Age Differences, Age Changes, and Generalizability in Marathon Running by Master Athletes

Area of Science:

• Sports Science
• Gerontology
• Biostatistics

Background:

• Marathon running performance data from Master athletes is often analyzed as a virtual cross-sectional study.
• This approach may overlook crucial temporal dimensions like cohort effects and historical transitions.
• Alternative models are needed to accurately interpret aging trends in athletic performance.

Purpose of the Study:

• To critique the virtual cross-sectional model for Master athlete marathon performance.
• To compare alternative models incorporating temporal dimensions (cohort, age changes, historical transitions).
• To evaluate the generalizability of findings across different performance quartiles.

Main Methods:

• Utilized archival marathon performance data from the Association of Road Racing Statisticians (937 men, 856 women).
• Employed mixed linear modeling (MLM) to analyze performance times.
• Compared models including 'entry cohort' age and 'elapsed age' for goodness of fit.

Main Results:

• A model incorporating linear and quadratic expressions of 'entry cohort' and 'elapsed age' showed the best fit.
• Women exhibited higher marathon performance times than men, with greater increases at older ages.
• Performance time increases were more pronounced in slower quartiles and interacted with gender and elapsed time.

Conclusions:

• The virtual cross-sectional model is insufficient for understanding aging in Master athletes.
• Temporal dimensions (cohort, elapsed age, gender) significantly influence marathon performance trends.
• Performance declines with age are more substantial in slower athletes and women.