Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Banister TRIMP× | Nisbah Beban Kerja Akut-Kronik× | GPS Pergerakan Masa× | |
|---|---|---|---|
| Bidang | Sains Sukan | Sains Sukan | Sains Sukan |
| Keluarga | Hypothesis test | Hypothesis test | Hypothesis test |
| Tahun asal≠ | 1975 | 2016 | 2010 |
| Pengasas≠ | Eric Banister | Tim Gabbett | Osgnach & Di Prampero |
| Jenis≠ | mathematical modeling | workload monitoring | GPS tracking |
| Sumber perintis≠ | 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 ↗ | 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 ↗ | Gregory, P., & Drust, B. (2007). Physical demands of rugby union: quantification of accelerations and movements patterns in play. Journal of Strength and Conditioning Research, 21(2), 309-314. link ↗ |
| Alias≠ | TRIMP, training impulse, fitness-fatigue model | ACWR, workload ratio, training load balance | GPS analysis, movement tracking, workload quantification, physical demands |
| Berkaitan≠ | 3 | 3 | 4 |
| Ringkasan≠ | 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 Although superseded by newer frameworks, the Banister model remains influential and intuitive. | 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. | Time-motion analysis with GPS and micro-sensor technology quantifies the movement patterns, workload, and physical demands during training or match play in team sports. Pioneered by Osgnach and colleagues (2010), modern GPS units track athletes' positions in real-time, calculating distance covered, velocity profiles, and acceleration/deceleration frequencies. Combined with heart rate and other sensor data, GPS analysis provides comprehensive workload quantification enabling coaching staff to monitor player fatigue, balance training intensity, and prevent injury. GPS is now standard in elite soccer, rugby, Australian Rules football, and other intermittent sports. |
| ScholarGateSet data ↗ |
|
|
|