Participation and performance trends in elderly marathoners in four of the world's largest marathons during 2004-2011

Impact of marathon performance on muscles stiffness in runners over 50 years old

Introduction

The research examines the relationship between marathon performance and muscle stiffness changes from pre to marathon in recreational runners aged 50+ years.

Methods

Thirty-one male long-distance runners aged 50-73 years participated in the experiment. The muscle stiffness of quadriceps and calves was measured in two independent sessions: the day before the marathon and 30 min after the completed marathon run using a Myoton device.

Results and Discussion

The 42.195-km run was completed in 4.30,05 h ± 35.12 min, which indicates an intensity of 79.3% ± 7.1% of HRmax. The long-term, low-intensity running exercise (marathon) in older recreational runners and the low level of HRmax and VO2max showed no statistically significant changes in muscle stiffness (quadriceps and calves). There was reduced muscle stiffness (p = 0.016), but only in the triceps of the calf in the dominant (left) leg. Moreover, to optimally evaluate the marathon and adequately prepare for the performance training program, we need to consider the direct and indirect analyses of the running economy, running technique, and HRmax and VO2max variables. These variables significantly affect marathon exercise.

Relationship between running performance and weather in elite marathoners competing in the New York City Marathon

It is well known that weather and pacing have an influence on elite marathon performance. However, there is limited knowledge about the effect of weather on running speed in elite marathoners. The aim of the present cross-sectional study was to investigate potential associations between running speed and weather variables in elite runners competing in the 'New York City Marathon' between 1999 and 2019. Data from all official female and male finishers with name, sex, age, calendar year, split times at 5 km, 10 km, 15 km, 20 km, 25 km, 30 km, 35 km, 40 km and finish and hourly values for temperature (°Celsius), barometric pressure (hPa), humidity (%) and sunshine duration (min) between 09:00 a.m. and 04:00 p.m. were obtained from official websites. A total of 560,731 marathon runners' records were available for analysis (342,799 men and 217,932 women). Pearson and Spearman correlation analyses were performed between the average running speed and the weather variables (temperature, pressure, humidity and sunshine). Ordinary Least Squares (OLS) regressions were also performed. The runner´s records were classified into four performance groups (all runners, top 100, top 10 and top 3) for comparison. Differences in running speed between the four performance groups were statistically significant (p < 0.05) for both men and women. Pearson (linear) correlation indicated a weak and positive association with humidity in the top 10 (r = 0.16) and top 3 (r = 0.13) performance groups that the running speed of the elite runners was positively correlated with humidity. Regarding sunshine duration, there was a weak and positive correlation with the running speed of the elite groups (r = 0.16 in the top 10 and r = 0.2 in the top 3). Spearman correlation (non-linear) identified a weak but negative correlation coefficient with temperature in all runners' groups. Also, non-linear positive correlation coefficients with humidity and sunshine can be observed in the Spearman matrixes. A Multivariate Ordinary Least Squares (OLS) regression analysis showed no predictive power of weather factors. For elite runners competing in the 'New York City Marathon' between 1999 and 2019, the main findings were that elite runners became faster with increasing humidity and sunshine duration while overall runners became slower with increasing temperature, increasing humidity and sunshine duration. Weather factors affected running speed and results but did not provide a significant predictive influence on performance.

Increased Participation and Decreased Performance in Recreational Master Athletes in "Berlin Marathon" 1974-2019

The aspect of participation and performance trends in marathon running has been investigated mainly in marathons held in the United States of America (e.g., “New York City Marathon,” “Boston Marathon”), but not for the fastest course in the world, the “Berlin Marathon” held in Berlin, Germany. This study aimed to examine trends in participation and performance in the “Berlin Marathon” on all its previous 46 editions from 1974 to 2019, the largest dataset ever studied in this event with 696,225 finishers (after data cleaning). Athletes in all age groups increased their participation, except for male athletes aged 20–49 years and athletes of both sexes above 79 years of age. This overall increase in participation was more pronounced in women, but still, there are more men than women participating in “Berlin Marathon” nowadays. All age group athletes decreased their performance across years overall, whereas the top ten recreational athletes improved their performance over the years. Our findings improved the knowledge about the evolution of male and female marathoners across calendar years, especially for the fastest marathon race in the world, the “Berlin Marathon.”

Trends in Weather Conditions and Performance by Age Groups Over the History of the Berlin Marathon

The effect of different environmental conditions such as temperature, wind, barometric pressure, and precipitation has been well investigated in elite marathoners, but not by age categories (i.e., age group marathoners). The aim of the study was to investigate the potential influence of environmental conditions such as temperature, precipitation, and atmospheric pressure on marathon performance in age group marathoners competing in the ‘Berlin Marathon’ from 1974 to 2019. A total of 869,474 valid finisher records were available for analysis, of which 711,136 correspond to males and 158,338 to females. The influence of temperature, atmospheric pressure, and precipitation on marathon race times was investigated in age group marathoners grouped in 5-year-intervals. Within the 46 years of Berlin marathons under investigation, there was some level of precipitation for 18 years, and 28 years without any rain. Sunshine was predominant in 25 of the events, whilst in the other 21 years, cloud cover was predominant. Marathon race times were significantly and positively correlated with age (i.e., older runners were slower than younger runners) where the correlation was higher for males than for females. Marathon race times were significantly and positively correlated with both the hours of sunshine and the daily maximum temperature. The fastest marathon runners (meaning the minimum times) achieved the fastest race times on race days with higher maximum temperatures (i.e., 15–30°C). Daily maximum temperatures showed an influence on age group marathoners from age group 35–40 years and older. Higher precipitation levels impaired performance across most age groups. In summary, higher daily maximum temperatures (i.e., >15°C) and higher precipitation levels impaired performance of master marathoners (i.e., 35–40 years and older) competing in the ‘Berlin Marathon’ in the last 45 years. Master marathoners should start in marathon races with temperatures < 15°C and no precipitation in order to achieve a fast marathon race time.

The week after running a marathon: Effects of running vs elliptical training vs resting on neuromuscular performance and muscle damage recovery

Our aim was to compare the effects of two exercise modalities vs resting on the time course of neuromuscular performance and muscle damage recovery during the week after running a marathon. Sixty-four finishers from a road marathon completed the study (54 men and 10 women; 39 ± 4 years; 3 h 35 min ± 21 min). The day before the race, within 15 min after finishing the marathon and at 24, 48, 96, 144 and 192 h postrace, lactate dehydrogenase and creatine kinase were analysed. Participants also performed a squat jump (SJ) test before and after the marathon and at 48, 96 and 144 h postrace. On their arrival to the finish line, participants were randomized into one of the three intervention groups: running (RUN), elliptical training (ELIP) and resting recovery (REST). RUN and ELIP groups exercised continuously for 40 min at a moderate intensity (95-105% of the HR corresponding to the first ventilatory threshold) at 48, 96 and 144 h after the marathon. Neither 'Intervention' factor nor 'Intervention x Time' interaction effects were revealed for muscle damage blood markers (p > 0.05). On the other hand, RUN group evidenced an enhancement in SJ performance 96 h post-marathon as compared with REST group (108.29 ± 10.64 vs 100.58 ± 9.16%, p = 0.020, d = 0.80). Consequently, return to running at 48 h post-marathon does not seem to have a negative impact on muscle damage recovery up to eight days post-race and it could be recommended in order to speed up neuromuscular recovery.

The Impact of the COVID-19 Pandemic on Endurance and Ultra-Endurance Running

Background and objectives: The COVID-19 outbreak has become a major health and economic crisis. The World Health Organization declared it a pandemic in March 2020, and many sporting events were canceled. Materials and Methods: We examined the effects of the COVID-19 pandemic on endurance and ultra-endurance running (UER) and analyzed finishes and events during the COVID-19 pandemic (observation period March 2020–October 2020) to the same time period pre-COVID-19 outbreak (March 2019–October 2019). Results: Endurance finishes decreased during the pandemic (459,029 to 42,656 (male: 277,493 to 25,582; female 181,536 to 17,074; all p < 0.001). Similarly, the numbers of endurance events decreased (213 vs. 61 events; p < 0.001). Average marathon finishing times decreased during the pandemic in men (5:18:03 ± 0:16:34 vs. 4:43:08 ± 0:25:08 h:min:s (p = 0.006)) and women (5:39:32 ± 0:19:29 vs. 5:14:29 ± 0:26:36 h:min:s (p = 0.02)). In UER, finishes decreased significantly (580,289 to 110,055; p < 0.001) as did events (5839 to 1791; p < 0.001). Popular event locations in United States, France, UK, and Germany decreased significantly (p < 0.05). All distance and time-limited UER events saw significant decreases (p < 0.05). Conclusions: The COVID-19 pandemic has had a significant effect on endurance and UER, and it is unlikely that running activities return to pre-pandemic levels any time soon. Mitigation strategies and safety protocols should be established.

The Optimal Weight Carriage System for Runners: Comparison Between Handheld Water Bottles, Waist Belts, and Backpacks

In endurance running, where fluid and nutritional support is not always readily available, the carriage of water and nutrition is essential. To compare the economy and physiological demands of different carriage systems, 12 recreational runners (mean age 22.8 ± 2.2 years, body mass index 24.5 ± 1.8 kg m⁻², VO₂max 50.4 ± 5.3 ml kg⁻¹ min⁻¹), completed four running tests, each of 60-min duration at individual running speeds (mean running speed 9.5 ± 1.1 km h⁻¹) on a motorized treadmill, after an initial exercise test. Either no load was carried (control) or loads of 1.0 kg, in a handheld water bottle, waist belt, or backpack. Economy was assessed by means of energy cost (CR), oxygen cost (O₂ cost), heart rate (HR), and rate of perceived exertion (RPE). CR [F(2,20) = 37.74, p < 0.01, η_p² = 0.79], O₂ cost [F(2,20) = 37.98, p < 0.01, η_p² = 0.79], HR [F(2,18) = 165.62, p < 0.01, η_p² = 0.95], and RPE [F(2,18) = 165.62, p < 0.01, η_p² = 0.95] increased over time, but no significant differences were found between the systems. Carrying a handheld water bottle, waist belt, or backpack, weighing 1.0 kg, during a 60-min run exhibited similar physiological changes. Runners’ choice may be guided by personal preference in the absence of differences in economy (CR, O₂ cost, HR, and RPE).

Trunk-pelvis coordination during load carriage running

Understanding the influence of load carriage on trunk-pelvis coordination and its variability has important functional implications for athletes who need to run with load. The aim of this study was to examine the influence of load carriage on trunk-pelvis coordination in running. Thirty healthy adults performed running while wearing a 20% bodyweight backpack, and without load. Vector coding was used to quantify trunk-pelvis segmental coordination and its variability during the stance phase of running. The four coordination patterns were: 1) anti-phase (segments moving in opposite directions), in-phase (segments moving in same directions), trunk-only phase (only trunk movement), and pelvic-only phase (only pelvic movement). For each plane, the percentage of stance phase spent in a specific coordination pattern was quantified. Coordination variability for each plane was averaged over the stance phase. Mixed effects models were used to analyse the effects of load, adjusted for the covariate of sex, on coordination and its variability. Running with load increased trunk-only coordination in the sagittal plane (P < 0.001), increased anti-phase coordination in the frontal plane (P < 0.001), reduced trunk-only phase coordination in axial rotation (P < 0.001), and increased coordination variability in all three planes (Flexion-Extension: P < 0.001; Lateral flexion: P = 0.03; Axial rotation: P < 0.001). Future studies would benefit from investigating how trunk-pelvis coordination and its variability alters candidate end-point variability indices (e.g. COM displacement), and its functional implications in load carriage running.

Using Accelerometry for Evaluating Energy Consumption and Running Intensity Distribution Throughout a Marathon According to Sex

The proportion of females participating in long-distance races has been increasing in the last years. Although it is well-known that there are differences in how females and males face a marathon, higher research may be done to fully understand the intrinsic and extrinsic factors affecting sex differences in endurance performance. In this work, we used triaxial accelerometer devices to monitor 74 males and 14 females, aged 30 to 45 years, who finished the Valencia Marathon in 2016. Moreover, marathon split times were provided by organizers. Several physiological traits and training habits were collected from each participant. Then, we evaluated several accelerometry- and pace-estimated parameters (pacing, average change of speed, energy consumption, oxygen uptake, running intensity distribution and running economy) in female and male amateur runners. In general, our results showed that females maintained a more stable pacing and ran at less demanding intensity throughout the marathon, limiting the decay of running pace in the last part of the race. In fact, females ran at 4.5% faster pace than males in the last kilometers. Besides, their running economy was higher than males (consumed nearly 19% less relative energy per distance) in the last section of the marathon. Our results may reflect well-known sex differences in physiology (i.e., muscle strength, fat metabolism, VO_2max), and in running strategy approach (i.e., females run at a more conservative intensity level in the first part of the marathon compared to males). The use of accelerometer devices allows coaches and scientific community to constantly monitor a runner throughout the marathon, as well as during training sessions.

The Determinants of Marathon Performance: An Observational Analysis of Anthropometric, Pre-race and In-race Variables

Researchers investigating the determinants of marathon performance have previously focused on pre-race (e.g. training) or in-race (e.g. pacing) variables, but not both. This cross-sectional study, therefore, sought to elucidate the relationship between training behaviours, in-race pacing and anthropometric variables with eventual marathon finish time. A self-report questionnaire collecting athletes' anthropometrics, training behaviours and recent race times was administered to 260 participants in the 2018 Dublin Marathon. Participants' race numbers were cross referenced with in-race split times and finish times to determine their race performance. The accuracy or pragmatism of participants' target finish time was calculated using a 'predicted' time based on their training and previous race performances and subtracting this value from their eventual finish time. Multiple regression analysis examined the influence of age, body mass index (BMI), marathon experience, training history, target finish time pragmatism and in-race pace variance on marathon performance. The model was statistically significant and predicted marathon finish time F(7,252) = 217.761, p < 0.0005, adj. R2 = 0.858. Marathon experience (p = 0.01, Beta = 0.06), a pragmatic target finish time (p < 0.0005, Beta = -0.36), training history (p < 0.0005, Beta = 0.76) and in-race pace variance (p < 0.0005, Beta = 0.26) made statistically significant contributions to the overall regression model. A marathoners' training history accounts for the greatest variance in their overall performance, followed by the pragmatism of their target finish time and their in-race pace variance. This study provides the first indication of the combined relative importance of anthropometric, training and pacing variables to marathon performance.

Estimation of energy consumed by middle-aged recreational marathoners during a marathon using accelerometry-based devices

As long-distance races have substantially increased in popularity over the last few years, the improvement of training programs has become a matter of concern to runners, coaches and health professionals. Triaxial accelerometers have been proposed as a one of the most accurate tools to evaluate physical activity during free-living conditions. In this study, eighty-eight recreational marathon runners, aged 30–45 years, completed a marathon wearing a GENEActiv accelerometer on their non-dominant wrist. Energy consumed by each runner during the marathon was estimated based on both running speed and accelerometer output data, by applying the previously established GENEActiv cut-points for discriminating the six relative-intensity activity levels. Since accelerometry allowed to perform an individualized estimation of energy consumption, higher interpersonal differences in the number of calories consumed by a runner were observed after applying the accelerometry-based approach as compared to the speed-based method. Therefore, pacing analyses should include information of effort intensity distribution in order to adjust race pacing appropriately to achieve the marathon goal time. Several biomechanical and physiological parameters (maximum oxygen uptake, energy cost of running and running economy) were also inferred from accelerometer output data, which is of great value for coaches and doctors.

Age Differences, Age Changes, and Generalizability in Marathon Running by Master Athletes

This study examines the world’s Top 100 age class performance times by Master athletes in marathon running. The predominant paradigm for this type of research assumes that the outcomes represent a “virtual” cross-sectional study with important implications about aging. This article critiques this perspective and presents alternative models that include temporal dimensions that relate to cohort differences, age changes and historical transitions. One purpose of this study is to compare these models with respect to goodness of fit to the data. A second purpose is to evaluate the generalizability of findings from the fastest divisional age class quartile to the slower quartiles. Archival listings by the Association of Road Racing Statisticians include a maximum of 100 fastest age class performances in marathon running performances by men and women. This database includes 937 performances by 387 men performances and 856 performances by 301 women. The mean ages are 62.05 years for men and 60.5 years for women. The mean numbers of performances per runner are 6.64 for men and 6.4 for women. Analysis by mixed linear modeling (MLM) indicates best goodness of fit for logarithms of performance time by a model that includes linear and quadratic expressions of age at entry into the database (termed “entry cohort”) and subsequent age changes (termed “elapsed age”) as variables. Findings with this model show higher performance times in women than men. Rates of increase in performance time are higher at older cohort ages and elapsed ages. Performance time increases with interactions between cohort age and elapsed age, cohort age and gender, and elapsed age and gender (i.e., with greater increases in women than men). Finally, increases in performance time with cohort age and elapsed age are higher in slower than faster performance quartiles, with athletes in the faster quartiles more likely to have multiple data entries and athletes in the slower quartiles single data entries. Implications of these findings are discussed.

Quick Recovery of Renal Alterations and Inflammatory Activation after a Marathon

Purpose

We evaluated the incidence of acute kidney injury in a cohort of marathon participants.

Methods

We conducted a prospective observational study focused on evaluating the incidence of kidney damage after a marathon, and its evolution in the first 48 h after the marathon in 88 runners who completed the Valencia Marathon.

Results

From the 88 participants, 42 (48.28%) presented with acute kidney injury, mainly grade 1 (95.20%). Microscopic haematuria was observed in 29 runners (33%). Levels of interleukin 6, leukocytes, and neutrophils were markedly increased at the marathon's finish line.

Conclusions

Our results confirmed that there are slight transient changes in glomerular filtration rate and inflammatory activation after a marathon.

Establishing cut-points for physical activity classification using triaxial accelerometer in middle-aged recreational marathoners

The purpose of this study was to establish GENEA (Gravity Estimator of Normal Everyday Activity) cut-points for discriminating between six relative-intensity activity levels in middle-aged recreational marathoners. Nighty-eight (83 males and 15 females) recreational marathoners, aged 30–45 years, completed a cardiopulmonary exercise test running on a treadmill while wearing a GENEA accelerometer on their non-dominant wrist. The breath-by-breath V̇O₂ data was also collected for criterion measure of physical activity categories (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous). GENEA cut-points for physical activity classification was performed via Receiver Operating Characteristic (ROC) analysis. Spearman’s correlation test was applied to determine the relationship between estimated and measured intensity classifications. Statistical analysis were done for all individuals, and separating samples by sex. The GENEA cut-points established were able to distinguish between all six-relative intensity levels with an excellent classification accuracy (area under the ROC curve (AUC) values between 0.886 and 0.973) for all samples. When samples were separated by sex, AUC values were 0.881–0.973 and 0.924–0.968 for males and females, respectively. The total variance in energy expenditure explained by GENEA accelerometer data was 78.50% for all samples, 78.14% for males, and 83.17% for females. In conclusion, the wrist-worn GENEA accelerometer presents a high capacity of classifying the intensity of physical activity in middle-aged recreational marathoners when examining all samples together, as well as when sample set was separated by sex. This study suggests that the triaxial GENEA accelerometers (worn on the non-dominant wrist) can be used to predict energy expenditure for running activities.

Performance Trends in Master Butterfly Swimmers Competing in the FINA World Championships

Performance trends in elite butterfly swimmers are well known, but less information is available regarding master butterfly swimmers. We investigated trends in participation, performance and sex differences in 9,606 female and 13,250 male butterfly race times classified into five-year master groups, from 25-29 to 90-94 years, competing in the FINA World Masters Championships between 1986 and 2014. Trends in participation were analyzed using linear regression analysis. Trends in performance changes were investigated using mixed-effects regression analyses with sex, distance and a calendar year as fixed variables. We also considered interaction effects between sex and distance. Participation increased in master swimmers older than ~30-40 years. The men-to-women ratio remained unchanged across calendar years and master groups, but was lower in 200 m compared to 50 m and 100 m. Men were faster than women from 25-29 to 85-89 years (p < 0.05), although not for 90-94 years. Sex and distance showed a significant interaction in all master groups from 25-29 to 90-94 years for 200m (p < 0.05). For 50 m and 100 m, a significant sex × distance interaction was observed from 25-29 to 75-79 years (p < 0.05), but not in the older groups. In 50 m, women reduced the sex difference in master groups 30-34 to 60-64 years (p < 0.05). In 100 m, women decreased the gap to men in master groups 35-39 to 55-59 years (p < 0.05). In 200 m, the sex difference was reduced in master groups 30-34 to 40-44 years (p < 0.05). In summary, women and men improved performance at all distances, women were not slower compared to men in the master group 90-94 years; moreover, women reduced the gap to men between ~30 and ~60 years, although not in younger or older master groups.

Declines in marathon performance: Sex differences in elite and recreational athletes

The first aim of this study was to determine the age group at which marathon performance declines in top male and female runners and to compare that to the runners of average ability. Another aim of this of this study was to examine the age-related yearly decline in marathon performance between age group winners and the average marathon finisher. Data from the New York (NYC), Boston, and Chicago marathons from 2001–2016 were analyzed. Age, sex, and location were used in multiple linear regression models to determine the rate of decline in marathon times. Winners of each age group were assessed in 5-year increments from 16 through 74 years old (n = 47 per age group). The fastest times were between 25–34 years old, with overall champion males at 28.3 years old, and overall champion females at 30.8 years old (p = 0.004). At 35 years of age up to 74 years of age, female age group winners had a faster yearly decline in marathon finishing times compared to male age group winners, irrespective of marathon location [women = (min:sec) 2:33 per year, n = 336; men = 2:06 per year, n = 373, p < 0.01]. The median times between each age group only slowed beginning at 50 years old, thereafter the decline was similar between both men and women (women = 2:36, n = 140; men = 2:57, n = 150, p = 0.11). The median times were fastest at Boston and similar between Chicago and NYC. In conclusion, the rate of decline at 35 years old up to 74 years old is roughly linear (adjusted r² = 0.88, p < 0.001) with female age group winners demonstrating 27 s per year greater decline per year compared to male age group winners.

Performance trends in age-group runners from 100 m to marathon-The World Championships from 1975 to 2015

This study examined changes in performance in age-group track runners across years from 1975 to 2015 for 100, 200, 400, 800, 1500, 5000, 10 000 m, and marathon and the corresponding sex differences. Athletes were ranked in 5-year age-group intervals from 35-39 to 95-99 years. For all races and all years, the eight female and male finalists for each age-group were included. Men were faster than women and this observation was more pronounced in the shorter distances. The younger age-groups were faster than the older age-groups and age exerted the largest effect on speed in 800 m and the smallest in marathon. There was a small variation of speed by calendar years. The competition density varied by sex and race distance. Half of participants were from USA, Germany, Australia, and Great Britain, but the participants' nationality varied by sex and race distance. In summary, the variation of competitiveness by sex in short race distances might be important for athletes and coaches. Considering the event's competitiveness and that athletes are participating in both 100 and 200 m or in 200 and 400 m, master women should be oriented to 200 m and master men should be oriented to 100 and 400 m.

A technique to determine the fastest age-adjusted masters marathon world records

Introduction/Purpose

This study’s purpose was to develop and employ a technique to determine the fastest masters marathon world records (WR), ages 35–79 years, adjusted for age (WRadj).

Methods

From single-age WR data, a best-fit polynomial curve (WRpred1) was developed for the larger age range of 29–80 years for women and 30–80 years for men to improve curve stability in the 35–79 years range. Due to the relatively large degree of data scatter about the curve and the resultant age bias in favor of older runners, a subsample was constituted consisting of those with the lowest WR/WRpred1 ratio within each five-year age group (N = 11). A new polynomial best-fit curve (WRpred2) was developed from this subsample to become the standard against which WR would be compared across age. WRadj was computed from WR/WRpred2 for all runners, 35–79 years, from which the top ten fastest were then determined.

Results

The WRpred2 model reduced data scatter and eliminated the age bias. Tatyana Pozdniakova, 50 years, WR = 2:31:05, WRadj = 2:12:40; and Ed Whitlock, 73 years, WR = 2:54:48, WRadj = 1:59:57, had the fastest WRadj for women and men, respectively.

Conclusions

This technique of iterative curve-fitting may be an optimal way of determining the fastest masters WRadj and may also be useful in better understanding the upper limits of human performance by age.

Performance trends in master freestyle swimmers aged 25-89 years at the FINA World Championships from 1986 to 2014

Performance trends in elite freestyle swimmers are well known, but not for master freestyle swimmers. We investigated trends in participation, performance, and sex difference in performance of 65,584 freestyle master swimmers from 25-29 to 85-89 years competing in FINA World Masters Championships between 1986 and 2014. The men-to-women ratio was calculated for each age group, and the trend across age groups was analyzed using single linear regression analysis. Trends in performance changes were investigated using a mixed-effects regression model with sex, distance, and calendar year as fixed variables. Participation increased in women and men in older age groups (i.e., 40 years and older). Women and men improved race times across years in all age groups and distances. For age groups 25-29 to 75-79 years, women were slower than men, but not for age groups 80-84 to 85-89 years. In 50, 100, and 200 m, women reduced the sex difference from 1986 to 2014 in age groups 30-34 to 75-79 years. In 400 m, women reduced the gap to men across time in age groups 40-44, 45-49, and 55-59 years. In 800 m, sex difference became reduced across time in age groups 55-59 and 70-74 years. In summary, participation increased from 1986 to 2014 in women and men in older age groups, women and men improved across time performance in all distances, and women were not slower compared to men in age groups 80-84 to 85-89 years. We expect a continuous trend in increasing participation and improved performance in master freestyle swimmers.

Half-marathoners are younger and slower than marathoners

Age and performance trends of elite and recreational marathoners are well investigated, but not for half-marathoners. We analysed age and performance trends in 508,108 age group runners (125,894 female and 328,430 male half-marathoners and 10,205 female and 43,489 male marathoners) competing between 1999 and 2014 in all flat half-marathons and marathons held in Switzerland using single linear regression analyses, mixed-effects regression analyses and analyses of variance. The number of women and men increased across years in both half-marathons and marathons. There were 12.3 times more female half-marathoners than female marathoners and 7.5 times more male half-marathoners than male marathoners. For both half-marathons and marathons, most of the female and male finishers were recorded in age group 40-44 years. In half-marathons, women (10.29 ± 3.03 km/h) were running 0.07 ± 0.06 km/h faster (p < 0.001) than men (10.22 ± 3.06 km/h). Also in marathon, women (14.77 ± 4.13 km/h) were running 0.28 ± 0.16 km/h faster (p < 0.001) than men (14.48 ± 4.07 km/h). In marathon, women (42.18 ± 10.63 years) were at the same age than men (42.06 ± 10.45 years) (p > 0.05). Also in half-marathon, women (41.40 ± 10.63 years) were at the same age than men (41.31 ± 10.30 years) (p > 0.05). However, women and men marathon runners were older than their counterpart half-marathon runners (p < 0.001). In summary, (1) more athletes competed in half-marathons than in marathons, (2) women were running faster than men, (3) half-marathoners were running slower than marathoners, and (4) half-marathoners were younger than marathoners.

Increase in participation but decrease in performance in age group mountain marathoners in the 'Jungfrau Marathon': a Swiss phenomenon?

Participation and performance trends for age group marathoners have been investigated for large city marathons such as the ‘New York City Marathon’ but not for mountain marathons. This study investigated participation and trends in performance and sex difference in the mountain marathon ‘Jungfrau Marathon’ held in Switzerland from 2000 to 2014 using single and mixed effects regression analyses. Results were compared to a city marathon (Lausanne Marathon) also held in Switzerland during the same period. Sex difference was calculated using the equation ([race time in women] − [race time in men]/[race time in men] × 100). Changes in sex differences across calendar years and were investigated using linear regression models. In ‘Jungfrau Marathon’, participation in all female and male age groups increased with exception of women in age groups 18–24 and men in age groups 30–34, 40–44 and 60–64 years where participation remained unchanged. In ‘Lausanne Marathon’, participation increased in women in age groups 30–34 to 40–44 years. In men, participation increased in age groups 25–29 to 44–44 years and 50–54 years. In ‘Jungfrau Marathon’ runners became slower across years in age groups 18–24 to 70–74 years. In ‘Lausanne Marathon’, runners became slower across years in age groups 18–24 and 30–34 to 65–69 years, but not for 25–29, 70–74 and 75–79 years. In ‘Jungfrau Marathon’, sex difference increased in age groups 25–29 (from 4 to 10 %) and 60–64 years (from 3 to 8 %) but decreased in age group 40–44 years (from 12 to 6 %). In ‘Lausanne Marathon’, the sex difference showed no changes. In summary, participation increased in most female and male age groups but performance decreased in most age groups for both the mountain marathon ‘Jungfrau Marathon’ and the city marathon ‘Lausanne Marathon’. The sex differences were lower in the ‘Jungfrau Marathon’ (~6–7 %) compared to the ‘Lausanne Marathon’ where the sex difference was ~10–12 % from age groups 18–24 to 55–59 years. These unexpected findings might be a typical Swiss phenomenon. Future studies need to investigate whether this trend can also be found in other endurance sports events held in Switzerland and other mountain marathons held in other countries.