Alan J. Walker, PhD. – PhD., IFNH Center for Health and Human Performance, Rutgers University
Harry P. Cintineo, MS, CSCS – PhD Student, Rutgers University
Bridget A. McFadden, MBS, CSCS*D – PhD Candidate, Rutgers Center for Health and Human Performance
David J. Sanders, MS, CSCS*D – Research Assistant, Rutgers University
Brittany N. Bozzini, CSCS – PhD Student, IFNH Center for Health and Human Performance, Rutgers University
Research suggests endurance athletes manipulate training prior to a competition to achieve optimal performance capacity on race day. Tracking the physiological response, via blood biomarkers and performance changes, to this manipulation of training can provide additional information on the effectiveness of training. PURPOSE: To track changes in performance, body composition, training load (TL), and various biomarkers in competitive triathletes for a two-month preparation phase prior to a competition. METHODS: Male (N=6; Mage=45.16±13.23yrs; Mhheight=167.64±8.75cm) competitive triathletes were used in this study. Athletes participated in three testing sessions consisting of body composition assessment, blood draws, and performance testing. Testing was performed two months (T1), one month (T2), and in conjunction with the taper prior to competition (T3). Athletes arrived fasted and euhydrated between 0700-0900h following a rest day. Body composition assessments included body weight (BW), body fat percentage (BF%), and lean body mass (LBM). Athletes then underwent blood draws for analysis of total and free testosterone (TT, FT), total and free cortisol (TC, FC), creatine kinase (CK), sex-hormone binding globulin (SHBG), and insulin-like growth factor 1 (IGF-1). They were then allowed to eat and hydrate an hour prior to performance testing. An intermittent lactate threshold protocol was used to measure VO2peak and velocity at lactate threshold (VLT). Athletes self-reported weekly distance and duration for each training modality. RM MANOVAs with univariate follow-ups were conducted with significance set at P< .05, and effect size (ES) was calculated using Cohen’s d. RESULTS: Over this training block, there were no changes in VO2peak, VLT, BW, BF%, or LBM (P >0.21). There were no significant changes in cycling and swimming distance or cycling, swimming, and running duration (P >0.11). There was a significant increase in weekly running distance (Δdis=7.85+5.2 km; P< 0.05, ES=0.65) with a trending increase in weekly total exercise duration (Δdur=2.72+1.33 hrs; P=0.097, ES=0.66). There were no significant changes in TT, FT, TC, CK, or SHBG (P >0.13). There was a significant increase from T1-T3 in FC (ΔFC=5.52+1.32 nmoL; P< 0.05, ES=0.62) with a trending increase from T1-T3 in IGF-1 (ΔIGF-1= 29.83+12.94 ng/mL; P=0.069, ES=0.85). CONCLUSIONS: These results show that despite a moderate increase in TL with moderate-large ES, the external stimulus of the training was insufficient in producing performance improvements. It appears these athletes focus more on maintenance rather than manipulating TL to peak for competition. These results suggest greater manipulation in TL can be used to produce favorable changes in fitness prior to a competition. Furthermore, these results highlight the difficulty for competitive, non-professional, athletes to find balance among training and other life stressors to adapt and improve fitness for competition. PRACTICAL APPLICATIONS: A more structured periodization plan should be used to produce a greater physical and physiological challenge for athletes to produce meaningful performance improvements. Utilization of biomarkers and monitoring TL can be used to fine-tune manipulations in training to ensure a minimal effective training dose is met to induce adaptations. Monitoring is vital during the peaking and tapering phases immediately prior to competition to prevent performance decrements from accumulated training stress.