Prediction of endurance running performance for middle-aged and older runners

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.

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.

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

OBJECTIVES

Marathoners rely on expert-opinion and the anecdotal advice of their peers when devising their training plans for an upcoming race. The accumulation of results from multiple scientific studies has the potential to clarify the precise training requirements for the marathon. The purpose of the present study was to perform a systematic review, meta-analysis and meta-regression of available literature to determine if a dose-response relationship exists between a series of training behaviours and marathon performance.

DESIGN

Systematic review, meta-analysis and meta-regression.

METHODS

A systematic search of multiple literature sources was undertaken to identify observational and interventional studies of elite and recreational marathon (42.2km) runners.

RESULTS

Eighty-five studies which included 137 cohorts of runners (25% female) were included in the meta-regression, with average weekly running distance, number of weekly runs, maximum running distance completed in a single week, number of runs ≥32km completed in the pre-marathon training block, average running pace during training, distance of the longest run and hours of running per week used as covariates. Separately conducted univariate random effects meta-regression models identified a negative statistical association between each of the above listed training behaviours and marathon performance (R² 0.38-0.81, p<0.001), whereby increases in a given training parameter coincided with faster marathon finish times. Meta-analysis revealed the rate of non-finishers in the marathon was 7.27% (95% CI 6.09%-8.65%).

CONCLUSIONS

These data can be used by athletes and coaches to inform the development of marathon training regimes that are specific to a given target finish time.

Prediction Equations for Marathon Performance: A Systematic Review

PURPOSE

Despite the volume of available literature focusing on marathon running and the prediction of performance, no single prediction equations exists that is accurate for all runners of varying experiences and abilities. Indeed the relative merits and utility of the existing equations remain unclear. Thus, the aim of this study was to collate, characterize, compare, and contrast all available marathon prediction equations.

METHODS

A systematic review was conducted to identify observational research studies outlining any kind of prediction algorithm for marathon performance.

RESULTS

Thirty-six studies with 114 equations were identified. Sixty-one equations were based on training and anthropometric variables, whereas 53 equations included variables that required laboratory tests and equipment. The accuracy of these equations was denoted via a variety of metrics; r2 values were provided for 68 equations (r2 = .10-.99), and an SEE was provided for 19 equations (SEE 0.27-27.4 min).

CONCLUSION

Heterogeneity of the data precludes the identification of a single "best" equation. Important variables such as course gradient, sex, and expected weather conditions were often not included, and some widely used equations did not report the r2 value. Runners should therefore be wary of relying on a single equation to predict their performance.

Different Training Modalities Improve Energy Cost and Performance in Master Runners

Purpose: The aim of this study was to compare the effects of continuous moderate-intensity and discontinuous high-intensity training on running performance in master runners.

Methods: Thirty-four male master runners (47.2 ± 7.4 years) were assigned to three different groups: continuous moderate-intensity training (CMIT), discontinuous high-intensity training (DHIT), and control group (CON). CMIT and DHIT performed 8-week of supervised training (3 session·wk⁻¹; ~35 km·wk⁻¹) while CON maintained their normal training habits (3–4 session·wk⁻¹; ~50 km·wk⁻¹). Peak oxygen consumption (V˙O_2peak) and peak running speed (v_peak) during incremental treadmill exercise, gas exchange threshold (GET), speed at GET, energy cost of running (Cr), and 5-km performance were evaluated before and after training.

Results: Following the training period, both CMIT and DHIT significantly reduced Cr (−4.4 and −4.9%, respectively, P < 0.05), increased speed at GET (3.4 and 5.7%, P < 0.05) and improved 5-km time-trial performance (3.1 and 2.2%, P < 0.05) whereas no differences were found for V˙O_2peak and GET (as %V˙O_2peak). After training, v_peak improved only for DHIT (6%, P < 0.05). No differences were found in any variable for CON.

Conclusions: This study indicates that both CMIT and DHIT may positively affect running performance in middle-aged master runners. This improvement was achieved despite a significant reduction of the amount of weekly training volume.

An empirical study of race times in recreational endurance runners

Background

Studies of endurance running have typically involved elite athletes, small sample sizes and measures that require special expertise or equipment.

Methods

We examined factors associated with race performance and explored methods for race time prediction using information routinely available to a recreational runner. An Internet survey was used to collect data from recreational endurance runners (N = 2303). The cohort was split 2:1 into a training set and validation set to create models to predict race time.

Results

Sex, age, BMI and race training were associated with mean race velocity for all race distances. The difference in velocity between males and females decreased with increasing distance. Tempo runs were more strongly associated with velocity for shorter distances, while typical weekly training mileage and interval training had similar associations with velocity for all race distances. The commonly used Riegel formula for race time prediction was well-calibrated for races up to a half-marathon, but dramatically underestimated marathon time, giving times at least 10 min too fast for half of runners. We built two models to predict marathon time. The mean squared error for Riegel was 381 compared to 228 (model based on one prior race) and 208 (model based on two prior races).

Conclusions

Our findings can be used to inform race training and to provide more accurate race time predictions for better pacing.

Electronic supplementary material

The online version of this article (doi:10.1186/s13102-016-0052-y) contains supplementary material, which is available to authorized users.

Relationship of anthropometric and training characteristics with race performance in endurance and ultra-endurance athletes

A variety of anthropometric and training characteristics have been identified as predictor variables for race performance in endurance and ultra-endurance athletes. Anthropometric characteristics such as skin-fold thicknesses, body fat, circumferences and length of limbs, body mass, body height, and body mass index were bi-variately related to race performance in endurance athletes such as swimmers in pools and in open water, in road and mountain bike cyclists, and in runners and triathletes over different distances. Additionally, training variables such as volume and speed were also bi-variately associated with race performance. Multi-variate regression analyses including anthropometric and training characteristics reduced the predictor variables mainly to body fat and speed during training units. Further multi-variate regression analyses including additionally the aspects of previous experience such as personal best times showed that mainly previous best time in shorter races were the most important predictors for ultra-endurance race times. Ultra-endurance athletes seemed to prepare differently for their races compared to endurance athletes where ultra-endurance athletes invested more time in training and completed more training kilometers at lower speed compared to endurance athletes. In conclusion, the most important predictor variables for ultra-endurance athletes were a fast personal best time in shorter races, a low body fat and a high speed during training units.

Runners in their forties dominate ultra-marathons from 50 to 3,100 miles

OBJECTIVES

This study investigated performance trends and the age of peak running speed in ultra-marathons from 50 to 3,100 miles.

METHODS

The running speed and age of the fastest competitors in 50-, 100-, 200-, 1,000- and 3,100-mile events held worldwide from 1971 to 2012 were analyzed using single- and multi-level regression analyses.

RESULTS

The number of events and competitors increased exponentially in 50- and 100-mile events. For the annual fastest runners, women improved in 50-mile events, but not men. In 100-mile events, both women and men improved their performance. In 1,000-mile events, men became slower. For the annual top ten runners, women improved in 50- and 100-mile events, whereas the performance of men remained unchanged in 50- and 3,100-mile events but improved in 100-mile events. The age of the annual fastest runners was approximately 35 years for both women and men in 50-mile events and approximately 35 years for women in 100-mile events. For men, the age of the annual fastest runners in 100-mile events was higher at 38 years. For the annual fastest runners of 1,000-mile events, the women were approximately 43 years of age, whereas for men, the age increased to 48 years of age. For the annual fastest runners of 3,100-mile events, the age in women decreased to 35 years and was approximately 39 years in men.

CONCLUSION

The running speed of the fastest competitors increased for both women and men in 100-mile events but only for women in 50-mile events. The age of peak running speed increased in men with increasing race distance to approximately 45 years in 1,000-mile events, whereas it decreased to approximately 39 years in 3,100-mile events. In women, the upper age of peak running speed increased to approximately 51 years in 3,100-mile events.

Lactate-driven equine conditioning programmes

Equine conditioning programmes are rarely driven by science. Indeed, the scientific literature on conditioning responses often refers to conventional technique rather than physiological driving parameters. This, alongside poor classification of conditioning protocols, has reduced the possibility of comparative data analysis. Recent interest into lactate-driven conditioning programmes has driven this review which provides a summary of equine protocols used to date and their responses. Key areas identified for further standardisation and/or investigation include (1) the treadmill acclimation protocol and markers of its efficiency, (2) the design and frequency of standardised exercise tests used, and (3) the interpretation of data for the development of effective and realistic conditioning programmes.

Characteristics Associated with 10-km Running Performance among a Group of Highly Trained Male Endurance Runners Age 21–63 Years

This study assessed physiological and cardiac factors associated with 10-km running performance in a group of highly trained endurance runners age 21–63 years. Participants (N= 37) underwent a resting echocardiograph and incremental treadmill running test. They also provided information on their recent 10-km races. Data were analyzed using “best subsets” multiple regression. Declines with age were found for 10-km running speed (0.26 m · s−1· decade−1), maximum heart rate (4 beats/decade), VO2peak(6 ml · kg−1· min−1· decade−1), velocity at lactate threshold (1 m · s−1· decade−1), and VO2at lactate threshold (4 ml · kg−1· min−1· decade−1). The percentage of VO2peakat which lactate threshold occurred increased with age by 1.5% per decade. The rate of change of displacement of the atrioventricular plane at the left free wall and septum both declined by 1 cm · s−1· decade−1. The best single predictor of 10-km running speed was velocity at lactate threshold.

Maximal aerobic power, lactate threshold, and running performance in master athletes

PURPOSE

This study sought to determine how lactate threshold (LT) is related to running performance in older male and female runners, if LT changes significantly with age, and if gender alters the relationship between LT and performance in older runners.

METHODS

Subjects were 168 master runners (111 men, 57 women) selected from a longitudinal study, who ran at least 10 miles x wk(-1) for 5 yr or more. VO2max was measured on a treadmill and body composition by hydrostatic weighing. Blood samples taken each minute of exercise were analyzed for lactate concentration and LT determined as the breakpoint in lactate accumulation. Performance times and training histories were self-reported by questionnaire.

RESULTS

Men had significantly greater body mass, fat-free mass (FFM), and VO2max (L x min(-1); mL x kg(-1) x min(-1)) than women. FFM and VO2max (L x min(-1); mL x kg(-1) x min(-1)) declined with age in both men and women. Running performance was significantly different between men and women and declined with age in both. LT (L x min(-1); mL x kg(-1) x min(-1)) was significantly different between men and women, and declined significantly with age in men, whereas LT (%VO2max) did not differ between men and women and increased significantly with age in both. VO2max (mL x kg(-1) x min(-1)) was the most significant predictor of performance in both men and women, whereas LT (L x min(-1)) added to the prediction of 5-km and 10-km performance in women.

CONCLUSION

The results of this study demonstrate that VO2max (mL x kg(-1) x min(-1)) is a better predictor of performance than LT in older male and female runners. Additionally, LT as a percentage of VO2max increases significantly with age.

Predicting competition performance in long-distance running by means of a treadmill test

PURPOSE

The purpose of this study was to examine the power of 16 parameters beside the individual anaerobic threshold (IAT) in predicting performance in various competition distances.

METHODS

This study examined 427 competitive runners to test the prediction probability of the IAT and other parameters for various running distances. All runners (339 men, 88 women; ages, 32.5 +/- 10.14 yr; training, 7.1 +/- 5.53 yr; training distance, 77.9 +/- 35.63 km.wk-1) performed an increment test on the treadmill (starting speed, 6 or 8 km.h-1; increments, 2 km.h-1; increment duration, 3 min to exhaustion). The heart rate (HR) and the lactate concentrations in hemolyzed whole blood were measured at rest and at the end of each exercise level. The IAT was defined as the running speed at a net increase in lactate concentration 1.5 mmol.L-1 above the lactate concentration at LT.

RESULTS

Significant correlations (r = 0.88-0.93) with the mean competition speed were found for the competition distances and could be increased using stepwise multiple regression (r = 0.953-0.968) with a set of additional parameters from the training history, anthropometric data, or the performance diagnostics.

CONCLUSIONS

The running speed at a defined net lactate increase thus produces an increasing prediction accuracy with increasing distance. A parallel curve of the identity straight lines with the straight lines of regression indicates the independence of at least a second independent performance determining factor.

Cardiorespiratory responses to cycling exercise in trained and untrained healthy elderly: with special reference to the lactate threshold

The fastest growing age group in the United States and Japan is the elderly. There is a need to develop appropriate exercise training guidelines designed specifically for healthy older persons. Recent reports have shown that the lactate threshold (LT) can be used to evaluate the clinical significance of aerobic power (VO2max) and its effect of exercise training in the elderly. However, there is a lack of research comparing the LT between well-trained and sedentary elderly individuals. Also, the effect of exercise training on the heart rate (HR) at LT needs further investigation. The purpose of this study was to compare the LT levels between the older trained men (T group; n = 72, age = 71.3 +/- 5.8 yr, range 60-85 yr) and apparently healthy but untrained elderly men (U group; n = 172, age = 72.2 +/- 5.7 yr, range 60-93 yr). The LT was measured during an incremental cycle ergometer test. A low relationship was found between VO2 corresponding to LT (VO2LT) and age in the T (r = 0.20, P < 0.05) and U groups (r = 0.43, P < 0.05). A significant difference was found in the VO2LT between the T and U groups. The absolute VO2LT corresponded to approximately 6 and 4 METs for the T and U subjects, respectively. However, there was no significant difference in HR corresponding to LT (HRLT) between the two groups (T; 109 +/- 19 b.min-1, U; 107 +/- 13 b.min-1). The data show that the absolute VO2LT is higher for T than U elderly subjects and is associated with a HR of approximately 108 b.min-1 for both groups. Recommended exercise intensity in terms of HR may not differ between trained and untrained elderly men.