The platform 'Science for Sport' aims to inform coaches worldwide in the most effective way possible, providing concise lessons. Recently, the platform announced four cost-effective methods for calculating training load, which may be particularly useful for coaches with limited funding and resources. 

These methods include two internal and two external approaches.

One of the internal methods is the Perceived Effort Rating (RPE).

Perceived Exertion (RPE) is a widely recognized method for tracking an athlete's effort after a workout, which is a significant topic in performance measurement classes. The traditional 10-point scoring scale is used, where athletes assign a numerical value to their efforts, with 10 representing the maximum effort for a given session.

Additionally, health surveys may be employed.

Wellness surveys are self-report surveys in which athletes score their perceived level of well-being based on several parameters, such as sleep quality, muscle soreness, and mood. An example question is, 'How would you rate your sleep from the previous night?' with response options ranging from 'very poor' to 'excellent'.       

 

Additionally, this survey may include a countermovement jump test to assess lower body power.

The countermovement jump (CMJ) is a functional neuromuscular test used to measure performance in a sequence of movements and as an indicator of fatigue. Many trainers who lack access to power plates often use the MyJump2 app for iOS and Android as an alternative.

To record the total

 

volume load of the workout in a cost-effective manner, monitoring volume load is recommended. Volume load is the product of the load used, the number of repetitions, and the weight lifted. For instance, if an athlete squats 100 kg for 5 repetitions, the external load for this lifting set will be 500 kg. To gain insight into an athlete's internal load, RPE-based metrics and surveys can be used, while CMJ performance and Volume Load can provide insight into an athlete's external load. When combined, these components provide a versatile, cost-effective, and easy-to-implement load monitoring model.