# Quantitative Fatigue Self-Assessment in NCAA (DI) Men’s Lacrosse Athletes: Case Study

Written By: Andrew Patton

In the strength and conditioning community, it is well known that in terms of athletic performance, there is not an endless ability for an athlete to respond to by increasing training stimuli (Baechle & Earle, 2008; Israetel, Hoffman, & Wesley Smith, 2015). In more colloquial terms, we know that more work is not better under all circumstances. For individual strength sports, it is fairly easy to quantify both the work and the “more”.

For each primary exercise, this can be calculated and the lifter’s overall training volume is found. Increases and decreases in workload can be estimated based on percentage or absolute measures. Similar training volumes and adjustments can be calculated for time/distance sports such as track, swimming, cycling, etc., where instead of load, there is effort or intensity. When training volume is increased, there is also a corresponding increase in short term fatigue (Baechle & Earle, 2008; Saw, Main, & Gastin, 2016). Further, when that lifter continues to increase their training volume, they will eventually hit their maximal recoverable volume (MRV), or the amount of volume where they can no longer benefit from further increases in workload. Given our previous formula, we can quantify this MRV with assuming an athlete keeps detailed records of weight lifted. However, strength athletes are unique in that their lifting is their sport, there is no addition sport specific practice. When we instead turn our attention to collegiate team sport athletes (football, basketball, soccer, etc.) we see that lifting and field practice must both be considered together as part of the training load; three hours of hard football four times a week will limit the amount of work that is possible in the weight room compared to that same population with no field time. Figure 1 shows what a theoretical distribution of too much accumulated fatigue would look like broken out by cause, based on example MRV charts from Israetel, Hoffman, & Wesley Smith (2015).

Taking a step back, we should understand that the reason a strength and conditioning coach is concerned about MRV is that beyond the MRV, their fatigue accumulation will likely inhibit on field performance. A common example is freshmen who fade towards the end of a season – which could demonstrate an accumulation of fatigue that they have not yet adapted to, unlike their older teammates with more training time. Therefore, ensuring that as coaches we are not consistently exceeding MRV’s is crucial to ensuring high on-field performance. However, measuring this cumulative MRV for each athlete is somewhere between extremely resource intensive and impossible. It is possible to measure biomarkers of exposure to exceeding the MRV, but the costs, invasiveness, and time (as well as a lack of consensus in the literature) makes biomonitoring impractical for most collegiate athletics settings (Hecksteden et al., 2016; Palacios et al., 2015; Saw et al., 2016; Wiewelhove et al., 2015). However, there have been non-invasive survey methods created that attempt to have the athletes answer questions to determine fatigue levels, and these surveys were found to be more sensitive to predicting changes in training loads than any biomarkers (Saw et al., 2016). The effective surveys were found to have between 22-76 individual questions, which again creates a major problem for data collection when dealing with 50 individual athletes between 18-22 years old with minimal free time and attention spans (Saw et al., 2016).

### Goals

In order to attempt to accurately, and almost importantly, conveniently, assess athlete fatigue levels, a simple athlete self-assessment was created and implemented over the course of a fall pre-season in an NCAA DI Men’s Lacrosse team from early September through early December. Data was collected on a self-report basis when the players showed up to the facility on days with a scheduled lift (approximately three days per week). Instructions were given that fatigue scores were to be filled in from 1-4 with 1 designated as “terrible” and 4 as “awesome”. Further instructions were provided to disregard injury status, so that a player with a broken leg could conceivably have a 4 for physical fatigue status. An even number of ratings was provided to prevent athletes from defaulting to the middle choice.

Aside from periodic team reminders to fill in the binder (which was left in the locker room), there were no requirements to fill the binder nor punishments for not doing so. This was done with the belief that if forced to fill in the binder, athletes would not put as much effort into doing so, and having fewer more accurate data points would be better than more less accurate data points. Following the collection of data, the goal was to determine if there were fatigue (either mental or physical) trends associated by position, year, and playing time, or if there were changes in fatigue measures following notable events in the course of the pre-season including conditioning tests, scrimmages, long weekends of rest, etc.

### Methods

Following the collection of the hand written self-assessment sheets, data was formatted into tabular form. Data analysis was conducted in R 3.3.1 (R Core Team, 2016) and STATA 14 (StataCorp, 2015). Graphics and tables were produced in Microsoft Excel 2013, R 3.3.1, and STATA.

### Descriptive Statistics

The initial step in the analysis was to determine basic summary statistics and response rates. Table 3 indicates by position the number of players, number of data points, and mean daily survey response rate. Table 4 presents summary fatigue broken out by position and both physical and mental scores. Of note, the sample size for the face-off (fogo) group is on the small side, with the fewest number of players and data points, as well as the lowest response rate. However, I believe that it is important to retain this group as their physical loads in practice are substantively different than those of other groups. Similarly, when considering team statistics, midfield responses will carry the heaviest weight, given that they make up the largest proportion of the team. Data distributions and densities are presented in Figure 1.

### Hypothesis I Analysis

The first study aim was to determine if there was a quantifiable relationship between mental and physical scores. It was my hypothesis that there would be a moderately strong positive linear relationship between the two across all position groups, as school stress and on-field stress can often be a cumulative process. In order to investigate this relationship, simple linear regression was performed on each of the mental-physical score pairs by position group. Table 5 presents the tabular results of the regression and the significance level for the correlation. Figure 2 shows the graphical results of the regression analysis.

Following regression, all positions except goalie displayed minimal to marginal correlation coefficients and were positively related. The regression coefficients for goalies did not reach the level of significance to reject the null of no relation.

### Hypothesis I Discussion

The link between physical and mental fatigue scores was not surprising, as substantial physical stress and fatigue has been long known to add to mental fatigue as well. .

It is somewhat noteworthy that attack and midfield are more similar than compared with LSM’s, fogo’s, and defense, as these two groups practice separately for some portion of each day.

### Hypothesis II Analysis

The second aim of the study was to determine if mean fatigue scores differed by position group, as the physical demands of each position are different. The analysis was performed using Welch’s t-tests as the Bartlett’s test indicated unequal variances. The results are presented below in Table 6 with a Bonferroni correction.

Based on these results, only goalies and fogos had any mean differences in their physical and mental scores.

### Hypothesis II Discussion

The mean difference found in the goalies has a simple explanation. The positional needs of goalies vary substantially from other positions, as they do not need the same level of general baseline cardio or speed endurance. Further, they do not run in practice during drills, so they do not have as many opportunities to develop those conditioning attributes. Therefore, when team wide conditioning is conducted, they are often the most negatively physically impacted, which can manifest in terms of soreness and fatigue. Understanding why the fogos would have lower mean fatigue scores is more difficult, as they generally have similar levels of baseline conditioning as the other field position players (and empirically do in this situation). There is potentially a sample size issue as well, as there are only three fogos on the roster.

### Hypothesis III

The third aim of the study was to determine the influence of temporality with regards to fatigue measurements. This aim is strongly related to the usefulness of the survey as a team measure as opposed to an individual measure. The hypothesis is that on days following major fatigue inducing events, fatigue scores would be lower than on previous days. Comparisons to be tested include: Mondays vs. Wednesdays vs. Fridays (IIIA) and days prior to scrimmages and after (IIIB).

### Hypothesis IIIA Analysis

In order to conduct the analysis by day by position, each score was assigned to the corresponding day the score was recorded. From there, ANOVA’s were conducted for score by day for each   position. All F scores were approximately 1.0, with p-values >0.05, indicating that there were no mean differences across days within each position group.

### Hypothesis IIIA Results

Given the conventional belief that weekends and days off help with recovery and that full weeks of practice, conditioning, and lifting would increase fatigue, this result was surprising. The two possible interpretations of these results are that athletes do not dedicate enough time and energy to recovery, or that the survey itself is entirely flawed.

### Hypothesis IIIB Analysis

Operating under the hypothesis that competition will increase physical fatigue and decrease mental fatigue (increased effort and decreased pressure) compared to days previously, the Wednesday and Friday scores prior to a Sunday scrimmage were compared to the following Monday and Wednesday. Figure 2 shows the results of that comparison, with the acknowledgement that samples sizes were too small for robust statistical analysis. Generally, there is no trend across positions or type of fatigue.

### Hypothesis IIB Discussion

When considering the generally small samples sizes, it is useful to look at the midfield results (2/3rds of all samples) as the theoretically most accurate or representative sample. They show results that are close to no difference, and trending away from the hypothesized value. Either the players were not substantively fatigued by a scrimmage (contrary to practical school of thought), or similar to IIIA, the survey is not useful.

### General Discussion

The goal of this data collection was to determine if the use of a two category, discrete value, numerical survey could help coaches understand mental and physical fatigue resulting from non-quantifiable volume and intensity metrics. The current knowledge base suggests that there are surveys that fit these needs, but they tend to be proprietary and complex. When coaching teams of 30-50 collegiate athletes, dedicating that much time and resources to collecting the data and analyzing the results is not practical for the clear majority of coaching staffs or athletic departments. Similar hurdles are faced with biomonitoring athletes for fatigue indicators. This survey was designed to be intuitive and easy to use for the athletes and coaches. However, with the increase in simplicity comes a potential decrease in efficacy and sensitivity. While there was never an expectation that at on a micro level this survey could be used to analyze single discrete events, the hope was that it would provide value for team wide planning in terms of fatigue management.

During the collection phase, the data was periodically monitored as the plan was to implement real time collection and analysis via Google Drive in the future. While conducting the check-ins individuals who reported more than one day of two or any day of one were talked to prior to lifting to get a sense for why they were fatigued and how adjusting the lift might, or might not, be necessary. From these individual cases, the survey had some use, as single lift volume and intensity was lowered for several athletes on a case by case basis. However, based on the results of the team wide positional analysis, there was essentially no utility to the survey. The only meaningful results were gathered from the levels of fatigues in the goalies in early season conditioning, something that would have been apparent simply from watching the running. Perhaps the survey goals were not explained properly, or the measurement metric was incorrect, or the athletes were afraid of repercussions for recording high levels of fatigue, but the simple survey did not function as a useful tool in terms of understanding team level fatigue burdens. Data collection will occur throughout the remainder of the season and the analysis will be expanded and updated as necessary.

References:

Baechle, T., & Earle, R. (2008). Essentials of Strength Training and Conditioning (Third). National Strength and Conditioning Association.

Hecksteden, A., Skorski, S., Schwindling, S., Hammes, D., Pfeiffer, M., Kellmann, M., … Meyer, T. (2016). Blood-Borne Markers of Fatigue in Competitive Athletes – Results from Simulated Training Camps. PloS One, 11(2), e0148810. https://doi.org/10.1371/journal.pone.0148810

Israetel, M., Hoffman, J., & Wesley Smith, C. (2015). Scientific Principles of Strength Training. Juggernaut Training Systems.

Palacios, G., Pedrero-Chamizo, R., Palacios, N., Maroto-Sánchez, B., Aznar, S., González-Gross, M., & EXERNET Study Group. (2015). Biomarkers of physical activity and exercise. Nutricion Hospitalaria, 31 Suppl 3, 237–44. https://doi.org/10.3305/nh.2015.31.sup3.8771

R Core Team. (2016). R: A Language and Environment for Statistical Computing. Retrieved from https://r-project.org

Saw, A. E., Main, L. C., & Gastin, P. B. (2016). Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. British Journal of Sports Medicine, 50(5), 281–291. https://doi.org/10.1136/bjsports-2015-094758

StataCorp. (2015). Stata Statistial Software: Release 14. College Station, TX.

Wiewelhove, T., Raeder, C., Meyer, T., Kellmann, M., Pfeiffer, M., & Ferrauti, A. (2015). Markers for Routine Assessment of Fatigue and Recovery in Male and Female Team Sport Athletes during High-Intensity Interval Training. PLOS ONE, 10(10), e0139801. https://doi.org/10.1371/journal.pone.0139801