It is common practice for endurance athletes to manipulate training load prior to an important competition, known as tapering. An effective strategy aims to alleviate accumulated fatigue, whilst maximising physiological adaptation and facilitating a peak performance. Improvements in performance of 0.5 to 6.0% have been reported after a successful taper, a margin that could potentially have a dramatic influence on performance outcome at the elite level. This thesis explored the strategies currently employed by elite endurance athletes and investigated novel training manipulations during the taper to further enhance performance, to gain a more thorough understanding of the physiological mechanisms, and to identify a minimally invasive physiological biomarker capable of monitoring recovery status during the taper. Tapering strategies in elite endurance athletes were shown to be individualised and influenced by the preceding training load. Algorithms were developed, capable of explaining a large proportion of the variance (53-95%) in tapering strategy training variables (with the exception of interval volume), for a given pre-taper training load (Chapter III). A tapering strategy implemented using the algorithms was most likely to improve 1,500 m treadmill performance (ES = 0.53). When the intensity of final interval session was increased from 100% to 115% race speed, the effect on treadmill performance was unclear (ES = 0.22) and perhaps due to insufficient recovery to respond positively to the increased intensity interval session (Chapter IV). When continuous volume was reduced further (by 60%), the novel high intensity strategy was very likely to improve 1,500 m track performance (ES = 0.74), compared to the algorithm-derived taper (ES = 0.40) (Chapter VI). In middle-distance runners, training above race speed in the final days of the taper might be more beneficial than current practice, although training volume must be further reduced to compensate. It was possible to measure plasma concentrations of interleukin-6 and soluble interleukin-6 receptor from capillary samples (Chapter II), although these markers in addition to C-reactive protein, testosterone and cortisol were not sensitive enough to detect changes in recovery status during tapering (Chapters IV and V). Measures of muscle maximum voluntary contraction force (algorithm-derived taper: 9%; ES = 0.39; novel taper: 6%; ES = 0.29), and rate of force development (algorithm-derived taper: ES = 0.53; novel taper: ES = 0.26) improved in response to tapering (Chapter IV), and could represent alternative non-invasive markers of recovery and taper effectiveness to facilitate peak performance
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