📜References

Bird, Stephen. (2013). Sleep, Recovery, and Athletic Performance: A Brief Review and Recommendations. Strength and Conditioning Journal. 35. 43-47.

Sleep has been identified as an important factor contributing to optimal athletic performance. However, the psychosociophysiological (psychological, social and physiological) stresses placed on elite athletes often results in an increased stress/fatigue state and presents a phenomenon which may result in an inability to gain appropriate sleep. Improving an athlete’s sleep hygiene is seen as a key strategy that could have powerful implications for athletic performance. Aside from direct physiological implications associated with improved sleep hygiene, an athlete’s sleep perception may influence psychological factors including confidence, anxiety and motivation, and thereby influence performance indirectly through such factors. Therefore, the purpose of this paper is to (i) overview the impact of sleep on recovery and athletic performance; (ii) outline sleep hygiene strategies; and (iii) provide sleep recommendations for athletes and coaches. The sleep hygiene strategies presented in this paper represent a practical approach to improve sleep perception in elite athletes.

Dotan, Raffy. (2022). A critical review of critical power. European Journal of Applied Physiology. 122. 1-30. 10.1007/s00421-022-04922-6.

The elegant concept of a hyperbolic relationship between power, velocity, or torque and time to exhaustion has rightfully captivated the imagination and inspired extensive research for over half a century. Theoretically, the relationship’s asymptote along the time axis (critical power, velocity, or torque) indicates the exercise intensity that could be maintained for extended durations, or the “heavy–severe exercise boundary”. Much more than a critical mass of the extensive accumulated evidence, however, has persistently shown the determined intensity of critical power and its variants as being too high to maintain for extended periods. The extensive scientific research devoted to the topic has almost exclusively centered around its relationships with various endurance parameters and performances, as well as the identification of procedural problems and how to mitigate them. The prevalent underlying premise has been that the observed discrepancies are mainly due to experimental ‘noise’ and procedural inconsistencies. Consequently, little or no effort has been directed at other perspectives such as trying to elucidate physiological reasons that possibly underly and account for those discrepancies. This review, therefore, will attempt to offer a new such perspective and point out the discrepancies’ likely root causes.

Esteve, Jonathan & Foster, Carl & Seiler, Stephen & Lucia, Alejandro. (2007). Impact of Training Intensity Distribution on Performance in Endurance Athletes. Journal of strength and conditioning research / National Strength & Conditioning Association. 21. 943-9. 10.1519/R-19725.1.

The purpose of this study was to compare the effect of 2 training programs differing in the relative contribution of training volume, clearly below vs. within the lactate threshold/maximal lactate steady state region on performance in endurance runners. Twelve subelite endurance runners (who are specialists in track events, mostly the 5,000-m race usually held during spring-summer months and who also participate in cross-country races [9-12 km] during fall and winter months) were randomly assigned to a training program emphasizing low-intensity (subthreshold) (Z1) or moderately high-intensity (between thresholds) (Z2) training intensities. At the start of the study, the subjects performed a maximal exercise test to determine ventilatory (VT) and respiratory compensation thresholds (RCT), which allowed training to be controlled based on heart rate during each training session over a 5-month training period. Subjects performed a simulated 10.4-km cross-country race before and after the training 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 spent in zones 1 and 2 was controlled to have relatively more low-intensity training in Z1 (80.5 +/- 1.8% and 11.8 +/- 2.0%, respectively) than in Z2 (66.8 +/- 1.1% and 24.7 +/- 1.5%, respectively), whereas the contribution of high-intensity (zone 3) training was similar (8.3 +/- 0.7% [Z1] and 8.5 +/- 1.0% [Z2]). The magnitude of the improvement in running performance was significantly greater (p = 0.03) in Z1 (-157 +/- 13 seconds) than in Z2 (-121.5 +/- 7.1 seconds). These results provide experimental evidence supporting the value of a relatively large percentage of low-intensity training over a long period ( approximately 5 months), provided that the contribution of high-intensity training remains sufficient.

Hauser, Thomas & Adam, Jennifer & Schulz, Henry. (2014). Comparison of calculated and experimental power in maximal lactate-steady state during cycling. Theoretical biology & medical modelling. 11. 25. 10.1186/1742-4682-11-25. Background The purpose of this study was the comparison of the calculated (MLSSC) and experimental power (MLSSE) in maximal lactate steady-state (MLSS) during cycling.

Methods 13 male subjects (24.2 ± 4.76 years, 72.9 ± 6.9 kg, 178.5 ± 5.9 cm, V˙O2max: 60.4 ± 8.6 ml min−1 kg−1, V˙Lamax: 0.9 ± 0.19 mmol l-1 s-1) performed a ramp-test for determining the V˙O2max and a 15 s sprint-test for measuring the maximal glycolytic rate (V˙Lamax). All tests were performed on a Lode-Cycle-Ergometer. V˙O2max and V˙Lamax were used to calculate MLSSC. For the determination of MLSSE several 30 min constant load tests were performed. MLSSE was defined as the highest workload that can be maintained without an increase of blood-lactate-concentration (BLC) of more than 0.05 mmol l−1 min−1 during the last 20 min. Power in following constant-load test was set higher or lower depending on BLC.

Results MLSSE and MLSSC were measured respectively at 217 ± 51 W and 229 ± 47 W, while mean difference was −12 ± 20 W. Orthogonal regression was calculated with r = 0.92 (p < 0.001).

Conclusions The difference of 12 W can be explained by the biological variability of V˙O2max and V˙Lamax. The knowledge of both parameters, as well as their individual influence on MLSS, could be important for establishing training recommendations, which could lead to either an improvement in V˙O2max or V˙Lamax by performing high intensity or low intensity exercise training, respectively. Furthermore the validity of V˙Lamax -test should be focused in further studies.

Hooper, Dr & Mackinnon, Laurel. (1995). Monitoring Overtraining in Athletes. Sports Medicine. 20. 10.2165/00007256-199520050-00003.

Leo, Peter & Spragg, James & Podlogar, Tim & Lawley, Justin & Mujika, Iñigo. (2022). Power profiling and the power-duration relationship in cycling: a narrative review. European Journal of Applied Physiology. 122. 10.1007/s00421-021-04833-y.

Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P ( t ) = W ′/ t + CP ( W ′—work capacity above CP; t —time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist’s power profile.

Sanders, Dajo & Heijboer, Mathieu. (2019). Physical Demands and Power Profile of Different Stage Types within a Cycling Grand Tour. European Journal of Sport Science. 19. 10.1080/17461391.2018.1554706.

This study aims to describe the intensity and load demands of different stage types within a cycling Grand Tour. Nine professional cyclists, whom are all part of the same World-Tour professional cycling team, participated in this investigation. Competition data were collected during the 2016 Giro d’Italia. Stages within the Grand Tour were classified into four categories: flat stages (FLAT), semi-mountainous stages (SMT), mountain stages (MT) and individual time trials (TT). Exercise intensity, measured with different heart rate and power output based variables, was highest in the TT compared to other stage types. During TT’s the main proportion of time was spent at the high-intensity zone, whilst the main proportion of time was spent at low intensity for the mass start stage types (FLAT, SMT, MT). Exercise load, quantified using Training Stress Score and Training Impulse, was highest in the mass start stage types with exercise load being highest in MT (329, 359 AU) followed by SMT (280, 311 AU) and FLAT (217, 298 AU). Substantial between-stage type differences were observed in maximal mean power outputs over different durations. FLAT and SMT were characterised by higher short-duration maximal power outputs (5–30 s for FLAT, 30 s–2 min for SMT) whilst TT and MT are characterised by high longer duration maximal power outputs (>10 min). The results of this study contribute to the growing body of evidence on the physical demands of stage types within a cycling Grand Tour.