Vol.:(0123456789)
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Journal of Muscle Research and Cell Motility (2023) 44:115–122 
https://doi.org/10.1007/s10974-022-09633-1
REVIEW
Participation and performance characteristics in half‑marathon run: 
a brief narrative review
Pantelis Theodoros Nikolaidis1 · Beat Knechtle2
Received: 13 April 2022 / Accepted: 17 October 2022 / Published online: 3 November 2022 
© The Author(s) 2022
Abstract
Half-marathon (HM) is a running sport of increasing popularity in both sexes and in all age groups worldwide during the 
last years. Many studies have examined several aspects of HM, such as performance and participation trends, sex and age 
differences, physiological correlates, and training; however, no comprehensive review has ever been contacted to summa-
rize the recently accumulated knowledge. Therefore, the aim of the present study was to review all previous research in this 
sport, focusing on participation and performance aspects. It was shown that HM runners had similar anthropometric and 
physiological characteristics as full-marathon runners which should be attributed to the affinity of these two races in terms 
of metabolic demands. Performance in HM was related with superior scores in aerobic capacity (maximal oxygen uptake, 
anaerobic threshold and running economy) and training characteristics (sport experience, weekly distance, training speed, 
frequency of sessions and long single endurance run distance), and lower scores in adiposity-related scores (e.g. body mass, 
body mass index, body fat percentage and skinfold thickness). Considering the popularity of HM race and the lack of many 
original studies (compared to FM race), this is an exciting field for scientific research with a large potential for practical 
applications, since the majority of HM runners are amateur runners in need of sex-, age- and performance-tailored exercise 
prescription.
Keywords Aerobic capacity · Anaerobic threshold · Endurance · Exercise · Nutrition · Participation · Running economy
Introduction
Half-marathon (HM) is a running event of increasing popu-
larity in both sexes and in all age groups worldwide during 
the last years (Bonet et al. 2022). Although full-marathon 
(FM) is the most popular endurance running distance, most 
runners participate in HM (Cribari et al. 2013). Many studies 
have examined several aspects of HM, such as performance 
and participation trends, sex and age differences, physio-
logical correlates, and training,however, no comprehensive 
review has ever been contacted to summarize the accumu-
lated knowledge. Therefore, the aim of the present study is 
to review all previous research covering all aspects such as 
epidemiological trends, the role of age and sex, especially 
focusing on the physiological aspects.
Participation
Main aspects of participation included the numbers of par-
ticipants in HM and whether these numbers would change 
across calendar years, sex differences in participation (typi-
cally examined using the men-to-women ratio) and age 
across years, whereas these aspects may vary by national-
ity. In 2016, more finishers and events were observed in HM 
than in FM (Fig. 1). Participation trends in HM have been 
examined with regards to FM in a single country, Switzer-
land, where those participating in HM are three times more 
than those competing in FM races (Anthony et al. 2014). 
It has been observed in 226,754 HM and 86,419 FMrun-
ners competing in Switzerland between 2000 and 2010 that 
the number of HM increased from 2000 to 2010 for both 
men (+ 231%) and women (+ 299%), whereas the number 
of male and female FM runners increased until 2005 only 
and decreased thereafter (Anthony et al. 2014). A study on 
 * Pantelis Theodoros Nikolaidis 
 
pnikolaidis@uniwa.gr
1 
School of Health and Caring Sciences, University of West 
Attica, Ag. Spyridonos, 122 43 Egaleo, Athens, Greece
2 
Institute of Primary Care, University of Zurich, Zurich, 
Switzerland
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Journal of Muscle Research and Cell Motility (2023) 44:115–122
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508,108 runners (125,894 female and 328,430 male HM and 
10,205 female and 43,489 male FM) competing between 
1999 and 2014 in all flat HM and FM held in Switzerland 
showed that the number of women and men increased across 
years in both HM and FM, and there were 12.3 times more 
female HM than female FM and 7.5 times more male HM 
than male FM (Knechtle et al. 2016a). These different pro-
portions of women and men competing in HM and FM races 
indicated that HM was a sport where relatively more women 
participated compared to FM. The abovementioned prelimi-
nary studies (Anthony et al. 2014; Knechtle et al. 2016a) 
highlighted a larger participation in HM than in FM races, 
and this trend was more striking in women than in men. An 
explanation of this discrepancy between sexes might be that 
women could be considered as more ‘novice’ runners than 
men, and consequently, should run more HM before enter-
ing FM races.
The participation and performance in HM may vary by 
nationality, and it would be interesting to focus on trends 
concerning East African runners who were considered as 
experts in long distance running events (Knechtle et al. 
2016b).Actually, a study of ~ half million HM and FM run-
ners originating from 126 countries and competing between 
1999 and 2014 in all road-based HM and FM held in Swit-
zerland reported that, in HM, 48 women (0.038%) and 63 
men (0.019%) were from Ethiopia and 80 women (0.063%) 
and 134 men (0.040%) from Kenya, whereas in FM, three 
women (0.029%) and 15 men (0.034%) were from Ethiopia 
and two women (0.019%) and 33 men (0.075%) from Kenya 
(Knechtle et al. 2016b). These findings suggested that the 
largest percentage of participants in HM is of local origin.
In both women and men, the best performance in HM and 
FM held in Switzerland was achieved by East African run-
ners with Ethiopian and Kenyan runners being the youngest 
in both sexes and formats of race (Knechtle et al. 2016b). 
These findings showed that East-African runners were the 
fastest in both HM and FM although they represented the 
smallest percentage of participants (Knechtle et al. 2016b). 
This observation was in agreement with an analysis of the 
world’s best HM runners during 1999–2015, where it was 
shown that most of them were Kenyans (30% in women 
and 57% in men) (Nikolaidis et al. 2017). According to 
the abovementioned effect of nationality, the characteris-
tics of HM differed from country to country, e.g. the local 
people tended to participate more to races taking place in 
their country than foreigners. Furthermore, the participa-
tion and performance may change across years. Actually, a 
study examined the changes in participation, performance 
and age of East African runners competing in HM and FM 
held in Switzerland between 2000 and 2010 indicated that 
across time, the number of Kenyan and Ethiopian finishers 
remained stable while the number of Non-African finishers 
increased for both women and men in both HM and FM 
(Cribari et al. 2013). This difference in participation trends 
across years by nationality might be due to the increase of 
local ‘recreational’ runners, while the number of the most 
competitive runners coming from abroad would remain sta-
ble. To sum up, more runners compete in HM than in FM, 
and the fastest HM runners are East Africans.
Age of peak performance
Every sport has its own age of peak performance and thus, 
it would be important to estimate at which age HM runners 
achieve their peak performance in order to set long-term 
training goals. The largest part of the finishers in HM and 
FM held in Switzerland of both genders was assigned to 
age group 40–44 years in HM (19.5% of the total number 
of finishers) and FM (22.0% of finishers) (Anthony et al. 
2014). For both HM and FM races, most of the female and 
male finishers were recorded in age group 40–44 years 
(Knechtle et al. 2016a; Knechtle and Nikolaidis 2018). 
In HM, women (41.4 years) were at the same age as men 
(41.3 years),in FM, women (42.2 years) were at the same 
age than men (42.1 years),however, women and men FM 
runners were older than their counterpart HM runners (Kne-
chtle et al. 2016a). With regards to the age of peak perfor-
mance, it may differ depending on the performance level, 
i.e. whether all or the top finishers were considered. For 
instance, in the world’s largest HM race—the Göteborgs-
Varvet—U40 was the fastest age group when all finishers 
were analyzed, whereas U35 were the fastest when the top 
10 were considered (Knechtle and Nikolaidis 2018). Moreo-
ver, an analysis of the world’s best HM runners indicated 
Fig. 1  Finishers and events in 
USA in 2016. Source: http:// 
www. runni ngusa. org (accessed 
on 16/9/2017)
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an age of 26–27 years, which was younger than in FM 
and 100 km ultra-marathon races (Nikolaidis et al. 2017) 
(Fig. 2). This observation was confirmed by a study using 
non-linear regression on world records in HM, where the age 
peak performance was ~ 27 years (Nikolaidis et al. 2018).
The effect of age on HM performance differed from other 
endurance sports (Sterken 2005). It has been shown that 
age-related losses in endurance performance did not occur 
before the age of 50 years with mean FM and HM race times 
being identical for the age groups 20–49 years, whereas age-
related performance decreases of the 50–69-year-old sub-
jects were only in the range of 2.6–4.4% per decade (Leyk 
et al. 2007). These results suggested that the majority of 
older athletes were able to maintain a high degree of physi-
cal plasticity supporting the hypothesis that lifestyle factors 
had considerably stronger influences on functional capacity 
than the factor age (Leyk et al. 2007). No significant age-
related decline in performance appeared before the age of 
55 years, whereas only a moderate decline is seen thereafter 
(Leyk et al. 2010). Performance losses in middle age were 
mainly due to a sedentary lifestyle, rather than biological 
aging (Leyk et al. 2010). In summary, the average age of HM 
runners was 40–44 years, and the age of peak performance 
was younger than 35–40 years.
Performance trends
Performance in HM might be examined using either race 
time or average speed. In HM held in Switzerland, women 
(10.29 ± 3.03 km/h) were faster than men (10.22 ± 3.06 km/h) 
as well as in FM, women (14.77 ± 4.13 km/h) were faster 
than men (14.48 ± 4.07 km/h) (Knechtle et al. 2016a). 
Slower HM race time by 13% (10% in FM) in women than 
in men was observed in a study of all and top 10 finishers 
aged 20–79 years (Leyk et al. 2007). Moreover, a sex dif-
ference of ~ 14% was noted in the world best HM runners 
(Nikolaidis et al. 2017). Sex differences in performance may 
also be attributed to the different adaptation to long-term 
exercise between women and men. A study on 16 males and 
16 females preparing for a HM revealed a larger increase in 
the average daily metabolic rate in men than in women sug-
gesting exercise stimulates more habitual physical activity 
and diet-induced thermogenesis in men than in women (Mei-
jer et al. 1991). The variation of performance from a race to 
race seems to depend on competitive experience and attitude 
toward competing and was found 4.2% for the fastest quartile 
of men runners in HM with men, slower and younger run-
ners presenting more variation (Hopkins and Hewson 2001). 
Physiological, anthropometric and training correlates of 
performance were examined in following sections,however, 
they might differ depending on performance level, consider-
ing that in the elite level other factors (e.g. shoe technology) 
would play an important role (Goss et al. 2022).
Physiological correlates of performance
Physical fitness is classified as health- (consisting of body 
composition, aerobic capacity, muscular strength, muscular 
endurance and flexibility, i.e. components related directly 
to health) or sport-related (consisting of those components 
related to sport performance such as speed or reaction time). 
Based on the relatively long duration of a HM, it is reason-
able to assume that performance in this sport relates to maxi-
mal oxygen uptake  (VO2max), since it relies mostly on the 
aerobic energy transfer system. One of the oldest studies on 
HM (Williams and Nute 1983) already identified  VO2max 
and anaerobic threshold as correlates of race time (r = −0.81 
and r = −0.88, respectively). In addition, in male recreational 
runners, HM race time correlated with VO2max (r = −0.64), 
speed at VO2max (r = −0.84) and anaerobic threshold 
(r = −0.79) (Alvero-Cruz et al. 2019). These relationships 
were confirmed in HM runners with asthma, too,actually, 
the HM pace of asthmatic runners correlated largely with 
 VO2max (r = 0.86) and almost perfectly with running speed 
Fig. 2  Race time and age of the best runners by race distance and sex. Based on Nikolaidis and colleagues Nikolaidis et al. 2017. Error bars rep-
resent standard deviations. HM = half-marathon, FM = full-marathon
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Journal of Muscle Research and Cell Motility (2023) 44:115–122
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at a blood lactate concentration of 2 mmol.L−1 (r = 0.97) 
(Freeman et al. 1990). In amateur runners (nine men, age 
36 years), HM time was almost perfectly correlated with 
 VO2max (r = 0.91) and speed corresponding to  VO2max 
(r = 0.90) (Santos et al. 2012). Furthermore, a comparison 
among 400–800 m, 1500–3000 m and HM women runners 
showed that HM runners had the highest  VO2max (Nurme-
kivi et al. 1998). In runners, the pace of HM was comparable 
to the maximal lactate steady state velocity (Legaz-Arrese 
et al. 2011).
Considering the physiological relevance of HM and FM 
races, previous research examined the relationship of per-
formances in these two races (Salinero et al. 2017; Karp 
2007; Coyle 2007). A research on 84 male amateur FM run-
ners (aged 41.0 years, finish time 226.0 min) showed a very 
large correlation between FM and HM race time (r = 0.81) 
(Salinero et al. 2017). In 2004 U.S. Olympic Marathon Tri-
als qualifiers (104 men, 151 women), FM performance cor-
related to HM performance (Karp 2007). Maintaining the 
world record pace for the HM in the FM would lead to run 
a FM in 1:58 h:min (Coyle 2007).
In addition to the abovementioned correlation studies, an 
approach to study determinants of performance in HM is to 
develop prediction equations of race time based on corre-
lates (Pérez et al. 2012; Gómez-Molina et al. 2017), which 
include usually two steps, first, the development of an equa-
tion in a sample of runners, and second, the validation of this 
equation in another sample. For instance, a study on male 
runners considered training-related and anthropometric vari-
ables, and laboratory data from a graded exercise test (GXT) 
on a treadmill  (VO2max, speed at the anaerobic threshold, 
peak speed) and biomechanical variables (contact and flight 
times, step length and step rate) (Gómez-Molina et al. 2017). 
This study found that HM race time could be predicted to 
90.3% by variables related to training and anthropometry, 
94.9% by physiological variables, 93.7% by biomechanical 
parameters and 96.2% by a general equation, and using these 
equations, the predicted time was significantly correlated 
with performance (r = 0.78, 0.92, 0.90 and 0.95, respec-
tively) (Gómez-Molina et al. 2017). Moreover, HM race 
speed could be predicted as V21k (km/h) = (V2*1.085) + (−
0.282*bLA2) −0.131, r2 = 0.97, ETE = 0.414 km/h, where 
V2 was the speed during a test in track at constant pace 
over 2400 m slightly higher than the competition expected 
pace and bLA2 blood lactate (Pérez et al. 2012). A research 
examined the ratio between running speed and heart rate 
(HR) as predictor for aerobic capacity (based on the assump-
tion that lower HR at a given speed is expected for more fit 
individuals), and subsequently race time in 10 km, HM and 
FM (Altini et al. 2017). This study showed that the speed 
to HR ratio provides higher accuracy in aerobic capacity 
estimation compared to resting HR or no-physiological data, 
and large correlations between aerobic capacity and race 
time (r = 0.56–0.61) (Altini et al. 2017). In addition to the 
comparison with laboratory exercise testing, HM perfor-
mance has been investigated with field tests such as Cooper 
test (Alvero-Cruz et al. 2019; Alvero-Cruz et al. 2020). For 
instance, Alvero-Cruz and colleagues (Alvero-Cruz et al. 
2019; Alvero-Cruz et al. 2020) observed an almost perfect 
correlation between HM race time and Cooper test distance, 
and high predictive validity of this test.
Another methodological approach that may provide indi-
rect information for the identification of correlates of per-
formance is to examine the acute physiological responses 
to a HM by comparing values pre- and post-race (Dressen-
dorfer 1991). For instance, runners were tested in a graded 
exercise test before and after a HM,time to exhaustion (6.0 
vs 4.1 min),  VO2max (60.0 vs 56.3 ml.kg−1.min−1), peak 
respiratory exchange ratio (RER; 1.18 vs 1.06), and peak 
La (9.7 vs 7.8 mmol.L−1) decreased after the HM (Dres-
sendorfer 1991). Other studies focus on muscular acute 
responses to a HM (Boccia et al. 2017 Boccia et al. 2017).
For instance, intermittent isometric rapid contractions of the 
knee extensor muscles were performed the day before and 
immediately after a HM, where it was observed that HM had 
a greater negative effect on repeated, rather than on single, 
attempts of maximal force production (Boccia et al. 2017). 
In another study of this research group that examined pre- 
and post-HM race maximum voluntary contractions of knee 
extensor muscles, it was found that post-race knee extensors 
showed a decreased strength (−13.9%) and a reduction in 
EMG amplitude (−13.10%) and in CV (−6.8%, p = 0.032) 
(Boccia et al. 2017). Moreover, compared to FM, a HM race 
induced smaller vertical jump height reduction and less self-
reported muscle pain suggesting less muscular fatigue in 
HM than in FM race (Coso et al. 2017). The abovemen-
tioned studies (Boccia et al. 2017; Boccia et al. 2017; Coso 
et al. 2017) highlighted the importance of muscular fitness 
in addition to aerobic capacity for HM race. In line with the 
findings of the correlation studies presented in this section, 
a comparative study among different performance groups 
highlighted the role of physiological characteristics (Ogueta-
Alday et al. 2018). Particularly, Oguea-Alday and colleagues 
(Ogueta-Alday et al. 2018) found that HM runners with race 
time 70 min had superior  VO2max and running economy 
than those with race time 80 min, 90 min and 105 min. In 
summary, performance on HM race would depend mostly on 
 VO2max and other indices of aerobic capacity.
Anthropometric and training correlates 
of performance
Performance in HM is not related only to physiological char-
acteristics, but also to anthropometry and training habits 
(Campbell 1985; Friedrich et al. 2014; Knechtle et al. 2012; 
Zillmann et al. 2013). For instance, a study in university HM 
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runners observed that distance run per week and number of 
weeks training for the event, and body mass index (BMI) were 
predictors of HM race time (Campbell 1985). In female and 
male recreational HM runners, HM race time was related 
to body fat percentage (BF), running speed during training, 
and BMI was predictor of performance only in men (Frie-
drich et al. 2014), whereas elsewhere race time was more 
strongly associated with anthropometry in women than men 
(Knechtle et al. 2010). For male HM runners, BMI, BF and 
speed in running during training were related to race time 
(Zillmann et al. 2013). Furthermore, in female finishers of 
the ‘Half Marathon Basel’, race time was largely related to 
body mass, BMI, BF (r = 0.48–0.60), and could be predicted 
by the formula ‘166.7 + 1.7*midaxilla skinfold – 6.4*speed 
in training’(R2 = 0.71) (Knechtle et al. 2011). In HM, FM and 
UM master runners (> 35 years old), BF and training charac-
teristics, not skeletal muscle mass, were associated with run-
ning times (Knechtle et al. 2012). A comparison between HM 
and FM men runners showed that HM runners were heavier, 
had longer legs, thicker upper arms, a thicker thigh, a higher 
sum of skinfold thicknesses, a higher body fat percentage and 
a higher skeletal muscle mass, fewer years of experience, com-
pleted fewer weekly training kilometers, and fewer weekly run-
ning hours (Zillmann et al. 2013). The relationship of HM with 
training characteristics might be explained by exercise-induced 
chronic cardiometabolic adaptations resulting in improvements 
in  VO2max, a major predictor of performance (Meyler et al. 
2021).
A comparative study between HM and FM races showed 
that a fast race time was associated with high weekly train-
ing volume (> 32 km) and a long training single distance 
(> 21 km) in HM, and high weekly training volume (> 65 km) 
in FM runners (Fokkema et al. 2020). Furthermore, a research 
among groups different performance groups confirmed the role 
of anthropometric and training characteristics on HM perfor-
mance (Ogueta-Alday et al. 2018), where runners with race 
time 70 min had more sport experience and weekly running 
distance, and less body mass, BMI and skinfold thickness than 
those with race time 80 min, 90 min and 105 min. In summary, 
performance on HM race would depend mostly on  VO2max 
and other indices of aerobic capacity. To sum up, HM per-
formance depended on weekly running distance, number of 
training units, training running speed, BMI and BF (Campbell 
1985; Friedrich et al. 2014; Knechtle et al. 2012; Zillmann 
et al. 2013). Therefore, a practical advice for recreational run-
ners wishing to improve their race time might be to increase 
training (distance, running speed and frequency) and decrease 
BMI and BF.
Pacing
Pacing in sport refers to the conscious or subconscious 
regulation of performance (e.g. speed during a race) in 
order to achieve a goal (Thiel et al. 2018; Micklewright 
2019). Although pacing is related to performance in endur-
ance sports (the less variable the pacing, the faster the 
race time), little information exist with regards to pac-
ing strategies of HM runners (Hanley 2015). The relevant 
literature has been developed recently (Nikolaidis et al. 
2019 Cuk et al. 2019; Cuk et al. 2020; Nikolaidis et al. 
2019). A methodological approach to analyze pacing in 
HM has been to consider speed for intermediate segments, 
e.g. every 5 km (0–5, 5–10, 10–15 and 15–20 km) and 
end 1.1 km segments (Hanley 2015). An analysis of all 
finishers in Ljubljana 2017 HM showed a positive pacing 
with every segment being slower than its precedent one 
(Nikolaidis et al. 2019). In elite (finishers in the IAAF 
World Half Marathon Championships) HM runners, it was 
observed that the fastest finishers maintained split speed 
from 5 to 15 km, whereas slower runners decreased speed 
after 5 km (Hanley 2015). Compared to FM, an analysis of 
recreational HM runners observed a more even pacing in 
Ljubljana (Nikolaidis et al. 2019) and Vienna races (Cuk 
et al. 2020).A research of the pacing in the Great West 
Run HM showed that RPE scales with the proportion of 
exercise time that remains inferring that the brain uses 
a scalar timing mechanism (Faulkner et al. 2008). With 
regards to the variation of pacing by age, a research in 
Vienna City HM race revealed a positive pacing strategy, 
i.e. speed decreased across race, with an end spurt in all 
age groups and larger variation of speed in the younger age 
groups (Cuk et al. 2019). Additionally, a more even pacing 
was observed to relate with high training volume and long 
single training run (Fokkema et al. 2020). In summary, 
similarly to other endurance races, pacing was associated 
with performance in HM race with faster runners adopting 
a more even pacing than their slower counterparts. Thus, 
recreational runners would be recommended to maintain 
running speed during a HM race.
Practical applications
With regards to the popularity of HM race, the findings of 
research on this field would concern an increasing number 
of HM runners. The fact that HM race has been a sport 
activity recently developed would imply that it could 
not be ‘covered’ by scientific and professional knowl-
edge derived from long distance running – such as 5 km 
and 10 km – and FM race. This observation highlighted 
the need to develop further the research on this field. 
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Journal of Muscle Research and Cell Motility (2023) 44:115–122
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In this context, the present review attempted to address 
fundamental questions on the identity of HM runners 
(sex, age and nationality), performance trends and cor-
relates, to develop practical information for professionals 
working with runners. For instance, the knowledge of the 
age of peak performance can aid setting long-term per-
formance goals considering the age of runners. Several 
original research studies were identified that examined 
correlates of HM performance and enhanced our knowl-
edge on this field. Based on these findings, practitioners 
working with HM runners should aim to optimize aerobic 
capacity (e.g.,  VO2max and anaerobic threshold), train-
ing indices (e.g., weekly running distance and running 
speed) and adiposity-related parameters. Although these 
parameters clearly were related to HM performance in 
recreational runners that might be considered as an het-
erogeneous group, a challenge for future studies would be 
to examine the variation of their importance depending 
on performance level.
Conclusion
In conclusion, performance in HM was related with supe-
rior scores in aerobic capacity (maximal oxygen uptake, 
anaerobic threshold and running economy) and training 
characteristics (sport experience, weekly distance, training 
speed, frequency of sessions and long single endurance 
run distance), and lower scores in adiposity-related scores 
(e.g. body mass, body mass index, body fat percentage and 
skinfold thickness) (Fig. 3). Considering the popularity of 
HM race and the lack of many original studies (compared 
to FM race), this is an exciting field for scientific research 
with a large potential for practical applications.
Acknowledgements Figure 3 is credited to Dr. Celine Dewas.
Author contributions All authors wrote the main manuscript text and 
prepared figures. All authors reviewed the manuscript.
Funding Open access funding provided by HEAL-Link Greece.
Fig. 3  Physiological, anthropo-
metric and training characteris-
tics influencing half-marathon 
performance
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Journal of Muscle Research and Cell Motility (2023) 44:115–122 
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Data availability All data are available by the corresponding author 
upon reasonable request.
Declarations 
Conflict of interest The authors declare no conflict of interest.
Open Access This article is licensed under a Creative Commons Attri-
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Participation and performance characteristics in half-marathon run: a brief narrative review.
Nikolaidis, Pantelis Theodoros,Knechtle, Beat