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

José Alvero-Cruz,Manuel García,Beat Knechtle,Fernando Alacid,Lorena Correas-Gómez,Elvis Carnero,Thomas Rosemann,Pantelis Nikolaidis

doi:10.3390/ijerph17218289

Abstract

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.

Highlights

The great popularity of long-distance running has seen an unprecedented increase in the last 10 years
One of the difficulties we encountered in comparing the different equations is that there is no consensus on the definition of the type of athletes, with each author having named the type of subjects involved
We found great differences in the number of athletes participating in the studies, ranging from eight subjects [24,36] to 427 including both men and women [28]

Summary

Introduction

The great popularity of long-distance running has seen an unprecedented increase in the last 10 years This has generated, in coaches and athletes, a great interest in the development of performance prediction models based on linear regression equations, with the aim of helping many athletes in their preparation for competitions. Accurate knowledge is frequently difficult to obtain, especially in long-distance races, as it would involve high training loads, which can, at times, indicate poor race planning in inexperienced runners who normally use polarized training methods [3] This and other factors associated with the control of training, result in predictive models being recognized and useful for coaches or professional runners. Transferring the results and conclusions obtained from amateur athletes to high-level athletes is not advisable [6]

Objectives

Methods

Results

Discussion

Conclusion