In section 4.4, a number of subjects were identified as having significant differences between season averages on side 1 and side 2 in multiple axes (Table 4-9 and 4-10). The link between these results and the propensity for these subjects to record incidents of missed or modified training will be examined in this section.
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Aims
Investigate the link between a significant difference between side 1 and side 2 within an axis and CMD condition and subjects whose training was modified for “load” or “groin” reasons
Methods
Subjects
All participants within the cohort described in section 5.3.1 (General Methods) were used for this study.
Data collection
GPS and accelerometer data were collected in accordance with the procedures outlined in General Methods, section 5.3.2.1. Missed and modified training information was collected and collated in accordance with the procedures outlined in General Methods, section 5.3.2.2.
Data analysis via the analysis tool
Data were analysed in accordance with the procedures outlined in General Methods, section 5.3.3
Missed and Modified Training and Game activity
Incidents of missed and modified training and game activity were identified as per the methods outlined in section 5.4.2.4
Season average within-section and between-section co-efficient of multiple determination results
Season average within-section and between-section CMD results were calculated and subjects whose season averages were significantly different from side 1 to side 2 were identified as per the methods outlined in section 4.4.2.3.
Data Analysis
Subjects who recorded an instance of modified training or game activity due to a “load” or “groin” reason at any stage during the season were identified. Only the “load” and “groin” classifications will be examined as modifications due to these reasons are in general long term issues as opposed to the “structural” classification which included many acute ligament sprains (especially ankle sprains). The “soft tissue” category was excluded due to the small number of instances (four) that were identified throughout
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the year. These results were then compared to the subjects who were identified as having a significant difference in their average CMD values between side 1 and side 2 within an axis and CMD analysis condition. The link between a significant difference between sides within an axis and CMD analysis condition and the presence of an incident of “load” or “groin” modification was examined by classifying each subject as a true positive, false positive, true negative and false negative where the presence of a significant difference in season average CMD represented the condition and the existence of an incident of training modification due to “load” or “groin” represented the test. These combinations of condition and test result are further outlined in Table 5-18. For the test element to be positive within an axis either within-section or between- section analysis conditions must have a significant difference on both sides (i.e. the average of side 1 needed to be outside the 99% confidence interval for the average of side 2 and the average of side 2 needed to be outside the 99% confidence interval of side 1). The test condition was determined for all three axes.
Table 5.18 Combinations of condition and test result
Side to side difference in season average CMD
Instance of Training Modification
False positive Yes No
True Negative No No
False negative No Yes
True Positive Yes Yes
Results
The total number subjects with modified training or game activity at any time during the year by reason for the modification are shown in Table 5-19. These are broken down further by instances where the test (a significant difference in season average CMD) has predicted the condition (the presence of a training or game modification) in Table 5-20 for training or game modifications due to “load”, and Table 5-21 for training or game modifications due to “groin”. The overall number of subjects where the test correctly predicted the condition (i.e. true positive or true negative results) is shown in Table 5-22.
Table 5.19 Number of subjects with modified and unmodified training by reason for modification
Modified Unmodified
Load 10 11
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Table 5.20 Condition and test result for "load" modifications
z-axis y-axis x-axis
True Positive 50% 60% 30%
True Negative 82% 27% 55%
False positive 18% 73% 45% False negative 50% 40% 70%
Table 5.21 Condition and test result for "groin" modifications
z-axis y-axis x-axis
True Positive 56% 78% 67%
True Negative 75% 42% 92%
False positive 25% 58% 8%
False negative 44% 22% 33%
Table 5.22 Overall instances (out of 21) where the test has correctly predicted the condition
z-axis y-axis x-axis
Correct Load 14 9 9
Correct Groin 14 12 15
Discussion
Overall, the presence of a significant difference in season average CMD between sides within an axis and analysis condition does not appear to be an effective test for
whether there will be a training modification at any time during the year for reasons of “load” or “groin”. Though a side to side difference in the z-axis correctly predicted the presence of a training modification in 66% of all subjects, this would not be an effective practical tool if used in isolation to definitively indicate the risk of the need for a training modification over the course of a season, especially given the high number of false negative results. The fewest number of false negative results were found in the y-axis “groin” (two false negative results) and the x-axis “groin” (three false negative results), indicating this method used in isolation is poor at correctly identifying subjects who were at an increased risk of requiring a training modification due to either “groin” or “load” reasons during the year. It is possible that when used in combination with other testing and monitoring tools that the predictive power of this method will enhanced, and that it could be integrated into a barrage of tests that when used together reliably predict “groin” and “load” training modifications during the season.
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There were, however, some very encouraging results when the false positive results are examined. There was only one subject who required an activity modification for “groin” reasons who did not have a significant difference between sides on in the x- axis. This would indicate that this method of analysing the season long results has some merit for identifying subjects who are at a reduced risk of requiring an activity modification due to “groin”. In addition, there were only two of eleven subjects who recorded a false positive result for “load” modifications in the z-axis, and three of twelve subjects who recorded a false positive result for “groin” in the z-axis, indicating that the lack of significant differences in the z-axis may potentially have some practical use in identifying subjects at a reduced risk of requiring “groin” or “load” modifications during the season. There are a number of practical implications for these results. Predicting a reduced likelihood of the need to modify training would be extremely useful information to have when designing training programs. It would also be beneficial when combined with other testing and screening tools in the diagnosis of injury when pain is reported. There are also implications for recruitment of athletes, in that if this information were available prior to recruitment it could aid in the selection of athletes who are most likely to be available for selection more often, a significant issue when considering the return on investment in both the athlete and support staff for professional sporting clubs. It must be noted that all subjects with available data were used for this study, and there was no exclusion for subjects who may have had significant differences between sides within an axis for reasons such as a previous anterior cruciate ligament (ACL)
reconstruction (as discussed in section 4.4). There may be some merit in excluding subjects with previous ACL reconstructions to aid in identifying subjects at a reduced risk of an activity modification due to “groin” or “load” as excluding subjects who
already have a side to side difference will reduce the proportion of subjects who have a positive test, which has the potential to reduce the number of false positive and true positive results. For instance, by excluding subjects who had a side to side difference in at least 90% of all analysis conditions (subjects 2, 10 and 19) two false positive results would be excluded in each axis of the “load” condition and one false positive result would be excluded in each axis of the “groin” condition. The net result of this is that in the “load” condition, the z-axis predicts 100% of true negative results, and in the “groin” condition the x-axis predicts 100% of the true negative results. There would consequently be merit in conducting further research to establish whether exclusion due to criteria such as a previous ACL reconstruction would enhance the ability of this analysis method to identify subjects who were at a reduced risk of requiring a training modification due to “load” or “groin”.
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Conclusion
The link between a significant difference between side 1 and side 2 within an axis and CMD condition and subjects whose training was modified for “load” or “groin” reasons was investigated, and it was found that this analysis method has some merit in
identifying subjects who were at reduced risk of requiring a training modification, particularly in the x-axis for “groin” modifications. These findings are amplified if subjects with difference in stride variability from side to side across at least 90% of analysis conditions are excluded.
When used in isolation this method does not appear to be able to identify subjects who were at an increased risk of requiring a training modification, however further
investigation may identify enhanced predictive value when this tool is combined with other athlete screening and monitoring tools.