Christof Schwiening
Department of PDN, University of Cambridge, UK

https://doi.org/10.36866/pn.98.19

Age affects how our physiology adapts to stresses. As we become older the balance between the rate of accumulation and retention of knowledge, skill and physical damage changes. Ageing is often characterised by the shift in balance between the damage accrued from training and any potentially beneficial gain of function by physiological adaptations and hypertrophy. This damage, the slow recovery and the associated pain is often used as an ‘excuse’ for poor performance amongst the over 40s.

There is a tendency to accept declining mental and physical performance as an inevitable consequence of growing old. But, it is worth considering how much of the decline in physiological function actually results from true time-dependent biochemical ageing and how much is the result of changing behaviour or inappropriate stresses to our physiological systems. There is a general presumption that making life easy for the elderly is a good thing. Whilst in the short term this may well be true, from a physiological feedback perspective there is the possibility that by removing too many training stresses, life ultimately remains just as hard but with an atrophied physiology. There are few better ways to illustrate the critical nature of imposed stresses than to consider athletic performance.

The very fastest are usually young

World Record athletic performance has always been the preserve of the relatively young although how young depends upon the nature of the event. At the age of 40 Elena Zvereva became the oldest World Champion (discus)[1] in athletics and continued competing at World Championship events until the age of 49. World Records at running events are typically set by younger athletes (25-35 years) with peak performance declining [2] in a smooth but not linear age-related fashion (Figure 1).

Age-grading: a fairer form of competition

The recent explosion of interest in competitive amateur running has, in part, encouraged the use of normative tables to compare athletes – not just of different ages, but men and women too[4]. In 1989 The World Association of Veteran Athletes (WAVA) first published age-graded performance tables for athletics events allowing men and women of all ages to compete on a ‘physiologically’ level playing field. Such tables are currently maintained by World Masters Athletics (WMA)[5] and form the basis for calculating age and sex-graded performance. Such correction factors play a critical role in encouraging mass participation in athletics events not just because they allow competition between the generations, but also because they reveal elite-level performances (optimization of physiology) that would otherwise go largely unrecognized.

Engaging the ageing in exercise

A good example of this is the parkrun[6] movement – a 5km timed run that takes place at 9 am every Saturday morning at 309 locations across the UK involving nearly 50,000 runners each week with ~1% of the UK population having taken part in at least one event. The results from each event are posted online together with the age (and sex)-graded performance expressed as a percentage of the appropriate World Record performance. Using my local parkrun as an example we have one athlete[7] who has posted a 95.8% age-graded performance and regularly achieves over 90% on a course that is far from optimal. Despite these near World Record performances (~27 mins) she rarely finishes higher than 200th place (she runs in the 75-79 age group) although she easily beats the average female time of ~29.5 mins. Whilst Mary represents the extreme end of athletic performance and has clearly been able to optimize her gradually declining ability to adapt, it is worth considering to what extent the general population is actually limited by biochemical age. The first example that springs to mind is Steve Way. In 2007, aged 33, he was in slightly worse state than the ‘average’ man: he weighed 105 kg and smoked 20 cigarettes a day[8]. However, by the age of 40, through simple training, he had worked his way to finishing 15^th at the London Marathon albeit still 12 min behind the absolute World Record time. Whilst it must be the case that to some extent Steve has been lucky in terms of his genetics and biomechanics it is worth looking at what it takes, in terms of training load, to produce a fast marathon time regardless of age.

How the amateur can run a fast marathon

Those of you engaged in running will know about the many and varied training loads that are used to force the adaptations necessary for a fast marathon. The list includes; the long run, tempo running, interval training, speed work, hill work, core exercises, dynamic and static flexibility and easy runs. For the novice the complexity often obscures what is actually important. For most amateur runners marathon racing does not involve running particularly quickly – it is just requires that the pace can be maintained without any dramatic decline, i.e. not hitting “the wall” (Rapoport, 2010). Tanda (2011) provides some insight into what elements of training are important for determining marathon performance and therefore also for how to approach, but not hit, the wall. His rather simple analysis correlated training data with marathon performance times. His conclusion was that marathon finishing times could be predicted, with reasonable accuracy (SEE ~4 mins), by knowing how far someone has run over an 8 week period and how long it took. The precise make-up of the training seemed to play only a minor role. I was surprised by this – could it really be so simple? Re-plotting my own training data[9] I found that the predictive equation correlated well with my own race performances (Figure 2) with similar scatter to that reported by Tanda (2011).

Do more and get faster: correlation and causation

It would thus appear, to a first approximation, that athletes are a product of the training loads that they are either willing to apply or can tolerate. The problem with ageing may therefore be one of finding methods that produce appropriate training loads and making them acceptable. Certainly the parkrun begins to fill that gap by providing an accessible acceptable event on a regular basis. However, those wishing to achieve their optimal performance may well be able to do better by looking at what we now refer to as the Tanda parameter space. The Tanda equation predicts equal performance for runners who train in different fashions: fast and short or long and slow. The performance differences are dependent on just how short and fast or how long and slow. A little knowledge of physics (kinetic energy is proportional to speed squared) and experience is sufficient to suggest that these two diametrically opposed training strategies are not equally accessible to the older athlete: training fast may cause more damage (Hespanhol Jr, 2013) than benefit. It is likely that by running slower damage may be reduced allowing a greater training stimulus to occur, through an increase in distance, resulting in better performances predictions (Tanda, 2011) regardless of sex or age. Certainly my own personal experience suggests that there may be some mileage in this approach (Figure 1).

References

Hespanhol Jr LC, Costa LO P, Lopes AD(2013) Previous injuries and some training characteristics predict running-related injuries in recreational runners: a prospective cohort study. J Physiotherapy 59, 263–269.

Rapoport B (2010) Metabolic factors limiting performance in marathon runners. PLoS Comput Biol 6, 1–13.

Tanda G (2011) Prediction of marathon performance time on the basis of training indices. Journal of Human Sport and Exercise, 6, 511–520.

Web Sources

[1] IAAF International Association of Athletics Federations (2015) IAAF: Athletics Discipline – Discus – Disciplines – iaaf.org. http://www.iaaf.org/disciplines/throws/discus-throw Accessed 25 February 2015

[2] WMA (2015) Records Outdoor Men. World-masters-athletics.org http://www.world-masters-athletics.org/records/outdoor-men Accessed 25 February 2015

[2b] WMA (2015) Records Outdoor Women. World-masters-athletics.org http://www.world-masters-athletics.org/records/outdoor-women Accessed 25 February 2015

[3] The power of ten (2015) Athlete Profile Thepowerof10.info http://www.thepowerof10.info/athletes/profile.aspx?athleteid=116274 Accessed 25 February 2015

[4] Proctor L (2010) Running: A race against gender. BBC News Magazine http://www.bbc.co.uk/news/magazine-11400138 Accessed 25 February 2015

[5] WMA (2015) http://www.world-masters-athletics.org Accessed 25 February 2015

[6] parkrun UK (2015) (Parkrun.org.uk)http://www.parkrun.org.uk Accessed 25 February 2015

[7] parkrun UK (2015) athlete history | Cambridge parkrun Parkrun.org.uk http://www.parkrun.org.uk/cambridge/results/athletehistory/?athleteNumber=180088 Accessed 25 February 2015

[8] Dirs, B (2014) Steve Way: From cigarettes and alcohol to marathon man. BBC Sport http://www.bbc.co.uk/sport/0/get-inspired/27994073 Accessed 25 February 2015

[9] Strava (2015) Christof Schwiening | Runner on Strava https://www.strava.com/athletes/1727245 Accessed 25 February 2015