Does Polarized Training Improve Performance in Recreational Runners?

Universidad Europea de Madrid, Madrid, Spain.
International journal of sports physiology and performance (Impact Factor: 2.25). 05/2013; DOI: 10.1123/IJSPP.2012-0350
Source: PubMed

ABSTRACT PURPOSE: To quantify the impact of training intensity distribution on 10k performance in recreational athletes. METHODS: 30 endurance runners were randomly assigned to a training program emphasising low-intensity, sub-ventilatory threshold (VT), polarized endurance training distribution (PET), or moderate high-intensity (between thresholds), endurance training program (BThET). Before the study, the subjects performed a maximal exercise test to determine VT and respiratory compensation threshold (RCT), which allowed training to be controlled based on heart rate during each training session over the 10wk intervention period. Subjects performed a 10km race on the same course before and after the intervention period. Training was quantified based on the cumulative time spent in 3 intensity zones: zone 1 (low intensity; <VT), zone 2 (moderate intensity; between VT and RCT), and zone 3 (high intensity; >RCT). The contribution of total training time in each zone was controlled to have more low-intensity training in PET (±77/3/20), whereas for BThET the distribution was higher in zone 2 and lower in zone 1 (±46/35/19). RESULTS: Both groups significantly improved their 10k time (39min18s±4min54s vs 37min19s±4min42s, P<0.0001 for PET; 39min24s±3min54s vs 38min0s±4min24s P<0.001 for BThET). Improvements were 5.0% vs 3.6%, ~41 seconds difference at Post. This difference was not significant. However, a subset analysis comparing those 12 runners who actually performed the most PET (n=6) and BThET (n=6) distributions showed greater improvement in PET by 1.29 standardized Cohen effect size units (90% CI 0.31 to 2.27, p=0.038). CONCLUSIONS: Polarized training can stimulate greater training effects than between-thresholds training in recreational runners.

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    ABSTRACT: Purpose: The authors directly compared 3 frequently used methods of heart-rate-based training-intensity-distribution (TID) quantification in a large sample of training sessions performed by elite endurance athletes. Methods: Twenty-nine elite cross-country skiers (16 male, 13 female; 25 ± 4 y; 70 ± 11 kg; 76 ± 7 mL · min-1 · kg-1 VO2max) conducted 570 training sessions during a ~14-d altitude-training camp. Three analysis methods were used: time in zone (TIZ), session goal (SG), and a hybrid session-goal/time-in-zone (SG/TIZ) approach. The proportion of training in zone 1, zone 2, and zone 3 was quantified using total training time or frequency of sessions, and simple conversion factors across different methods were calculated. Results: Comparing the TIZ and SG/TIZ methods, 96.1% and 95.5%, respectively, of total training time was spent in zone 1 (P < .001), with 2.9%/3.6% and 1.1%/0.8% in zones 2/3 (P < .001). Using SG, this corresponded to 86.6% zone 1 and 11.1%/2.4% zone 2/3 sessions. Estimated conversion factors from TIZ or SG/TIZ to SG and vice versa were 0.9/1.1, respectively, in the low-intensity training range (zone 1) and 3.0/0.33 in the high-intensity training range (zones 2 and 3). Conclusions: This study provides a direct comparison and practical conversion factors across studies employing different methods of TID quantification associated with the most common heart-rate-based analysis methods.
    International journal of sports physiology and performance 01/2014; 9(1):100-7. · 2.25 Impact Factor
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    ABSTRACT: The concept of fatigue has inspired a wide body of research since the earliest studies in muscular and exercise physiology (Noakes, 2012). During the last decades, intense debates (Amann and Secher, 2010) have been conducted among sport and exercise physiologists in an attempt to clarify the mechanisms that regulate fatigue. It is interesting to note that various models have been proposed for its understanding along with the vast number of studies generated in this area (Abiss and Laursen, 2005; Amann and Dempsey, 2008; Millet, 2011). Given this important lack of scientific consensus, it is therefore pertinent to clarify if we are thinking about the complex phenomenon of fatigue in the right way. From a historical perspective, the whole picture of fatigue research would evoke the old Indian tale of the blind men and the elephant. Briefly, this parable described several blind men touching an elephant to figure out what it looked like with a subsequent discussion on the nature of the elephant from their own perspectives. Every man touched only a single part of the elephant's body, leading each of them to guess differently about the object’s features. Similarly, fatigue researchers have strongly defended different theories without assuming that they could only be aware of a limited piece of the whole fatigue phenomenon. Thus, the absence of agreement between fatigue specialists could have its origin with the technological dependence of fatigue research. That is, the epistemological approximation to fatigue has been more inductive than deductive as laboratory facilities with restricted findings (e.g. maximum oxygen uptake; VO2max) dictated the elaboration of further explanatory theories (e.g. Anaerobic/Cardiovascular Model). In this regard, some caution should be considered with the new technological advances in brain and muscle imaging as these could be performed under the same erroneous process. This consideration is very important given that new technologies are also capable of possessing important methodological limitations under exercise conditions that may limit our understanding of fatigue. Although it seems that we will see a new era in fatigue research, the risk of elaborating on biased knowledge because of technology limitations should not be ignored.
    Frontiers in Physiology 10/2013; 4:309.


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