Category: training

heart rate, monitoring, Sports Science, Technology, training October 12, 2010

Monitoring training load: quo vadis? #2

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!

monitoring, Periodization, Sports Science, training September 19, 2010

Monitoring training load in Team Sports: Quo vadis? #1

It is the beginning of the season for many team sports and it is the typical time when sports scientists start to struggle with manipulating the training load and making sure the players can survive a long season producing great performances.

I will try to analyse the current trends in the literature and provide some comments and some possible advice on how to put in place a meaningful and practical monitoring system to be able to inform the coaching process.

It is widely recognised that appropriate periodisation of training is fundamental for
optimal performance in sport. Until recently, it has been very difficult to quantify the
training loads (TLs) in team sports players due to the difficulty in measuring the various types of stress encountered during training and competition. Wearable sensors and well established psychometric tools as well as easy access to field-based biochemistry nowadays allow the collection of various data to be able to quantify and understand the training load as well as track the progression of the players’ performances. This can provide the basis for a critical assessment of the training process and feedback to the players and coaching staff of the progression.

Few comments before discussing the methods for data collection.

Training monitoring is becoming a standard operating procedure for many strength and conditioning coaches and sports scientists which is a good thing. However there are certain aspects that needs to be taken into consideration in order to understand the limitations of some training monitoring approaches as well as the potential of such methods to impact practice.

The latter is the most important aspect to be taken into consideration. Training monitoring becomes a useful thing to do ONLY if guides practice and informs the coaching process. Otherwise it becomes just a data collection exercise. I have seen many S&C coaches use a variety of tools and tests and despite the fact they have some nice continuous data it is clear that such data did not affect practice as training programmes continued in the same way despite the information available on training load and some effects.

So, first rule: training monitoring is a great way to understand how much work your athletes are doing and how they cope with it. Great thing to do only if it helps you in changing and evaluating your training plans.

The other aspect to consider is the limitations of what you measure, when you measure it and how many time you measure it. All this information helps in understanding what the information tells you and what parameter of your training programme you should change according to the results observed.

Training monitoring needs two main parameters to be measured:

1) The amount of training your athlete is performing (the INPUT)

2) How the athlete is coping with the amount of training (the OUTPUT)

The INPUT can be measured in various ways and should contain some information on how much work the athlete has performed (such weights lifted in each session, distance covered in training and also the perception of how hard the session has been). The list can be more extensive, but frankly your ability to collect more and better data is limited by the equipment you have access to. Heart rate monitors, GPS and accelerometers, power meters in the gym are all available nowadays and allow a lot of measurements to be collected in team sports to help you gain more info on the intensity and the amount of training performed. I have presented few technologies in this blog and aim to do more in the future, so plenty of solutions for you to try.

However, not many people have access to technology (in particular the expensive software and hardware kits for more complex multisensor data collection). So, let’s discuss some simple training quantification methods and their applications.

This will require the use of spreadsheets to facilitate the calculations and the data collection as well as provide you the possibility to create reports and graphs. If you don’t have access to Microsoft ® Excel don’t worry! You can in fact download open office for free from here and have access to a free suite which allows you to have spreadsheets, graphs and presentations at no cost!

The Session RPE method

The session-RPE method of monitoring TL in team players requires each athlete to
provide a Rating of Perceived Exertion (RPE) for each exercise session along with a measure of training time (as suggested by Foster et al., 2001).

To calculate a measure of session intensity, athletes are asked within 30-minutes of finishing their workout a simple question like “How was your workout?” A single number representing the magnitude of TL for each session is then calculated by the multiplication of training intensity (RPE from Table 1) by the training session duration (mins).

Table 1. The modified RPE scale proposed by Foster et al. 2001

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 = Session RPE x duration (mins)

For example, to calculate the TL for a training session 60-minutes in duration with the
athletes RPE being 5, the following calculation would be made:

TL = 5 x 60 = 300 AU (arbitrary units)

With a simple spreadsheet it is is therefore possible to track the training load of a team very easily just by recording the duration of training and making sure that each player at the end of each session provides you with the perceived exertion for that session.

Here is an example of what a score of a typical training period could look like:

The Black dotted line represents the average Session RPE for the team and each colour represents one of the players. In this way, it is possible to track how the overall training load is progressing and how each individual compares to the team.

The data can also be useful to track down the team’s session RPE and understand if overall the training load is going in the direction planned.

Further simple calculations of training ‘monotony’ and ‘strain’ can also be made from
session-RPE variables.

Training monotony is a simple measure day to day variability in training that has been suggested to be related to the onset of overtraining when monotonous training is combined with high training loads (see Foster, 1998).

Training monotony is calculated from the average daily TL divided by the standard deviation of the daily TL calculated over a week.

MONOTONY= DAILY TL/SD of TL over a week

Training strain can also be calculated as follows:

TRAINING STRAIN = weekly TL x monotony

The table below provides a simple example of a weekly training load in a semi-professional handball team with all the variables calculated.

Recent work conducted using RPE from 20 soccer players during 67 small sided-games soccer training sessions (Coutts, Rampinini, Castagna, Marcora, & Impellizzeri, 2007a) has shown that the combination of blood lactate and HR measures during small-sided games were better related to RPE than HR or blood lactate measures alone. This work suggested that RPE is a valid method of estimating global training intensity in soccer. There isn’t such evidence in other sports, however nothing stops practitioners to try and see if it helps with their coaching process.

This is the first article of a series aimed at discussing the issue of monitoring training. I aim to present practical solutions to be able to start quantifying and understanding adaptations in team sports athletes.

Enough for now, time to get your spreadsheets sorted and start calculating what your players are doing so you are ready to apply the techniques presented in the next article!

handball, performance, training December 31, 2009

Handball and sports science….why so behind?

Handball (also known as team handball, Olympic handball or European handball) is a team sport in which two teams of seven players each (six outfield players and a goalkeeper) pass and bounce a ball to throw it into the goal of the opposing team. The team with the most goals after two periods of 30 minutes wins. Handball is by far my favourite sport. I played handball for many years in Italy (a bit more seriously) and for a couple of years in Scotland (for fun) and was a coach for few years and despite the fact I am not coaching handball anymore, I still cannot believe how old fashioned handball training is.

Handball is a professional sport in many countries. In Germany, Spain, Denmark and Norway and many other European countries (mainly in Eastern Europe) there are full time coaches, players and support staff. The European Champion’s League is televised on Eurosport and in some games you can see more than 15000 supporters watching the game! In few words…it is a serious business!

(The Croatian Ivano Balic….possibly the best player in the World for Men’s Handball)

(The Hungarian Anita Görbicz….possibly the best player in the World for Women’s Handball)

If you have never seen a game of handball….you can get some ideas of how it is played on YOUTUBE.

Handball is a sport which is growing very fast in terms of spectators and media coverage, and is one of the top sports in Europe in terms of employment opportunities for coaches and sports scientists. Handball is an Olympic sport since 1972 in its indoor version. However, despite all the media interest, the sponsorships and the fact that Olympic medals are at stake, very little is available in terms of sports science. Very few research activities have been conducted and there is a paucity of published literature. A simple analysis on pubmed using the keyword “Handball” provides 343 entries (with many papers on injury rates and/or on a different sport also called Handball and played mostly in the USA and Canada). A keyword search for Basketball presents 1734 papers, and volleyball presents 693. In simple terms, there is clearly a paucity of information on Handball.

When we then analyse the scientific literature available, we then realise how little has been published on training and performance aspects as most of the literature refers to injuries in Handball.

A very recent review from Ziv and Lidor (Ziv, Gal and Lidor, Ronnie(2009) ‘Physical characteristics, physiological attributes, and on-court performances of handball players: A review’, European Journal of Sport Science, 9: 6, 375 — 386) has summarised all the available literature on handball and highlighted how little is known about this wonderful sport.

I have previously discussed on this blog specific aspects of physical preparation of handball players. However I would like to point out again that little is known about physiological demands and most of all about physiological characteristics of elite handball players. Ziv and Lidor summarised in Table 1 of their paper what has been so far published:

From the paucity of data on elite performers, you can clearly see that elite handball players are bigger and have more muscle mass than non elite. When I worked in Italy we used to benchmark our youth national teams and seniors with the World elite and it was always clear that in order to be World leading in this sport you needed height and fat free mass in particular in some playing positions.

Endurance capacity has always been a matter of discussion in the Handball coaching community. Despite the fact that Handball is clearly an intermittent sport (played on a 40m court!), a lot of attention was always devoted to endurance capacity. However data clearly show that handball players have a VO2max of 50-60 ml.kg.min-1, indicating that probably endurance capacity per se is not the most important performance-limiting factor. This has been supported by one scientific paper published by Gorostiaga et al. In fact, they conducted a study that examined endurance capacity in elite and amateur handball players while running at 10, 12, 14, and 16 km/h found no differences in mean blood lactate concentration or in mean heart rate (Gorostiaga et al., 2005). The mean running velocity and heart rate that elicited a blood lactate concentration of 3.0 mmol. l-1 were similar in both elite and amateur players, suggesting that endurance capacity per se does not differentiate elite from amateur handball players. In addition, no significant differences in endurance running at 10, 12, 14, and 16 km/h were observed in elite players over the course of a season (Gorostiaga et al., 2006). Despite this, a lot of handball coaches still put emphasis on training endurance capacity mostly in the form of long steady state running. However, it is important to state that we don’t have data on the top 5 national teams in the World and we have no idea if they are really different from the rest. Also, considering how fast the game is now played, we should clearly reconsider how to train handball players as I suspect metabolic demands are a lot higher than the ones recorder in the early 80s.

The most recent work on motion analysis characteristics is the one published by Luig et al. (2008) which conducted time-motion analyses during nine games of the 2007 men’s World Cup. The analyses were conducted using a computerized match analysis system. Four movement categories were defined in this study: walking, slow running, fast running, and sprinting. Playing time was significantly higher in wings (37.37 +/- 2.37 min) and goalkeepers (37.11 +/- 3.28 min) than backcourt players (29.16 +/- 1.70 min) and pivots (29.3 +/- 2.70 min). Total distance covered was higher in wings (3710.6 +/- 210.2 m) than in backcourt players (2839.9 +/- 150.6 m) and pivots (2786.9 +/-238.8 m). As anticipated, goalkeepers covered the shortest total distance (2058.1 +/-90.2 m). The total distance covered by field players consisted of 34.3 +/- 4.9% walking, 44.7 +/-5.1% slow running, 17.9 +/- 3.5% fast running, and 3.0 +/- 2.2% sprinting. Compared with other players, wings covered significantly shorter distances while slow running but significantly longer distances while fast running and sprinting. The distances covered are a lot less of what was reported in the 80s and is possibly due to how the game has changed with a better use of substitutions during the game to make sure players can perform fast movements for almost 21% of the total distance covered. To date, no study has been performed on oxygen consumption during handball games, but it is quite easy to predict what to expect considering the fast pace of handball playing. Quite simply, there is no information on handball performance which can help coaches identifying what the real demands are in particular at the very elite end of performance. I am sure data exist possibly in languages I cannot read and understand, I would be in fact very surprised if the elite handball nations don’t have such data to identify what they need in order to win an Olympic medal.

More data exist on strength and power capabilities and how training can influence throwing speed:

Again, few data here, but it seems feasible to suggest that strength training can improve throwing speed at least in non-elite players. The effectiveness of strength training on improving throwing speed in World class players is debatable mainly because there are no data to support or disprove this possibility. However, considering that Gorostiaga et al found a significant correlation between total strength training time and standing throwing velocities (r=0.58), and with my group we always improved throwing speed in national team players following a strength training programme, it seems feasible to suggest that strength training can improve throwing speed even in elite players. Throwing speed is of course only one part of the story, as accuracy is needed in order to score a goal as well as the ability to “beat” the goalkeeper. Incredibly there is virtually no information on interventions able to improve accuracy in handball players….

Handball is quite a demanding sport and being a “contact” sport eposes the players to a relatively high risk of injuries. Data from Beijing Olympics (Junge et al. 2009) clearly shows the high injury rates observed in Handball. 92% of the injuries occurred in competition. Of course during the Olympics teams tend to lower the intensity of training sessions and minimise physical contact in order to avoid injuries. However, it would be interesting to investigate injury rates in training and competition to find out if training sessions are too far from the competition demands as I suspect this is the case in many countries.

Who is going to invest in research activities on handball performance? Which country will be able to identify marginal gains to produce better players? Which country will be able to identify nutritional strategies to maximise performance in handball?

Maybe in few years we will have an answer, in the meantime, we can enjoy the competitions and hope that more research activities will be published on peer reviewed journals and websites for the good of coaches and players.