After having presented a simple method to monitor training load without the need of expensive equipment, it is now the time to discuss other methods which involve the use of equipment.
The first and obvious one is monitoring training with the use of heart rate monitors. Thanks to the development of technology it is nowadays possible to measure in real time heart rate (HR) of numerous players on the field without the need for them to wear a watch or a recording device. Many companies in fact provide telemetry systems capable of storing and transmitting heart rate values recorded during training and/or competition. When I first started working in this field may years ago I remember the excitement of being able to measure HR during training and be able to download the files for analysis using the conventional heart rate bands and watches. The cost was prohibitive (there was no way I could afford 20 watches + HR bands!), it took ages to download the files with 1 interface connected to a serial port, and most of all, because athletes needed to wear a watch…we had to be creative about where to place it and also be prepared to sacrifice a few in some contact sports or due to falls.
Nowadays, it is very easy! The current systems can transmit information in real time, it is possible to measure many athletes at the same time and it is possible to store and analyse all data immediately after the end of each training session. Furthermore, due to the improved quality of the sensors used and the software and hardware developments, it is also possible to measure R-R intervals and analyse heart rate variability (HRV).
Heart rate can be considered as a reliable indicator of the physiological load both for immediate training monitoring and for post-training analysis in almost every sport. However, considering the influence of psychological components like anxiety and stress on HR, it is feasible to suggest that an appropriate assessment of training intensity should also consider this limitation of HR monitoring.
Typical training plans of team sports are characterised by a combination of technical and tactical specific drills, small sided games, or general types of team drills. In the above situations, all members or small groups of the team perform similar tasks. The determination of training intensity and training stress is an extremely important parameter for training planning and for appropriate distribution of training load in elite athletes competing in team sports.
The following methods have been suggested to be effective in quantifying the training load:
The Training Impulse [TRIMP] method
Proposed by Bannister et al. (1975), characterised by the following equation:
TRIMP = training time (minutes) x average heart rate (bpm).
For example, 30 minutes at 145 bpm. TRIMP = 30 x 145 = 4350
This approach is very simple, however it does not distinguish between different levels of training. So it has been used mainly to determine general load in aerobic-endurance sessions.
TRIMP TRAINING ZONES METHOD
Developed by Foster et al (2001) is based on assigning a coefficient of intensity to five HR zones expressed as a % of HRmax:
1. 50-60% HRmax
2. 60-70% HRmax
3. 70-80% HRmax
4. 80-90% HRmax
5. 90-100% HRmax
The zone number is used to quantify training intensity; TRIMP is calculated as the cumulative total of time spent in each training zone.
For example
- 30 minutes at 140 bpm. Max HR = 185 bpm. %max HR = 140/185 x 100 = 76%. Therefore, training intensity = 3.
TRIMP = training volume (time) x training intensity (HR zone) = 30 x 3 = 90.
- 25 minutes at 180 bpm. Max HR = 185 bpm. %max HR = 97%.
Training intensity = 5. TRIMP = 25 x 5 = 125
The zone TRIMP calculation method can distinguish between training levels while remaining mathematically simple, however this can only quantify aerobic training and it does not allow quantification of strength, speed, anaerobic and technical sessions.
TRIMP Zones + RPE
Combining the two methods allows the determination of training intensity not only from a cardiovascular standpoint, but also taking into account the perception of effort and can be extended to strength training to be able to collect a cumulative training load score.
EPOC (excess post-exercise oxygen consumption) Methods
EPOC is basically the excess oxygen consumed during recovery from exercise as compared to resting oxygen consumption. The EPOC prediction method has been developed to provide a physiology-based measure for training load assessment.
EPOC is predicted only on the basis of heart rate derived information. The variables used in the estimation are current intensity (%VO2max) and duration of exercise (time between two sampling points, Dt) and EPOC in the previous sampling point. The model is able to predict the amount of EPOC at any given moment. No post-exercise measurement is needed. The model can be mathematically described as follows:
EPOC (t) = f(EPOC(t-1), exercise_intensity(t), Dt) (Saalasti, 2003)
At low exercise intensity (<30-40%VO2max), EPOC does not accumulate significantly after the initial increase at the beginning of exercise. At higher exercise intensities (>50%VO2max), EPOC accumulates continuously. The slope of accumulation gets steeper with increasing intensity.
(The following figure is from Firstbeat Technologies Withepaper)
The relationship between measured and HR derived EPOC has been shown to be significantly large suggesting this method as an alternative solution to determine training load with minimally invasive procedures such as wearing a chest band (Rusko et al., 2003).
And by the same authors has been shown to be related to blood lactate.
The EPOC approach has been nowadays introduced by various HR monitors manufacturers (www.suunto.com and www.firstbeattechnologies.com).
(Figure above from www.suunto.com)
Various manufacturers are now developing innovative approaches to describe training loads based on HR measurements (e.g. http://www.polar.fi/en/b2b_products/team_sports/software/polar_team2_software) and more will be available soon due to the ability for the current systems to record with high accuracy also R-R intervals and derive training stress information from Heart Rate Variability indices.
I will write more on these in the next posts on this interesting topic…this is it for now…stay tuned!
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