The majority (81%) of Olympic swimming events are contested in under two minutes and twenty seconds. Despite this relatively short time frame, traditional training practices of competitive swimmers tend to involve extensive training sessions. Integrated planning and periodization are at the forefront of achieving success and coaches strategically fluctuate Training Load and recovery to push the limits of adaptation and avoid overtraining, injury, or detraining. Training load monitoring is a key element of this.

The cyclical motion, coupled with the high volume training sessions means the competitive swimmer is at risk of overuse injuries. Average injury rates of four injuries for every thousand hours of training have been reported (Wolf et al. 2009) with the shoulder, lower back, and knee being most common (Matsuura et al. 2019). Unsurprisingly, competitive swimmers have a significantly higher incidence of injury in training than in competition.

Avoiding overtraining is a key aspect of the planning process, not only to safeguard the long-term health of the athlete but to maximize their ability to train and perform without interruption. Finding a balance between Training Load and recovery is crucial to training consistently and effectively. To this end, an individual athlete’s training load needs to be monitored.

The rise of training load monitoring

In recent years, the popularity of training load monitoring has grown considerably due to increased sports science support, technological developments, and the growing professionalisation of sport.

Training Load can be divided into internal and external loads, with external loads describing the quantification of work done and internal loads describing the individuals’ response to that work

(Drew and Finch 2016)

Training Load encompasses swimming volume, the intensity of effort, session frequency and dry-land training. Customarily, swimming coaches rely heavily on external Training Load measures in the prescription of training sessions. Set distances, combined with prescribed target times are most commonly used, allowing the coach to draw on anecdotal evidence and professional experience as to the level of effectiveness. Traditionally, internal Training Load in swimming has focussed on heart rate and blood lactate. There is a consensus that both internal and external loads should both be collected and analysed to provide a clear picture of the athletes’ Training Load.

What training load monitoring measures are most practical in swimming?

Traditional measures such as heart rate, while widely used are still impractical outside the upper echelons of the sport. Even though new technologies have made it possible to track heart rate in real-time, it still has its difficulties.

Swimming training comprises pool-based training, with a variety of session targets (speed, aerobic, anaerobic, etc.) and dryland (gym, rehabilitation, mobility, cross-training) training. The use of a Training Load measure relying on heart rate (E.g. TRIMP) may not be accurately transferable to all types of training activities.

An athlete’s training load cannot be accurately reviewed and acted upon unless all elements are considered. While it is important to quantify pool-based load, the assessment of dryland and competition-based load is of equal importance. Using measures such as live heart rate is not a viable option in the competition environment and is limited in a gym setting. Therefore, using a subjective rating of internal training load (RPE), alongside an external measure (duration) and swim volume may be best placed to gather an accurate representation of all the elements of a modern-day swim programme.

A case study approach to training load monitoring

Building a training load monitoring profile using RPE for an athlete requires consistency in data collection and time for analysis and review. It essentially is a process that resembles stacking blocks to make a structure. Each block represents a load of a particular session, as you accumulate sessions day by day you stack the blocks together to build whatever you want to see and review.

Step 1

The first step is to know how to make each block.

Ask your athletes to subjectively rate the intensity of the training session according to the modified Borg scale.

• Use a simple question, such as ‘‘How hard was your session?’’ (Ensure they consider the session as a whole). The athlete then indicates the intensity of the training session by referring to a numerical value on the scale. 
• RPE is then multiplied by the total duration (minutes) of the training session. 
• This gives you the session’s total load.

For example, a swimmer attends swim training on Monday morning. The coach planned an aerobic set designed for recovery. The session is 90 minutes long with a target of 5,000m. The swimmer finishes the session after swimming 5,000m and rates the session as a 3 or “moderate” on the RPE scale. What is the swimmers’ load for the session?

Load = RPE x Duration (Min)

3 x 90 = 270

Therefore, Load = 270au

Step 2

The second step is to make multiple blocks of load for each day of the week. This is done by asking the athlete to report the RPE and duration of every session across a week.

Once you collect consistent load data over time, you will have enough blocks to build a graphical representation of your training load. Using the data above we can visualise:

While this information seems quite basic, once you build a chronic profile of the athlete, more significant trends tend to become apparent. The data below is representative of a single athlete over 24 weeks.

Using the information wisely

Ultimately, the role of training load monitoring is to inform the decision-making processes. Decision-making is fundamental in elite sport and it is often viewed as being linked to success during competition. Open, fast, and dynamic decisions are made regularly on the field of play. However, there are many key decisions made outside of competition in the daily training environment which are complex and have lasting consequences.

Best practice in the decision-making process in sport seems to favour multidisciplinary collaboration. Incorporating a support staff, athlete, and coach. Using graphical visualisation to look for trends in the data can be a significant aspect of the decision-making process.

Performance or injury prediction in sport has been seen as the holy grail in recent years. However, training load monitoring has not been proven to be a definitive predictive tool. This is primarily due to the multifactorial nature of swimming performance and injury prevention. training load monitoring can have an association with the risk of injury but has not been found to conclusively predict injury (Impellizzeri et al. 2020). Considering the lack of predictive qualities, training load monitoring should be solely used to provide information to be used in combination with a coaches’ experience, allowing informed decision-making processes to occur.

Barriers to training load monitoring

Training load monitoring requires consistent data collection, interpretation and action. However, there are three recurring barriers to accurate training load monitoring.

1. Athlete adherence to providing Training Load data post-session, 
2. A coaches’ reluctance to engage with the information, 
3. NGB’s providing sufficient financial support.

Athlete Adherence

Successful implementation of training load monitoring is strongly related to end-user buy-in. Athletes have reported that ease of reporting and feedback on their Training Load data are significant factors in their adherence. This is where modern, easy-to-use monitoring apps can play a significant part in the process. As discussed in our previous blog, Monitoring your athletes like the pros, apps such as RYPT allow the athlete to easily input their session Training Load data instantly post-session. As athlete feedback is also a key consideration, the ability of these applications to provide simple data dashboards for the athlete to review in conjunction with their coaching staff is a major aspect of the process.  

Coaches’ reluctance

The coaches’ reluctance to engage with the information is also a key consideration. The initial decision to implement a training load monitoring system should be dependent on several factors. Firstly, an identified need for the system must be present. Secondly, the sports organisation must have the capacity to meet that need, a clear method to obtain the data, and an intention to use the data. Thirdly, a commitment to the process from the coaching team is of utmost importance (Saw et al. 2017). This stakeholder engagement process can be improved through formal or informal education of those involved, including clear protocols on how the system is used, whose responsibility is the data, and how it will benefit the sports organisation and the individuals involved.

Sufficient Support

The commitment to Training Load practices should be mirrored by the NGB involved. An effective leadership model designs their organisation to promote innovation alongside their day-to-day operational duties. Conversely, low-performing NGB’s show an ability to complete their day-to-day resources but cite a lack of time and resources for pursuing innovations (Harris et al. 2021). Financial investment from the NGB to pursue innovation is important in this regard.  The ability to have an effective training load monitoring system or product partner in place, as well as a sole staff position dedicated to the role of training load monitoring and sport science services, is crucial to an effective system. This increased support through providing a skilled and knowledgeable practitioner would have a profound effect on the organisations’ ability to roll out best practice services to their athletes.

References

1. Drew, M., Finch, C. (2016) ‘The relationship between training load and injury, illness and soreness: a systematic and literature review.’, Sports Medicine, 46(6), 861–883.
2. Harris, S.J., Metzger, M.L., Duening, T.N. (2021) ‘Innovation in national governing bodies of sport: investigating dynamic capabilities that drive growth’, European Sport Management Quarterly, 21(1), 94–115.
3. Impellizzeri, F.M., Menaspà, P., Coutts, A.J., Kalkhoven, J., Menaspà, M.J. (2020) ‘Training load and its role in injury prevention, part I: back to the future’, Journal of Athletic Training, 55(9), 885–892.
4. Matsuura, Y., Hangai, M., Koizumi, K., Ueno, K., Hirai, N., Akuzawa, H., Kaneoka, K. (2019) ‘Injury trend analysis in the Japan national swim team from 2002 to 2016: effect of the lumbar injury prevention project’, BMJ Open Sport & Exercise Medicine, 5(1), e000615.
5. Saw, A.E., Kellmann, M., Main, L.C., Gastin, P.B. (2017) ‘Athlete self-report measures in research and practice: considerations for the discerning reader and fastidious practitioner’, International Journal of Sports Physiology and Performance, 12(s2), S2-127-S2-135.
6. Wolf, B.R., Ebinger, A.E., Lawler, M.P., Britton, C.L. (2009) ‘Injury patterns in Division I collegiate swimming’, The American Journal of Sports Medicine, 37(10), 2037–2042.