Monitoring Acute Effects on Athletic Performance with Mixed Linear Modeling

VANDENBOGAERDE, T. J. and W. G. HOPKINS. Monitoring Acute Effects on Athletic Performance with Mixed Linear Modeling. Med. Sci. Sports Exerc., Vol. 42, No. 7, pp. 1339-1344, 2010. There is a need for a sophisticated approach to track athletic performance and to quantify factors affecting it in practical settings. Purpose: To demonstrate the application of mixed linear modeling for monitoring athletic performance. Methods: Elite sprint and middle-distance swimmers (three females and six males; aged 21-26 yr) performed 6-13 time trials in training and competition in the 9 wk before and including Olympic-qualifying trials, all in their specialty event. We included a double-blind, randomized, diet-controlled crossover intervention, in which the swimmers consumed caffeine (5 mg.kg(-1) body mass) or placebo. The swimmers also knowingly consumed varying doses of caffeine in some time trials. We used mixed linear modeling of log-transformed swim time to quantify effects on performance in training versus competition, in morning versus evening swims, and with use of caffeine. Predictor variables were coded as 0 or 1 to represent absence or presence, respectively, of each condition and were included as fixed effects. The date of each performance test was included as a continuous linear fixed effect and interacted with the random effect for the athlete to represent individual differences in linear trends in performance. Results: Most effects were clear, owing to the high reliability of performance times in training and competition (typical errors of 0.9% and 0.8%, respectively). Performance time improved linearly by 0.8% per 4 wk. The swimmers performed substantially better in evenings versus mornings and in competition versus training. A 100-mg dose of caffeine enhanced performance in training and competition by similar to 1.3%. There were substantial but unclear individual responses to training and caffeine (SD of 0.3% and 0.8%, respectively). Conclusions: Mixed linear modeling can be applied successfully to monitor factors affecting performance in a squad of elite athletes.