R. C. Richard Davison

The landscape of competitive cycling has undergone a remarkable transformation over the past four decades, with one of the most significant developments being the explosive growth in masters category participation ( Shoak et al. , 2013 ; Davison and Smith, 2022 ). This phenomenon represents not merely a demographic shift, but a fundamental reimagining of athletic longevity and competitive engagement across the lifespan ( Appleby and Dieffenbach, 2016 ; Macgregor et al. , 2017 ). Masters cycling, formally defined by many national and international cycling federations (e.g., USA Cycling, British Cycling) define masters categories, typically starting at 30 or 35 years of age, often in 5-year increments. This has recently evolved from a niche pursuit into a dominant force within the broader cycling ecosystem, challenging traditional assumptions about age-related performance decline and redefining what it means to be a competitive athlete in the modern era. The examples of the UCI Grand Fondo World Series ( www.ucigranfondoworldseries.com ) and the UCI World Gravel Series ( www.ucigravelworldseries.com ) both culminating in respective age—group World Championships serve as examples of the increased competitive opportunities for the older cyclists with events up to 8000 competitors and a world championship of up to 3000 competitors mostly over 35 years of age. Perhaps most remarkably, the growth in masters cycling participation has been accompanied by continued improvements in age-group performance standards. Legendary masters cyclist Robert Marchand set world records for one-hour track cycling at both 102 and 105 years of age ( Davison and Smith, 2022 ). These exceptional performances, while representing the outer bounds of human capability, illustrate the potential for sustained high-level performance that contradicts traditional assumptions about aging and athletic capacity.

Power quantifies the work accomplished by the cyclist per unit time and is commonly measured in Watts ( Winter et al. , 2016 ) and, in the context of cycling, reflects the cyclist's ability to effectively apply force to the pedals. The capacity to generate power across different time intervals is a principal determinant of performance in most cycling events and serves as a critical metric for evaluating both training adaptations and competitive outcomes. Its instantaneous and objective nature makes it an indispensable tool for designing specific training sessions, assessing exertional demands, and monitoring fatigue levels.

The scientific pursuit of optimal cycling performance has historically relied on group-mean responses to standardised interventions, yet practitioners increasingly recognise that individual athletes may deviate substantially from population averages ( Pickering and Kiely, 2019 ). These individual differences manifest in four main domains: genetic polymorphisms that influence physiological ceilings and maximal adaptive rates ( Bouchard et al. , 2011a ), heterogeneous homeostatic stress and adaptations to equivalent training stimuli ( Bossi et al. , 2024a ), variable response to nutritional interventions ( Lolli et al. , 2025 ), and personalised pacing that matches unique physiological profiles ( Foster et al. , 2023 ). Understanding these factors matters not only for elite performance optimisation but also for broader participation, as individualised approaches may reduce dropout rates among recreational cyclists who do not respond to generic training programmes and therefore fail to achieve the expected health benefits of regular exercise ( Mann et al. , 2014 ). The critical challenge lies in distinguishing genuine biological heterogeneity from measurement error and random fluctuation, a methodological hurdle that pervades the scientific literature ( Atkinson and Batterham, 2015 ; Bossi, 2024 ). Accordingly, this chapter is an attempt to synthesise evidence across the aforementioned domains while acknowledging the limitations of existing research that purports to identify responders and non-responders.

Incremental exercise testing (IET) in cycling involves a gradual increase in exercise intensity until either a predetermined physiological response or volitional exhaustion. This progressive approach allows for the measurement of various physiological parameters, typically targeting a work rate, VO 2max or heart rate ( Bentley et al. , 2007 ). This enables precise quantification of key determinants of aerobic and anaerobic metabolism, cardiovascular function, and muscular endurance ( Michalik et al. , 2021 ).

Body composition is a critical determinant of performance in many cycling disciplines ( Lippi and Mattiuzzi, 2020 ; Phillips and Hopkins, 2020 ; Smith et al. , 2024 ). Unlike some other sports where absolute power or size might be advantageous for example in track and field athletics, in cycling, particularly in events involving prolonged efforts and uphill gradients, carrying excess body mass that does not contribute to power output can be significantly detrimental ( Jurov et al. , 2020 ; Mujika and Padilla, 2001 ). This understanding has led many cyclists to strive for a body mass that is as minimal as possible while maintaining adequate power.

The study of human performance on a bicycle is not a recent phenomenon. Indeed, the cycle ergometer has been a cornerstone of physiological research for over a century, evolving from the rudimentary friction-braked devices of the late 19th century to the sophisticated laboratory instruments of today ( Vandewalle & Driss, 2015 ). This long history provided the foundation to investigate the fundamental principles of exercise physiology, many of which were first articulated by Nobel laureate A.V. Hill. His pioneering work distinguishing aerobic from anaerobic metabolism and defining concepts such as “maximum O 2 intake” established the theoretical questions that the ergometer was uniquely positioned to answer in the exercising human ( Bassett, 2002 ). The subsequent standardisation and widespread adoption of laboratory ergometry laid the groundwork for an explosion of research. In recent decades, the technological leap from stationary ergometers to mobile, on-bike power meters has coincided with a dramatic growth in scientific publications, rising from 855 in 1985 to over 18,000 in 2024 ( Figure 1.1 ).

The Routledge Handbook of Cycling Science brings together internationally recognised experts to showcase the growing body of both experimental and applied

Purpose The Clara cell protein CC16, secreted from Clara cells in the lung, is discussed as a potential biomarker for toxic effects on the airways. An increased concentration of CC16 in serum may be caused by increased permeability of the lungs. To investigate the changes in P-CC16 in response to an intense exercise bout performed at different times of day (9 am and 4 pm) of highly trained individuals. Method Using a crossover randomized design, 8 runners (mean VO_2max 71 ml kg⁻¹ min⁻¹, SD 6) performed a 10-km time trial run, at 9 am and 4 pm, in an environmental chamber set at 6 °C. Lung function tests and blood sampling occurred at baseline, immediately post and 1 h post time trial. Result Diurnal differences (P < 0.05) were found for blood neutrophil and lymphocyte counts; with higher values at 4 pm. P-CC16 was higher at the pre- and post-trial time point at 9 am compared to 4 pm. Lung function was not different between or within trials. Conclusion Morning trial in cold condition caused more physiological strain compared to the same trial in the evening. However, this extra stress caused by zeitgebers could be a useful strategy for athletes, coaches, and general population to improve their running performance and protect their health in cold conditions in the long-term plan.

Aims and objectives.  To enable people with Type 1 diabetes to exercise safely by investigating the reproducibility of the glucose response to an algorithm for carbohydrate and insulin adjustment during and after exercise compared to their self‐management strategies.Background.  Difficulties in managing blood glucose levels in Type 1 diabetes whilst exercising is known to deter people from exercise. Currently there is a limited evidence base to aid health care professionals enable people with diabetes to exercise safely. This study seeks to address this gap.Design.  A quasi‐experimental study was undertaken amongst people with Type 1 diabetes.Methods.  Over 14 days, 14 participants undertook four exercise sessions (40 minutes at 50%VO_2max). Two sessions were undertaken in week 1 self‐managing their diabetes and two sessions in week 2 using an algorithm for carbohydrate and insulin adjustment.Results.  The mean reduction of glucose levels detected by Continuous Glucose Monitoring during exercise was 3·1 (SD 2·03) mmol/l. Time spent within the range of 4–9 mmol/l during exercise was not significantly different between the self‐managed and the algorithm weeks (−3–22·4 min). The mean reduction of blood glucose for each individual over all four exercise sessions ranged between 0·8–5·95 mmol/l. The technical error between days one and two was 2·4 mmol/l (CV = 33·2%) and between days 3–4 the technical error was 2·7 mmol/l (CV = 33·7%).Conclusions.  The results provide useful data about the reproducibility of the blood glucose response to moderate intensity exercise, despite the variability of individual responses 40 minutes of moderate intensity exercise decreases Continuous Glucose Monitoring glucose by 3 mmol/l with or without a 30% decrease of insulin before exercise.Relevance to clinical practice.  This information provides valuable baseline information for people with diabetes and health care professionals who wish to encourage physical activity and undertake further research in this area.

(2009). The use of sports science in preparation for Olympic competition. Journal of Sports Sciences: Vol. 27, The use of sports science in preparation for Olympic competition, pp. 1363-1365.