Training is essential from patients to athletes for sustaining a high quality of life, remaining functionally independent or improving physical performance. They exercise to improve their cardiorespiratory fitness and muscle power in order to enhance their exercise tolerance. However, most training programs have been designed using a ‘one-size-fits-all’ approach, although there is substantial individual variation in training adaptations. The goal of this research is to understand why people respond differently to training and to design effective training programmes for each individual.
With this Revanche proposal, a subset will be made of the experiment from Stephans original VENI application. This will include a 3-month training intervention study in recreationally-active adults (n=56). The aim of this study is twofold: 1) to proof that sufficient training effects can be obtained within 3 months and 2) to demonstrate the use of artificial intelligence to explore individual training adaptations based on a small sample size. These findings will help to explore how the individual adults can best maintain a high physical performance and health-related quality of life, based on a personalized approach.
The proposed study conrtibutes to become an expert in the currently emerging field of data science in exercise physiology, focusing on individualized adaptations. In addition, it will increase chances of success in other research projects, such as the ERC starting grant.