Science
Load and wellness monitoring in RPE Pulse is grounded in published evidence. Combining subjective and load signals helps coaches prioritize athlete follow-up versus looking at each indicator alone. That is not the same as predicting an injury in an individual.
Murray, N. B., et al. (2017). Calculating acute:chronic workload ratios using exponentially weighted moving averages provides a more sensitive indicator of injury likelihood than rolling averages. British Journal of Sports Medicine, 51(9), 749-754.
EWMA weights recent load more heavily and responded more sensitively than rolling averages. RPE Pulse adopts EWMA as a load signal, not an individual predictor.
Impellizzeri, F. M., et al. (2020). Acute:Chronic Workload Ratio: Conceptual Issues and Fundamental Pitfalls. International Journal of Sports Physiology and Performance, 15(6), 907-913.
Highlights conceptual and mathematical limitations of ACWR. It is therefore interpreted with wellness, strain, context, and athlete history, never as a standalone diagnosis.
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.
ACWR ≥ 1.5 is associated with ~2–4× higher group-level relative injury risk vs the ~0.8–1.3 zone. Population association, not individual prediction.
Blanch, P., & Gabbett, T. J. (2016). Has the athlete trained enough to return to play safely? British Journal of Sports Medicine, 50(8), 471-475.
Useful ACWR zone around 1.0–1.15 for adaptation / return to play.
Soligard, T., et al. (2016). How much is too much? (Part 2) IOC consensus statement. British Journal of Sports Medicine, 50(17), 1043-1052.
ACWR > 1.3 sustained across multiple weeks is linked to elevated risk (IOC load/illness consensus).
Hooper, S. L., & Mackinnon, L. T. (1995). Monitoring overtraining in athletes. Recommendations. Sports Medicine, 20(5), 321-327.
Foundation of modern daily wellness tracking and overtraining monitoring (not a modern injury odds ratio).
Foster, C. (1998). Monitoring training in athletes with reference to overtraining syndrome. Medicine & Science in Sports & Exercise, 30(7), 1164-1168.
High monotony correlates with chronic fatigue / overreaching (fatigue outcome, not an injury OR).
Saw, A. E., Main, L. C., & Gastin, P. B. (2016). Monitoring the athlete training response: subjective measures. British Journal of Sports Medicine, 50(5), 281-291.
Subjective measures work best vs an athlete’s own history (well-being / training response).
Borresen, J., & Lambert, M. I. (2009). The quantification of training load… Sports Medicine, 39(9), 779-795.
Low variation (high monotony) is linked to greater overload / poor adaptation risk.
Halson, S. L. (2014). Monitoring training load to understand fatigue in athletes. Sports Medicine, 44(Suppl 2), S139-S147.
High monotony reduces adaptive capacity (fatigue and load framework).
Subjective measures work best against an athlete’s own history. Adherence informs confidence in the calculation, not physiological risk. The RPE Pulse Score and load metrics are monitoring tools: not a medical diagnosis and not a validated injury predictor.
Association ≠ individual prediction. Literature thresholds (e.g. ACWR) describe group-level relative risk; they do not diagnose or guarantee injury prevention.