The Development of a Personalised Training Framework: Implementation of Emerging Technologies for Performance

Abstract

Over the last decade, there has been considerable interest in the individualisation of athlete training, including the use of genetic information, alongside more advanced data capture and analysis techniques. Here, we explore the evidence for, and practical use of, a number of these emerging technologies, including the measurement and quantification of epigenetic changes, microbiome analysis and the use of cell-free DNA, along with data mining and machine learning. In doing so, we develop a theoretical model for the use of these technologies in an elite sport setting, allowing the coach to better answer six key questions: (1) To what training will my athlete best respond? (2) How well is my athlete adapting to training? (3) When should I change the training stimulus (i.e., has the athlete reached their adaptive ceiling for this training modality)? (4) How long will it take for a certain adaptation to occur? (5) How well is my athlete tolerating the current training load? (6) What load can my athlete handle today? Special consideration is given to whether such an individualised training framework will outperform current methods as well as the challenges in implementing this approach.

Keywords: cfDNA; genetics; machine learning; metabolomics; miRNA; monitoring; pharmacogenomics.

Figure 1

Summary of epigenetic influences on training-induced adaptation and fatigue.

Figure 2

An overview of the development of the personalised training process.

Figure 3

A framework for the implementation of various emerging technologies to enhance daily training practice.