The Current State of Subjective Training Load Monitoring: Follow-Up and Future Directions

Sports Medicine - Open

Table 2 Model summaries for performance outcomes in two different groups of elite international athletes

Variable	Estimate [95% CI]	Standard error	Pr( >\|t\|)	f²	Effect size
Athletics (n = 4, performance measures = 29)
(Intercept)	0.724 [− 0.872, 2.321]	0.815	0.38
Chronic 14-d SMA	0.002 [− 0.003, 0.008]	0.003	0.49	0.008	Trivial
TSB 9:14-d SMA	0.019 [0.003, 0.035]	0.008	0.03*	0.214	Moderate
TSB 9:14-d SMA CH28-d	− 0.013 [− 0.023, − 0.004]	0.005	0.01*	0.313	Moderate
%INJ	0.15 [− 1.583, 1.886]	0.885	0.87	0.011	Trivial
Basketball (n = 13, performance measures = 171)
(Intercept)	2.075 [1.077, 3.073]	0.509	< 0.001***
Chronic 21-d SMA	− 0.001 [− 0.002, 0.001]	0.001	0.23	0.006	Trivial
TSB 9:21-d SMA	− 0.002 [− 0.004, − 0.000]	0.001	0.04*	0.023	Small
TSB 9:21-d SMA CH21-d	0.003 [0.002, 0.004]	0.001	< 0.001***	0.192	Moderate
%INJ	− 0.238 [− 0.611, 0.135]	0.190	0.21	0.008	Trivial

TL training load, TSB training-stress balance, CH change in TL measure prior to competition, d days, SMA simple moving averages, EWMA-W exponentially weighted moving average as per Williams et al. [23], EWMA-L exponentially weighted moving average as per Lazarus et al. [24]
^*p < 0.05; ***p < 0.001; f², Cohen’s marginal effect size