An evaluation of the training determinants of marathon performance: A meta-analysis with meta-regression

Quantitative Analysis of 92 12-Week Sub-elite Marathon Training Plans

BACKGROUND

A typical training plan is a mix of many training sessions with different intensities and durations to achieve a specific goal, like running a marathon in a certain time. Scientific publications provide little specific information to aid in writing a comprehensive training plan. This review aims to systematically and quantitatively analyse the last 12 weeks before a marathon as recommended in 92 sub-elite training plans.

METHODS

We retrieved 92 marathon training plans and linked their running training sessions to five intensity zones. Subsequently, each training plan was grouped based on the total running volume in peak week into high (> 90 km/week), middle (65-90 km/week), and low (< 65 km/week) training volume plan categories.

RESULTS

In the final 12 weeks before a race, recommended weekly running volume averaged 108 km, 59 km, and 43 km for high, middle, and low distance marathon training plans. The intensity distribution of these plans followed a pyramidal training structure with 15-67-10-5-3%, 14-63-18-2-3%, and 12-67-17-2-2% in zones 1, 2, 3, 4, and 5, for high, middle, and low volume training plans, respectively.

CONCLUSIONS

By quantitatively analysing 92 recommended marathon training plans, we can specify typical recommendations for the last 12 weeks before a marathon race. Whilst this approach has obvious limitations such as no evidence for the effectiveness of the training plans investigated, it is arguably a useful strategy to narrow the gap between science and practice.

Prediction of half-marathon performance of male recreational marathon runners using nomogram

BACKGROUND

Long-distance running is a popular competitive sport. We performed the current research as to develop an easily accessible and applicable model to predict half-marathon performance in male recreational half-marathon runners by nomogram.

METHODS

Male recreational half-marathon runners in Zhejiang Province, China were recruited. A set of literature-based and panel-reviewed questionnaires were used to assess the epidemiological conditions of the recruited runners. Descriptive and binary regression analyses were done for the profiling and identification of predictors related to higher half-marathon performance (completing time ≤ 105 min). Participants were assigned to the training set (n = 141) and the testing set (n = 61) randomly. A nomogram was used to visually predict the half-marathon performance, and the receiver operating characteristic (ROC) was used to evaluate the predictive ability of the nomogram.

RESULTS

A total of 202 participants (median age: 49 years; higher half-marathon performance: 33.7%) were included. After multivariate analysis, three variables remained as significant predictors: longer monthly running distance [adjusted odds ratio (AOR) = 0.992, 95% confidence interval (CI): 0.988 to 0.996, p < 0.001], faster mean training pace (AOR = 2.151, 95% CI: 1.275 to 3.630, p < 0.001), and better sleep quality [the Pittsburgh Sleep Quality Index (PSQI), AOR = 2.390, 95% CI: 1.164 to 4.907, p = 0.018]. The AUC of the training and testing sets in nomogram were 0.750 and 0.743, respectively. Further ternary and linear regression analyses corroborated the primary findings.

CONCLUSIONS

This study developed a nomogram with good potential to predict the half-marathon performance of recreational runners. Our results suggest that longer monthly running distance, faster mean training pace and better sleep quality notably contribute to better half-marathon performance.

A preliminary exploration of the regression equation for performance in amateur half-marathon runners: a perspective based on respiratory muscle function

This document presents a study on the relationship between physical characteristics, respiratory muscle capacity, and performance in amateur half-marathon runners. The aim of this study was to establish a preliminary predictive model to provide insights into training and health management for runners. Participants were recruited from the 2023 Beijing Olympic Forest Park Half-Marathon, comprising 233 individuals. Personal information including age, gender, height, weight, and other relevant factors were collected, and standardized testing methods were used to measure various parameters. Correlation analysis revealed significant associations between gender, height, weight, maximum expiratory pressure, maximal inspiratory pressure, and half-marathon performance. Several regression equations were developed to estimate the performance of amateur marathon runners, with a focus on gender, weight, maximum expiratory pressure, and height as predictive factors. The study found that respiratory muscle training can delay muscle fatigue and improve athletic performance. Evaluating the level of respiratory muscle capacity in marathon athletes is crucial for defining the potential speed limitations and achieving optimal performance. The information from this study can assist amateur runners in optimizing their training methods and maintaining their physical wellbeing.

Sleep Health, Individual Characteristics, Lifestyle Factors, and Marathon Completion Time in Marathon Runners: A Retrospective Investigation of the 2016 London Marathon

Despite sleep health being critically important for athlete performance and well-being, sleep health in marathoners is understudied. This foundational study explored relations between sleep health, individual characteristics, lifestyle factors, and marathon completion time. Data were obtained from the 2016 London Marathon participants. Participants completed the Athlete Sleep Screening Questionnaire (ASSQ) along with a brief survey capturing individual characteristics and lifestyle factors. Sleep health focused on the ASSQ sleep difficulty score (SDS) and its components. Linear regression computed relations among sleep, individual, lifestyle, and marathon variables. The analytic sample (N = 943) was mostly male (64.5%) and young adults (66.5%). A total of 23.5% of the sample reported sleep difficulties (SDS ≥ 8) at a severity warranting follow-up with a trained sleep provider. Middle-aged adults generally reported significantly worse sleep health characteristics, relative to young adults, except young adults reported significantly longer sleep onset latency (SOL). Sleep tracker users reported worse sleep satisfaction. Pre-bedtime electronic device use was associated with longer SOL and longer marathon completion time, while increasing SOL was also associated with longer marathon completion. Our results suggest a deleterious influence of pre-bedtime electronic device use and sleep tracker use on sleep health in marathoners. Orthosomnia may be a relevant factor in the relationship between sleep tracking and sleep health for marathoners.

The Relative Importance of Training and Social Support for Runners' Performance: A Cross-Sectional Study

BACKGROUND

Running participation/performance is a complex system. Understanding the variables associated with these behaviors may help to increase population physical activity and improve performance. This study aimed to investigate social and training variables important for running performance using a network approach.

METHODS

This cross-sectional study sampled 1151 non-professional Brazilian runners of both sexes (women, 38.2%; men, 61.7%). A questionnaire was available for eligible participants using an online platform, and information regarding training (volume and running pace) and social variables (participation in a running event, participation in a running group, influence on running, runners in the family, and childhood sport) related to runners' performance was obtained. The Chi-square test and network model were estimated by sex.

RESULTS

Training characteristics tend to be clustered. For both sexes, the training volume node presented the highest expected influence in the network (1.69 for women and 2.16 for men). Centrality indicators of social variables show that sports childhood participation and the presence of runners in the family were the most important nodes of network connection for women and men, respectively.

CONCLUSION

Based on these findings, it was concluded that sports participation during childhood and the practice of running by other family members were important factors to connect variables in the network. These findings have practical applications for health policymakers, highlighting the need to develop educational programs to increase sports participation during childhood and within families.

Estimating the cost of training disruptions on marathon performance

Completing a marathon usually requires at least 12-16 weeks of consistent training, but busy lifestyles, illness or injury, and motivational issues can all conspire to disrupt training. This study aims to investigate the frequency and performance cost of training disruptions, especially among recreational runners. Using more than 15 million activities, from 300,000 recreational runners who completed marathons during 2014-2017, we identified periods of varying durations up to 16 weeks before the marathon where runners experienced a complete cessation of training (so-called training disruptions). We identified runners who had completed multiple marathons including: (i) at least one disrupted marathon with a long training disruption of ≥ 7 days; and (ii) at least one undisrupted marathon with no training disruptions. Next, we calculated the performance cost of long training disruptions as the percentage difference between these disrupted and undisrupted marathon times, comparing the frequency and cost of training disruptions according to the sex, age, and ability of runner, and whether the disruptions occurred early or late in training. Over 50% of runners experienced short training disruptions up to and including 6 days, but longer disruptions were found to be increasingly less frequent among those who made it to race-day. Runners who experience longer training disruptions ( ≥ 7 days) suffer a finish-time cost of 5-8% compared to when the same runners experienced only short training disruptions (<7 days). While we found little difference (<5%) in the likelihood of disruptions-when comparing runners based on sex, age, ability, and the timing of a disruption-we did find significant differences in the the cost of disruptions (10-15%) among these groups. Two sample t -tests indicate that long training disruptions lead to a greater finish-time cost for males (5%) than females (3.5%). Faster runners also experience a greater finish-time cost (5.4%) than slower runners (2.6%). And, when disruptions occur late in training (close to race-day), they are associated with a greater finish-time cost (5.2%) than similar disruptions occurring earlier in training (4.4%). By parameterising and quantifying the cost of training disruptions, this work can help runners and coaches to better understand the relationship between training consistency and marathon performance. This has the potential to help them to better evaluate disruption risk during training and to plan for race-day more appropriately when disruptions do occur.

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.

Oxygen Uptake Measurements and Rate of Perceived Exertion during a Marathon

Although the marathon race has been democratized, it remains complex due to the famous “hitting the wall” phenomenon after the 25th km. To characterize this “wall” from a physiological and Rate of Perceived Exertion (RPE) perspective in recreational marathon runners, we report first continuous breath-by-breath gas exchange measurements during an actual marathon race. In order to test the hypothesis that RPE could be a candidate for controlling the marathon pace, this study examined the relationship between RPE and the physiological variables time course throughout a marathon. Only the respiratory frequency and heart rate increased progressively during the race in all the runners, while the oxygen uptake and ventilatory rate followed different kinetics according the individuals. However, the indexation of the physiological parameters and speed by RPE showed the same decreased tendency for all the runners. In conclusion, these results suggest that running a marathon must be self-paced with the RPE.

Who Is Running in the D-A-CH Countries? An Epidemiological Approach of 2455 Omnivorous, Vegetarian, and Vegan Recreational Runners-Results from the NURMI Study (Step 1)

Accompanied by the growing popularity of distance running, the prevalence of vegan and vegetarian diets in endurance runners has increased across the globe and especially in German-speaking (D-A-CH: Germany, Austria, Switzerland) countries. The present study aimed to investigate and compare the epidemiological characteristics associated with diet types and running behaviors of recreational endurance runners. From a total number of 7422 runners who started to fill in the online survey, 3835 runners completed the questionnaire. After data clearance, 2455 distance runners (mean age: 37 years; 56% females, 44% males) were selected as the final sample and classified as 1162 omnivores (47.4%), 529 vegetarians (21.5%), and 764 vegans (31.1%). Sociodemographic information and general characteristics in training and competition were evaluated using a questionnaire-based approach. A significant association was found between diet type and race distance (p < 0.001). In females, vegan ultra-marathoners and omnivorous half-marathoners had better individual running records among dietary groups. Sex differences in running performance had a minimizing trend with increasing race distance. Most runners reported independent race preparation (90%) over less than four months (73%). From an epidemiological viewpoint, the present findings suggest a central role of plant-based diets in running performance and behaviors among active distance runners in D-A-CH countries and that vegetarian and vegan diets are compatible with competitive running.

Training and Racing Behaviors of Omnivorous, Vegetarian, and Vegan Endurance Runners-Results from the NURMI Study (Step 1)

As a key modulator of training adaptations and racing performance, nutrition plays a critical role in endurance runners' success, and the training/racing behaviors of runners are potentially affected by their diet types. The present study aimed to investigate whether distance runners with a vegan diet (i.e., devoid of foods or ingredients from animal sources), vegetarian diet (i.e., devoid of meat and flesh foods), and omnivorous diet (i.e., a mixed diet with no restriction on food sources) have different training and racing patterns in general and based on race distance subgroups. A total of 3835 recreational runners completed an online survey. Runners were assigned to dietary (omnivorous, vegetarian, and vegan) and race distance (<21 km, half-marathon, and marathon/ultra-marathon) groups. In addition to sociodemographic information, a complete profile of data sets focusing on running and racing behaviors/patterns was evaluated using a questionnaire-based epidemiological approach. There were 1272 omnivores (47% females), 598 vegetarians (64% females), and 994 vegans (65% females). Compared to vegans and vegetarians, omnivorous runners prepared for a longer time period for running events, had a higher number of half-marathons and marathons completed with a better finish time, and had more reliance on training under supervision (p < 0.05). The present findings indicate an important association of diet types with patterns of training and racing amongst endurance runners that may be related to different motives of omnivorous, vegetarian, and vegan runners for participating in events.

Relationship between Biological, Training, and Physical Fitness Variables in the Expression of Performance in Non-Professional Runners

Sports performance is a multifactorial trait that can be associated with individual and environmental characteristics. In this study, the sample comprised 35 male runners, enrolled in the "InTrack" project. Information regarding variables related to runners' training was obtained via an online questionnaire, while anthropometric and body composition variables, as well as physical fitness components (muscular power, isometric strength, local muscular endurance, agility, and aerobic capacity) were measured, and a global physical fitness score (based on physical fitness components measured) was computed. The Weltman test (3200 m) was used to estimate runners' pace and their stride frequency. Linear regression was used, taking the running pace as dependent variable. The final model, comprising biological, physical fitness, spatiotemporal, and training variables, explained 86% of the running performance variance. Muscular power (β = -1.02; 95% CI = (-1.69)-(-0.35)), abdominal muscle endurance (β = -4.81; 95% CI = (-7.52)-(-2.10)), isometric strength (β = -422.95; 95% CI = (-689.65)-(-156.25)), global physical fitness (β = 27.14; 95% CI = 9.52-45.03), and stride frequency (β = -2.99; 95% CI = (-4.29)-(-1.69)) were significantly associated with performance, meaning that better results in tests and increasing the stride frequency leads to better performance. Individual characteristics and physical fitness components were demonstrated to be significant predictors for running performance.

Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners

Every year millions of people, from all walks of life, spend months training to run a traditional marathon. For some it is about becoming fit enough to complete the gruelling 26.2 mile (42.2 km) distance. For others, it is about improving their fitness, to achieve a new personal-best finish-time. In this paper, we argue that the complexities of training for a marathon, combined with the availability of real-time activity data, provide a unique and worthwhile opportunity for machine learning and for recommender systems techniques to support runners as they train, race, and recover. We present a number of case studies-a mix of original research plus some recent results-to highlight what can be achieved using the type of activity data that is routinely collected by the current generation of mobile fitness apps, smart watches, and wearable sensors.

Development and Validation of Prediction Equation of "Athens Authentic Marathon" Men's Race Speed

Aim

Despite the increasing popularity of outdoor endurance running races of different distances, little information exists about the role of training and physiological characteristics of recreational runners. The aim of the present study was (a) to examine the role of training and physiological characteristics on the performance of recreational marathon runners and (b) to develop a prediction equation of men’s race time in the “Athens Authentic Marathon.”

Methods

Recreational male marathon runners (n = 130, age 44.1 ± 8.6 years)—who finished the “Athens Authentic Marathon” 2017—performed a series of anthropometry and physical fitness tests including body mass index (BMI), body fat percentage (BF), maximal oxygen uptake (VO₂max), anaerobic power, squat, and countermovement jump. The variation of these characteristics was examined by quintiles (i.e., five groups consisting of 26 participants in each) of the race speed. An experimental group (EXP, n = 65) was used to develop a prediction equation of the race time, which was verified in a control group (CON, n = 65).

Results

In the overall sample, a one-way ANOVA showed a main effect of quintiles on race speed on weekly training days and distance, age, body weight, BMI, BF, and VO₂max (p ≤ 0.003, η² ≥ 0.121), where the faster groups outscored the slower groups. Running speed during the race correlated moderately with age (r = −0.36, p < 0.001) and largely with the number of weekly training days (r = 0.52, p < 0.001) and weekly running distance (r = 0.58, p < 0.001), but not with the number of previously finished marathons (r = 0.08, p = 0.369). With regard to physiological characteristics, running speed correlated largely with body mass (r = −0.52, p < 0.001), BMI (r = −0.60, p < 0.001), BF (r = −0.65, p < 0.001), VO₂max (r = 0.67, p < 0.001), moderately with isometric muscle strength (r = 0.42, p < 0.001), and small with anaerobic muscle power (r = 0.20, p = 0.021). In EXP, race speed could be predicted (R² = 0.61, standard error of the estimate = 1.19) using the formula “8.804 + 0.111 × VO₂max + 0.029 × weekly training distance in km −0.218 × BMI.” Applying this equation in CON, no bias was observed (difference between observed and predicted value 0.12 ± 1.09 km/h, 95% confidence intervals −0.15, 0.40, p = 0.122).

Conclusion

These findings highlighted the role of aerobic capacity, training, and body mass status for the performance of recreational male runners in a marathon race. The findings would be of great practical importance for coaches and trainers to predict the average marathon race time in a specific group of runners.

Running Performance Variability among Runners from Different Brazilian States: A Multilevel Approach

The ecological model theory highlights that human development (or a given behavior) is the result of the interaction of variables derived from different levels, comprising those directly related to the subjects and those related to the environment. Given that, the purpose of this study is to establish whether runners' performance may vary among different Brazilian states, as the factors associated with this difference. The sample comprised 1151 Brazilian runners (61.8% men) that completed an online questionnaire, providing information about biological (sex, age, height, and weight), training (running pace, frequency and volume/week, and motivation), sociodemographic (place of residence and wage) aspects, and perceptions about the environmental influences on the practice. Information about state variables was obtained from official institutes, and comprised the human development index (HDI), athletics events, and violence index. Multilevel analysis was conducted in HLM software. State-level characteristics explained ≈3% of the total variance in running performance. Of the total variance explained for the individual level, 56.4% was associated with male sex (β = -54.98; p < 0.001), age (β = 1.09; p < 0.001), body mass index (β = 6.86; p < 0.001), economic status (β = 6.23; p = 0.003), the perception of the natural environment (β = 7.58; p = 0.02), training frequency (β = -16.64; p < 0.001), and weekly volume (β = -0.30; p < 0.001). At the state level, only athletics events presented a positive and significant influence on performance. There is a significant role of the environment on the explanation of running performance variability, and given the diversity across states, environmental variables should not be neglected, as they are relevant to the exploration of other variables possibly related to running performance.

Training and Racing Behavior of Recreational Runners by Race Distance-Results From the NURMI Study (Step 1)

The present study investigated pre-race preparation of a large sample of recreational runners competing in different race distances (e.g., shorter than half-marathon, half-marathon, marathon and ultra-marathon). An online questionnaire was used and a total of 3,835 participants completed the survey. Of those participants, 2,864 (75%) met the inclusion criteria and 1,628 (57%) women and 1,236 (43%) men remained after data clearance. Participants were categorized according to race distance in half-marathon (HM), and marathon/ultra-marathon (M/UM). Marathon and ultra-marathon data were pooled since the marathon distance is included in an ultra-marathon. The most important findings were (i) marathon and ultra-marathon runners were more likely to seek advice from a professional trainer, and (ii) spring was most commonly reported across all subgroups as the planned season for racing, (iii) training volume increased with increasing race distance, and (iv) male runners invested more time in training compared to female runners. In summary, runners competing in different race distances prepare differently for their planned race.

Clinical Trial Registration:www.ClinicalTrials.gov, identifier ISRCTN73074080. Retrospectively registered 12th June 2015.

Predictive Performance Models in Long-Distance Runners: A Narrative Review

Physiological variables such as maximal oxygen uptake (VO₂max), velocity at maximal oxygen uptake (vVO₂max), running economy (RE) and changes in lactate levels are considered the main factors determining performance in long-distance races. The aim of this review was to present the mathematical models available in the literature to estimate performance in the 5000 m, 10,000 m, half-marathon and marathon events. Eighty-eight articles were identified, selections were made based on the inclusion criteria and the full text of the articles were obtained. The articles were reviewed and categorized according to demographic, anthropometric, exercise physiology and field test variables were also included by athletic specialty. A total of 58 studies were included, from 1983 to the present, distributed in the following categories: 12 in the 5000 m, 13 in the 10,000 m, 12 in the half-marathon and 21 in the marathon. A total of 136 independent variables associated with performance in long-distance races were considered, 43.4% of which pertained to variables derived from the evaluation of aerobic metabolism, 26.5% to variables associated with training load and 20.6% to anthropometric variables, body composition and somatotype components. The most closely associated variables in the prediction models for the half and full marathon specialties were the variables obtained from the laboratory tests (VO₂max, vVO₂max), training variables (training pace, training load) and anthropometric variables (fat mass, skinfolds). A large gap exists in predicting time in long-distance races, based on field tests. Physiological effort assessments are almost exclusive to shorter specialties (5000 m and 10,000 m). The predictor variables of the half-marathon are mainly anthropometric, but with moderate coefficients of determination. The variables of note in the marathon category are fundamentally those associated with training and those derived from physiological evaluation and anthropometric parameters.

Diverse Effects of Thermal Conditions on Performance of Marathon Runners

Heat exposure affects human performance in many ways. Both physiological (i.e., glycogen sparing, oxygen uptake, thermoregulation) and biomechanical mechanisms (i.e., contact time, knee flexion, muscle activity) are affected, hence reducing performance. However, the exposure affects persons differently. Not all athletes necessarily experience an identical thermal condition similarly, and this point has been overlooked to date. We analyzed endurance performances of the top 1000 runners for every year during the last 12 New York City Marathons. Thermal conditions were estimated with wet-bulb globe temperature (WBGT) and universal thermal climate index (UTCI). Under identical thermal exposure, the fastest runners experienced a larger decline in performance than the slower ones. The empirical evidence offered here not only shows that thermal conditions affect runners differently, but also that some groups might consistently suffer more than others. Further research may inspect other factors that could be affected by thermal conditions, as pacing and race strategy.