The purpose of this study was to develop a scientifically grounded methodologyfor managing triathletes’ training in which artificial intelligence (AI) is used as a decision-supporttool for the coach. The study followed a design-oriented methodological approach. Scientificliterature on endurance monitoring, training load, heart rate variability, wearable technology,and machine learning was analysed; data requirements were defined; an algorithm for dailyreadiness assessment and weekly load adjustment was developed; and safety and pedagogicalcontrol criteria were formulated. The resulting methodology integrates swimming, cycling, andrunning data with heart rate, heart rate variability, sleep, perceived exertion, and subjective wellbeing. A closed loop is proposed: data acquisition, quality control, state assessment,recommendation, coach decision, and feedback. AI classifies the athlete’s state, identifies atypicalchanges, and proposes adjustments to volume, intensity, and recovery. Final authority, however,remains with the coach and, where appropriate, a medical professional. The practical value of themethodology lies in its potential use by sports schools, clubs, and individually coached athletes. Acontrolled trial using real athlete data is required to determine its effectiveness
| Mualliflar | Abdugabbarov, Asilbek |
|---|---|
| Jurnal | Педагогик акмеология |
| Nashr sanasi | 2026-08-12 |
| Jild | 7 |
| Son | 36 |
| Til | Ingliz |
triathlon, artificial intelligence, machine learning, triathlon, artificial intelligence, machine learning, triathlon, artificial intelligence, machine learning
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