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Background Running is a popular sport with high injury rates. Although risk factors have intensively been investigated, synthesized knowledge about the differences in injury rates of female and male runners is scarce. Objective To systematically investigate the differences in injury rates and characteristics between female and male runners. Methods Database searches (PubMed, Web of Science, PEDro, SPORTDiscus) were conducted according to PRISMA guidelines using the keywords “running AND injur*”. Prospective studies reporting running related injury rates for both sexes were included. A random-effects meta-analysis was used to pool the risk ratios (RR) for the occurrence of injuries in female vs. male runners. Potential moderators (effect modifiers) were analysed using meta-regression. Results After removal of duplicates, 12,215 articles were screened. Thirty-eight studies were included and the OR of 31 could be pooled in the quantitative analysis. The overall injury rate was 20.8 (95% CI 19.9–21.7) injuries per 100 female runners and 20.4 (95% CI 19.7–21.1) injuries per 100 male runners. Meta-analysis revealed no differences between sexes for overall injuries reported per 100 runners (RR 0.99, 95% CI 0.90–1.10, n = 24) and per hours or athlete exposure (RR 0.94, 95% CI 0.69–1.27, n = 6). Female sex was associated with a more frequent occurrence of bone stress injury (RR (for males) 0.52, 95% CI 0.36–0.76, n = 5) while male runners had higher risk for Achilles tendinopathies (RR 1. 86, 95% CI 1.25–2.79, n = 2). Meta-regression showed an association between a higher injury risk and competition distances of 10 km and shorter in female runners (RR 1.08, 95% CI 1.00–1.69). Conclusion Differences between female and male runners in specific injury diagnoses should be considered in the development of individualised and sex-specific prevention and rehabilitation strategies to manage running-related injuries.
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Vol.:(0123456789)
Sports Medicine (2021) 51:1011–1039
https://doi.org/10.1007/s40279-020-01412-7
SYSTEMATIC REVIEW
Sex‑Specific Differences inRunning Injuries: ASystematic Review
withMeta‑Analysis andMeta‑Regression
KarstenHollander1,2 · AnnaLinaRahlf3 · JanWilke4 · ChristopherEdler5 · SimonSteib6 · AstridJunge1,7 ·
AstridZech3
Accepted: 10 December 2020 / Published online: 12 January 2021
© The Author(s) 2021
Abstract
Background Running is a popular sport with high injury rates. Although risk factors have intensively been investigated,
synthesized knowledge about the differences in injury rates of female and male runners is scarce.
Objective To systematically investigate the differences in injury rates and characteristics between female and male runners.
Methods Database searches (PubMed, Web of Science, PEDro, SPORTDiscus) were conducted according to PRISMA
guidelines using the keywords “running AND injur*”. Prospective studies reporting running related injury rates for both
sexes were included. A random-effects meta-analysis was used to pool the risk ratios (RR) for the occurrence of injuries in
female vs. male runners. Potential moderators (effect modifiers) were analysed using meta-regression.
Results After removal of duplicates, 12,215 articles were screened. Thirty-eight studies were included and the OR of 31
could be pooled in the quantitative analysis. The overall injury rate was 20.8 (95% CI 19.9–21.7) injuries per 100 female
runners and 20.4 (95% CI 19.7–21.1) injuries per 100 male runners. Meta-analysis revealed no differences between sexes for
overall injuries reported per 100 runners (RR 0.99, 95% CI 0.90–1.10, n = 24) and per hours or athlete exposure (RR 0.94,
95% CI 0.69–1.27, n = 6). Female sex was associated with a more frequent occurrence of bone stress injury (RR (for males)
0.52, 95% CI 0.36–0.76, n = 5) while male runners had higher risk for Achilles tendinopathies (RR 1. 86, 95% CI 1.25–2.79,
n = 2). Meta-regression showed an association between a higher injury risk and competition distances of 10km and shorter
in female runners (RR 1.08, 95% CI 1.00–1.69).
Conclusion Differences between female and male runners in specific injury diagnoses should be considered in the develop-
ment of individualised and sex-specific prevention and rehabilitation strategies to manage running-related injuries.
Supplementary Information The online version contains
supplementary material available at https ://doi.org/10.1007/s4027
9-020-01412 -7.
* Karsten Hollander
karsten.hollander@medicalschool-hamburg.de
Extended author information available on the last page of the article
Key Points
There were no differences between female and male run-
ners in overall injury rates.
Female runners had more bone stress injuries.
Male runners had more Achilles tendon injuries.
Shorter competition distances increase the risk of injury
for female runners.
1 Introduction
Running is a very popular sport practiced all over the world.
While regular physical activity and sports such as running
are beneficial for prevention and rehabilitation of many
health complaints (“exercise is medicine”) [1, 2], running
is frequently associated with high injury prevalence and
incidence rates [35].
For injury prevention, risk factors need to be well
understood [6]. Risk factors for running are manifold
and consist of training load, biomechanical, anatomical
and anthropometrical variables [712]. While some previ-
ous studies exclusively investigated either male [9, 13] or
female [1416] runners, sex has been suggested to be a
risk factor for specific injury patterns in running, as well
as for overall injury risk [7, 17, 18]. This is supported by
a study investigating injury rates for female and male elite
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1012 K.Hollander et al.
running athletes [19]. Analysing data collected during 14
international athletics championships, Edouard etal. [19]
showed that male elite athletes had lower injury incidence
rates for bone stress injuries (BSI) than female counter-
parts. However, injury risks differed between sexes for
running disciplines (from middle distances upwards)
although only with a small to trivial relative risk (1.5
for middle distances, 0.9 for long distances and 1.3 for
marathon running) [19].
Including and investigating both sexes in running injury
research is in line with evidence for the different risks
between female and male athletes for specific types of inju-
ries such as anterior cruciate ligament ruptures or concus-
sions in different team sports as well as ankle sprains in all
sports [2022]. However, considering the current literature,
it is difficult to derive conclusive summaries about differ-
ences in overall or specific injury epidemiology for both
sexes in specific sports [18]. To develop and optimize indi-
vidualized prevention and treatment options for running
injuries, it is crucial to understand if and to what extent
injury epidemiology differs between the sexes. Therefore,
the aim of this systematic review was to evaluate the differ-
ences in injury rates and characteristics between male and
female runners using meta-analytical techniques. First, dif-
ferences in overall injury rates were compared between both
sexes. Secondly, depending on the availability of sufficient
data, specific injury diagnoses were analysed regarding their
occurrence in female and male runners.
2 Methods
This study was conducted and presented according to
the PRISMA guidelines for reporting systematic reviews
and meta-analysis [23]. Prior to the start of the study, the
review protocol was registered in the PROSPERO database
(CRD4201911883).
2.1 Search Strategy andInclusion Criteria
Two independent investigators (K.H. and C.E.) conducted a
systematic literature search including articles from inception
till April 2020. Prospective cohort studies and randomized
controlled trials investigating healthy runners from differ-
ent age groups were included. The search was restricted to
articles from peer-reviewed journals published in English,
German, or Spanish languages. Furthermore, studies had to
report rates of running-related injuries for both sexes. Over-
all injury rates and injury rates for specific locations, diagno-
ses or injury mechanisms were considered. Included running
disciplines were middle distance and long-distance track as
well as cross-country, trail and road running. There was no
restriction to a specific injury definition. Reviews, systematic
reviews, commentaries, case studies, case series, cross-sec-
tional studies, retrospective studies and interventional arms
of randomised controlled trials (RCT) were excluded. For
RCTs, only untreated control groups were considered.
The search strategy using specific keywords (running
AND injur*) was applied to four different databases (Pub-
Med, Web of Science, PEDro, SPORTDiscus). All databases
were searched to identify relevant studies based on keywords,
title and abstract. Two independent investigators (C.E. and
K.H.) extracted relevant studies based on the inclusion cri-
teria first by readingthe title, the abstract and the full text, if
available. A third reviewer (A.Z.) was available for consensus
decisions. The bibliographical information of included arti-
cles was examined for further relevant references (backward
search). A forward search was done via citation tracking using
Web of Science® (Thomson Reuters).
2.2 Data Extraction
Study characteristics (design, running discipline, population,
age and number of participants) as well as prevalence and inci-
dence rates for both sexes were extracted. For prevalence rates,
number of injuries or number of injured runners were related to
the number of runners investigated. For incidence rates, number
of injuries and specific exposures (in hours, kilometres or ath-
lete exposure) were used. An athlete exposure (AE) is defined
as one athlete participating in one practice or competition [24].
When it was not possible to extract the data from an article
for specific running distances (e.g., pooling of overall injuries
for track disciplines), corresponding authors were contacted
by email to obtain the data. If specific data were not able to be
obtained, the respective study was included in the systematic
review but not in subsequent analyses.
2.3 Study Quality Assessment
Due to insufficient study quality assessment tools in sports
injury epidemiology, a new tool was developed by consensus
of K.H., A.J., A.L.R., A.Z. and S.S. on the basis of previ-
ously used tools [20, 22, 25, 26]. The modification ensured
that all relevant points regarding the quality of the study
design and important content-related information would be
considered—e.g., differences in methodological approaches
such as competition or season, or the type of data collection.
This tool consisted of 15 items on recruitment, reporting,
injury and exposure collection, injury definition and drop-
out (Table1).
The identified quality score was used to determine a high
(above the median) or low (below the median) study qual-
ity of the studies investigated (median score was 18). Two
independent reviewers (K.H., A.J.) with a third reviewer
(A.L.R.) for consensus assessed the study quality of the
included studies.
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1013
Sex-Specific Differences in Running Injuries
Publication bias was checked by visual inspection of fun-
nel plots (log risk ratio against standard errors) and regres-
sion test for funnel plot asymmetry.
2.4 Data Synthesis andStatistics
To compare injury risk between male and female runners,
risk ratios (RR) with 95% confidence intervals (CI) were
computed for each study. Meta-analytic pooling was done
using a random-effects model (DerSimonian and Laird
method [27]). Between-study heterogeneity was estimated
using Cochran’s Q and I2 statistics. To reveal potential publi-
cation biases, funnel plots were constructed if more than ten
studies were available [28]. Besides visually checking their
symmetry, Egger’s regression test was applied.
Following the calculation of pooled RRs, we used a
mixed-effects meta-regression model to identify variables
potentially affecting the outcome of the meta-analysis
[27]. The choice of tested moderators (effect modifiers)
was based on three criteria: (1) a plausible impact on the
tested variables, (2) reporting in the included studies, (3)
sufficient variation of the moderators’ values [29]. The
following moderators were submitted into the meta-regres-
sion model: performance/expertise level (recreational: no
competitions, competitive: participating in local competi-
tions, elite: qualifying for national or international com-
petitions); age (youth: < 18, adult: 18) competition dis-
tance (≤ 10km, > 10km); study quality (low: study quality
score < 18, high: study quality score 18), training dura-
tion (low: < 7.5h or high: 7.5h/week), training mileage
(low: < 64km/week, high: ≥ 64/week). Moderator analyses
were performed if ten or more studies were available [28].
If a significant moderator was detected, a subgroup analy-
sis comparing the respective values of the moderator was
performed using the meta-analytic procedures described
above.
All calculations were performed using algorithms of
the metaphor package embedded in R (R Foundations for
Statistical Computing, Vienna, Austria) as well as the soft-
ware JAMOVI [30] and OpenMeta [Analyst] software (OS
X version 10.12 obtained from http://www.cebm.brown
.edu/openm eta/).
3 Results
3.1 Search Results
The search returned 15,914 studies and 29 additional stud-
ies were identified through other sources. After removing
3699 duplicates and applying inclusion criteria, a total
of 38 studies were considered eligible [7, 19, 24, 3165].
Thirty-one of them could be included in the quantitative
analysis. Seven studies reported on the same data sets as
other included studies and were excluded from the quan-
titative analysis [19, 3133, 39, 40, 65]. The full literature
search process is displayed in Fig.1.
Table 1 Risk of bias assessment tool
Question Rating
Are the sources and methods of participant recruitment clearly
described?
Yes (1), no (0)
Are the relevant characteristics (n, age, sex, sport, level of competition)
of the study population reported?
Yes (1), no (0)
Does the study cover season and/or tournaments/championships? Season (2), tournaments (1), not reported (0)
Are exposure data recorded? Yes (1), no (0)
Is the frequency of data collection reported? Yes (1), no (0)
If yes: ≥ Daily (3), ≥ weekly (2), ≥ monthly (1), not reported (0)
Is a clear injury definition provided? Yes (1), no (0)
If yes: Medical attention (3), time loss (2), other (1), no clear definition (0)
Is the method for assessing exposure described? Yes (1), no (0)
If yes: Individual data collection (2), exposure estimated (1), not reported (0)
Is the method for assessing injury reported? Yes (1), no (0)
If yes: Briefed medical personnel (3), medical personnel (2), coach, self-
report, media reports (1), not reported (0)
Are characteristics of injury reported (location, type, mechanism, sever-
ity, recurrent)?
Yes (1), no (0)
If yes: Complete (2), partly (1), no (0)
Is the drop out < 30% drop out? Yes (1), no (0)
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1014 K.Hollander et al.
3.2 Characteristics ofIncluded Studies
Of the included studies, 36 reported injury data from 35,689
participants (40.8% female). Two studies reporting on inju-
ries from the National Collegiate Athletic Association
(NCAA) database did not state the number of athletes but
did report the athlete exposure (242,244 athlete exposures
with 46.7% females [24] and 276,207 athlete exposures with
50.7% females [59]). Most studies were prospective cohort
studies (n = 37), while the control group (not receiving any
intervention) from one randomised controlled study met the
inclusion criteria [42]. Twenty-three studies investigated
road runners, 11 track runners (middle and long distance),
10 cross-country runners and 3 studies reported on trail
running/orienteering (Table2). Studies from major competi-
tions (European or World Championships) reported concur-
rently on track and road running (half or full marathon) [19,
3133, 41]. Regarding competition level, 18 studies reported
on novice and recreational runners, 11 on competitive and 9
on elite runners. Study characteristics of all included studies
are summarized in Table2.
3.3 Study Quality
The two independent reviewers evaluating study quality
agreed on 441 of 570 evaluated items (agreement = 77.4%).
The scores for study quality ranged between 9 and 23 out of
24 points with a median of 18 and a mean ± SD of 16.8 ± 4.1.
Fig. 1 Flow diagram displaying the literature search
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1015
Sex-Specific Differences in Running Injuries
Table 2 Study characteristics
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Nicholl etal.
[54]
Marathon Sheffield
Marathon
(1982) par-
ticipants
Recreational 53/2236 over 18 1day Contact with
medical staff
at first-aid
posts
One full
marathon
18 injured
runners
per 100
marathon
runners
Female: 32
injured run-
ners per 100
marathon
runners;
Male: 17.5
injured run-
ners per 100
marathon
runners
14
Hughes etal.
[47]
Road racing Chicago Dis-
tance Clas-
sic (20km)
Recreational 188/1071 32.3 (range
9—75)
1day Self-reported
specific
orthopaedic
problems
20km race 28.4 injured
runners per
100 runners
Female: 54.3
injured run-
ners per 100
runners;
Male: 31.6
injured run-
ners per 100
runners
11
Johansson
[49]
Orienteers College stu-
dents
Elite 33/56 17.5 ± 1.5 1year Time loss
training or
competition
injuries
Daily train-
ing logs,
monthly
reports of
training
3 injuries per
1000h;
74 injuries
per 100 run-
ners
Female: 72.7
injuries per
100 runners;
Male: 75.0
injuries per
100 runners
20
de Loes and
Goldie [38]
Road/Trail Population
based (Swe-
den)
Recreational 2505/3530 15–59 1year Medically
diagnosed:
injury reg-
istry from
hospitals
and sports
medicine
physician.
Validated by
telephone
interview
Data were col-
lected from
representa-
tive sample
via question-
naire, then
extrapola-
tion to
whole
population
0.7 injuries
per 1000h
Female: 0.7
injuries per
1000h;
Male: 0.7
injuries per
1000h
14
McLain and
Reynolds
[52]
Cross-country High school
students
Competitive
(high school)
40/54 NA 1year Athletic
trainer: Any
time loss
incident
resulting
from athletic
participation
NA 10.7 injuries
per 100 run-
ners
Female: 7
injured run-
ners per 100
runners;
Male: 13
injured run-
ners per 100
runners
11
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1016 K.Hollander et al.
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Walter etal.
[63]
Road runners
(10 Miles)
Community
running
events
(4–22.4km)
in Ontario
Recreational 301/980 over 14 52weeks Injuries severe
enough to
reduce the
number
of miles
run, take
medicine, or
see a health
professional
NA 48.4 injured
runners per
100 runners
Female: 45.5
injured run-
ners per 100
runners;
Male: 49.3
injured run-
ners per 100
runners
13
Bennell etal.
[35]
Track Victoria ath-
letics
Competitive
(college)
26/28 17–26 48weeks Stress frac-
ture: medi-
cal imaging
after clinical
evaluation
Structured
interview:
hours per
week, weeks
without run-
ning,
0.7 stress
fractures per
1000h
25.9 runners
with stress
fractures per
100 runners
Female: 30.8
runners
with stress
fractures per
100 runners;
Male: 21.4
runners
with stress
fractures per
100 runners
21
Beachy etal.
[34]
Cross-country High school
students
(Punahou,
Hawai)
Competitive
(high school)
787/501 NA 8years Any athlete
complaint
that required
the attention
of the ath-
letic trainer,
regardless
of the time
lost from
activity
NA 65 injuries
per 100 run-
ners
Female: 65
injuries per
100 girls;
Male: 66
injuries per
100 boys
14
Colbert etal.
[36]
Road running Patients from
Cooper
Clinic
Recreational 220/1771 NA 8years Clinical visit NA 26.3 injured
runners per
100 runners
Female: 25.0
injured run-
ners per 100
runners;
Male: 26.4
injured run-
ners per 100
runners
9
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1017
Sex-Specific Differences in Running Injuries
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Rauh etal.
[58]
Cross country High school
students
(Washington
state)
Competitive
(high school)
1202/2031 NA 15years An injury was
defined as
a medical
problem
resulting
from athletic
participation
that required
an athlete to
be removed
from a
practice or
competi-
tive event
or to miss a
subsequent
practice or
competitive
event
An AE was
defined as
any practice
or meet
(competi-
tion) in
which there
was the
possibility
of sustaining
an athletic
injury
13.1 injuries
per 1000
AEs
Female: 16.7
injuries per
1000 AEs;
Male: 10.9
injuries per
1000 AEs
18
Steinacker
etal. [61]
Marathon Berlin
Marathon
participants
Recreational 22/36 44.5 24weeks Self-reported
orthopaedic
problems
(Survey)
NA 46.6 injured
runners per
100 runner
Female: 41.6
injured run-
ners per 100
runners;
Male: 54.5
injured run-
ners per 100
runners
11
Taunton etal.
[62]
Road race
(10km)
Vancouver
Sun Run
(10km)
Recreational 635/205 NA 13weeks Self-reported
pain (Sur-
vey) with
medical
confirmation
NA 29.5 injured
runners per
100 runner
Female: 30.2
injured run-
ners per 100
runners;
Male: 28.3
injured run-
ners per 100
runners
14
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1018 K.Hollander et al.
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Dane etal.
[37]
Road running College stu-
dents
Competitive
(college)
47/45 17–28 One season
(12weeks)
Medically
diagnosed:
contusions,
bleeding,
wounds, and
fractures,
except small
bruises,
were clas-
sified as
injuries
NA 57.1 injured
runners per
100 runners
Female: 52
injured run-
ners per 100
runners;
Male: 60
injured run-
ners per 100
runners
10
Rauh etal.
[57]
Cross country College
students
(Seattle)
Competitive
(college)
186/235 NA One season Muscle, joint,
or bone
problem/
injuries of
the back
or lower
extremity
requiring
the runner to
be removed
from a prac-
tice or meet
or to miss a
subsequent
one
An AE was
any practice
or competi-
tive event
where a
runner was
at risk of
sustaining
an injury
17.0 injuries
per 1000
AEs
Female: 19.6
injuries per
1000 AEs;
Male: 15.0
injuries per
1000 AEs
20
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1019
Sex-Specific Differences in Running Injuries
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Plisky etal.
[56]
Cross country High school
students
(Wisconsin)
Competitive
(college)
46/59 NA 13weeks Medial
tibial stress
fracture:
pain in the
tibial region,
exacerbated
with repeti-
tive weight-
bearing
activity, and
localized
pain with
pal- pation
along the
distal two
thirds of the
posterior-
medial tibia
AE: any
practice or
competitive
event
2.8 stress
fractures per
1000 AEs
Female:
4.3 stress
fractures per
1000 AEs;
Male: 1.7
stress
fractures per
1000 AEs
21
Alonso etal.
[32]
Track + Mara-
thon
2007 IAAF
World cham-
pionships
(Osaka)
participants
Elite 249/267 17–37 9days All mus-
culoskel-
etal injuries
regardless
of the conse-
quences
with respect
to the
athlete’s
absence
from compe-
tition or
training
Number of
competing
athletes
150 competi-
tion injuries
per 1000
athletes
Time-loss
injuries
per 1000
registered
athletes
- Female:
800m: 22,
1500m:
26, 3000m
SC: 48,
5000m: 38,
10000m:
158, mara-
thon 61;
- Male:
800m: 43,
1500m:
24, 3000m
SC: 79,
10000m:
91, mara-
thon: 118
22
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1020 K.Hollander et al.
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Alonso etal.
[33]
Track + Mara-
thon
2009 IAAF
World cham-
pionships
(Berlin)
participants
Elite 244/312 NA 9days All time-loss
muscu-
loskel-
etal injuries
(traumatic
and overuse)
regardless
of the conse-
quences
with respect
to the
athlete’s
absence
from compe-
tition or
training
The number
of compet-
ing athletes
was defined
as all
athletes who
started at
least once in
a discipline
MD: 173.3
injuries
per 1000
registered
athlete
LD: 151.1
injuries
per 1000
registered
athletes
Injuries
per 1000
registered
athletes
- Female:
800m 46.5
1500m 71.4
3000m SC:
48.8
5000m 43.5
10000m 90.9
Marathon 0
- Male:
800m 0
1500m 37.0
3000m SC:
26.5 5000m
102.6
10000m 32.3
Marathon
30.6
22
Buist etal. [7] Road racing
(4 Miles)
Groningen 4
mile
Recreational 422/207 43.7 ± 9.5 8weeks Any time loss
running-
related
musculo-
skeletal pain
at the lower
extremity or
back
Exposure as
given by
training
programme
25.9 injured
runner per
100 runners;
30.1 injuries
per 1000h
Injury inci-
dence rate
per 1000h -
Female: 27.5,
Male: 35.0
Mean Preva-
lence -
Female: 23.4
injured run-
ners per 100
runners
Male: 31.4
injured run-
ners per 100
runners
18
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1021
Sex-Specific Differences in Running Injuries
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Alonso etal.
[31]
Track + Mara-
thon
2011 IAAF
World cham-
pionship
(Daegue)
participants
Elite 208/268 17–42 9days All mus-
culoskel-
etal injuries
regardless
of the conse-
quences
with respect
to the
athlete’s
absence
from compe-
tition or
training
Number of
competing
athletes
MD: 176.1
injuries
per 1000
registered
athletes
LD: 187.8
injuries
per 1000
registered
athletes
Time-loss
injuries
per 1000
registered
athletes
- Female:
800m: 55.6,
1500m:
57.1,
3000m SC:
0, 5000m:
125 injuries,
10000m:
52.6,, mara-
thon 53.6;
- Male:
800m: 22.7,
1500m:
76.9,
3000m SC:
0, 5000m:
122 injuries,
10000m:
47.6 injuries
marathon:
220.6
22
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1022 K.Hollander et al.
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Jacobsson
etal. [48]
Track
(MD + LD)
Swedish
national
team
Elite 54/55 17–37 52weeks Self-reported
musculo-
skeletal
pain, sore-
ness or
injury with
time loss
or changes
in normal
training/
competition
Self-reported
athletic
training par-
ticipation
83 injured
runners per
100 runners
Adults:
Female: 74
injured run-
ners per 100
runners;
Male: 81
injured run-
ners per 100
runners
Youth:
Female: 57
injured run-
ners per 100
runners;
Male: 58
injured run-
ners per 100
runners
19
Edouard etal.
[40]
Track (MD) European Ath-
letics indoor
champion-
ships Paris
2011 partici-
pants
Elite 125/75 NA 3days Any muscu-
loskeletal
complaint
and concus-
sion that
received
medical
attention
regardless of
time loss
Athletes’
exposure in
competition
MD: 53
injuries
per 1000
registered
athletes
Injuries
per 1000
registered
athletes –
Female:
800m: 47.6
3000m 150.0
- Male:
800m: 107.1
3000m 34.5
21
Nielsen etal.
[55]
Road racing DANO-RUN
study
Novice runners 441/432 37.2 ± 10.3 1year Any muscu-
loskeletal
complaint
of the lower
extremity
or back
caused by
running that
restricted
the amount
of running
for at least
1week
Online diary:
GPS or
manually
kilometers
23.1 injured
runner per
100 runners
Female: 21.8
injured run-
ners per 100
runners;
Male: 24.5
injured run-
ners per 100
runners
23
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1023
Sex-Specific Differences in Running Injuries
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Edouard etal.
[39]
Track (MD) European
Athletics
champion-
ships Hel-
sinki 2012
participants
Elite 66/164 NA 3days Any muscu-
loskeletal
complaint
and concus-
sion that
received
medical
attention
regardless of
time loss
Athletes’
exposure in
competition
MD: 53
injuries
per 1000
registered
athletes
Injuries
per 1000
registered
athletes
-Female:
800m: 41.7
1500m: 30.3
3000m: 142.9
5000m: 347.8
10000m:
176.5
- Male:
800m: 69.8,
1500m:
171.4,
3000m: 275.9
5000m: 71.4
10000m
103.4
21
Changstrom
etal. [24]
Cross-country National High
School
Sports-
Related
Injury
Surveillance
System
(2011–
2012),
(USA)
Competitive
(high school)
NA 13–19 2years (Stress)
fractures,
concussions
and dental
injuries with
or without
time loss.
All injuries
with time
loss requir-
ing medical
attention
Athlete expo-
sure (AE)
7.8 stress
fractures
per 100,000
AEs
Female:
10.6 stress
fractures
per 100,000
AEs; Male:
5.4 stress
fractures
per 100,000
AEs
19
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1024 K.Hollander et al.
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Edouard etal.
[19]
Track + Mara-
thon
All athletic
world cham-
pionships
(2007–2014)
Elite 1302/1573 NA (3–9days per
championship)
All mus-
culoskel-
etal injuries
(traumatic
and overuse)
and concus-
sion newly
incurred
during
competition
or training
regardless
of the conse-
quences
with respect
to the
athlete’s
absence
from compe-
tition or
training
Total number
of registered
athletes
N/A Injuries
per 1000
registered
athletes
- Female:
MD 94.6
LD 155.3
Marathon
153.3
- Male:
MD 108.5
LD 141.4
Marathon
195.5
21
Kluitenberg
etal. [50]
Road racing NLstart2run Novice runners 1332/364 43.3 6weeks A muscu-
loskeletal
complaint
of the lower
extremity
or back that
hampered
running
ability
for three
consecutive
training ses-
sions
Weekly
running
frequency
and running
exposure (in
minutes) for
each training
session
27.5 injuries
per 1000h
Female: 10.4
injured run-
ners per 100
runners;
Male: 12.6
injured run-
ners per 100
runners
17
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1025
Sex-Specific Differences in Running Injuries
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Hespanhol
Junior etal.
[45]
Road racing
(10 Miles)
Tilburg Ten
Miles
Recreational 31/22 44.1 18weeks OSTRC: All
running-
related
injuries
Online ques-
tionnaires
completed
every fort-
night
Mean preva-
lence: 30.8
injured run-
ners per 100
runners and
cumulative
prevalence
60.4 injured
runners per
100 runners
Mean preva-
lence
Female:
11.5%
injured run-
ners per 100
runners
Male: 19.3
injured run-
ners per 100
runners
18
Hespanhol
Junior etal.
[44]
Trailrunning Dutch trail
runners
Recreational 57/171 43.4 6months OSTRC: All
running-
related
injuries
Online ques-
tionnaires
completed
every fort-
night
10.7 injuries
per 1000h;
mean
prevalence
22.4%
Injury inci-
dence rate
per 1000h -
Female 9.1
Male: 11.3
Mean Preva-
lence:
Female:
20.7%;
Male: 23.0%
17
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1026 K.Hollander et al.
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Rizzone etal.
[59]
Cross country NCAA
(2004–2014)
Competitive
(college)
NA NA 9years Time loss
stress
fractures
that required
medical
attention:
(1) occurred
due to
participation
in a school-
sanctioned
practice or
competition,
(2) required
attention
from an AT
or physician,
(3) resulted
in at least
24h of time
missed from
participa-
tion, and
(4) had a
reported
diagnosis
of stress
fracture
AE: 1 student-
athlete
participating
in 1 NCAA-
sanctioned
practice or
competition
22.4 stress
fractures
per 100,000
AEs
Female:
28.6 stress
fractures
per 100,000
AEs;
Male: 16.1
stress
fractures
per 100,000
AEs
18
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1027
Sex-Specific Differences in Running Injuries
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Messier etal.
[53]
Road racing TRAILS study Recreational 128/172 Range 18—60 104weeks Overuse
running
injuries:
grade 1,
maintained
full activity
in spite of
symptoms;
grade 2,
reduced
weekly
mileage;
and grade 3,
interrupted
all training
for at least
2weeks
NA 66 injured
runners per
100 runners
Female: 73
injured run-
ners per 100
runners;
Male: 62
injured run-
ners per 100
runners
15
Winter etal.
[64]
Road running Runners from
local run-
ning club
Recrea-
tional + Com-
petitive
35/57 18—65 52weeks Pain prevent-
ing the
runner from
performing
or complet-
ing at least
one training
session
Training
diary with
information
on running
sessions
per week,
distance and
duration of
runs
51.3 injured
runners per
100 runners
Female: 54.8
injured run-
ners per 100
runners;
Male: 48.9
injured run-
ners per 100
runners
20
Fokkema
etal. [42]
Road racing INSPIRE
trial (NN
City Pier
City The
Hague, NN
Marathon
Rotterdam,
Ladies Run
Rotterdam)
Recreational 553/629
(Control
group)
41.4 ± 12 4.5 ± 1.6months Injuries of the
lower back
or lower
extremities
caused by
running
with change
of training
for at least
1week, a
medical visit
or medica-
tion
NA 36.7 injured
runners per
100 runners
Female: 35.8
injured run-
ners per 100
runners;
Male: 38.3
injured run-
ners per 100
runners
15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1028 K.Hollander et al.
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Hayes etal.
[43]
Cross-country NCAA (Ivy
League &
New Eng-
land Small
College
Athletics)
Competitive
(college)
57/10 17–21 1 US-cross-
country season
Self-reported
Injuries that
were not
present dur-
ing adminis-
tration of the
pre-season
survey
Average and
maximum
weekly
mileage in
increments
of 10 miles
(e.g. 31–40
miles per
week)
53 injured
runners per
100 runners
(over one
season)
Female: 51
injured run-
ners per 100
runners;
Male: 55
injured run-
ners per 100
runners
12
Lagas etal.
[51]
Road racing INSPIRE
trial (NN
City Pier
City The
Hague, NN
Marathon
Rotterdam,
Ladies Run
Rotterdam)
Recreational 909/1020 41.9 ± 12.1 20.5 ± 7weeks Self-reported
Achilles
tendinopa-
thy caused
by running
with change
of training
for at least
1week, a
medical visit
or medica-
tion
NA 5.2 injured
runners per
100 runners
Female: 3.6
injured run-
ners per 100
runners;
Male: 6.6
injured run-
ners per 100
runners
12
Ruffe etal.
[60]
Cross-country High school
students
(California)
Competitive
(high school)
80/68 15.6 1 US-cross-
country season
Muscle, bone,
or joint
problem/
injury of the
low back
or lower
extremity
requiring
removal
from
training/
competitions
or leading
to missed
subsequent
training/
competitions
Runners’ daily
participation
in prac-
tices and
competitive
events
33.1 injured
runners per
100 runners
(over one
season)
Female: 38.8
injured run-
ners per 100
runners;
Male: 26.5
injured run-
ners per 100
runners
16
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1029
Sex-Specific Differences in Running Injuries
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Winter etal.
[65]
Road running Runners from
local run-
ning club
Recrea-
tional + Com-
petitive
35/57 18—65 52weeks Pain prevent-
ing the
runner from
performing
or complet-
ing at least
one training
session
assessed by
an experi-
enced health
or medical
professional
Average kilo-
meters per
week; aver-
age duration
(minutes)
per week;
average
frequency
per week
51.3 injured
runners per
100 runners
Female: 54.8
injured run-
ners per 100
runners;
Male: 48.9
injured run-
ners per 100
runners
21
Edouard etal.
[41]
Track + Mara-
thon
IAAF World
and Euro-
pean Cham-
pionships
participants
Elite MD: 742/943;
LD 656/793;
Marathon
464/550
NA 78days
(3–9days per
championship)
All mus-
culoskel-
etal injuries
(traumatic
and overuse)
and concus-
sion newly
incurred
during
competition
or training
regardless
of the conse-
quences
with respect
to the
athlete’s
absence
from compe-
tition or
training
Total number
of registered
athletes
Injuries
per 1000
registered
athletes—
MD: 97
LD: 126
Marathon:
139
Injuries
per 1000
registered
athletes
- Female:
MD 84.9
LD 128
Marathon
118.5
Male:
MD 106
LD 123.6
Marathon
156.4
19
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1030 K.Hollander et al.
Most studies (> 90%) reported recruitment procedures,
injury assessment and documented injury characteristics.
Fewer studies achieved maximal points due to not record-
ing individual exposure data (50.0% of maximal points) or
exposure data at all (57.9% of maximal points) as well as
not using a medical attention definition (56.1% of maximal
points). The results of the study quality assessment are pre-
sented in Electronic Supplementary Material TableS1.
Except for one outlier [47], visual inspection of the funnel
plot (Fig.2) showed a symmetrical distribution of the log
risk ratios and the regression test for funnel plot asymmetry
(− 0.150; p= 0.881) suggested no indication of publication
bias.
3.4 Overall Injury Rates
The overall injury rate was 20.4 (95% CI 19.7–21.1) injuries
per 100 male runners and20.8 (95% CI 19.9–21.7) injuries
per 100 female runners. Meta-analytic pooling did not reveal
differences between female and male runners’ injury rates
per runner (n = 21; RR 0.99, 95% CI 0.90–1.10; p = 0.84;
I2 = 72.31) or per specific exposures (n = 6; RR 0.94, 95%
CI 0.69–1.27; p = 0.669; I2 = 85.93) (Figs.3 and 4). Due to
the small number (n = 6) of studies reporting injuries per
exposure (athlete exposures (n = 2) or hours (n = 4)), no
aggregation of overall injury rates per specific exposures
was performed.
3.5 Meta‑Regression
Moderator analyses of injury RR rates per runner revealed
an association of a higher injury risk in men and compe-
tition distances exceeding distances of 10km (p = 0.002)
(Table3). Specifically, the subgroup meta-analysis of com-
petition distance showed a significantly higher RR of 1.08
(95% CI: 1.04–1.39) for female runners with competition
Table 2 (continued)
Study Sport Cohort/
populations,
(Country)
Level Number of
participants
(female/male)
Age (years) Duration of data
collection
Injury defini-
tion
Exposure
measurement
Injury rates
(overall)
Injury rates
(female/male)
Risk
of bias
score
Hofstede etal.
[46]
Half- + Mara-
thon
SUMMUM
study
(Utrecht
Marathon)
Recreational 71/90 40.7 ± 11.7 16weeks OSTRC ques-
tionnaire,
injuries with
a moderate
to severe
reduction in
training or
competition
or time loss
NA 44.1 substan-
tial injuries
per 100
runners
Female: 52.1
substantial
injuries per
100 runners;
Male: 37.8
substantial
injuries per
100 runners
14
NA not available, 3000m SC 3000m steeplechase, AE athlete exposure, MD middle distance, LD long distance, IAAF International Amateur Athletics Federation, GPS global positioning sys-
tem, NCAA National Collegiate Athletic Association, OSTRC Oslo Sports Trauma Research Center
Fig. 2 Funnel plot of the overall differences between injury rate of
female and male runners (log risk ratios against standard error)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1031
Sex-Specific Differences in Running Injuries
distances ≤ 10km. For competition distances > 10 km, the
comparison approached but failed to reach significance
although the RR of 0.77 (95% CI: 0.58–1.02) was sugges-
tive of a lower probability of injury in male runners (Fig.5).
No meta-regression was performed for specific injuries and
moderators training duration or training mileage due to
absence of more than ten studies reporting these variables
[28].
3.6 Specific Injury Rates
Data for two specific running-related injuries were available
for synthesis.
3.6.1 Bone Stress Injuries
Four studies reported on bone stress injuries with a pooled
decreased probability for male runners (estimated RR
0.52, 95% CI 0.36–0.76, p < 0.001; I2 = 0) (Fig.6).
3.6.2 Achilles Tendinopathy
Furthermore, data pooling for two studies reporting injury
rates for Achilles tendinopathy revealed an increased
chance for male runners to have an Achilles tendon injury
(estimated RR 1.86, 95% CI 1.25–2.79, p = 0.022; I2 = 0%)
(Fig.7).
Fig. 3 Forest plot depicting the meta-analytical results comparing risk ratios for male and female runners regarding injuries per 100 runners
Fig. 4 Forest plot depicting the meta-analytical results comparing risk ratios for male and female runners regarding injuries per exposure (hours
or athlete exposures)
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1032 K.Hollander et al.
4 Discussion
The aim of this analysis was to systematically ana-
lyse the literature to reveal sex-related differences in
running-related injury rates and characteristics. While no
differences between sexes were found for overall running-
related injuries, female runners were more likely to sustain
bone stress injuries while male runner were more prone to
Achilles tendinopathies. Meta-regression showed that for
Table 3 Results of the
moderator analysis for injury
risk ratio rates per 100 female
or male runners
95% CI 95% confidence interval
Moderator No of com-
parisons (k)
Z p Risk ratio estimate (95% CI) Tau2/Q
Risk of bias 22 0.0299/68.1
Intercept − 0.88 0.378 0.051 (− 0.167 to 0.063)
Moderator 1.42 0.156 0.151 (− 0.058 to 0.359)
Level 20 0.0385/63.1
Intercept 0.68 0.495 0.044 (− 0.083 to 0.171)
Moderator 1.05 0.293 0.110 (− 0.095 to 0.316)
Age 15 0.0224/27.3
Intercept 0.87 0.387 0.116 (− 0.146 to 0.378)
Moderator − 0.81 0.419 0.119 (− 0.407 to 0.170)
Competition distance 14 0.0311/44.7
Intercept 1.71 0.088 0.144 (− 0.021 to 0.309)
Moderator − 3.05 0.002 − 0.387 (− 0.636 to − 0.138)
Fig. 5 Forest plot depicting the meta-analytical results for sub-anal-
ysis (competition distance) of risk ratios for male and female runners
regarding injuries per 100 runners. Subgroup 1 (a) represents studies
investigating runners competing in distances below or equal to 10km
and subgroup 2 (b) in distances above 10km
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1033
Sex-Specific Differences in Running Injuries
competition distances of10kmand shorter, female runners
had higher risk for injuries than malerunners.
4.1 No Differences inOverall Injury Rates
betweenFemale andMale Runners
Despite pooling data from all available epidemiological stud-
ies, no differences in overall injury rates between female and
male runners were found in this systematic review. This was
the case for both studies reporting injuries per runner and inju-
ries per specific exposures. The injury rates of 20.4 (male) and
20.8 (female) per 100 runners are in accordance with summa-
ries of injury rates from the last three decades [4]. Nonethe-
less, these rates are at the lower spectrum of published injury
rates that were reported to be up to 79.3% [66].
4.2 Shorter Competition Distances Increase theRisk
ofInjury forFemale Runners
Injury rates depend on several factors that need to be taken
into consideration, such as systematic factors (age, BMI),
running-/training-related factors (training frequency, train-
ing and racing distance, experience, level of running, foot-
wear, biomechanics), health factors (injury history) and
lifestyle factors (drinking, smoking) [6670].
Not all of these factors were reported in each study and
may vary between investigated populations. Therefore, the
moderator analysis was incorporated into this study. Only
competition distance was a statistically significant modera-
tor for an increased risk of female runners compared to male
runners when running competition distances of 10km and
shorter. Furthermore, the subanalysis revealed a tendency of
increased injury risk for male runners for longer distances
than 10km. This is in accordance with the finding that male
runners had a higher risk of sustaining injuries compared to
female runners when running high mileages (> 64km/week)
[18]. While running higher mileages are associated with
longer competition distances, this can only be used as an
estimate for this discussion. Unfortunately, there was insuf-
ficient reporting of training load (time or mileage) in the
included studies. For future studies reporting data on injury
epidemiology or risk factors, it is strongly recommended to
report the training load [71, 72].
4.3 Bone Stress Injuries Occur Twice asOften
inFemale thaninMale Runners
Female runners had twofold higher risk of having a bone
stress injury compared to male runners in this review. A
bone stress injury is an injury pattern with known sex dif-
ferences for epidemiology and risk factors [73]. Bone stress
injuries are common running-related overuse injuries due
to cumulative microtrauma to the bone [74]. Especially in
younger ages, females seem to have a higher risk for bone
stress injuries compared to male runners. For example,
Changstrom etal. [24] reported a twofold risk and Plisky
etal. [56] a 2.5-fold risk for female high school runners
of sustaining a bone stress injury compared to male high
school runnersin cross-country. In older collegiate ath-
letes, female cross-country runners were found to have
28.6 injuries per 100,000 athlete exposures (AE) compared
to 16.4 injuries per 100,000 AE in males, representing a
Fig. 6 Forest plot depicting the meta-analytical results comparing risk ratios for male and female runners regarding bone stress injuries
Fig. 7 Forest plot depicting the meta-analytical results comparing risk ratios for male and female runners regarding Achilles tendinopathy
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1034 K.Hollander et al.
statistically significant rate ratio of 1.8 [59]. In outdoor
track (100m–1500m), this difference was even higher (22.3
injuries/100,000AE for females and 7.2 injuries/100,000AE
for males, risk ratio of 3.1) [59]. One possible explanation
that has been discussed was the association of bone stress
injuries with the female athlete triad (low energy availabil-
ity, menstrual disturbance and low bone mineral density)
to explain the higher risk for bone stress injuries in female
runners [35, 75, 76]. However, while the term female athlete
triad is used only for female athletes, the more current and
more detailed concept of relative energy deficiency of sports
(RED-S) has also been described for male athletes [7779].
Despite using the same initial treatment (activity modifica-
tion, protected or non-weight bearing) of bone stress injuries
for both sexes, the further treatment differs between female
and male runners, depending on specific risk factors, such as
elevated RED-S risk, biomechanics (load rates, hip adduc-
tion, rearfoot eversion), altered hormonal status or calcium
and vitamin D intake [14, 15, 73, 80]. In summary, bone
stress injuries are more prevalent in female runners and
treatment/rehabilitation strategies should incorporate sex
as an important variable. Nonetheless, in the prevention of
bone stress injuries consideration of the sex would probably
benefit from awareness of RED-S, including screening for
low energy availability and low mineral bone density.
4.4 Achilles Tendinopathies Occur Twice asOften
inMale Compared toFemale Runners
Data from two studies showed that male runners had almost
twice the risk of having an Achilles tendinopathy as female
runners [49, 51]. This is in accordance with a systematic
review on the pathogenesis of Achilles tendinopathy [81].
The Achilles tendon transmits the generated forces from
the gastrocnemius-soleus muscle complex and, thus, is an
important tendon for propulsion during running. However,
the Achilles tendon has a poor blood supply and, therefore,
is prone to overuse injuries, such as a tendinopathy [81]. The
lifetime prevalence has been reported as high as 40–50%
in runners [13, 82] and a recent 1-year prospective study
determined the incidence rate in a cohort of recreational
runners to be 5.2% [51]. While the amount of loading is the
key factor in the etiology of Achilles tendinopathy, there
are several intrinsic (age, stress, genes, biomechanics, body
composition) and extrinsic factors (footwear) modulating the
risk for this injury [83]. Recent studies found biomechanical
(footstrike pattern, ankle dorsiflexion moments) and train-
ing-related parameters (changes in training, cold weather,
footwear, use of compression socks, mileage) as possible
risk factors [10, 51, 8486]. This summary of (possible) risk
factors does not directly explain the increased probability
for male runners to have an Achilles tendinopathy. There-
fore, we can only speculate about the possible mechanisms.
One recently published study discusses the mechanism of
the lifetime cumulated load (together with running years)
which might be higher in male runners than in female run-
ners [87]. Chronic loading needs to be taken into account
when evaluating the risk for Achilles tendinopathies.
Another explanation might be found in the hormonal dif-
ferences between women and men. For example, estrogen is
associated with collagen synthesis and could therefore influ-
ence tendon healing capacity [88, 89]. Furthermore, estro-
gen deficiency has been reported to negatively affect tendon
metabolism and healing [90]. Hormonal fluctuations that are
typical for the menstrual cycle have not been associated with
modifications of tendon function [90, 91]. A review summa-
rizes that high or low levels of sexual hormones (estrogen,
progesterone and testosterone) are not directly causing ten-
dinopathies but may play a role in tendon pathologies [92].
Therefore, individual hormonal status should be taken into
account for injury risk of female and male runners as well
as for their therapies and prevention [92].
4.5 Results oftheCurrent Review inContrast
withandinAddition toOther Systematic
Reviews
This was the first systematic review on sex-specific differ-
ences in running injuries incorporating a meta-regression
analysis to determine moderating variables and shall be
discussed in light of other systematic reviews on this topic.
This systematic review contrasts the findings of the
systematic review by van der Worp etal. [18], who found
female runners at a lower overall risk of sustaining an injury
than male runners. This finding was particularly found in
men under 40years. However, when assessing the evidence
level the authors called for caution in the interpretation of
their findings since these were based on only five high-
quality and one low-quality studies. In contrast, our review
included epidemiological studies reporting injury rates
separately for both sexes. With this approach, 26 studies
were included and meta-analyses showed no sex differences
for overall running injuries when calculated per runner or
per exposure (hours or AE). Furthermore, we were able to
conduct a meta-regression analysis showing ahigher injury
risk for female runners in competition distances of10 km
and shorter as well as atendency for a higher injury risk for
male runners in competition distances longer than 10km.
This is a new finding and in line with the increased risk for
male runners with a high weekly mileage (> 64km), which
is typically needed for longer competition distances [18].
The systematic review by Wright etal. [93] found female
sex to be a primary risk factor for lower extremity bone
stress injuries despite conflicting evidence using an explora-
tory meta-analysis incorporating three etiological studies [6,
94, 95]. The meta-analysis found a similar 2.3-fold increased
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1035
Sex-Specific Differences in Running Injuries
probability for female runners. Our meta-analysis supports
these findings and underlines the evidence for female run-
ners to be more prone to bone stress injuries based on five
included prospective studies [24, 35, 49, 56, 59]. Female
sex as a risk factor for medial tibial stress syndrome has also
been described by a meta-analysis in active individuals (not
exclusively runners [96]).
4.6 Limitations andMethodological Considerations
ofCurrent Research
This systematic review summarised data from 38 prospec-
tive studies representing more than 35,689 participants
(from 36 studies) and 518,000 athlete exposures (from 2
studies). While the distribution between female and male
runners (40.8–50.7% females) was similar and no overall
differences were found, breakdown of injury data regarding
sex and according to location or diagnosis was only possible
in six studies. Consequently, the available literature included
in this systematic review did not allow conclusions on the
sex-dependent epidemiology of pathologies other than bone
stress injuries and Achilles tendinopathies.
The meta-regression approach of this study included
several potential moderators. However, considering the
multifactorial aetiology of running-related injuries, other
confounding bias such as biomechanical or psychologi-
cal variables may have influenced the injury risk. Another
limitation was the moderate to high heterogeneity of studies
included in the overall injury meta-analyses, emphasizing
the need for further studies with a clear injury definition and
uniform data collection methods [71, 97].
Regarding quality, future studies would benefit from doc-
umenting exposure data and using medical attention injury
definitions. Furthermore, moderator analysis was only pos-
sible for 1 outcome (overall injuries per 100 runners) due to
missing descriptive information on study populations (such
as mileage, training duration, competition distances). As
seen in Table2, there are several different data collection
methods applied and injury definitions used to determine a
running injury. In accordance with recent consensus state-
ments in injury epidemiology [71] and a Delphi consensus
on running injuries [98], we encourage future running injury
research to follow these guidelines to improve the homo-
geneity of studies. From this, future meta-analyses would
benefit from comparing rates of injuriesbetween studies
[97, 99].
5 Conclusion
Sex does not seem to represent a specific risk factor when
considering the overall occurrence of injuries in running.
However, female runners more frequently sustain bone stress
injuries, while male runners have higher risk of developing
Achilles tendinopathies. Preventive measures targeting these
diagnoses may therefore be more effective when account-
ing for sex-specific aspects such as hormonal changes or
biomechanical characteristics. Regarding moderators, there
is a paucity of evidence although meta-regression identi-
fied running competition distance (cut-off 10km) as a factor
associated with higher injury rates in male runners.
Declarations
Funding Open Access funding enabled and organised by Projekt
DEAL. The research fellowship of Karsten Hollander wasfunded by
the German Research Foundation (Grant Number HO 6214/2-1). No
sources of funding were used to assist in the preparation of this article.
Conflict of Interest Karsten Hollander, Anna Rahlf, Jan Wilke, Chris-
topher Edler, Simon Steib, Astrid Junge and Astrid Zech have no con-
flicts of interest relevant to the content of this review.
Ethics approval Not applicable.
Consent to participate Not applicable.
Consent for publication Not applicable.
Data Availability Statement The data from the current study are pre-
sented in the article/electronic supplementary material and are avail-
able from the corresponding author upon request.
Code availability Not applicable.
Author Contributions KH: conceptualization, methodology, litera-
ture search, study quality assessment, formal analysis, writing (origi-
nal draft preparation). ALR: conceptualization, methodology, study
quality assessment, writing (review and editing). JW: formal analysis,
visualization, writing (review and editing). CE: literature search, writ-
ing (review and editing). SS: conceptualization, methodology, writing
(review and editing). AJ: conceptualization, methodology, study quality
assessment, writing (review and editing). AZ: conceptualization, meth-
odology, literature search, writing (review and editing), supervision.
All authors read and approved the final manuscript.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.
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Aliations
KarstenHollander1,2 · AnnaLinaRahlf3 · JanWilke4 · ChristopherEdler5 · SimonSteib6 · AstridJunge1,7 ·
AstridZech3
1 Medical School Hamburg, Hamburg, Germany
2 Department ofPhysical Medicine andRehabilitation,
Spaulding National Running Center, Harvard Medical
School, Cambridge, MA, USA
3 Department ofHuman Movement Science andExercise
Physiology, Institute ofSport Science, Friedrich Schiller
University Jena, Jena, Germany
4 Department ofSports Medicine andExercise Physiology,
Goethe University Frankfurt, Frankfurt, Germany
5 Prevention, Rehabilitation andInterdisciplinary Sports
Medicine, BG Trauma Hospital ofHamburg, Hamburg,
Germany
6 Department ofHuman Movement, Training andActive
Aging, Institute ofSports andSports Science, Heidelberg
University, Heidelberg, Germany
7 Swiss Concussion Center, Schulthess Klinik, Zürich,
Switzerland
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
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... Unfortunately, most of these studies have been carried out on male subjects. However, one study included only female runners and found that vitamin C supplementation was associated with a slowing of running speeds during training, although no effect on 5 km time trial performance was seen [96]. Thus, while AO supplementation may have a greater effect on reducing oxidative stress in females, the implications of this are unclear [97]. ...
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Full-text available
Background There is evidence of sex differences in the physiology of endurance exercise, yet most of the advice and guidelines on training, racing, nutrition, and recovery for ultramarathons are based on research that has largely excluded female athletes. The objective was therefore to review the current knowledge of sex differences in ultramarathon runners and determine if sufficient evidence exists for providing separate guidelines for males and females. Methods This systematic review was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Three databases were searched for studies investigating differences in elite and recreational male and female ultramarathon runners. Studies were included if they compared males and females and looked at outcomes relating to the performance or health of ultramarathon runners. The quality of the included studies was determined using the Grading of Recommendations Assessment Development and Evaluation (GRADE) approach. Results The search strategy identified 45 studies that met the inclusion criteria. Most studies were observational in design, with only three papers based on randomised controlled trials. The overall quality of the evidence was low. Sex differences in the predictors of ultramarathon performance; physiological responses to training, racing, and recovery; chronic and acute health issues; and pacing strategies were found. There were areas with contradictory findings, and very few studies examined specific interventions. Conclusion The results from this review suggest that the development of sex-specific guidelines for ultramarathon coaches and athletes could have a significant effect on the performance and health of female runners. At present, there is insufficient high-quality evidence on which to formulate these guidelines, and further research is required.
... Stress fractures likely have a 1-2% incidence in athletes in general [1,2]. In runners, a more vulnerable population, incidence rates likely range between 3.2 and 21% with female runners having a greater susceptibility [1,3,4]. Specifically, the incidence of femoral shaft stress fractures is difficult to ascertain as most studies do not differentiate femoral shaft fractures from femoral neck fractures, but one study reported a 21% incidence in college athletes [5]. ...
Article
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Stress fractures likely have a 1–2% incidence in athletes in general. In runners, a more vulnerable population, incidence rates likely range between 3.2 and 21% with female runners having greater susceptibility. The incidence of femoral shaft stress fractures is less well known. New basic and translational science research may impact the way clinicians diagnose and treat femoral stress fractures. By using a fictitious case study, this paper applies bone science to suggest new approaches to evaluating and treating femoral shaft stress fractures in the running population.
... Biological sex is also considered a risk factor for ankle sprain. Although there are reports of comparable incidence rates between males and females [22], a 2014 systematic review concluded that females suffer from ankle sprains at higher rates than their male counterparts [23], which aligns with recent evidence indicating sex-specific general injury patterns in team sports [24] and running [25]. Sex-based differences in factors such as joint laxity and sensorimotor control may contribute to this injury rate discrepancy [26,27], and there is early evidence that ankle sprain injury history influences future ankle sprain risk in males but not females [15]. ...
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Full-text available
Background Ankle sprains remain prevalent across most team sports. However, despite divergent ankle sprain injury rates in male and female athletes, little is known about potential sex-specific risk factors for ankle sprain. Objective To systematically investigate the sex-specific risk factors for ankle sprain. Methods Combinations of the key terms were entered into PubMed, Web of Science, Embase and Cochrane Library databases, and prospective studies reporting ankle sprain risk factors in males or females were included for meta-analysis. Results Sixteen studies were eligible for inclusion, for a total of 3636 athletes (735 female) and 576 ankle sprains (117 female). Out of 21 prognostic factors, previous ankle sprain injury (odds ratio = 2.74, P < .001), higher body mass index (SMD = 0.50, P < 0.001), higher weight (SMD = 0.24, P = 0.02), lower isometric hip abduction strength (SMD = − 0.52, P < 0.0001) and lower dynamic balance performance (SMD = − 0.48 to − 0.22, P < 0.001–0.04) were identified as risk factors in male athletes. In female athletes, out of 18 factors eligible for meta-analysis, only lower concentric dorsiflexion strength was identified as a risk factor (SMD = − 0.48, P = 0.005). Conclusion This meta-analysis provides novel evidence for different risk factor profiles for ankle sprain injuries between female and male athletes. Further studies, particularly in female athletes, are needed to strengthen the evidence.
... This finding of higher sport injuries in male than female athletes is consistent with the report of Owoeye et al observed in athletes during a national sport festival in Nigeria, but in contrast with the reports of some previous studies in other populations and sporting events (20,21). Several reasons have been postulated for sex differences in athletes (33)(34)(35)(36). Also, this study revealed that majority of the athletes who sustained sport injuries were within the age range of 20 and 25 years. ...
Article
Objective. Profiling epidemiological patterns and interventional methods for sport injuries in recurring sporting events like the Nigerian university games are useful for planning sport injury prevention and management programs. This study was designed to characterize the pattern of sports injuries and interventions implemented during the 24 th Nigerian university games. Material and Method. This was a retrospective case chart review of 159 athletes who reported for medical interventions during the 24 th Nigerian university games in 2014. Data extracted from the charts include age, gender, and incidence of sport injuries; sport types; body area affected by injury; injury presentation; stage of competition; date of injury occurrence in the competition; sport category and date of return to play; treatment(s) received; and medical personnel involved in treatments. Descriptive statistics of frequency and percentage were used to summarize data. Results. Higher incidences of sports injuries were observed among males, those within 20-25 years age range, athletes from the host institution, footballers, and body sites involving skin/ankle/foot. Furthermore, observed sport injuries were mostly sprain (50.9%) and strain (22.0%), and the injuries were commonly at the round stages of competition (67.9%), occurring 2 to 3 days after the commencement of the competition (35.8% to 18.9%), and was more in contact sports (66.7%). Cryotherapy was the most employed treatment (77%) with physiotherapists predominantly involved in injury management (43%). Conclusion. Sprain and strain affecting the lower extremities were the most common sport injuries in Nigeria university games, with higher incidences in males, participants from the host institution, and footballers. Cryotherapy was the most frequently used modality for the treatment of sport injuries.
... Musculoskeletal injuries (MSKIs) are ubiquitous during initial entry military training, impacting 12%-22% of all recruits, 1 2 with overuse injuries in the lower extremities the most frequent. [3][4][5] A common mechanism for overuse injuries is running, 6 an activity that is an integral and compulsory part of US Coast Guard (USCG) training and a requirement for graduation. Therefore, it is important to understand the factors, including the impact of footwear, that may influence the risk of MSKI. ...
Article
Introduction Musculoskeletal injuries (MSKIs) are ubiquitous during initial entry military training, with overuse injuries the most common. A common injury mechanism is running, an activity that is integral to US Coast Guard (USCG) training and a requirement for graduation. The purpose of this study was to assess the effects of a policy that allowed for athletic footwear choice on risk of lower quarter MSKI in USCG recruits. Methods A retrospective cohort study was performed that included 1230 recruits (1040 men, 190 women) who trained under a policy that allowed self-selection of athletic footwear and 2951 recruits (2329 men, 622 women) who trained under a policy that mandated use of prescribed uniform athletic shoes and served as controls. Demographic data and physical performance were derived from administrative records. Injury data were abstracted from a medical tracking database. Unadjusted risk calculations and multivariable logistic regression assessing the effects of group, age, sex, height, body mass and 2.4 km run times on MSKI were performed. Results Ankle-foot, leg, knee and lumbopelvic-hip complex injuries were ubiquitous in both groups (experimental: 13.13 per 1000 person-weeks; control: 11.69 per 1000 person-weeks). Group was not a significant factor for any of the injuries assessed in either the unadjusted or adjusted analysis, despite widespread reports of pain (58.6%), perceived injury attribution (15.7%), perceived deleterious effect on performance (25.3%), general dissatisfaction (46.3%) and intended discontinuance of use following graduation (87.7%). Conclusion MSKI continues to be a major source of morbidity in the recruit training population. The policy that allowed USCG recruits to self-select athletic footwear did not decrease or increase the risk of MSKI. While regulations pertaining to footwear choice did not influence injury outcomes, there was general dissatisfaction with the prescribed uniform athletic footwear conveyed by the recruits and widespread reports of discomfort, perceived deleterious effects from wear and intended discontinued use following training completion.
... These findings support quantifying the risk associated with sex and age of recruits undergoing basic training. In sporting populations, age [16] and sex [17] influence injury incidence; which has also been observed in military populations, undergoing basic training, for age [18] and sex [19]. Knowledge of these key influences are important when developing physical training and injury prevention programs. ...
Article
Full-text available
Purpose A lack of published epidemiological data among police recruits presents a major challenge when designing appropriate prevention programs to reduce injury burden. We aimed to report the injury epidemiology of Western Australian (WA) Police Force recruits and examine sex and age as injury risk factors. Methods Retrospective analyses were conducted of prospectively collected injury data from WA Police Force recruits between 2018–2021. Injury was defined as ‘time-loss’ and injury incidence rate per 1000 training days (Poisson exact 95% confidence intervals) was calculated. For each region and type of injury, the incidence, severity, and burden were calculated. The association between age, sex, and injury occurrence were assessed using Cox regression time-to-event analysis. Results A total of 1316 WA Police Force recruits were included, of whom 264 recruits sustained 304 injuries. Injury prevalence was 20.1% and the incidence rate was 2.00 (95%CI 1.78–2.24) injuries per 1000 training days. Lower limb injuries accounted for most of the injury burden. Ligament/ joint injuries had the highest injury tissue/pathology burden. The most common activity injuring recruits was physical training (31.8% of all injuries). Older age (Hazard Ratio = 1.5, 95%CI = 1.2 to 1.9, p = 0.002) and female sex (Hazard Ratio = 1.4, 95%CI = 1.3 to 1.6, p < 0.001) increased risk of injury. Conclusion Prevention programs targeting muscle/tendon and ligament/joint injuries to the lower limb and shoulder should be prioritised to reduce the WA Police Force injury burden. Injury prevention programs should also prioritise recruits who are over 30 years of age or of female sex, given they are a higher risk population.
... They concluded that differences between female and male runners in specific injury Foot Orthoses and Runners diagnoses should be considered in the development of individualized and sex-specific prevention and rehabilitation strategies to manage running-related injuries. 43 Francis et al 44 reported that the predominant injury in female runners is to the knee. Male runners have a more even distribution of injury between the knee, shank, and ankle-foot complex. ...
Article
Context: A variety of approaches have been proposed to prevent lower limb injuries in runners. However, the evidence for the effectiveness of interventions to reduce lower limb pain and injury after intensive running is very weak. Objective: The authors performed a systematic review to investigate the effects of foot orthoses on pain and the prevention of lower limb injuries in runners. Evidence acquisition: The authors searched the MEDLINE/PubMed, Physiotherapy Evidence Database, Scielo, and Cochrane Central (from inception to February 2022) databases for randomized controlled trials that evaluated the effects of foot orthoses in runners. The authors then calculated mean differences and 95% confidence intervals from these trials. Heterogeneity was assessed using the I2 test. Furthermore, the authors compared the criteria between runners with foot orthoses and ones with no intervention (control group). Evidence synthesis: Twelve studies (5321 runners) met our review criteria. The control and the foot orthoses group sustained 721 (37%) and 238 (24%) injuries, respectively. Compared with the control group, the use of foot orthoses resulted in a significant reduction in lower limb injury risk (risk ratio = 0.6; 95% confidence interval, 0.5-0.7; P = .00001, I2 = 54%; 7 studies, N = 2983: moderate-quality evidence). Moreover, the foot orthoses group corresponded to a 40% reduction in the risk of developing lower limb injuries. Conclusions: The use of foot orthoses may help reduce the incidence of lower limb injuries and pain in runners.
... However, runningrelated injuries are common with incidences being as high as 80% (van der Worp et al., 2015;van Gent et al., 2007). Despite considerable effort, their management remains a major challenge (Barton et al., 2016;Hollander et al., 2021;Mulvad, Nielsen, Lind, & Ramskov, 2018). ...
... more often in female than in male athletes and 3.1-3.6fold more often in female than in male military recruits 11,12,[30][31][32][33][34] . The higher incidence in both female athletes and female military recruits might be partially explained by the association of bone stress injuries with low energy availability, a condition that is typically more prevalent in active women than in men. ...
Article
Bone stress injuries, including stress fractures, are overuse injuries that lead to substantial morbidity in active individuals. These injuries occur when excessive repetitive loads are introduced to a generally normal skeleton. Although the precise mechanisms for bone stress injuries are not completely understood, the prevailing theory is that an imbalance in bone metabolism favours microdamage accumulation over its removal and replacement with new bone via targeted remodelling. Diagnosis is achieved by a combination of patient history and physical examination, with imaging used for confirmation. Management of bone stress injuries is guided by their location and consequent risk of healing complications. Bone stress injuries at low-risk sites typically heal with activity modification followed by progressive loading and return to activity. Additional treatment approaches include non-weight-bearing immobilization, medications or surgery, but these approaches are usually limited to managing bone stress injuries that occur at high-risk sites. A comprehensive strategy that integrates anatomical, biomechanical and biological risk factors has the potential to improve the understanding of these injuries and aid in their prevention and management. Bone stress injuries, commonly referred to as stress reactions or stress fractures, result from repeated overloading of bone and are thought to involve an imbalance in microdamage formation and repair. This Primer provides an overview of the epidemiology, pathobiology, risk factors, diagnostic approaches, treatments and consequences of bone stress injuries.
Article
Background Sex differences in sports medicine are well documented. However, no studies to date have reviewed the rate at which sex is reported and analyzed in the athlete-specific orthopaedic sports medicine literature. Purpose To determine the rates of reporting and analyzing patient sex in athlete-specific sports medicine literature. Study Design Systematic review; Level of evidence, 4. Methods Articles published by the 3 journals of the AOSSM ( American Journal of Sports Medicine [ AJSM], Orthopaedic Journal of Sports Medicine, and Sports Health: A Multidisciplinary Approach) between 2017 and 2021 were considered for inclusion. Original sports medicine research studies that isolated athletes were included. Studies that isolated sports that are predominantly single sex at the college and/or professional levels (football, baseball, softball, and wrestling) were excluded. Results Of the 5140 publications screened, 559 met the inclusion criteria. In total, 93.9% of all studies reported patient sex, and 34.7% of all studies analyzed patient sex. However, 143 studies only included males and 50 studies only included females (n = 193). When excluding these single-sex studies, analysis of the remaining 366 studies found that the rate of sex-specific analysis increased to 53.0%. Rates of reporting patient sex did not significantly differ by journal or by year. Similarly, rates of analyzing patient sex did not differ by year, but Sports Health analyzed sex the most frequently, and AJSM analyzed sex the least frequently ( P = .002). Studies that isolated college (84.1%), youth (66.7%), or recreational (52.6%) athletes analyzed sex at or above the overall rate of 53.0%, but studies of elite athletes (35.7%) tended to analyze sex less frequently. Conclusion Patient sex is well reported in the athlete-specific sports medicine literature (93.9% of included studies reported sex), demonstrating that most studies include sex as a demographic variable. However, patient sex was analyzed only in 53.0% of studies that included both male and female patients. Given that athlete-specific sex differences are known to exist within the field of sports medicine, many studies that could benefit from using patient sex as a variable for analysis likely fail to do so.
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Background: While some studies have failed to reveal any significant relationship between magnetic resonance imaging (MRI) grading and return to sports after bone stress injuries, others have reported either a linear or nonlinear relationship. Purpose: To evaluate the prognostic value of MRI grading for time to return to sports and rate of return to sports after bone stress injuries. Study Design: Systematic review and meta-analysis. Methods: A systematic search was performed in PubMed, Web of Science, SPORTDiscus, and Google Scholar. Studies reporting return to sports data after bone stress injuries using MRI grading systems were included in this review. The risk of bias was evaluated using the Quality in Prognosis Studies tool. Meta-analyses were performed to summarize the mean time to return to sports. The Pearson correlation was used to determine the relationship between time to return to sports and MRI grade. A meta-analysis of proportions was conducted to determine the percentage of athletes who successfully returned to sports. Results: A total of 16 studies with 560 bone stress injuries met inclusion criteria. Higher MRI-based grading was associated with an increased time to return to sports (P < .00001). Pooled data revealed that higher MRI-based grading correlated with a longer time to return to sports (r = 0.554; P = .001). Combining all anatomic locations, the mean time to return to sports was 41.7 days (95% CI, 30.6-52.9), 70.1 days (95% CI, 46.9-93.3), 84.3 days (95% CI, 59.6-109.1), and 98.5 days (95% CI, 85.5-112.6) for grade 1, 2, 3, and 4 injuries, respectively. Trabecular-rich sites of injury (eg, pelvis, femoral neck, and calcaneus) took longer to heal than cortical-rich sites of injury (eg, tibia, metatarsal, and other long-bone sites of injury). Overall, more than 90% of all athletes successfully returned to sports. Conclusion: The findings from this systematic review indicate that MRI grading may offer a prognostic value for time to return to sports after the nonsurgical treatment of bone stress injuries. Both MRI grade and location of injury suggest that individually adapted rehabilitation regimens and therapeutic decisions are required to optimize healing and a safe return to sports.
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Despite the worldwide popularity of running as a sport for children, relatively little is known about its impact on injury and illness. Available studies have focused on adolescent athletes, but these findings may not be applicable to preadolescent and pubescent athletes. To date, there are no evidence or consensus-based guidelines identifying risk factors for injury and illness in youth runners, and current recommendations regarding suitable running distances for youth runners at different ages are opinion based. The International Committee Consensus Work Group convened to evaluate the current science, identify knowledge gaps, categorise risk factors for injury/illness and provide recommendations regarding training, nutrition and participation for youth runners.
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Problem: Awareness and knowledge of the Female Athlete Triad (Triad) among physicians and allied health professionals is well studied; however, awareness of Relative Energy Deficiency in Sport (RED-S) is largely unknown. The purpose of this study is to assess awareness and comfort in treating patients with Triad and RED-S among providers attending a sports medicine conference. Methods: Cross-sectional study of physicians and allied health professionals attending a sports medicine conference. Conference attendees (n=163, 54% female) completed a survey on awareness of and confidence in treating Triad and RED-S (response rate=42%). Results: Most participants (76%) were aware of Triad compared to fewer with awareness regarding RED-S (29%). More participants (33%) reported feeling comfortable treating athletes with the Triad compared to RED-S (13%) (p<0.001). There was no significant difference between physicians and allied health providers in outcome measures except that physicians trended towards being more likely to have heard of RED-S (p=0.07). Physicians with fellowship training in sports medicine reported greater comfort treating both Triad and RED-S compared with non-fellowship trained physicians (all p<0.05). Discussion: Knowledge in treating athletes with Triad and RED-S is low across professions, training backgrounds, and practice locations. Educational efforts are necessary for both recognition and clinical management skills.
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The footstrike pattern of an athlete is understood as the way the foot touches the ground. Over the years, several definitions and techniques to classify and quantify footstrike patterns have been described. Therefore, this narrative review summarizes the existing classifications of footstrike patterns, gives suggestions for further use of these classifications, and provides a summary of the relationship between footstrike patterns and the occurrence of overuse injuries. Footstrike patterns are classified by using nominal (e.g. forefoot strike, midfoot strike, rearfoot strike) or continuous variables (e.g. footstrike angle). Possible assessments include visual, video-based, 3D-biomechanical, force plate-based or inertial measurement unit-based analysis. Scientists, coaches, and clinicians can choose between different methods to analyze footstrike patterns in runners. All approaches to classify footstrike patterns have advantages and limitations. In certain situations, it might be beneficial to combine these methods. Despite great efforts in analyzing footstrike patterns, relationships between footstrike patterns and running-related injuries are mostly unclear at present. Based on the current literature, causal links to overuse injuries, recommendations to change running technique, and other simplifications solely based on the footstrike pattern must be considered critically.
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Injury and illness surveillance, and epidemiological studies, are fundamental elements of concerted efforts to protect the health of the athlete. To encourage consistency in the definitions and methodology used, and to enable data across studies to be compared, research groups have published 11 sport-specific or setting-specific consensus statements on sports injury (and, eventually, illness) epidemiology to date. Our objective was to further strengthen consistency in data collection, injury definitions and research reporting through an updated set of recommendations for sports injury and illness studies, including a new Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist extension. The IOC invited a working group of international experts to review relevant literature and provide recommendations. The procedure included an open online survey, several stages of text drafting and consultation by working groups and a 3-day consensus meeting in October 2019. This statement includes recommendations for data collection and research reporting covering key components: defining and classifying health problems; severity of health problems; capturing and reporting athlete exposure; expressing risk; burden of health problems; study population characteristics and data collection methods. Based on these, we also developed a new reporting guideline as a STROBE Extension—the STROBE Sports Injury and Illness Surveillance (STROBE-SIIS). The IOC encourages ongoing in- and out-of-competition surveillance programmes and studies to describe injury and illness trends and patterns, understand their causes and develop measures to protect the health of the athlete. Implementation of the methods outlined in this statement will advance consistency in data collection and research reporting.
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This report aims to introduce the fundamental features of the free Jamovi software to academics in the field of educational measurement for use at undergraduate and graduate level research. As such, after introducing the R based interface and the integrated development environment, the core functions of Jamovi are presented, the installation for GNU7Linux, Windows, and MacOS is explained and screenshots of frequently conducted statistical analyses are provided. Additionally, the module support of Jamovi is presented, along with a use case scenario on developing further functionality for Jamovi using modules. Specifically, conducting meta-analysis and Bayesian statistics using modules in Jamovi are explained through examples.
Article
Purpose: Despite the health benefits of running, the prevalence of running injuries (RRI) remains high. The underlying risk factors between these injuries are still not well understood. Therefore, the aim of this study was to compare biomechanical, anthropometric and demographic injury risk factors between different locations in injured recreational runners. Methods: In this retrospective case-control analysis, 550 injured runners (49.6% female) with a medically diagnosed RRI were included. All runners had undergone an instrumented treadmill analysis to determine habitual footstrike pattern, vertical instantaneous load rate, peak vertical ground reaction force (vGRF) and cadence. Injuries were classified by location according to a recent consensus statement. A logistic regression model was used to determine the association between the biomechanical parameters and RRI locations. As injuries can be associated with age, sex and BMI, these variables were also entered into the logistic regression. Results: Strike pattern and peak vGRF were the only biomechanical variable distinguishing an injury from the group of injuries. A midfoot strike differentiated Achilles tendon injuries (OR 2.27; 90%CI 1.17 to 4.41) and a forefoot strike distinguished posterior lower leg injuries (OR 2.59; 90%CI 1.50 to 4.47) from the rest of the injured group. Peak vGRF was weakly associated with hip injuries (OR 1.14; 90%CI 1.05 to 1.24). Female sex was associated with injuries to the lower leg (OR 2.65; 90%CI 1.45 to 4.87) and hip/groin (OR 2.22; 90%CI 1.43 to 3.45). Male sex was associated with Achilles tendon injuries (OR 1.923: 90%CI 1.094 to 3.378). Conclusion: Sex, foot strike pattern and vGRF were the only factors that distinguished specific injury locations from the remaining injury locations.
Article
Background: The role of biomechanical variables of running gait in the development of running related injury has not been clearly elucidated. Several systematic reviews have examined running biomechanics and its association with particular running related injuries. However, due to retrospective designs, inferences into the cause of these injuries are limited. Although prospective studies have been completed, no quantitative analysis pooling these results has been completed. Methods: A systematic review of MEDLINE, CINAHL, and PubMed was completed. Articles included used prospective study designs, human subjects currently completing a regular running program, and a minimum 12-week follow-up period. Excluded articles had no biomechanical data reported, participants who were beginning runners or military recruits, or had an intervention provided. Findings: Thirteen studies met these criteria. Pooled analyses were completed if two or more studies were available with samples that investigated the same sex and competition level. A qualitative synthesis was completed when pooled analysis was not possible. Five unique running samples were identified and allowed for pooled analyses of variables in mixed-sex collegiate runners and female recreational runners. Moderate evidence exists for increased hip adduction and reduced peak rearfoot eversion as risk factors for running related injury in female recreational runners. Variables differed in other samples of runners. Interpretation: A runner's sex and competition level may affect the relationship between biomechanical factors and the development of running related injury. Hip adduction and rearfoot eversion may be important factors related to running related injury in female recreational runners. Further investigation of biomechanical factors in running injury is warranted.
Article
Background Because of the complex and multifaceted nature of running injuries, a multifactorial approach when investigating running injuries is required. Hypothesis Compared with uninjured runners, injured runners would exhibit different running biomechanics, display more fatigue changes, and would run a greater weekly running volume; more injured runners would also report having a previous injury. Study Design Prospective cohort study. Level of Evidence Level 4. Methods At commencement of the study, data were collected on demographics, anthropometrics, training history, previous injury history, and center-of-mass accelerations during a long-distance overground run. Participants completed weekly training diaries and were monitored for 1 year for an injury. Results A total of 76 runners completed the study, with 39 (22 male; 17 female) reporting an injury. Compared with male uninjured runners, male injured runners were heavier and ran a greater weekly distance. Male runners (injured and uninjured) exhibited increases in mediolateral center-of-mass accelerations during the run. Compared with female uninjured runners, female injured runners were heavier, ran with longer flight times and lower step frequencies, and more of them had reported an injury in the previous year and had increased speed training in the weeks prior to injury. Over 60% of male injured runners and over 50% of female injured runners had increased their weekly running distance by >30% between consecutive weeks at least once in the 4 weeks prior to injury. Conclusion Factors that may be related to injury for male runners include being heavier, running a greater weekly distance, and exhibiting fatigue changes in mediolateral center-of-mass accelerations. Factors that may be related to injury for female runners include being heavier, having an injury in the previous year, running with longer flight times and lower step frequencies, and increasing speed training prior to injury. Increases in weekly running distance in 1 consecutive week (particularly >30%) needs to be monitored in training, and this along with the other factors found may have contributed to injury development. Clinical Relevance This study found that multiple factors are related to running injuries and that some factors are sex specific. The findings can aid in injury prevention and management.
Article
Background Shoe cushioning is expected to protect runners against repetitive loading of the musculoskeletal system and therefore running-related injuries. Also, it is a common belief that heavier runners should use footwear with increased shock absorption properties to prevent injuries. Purpose The aim of this study was to determine if shoe cushioning influences the injury risk in recreational runners and whether the association depends on the runner’s body mass. Study Design Randomized controlled trial; Level of evidence, 1. Methods Healthy runners (n = 848) randomly received 1 of 2 shoe prototypes that only differed in their cushioning properties. Global stiffness was 61.3 ± 2.7 and 94.9 ± 5.9 N/mm in the soft and hard versions, respectively. Participants were classified as light or heavy according to their body mass using the median as a cut-off (78.2 and 62.8 kg in male and female runners, respectively). They were followed over 6 months regarding running activity and injury (any physical complaint reducing/interrupting running activity for at least 7 days). Data were analyzed through time-to-event models with the subhazard rate ratio (SHR) and their 95% confidence interval (CI) as measures of association. A stratified analysis was conducted to investigate the effect of shoe cushioning on the injury risk in lighter and heavier runners. Results The runners who had received the hard shoes had a higher injury risk (SHR, 1.52 [95% CI, 1.07-2.16]), while body mass was not associated with the injury risk (SHR, 1.00 [95% CI, 0.99-1.01]). However, after stratification according to body mass, results showed that lighter runners had a higher injury risk in hard shoes (SHR, 1.80 [95% CI, 1.09-2.98]) while heavier runners did not (SHR, 1.23 [95% CI, 0.75-2.03]). Conclusion The injury risk was higher in participants running in the hard shoes compared with those using the soft shoes. However, the relative protective effect of greater shoe cushioning was found only in lighter runners. Registration NCT03115437 (ClinicalTrials.gov identifier)