Assembled and summarized in part by AI – ChatGPT with the guidance of a human being (Jim C)
Selected past studies on age-related athletic performance:
Nikolaidis, P. T., & Knechtle, B. (2018). The age-related performance decline in marathon cross-country skiing – the Engadin Ski Marathon. Journal of Sports Sciences, 36(6), 599–604. DOI: 10.1080/02640414.2017.1325965.
Data: Engadin Ski Marathon, 1998–2016.
Question: How does maximum cross-country skiing performance change with age?
Approach: Examined performance across one-year age intervals and modeled the age-performance relationship.
Major result: The age-performance relationship is distinctly nonlinear. The estimated age of peak performance depended on sex and on how performance was defined.
Knechtle, B., & Nikolaidis, P. T. (2018). The age of peak marathon performance in cross-country skiing—The “Engadin Ski Marathon.” Journal of Strength and Conditioning Research, 32(4), 1131–1136. DOI: 10.1519/JSC.0000000000001931.
Data: 162,991 men and 34,833 women competing in the Engadin between 1998 and 2016—nearly 198,000 performances.
Approach: Examined all finishers and fastest performances using one-year age intervals.
Major result: Among the fastest competitors within one-year age intervals, peak speed occurred at 29 years for men and 24 for women. Results differed considerably when all competitors were considered.
Ouimet, R. (2023). Maximum performance of master cross-country skiers in loppets: Relationship with age. SportRxiv. DOI: 10.51224/SRXIV.277.
Data: Nine Worldloppet races, comprising 89 events from 1995–2005, with 190,304 male and 24,917 female Masters participants.
Approach: A boundary-line method identified maximum performance by age category. A modified power function modeled relative maximum speed; loppets were treated as a random factor.
Major result: Performance declined with age, but importantly the decline was smaller for classic than freestyle skiing and smaller for men than women. The model explained about 70% of the variation. Ouimet also derived preliminary skiing age-grading factors.
Nikolaidis, P. T., Rosemann, T., & Knechtle, B. (2019). The differences in pacing among age groups of amateur cross-country skiers depend on performance. Journal of Human Kinetics, 66, 167–176.
Data: 105,565 Engadin finishers, 1998–2016, with split speeds measured at 10, 20 and 35 km.
Major result: Age differences in pacing depended upon overall performance level. The interaction between age and performance was considerably more complicated than simply “older skiers slow down more.”
Carlsson, M., Assarsson, H., & Carlsson, T. (2016). The influence of sex, age, and race experience on pacing profiles during the 90 km Vasaloppet ski race. Open Access Journal of Sports Medicine, 7, 11–19. DOI: 10.2147/OAJSM.S101995.
Data: Vasaloppet, the 90-km Swedish ski marathon.
Lepers, R., Burfoot, A., & Stapley, P. J. (2021). Sub 3-hour marathon runners for five consecutive decades demonstrate a reduced age-related decline in performance. Frontiers in Physiology, 12, 649282. DOI: 10.3389/fphys.2021.649282.
Design: This is fundamentally different from most studies. The authors identified runners who achieved sub-three-hour marathons over five consecutive calendar decades and examined their performance longitudinally.
Major result: The within-runner deterioration was substantially smaller than one might predict from comparisons of different athletes at different ages. The authors explicitly point out the inherent limitation of estimating aging from age-group records: the records at different ages generally belong to different people.
Leyk, D., Erley, O., Ridder, D., Leurs, M., Rüther, T., Wunderlich, M., Sievert, A., Baum, K., & Essfeld, D. (2007). Age-related changes in marathon and half-marathon performances. International Journal of Sports Medicine, 28(6), 513–517. DOI: 10.1055/s-2006-924658.
Data: 405,515 performances initially; 300,757 runners after repeated performances were excluded. Age range: 20–79.
Major result: Mean marathon and half-marathon performance was remarkably similar from ages 20–49. Significant deterioration did not appear until approximately 50, and performance losses among 50–69-year-olds were only about 2.6–4.4% per decade.
Nikolaidis, P. T., Alvero-Cruz, J. R., Villiger, E., Rosemann, T., & Knechtle, B. (2019). The age-related performance decline in marathon running: The paradigm of the Berlin Marathon. International Journal of Environmental Research and Public Health, 16(11), 2022. DOI: 10.3390/ijerph16112022.
Data: 387,222 finishers, 2008–2018.
Major result: Peak performance was estimated at age 32 for women and 34 for men using one-year age groups. Women aged 60–64 and men aged 55–59 still averaged approximately 90% of peak running speed.
Zavorsky, G. S., Tomko, K. A., & Smoliga, J. M. (2017). Declines in marathon performance: Sex differences in elite and recreational athletes. PLoS ONE, 12(2), e0172121. DOI: 10.1371/journal.pone.0172121.
Data: Boston, Chicago and New York City marathons.
Major contribution: Distinguished between top age-group performance and ordinary recreational performance.
Major result: High-level performance began declining around age 35, while the decline in median recreational performance appeared considerably later.
Knechtle, B., Assadi, H., Lepers, R., Rosemann, T., & Rüst, C. A. (2014). Relationship between age and elite marathon race time in world single age records from 5 to 93 years. BMC Sports Science, Medicine and Rehabilitation, 6, 31. DOI: 10.1186/2052-1847-6-31.
Data: World single-age marathon records from age 5 through 93.
Approach: Both linear and nonlinear regression.
Connick, M. J., Beckman, E. M., & Tweedy, S. M. (2015). Relative age affects marathon performance in male and female athletes. Journal of Sports Science and Medicine, 14(3), 669–674.
Knechtle, B., Rüst, C. A., Rosemann, T., & Lepers, R. (2012). Age-related changes in 100-km ultra-marathon running performance. Age, 34(4), 1033–1045. DOI: 10.1007/s11357-011-9290-9.
Data: 100-km Lauf Biel, Switzerland, 1998–2010.
Tanaka, H., & Seals, D. R. (2008). Endurance exercise performance in Masters athletes: Age-associated changes and underlying physiological mechanisms. The Journal of Physiology, 586(1), 55–63. DOI: 10.1113/jphysiol.2007.141879.
Major conclusion: Endurance performance is generally well maintained until roughly age 35, decreases modestly through approximately 50–60, and then declines progressively more rapidly.
The physiological factor most strongly associated with this deterioration is declining VO₂max. Lactate threshold contributes, while exercise economy appears comparatively well preserved in trained older athletes.
Lepers, R., & Stapley, P. J. (2016). Master athletes are extending the limits of human endurance. Frontiers in Physiology, 7, 613. DOI: 10.3389/fphys.2016.00613.
Scope: Running, swimming, cycling, triathlon and ultra-endurance sports.
Major conclusion: The rate of age-related decline depends on mode of locomotion, event duration and sex. VO₂max is again identified as the physiological determinant most affected by age, while exercise economy and lactate threshold deteriorate less. Maintaining training stimulus appears particularly important in limiting decline.
What these studies collectively imply
Three findings seem particularly well established:
1. The relationship between performance and age is nonlinear. There is little scientific justification for assuming that performance deteriorates by, for example, exactly 1% per year from age 30 through 80. Leyk et al., Tanaka & Seals, the Berlin Marathon work and the Engadin studies all point toward an increasingly steep decline with advanced age.
2. Second, cross-sectional and longitudinal aging are different quantities. Lepers et al. make this particularly clear: comparing today’s 70-year-olds with today’s 40-year-olds does not necessarily tell us how much a particular 40-year-old will slow by age 70.
3. Third, the performance curve appears to be sport- and technique-specific. Ouimet’s Worldloppet analysis actually found less age-related decline in classic than freestyle skiing. Because that finding comes from cross-sectional data and a preprint, it is better regarded as a hypothesis to test than an established physiological fact.
Longitudinal data are needed to confirm or refine the age-grading factors, particularly for skiers aged 60 and older.
A strong analysis would therefore estimate a longitudinal age curve from repeated individual performances, adjust each race for annual conditions, use pace/speed rather than raw finishing time where distances vary, and then test whether the curve differs by sex and classic versus freestyle.