1 3

Eur J Appl Physiol (2014) 114:1429–1438DOI 10.1007/s00421-014-2870-5

OrIgInAl ArtIclE

Odd‑impact loading results in increased cortical area and moments of inertia in collegiate athletes

Lee Weidauer · Maggie Minett · Charles Negus · Teresa Binkley · Matt Vukovich · Howard Wey · Bonny Specker

received: 5 December 2013 / Accepted: 4 March 2014 / Published online: 25 March 2014 © Springer-Verlag Berlin Heidelberg 2014

Conclusions load-specific changes resulting in improved measures of bone strength take place in athletes during a competitive season. these changes may result in improved resistance to fractures and stress fractures.

Keywords Bone · Athletes · Female · Stress fracture · PQct

AbbreviationsaBMD Areal bone mineral densityAncOVA Analysis of covarianceAnOVA Analysis of varianceBMc Bone mineral contentDXA Dual-energy X-ray absorptiometryHSA Hip structural analysisMOI Moment of inertiancAA national collegiate athletic associationpQct Peripheral quantitative computed tomographyvBMD Volumetric bone mineral density

Introduction

the bones of collegiate athletes are subjected to different loading environments depending on the sport. Differences in the bone characteristics of athletes have been inves-tigated extensively in cross-sectional studies (Heinonen et al. 1993, 1995; nikander et al. 2005, 2008; taaffe et al. 1997; liu et al. 2003; rantalainen et al. 2011; Weidauer et al. 2012; Haapasalo et al. 2000). However, few studies have investigated bone changes that may take place over the course of a competitive collegiate season and compared these changes to non-athletes during the same time period.

Bone loading consists of both ground reaction forces and muscle forces acting on the bone, and the direction and

Abstract Purpose the purpose of this study was to investigate tib-ial changes in volumetric bone mineral density and geom-etry that take place in athletes from pre- to post-season.Methods Female college athletes (n = 36) and ten con-trols recruited from the student population were included in the study. Participants had their left tibia scanned by pQct at 4, 20, and 66 % of the overall length from the distal end before and after their competitive seasons. Subjects were divided into four groups: non-athlete (controls, n = 10), moderate-impact (cross-country runners, n = 13), high-impact (volleyball and basketball, n = 11), and odd-impact (soccer, n = 12).Results Anterior–posterior and medial–lateral diam-eter increased at the 4 % site in control subjects. In the moderate-impact group, medial–lateral moment of inertia (MOI) increased by 1.2 ± 1.8 (mean ± SD) percent at the 20 % site. In high-impact group, anterior–posterior MOI increased by 1.6 ± 2.0 percent at the 66 % site. In odd-impact group, cortical area (1.4 ± 2.3 %) and cortical thick-ness (1.8 ± 2.8 %) increased at the 20 % site increased, as did the polar MOI (1.8 ± 2.2 %) at the 66 % site.

communicated by Olivier Seynnes.

l. Weidauer (*) · M. Minett · t. Binkley · H. Wey · B. Specker Ethel Austin Martin Program in Human nutrition, South Dakota State University, Box 506, Brookings, SD 57007, USAe-mail: lee.Weidauer@sdstate.edu

c. negus l-3 Applied technologies Inc., 10770 Wateridge circle, Suite 200, San Diego, cA 92121, USA

M. Vukovich Health and nutritional Sciences Department, South Dakota State University, Box 2203, Brookings, SD 57007, USA

1430 Eur J Appl Physiol (2014) 114:1429–1438

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magnitude of these loads varies by sport. Previous stud-ies using dual-energy X-ray absorptiometry (DXA) have reported load-specific differences in areal bone mineral density (aBMD) (Heinonen et al. 1993, 1995; nikander et al. 2005, 2008). Other studies using peripheral quanti-tative computed tomography (pQct) have reported higher cortical volumetric bone mineral density (vBMD) in non-athletes than in athletes (liu et al. 2003; rantalainen et al. 2011; Weidauer et al. 2012). the authors suggested that the lower cortical vBMD in the athletes may have been the result of increased remodeling resulting in increased cortical bone porosity. In addition to the effects of activ-ity on cortical bone, greater trabecular vBMD has been associated with greater levels of physical activity (lorent-zon et al. 2005). greater trabecular vBMD also has been reported in mogul and down-hill skiers than in inactive ref-erents (nikander et al. 2008). loading experienced by ath-letes also may result in greater bone size (liu et al. 2003; nikander et al. 2008; Haapasalo et al. 2000) which may serve a protective role against fractures and stress fractures (Popp et al. 2009; crossley et al. 1999; Beck et al. 2000; giladi et al. 1991; Franklyn et al. 2008). greater cross-sec-tional bone area (nikander et al. 2005) and cortical thick-ness (nikander et al. 2008, 2009) at the hip and periosteal circumference (Weidauer et al. 2012) at the tibia have been reported in athletes as compared to non-athlete controls, and a study of twins reported that long-term physical activ-ity had a positive effect on measures of bone strength (Ma et al. 2009). Based on these studies, it appears as though some adaptations are occurring in athletes, but how bones change throughout a competitive season remains uncertain.

While many cross-sectional studies have reported differ-ences in aBMD, cortical and trabecular vBMD, and geom-etry as a result of participation in sports, few studies have reported changes in bone throughout a competitive season. One study reported a significant loss in whole body bone mineral content (BMc) throughout the course of a com-petitive season in male basketball players as well as during times of intense off-season training (Klesges et al. 1996). However, a study of sedentary subjects reported an increase in trabecular density of the tibia as a result of participation in a 13-week exercise program (Evans et al. 2012). One possible explanation for the differing results seen in these studies is the inherent error within DXA scans. Previous studies have reported inaccuracies in BMD measures using DXA due to changes in body composition (Bolotin et al. 2001, 2003), but no such data exist for pQct. Stress frac-tures are considered to be an overuse injury due to greater bone resorption compared to formation and the inability to repair microdamage. therefore, a logical conclusion is that if athletes are losing bone mass throughout the course of a competitive season this may place them at a greater risk for stress fractures.

cross-sectional studies have reported smaller tibial area (Beck et al. 1996; giladi et al. 1991), lower femur aBMD (Beck et al. 1996), and femoral neck aBMD (Pouilles et al. 1989) in military recruits with stress fractures. In addition, lower spine, femoral neck, and proximal femur aBMD have been reported in female athletes who developed a stress fracture (Myburgh et al. 1990; Bennell et al. 1996). these studies provide evidence of possible associations between aBMD, bone geometry, and the development of stress fractures.

the purpose of this study was to investigate tibial changes in vBMD and bone geometry during a competitive season in female college athletes. We hypothesized that ath-letes participating in high-impact loading sports will have the greatest decrease in cortical vBMD due to increased remodeling. Furthermore, we hypothesized that measures of bone size will increase in high- and odd-impact loading groups due to the greater loads being placed on their bones during a competitive season.

Methods

Subjects

Subjects included 36 national collegiate Athletic Asso-ciation (ncAA) Division 1 female athletes (12 soccer, 13 cross-country runners, 6 volleyball, and 5 basketball). In addition, 13 non-athlete females were recruited from cam-pus to serve as controls. non-athlete controls could not be participating in physical activity more than two times per week or for a total of more than 60 min per week. Partici-pants were evenly distributed among classes (12 freshmen, 12 sophomores, 11 juniors, and 14 seniors). For the analy-sis, subjects were divided into four groups based on loading environment experienced in the different sports: non-ath-lete (controls, n = 13), moderate-impact repetitive loading (cross-country runners, n = 13), high-impact (volleyball and basketball players, n = 11) and odd-impact (soccer players, n = 12). For the purpose of this study, moderate-impact repetitive loading was defined as activity taking place in a forward direction with the majority of the foot-to-ground impacts being moderate-impact. High-impact was defined as activity in which jumping on a hard surface along with a moderate amount of cutting predominates the movement patterns. Odd-impact activity was defined as taking place on a soft, uneven surface and requiring move-ment in all directions.

Procedures

All participants completed two study visits. the first visit took place prior to the beginning of the competitive season

1431Eur J Appl Physiol (2014) 114:1429–1438

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(August and September) and the second visit took place at the end of the season and intense training period (March and April). Visits with control subjects were completed at the same time as the athletes and all visits were separated by 27–31 weeks.

Height was measured to the nearest 0.5 cm using a port-able stadiometer (Seca Model 225) and weight was meas-ured to the nearest 0.1 kg using a digital scale (Seca Model 770). lean and fat mass measurements were obtained by total body dual-energy X-ray absorptiometry (DXA; Apex Version 3.3, Discovery, Hologic, Bedford, MA). lean, fat, and bone mass were obtained using tissue analysis method 1, non-nHAnES reference data included in the software. Our institutional coefficient of variation for DXA outcome measures is less than 1.4 %. Prior to each visit, partici-pants also kept a 72-h diet record to determine vitamin D, calcium, and macronutrient intakes. Diets were analyzed using the Food Processor Software (ESHA research Ver-sion 10.2). Questionnaires were administered to determine whether or not the participants had regular (every 21–35 days) or irregular menstrual periods. 39 participants had regular menstrual periods while 10 had irregular periods. In addition to the information obtained from the athletes, coaches were interviewed to obtain information regard-ing the amount of time the teams were training during the week.

Bone images were obtained at 4, 20, and 66 % of the tibia length from the distal end using the Xct 3000 (Ortho-metrix, Inc.). Voxel size was set to 0.5 mm at a speed of 20 mm/s. tibia length was measured using a segmometer (rosscraft) as the total distance between the medial con-dyle and the medial malleolus of the tibia and the meas-urement was entered into the software program prior to the start of imaging. A scout view scan was obtained to place a reference line at the endplate of the tibia and the software then located the slices to image at 4, 20, and 66 % of the tibia length from the distal end (Fig. 1). Our institution’s

coefficient of variation for the Xct 3000 bone outcomes ranges from 1 to 3 %. All images were screened for motion artifacts and analyzed using Bone Alignment and Measure-ment software (BAMpack:l-3 AtI, San Diego, cA). the BAMpack software allows for the analysis of six equally spaced sectors around the cortical shell.

the 4, 20, and 66 % slices were chosen for specific rea-sons. the 4 % site was chosen to represent an area that is predominantly trabecular bone, while the 20 % slice was selected because it contained primarily cortical bone and based on information from our sports medicine staff the majority of tibial stress fractures in our athletes were occurring in the distal third of the tibia. Finally, the 66 % slice was selected to allow us to determine muscle cross-sectional area of the lower leg. While previous studies have reported cross-sectional effects of training on these param-eters, our study used information from all three sites to help us understand the effects of different types of physical activity on changes to trabecular bone, cortical bone, and muscle throughout a competitive season. these data add to the understanding of why some people develop stress frac-tures and some do not.

Analysis

Baseline differences among groups in age, height, weight, body fat percentage, lean mass, and fat mass were ana-lyzed using AnOVA. Baseline differences in bone meas-ures were analyzed using AncOVA after adjusting for height, lean mass, and fat mass. Post hoc comparisons of baseline differences among groups were performed using tukey’s HSD. Wilcoxon signed-rank tests were performed to determine the significance of the change in outcome variables in each of the loading groups. Percent change in outcome variables was calculated for each individual par-ticipant and used as dependent variables for testing among and between group differences. All dependent variables were modeled using multiple linear regression with base-line height, baseline lean mass, baseline fat mass, men-strual status (regular or irregular), loading group, base-line and follow-up calcium, vitamin D and macronutrient intakes, year in school, and the baseline measure of the outcome variable as predictors of the outcome measures. Only variables that remained significant at p < 0.05 were included in the final models. Among group differences in the percent change in bone parameters were assessed using multiple linear regression after adjusting for covari-ates. Dunnett’s test was used to compare adjusted and unadjusted mean percent change of the athlete groups to the control group. In addition, all pairwise comparisons of adjusted and unadjusted mean percent changes were assessed using tukey’s HSD. All analyses were performed using Stata release 11 (Statacorp lP).Fig. 1 location of pQct images at the 4, 20, and 66 % slices

1432 Eur J Appl Physiol (2014) 114:1429–1438

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Results

Subject characteristics

Baseline and follow-up data for each impact-loading group are given in table 1. total dietary intake and macronutri-ent intakes did not differ among any of the groups. Over-all mean caloric intake was 2,354 ± 571 kcal/day, calcium intake was 1,113 ± 379 mg/day, and vitamin D intake was 171 ± 123 IU/day. total dietary intake and macronutrient intake did not contribute significantly to any of the sta-tistical models. training information was obtained from coaches and is given in table 2.

Baseline measurements

At the 4 % tibia site, baseline trabecular vBMD was lower in control subjects than in the high-, moderate-, or odd-impact groups and the odd-impact group had greater trabecular vBMD than the moderate-impact group (table 3). Anterior–posterior diameter at the 4 % site did not differ among groups at baseline, but medial–lateral

diameter at the 4 % site was greater in the high-impact group than in the control, moderate- or odd-impact groups.

At the 20 % tibia site, baseline cortical area was greater in the high-impact group than in the control or odd-impact groups but was not different from the moderate-impact group. total cross-sectional area and polar and anterior–posterior moment of inertia (MOI) were all greater in the high-impact group than in any of the other groups (table 3). In addition, medial–lateral MOI was greater in the high-impact group than in the control and odd-impact groups, but was not different than that in the moderate-impact group.

At the 66 % site, baseline cortical area was higher in the high-impact group than in the control and odd-impact groups and the moderate-impact group had greater corti-cal area than the control group. cortical thickness and total cross-sectional area were both higher at baseline in the high-impact group than in the control group. Polar, medial–lateral, and anterior–posterior MOI at the 66 % site were higher in the high-impact group than in the odd-impact and control groups at baseline.

Table 1 Participant characteristics

Data are mean ± SD

Control group college aged females who did not participate in regular physical activity, High-impact group basketball and volleyball players, Low-impact group long distance runners, Odd-impact group soccer players

Variables with the same superscript within a column are different at baseline (tukey’s HSD at p < 0.05)

group Age (years) Height (cm) Weight (kg) Body fat (%) Fat mass (kg) lean mass (kg)

control (n = 10)

Baseline 19.2 ± 1.4 167.8 ± 5.4A 66.9 ± 10.9AB 25.9 ± 5.3ABc 17.3 ± 6.7AB 49.6 ± 8.1A

Follow-up 19.7 ± 1.4 167.8 ± 5.6 68.8 ± 12.8 23.0 ± 4.7 15.8 ± 5.3 53.0 ± 10.9

High (n = 11)

Baseline 19.9 ± 1.2 177.3 ± 6.4ABc 76.3 ± 5.4AcD 21.2 ± 3.8A 16.2 ± 3.9 60.1 ± 5.8ABc

Follow-up 20.4 ± 1.2 176.8 ± 6.6 76.4 ± 6.0 20.3 ± 5.2 15.5 ± 3.5 60.9 ± 6.6

low (n = 13)

Baseline 19.9 ± 1.3 166.6 ± 6.3B 57.8 ± 7.4Bc 19.2 ± 3.8B 11.1 ± 2.3A 46.8 ± 7.4B

Follow-up 21.4 ± 1.3 166.5 ± 5.7 57.9 ± 7.9 21.6 ± 4.1 12.5 ± 2.0 45.4 ± 6.0

Odd (n = 12)

Baseline 19.5 ± 1.0 165.6 ± 6.3c 62.5 ± 6.8D 21.1 ± 4.0c 13.2 ± 3.4B 49.3 ± 4.7c

Follow-up 20.0 ± 1.0 165.3 ± 6.5 62.4 ± 6.2 22.2 ± 7.5 13.8 ± 4.3 48.6 ± 7.5

Table 2 training schedules among groups

a Soccer, Volleyball and Basketball athletes participate in 30 min of plyometric training once per week in the off-season

In-season practice Off-season practice In-season competition In-season lifting Off-season lifting

cross-country running

60–90 min of running per day 6 days/week

60–90 min of running per day 6 days/week

1 race of 5 km per week none none

Soccer 60 min of training per day 4 days/week

60 min of training 4 to 5 days/week

2 matches per week 1 day per week 3 days per weeka

Volleyball 120–150 min of practice per day 4 days/week

60 min per day 2 days/week

2 matches per week 2 days per week 3 days per weeka

Basketball 90 min of practice 4 days per week

60 min per day 2 days/week

2 games per week 2 days per week 3 days per weeka

control none none none none

1433Eur J Appl Physiol (2014) 114:1429–1438

1 3

Tabl

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dd (

n =

12)

Bas

elin

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trab

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(m

g/cm

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9 ±

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−0.

09 ±

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nS

293

± 1

16A

0.3

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0.6

nS

275

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21B

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08 ±

1.3

nS

291

± 2

4cD

−0.

03 ±

0.9

nS

tot

al c

SA (

mm

2 )77

1 ±

146

1.3

± 1

1.5*

0.01

882

± 1

420.

2 ±

0.6

nS

823

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35−

0.2

± 0

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S74

0 ±

215

0.7

± 0

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S

AP

diam

eter

(m

m)

34 ±

30.

9 ±

0.9

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0236

± 2

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2 ±

1.3

nS

34 ±

2−

0.02

± 0

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± 2

−0.

3 ±

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nS

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dia

met

er (

mm

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± 2

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1 ±

0.9

A, *

0.01

41 ±

3A

Bc

−0.

6 ±

1.7

An

S38

± 2

B0.

1 ±

1.5

nS

38 ±

3c

−0.

4 ±

1.1

nS

20 %

Site

cor

tical

vB

MD

(m

g/cm

3 )1,

174

± 1

60.

4 ±

0.5

nS

1,16

4 ±

19

0.3

± 0

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179

± 1

30.

4 ±

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0.2

± 0

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S

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e ar

ea (

mm

2 )20

0 ±

20A

0.1

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9 ±

27A

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211

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thi

ckne

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7 ±

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± 0

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nS

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± 1

3B0.

5 ±

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nS

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± 1

3c−

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± 0

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(mm

4 ×l0

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4.9

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4 ±

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± 4

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−0.

2 ±

2.6

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± 4

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± 1

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3.2

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± 3

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± 1

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± 2

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± 1

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± l.

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1 ±

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nS

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± 2

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± 3

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5 ±

1.8

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± 1

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Site

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tical

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(m

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2 ±

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nS

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0.1

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148

± 1

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6 ±

1.0

nS

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06 ±

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± 3

6Ac

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± 3

.6n

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9 ±

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1 ±

1.8

nS

304

± 2

9c0.

06 ±

1.8

nS

thi

ckne

ss (

mm

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3 ±

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± 0

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0.7

± 3

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1 ±

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1.2

± 3

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9 ±

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5 ±

3.5

nS

tot

al c

SA48

1 ±

22A

0.3

± 0

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S57

1 ±

18A

2.0

± 1

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6 ±

16

−2.

9 ±

2.9

nS

504

± 1

81.

4 ±

0.9

nS

Pol

ar M

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(mm

4 ×10

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.8 ±

11.

7A0.

7 ±

1.8

nS

61.4

± 7

.5A

B1.

8 ±

2.7

nS

51.1

± 1

0.2

1.1

± 2

.5n

S44

.6 ±

7.8

B1.

8 ±

2.2

*0.

05

Ml

MO

I (m

m4 ×

l03 )

29.0

± 8

.3A

0.8

± 1

.6n

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.6 ±

6.2

AB

1.9

± 3

.2n

S37

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7.6

1.3

± 2

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.0 ±

5.5

B2.

1 ±

2.5

nS

AP

MO

I (m

m4 ×

l03 )

10.8

± 3

.5A

0.3

± 2

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± 2

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8 ±

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± 2

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± 1

.9n

S

1434 Eur J Appl Physiol (2014) 114:1429–1438

1 3

Pre- to post-season changes

changes from pre- to post-season are summarized in tables 1 and 3 and were reported after controlling for base-line measurement, change in lean mass, and change in fat mass. the only change among controls was an increase in anterior–posterior and medial–lateral diameter at the 4 % distal tibia (Fig. 2).

In the moderate-impact group, no changes in cortical vBMD, thickness or area were observed (Fig. 3a); how-ever, an increase in medial–lateral MOI was observed at the 20 % site (Fig. 3b). Sector analysis revealed an increase in cortical vBMD in the lateral-posterior sector at the 20 % site (Fig. 4a) and at the anterior and medial-posterior sec-tors at the 66 % site in the moderate-impact group (Fig. 4b).

Among the high-impact group, there was an increase in anterior–posterior MOI at the 66 % distal tibia (Fig. 5b). Sector analysis revealed an increase in cortical vBMD in the posterior region of the 66 % site in the high-impact group (Fig. 4b).

In the odd-impact group, there was a significant increase in cortical area and cortical thickness at the 20 % distal tibia (Fig. 3a, b). Polar MOI increased significantly at the 66 % site (Fig. 5b).

Discussion

Exercise loading has been previously suggested to result in greater cortical area. In our study, baseline cortical area was greater in the high-impact group as compared to the odd-impact and control groups but did not differ from the mod-erate-impact group. While it is not surprising that the high-impact group had the greatest cortical area (liu et al. 2003; nikander et al. 2006), the results indicating lower cortical area in the odd-impact group compared to the high-impact group were not expected. Previous research using DXA hip structural analysis (HSA) has shown odd-impact loading may be favorable, or at least similar, to high-impact load-ing for increasing cortical cross-sectional area and cortical thickness of the femoral neck (nikander et al. 2005, 2009).

the lack of differences in the cortical area and thick-ness between high-impact and moderate-impact athletes is of particular interest. given that stress fractures are com-mon in moderate-impact loading athletes, we would have expected cortical area and thickness to be lower in the moderate-impact group. Perhaps muscle fatigue rather than bone characteristics play a key role in the development of stress fractures in these athletes due to increased strains being placed on the bones with fatigue (Milgrom et al. 2007). An increase in strain could make recovery from an exercise bout difficult and eventually lead to a stress fracture.

Similar to previous work by nikander et al. (2008) tra-becular vBMD was greater in all the athlete groups than in the control subjects, but trabecular vBMD did not change

Fig. 2 Mean (±SD) percent change in trabecular vBMD, anterior–posterior diameter, and medial lateral diameter at the 4 % slice after adjusting for baseline bone measurement, change in lean mass, and change in fat mass using multiple linear regression. An asterisk indi-cates a percent change that is significantly different from zero (Wil-coxon signed-rank test; p < 0.05) and bars with the same letter are significantly different from one another (tukey’s HSD; p < 0.05)

Fig. 3 Mean (±SD) percent change in cortical vBMD, cortical area, and cortical thickness (Fig. 2a) and in polar, medial–lateral, and ante-rior–posterior MOIs (Fig. 2b) at the 20 % site. An asterisk indicates a change that is significantly different from zero (Wilcoxon signed-rank test; p < 0.05)

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Fig. 4 results from sector analyses for changes in cortical vBMD at the 20 % site (a) and 66 % site (b) among loading groups are displayed. Signifi-cant changes are indicated by a * and a p value within the sector

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during the competitive season in any of the groups. Increases in trabecular vBMD have been reported previously in sed-entary subjects who began an aerobic or combined resist-ance and aerobic training regimen (Evans et al. 2012). Our athletic subjects may have already undergone similar adap-tations to those seen in the previous study of sedentary indi-viduals and that may explain why we did not see changes in trabecular vBMD. Our results showing higher trabecular vBMD at baseline among the athletes is consistent with this speculation and previous findings (nikander et al. 2008). An unexpected finding at the 4 % site was an increase in anterior–posterior and medial–lateral diameter in the control subjects but not in any other subjects. One explanation could be that the control subjects were previously completely sed-entary and then began an exercise program or participated in intramural athletics when they came to the university. Data from a bed rest study published in 2011 indicate that large improvements in bone parameters may be observed if an appropriate stimulus is provided (cervinka et al. 2011). If indeed some control subjects in our study began exercise but failed to report this at their follow-up visit, changes such as an increase in bone size may occur. Participants were screened for activity changes at the follow-up visit, but this was self-reported and therefore is subject to error.

In our study, both cortical area and cortical thickness increased at the 20 % site in the odd-impact loading group

while no changes in cortical vBMD, cortical area, or corti-cal thickness were observed at the 66 % site in any group. We hypothesized that the bone size would increase in both the high- and odd-impact groups, but no changes were observed in the high-impact group. Increases in cross-sectional area at the 38 % site have been reported in pre-viously sedentary subjects who began either an aerobic or combination aerobic and resistance training program (Evans et al. 2012); however, the same study also reported no changes at the 66 % site. Previous cross-sectional stud-ies have reported greater tibial cross-sectional area in athletic subjects compared with non-athletic referents (liu et al. 2003; Haapasalo et al. 2000; nikander et al. 2008), but it is unclear whether these increases result in a decreased stress fracture risk as the season progresses. cortical area in female runners (Popp et al. 2009) as well as section modulus and cortical thickness in female mili-tary recruits (Franklyn et al. 2008; Beck et al. 2000) have all been reported to be lower in subjects who developed a stress fracture and, therefore, we assume that subjects who have increases in measures of bone size and strength during the season would have a lower risk of developing a stress fracture.

Previous studies have reported that greater MOIs are related to a decreased risk of stress fracture (Milgrom et al. 1988, 1989). In our study, medial–lateral MOI increased at the 20 % site in the moderate- and odd-impact groups and polar MOI increased at the 66 % site in the odd-impact group. It is likely that these changes among the odd-impact group are a result of the loading environment as well as the uneven surfaces on which these individuals partici-pate. An unexpected finding from this study was the polar MOI increased in the odd-impact groups at the 66 % site but not at the 20 % site. loading environment is a probable explanation why we observed changes at the more proxi-mal location. Increases in anterior–posterior MOIs were observed at the 66 % site in the high-impact group. Mod-eling studies have indicated that forces during walking are tensile in the anterior tibia and compressive in the poste-rior tibia (Peterman et al. 2001; Kłodowski et al. 2011). It is likely that these forces are increased during high-impact activity and this may help to explain the improvements in anterior–posterior MOIs in these subjects.

While some changes in cortical vBMD were reported at different sectors, no discernible pattern based on loading group was evident. these findings were not entirely sur-prising due to the limited research suggesting that cortical vBMD may adapt to different loading patterns. A lack of adaptations in vBMD further emphasizes the importance of changes in bone geometry to accommodate loading.

the specificity of adaptations observed in this study brings about a potential mechanism of protection against stress fracture in these athletes. the direction-specific

Fig. 5 Mean (±SD) percent change in cortical vBMD, cortical area, and cortical thickness (Fig. 3a) and in polar, medial–lateral, and ante-rior–posterior moments of inertia (Fig. 3b) at the 66 % site. An aster-isk indicates a change that is significantly different from zero (Wil-coxon signed-rank test; p < 0.05)

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improvement in MOI brings about a potential mechanism in which the body adapts that has the potential to prevent stress fractures. More research is necessary to determine what underlying mechanisms may be present in athletes who develop stress fractures and if these mechanisms are related to load-specific adaptations.

limitations

this study is limited by our inability to control the activity level of our control subjects. Increases in physical activity in sedentary controls could affect the results of the study. In our study, the controls did increase their lean body mass significantly; however, we controlled for this in our models. In addition, the study is limited by a relatively small num-ber of participants.

Conclusion

In conclusion, adaptations in the bones of athletes appear to take place throughout a competitive season. the adapta-tions appear to be specific to the loading environment and result in improved measures of bone strength throughout a competitive season. these findings may be important in understanding why individuals develop stress fractures. Future studies should focus on how changes during a com-petitive season are related to injury occurrence.

Conflict of interest the authors declare that they have no conflict of interest.

Ethical standard All procedures and questionnaires used in this study were in compliance with international standards and were reviewed and approved by the Human Subjects review committee. Written informed consent was obtained from all participants prior to any testing.

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