I posted this on another forum but it looks as though it may have some value here.
I’ve spent the last several months trying to find another practical and non-invasive method for predicting/planning the overreaching state while also establishing one’s individual overtraining threshold.
Historically, endurance athletes have used training volume (miles per week) as an index with reasonable effectiveness. However, measurement of training volume alone ignores the critical importance of training intensity. For athletes training for strength and/or power, the use of volume as a training measure is incomplete because of the overriding importance of intensity.
Heart rate (HR) can be used as a measure of intensity since it demonstrates an almost linear relationship with VO2. Unfortunately, it does so only during steady-state, submaximal exercise. Beyond the anaerobic threshold, heart rate increases disproportionately and, thus, becomes a comparatively poor method of evaluating very high-intensity exercise. Furthermore, during overreaching, RPE (Rating of Perceived Exertion) for a given HR has been reported to increase, suggesting that RPE could be more sensitive to accumulated fatigue than HR.
So, what does that leave us with?
In an effort to answer that question, Foster et al. (1) developed a method of using RPE as a factor in quantifying training load. When this number (between 0-10) is multiplied by the duration (in minutes) of the training session, the “session RPE” or training load - a single number representing the magnitude of that training session - is derived.
Rating Descriptor
0 = Rest
1 = Very, Very Easy
2 = Easy
3 = Moderate
4 = Somewhat Hard
5 = Hard
6 = -
7 = Very Hard
8 = -
9 = -
10 = Maximal
Training Load = RPE x Duration (min)
However, training load is clearly not the only training related variable contributing to the genesis of the overtraining syndrome. “Training monotony” and “training strain” may also contribute to the negative adaptations to training.
Training monotony is a measure of the day-to-day variability of training within a given week. It is calculated as the average daily training load divided by the standard deviation over the course of a training week. It has been shown that greater daily variability (i.e. alternating “heavy” and “light” days) leads to positive adaptations whereas less variability can rapidly lead to decompensation.
Training Monotony = Daily Mean/Standard Deviation
Finally, since high training load and high training monotony are both factors related to negative adaptations to training, Foster et al. suggested that the product of training load and training monotony, “training strain”, may also relate to negative adaptations to training. It is this measure that they use to establish individual thresholds for overtraining. In other words, underperformance and/or illness will coincide with a certain level - the athletes individual threshold - of training strain. Indeed, they found that 84% of illnesses could be explained by a preceding spike in training load above the individual training threshold.
Training Strain = Training Load x Training Monotony
So, a weekly training log might look something like this…
Day --------- Training Session ------ Duration (min) - RPE ---- Load
Sunday ------- Cycle (100km) -------- 180 ------------- 5 ------ 900
Monday ------- Weight Training ------- 120 ------------ 7 ------ 840
Tuesday ------ Cycle (10km) ---------- 20 -------------- 2 ------ 40
Wednesday – Inline Roller Intervals – 90 ------------- 6 ------ 540
Thursday ----- Plyometrics ------------ 75 ------------- 7 ------ 525
Friday -------- Cycle (10km) ----------- 20 ------------- 2 ------ 40
Saturday ----- Weight Training -------- 120 ------------- 7 ------ 840
Daily Mean (average) Load ------------------------ 532
Daily Standard Deviation of Load ----------------- 367
Monotony (Daily Mean/Standard Deviation) ----- 1.44
Weekly Load (Daily Mean Load x 7) ------------- 3725
Strain (Weekly Load x Monotony) --------------- 5397
All of this information can be quite easily calculated using a spreadsheet program (i.e. Microsoft Excel) and, unless you’re a math geek, this may be the only way to calculate standard deviation.
So, how do you put this plan into motion?
Approximately 30 minutes following the conclusion of each training session, rate the global intensity using the RPE chart. Delay scoring the session so that particularly difficult or particularly easy segments toward the end of the exercise bout do not dominate your rating.
Multiply this number (0-10) by the duration of the entire training session (including warm-up, cooldown, and recovery intervals during the training session). In the case where multiple training sessions are performed on a given day, the training loads are summated.
Find the average load and standard deviation for the week. Use these numbers to derive monotony, weekly load, and strain. That’s it. I’m hoping that matching this information with other performance and health measures will allow me and others to fine-tune the training process.
References
(1) Foster, C. Monitoring training in athletes with reference to overtraining syndrome. Med. Sci. Sports Exerc., Vol. 30, No. 7, pp. 1164-1168, 1998.
(2) Foster, C., J.A. Florhaug, J. Franklin, L. Gottschall, L.A. Hrovatin, S. Parker, P. Doleshal, and C. Dodge. A new approach to monitoring exercise training. J. Strength Cond. Res. 15(1):109-115, 2001.
(3) Day, M.L., M.R. McGuigan, G. Brice, and C. Foster. Monitoring exercise intensity during resistance training using the session RPE scale. J. Strength Cond. Res. 18(2), 353-358, 2004.
(4) Impellizzeri, F. M., E. Rampinini, A. J. Coutts, A. Sassi, and S. M. Marcora. Use of RPE-based training load in soccer. Med. Sci. Sports Exerc., Vol. 36, No. 6, pp. 1042-1047, 2004.
(5) Sweet, T. W., C. Foster, M. R. McGuigan, and G. Brice, Quantitation of resistance training using the session rating of perceived exertion method. J. Strength Cond. Res. 18(4): 796-802, 2004.