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| Acute-Chronic Workload Ratio× | Banisterin TRIMP× | |
|---|---|---|
| Tieteenala | Liikuntatiede | Liikuntatiede |
| Menetelmäperhe | Hypothesis test | Hypothesis test |
| Syntyvuosi≠ | 2016 | 1975 |
| Kehittäjä≠ | Tim Gabbett | Eric Banister |
| Tyyppi≠ | workload monitoring | mathematical modeling |
| Alkuperäislähde≠ | Gabbett, T. J. (2016). The training-injury prevention paradox: should athletes be training smarter and harder? British Journal of Sports Medicine, 50(5), 273-280. DOI ↗ | Banister, E. W., Calvert, T. W., Savage, M. V., & Bach, T. (1975). A systems model of training responses and its relationship to muscular strength. Transactions of the ASME, 97(3), 177-183. link ↗ |
| Rinnakkaisnimet | ACWR, workload ratio, training load balance | TRIMP, training impulse, fitness-fatigue model |
| Liittyvät | 3 | 3 |
| Tiivistelmä≠ | The acute-chronic workload ratio (ACWR) is the ratio of acute training load (typically the past 1 week) to chronic training load (typically the rolling 4-week average). Formalized by Tim Gabbett (2016), ACWR is a widely adopted metric for predicting injury and illness risk in sports. The logic is straightforward: rapid increases in training load—when acute load spikes far above what the athlete has adapted to—exceed tissue tolerance and increase injury risk. Conversely, maintaining ACWR within optimal ranges (typically 0.8-1.3) is associated with better performance and lower injury incidence. ACWR monitoring is now standard in elite sports for load management. | The Training Impulse (TRIMP) model, developed by Eric Banister and colleagues (1975), quantifies the physiological stimulus of a training session by combining duration and intensity. The Banister fitness-fatigue model proposes that training effects on performance follow two opposing dynamics: fitness (beneficial) accumulates with time constant tau_f (~42 days) and fatigue (temporary decrement) accumulates faster but decays quickly (tau_d ~5-10 days). By tracking TRIMP and modeling these two processes, coaches can predict performance trajectories and optimize training load. Although superseded by newer frameworks, the Banister model remains influential and intuitive. |
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