Is ‘training volume’ a surrogate for the more important risk factor ‘jumping’?

Training volume and match participation represent strong risk factors for jumper's knee.2 The explanation for the difference in training and match exposure was that many of the athletes affected were selected for different training camps with junior national teams or their home clubs on weekends and school holidays.2 In other words, they were the most talented players. The differences we have observed in jump frequency during match play are easily explained; the best players see more playing time and get more hits, blocks and sets. But also during training it seems that skill level is a key factor. The best players are more likely to be given central roles, especially when the coach is drilling his starting six, and with their skills they are also more likely to stay on court in drills where the winner gets to keep going. It should be noted that even if player function was a significant factor among boys, the between-player differences were larger than those observed between player functions (table 1). During match play, there were no differences between player functions. This is in contrast to findings by Sheppard et al10 of 16 international top level matches (2004 Olympic Games and international test matches). It is possible that an even larger sample size would have revealed differences between player functions during match play in our study as well. A cross-sectional survey among 18–35-year-old Dutch volleyball players from different levels revealed that outside hitters and middle blockers were at greater risk for developing jumper's knee than setters,11 as originally shown by Lian et al,5 possibly because they tend to be taller, heavier and jump higher. Nevertheless, the current study documents that there are differences in jump frequency between players that could amplify the differences reported in total training and match exposure, and therefore exacerbate the risk for developing jumper's knee. However, to establish this link, prospective studies based on individual jump counts are needed.

Does the weekly jump rate contribute to the risk factor ‘sex’?

Several epidemiological studies have shown that sex is another risk factor for jumper's knee.1–3 ,8 In fact, Visnes and coauthors2 ,3 have shown that the male-to-female risk ratio was 4:1 in the total TVN cohort, and in the sample participating in the current study 12 of 26 boys were diagnosed with jumper's knee compared with only 1 of 18 girls. As there seems to be no difference in total training volume between sexes, the explanation favoured for the sex bias is that males jump higher than females, subjecting their tendons to higher loads.3 In the current study we show that males jump 2.6 times as much as females during training and 1.5 times as much during matches. Also, in the male group there was a greater disparity in jump frequency than within the female group. As can be seen from the player function patterns, the sex difference in jump frequency can at least partly be explained by differences in playing style with less jump setting and less involvement of the middle blockers in attacking and blocking among the girls, at least during training (see table 1 and figure 1). These findings could help explain the sex differences observed in jumper's knee prevalence. It seems that males are subjected to a much greater jump frequency during the vulnerable period transitioning from young talent to mature elite player, a phase where a large proportion of boys tend to develop tendon problems.

However, these two hypotheses that jump frequency is related to the risk for jumper's knee and can help explain the sex bias, require further research in prospective studies, where jump counts are measured as the exposure factor instead of just total training volume and match participation. In the current cross-sectional study, there was no difference in jump frequency between players with established jumper's knee and asymptomatic athletes. This is perhaps not surprising, as the study covers only 1 week of training and a few matches. Players who are already symptomatic may also have been limited by knee pain. Other limitations include differences in training attendance and match selection, which we have corrected for by comparing jump frequency per hour of training or per set played. We did not inform coaches of the purpose of the study, so we expect the sample is representative of normal practice routines. While all players were Norwegian, the coaches represented some of the world's leading volleyball nations of the world, so it is unlikely that the large disparity in jump frequency is a local phenomenon. A possible limitation is the precision of the jump count, which was conducted by only one observer, although uniquely qualified as a national team player. With up to 12 people jumping at the same time, a jump might escape notice. He also subjectively classified jumps as maximal, submaximal and hops. Finally, the courts were not completely covered by the cameras, and jumps performed in blind spots would be missed. However, it seems highly unlikely that these limitations could introduce systematic biases which would undermine the main conclusions.

Clinical implication: Are ‘jump counts’ a solution?

Baseball has introduced pitch counts to limit shoulder and elbow load in young and adolescent pitchers.12 However, manual jump counting in volleyball is time consuming. Analysing 1 h of practice video took approximately 3 h if the drills were relatively simple, and up to 6 h if the drills were complex with many players jumping at the same time. Therefore, introducing routine jump counts in training would require the development of wearable technology to collect and analyse data (Jon Jarning, Personal communication, 2014). In volleyball and other jumping sports, the best advice we currently can give is to keep a close eye on the most talented players, where their jumping ability is a key factor, to ensure that they are not overexposed to training and competition.3 ,13 ,14