totaling 5 measurement opportunities. Multi-level random
effects models were constructed and used to predict change from study entry in BMC parameters at each measurement occasion.
Results: A significant intervention effect was found at all bone sites. The effect was greatest immediately following the intervention (at 7 months)
but still significant three years after the intervention. At 7 months, intervention participants had BMC values that were 7.9%, 8.4%, 7.7% and 7.3%
greater than the controls at the LS, TH, FN and WB, respectively (p b 0.05), when the confounders of age, maturity and tissue mass were
controlled. Three years after the intervention had concluded the intervention group had 2.3%, 3.2%, 4.4% and 2.9% greater BMC than controls at
the LS, TH, FN and WB respectively (p b 0.05), when the confounders of age, maturity and tissue mass were controlled.
Conclusions: This provides evidence that short-term high impact exercise in pre-puberty has a persistent effect over and above the effects of normal
growth and development. If the benefits are sustained until BMC plateaus in early adulthood, this could have substantial effects on fracture risk.
© 2008 Elsevier Inc. All rights reserved.
Keywords: Detraining; Growth and development; Exercise intervention; Children; Mechanical loading; Peak bone mass
Introduction
Osteoporosis is a disorder in which loss of bone strength
leads to fragility fractures that most commonly occur among
older individuals. Skeletal fragility can result from numerous
mechanisms, including a failure to produce a skeleton of op-
timal mass and strength during growth [1]. Increasing evidence
supports exercise during growth as having the greatest poten-
tial to reduce osteoporosis risk later in life [24] and several
Bone 42 (2008) 710 718
www.elsevier.com/locate/bone
The work reported in this manuscript was supported by National Institutes
of Health RO1 AR45655-08.
Corresponding author. Bone Research Laboratory, WB 13, NES Department,
Oregon State University, Corvallis, OR 97331, USA. Fax: +1 541 737 1341.
E-mail addresses: kathy.gunter@oregonstate.edu (K. Gunter),
baxter.jones@usask.ca (A.D.G. Baxter-Jones), b.mirwald@shaw.ca
(R.L. Mirwald), halmstedt@lmu.edu (H. Almstedt), Arwen.Fuller@utah.edu
(A. Fuller), shantel.stark@oregonstate.edu (S. Durski),
Christine.snow@oregonstate.edu (C. Snow).
8756-3282/$ - see front matter © 2008 Elsevier Inc. All rights reserved.
doi:10.1016/j.bone.2008.01.002
researchers have reported positive effects of physical activity on
the whole body [5], lumbar spine [6] and hip [7,8] in growing
children.
Though exercise has been shown to enhance bone mass at all
ages, as children move further beyond puberty, the potential
for exercise to substantially influence the skeleton seems to
diminish [912]. Data from longitudinal studies of bone growth
and development indicate the greatest bone mineral accrual
occurs during the two years before and the two years following
accelerated linear growth or peak height velocity (PHV) [5,13].
The timing of PHV varies considerably between individuals
and systematically between boys and girls. Most studies show
girls achieving PHV between 1112 and boys slightly later
between 13 and 14 years of age [9,14,15]. The most effective
exercise interventions in children have been delivered prior to
or within this so called window of opportunity, when children
are entering and moving through puberty. Data from both pre
and early-pubertal exercise intervention studies both show
similar positive effects [6,11,1620].
Clearly early intervention is important and beginning exer-
cise just prior to, or during puberty is optimal. Beyond the
timing of the stimulus, the nature of the stimulus must be
carefully considered. Children who participate in activities such
as racquet sports [21,22], gymnastics [23,24], weight training
[25] and ballet [26] all exhibit greater bone mass at the loaded
sites compared to control subjects. These activities all confer
relatively large forces to the skeleton. For example, landing
forces in gymnastics have been measured at 1020 times body-
weight in young girls [27]. In comparison, non-weight bearing
activities such as swimming show no or very little effect on
bone in pre and early pubertal girls [24,28]. Thus, for optimal
skeletal benefits, the exercise stimulus should be weight-bear-
ing in nature, characterized by sufficiently large forces , and
occur in pre- or early puberty. Finally, the benefits must be
sustained through growth to affect osteoporosis risk in later life.
We previously reporte d a 3.5% and 4.5% greater increase
in bone mineral content (BMC) at the spine and hip respectively
in pre-pubertal boys and girls who participated in a 7-month
jumping intervention compared to control subjects [6]. Follow-
ing a year-long detraining period, the changes induced at the
hip by the jumping intervention were retained [7]. The im-
plications suggest that a bone mineral increase attributable
to exercise training prior to puberty may confer a signifi cant
advantage into adulthood. However, long-term follow-up is
necessary to determine the specific benefits. Additionally, the
former intervention was delivered by research personnel du-
ring general class time. In order to evaluate a real-world effect,
it is important to design programs that can be delivered by
practitioners in the field.
The purpose of the present study was to assess bone out-
comes in a new cohort of pre- and early pubertal boys and girls
following a 7-month school-based jumping intervention and
three subsequent years of detraining (no jumping intervention).
The intervent ion was implemented as part of the physical
education (PE) curriculum in two Corvallis, Oregon elementary
schools and delivered by physical education specialists during
PE class time. We hypothesized that children who participated
in the jumping intervention would accrue more BMC compa-
red to controls and therefore be at a considerable advantage in
optimizing peak bone mass accrual.
Materials and methods
Study design and participants
The subjects for this analysis were drawn from the BUGSY study (Building
Growing Skeletons in Youth), an ongoing longitudinal study of bone mineral
accretion in growing children. This study was initiated in the fall of 2002 and
participants were recruited from two participating elementary schools in Corvallis,
Oregon. At baseline children belonged to one of four age cohorts [7 (n=26), 8
(n=65) 9 (n=75) and 10 (n = 39) years of age]. These included 107 girls and 98
boys. The participating schools were randomly determined as either the
intervention (n= 101) or non-intervention (n =104) school. Researchers collecting
data on the primary outcome variables were blinded as to which school partici-
pants attended. The intervention lasted seven calendar months. Participants were
assessed at baseline and reassessed at 7 months (following the intervention) and
then annually thereafter for three consecutive years, at 19, 31 and 43 months
respectively. Each child provided their assent to participate and each child's parent
provided informed consent. Additionally, each parent and child pair completed
health history, food frequency and physical activity questionnaires as well as a
maturity assessment at each measurement interval. For inclusion in the multilevel
analysis subjects had to have complete data at each measurement occasion. At
baseline, 101 intervention children (n =47 boys; n = 54 girls) and 104 controls
(n=51 boys; n= 53 girls) had complete data (Table 1). At 43 months, 54% (n =24
boys; n = 32 girls) of intervention and 55% (n =27 boys; n =29 girls) of non-
intervention subjects remaining in the study had complete data (Table 2).
Anthropometric measures
Standing height, sitting height and weight were assessed at each visit.
Standing and sitting height were measured to the nearest 0.1 cm using a wall-
mounted stadiometer. A standard protocol outlined by Martin et al. [29] was
used to assess sitting height. Weight was measured to the nearest 0.1 kg using an
electronic weighing scale. Measurements were taken twice unless there was a
discrepancy greater than 0.4 cm (height and sitting height) or 0.4 kg (weight),
then a third measurement was taken. Recorded values were the average of two
measurements or the median of three. Leg length was calculated by subtracting
sitting height from standing height. For the analysis a change in height from
study entry (Δheight) was calculated.
Biological maturity
Peak height velocity (PHV) is a commonly used biological parameter in
growth studies which allows subjects to be aligned at comparable biological
rather than chronological ages [14]. It is also the only sexual maturational
landmark which occurs in both boys and girls and thus allows gender com-
parisons at the same maturational age to be performed [30]. In the present paper
bone measurements are considered in terms of time before and after PHV [14,31].
Since the majority of subjects had yet to reach PHV (average age of PHV is 12
in girls and 14 in boys) by the end of the study, age of attainment of PHV was
estimated by applying gender specific anthropometric prediction equations [32].
These equations use markers of somatic growth to predict how far a child is, in
years, from reaching PHV. All children demonstrate the same pattern of somatic
growth, just at different time periods and different tempos. Specifically, leg
length velocity peaks prior to peak height velocity and trunk length velocity
peaks after the peak in height velocity has occurred. The equations use these
growth characteristics, incorporating current height and sitting height para-
meters, adjusted for age and weight, to predict when peak height velocity will
occur. The equations were developed using data from a longitudinal study of
children's growth and then verified in two other similar longitudinal studies (31).
The prediction equation was applied at each measurement occasion. As subjects
approach PHV, the prediction increases in accuracy. Thus for the present analysis
age of PHV was predicted at the subject's last measurement occasion. Subjects
711K. Gunter et al. / Bone 42 (2008) 710718
were then classified as either pre or post PHVat each measurement occasion (Pre
PHV Maturity = 0, Post PHV Maturity =1).
Physical activity and nutritional assessments
Physical activity was assessed at each visit by parent and child using a self-
reported physical activity questionnaire developed for children and adolescents
[33]. This questionnaire was designed to obtain information about the amount of
time spent in both weight bearing and non-weight bearing physical activity over the
previous 12 months. Physical activity information is partitioned by general acti-
vity and participation in organized sports. For the present analysis two variables
were created; minutes of weight bearing activity per week (Weight Bearing), a
continuous variable at each measurement occasion, and sports activities, a cate-
gorical variable (where no sport activities, Sport Activity= 0 and 1 or more sport
activities, Sport Activity =1).
Dietary intake was assessed using the Harvard Medical School Youth Diet
Survey developed for children and adolescents between the ages of 918 years
[34]. This food frequency questionnaire is designed to be self-administered;
however, to improve accuracy, the questionnaire was filled out by parent and
child together [35]. A researcher familiar with the diet survey was available to
answer questions regarding the classification of foods and serving sizes. Visual
aids were also available to help participants and their parents to determine
appropriate responses. Completed food surveys were sent to Harvard Medical
School for analysis.
Bone mineral assessment
Bone mineral content (BMC; g) of the total body (TB), lumbar spine (LS), left
proximal femur (total hip [TH], femoral neck [FN], trochanter [TR]) were
evaluated using dual-energy X-ray absorptiometry (Hologic QDR 4500A; Hologic
Inc., Waltham, MA, USA). Bone measurements of the hip and lumbar spine have
an in-house precision error of 11.5% and the coefficient of variation (CV) for total
body BMC has been repeatedly determined by the manufacturer to be less than 1%
(Hologic, Inc.). Trained and qualified technicians blinded to the group status of the
participants, conducted all measurements. Over the four-year period during which
these data were collected, the Bone Research Laboratory acquired several upgrades
to its DXA software. During 2005 and 2006, all scans from each of the five
measurement occasions were re-analyzed using the latest software version
(Hologic QDR software, version 12.3, Delphi A) to ensure data accuracy. Two
researchers blinded to the group status of the participants were responsible for re-
analyzing all of the scan data. Spine and whole body scans were assigned to one
member of the research team and a second researcher analyzed all hip scans. Spine
and anthropometric phantoms were scanned daily and weekly, respectively, to
maintain quality assurance of the QDR 4500A. In addition to whole body BMC,
whole body lean and fat mass (g) were also measured.
After 3.6 years (43 months) of data collection, data were utilized from a total
of 857 DXA scans. These scans were from 199 individuals who were measured
on 2 or more occasions.
School-based exercise intervention
The specifics of the exercise intervention were similar to that which has been
reported elsewhere [6]. The current intervention differed in two ways: 1) There
were two participating schools; one serving as the intervention school and the
other as the non-intervention (control) school, and 2) the intervention was
delivered by physical education specialists during regularly scheduled PE clas-
ses, three times each week, excepting holidays. During the first two months,
children were introduced to the program and progressively trained to reach the
maximum of 100 jumps per session. Children averaged 90100 jumps per
session during the remaining 7 months of the school year. The average length of
Table 1
Characteristics at baseline
Intervention Non-intervention
Male (n = 47) Female (n = 54) Total (n= 101) Male (n = 51) Female (n= 53) Total (104)
Race (W/A/B/MR) 40/4/0/3 43/4/1/6 83/8/1/9 40/0/1/10 47/3/0/2 87/3/1/13
Age (years) 8.7 (0.9) 8.7 (0.8) 8.7 (0.9) 8.7 (0.8) 8.5 (0.9) 8.6 (0.9)
Biological age
Tanner stage I/II/III 45/2/0 48/6/0 93/8/0 51/0/0 50/2/1 101/2/1
Height (cm) 133.9 (6.9) 133.7 (8.6) 133.8 (7.8) 133.2 (6.8) 131.4 (7.8) 132.3 (7.4)
Weight (kg) 32.0 (7.7) 31.6 (8.5) 31.8 (8.1)
a
29.4 (5.0) 29.4 (6.8) 29.4 (5.9)
a
Relative lean mass (%) 75.5 (9.2)
d
72.6 (7.5)
d
74.9 (8.4) 78.6 (5.6)
d
72.6 (7.4)
d
75.6 (7.2)
Relative fat mass (%) 24.5 (9.2)
e
27.4 (7.5)
e
26.1 (8.4) 21.4 (5.6)
e
27.4 (7.4)
e
24.4 (7.2)
Bone variables (grams)
Whole body BMC 1137.2 (217.2) 1102.0 (224.0) 1116.7 (220.7)
b
1089.6 (159.4) 1032.5 (153.6) 1061.6 (158.4)
b
AP spine BMC 23.4 (4.0) 23.1 (4.6) 23.2 (4.3) 23.3 (3.3) 21.4 (3.3) 22.4 (3.4)
Total hip BMC 13.5 (3.5) 13.0 (3.8) 13.2 (3.7) 13.4 (2.9) 12.0 (2.5) 12.7 (2.8)
Femoral neck BMC 1.9 (0.5) 1.8 (0.4) 1.8 (0.4)
c
2.1 (0.5) 1.9 (0.5) 2.0 (0.5)
c
Trochanter BMC 3.0 (1.1) 3.2 (1.2) 3.1 (1.2) 2.9 (0.9) 2.9 (0.8) 2.9 (0.8)
Dietary intakes
Total calories (kcal) 2248 (596) 2101 (494) 2169 (545) 2204 (493) 2089 (553) 2147 (524)
Total calcium (mg) 1377 (455) 1368 (471) 1372 (461) 1365 (386) 1248 (426) 1307 (407)
Vitamin D (IU) 360 (170) 374 (177) 368(174) 348 (150) 301 (145) 325 (149)
Physical activity
Wt. bearing (min/wk) 276 (217) 257 (348) 266 (294) 372 (382) 199 (218) 286 (321)
PACER score 22.7 (12.0) 16.2 (6.9) 19.2 (10.0) 20.4 (9.6) 20.4 (10.8) 20.4 (10.2)
Team sports
% reporting none 12.8 37.5 26.2 19.6 45.1 32.4
% reporting 1 87.2 62.5 73.8 80.4 55.9 67.6
Values Mean (SD).
PACER Score is a measure of fitness, higher scores reflect better fitness [47].
Intervention greater than non-intervention,
a
p b 0.05,
b
p b 0.05.
Non-intervention greater than intervention,
c
p b 0.05.
Males greater than females,
d
p b 0.05.
Females greater than males,
e
p b 0.05.
712 K. Gunter et al. / Bone 42 (2008) 710718
follow-up during year one was 7-months. Testing occurred primarily between
mid October 2002 and mid May 2003. Both of the participating schools followed
a similar four-part lesson plan format in their PE classes [36]. Each 30-minute
class session included the following components: 1) warm up activity, 2) fitness
development, 3) lesson focus, 4) closing activity. The only difference between the
two programs was the inclusion of the jumping which was incorporated into the
fitness development component at the intervention school. University under-
graduate and graduate physical education teacher education students were pre-
sent in both intervention and control classrooms throughout the intervention to
help monitor jumps and assist in various aspects of the study. Compliance to the
intervention was approximately 93% with children attending an average of 67 ±
4.1 of the 72 jumping sessions during the intervention period.
Ground reaction forces
Peak ground reaction forces (GRF) have been assessed previously in two
separate studies [6] (in house, unpublished). The first study utilized a sample of
children who participated in a previous jumping intervention [6]. Participants
performed 100, two-footed jumps onto a 40 cm×60 cm force plate (model 9281B;
Kistler Instrument Corp., Amherst, NY, USA). Average peak GRF was appro-
ximately 8.5 times bodyweight (BW). The second study used a sub-sample of
subjects from the present study, who performed the same two-footed jumps
landing with one leg on each of two 40× 60 cm force plates (Bertec, Columbus,
OH) mounted flush with the floor. This allowed us to determine the GRF attri-
buted to each leg individually. Average peak GRF per leg was approximately
3.5 times BW. Thus, given the same jumping protocol and jump height, and
similarly aged intervention participants, we expect the GRF achieved during the
exercise intervention in this study were approximately 34 times BW for each leg.
Statistical analysis
Descriptive analyses were performed using SPSS software version 14.0
for Windows (SPSS Inc.). Values are reported as means (+/ SD), a level of
significance of p b 0.05 was used, and all statistical tests were two-tailed.
For longitudinal analyses, hierarchical (multilevel) random effects models
were constructed using a multilevel modeling approach as previously reported
(MlwiN version 1.0, Multilevel Models Project; Institute of Education, Uni-
versity of London, UK) [37]. Detailed description of multilevel modeling
as applied to bone data has been previously reported and complete details of
this approach are presented elsewhere [38]. In brief, hierarchical models were
developed for analyzing hierarchically structured data. In the present example
indices of bone accrual are measured repeatedly in individuals (level 1 of the
hierarchy) and between individuals (level 2 of the hierarchy). Analysis models
that contain variables measured at different levels of the hierarchy are known as
multilevel regression models.
For each bone site the change in BMC (Δ) from baseline was calculated. For
the multilevel analysis subjects therefore required a minimum of two measure-
ments, 199 out of the 205 subjects fulfilled this requirement. An individual
measured at study entry, 7 months, and then yearly for the following three years,
had 4 change (Δ) scores. Since the models can handle missing data, if data is
missing at random, subjects could miss an entire visit and still be included in the
analysis (e.g. assessed at baseline, 7 months, 19-months, missed the 31-month visit,
and returned at 43-months). However, for a particular visit to be included, all
variables in the model had to be measured and complete for that particular
Table 3
Number of subjects with DXA scans at each measurement occasion by
intervention group and gender
Intervention Non-intervention
Male Female Male Female Total
Baseline 48 56 50 51 205
7 months 46 55 48 50 199
19 months 43 47 45 46 181
31 months 39 42 35 44 160
43 months 24 32 27 29 112
Total 198 231 206 222 857
Table 2
Characteristics at 43 months
Intervention Non-intervention
Male (n = 24) Female (n =32) Total (n = 56) Male (n =27) Female (n = 29) Total (n = 56)
Race (W/A/B/MR) 21/2/0/1 29/0/1/2 50/2/1/3 19/0/0/8 27/1/0/1 46/1/0/9
Age (years) 12.5 (1.0) 12.2 (0.70) 12.3 (0.85) 12.3 (0.88) 12.0 (0.92) 12.1 (0.92)
Biological age
Tanner stage I/II/III/IV/V 4/5/8/6/1 7/9/7/6/3 11/14/15/12/4 7/5/8/6/1 5/7/4/12/1 12/12/12/18/2
Height (cm) 158.7 (8.6) 154.6 (7.7) 156.4 (8.3) 155.0 (8.3) 153.3 (9.5) 154.1 (8.9)
Weight (kg) 50.4 (13.8) 44.8 (8.4) 47.2 (11.3) 46.5 (11.0) 44.9 (11.8) 45.7 (11.3)
Relative lean mass (%) 74.9 (11.1) 74.4 (7.0) 74.7 (8.9) 77.1 (7.6) 74.3 (8.3) 75.6 (8.0)
Relative fat mass (%) 25.1 (11.1) 25.6 (7.0) 25.3 (8.9) 22.9 (7.6) 25.7 (8.3) 24.4 (8.0)
Bone variables (grams)
Whole body BMC 1851.1 (462.7) 1707.8 (347) 1770.0 (403.6) 1718.2 (337.3) 1693.4 (383.4) 1705.3 (358.9)
AP spine BMC 38.1 (11.4) 38.2 (11.6) 38.2 (11.4) 35.8 (8.7) 37.6 (11.4) 36.8 (10.1)
Total Hip BMC 28.5(9.2)
b
24.6 (6.2)
b
26.3 (7.8) 25.7 (7.5)
b
24.3 (6.1)
b
24.9 (6.8)
Femoral neck BMC 3.8 (0.96)
c
3.3(0.74)
c
3.5 (0.88) 3.6 (0.77)
c
3.3 (0.71)
c
3.4 (0.75)
Trochanter BMC 7.8 (3.3) 7.0 (2.1) 7.3 (2.7) 7.3 (2.9) 6.8 (2.1) 7.0 (2.5)
Dietary intakes
Total calories (kcal) 1986(499)
b
1924 (548)
b
1951 (523) 2223 (525)
b
1864 (568)
b
2037 (572)
Total calcium (mg) 1261 (360) 1233 (500) 1245 (441) 1264 (337) 1113 (416) 1185 (384)
Vitamin D (IU) 334 (135) 351 (188) 343 (166)
a
304 (156) 261 (140) 282 (149)
a
Physical activity
Wt. bearing (min/wk) 331 (187)
d
246 (163)
d
283 (177) 442 (274)
d
172 (123)
d
302 (248)
Team sports
% reporting none 29 22 25 30 24 27
% reporting 17178 757076 73
Values mean (SD).
Intervention greater than non-intervention,
a
p = 0.054.
Males greater than females,
b
p b 0.05,
c
p b 0.01,
d
p b 0.001.
713K. Gunter et al. / Bone 42 (2008) 710718
measurement occasion. Of the original sample 97%, 88%, 78% and 55% provided
eligible data after 7, 19, 31 and 43 months respectively (Table 3).
The following additive, multilevel regression models were adopted to
describe the Δ in site specific BMC parameter from study entry.
y
ij
¼ a
j
þ b
j
x
ij
þ k
1
z
ij
þ N k
n
z
ij
þ e
ij
ð1Þ
where: y is the ΔBMC (g) parameter on measurement occasion i in the j-th
individual; α
j
is the constant for the j-th individual; β
j
x
ij
is the slope of the ΔBMC
(g) parameter with time from study entry (years from start (i.e. time from fall 2002))
for the j-th individual; and k
1
to k
n
are the coefficients of various explanatory
variables (e.g. Baseline BMC, Sex, Race, Maturity, Δheight, ΔLean Mass, ΔFat
Mass, Weight Bearing, Sport Activity) at assessment occasion i in the j-th
individual, ɛ
ij
the level-1 residual (within individual variance) for the i-th as-
sessment of the ΔBMC (g) parameter in the j-th individual. Models were built in a
stepwise procedure, that is, predictor variables (κ-fixed effects) were added one at a
time, and likelihood ratio statistics were used to judge the effects of including
further variables. Predictor variables (κ) were accepted as significant if the
estimated mean coefficient (E) was greater than twice the standard error of the
estimate (SEE) that is, p b 0.05. If the retention criteria were not met, the predictor
variable was discarded. To allow for the non-linearity of growth, years from start
power functions were introduced into the linear models. Once confounders of
growth, development and physical activity were controlled an intervention variable
(Intervention Group) was added. This step allowed us to examine whether
participation in the intervention contributed to significant increases in BMC over
time. Again these variables were retained if the estimated mean coefficient (E) was
greater than twice the standard error of the estimate (SEE) that is, p b 0.05. Analysis
was performed by individuals blinded to the intervention coding.
The models were then used to predict change from study entry in BMC
parameters at each visit (ΔBMC). The numbers are presented as percentage
contribution of each predictor variable to the total ΔBMC value at different
visits. Mean values for each of the predictor variables at each measurement
occasion were used.
Results
Subject characteristics
The development of stature and weight were within normal
reference standards ranges for all chronologic al ages in both
genders, for both treatment and control groups over the four
years of follow-up. No differences were found between the
groups in either gender, for height and body mass develop-
ment. At baseline, drop-outs ve rsus those retained for four
years were not different in age, height, weight, bone and soft
tissue parameters, physical activity or nutrient intakes (p N 0.05).
There were no differences in race and gender distrib utions
between subjects who dropped out and those who were retained
in the study (p N 0.05).
Table 4
Multilevel regression models for change from study entry in whole body, lumbar spine AP aligned by years from study entry
Variables ΔBone Mineral Content (BMC)
(a) ΔWhole body (b) ΔLumbar LSAP
Random Level 1 (within individuals) Level 1 (within individuals)
Constant 800± 73 1.45 ±0.13
Level 2 (between individuals) Level 2 (between individuals)
Constant Years from start centered Constant Years from start centered
Constant 1737± 218 1094 ± 139 3.55 ±0.45 2.37 ± 0.29
Years from start centered 1094± 139 712 ± 106 2.37 ±0.29 1.50 ± 0.22
Fixed Estimates Estimates
Constant 30.2±15.0 0.88 ± 0.18
Years from start centered 35.6±7.7
Years from start centered
2
8.4± 1.5 0.27 ±0.06
Entry BMC 0.02±0.01
Entry Weight ––
Sex 8.9± 4.1 0.37± 0.15
Race ––
Maturity 43.6+7.3 2.44 +0.31
ΔHeight 8.8± 1.4 0.28 ± 0.04
ΔFM 7.1± 0.9 0.14 ± 0.04
ΔLM 22.6±1.5 0.81 ±0.06
Weight bearing ––
Sport activity ––
CA ––
Intervention 15.3±4.0 0.34 ±0.15
Interactions
InterventionSex ––
InterventionMaturity ––
Fixed effect values are Estimated Mean Coefficients± SEE (Standard Error Estimate) (ΔBMC g from study entry).Random effects values Estimated Mean Variance ±
SEE [years
2
].Years from Start Centered is Years from visit 2. Entry BMC BMC at baseline (g); Entry Weight Weight at baseline (kg); Sex (0 = Male,
1 = Female); Race (0=White, 1 = Non-white); Maturity (0 = pre-PHV, 1 = post-PHV); ΔHeight change in height from study entry (cm),ΔFM change in fat mass
(kg) from study entry; ΔLM change in lean mass (kg) from study entry; Weight Bearing weight bearing activities (minutes per week); Sport Activity (0 = no
sport activities, 1 = 1 or more sports activities); CA calcium intake (mg); Intervention (0= non- intervention, 1 =intervention). Intervention
Sex =Intervention
(0 = non-intervention, 1 = intervention) × Sex (0 = Male, 1 = Female); Intervention
Maturity =Intervention (0 = non-intervention, 1 = intervention) × Maturity (0 = pre-
PHV, 1 = post-PHV); p b 0.05 (mean N 2
SEE). = variables non significant and removed from the model.
714 K. Gunter et al. / Bone 42 (2008) 710718
At baseline, when the genders were combined, intervention
children were heavier than control children (pb 0.05), had greater
whole body bone mineral content (BMC) (pb 0.05) and lower
femoral neck BMC (pb 0.05) (Table 1). Age, reported physical
activity levels, sport participation and nutrient intakes were not
different between groups (pN 0.05). At 43-months there were no
differences between the intervention and control subjects in
age, ht., weight, total caloric intake, calcium intake, or reported
physical activity and sport participation, however intervention
subjects reported higher mean vitamin D intakes than controls
(343+ 166 IU vs. 282+ 149 IU, respectively; p =0.05) (Table 2).
Hierarchical analyses
Because the effects of growth, maturation, physical activity
and diet affect the development of BMC, these confounders were
controlled before treatment effects were investigated. Tables 4
and 5 summarize the results of such an analysis for changes in
whole body, lumbar spine and hip BMC from study entry. Years
from study start was centered around the middle visit, i.e. visit 2,
and thus allowed the intercept for all models to be in the middle of
the data rather than at study start. This was done because the
analysis focuses on change scores, and study start (visit 1) was not
directly modeled because subjects would have no change score at
this time point. Years from Start Centered was added as both a
fixed and random coefficient. The random effects coefficients
describe the two levels of variance [within individuals (level 1 of
the hierarchy) and between individuals (level 2 of the hierarchy)].
For all five models, the significant variances at level 1 of the
models indicate that ΔBMC was increasing significantly within
individuals over the 4 year observation period (E N 2
SEE)
pb 0.05). The between individuals variance matrix (level 2) for
each model indicated that individuals had significantly different
ΔBMC lines, both in terms of their intercepts (constant/constant,
pb 0.05), and the slopes of their lines (years from start centered/
years from start centered, p b 0.05). The variance of these inter -
cepts and slopes were positively correlated (constant/ years from
start centered, p b 0.05), at all sites. The variance between in-
dividuals was therefore different at different years from start
centered. To shape the individual curves, and thus make the models
non-linear, a second order polynomial of years from start centered
2
was added as a fixed effect. In all models (Tables 4 and 5)race,
calcium intake, sports activity and minutes of weight bearing
activity did not have significant independent effects on ΔBMC.
Table 5
Multilevel regression models for change from study entry for total hip, trochanter and femoral neck aligned by years from study entry
Variables ΔBone Mineral Content (BMC)
(a) ΔTotal Hip (b) ΔHip Trochanter (c) ΔFemo ral Neck
Random Level 1 (within individuals) Level 1 (within individuals) Level 1 (within individuals)
Constant 0.54±0.05 0.11 ± 0.01 0.02±0.001
Level 2 (between individuals) Level 2 (between individuals) Level 2 (between individuals)
Constant Years from start centered Constant Years from start centered Constant Years from start centered
Constant 1.15±0.15 0.66 ±0.09 0.18±0.02 0.10±0.01 0.04± .004 0.01±0.002
Years from start centered 0.66±0.09 0.44 ±0.07 0.10±0.01 0.06±0.01 0.01± 0.002 0.01 ± 0.001
Fixed Estimates Estimates Estimates
Constant –– 0.34±0.07
Years from start centered 0.60±0.11 0.25±0.04 0.15+0.03
Years from start centered
2
0.17±0.04 0.07±0.02 0.02+0.01
Entry BMC 0.08± 0.02 0.10 ± 0.02
Entry weight ––
Sex ––
Race ––
Maturity ––
ΔHeight 0.15± 0.02 0.03 ± 0.01 0.02 ± 0.01
ΔFM ––0.03±0.01
ΔLM 0.56±0.04 0.21±0.02 0.04±0.01
Weight bearing ––
Sport Activity ––
CA ––
Intervention 0.30±0.11 0.10±0.05 0.06±0.02
Interactions
Intervention
Sex ––
Intervention
Maturity –– 0.11 ± 0.04
Fixed effect values are Estimated Mean Coefficients ± SEE (Standard Error Estimate) (ΔBMC g from study entry).Random effects values Estimated Mean Variance± SEE
[years
2
].Years from Start Centered is Years from visit 2. Entry BMC BMC at baseline (g); Entry Weight Weight at baseline (kg); Sex (0 = Male, 1= Female); Race
(0= White, 1= Non-white); Maturity (0=pre-PHV, 1 =post-PHV); ΔHeight change in height from study entry (cm),ΔFM change in fat mass (kg) from study entry;
ΔLM change in lean mass (kg) from study entry; Weight Bearing weight bearing activities (minutes per week); Sport Activity (0=no sport activities, 1=1 or more
sports activities); CA calcium intake (mg); Interve ntion (0 = non- intervention, 1 = intervention). Intervention
Sex = Interve ntion (0 = non-intervention,
1= intervention)× Sex (0= Male, 1 = Female); Intervention
Maturity=Intervention (0= non-intervention, 1= intervention)× Maturity (0= pre-PHV, 1 =post-PHV);
pb 0.05 (mean N 2
SEE). = variables non significant and removed from the model.
715K. Gunter et al. / Bone 42 (2008) 710718
The model for whole body ΔBMC ( Table 4a) indicates that
once sex (girls have 8.9 g less BMC), maturity (if you are post
PHV you have 43.6 g more BMC), intial BMC, Δheight (1 cm
change predicts 8.8 g BMC), Δfat mass (1 kg predicts 7.1 g BMC)
and Δlean mass (1 kg predicts 22.6 g BMC) were controlled, a
significant independent intervention effect was found (p N 0.05)
representing an additional 15.3 g of BMC. To see if the in-
tervention was affected by either maturity status or gender, inter-
actions were added to the models. Neither of these interactions
(Intervention
Maturity; Intervention
Sex) had a significant
independent effect on BMC accrual (pN 0.05). An intervention
effect was also observed at the lumbar spine (LSAP) (Table 4b).
Table 5 shows the coefficients for the total hip, trochanter, and
femoral neck models. These models indicate that initial BMC (at
the total hip and trochanter only), Δheight, and Δlean mass
predict ΔBMC. A significant intervention effect was found at all
sites. At the femoral neck only, there was an independent in-
tervention by maturity interaction (p b 0.05). This indicates that
once the effects of time (years from start), change in stature and
change in lean mass are controlled, the intervention group accrue
an extra 0.06 g of BMC than controls. In addition, intervention
children who are post peak height velocity accrue an extra 0.11 g
of BMC than intervention children who are pre peak height
velocity. Fig. 1 presents the change in BMC (total hip, femoral
neck, whole body, lumbar spine) attributable to the intervention at
each visit and represents the amount of BMC obtained by the
children in the intervention above that which the non-intervention
children accrued in the same time period.
Discussion
Our aim was to study the long-term effects of a high-intensity
jumping program on the growing skele ton. We report that
children who participated in a 7-month school-based jumping
intervention consisting of 200300 jumping repetitions each
week of forces approximating 3.5 times BW (for each leg) had
significantly greater size and maturity-adjusted whole body,
spine, and hip BMC than control children. The intervention
effect is significant for each bone site assessed and at each
measured time point following the intervention (Fig. 1). Though
the effect of the intervention decreases over time, it nevertheless
persists even after controlling for growth and maturation.
Strengths of the study include the interventional, longitudinal
design, a highly specific exercise prescription, a rigorous statistical
approach, and a unique partnership with the local school district.
We confirmed our previous findings, that a single high intensity
impact exercise increases bone mass at the hip in growing child-
ren. Further, we demonstrate that these gains are evident three
years after the impact exercise is stopped. Finally , using a sophis-
ticated statistical approach, we identified individual growth tra-
jectories and were able to control for the independent effects of
growth, maturation and sex. Thus we report BMC gains due to the
intervention independent of normal growth and maturation.
With regard to limitations, while randomizing by school
may be considered a weakness, this approach allowed us to
insert the impact exercise into the regular PE class. Further,
our results are similar to those we previously reported using the
identical exercise prescription in a purely randomized, con-
trolled trial [6]. Between schools randomization facilitated a
partnership with the Corvallis school district where PE teachers
trained by researchers incorporated the jumping intervention
into their curriculum. Nevertheless, the limitation of randomiz-
ing in this way introduces a source of confounding and a
potential to interpret the findings as a school effect. However,
participating schools were within the same school district,
utilized identical curriculum (with the exception of the jumping
protocol), and were separated physically by a distance of 1 mile.
Furthermore, baseline data indicate that children were similar in
demographic characteristics. Thus, we believe that the results
observed are related to the treatment and not to the school.
Finally, due to the two-dimensional nature of the DXA as-
sessment, we did not measure bone geometry. Understanding
mineral distribution is important to understanding bone struc-
tural behavior. Given the diminishing effect of the intervention
on BMC observed in our data, it is possible that the sustained
benefits we observed may disappear before these children reach
skeletal maturity. However, Warden et al. [39] found that struc-
tural and strength benefits from exercise induced loading in
rats persisted even as initial increases in bone mass diminished
[39]. It is plausible that our jumping intervention induced a
structural change that we were not able to identify with DXA.
Future studies should include measures of bone geometry to
determine whether bone structure was affected by the interven-
tion and if so, whether those changes persist.
There are many reports of a positive skeletal response to exer-
cise in pre- and early pubertal children [6,8,1 1, 16,2 0,25,40 ,41].
The sustained effects we observed support the notion that exer-
cise in youth may have the potential to prevent fractures in adult-
hood. This potential is further supported by a recent report of an 8-
year follow-up of our previous intervention study[6] in which we
observed that the effects of a similar 7-month intervention persist
over 8 years[42]. In that study as in this one, the sustained effects are
Fig. 1. Effect of Intervention on Bone Mineral Accrual. Percent change in BMC
in jumpers above that of controls at each bone site [total hip, femoral neck, whole
body, lumbar spine] immediately following the intervention year (7 months), one
year of detraining (19 months), 2 years of detraining (31 months) and 3 years of
detraining (43 months). The intervention participants had between 7% and 8%
greater bone mass than controls immediately following the intervention, and
between 2% and 4% greater bone mass than controls four years later.
Results are
adjusted for entry BMC, ΔHt., Δfat mass, Δlean mass, maturity, and gender, and
are significant for each bone site, at each of the four measured intervals (p b 0.05).
716 K. Gunter et al. / Bone 42 (2008) 710718
attributed to a specific jumping program incorporated into either the
classroom or PE requiring only 10 min per day, 3 days per week,
overasingleschoolyear.Thepotential of including this program
into mandatory PE through the elementary school years could have
large, positive skeletal benefits in future generations.
Several investigations demonstrate the sustained benefits of
physical activity in young children [7,17,43,44]. These studies
suggest persistent effects after one year of detraining [7] and
when the stimulus is maintained [17,44]. Our data suggest
benefits persist for at least 3 years post intervention.
Research suggests environmental stimuli during a critical
window of time permanently affect subsequent structure, function
or developmental schedule of the organism [4547]. During early
life and development the embryo, fetus and infant are relatively
plastic in terms of metabolic function. The effect of environmental
exposure is likely to be more marked than at later ages, and the
influence is more likely to exert a fundamental effect on the
development of metabolic capacity. This has been characterized
as programming. Perhaps exercise during pre- or early puberty
results in a permanent alteration to bone's metabolic capacity. If
so, it is possible that exposure to sufficient mechanical stimuli
during a critical window of pre- or early puberty may provoke a
permanent change in bone metabolism that promotes enhanced
accrual throughout growth. Supporting this notion is a recent
study showing that short-term exercise in growing rats resulted in
lifelong benefits to bone structure and strength. Though we did
not assess bone structure, others have found that impact exercise
induces positive changes in bone structure in association with
increases in bone mass [8,17]. Given that short-term exercise in
youth affects bone mass and structure, and that changes in bone
mass are persistent following three years of detraining, it is
possible that the lifelong benefits from short-term exercise
observed in rats may translate to a human model.
We report a sustained effect on BMC accrual from a simple, high
impact jumping program. If this intervention became a regular
activity within a mandatory physical education curriculum, children
who choose not to engage in sport or physical activity outside of
school would gain skeletal benefit. Further, if the benefits are
sustained into adulthood, effectively increasing peak bone mass,
this could result in reductions in lifetime fracture risk.
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    • "A 3–8 % higher BMC was observed in children who participated in such programs lasting 7–9 months for at least three times per week compared to their peers who did not131415 17]. Taking into account the reported periods of those school-based intervention programs, a dose–response effect of WBE on bone accrual can be suggested [2,121314 18] . This suggestion is partly in line with the current WHO guidelines that recommend bone-strengthening exercises on at least three days a week [20] . "
    [Show abstract] [Hide abstract] ABSTRACT: Background: Physical activity (PA), weight-bearing exercises (WBE) and muscle strength contribute to skeletal development, while sedentary behaviour (SB) adversely affects bone health. Previous studies examined the isolated effect of PA, SB or muscle strength on bone health, which was usually assessed by x-ray methods, in children. Little is known about the combined effects of these factors on bone stiffness (SI) assessed by quantitative ultrasound. We investigated the joint association of PA, SB and muscle strength on SI in children. Methods: In 1512 preschool (2- < 6 years) and 2953 school children (6-10 years), data on calcaneal SI as well as on accelerometer-based sedentary time (SED), light (LPA), moderate (MPA) and vigorous PA (VPA) were available. Parents reported sports (WBE versus no WBE), leisure time PA and screen time of their children. Jumping distance and handgrip strength served as indicators for muscle strength. The association of PA, SB and muscle strength with SI was estimated by multivariate linear regression, stratified by age group. Models were adjusted for age, sex, country, fat-free mass, daylight duration, consumption of dairy products and PA, or respectively SB. Results: Mean SI was similar in preschool (79.5 ± 15.0) and school children (81.3 ± 12.1). In both age groups, an additional 10 min/day in MPA or VPA increased the SI on average by 1 or 2 %, respectively (p ≤ .05). The negative association of SED with SI decreased after controlling for MVPA. LPA was not associated with SI. Furthermore, participation in WBE led to a 3 and 2 % higher SI in preschool (p = 0.003) and school children (p < .001), respectively. Although muscle strength significantly contributed to SI, it did not affect the associations of PA with SI. In contrast to objectively assessed PA, reported leisure time PA and screen time showed no remarkable association with SI. Conclusion: This study suggests that already an additional 10 min/day of MPA or VPA or the participation in WBE may result in a relevant increase in SI in children, taking muscle strength and SB into account. Our results support the importance of assessing accelerometer-based PA in large-scale studies. This may be important when deriving dose-response relationships between PA and bone health in children.
    Full-text · Article · Sep 2015
    • "Following 12 months of sport specific training, the randomisation process will start in each sport group and participants will be divided into two sub-groups to perform a PJT programme as follows: 1) intervention programme groups, (sport + PJT) and 2) sport groups (sport only). It has been shown that 7 to 9 month PJT programmes can effectively improve BMC and/or BMD at different skeletal sites in children and adolescents and to maintain the benefits for 3 years after the intervention [52,87]. Therefore, a progressive PJT (~10 min/day) will be performed by intervention groups 3 to 4 times/week (depending on progression) as shown inTable 1. "
    File · Data · Apr 2015 · BMC Public Health
    • "Following 12 months of sport specific training, the randomisation process will start in each sport group and participants will be divided into two sub-groups to perform a PJT programme as follows: 1) intervention programme groups, (sport + PJT) and 2) sport groups (sport only). It has been shown that 7 to 9 month PJT programmes can effectively improve BMC and/or BMD at different skeletal sites in children and adolescents and to maintain the benefits for 3 years after the intervention [52,87]. Therefore, a progressive PJT (~10 min/day) will be performed by intervention groups 3 to 4 times/week (depending on progression ) as shown in Table 1. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Osteoporosis is a skeletal disease associated with high morbidity, mortality and increased economic costs. Early prevention during adolescence appears to be one of the most beneficial practices. Exercise is an effective approach for developing bone mass during puberty, but some sports may have a positive or negative impact on bone mass accrual. Plyometric jump training has been suggested as a type of exercise that can augment bone, but its effects on adolescent bone mass have not been rigorously assessed. The aims of the PRO-BONE study are to: 1) longitudinally assess bone health and its metabolism in adolescents engaged in osteogenic (football), non-osteogenic (cycling and swimming) sports and in a control group, and 2) examine the effect of a 9 month plyometric jump training programme on bone related outcomes in the sport groups. Methods/Design This study will recruit 105 males aged 12–14 years who have participated in sport specific training for at least 3 hours per week during the last 3 years in the following sports groups: football (n = 30), cycling (n = 30) and swimming (n = 30). An age-matched control group (n = 15) that does not engage in these sports more than 3 hours per week will also be recruited. Participants will be measured on 5 occasions: 1) at baseline; 2) after 12 months of sport specific training where each sport group will be randomly allocated into two sub-groups: intervention group (sport + plyometric jump training) and sport group (sport only); 3) exactly after the 9 months of intervention; 4) 6 months following the intervention; 5) 12 months following the intervention. Body composition (dual energy X-ray absorptiometry, air displacement plethysmography and bioelectrical impedance), bone stiffness index (ultrasounds), physical activity (accelerometers), diet (24 h recall questionnaire), pubertal maturation (Tanner stage), physical fitness (cardiorespiratory and muscular) and biochemical markers of bone formation and resorption will be measured at each visit. Discussion The PRO-BONE study is designed to investigate the impact of osteogenic and non-osteogenic sports on bone development in adolescent males during puberty, and how a plyometric jump training programme is associated with body composition parameters.
    Full-text · Article · Apr 2015
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