Modern cycling places high demands on the level of physical, technical, and psychophysiological preparation of athletes. Effective management of the training process is impossible without an objective assessment of the athlete's functional state. This article discusses modern methods and tools for diagnostics, including laboratory and field tests, heart rate variability (HRV) monitoring, lactate threshold analysis, the use of portable metabolic systems, as well as digital technologies used in sports medicine and the training process. The prospects for integrating artificial intelligence into the athlete assessment system are also discussed.

Keywords: cycling, functional state, lactate threshold, heart rate variability, sports medicine, metabolic analysis, exercise testing.

Introduction

Cycling is one of the most intense and energy-consuming disciplines, requiring athletes to have high endurance, coordination, and resistance to fatigue. The current level of athletic achievement is impossible without a scientifically based system for monitoring and assessing the functional state of athletes. The functional state is understood as the totality of physiological and biochemical characteristics of the body that determine performance and adaptive capabilities at the current moment in time.

The purpose of this article is to review current methods for assessing the functional state of cyclist athletes, analyze their effectiveness, and applicability in the practice of coaches and sports physicians.

1. Laboratory assessment methods

1.1 Lactate testing

One of the most common assessment methods is the analysis of lactate thresholds — aerobic and anaerobic. During an exercise test on a cycle ergometer, capillary blood is collected to determine the lactate concentration. The obtained values allow the determination of the anaerobic threshold (AnT) — the intensity at which a sharp increase in lactate in the blood begins, indicating the activation of anaerobic processes.

Lactate testing allows:

— determine individual training zones;

— assess the effectiveness of the training program;

— identify signs of overtraining or insufficient adaptation.

1.2 Gas analysis and VO₂ max

The use of metabolic telemetry systems, such as Cosmed, Cortex, or VO2 Master, allows the measurement of oxygen consumption (VO₂), carbon dioxide, and the respiratory exchange ratio. The maximum oxygen consumption (VO₂ max) reflects the aerobic capacity of the athlete and is widely used in the scientific and practical training of cyclists.

2. Field methods

2.1 Power-based testing

Cycling power meters (e.g., SRM, Garmin, Wahoo), which measure the mechanical power developed by the athlete, have become widespread. Standard tests include:

— 20-minute FTP test (Functional Threshold Power);

— Ramp-test — a step test to assess maximum power and calculate FTP;

— Critical Power Test — a series of rides of varying duration (3–20 min) to determine the power curve.

These tests allow coaches to adjust training zones and monitor the dynamics of the functional state during the competitive period.

2.2 Heart rate variability (HRV)

Heart rate variability (HRV) analysis is a non-invasive method of assessing the state of the autonomic nervous system and the level of recovery. A decrease in HRV indicators may indicate chronic fatigue, overtraining, or lack of sleep.

Modern applications (Elite HRV, HRV4Training, WHOOP) allow data to be collected at home and trends to be automatically analyzed, informing the coach about the athlete's readiness for load.

3. Biochemical markers and hormonal profile

Taking blood and urine for biochemical analysis can reveal electrolyte imbalances, changes in the levels of creatine kinase, cortisol, testosterone, as well as markers of inflammation. Regular monitoring of these indicators makes it possible to judge the effectiveness of recovery and the risk of overtraining.

4. The use of wearable technologies

Modern wearable devices (Garmin, Polar, Oura Ring) are capable of collecting extensive data on:

— heart rate;

— sleep quality;

— body temperature;

— blood oxygen level;

— stress level.

Integrating this data into platforms (TrainingPeaks, Today’s Plan, Xert) gives coaches and doctors the opportunity to see a complete picture of the athlete's condition in real-time.

5. New approaches and artificial intelligence

The use of machine learning and AI in sports analytics allows you to identify hidden patterns in large arrays of data. Algorithms can predict states of fatigue, recovery, and the optimal window for performing loads.

Examples of use:

— personalized adaptation of training plans;

— predictive analytics on injuries ;

— analysis of tactical decisions on the race using GPS and power.

6. Practical integration of methods

For the most complete picture of the functional state, it is necessary to combine various methods, for example:

— Field FTP testing + HRV monitoring + sleep tracking;

— Lactate threshold + metabolic analysis + biochemical markers;

— AI platforms + wattmeter data + subjective questionnaires (RESTQ-Sport, POMS).

Integrating data into a single system improves the accuracy of the assessment and the effectiveness of training process management.

Conclusion

Modern methods of assessing the functional state in cycling are rapidly developing, combining the achievements of physiology, biochemistry, technology, and data analytics. The key to successful preparation is not only data collection but also their competent interpretation. The development of individualized approaches and the integration of AI into sports practice open up new horizons in the preparation of elite cyclists.

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