• Subjective Indicators in Monitoring pt2

      by Mladen Jovanovic

      Similar to RPE during training, session-RPE is taken by answering a simple question “How was your workout?” and pointing to a scale. Training load is calculated by multiplying session-RPE and training duration in minutes (including warm-up).
      Training load = Session-RPE x Training_Duration (min)
      For example if session-RPE was 8/10 and the training duration was 90min, then training load is 720 (AU). Using this simple method of monitoring, weekly training load can be easily depicted and monitored. If a given training day has a multiple training sessions, they can be depicted as individual or summed as total daily workload. Data can be showed for an individual athlete or for the whole team.



      From a weekly training load, following numerical data can be calculated:
      Weekly training workload or the sum of daily workloads over the week
      Training monotony was calculated from the mean training load divided by the standard deviation of the training load over a 1-week period and basically represents daily variations in training workload (hard-easy, hard-medium-easy training days, etc). It is shown that training monotony is one of the most important factors inducing overtraining [8]
      Training strain or a product between weekly training load and training monotony
      It is important to stress that it is hard (and not even smart) to compare different types of workouts (i.e. strength training, glycolitic power/capacity, aerobic training, technical training) in terms of session-RPE and workloads based on subjective feeling. Or in other words, it is impossible to say that strength workout with 450 [AU] is easier/harder than aerobic workout with 550 [AU]. Those are apples and oranges and they stress different physiological systems (i.e. neuromuscular vs. cardiovascular). What can be done in this way is to compare the workouts of the same type and/or to compare the microcycles of the same/similar structure and aim/goal. The take home message is that it is very complex to say that this workout or this microcycle is harder/easier that that one, in terms of relying solely on session-RPE and training workload calculations based on it. Anyway, subjective monitoring of this kind is not enough per se for this type of evaluation, because “full picture” can be seen only with monitoring indicators of training workload (i.e. weight on the bar, rest duration and quality, velocity, distance, etc) and/or physiological indicators during the workout. Having all or most of that data can provide more insight for evaluations.
      One interesting trend worth mentioning was observed in the research done by Manzi et al. [24] where players with higher scores in Yo-Yo Intermitent Recovery Test L1 showed lower mean individual session-RPE indicating that the more endurance-fit players experienced a lower psychological/subjective workload to the same training sessions. Practically this means spending some time improving aerobic endurance and/or work capacity may yield long-term benefits (even if sport is not 'aerobic' per se, although this issue is worth an article in itself) by decreasing subjective indicators of the same training workloads and/or allowing training stress to be increased (which can be spend on developing technique skills or tactical skills or what have you) without negative effects. In strength and conditioning world where everybody is focusing on high intensity interval training (HIIT) and repeated-sprints ability (RSA), this is a point worth considering and usually forgotten.
      Mentioned variables resulting from simple tracking of session-RPE over the week/microcycle period, can be depicted over the longer period of time, for example one season or training year.



      This data can be used for two key purposes: (1) avoiding overtraining and injuries and (2) programming and adjusting/correcting the training workload.
      Numerous researches have showed a correlation between injury and illness rates along with overtraining incidents with the mentioned variables (weekly training workload, training monotony, training strain) resulting from simple tracking of session-RPE [2, 6, 8, 25]. Thus, monitoring of session-RPE for a single player and for the whole team represent powerful weapon of injury prevention and control/feedback of training workloads (comparison of planned workloads versus achieved). Not to say that you can prevent overtraining, underperformance (depressed mood state and emotional disorders [3]), injury and illness just by monitoring session-RPE, but rather by taking the corrective actions based on that data and the training plan and program.
      A paper by Baron et al. [3] even presents new model of monitoring emotional states with the aim of improving training process and avoiding overtraining and underperformance. The summary of decades long and millions of dollars worth research on overtraining can be summated by the simple question „How are you feeling today?“ (see section on immediate training effects), since psychological indicators are the first one affected, before performance drop or even before changes happen in the physiological variables [3]. Indeed, RPE appears to be most sensitive parameter for identifying overtraining [8], because physiological systems are more robust and probably become affected at a later stage in the overtraining process [3]. Overtraining may also influence the evolution of positive affective and affective loading responses, which might also be used to adjust training before underperformance occurs [3].
      Currently, a lot of coaches are concerned with implementation of prehab training (pre-rehabilitation training), single leg training, unstable surfaces, vibrating surfaces and other modern training gadgets, without seeing the big picture first, and that is good planning and programming of training and simple and effective monitoring of training workloads and adjustment based on that feedback data. Joel Jamieson, author of “Ultimate MMA Conditioning” book [14] and strength and conditioning coach of some of the most elite MMA fighters, in a private conversation with the author of this article stated very clearly that:
      “Strength and conditioning should be focused on improving performance and that happens by improving energy production and utilization through the systematic development of the biological systems. Injury prevention is most effectively managed through the proper applications of volumes and intensities of a well designed program because in my experience, most injuries tend to occur when there are a breakdown in the body’s adaptive abilities, not because some joint or joints didn’t have enough ROM or stability.” – E-mail received 5th July, 2010.
      Further utilization of monitoring of acute training effects by utilizing session-RPE is in control and adjustment of training workload. By simply monitoring session-RPE and resulting variables, a coach can control the implementation of training plan and program. Depending on the type of training session and training microcycle [11, 12, 18] a coach can expect to see certain types of daily and weekly training workloads assessed by session-RPE. This way a coach can compared what he wanted to achieve in terms of loading and what he actually achieved. Thus, session-RPE represent a very powerful tool in controlling, adjusting and correcting training program.
      By utilizing Peaking Index (PI) defined by Tudor Bompa [4], which is basically an index of peak shape or sport form [16], coach can plan in advance training loads based on the competition calendar. Based on Tudor Bompa [4]:

      Peaking index 5 is 50% preparedness, characteristic of the transitional period. Training workloads are very low due the rest and recovery goals.

      Peaking index 4 is 60% preparedness, characteristic of the preparatory period when athletes are not yet ready to play. Training workloads are the highest here, and since the fatigue is highest here athletes are not yet ready to play.

      Peaking index 3 is 70-80% preparedness typical for friendly games and games against weaker opponents. Training workloads are still high/medium here, and the training is still directed toward improving preparedness. Fatigue is medium.

      Peaking index 2 is 90% preparedness characteristic of the period and competitive games against opponents from the top of the table. Training workloads are medium level and fatigue is in medium/low level

      Peaking index 1 is 100% preparedness and is characteristic for Play-off periods, when peak form/shape is achieved. Training workloads are lowest and based on a concepts of peaking [4, 11, 12] which allow fatigue to dissipate and express the full preparedness.

      Using this simple five numbers, coach can plan the season and priorities in advance and thus training loads for each period, and by using session-RPE he can control improvisation of the training plan and program.
      In a very interesting article, Kelly and Coutts [20] presented very simple and effective method to plan training workloads during the season and control of it improvisation based on session-RPE. Training workloads are planned in advance based on (1) prediction of the match difficulty, (2) match location and travel and (3) training days between matches. Based on this model, training workload oscillates during the season based on the mentioned three factors with match difficulty being the one with greatest weight. This provides variations of training and undulations of training workload. In author’s opinion, this model provides great solution to planning and programming training in team sports, since traditional ideas on ‘peaking’ are not applicable during the team sports with very long season, except for the play-offs. Further explanation of practical utilization of Peaking Index, Kelly and Coutts model in periodizing, planning and programming training process would demand specific article, and it will be not addressed in more detail in this one.
      By monitoring immediate training effects, besides acute training effects that are already covered, prevention of injury, illness, overtraining and possibly staleness and under-performance based on planning, control and correction of training workloads can be achieved with more precision and ease, and provide further data.

      Stay tuned next month for Part 3!