Endurance research has to be interdisciplinary

Recently, in evolutionary terms, our endurance potential has become more visible in athletic performance than during hunting. Indeed, there has been a rapid rise in the volume of ultra-marathon events planned, the number of successful participants, and an increased amount of research into participation profiles and endurance performance.

Major gains in our understanding are likely to be identified using an interdisciplinary approach to sport and exercise science, rather than through fragmented, contextually isolated research (Balagué et al., 2017). Contextual isolation, and a failure to integrate knowledge from a number of disciplines may result in an insufficient understanding of sport-related phenomena, whilst an interdisciplinary approach may offer more valuable and informed directions (Balagué et al., 2017).  Indeed components in living organisms should not be analysed out of context, as they may behave differently when viewed in isolation rather than part of a network (Hristovski, 2013).

As with successes in cognitive science in recent decades, a cross-discipline, and interdisciplinary, approach has seen significant success in research and model development in disciplines as diverse as language emergence, natural language processing, working memory and mental imagery (Baddeley, 2012; Gong, Shuai, & Comrie, 2014; Moran, Guillot, Macintyre, & Collet, 2011; Wan et al., 2018).

Balagué, N., Torrents, C., Hristovski, R., & Kelso, J. A. (2017). Sport science integration: An evolutionary synthesis. European Journal of Sport Science, 17(1), 51-62. 

Hristovski, R. (2013). Synthetic thinking in (sports) science: The self-organization of the scientific language. Research in Physical Education Sport and Health, 2, 27–34.

Baddeley, A.D. (2012). Working memory: Theories, models and controversies. Annual Review of Psychology, 63: 1-20

Gong, T., Shuai, L., & Comrie, B. (2014). Evolutionary linguistics: Theory of language in an interdisciplinary space. Language Sciences, 41, 243-253. 

Wan, Y., Chen, Y., Shi, X., & Zhou, C. (2018). Constructing and validating word similarity datasets by integrating methods from psychology, brain science and computational linguistics. Soft Computing, 22(21), 6967-6979.