Observed response to regular exercise training differs widely between individuals even in tightly controlled research settings. However, the respective contributions of random error and true interindividual differences as well as the relative frequency of nonresponders are disputed. Specific challenges of analyses on the individual level as well as a striking heterogeneity in definitions may partly explain these inconsistent results. Repeated testing during the training phase specifically addresses the requirements of analyses on the individual level. Here we report a first implementation of this innovative design amendment in a head-to-head comparison of existing analytical approaches. To allow for comparative implementation of approaches we conducted a controlled endurance training trial (1 yr walking/jogging, 3 days/wk for 45 min with 60% heart rate reserve) in healthy, untrained subjects ( n = 36, age = 46 ± 8 yr; body mass index 24.7 ± 2.7 kg/m2; V̇o2max 36.6 ± 5.4). In the training group additional V̇o2max tests were conducted after 3, 6, and 9 mo. Duration of the control condition was 6 mo due to ethical constraints. General efficacy of the training intervention could be verified by a significant increase in V̇o2max in the training group ( P < 0.001 vs. control). Individual training response of relevant magnitude (>0.2 × baseline variability in V̇o2max) could be demonstrated by several approaches. Regarding the classification of individuals, only 11 of 20 subjects were consistently classified, demonstrating remarkable disagreement between approaches. These results are in support of relevant interindividual variability in training efficacy and stress the limitations of a responder classification. Moreover, this proof-of-concept underlines the need for tailored methodological approaches for well-defined problems. NEW & NOTEWORTHY This work reports a first implementation of a repeated testing training trial for the investigation of individual response. This design amendment was recently proposed to address specifically the statistical requirements of analyses on the individual level. Moreover, a comprehensive comparison of previously published methods exemplifies the striking heterogeneity of existing approaches.
Keywords: classification; interaction; personalized medicine; variance components.