The effect of training distribution, duration, and volume on VO2max and performance in trained cyclists: A systematic
Ben Cove1, Samuel Chalmers1, Maximillian J Nelson1
1Alliance for Research in Exercise, Nutrition, and Activity (ARENA), University of South Australia, Australia.
Objective:
This study aimed to systematically investigate whether polarized or non-polarized training leads to greater physiological and performance adaptations in cyclists.
Design:
A systematic review and meta-analysis were conducted, focusing on interventions categorized as polarized, non-polarized, or unclear. Inclusion criteria required participants to be at least recreationally trained cyclists (VO2max ≥ 59 ml/kg/min) and interventions lasting > 4 weeks.
Methods:
A multi-level random-effects meta-analysis using restricted maximum likelihood estimation was performed. A multivariate meta-regression assessed associations between training volume, VO2max, and time-trial performance.
Results:
Forty-one studies, comprising 81 training groups and 797 participants, were included. Training significantly improved VO2max across all groups (g = 0.42, 95 % confidence interval = 0.31-0.53, P ≤ 0.001) and time-trial performance (g = 0.39, 95 % confidence interval = 0.25-0.53, P ≤ 0.001), with no significant differences between training modalities (P > 0.05). Longer intervention durations positively influenced VO2max (g = 0.03, 95 % confidence interval = 0.02-0.05, P < 0.001) and time-trial performance (g = 0.04, 95 % confidence interval = 0.03-0.06, P < 0.001). No associations were found between weekly or total training volume and changes in VO2max or time-trial performance.
Conclusions:
Polarized and non-polarized training modalities yield comparable improvements in VO2max and time-trial performance in trained cyclists. Beyond achieving a necessary training volume, further increases do not appear to enhance performance. These findings encourage athletes and coaches to prioritize effective training distribution rather than fixating on total volume or a specific model.
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