Regardless of any rotational force data, this relationship suggests that the wearable units may be capable of measuring the degree of external stress rugby players experience during competition. As a result, this study still provides valuable information and may guide future research and clinical practice.
In a similar study (29), the authors also attempted to quantify the frequency, velocity, and acceleration of impacts in Australian football using the same formula used in rugby league research (30). It was reported that out of the 1578 “tackles” recorded by the accelerometer, only a mere 18% were correctly identified as tackles, while the other 82% were identified incorrectly (31, 32). It has been suggested that perhaps this large misinterpretation of tackling events is primarily due to the authors using an algorithm which was originally developed for rugby league (30). Though not enough information is currently known on this topic, if true, it suggests that algorithms must be developed specifically for each sport. Thus severe caution should be taken if you are attempting to use the algorithm developed for rugby league in another sport.
Number of Jumps
When the wearable units were first introduced, they did not include the accelerometer and gyroscope, meaning that only the locomotive demands of sports could be analysed using the GPS micro-sensor. In recent years, the inclusion of these additional micro-sensors has allowed further quantification of skill- and contact-based activities (e.g. jumping, kicking, marking, and tackling). To our knowledge, though no research has validated the use of accelerometers to measure the number of jumps in team sports, they have been validated in ski jumping (32, 33) and in laboratory environments (34). Consequently, the wearable units appear to be a reliable measure of jumping activities.
However, it is worth noting that during the ski jumping assessment, two units were worn: one on the sacrum, and the other on the thigh. It is also very important to understand that both of these environments are not as chaotic as other sports (e.g. football, rugby, and Australian football). More chaotic environments, which include other activities such as blocking and tackling, may produce more ”noise” within the data. More noise may lead to the jumping activities being masked or missed, thus decreasing the reliability of the data. As a result, future research should aim to compare the number of jumps derived from wearable units, with video-based technology in an attempt to highlight its reliability in more frantic environments.
This is certainly one of the more complicated metrics, and one that has not yet been discussed in this two-part review. Although still in its infancy, the metabolic power metric does express exciting qualities for future application. To simply describe the objective of this metric, its aim is to provide an overall ‘estimate’ of total internal energy expenditure during a performance. Simply meaning, that if accurate, this metric would enable sports scientists to non-invasively measure the energy expenditure of each athlete during training or competition. As a result, whilst its potential could be revolutionising, there are still a number of issues surrounding it.
Metabolic power (W·kg-1) has been referred to as a “shortcut” to define the amount of energy required, per unit of time, to perform the desired activity (35). In other words, metabolic power is a measure of the amount of ATP, per unit of time, necessary to perform the specific task (35, 36). In a traditional sense, metabolic power is an estimate of energy expenditure and is typically measured using a “gold-standard” protocol such as indirect calorimetry. Indirect calorimetry measures gas exchange (i.e. oxygen consumption and carbon dioxide production) and is often done in a laboratory using a Douglas bag. However, as this is impractical in clinical environments, alternative and more practical solutions have been developed (37, 38).
Professor di Prampero and colleagues have developed an indirect method of calculating metabolic power by measuring running speed and acceleration (37, 38). Put even simpler, this method attempts to estimate the energy cost of running. And although simplified, they basically measure running speed and the inclination of the body during acceleration to calculate this estimate of energy expenditure.
The metabolic power can be calculated by combining the energy cost of constant running speed with the energy cost of accelerating. The inclination of the body (i.e. the forward lean) is used to determine whether the athlete is accelerating or at constant speed, as athletes typically lean forward more during accelerating in order to place their centre of mass outside of their base of support. The gyroscope in the wearable technology is used to identify the inclination of the body during locomotion, whilst the GPS unit is used to identify the movement velocity of the athlete. Thus, the body inclination and movement velocity can be combined to estimate the energy expenditure of the athlete during running.
Whilst this method of estimating energy expenditure via running speed and the inclination of the body has been shown to be reliable during straight-line running (38), there are a number of concerns regarding the true validity and reliability of this method during sport-specific tasks. The following list provides some of the key concerns with this measure of metabolic power:
- It assumes each athlete’s running economy are the same.
- It assumes that deceleration movements have similar energy costs as forward running when the inclination of the body is equivalent.
- It assumes that lateral acceleration energy requirements are equal to linear running energy costs.
- It does not account for change of direction speed energy requirements.
To add to this problem, when using the GPS unit to calculate metabolic power, it also assumes that the wearable device is accurate for measuring the athlete’s speed and distance of locomotion – which in Part 1 of this review was shown to be highly-questionable. Therefore, layering a potentially unreliable metabolic power estimation on top of a small-moderately reliable measurement device (i.e. GPS), it should be recommended that significant caution is taken when interpreting this metric. There are also several other issues surrounding this metric which are unfortunately beyond the scope of this article.
If you wish to learn more about this metric then please read the two following articles:
- di Prampero PE, Botter A, Osgnach C. The energy cost of sprint running and the role of metabolic power in setting top performances. Eur J Appl Physiol (2015) 115:451–469. [PubMed]
- Buchheit M, Manouvrier C, Cassirame J, Morin JB. Monitoring Locomotor Load in Soccer: Is Metabolic Power, Powerful? Int J Sports Med 2015; 36: 1149–1155 [PubMed]
In addition to the above information, we also felt it may be beneficial to provide you with ‘arbitrary’ metabolic power zones classifications (Table 5) which have been adapted from research on professional football players (39). However, it is important to understand these zones have been constructed using arbitrary zones and based upon a specific population.