A.V. Lyubushkina¹
E.N. Bezuglov^2,3,4
A.M. Lazarev^2,3
1Sports medicine clinic "SmartRecovery", Moscow
²Sechenov First Moscow State Medical University (Sechenov University), Moscow
³Elite Sports Laboratory, Moscow Witte University
⁴Center for Sports Medicine, FMBA of Russia, Moscow

Corresponding author: lazarevartemii@yandex.ru

Abstract

Objective of the study was to collect a profile of elite-amateur female runners, who are able to run sub-3-hour marathon (S3HM) including their anthropometrical parameters, training regimes, and best non-marathon race performance and to detect predictor variables of faster marathon time.

Methods and structure. Twenty-one Russian female runners (mean weight 52.67 ± 3.88 kg, height 1.66 ± 0.059 m, body mass index (BMI) 19.16 ± 1.29, and years of training experience before their first S3HM 3.95 ± 3.8 years who had completed a S3HM answered a questionnaire. All runners had a main professional occupation other than sports. We gathered information on their anthropometric variables, diet and training regimes. One-third of the runners were vegan or vegetarian. S3HM time was positively correlated with 1-km time in runners with BMI ≤ 19.06 and in subjects running ≥ 344.5 km per month. S3HM time also correlated positively with the half-marathon best time, the sum of 1-, 5-, 10-km, and half-marathon best time and BMI in athletes running > 9 h per week. Running a S3HM requires a long preparation training period, long running distances, and is accompanied by weight loss. Predictor variables associated with faster marathon time become evident only upon accomplishing certain training times and distances.

Results and conclusions. Female marathon runners are able to run S3HM if they have no previous experience. Generally, it requires long training rounds (weekly 10.81 ± 4.28 h), lengthy running distances (weekly ~80 ± 39 km), and is accompanied by weight loss. Vegetarian diet is not a contraindication to run a S3HM. Predictor variables associated with faster marathon time become evident only upon accomplishing certain training times and running distances. S3HM time correlated positively with 1-km time in runners with BMI ≤ 19.06 and in athletes running ≥ 344.5 km per month. S3HM time also correlated positively with the half-marathon best time, the sum of 1, 5-, and 10-km times, and BMI in subjects running > 9 h per week.

Keywords: female runners, marathon, race predictors

Introduction. Long-distance running, including marathon running (42.195 km), enjoys growing popularity among recreational runners around the world. In 2016, around 5000 marathons were held worldwide, attracting more than 1.8 million participants ^{(Association of Road Racing Statisticians. Marathon Lists, n.d.)}.

Women did not participate in marathons up until the 1970s. However, several female runners took part in some races unofficially. The first woman to run a marathon was Kathrine Switzer in 1967 in Boston. The Amateur Athletic Union officially allowed women to participate in the marathon in 1971 [2]. On September 19, 1971, Beth Bonner became the first winner of the women’s division of the New York City Marathon at 19 years old; her race time was 2:55:22 ^{(Boston Athletic Association, n.d.)}. The women’s marathon Olympic debut was at the 1984 Summer Olympics in Los-Angeles. Joan Benoit Samuelson became the champion with a finish time of 2:24.52 ^{(International Association of Athletics Federations, n.d.)}. The current marathon world record for women is held by Brigid Kosgei with a finish time of 2:14:04, which was set at the 2019 Chicago marathon ^{(Bank of America Chicago Marathon, n.d.)}.

However, despite the growing popularity of marathon running, the number of female runners to complete a sub-3-hour marathon (S3HM) remains low. For instance, 21,295 female runners participated in the 2019 Chicago marathon, of which only 1% (n = 206) finished in under 3 h^{(Bank of America Chicago Marathon, n.d.)}. The particularly hard training requirements might explain such a moderate proportion for the S3HM (Esteve-Lanao et al., 2017).Heritability might also play an important role in running performance (de Moor et al., 2007).

There have been multiple attempts to determine the predictors of the marathon finish time (McKelvie et al., 1985)(Dotan et al., 1983)(Legaz Arrese et al., 2005), and several studies were conducted on top-class runners (Billat et al., 2001)(Karp, 2007). Several studies demonstrated an association between anthropometric and training variables and running performance. However, these studies were done on low-performance runners and did not specifically examine marathon races (Knechtle et al., 2015)(Schütz et al., 2019)(Knechtle et al., 2014)(Barandun et al., 2012)^{(Knechtle, Knechtle, Barandun, & Rosemann, 2011)}(Schmid et al., 2012)(Knechtle, Knechtle, Barandun, Rosemann, et al., 2011). Several studies on predictors of success at a marathon have been published (Bale et al., 1985)(Gordon et al., 2017)(Doherty et al., 2020). However, we could not identify studies demonstrating anthropometric and training parameters analysis in elite-amateur female marathon runners who finished an S3HM and had a main professional occupation other than sports. Therefore, studying predictor variables of S3HM performance might be of great practical interest for predicting marathon finish time and planning of training regimen. This study aimed to collect a profile of elite-amateur female runners, who are able to run sub-3-hour marathon (S3HM) including their anthropometrical parameters, training regimes, and best non-marathon race performance and to detect predictor variables of faster marathon time.

Methods. All procedures in this study were conducted following the ethical standards of the Sechenov University and National Research Committee and with the 1964 Helsinki Declaration and its later amendments. All study participants signed an informed consent form. The study was conducted during January–February 2020, utilizing a survey, which was mailed to study participants.

The study group comprised 21 female runners, who finished an S3HM in the 2018–2019 seasons. 23 potential participants were initially contacted for the research, 21 (91%) of them decided to complete the questionnaire. 17 (81%) filled the full questionnaire while 4 (19%) partially filled them. All study participants live and train in Russia, have no medical contraindications to sports, and have medical certificates for marathon running.

Inclusion criteria were as follows: (i) signed informed consent for study participation; (ii) age ≥ 18 years; (iii) finishing in at least one S3HM to the time point of research; (iv) main professional occupation other than sports to the time point of the first S3HM; (v) no activity in professional endurance sports under the age of 18 years.

Exclusion criteria were as follows: (i) refusal to participate in the study; (ii) age ≤ 18 years; (iii) running as a main professional occupation for at least 1 year during lifetime; and (iv) professional endurance sports activity under the age of 18 years. All runners had their primary professional occupation other than sports. They did not engage in endurance sports as adolescents. They were not trained by a professional coach. Instead, they had mentors who supervised them online and met only 1-2 times per year.

We assessed height, weight, age, training experience, and best race times of 1-, 5-, 10-km, half-marathon and marathon distances.

Design and procedures

Runners completed a questionnaire on their anthropometric and performance variables.

Statistical analysis

The Shapiro-Wilk test was used for testing the normality of data. Spearman’s rank correlation coefficient was used for the assessment of the relationships between S3HM time and height, weight, BMI, 1, 5, 10-km, and half-marathon best time, monthly running distance (km), weekly training time (h). The Mann-Whitney U test was used for comparing the results of groups of runners. Kruskal–Wallis one-way analysis of variance was used for comparing S3HM time in runners with different diets.

Statistical analysis was performed using R Statistical Software (version 3.3.3; R Foundation for Statistical Computing, Vienna, Austria) and SPSS v23.0 (IBM Corp., USA). Statistical significance was defined at P-value < 0.05.

Results

The anthropometric parameters of the runners are presented in Table 1. The average time of the first S3HM was 2:52:06 ± 0:07:16 min. The average weekly training time was 10.81 ± 4.28 h, and the monthly training distance was 317.8 ± 154.3 km (~80 ± 39 km per week). The best non-marathon race time is presented in Table 2. Training experience before the first S3HM was not normally distributed (Me; Min-Max; interquartile range, years: 2.50; 0.5–18.0; 3.5).

Most (95%, n = 20) of the runners were trained by coaches. Most (67%) of the runners (n = 14) finished the first S3HM in autumn, 19% (n = 4) and 14% (n = 3) in winter and summer, respectively.   The average weight loss during preparation rounds to the first S3HM was 3.01 ± 2.29 kg. At present, the participating runners continue training to achieve their next personal records. The average number of accomplished S3HM is 3.524 ± 3.76. One runner is pregnant and ceased training rounds. One-third (n = 7) of the runners were vegan or vegetarian. Eight (38%) of the runners had at least one significant injury that interrupted their training. S3HM time did not correlate with training experience before the S3HM, monthly running distance, weekly training time, 1-, 5-, 10-km, half-marathon best time, and the sum of the non-marathon races best time, as well as with weight, BMI, height, and alimentary habits.

Runners were then grouped according to the median values of the studied variables. The Mann-Whitney U test was used to compare the groups, and the correlation analysis was repeated.  The median weight was 53 kg. The ≤ 53 kg weight group of runners and the > 53 kg group of runners did not differ significantly in any of the analyzed variables. There was no significant correlation between BMI and S3HM time. The median height was 1.65 m. The group of runners with a height of ≤ 1.65 m did not differ from the group of runners with height > 1.65 m. There was no significant correlation between height and the S3HM time in both groups.

The median BMI was 19.06 kg/m2. The group of runners with BMI ≤ 19.06 did not differ from the runners with BMI > 19.06. The 1-km time in runners with BMI ≤ 19.06 correlated significantly with S3HM time (p = 0.003, cor = 0.96). The median monthly running distance was 344.5 km. The group of runners with monthly running distance < 344.5 km did not differ from the group with monthly distance ≥ 344.5 km. Best 1-km time correlated positively with S3HM time in athletes running ≥ 344.5 km per month (p = 0.021, cor = 0.79) (Fig. 1).

The median weekly training time was 9 h. The group of runners training ≤ 9 h per week differed from the runners training > 9 h per week in 5-km (p = 0.016), 10-km (p = 0.021), and half-marathon (p = 0.013) best times and the sum of 1-, 5-, 10-km, and half-marathon best times (p = 0.029).  In the group training > 9 h per week S3HM time correlated positively with half-marathon best time (p = 0.003, cor = 0.86), the sum of 1-, 5-, 10-km, and half-marathon best time (p = 0.015, cor = 0.9), and BMI (p = 0.028, cor = 0.72) (Fig. 2-4).

Figure 1. Correlation between S3HM and 1K best time in athletes running ≥ 344.5 km per month.

Figure 2. Correlation between S3HM and 21K best time in athletes running > 9 hours.

Figure 3. Correlation between S3HM and the sum of the non-marathon races best time in athletes running > 9 hours.

Figure 4. Correlations of BMI and S3HM in athletes running > 9 hours.

4. Discussion

The current study allowed us to collect a profile of female runners, including their anthropometrical parameters, training regimes, and best non-marathon race performance. S3HM time correlated positively with 1-km time in runners with BMI ≤ 19.06 and in athletes running ≥ 344.5 km per month. S3HM time also correlated positively with the half-marathon best time, the sum of 1, 5-, and 10-km times, and BMI in subjects running > 9 h per week. The main difference from other studies is that runners had their primary professional occupation other than sports. They did not engage in endurance sports as adolescents. They were not trained by a professional coach. Instead, they had mentors who supervised them online and met only 1-2 times per year. To our knowledge, this is the first study in elite-amateur runners with fast marathon time, who had a main professional occupation other than sports, which demonstrated an association between several training variables and marathon performance.

Bale et al. demonstrated the association between training mileage and running performance (Bale et al., 1985). This study was done on elite professional runners with marathon best times of 2 h 55 min or less, between 2 h 55 min and 3 h 8 min, and 3 h 18 min to 3 h 30 min. Interestingly, the runners in our study were older ( 32.11 ± 4.86 vs. 29.4 ± 7.6 years), weighted less (52.9 ± 4.8 vs 54.7 ± 5.6 kg), than the runners studied by Bale et.al., but had a comparable height (1.66 ± 0.06 vs 1.66 ± 0.04 m). The mean weekly distance covered by runners with a best marathon time ≤ 2 h 55 min was 106 ± 34 km, compared with ~80 ± 39 km in our study. Bale et al. concluded that the number of training sessions per week and the number of years of training were the best predictors of competitive performance at both 10 mile and marathon distances. We did not find a correlation between training h and S3HM time, as well as between training experience before the first S3HM and S3HM time. The main difference from our study is that the study by Bale et al. was done on elite professional runners (national and international distance runners and members of British Marathon Squad) (Bale et al., 1985). In contrast, our study was done on runners with a main professional occupation other than sports.

Gordon et al. considered training frequency, distance per session, and overall training experience to compare recreational runners within various marathon time groups (Gordon et al., 2017). These authors showed that training frequency and distance per session were significantly greater for the 2 h 30 min–3 h group compared to the 3 h 30 min–4 h and >4 h 30 min runner group. However, the 2 h 30 min to 3 h group in this study consisted almost exclusively of male athletes (one female and ten males). The mean weekly distance covered by these runners was 91.7 ± 31.6 km, which is higher than in our study. Therefore, faster marathon time cannot be reliably predicted in this setting (Gordon et al., 2017) .

Doherty et al. discovered a negative statistical association between the number of weekly runs, maximum running distance completed in a single week, number of runs ≥ 32 km completed in the pre-marathon training block, and the marathon finish time (Doherty et al., 2020). Even though these data were obtained in a meta-analysis, the dataset represented an over-bias towards male marathoners, which was noted as a limitation of the study by the authors themselves (Doherty et al., 2020). 

Schmid et al. found that a low calf circumference and a high running speed in training are associated with a fast marathon race time in female recreational runners (Schmid et al., 2012). However, the mean weekly distance covered by runners in this study was 34.6 ± 12.0 km, which is much lower than in our study. There was no single runner who finished the marathon faster than 3 h. Thus, the running performance of participants in this study was lower than in ours.  The runners in our study were younger (32.11 ± 4.86 vs. 47.1 ± 8.7 years), weighted less (52.9 ± 4.8 vs 59.1 ± 6.3 kg), than the runners studied by Schmid et.al., but had a similar height (1.66 ± 0.06 vs 1.66 ± 0.06 m).

The vegetarian diet is gaining popularity in sports. Wilson et al. found that 9% of recent marathon finishers adhere to vegan/vegetarian/pescatarian diet (Wilson, 2016). Nebl et al. states that a well-planned vegan diet, including supplements, can meet the athlete's requirements of vitamin B12, vitamin D and iron (Nebl et al., 2019), while Wirnitzer et al. found that vegetarian diet is associated with a good health status and is an equal alternative to an omnivorous diet for endurance runners (Wirnitzer et al., 2018). These data are supported by our study, where one-third of female runners were vegan/vegetarian. Thus, vegetarian diet is not a contraindication to run a S3HM.

This study showed that preparation training to run a S3HM is a serious challenge for female athletes without previous running experience. There are certain predictor variables, associated with faster marathon time. Disadvantages of our study are the relatively small sample group and the absence of such a variable as the skinfold or calf circumference measurement. However, the studied group might represent nearly all of the female S3HM finishers currently living in Russia. Further research should focus on a more thorough analysis of this group of runners and the impact of training regimens on their health and quality of life.  

5. Conclusions

To conclude, female marathon runners are able to run S3HM if they have no previous experience. Generally, it requires long training rounds (weekly 10.81 ± 4.28 h), lengthy running distances (weekly ~80 ± 39 km), and is accompanied by weight-loss. Vegetarian diet is not a contraindication to run a S3HM. Predictor variables associated with faster marathon time become evident only upon accomplishing certain training times and running distances. S3HM time correlated positively with 1-km time in runners with BMI ≤ 19.06 and in athletes running ≥ 344.5 km per month. S3HM time also correlated positively with the half-marathon best time, the sum of 1, 5-, and 10-km times, and BMI in subjects running > 9 h per week.

Funding: This research received no external funding.

Conflicts of Interest: The authors declare no conflict of interest.

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Table 1. Anthropometric parameters of the runners (normal distribution, Shapiro-Wilk Test).

Figure 1.

Figure 2.

Figure 3.

Figure 4.