The influence of growth and training loads on injury risk in competitive trampoline gymnasts

ABSTRACT

There is currently limited research exploring the relationship between growth, training load and injury in gymnasts. Twenty-one national level, trampoline gymnasts recorded training load and injury for 8-weeks. Percentage of predicted adult height (%PAH) was calculated using the Khamis–Roche method and used to define growth spurt status. Training load was calculated using the session rate of perceived exertion and analysed as differential loads and as a 7-day exponentially weighted moving average (EWMA7day). There was a significant non-linear association between %PAH and the probability of injury when adjusting for either training load metric (differential load, P = 0.015; EWMA7day; P = 0.008), with the highest injury risk estimated at ~90% PAH (circa growth spurt). The probability of injury significantly increased with increases in EWMA7day training load (RR: 1.88 95% CI: 1.21– 2.91, P = 0.005) but not with differential load. No significant interaction between %PAH, training load and the probability of injury were observed. Data suggest that competitive trampoline gymnasts are at an increased risk of injury during the adolescent growth spurt or with higher weekly training loads. Coaches should be educated and encouraged to identify periods of rapid growth and monitor training load, to reduce the risk of injury.

KEYWORDS:

• Growth spurt
• injury prevention
• peak height velocity
• gymnastics
• youth

Introduction

The adolescent growth spurt has been identified as a risk factor of injury in youth sport (Bergeron et al., 2015; Difiori et al., 2014; Johnson et al., 2019; Mckay et al., 2019). This may partly be explained by the growth plates becoming more fragile (Alexander, 1976; Brenner, 2007; Caine et al., 2006) and the development of bone and muscle occurring asynchronously (Iuliano‐Burns et al., 2001; Malina et al., 2004) during the adolescent growth spurt. In addition, rapid changes in body size, physique, functionality and biomechanics that occur during the adolescent growth spurt may lead to temporary decrements in motor performance during adolescence (i.e., adolescent awkwardness) (Quatman-Yates et al., 2012). In gymnastics, during the adolescent growth spurt, changes such as centre of mass position have previously been linked to a temporary loss or confusion of a skill (Patel et al., 2020). Similarly, these changes may further heighten the risk of injury (Difiori et al., 2014; Quatman-Yates et al., 2012; Wild et al., 2016). However, further research regarding adolescent awkwardness and subsequently its link to injury risk is warranted (Quatman-Yates et al., 2012). Although there is emerging evidence to suggest that the adolescent growth spurt serves as a risk factor for injury in young athletes (Bergeron et al., 2015; Difiori et al., 2014; Johnson et al., 2019; Mckay et al., 2019) our understanding of the processes, factors and mechanisms that underlie this association is limited.

Training load has also been identified as a risk factor for injury in youth sport (Bowen et al., 2017; Dennis et al., 2005; Malisoux et al., 2013; Møller et al., 2017; Murray, 2017; Watson et al., 2017). Recent studies have found the risk of injury increases when there is a “spike” or an accumulation of high training loads (Bowen et al., 2017; Møller et al., 2017; O’keeffe et al., 2020; Watson et al., 2017). Currently, the training loads undertaken by youth trampoline gymnasts is unknown, and research regarding monitoring trampoline training load is limited (Gao et al., 2009; Van Der et al., 2007). Subsequently, the relationship between training loads and injury remains unclear in this setting.

Although there are multiple studies investigating the relationship between training load and injury in youth athletes, there is only limited cross-sectional research specifically exploring the interaction between growth, training load and injury (Jayanthi et al., 2015). The impact of training load on injury may be amplified during developmental stages when the athlete is physically and athletically less able to cope with the demands of higher training intensities or volumes (Difiori et al., 2014). A better understanding of the interaction between training load and growth, particularly rapid growth, is therefore warranted and may ultimately contribute to reducing the risk of injury in youth athletes. This may be of particular interest in trampoline gymnasts, where injuries have been reported to be common amongst young, competitive trampoline gymnasts (Grapton et al., 2013). Additionally, injuries may impact on long-term development, future participation in trampolining or other sports and could consequently lead to long-term health problems (Webborn, 2012).

The aim of the study was to understand the interactions between growth and training load on injury risk in competitive, trampoline gymnasts. It was hypothesised that gymnasts in the adolescent growth spurt would report more injuries than those pre- or post-growth spurt. It was also hypothesised that gymnasts experiencing spikes in training load or accumulation of high loads would report pain or injuries more frequently. A secondary aim of the study was to describe the training load of competitive, trampoline gymnasts towards a major competition. It is anticipated that this study will contribute to understanding how the risk of injury can be reduced in competitive, youth gymnasts.

Methods

Study design

A prospective cohort study design was used to understand the interaction between growth, training load and injury in competitive trampoline gymnasts. Training load and injury data were captured through custom-made training diaries during the lead up to a national competition and anthropometrics were collected at national training camps to calculate growth spurt status.

Participants

Forty-two (16 female) gymnasts (aged 9−19 years) from the Trampoline (TRA) programmes at British Gymnastics were invited to take part in the study. As part of the inclusion criteria, gymnasts were on a Great Britain (Foundation, Development or Junior, n = 38) or England pathway programme (n = 4). Additionally, gymnasts were required to be healthy at the start of data collection and aiming to compete at a national competition (British Championships and or English Championships).

Recruitment

Gymnasts were invited to take part via email invitations to their parents from programme-specific administrators. Participation was at the gymnast and parent’s discretion. The parents of gymnasts under the age of 16 were provided with a participant information sheet and completed a written consent form on behalf of their child to participate in the study. Gymnasts under 16 were also required to complete a written assent form. Gymnasts over 16 were provided with a participant information sheet and completed a written consent form to participate in the study. The study was approved by the University of Bath Research Ethics Approval Committee for Health.

Training load and injury

Each gymnast was given a custom-made, discipline-specific training diary (Appendix 1) and was requested to complete the diary during the training cycle leading up to competition. Capturing training load leading up to a national competition was chosen to control the type of gymnastic training completed by gymnasts i.e., more routine training rather than learning new skills. Training data was collected for a period of 8 weeks up until, but not including the final competition (or podium training). This time frame was selected by the National Coaches as it is believed this is the minimum time required to prepare for a competition. Data collection took place between July 2019 – September 2019.

Training diaries contained templates for gymnasts to complete. Gymnasts were requested to fill in information regarding trampoline specific and non-trampoline training following each training session. Trampoline and non-trampoline training were separated into two different tables. The number of contacts on the trampoline (the number of skills performed) and the total difficulty (total DD) of skills were also recorded for each trampoline session as requested by National Coaches. Non-trampoline training included training in other sports, physical preparation/ strength and conditioning, and off-trampoline warm-ups. Both trampoline-specific and non-trampoline tables included duration (min), rating of perceived exertion (RPE [1–10]), training load measurements, and whether training was modified from programmed training. Gymnasts were also encouraged to record and provide brief details (e.g., location) of any pain or injury from each training session. Therefore, an “all-complaints” definition of injury was used in this study (Bahr et al., 2020).

Percentage of predicted adult height & growth spurt status

During the data collection period, height (Stadiometer, Seca 213, Germany) and body weight (Electronic column scales, Seca 767, Germany; last calibrated 19/02/2016) were measured a minimum of once by the principal researcher when gymnasts attended a national training camp. A standard method of measuring height to the nearest millimetre was used. The anthropometric data was then used alongside sex, age and mid-parent height to calculate current predicted adult height (PAH) using the Khamis–Roche method (Khamis & Roche, 1994) for each gymnast. Parent height was self-reported and adjusted for over-estimation (Epstein et al., 1995). The percentage of PAH (%PAH) was then calculated by dividing the gymnast’s PAH by the gymnast’s current height. This method has been validated in American and British youth athletes (Cumming et al., 2014; Malina et al., 2007; Myburgh et al., 2019). Predicted adult height for any gymnast over the age of 18 was calculated from their last height and weight measurement taken whilst under the age of 18. Growth spurt status was defined using %PAH (Pre: <85%, Circa: 85–96%, Post: 96%<) (Parr et al., 2020; Sanders et al., 2017). The median error bound for this method is 2.2 ± 0.6 cm in males and 1.7 ± 0.6 in females between 4.0 and 17.5 years of age (Khamis & Roche, 1994).

Statistical analysis

Descriptive statistics were calculated for participants and training diary variables. Session-RPE (sRPE) [duration x RPE] was calculated as a load variable for all trampoline and non-trampoline training. Session-RPE is a reliable and valid method of collecting training load (Foster et al., 2021; Haddad et al., 2017). Any missing duration data from competition or programme camps were added to the dataset, as these durations were known. Missing RPE data (6% of 1240 training sessions), was estimated from RPE of similar and recent training sessions. The number of contacts and total difficulty variables were not analysed in this study as these variables could not be combined with non-trampoline training to calculate total training load. Training load throughout the 8 weeks was analysed as differential load [smoothed rate of week-to-week changes in load] (Lazarus et al., 2017; Tysoe et al., 2020) and a 7-day exponentially weighted moving average (EWMA_7day)(Williams et al., 2017). No latent period was included, as the derived measures were updated and analysed daily. Prevalence of injury amongst gymnasts was calculated for each week by dividing the number of gymnasts reporting an injury by the total number of gymnasts completing a training diary (Clarsen et al., 2014). The same method was used to calculate the prevalence of injury that resulted in modified training.

Descriptive statistics and statistical tests were performed using R Studio (version 3.3.6, The R Foundation for Statistical Computing Platform, Vienna, Austria), using the ‘lme4ʹ package for modelling (Bates et al., 2014). Generalised linear mixed-effects models were used to model the associations between growth, training load and reported injury, using a binomial distribution and complementary log–log link function. Predictor variables (growth and training load variables [differential load and EWMA_7day]) were modelled as numeric-fixed effects. Multivariate models were used, such that the associations between each training load metric and injury risk were controlled for %PAH (and vice versa). Non-linear relationships were explored by including polynomial terms in the models and were retained when the squared term was significant. Gymnast ID was modelled as a random effect. The interaction effect of sex was evaluated by using an initial term in the model. An interaction effect between each training load measure and %PAH was also assessed in separate models. Linear predictor variables were evaluated as the change in risk associated with a 2SD increase in the predictor variable (Hopkins et al., 2009). Statistical significance was defined as P < 0.05.

Results

Participant characteristics

A total of 21 (8 female) trampoline gymnasts (Table 1), completed the training dairies over an 8-week build up to a national competition (British Championships = 20; English Championships = 1). Sixteen (7 female) of the 21 gymnasts competed at both English and British Championships. Of the 42 invited trampoline gymnasts, 21 (eight female) gymnasts did not complete the study; 1 gymnast declined participation, 1 gymnast withdrew with an injury, 2 gymnasts did not return their diaries, and 17 gymnasts did not complete their training diaries over the 8-week period.

Table 1. Participant characteristics

Participants		Age (yrs.)	Height (cm)*	Body mass (kg)*	% of adult height*	Growth spurt status
Male (n = 13)	10–19; 13.4 ± 2.5		141.4–183.9; 157.7 ± 13.7	32.70–76.7; 47.81 ± 15.11	80.0–101.6; † 89.4 ± 7.0	Pre = 6, Circa = 4, Post = 3
Female (n = 8)	10–17; 13.1 ± 2.0		147.9–163.2; 158.1 ± 5.13	36.75–60.15; 50.13 ± 8.77	87.7–99.5; 95.8 ± 3.9	Pre = 0, Circa = 3, Post = 5

Min-Max; Mean ± SD (Age, height, weight & percentage of adult height from data collected closest to competition). *Estimated for 3 (1 male, 2 female) gymnasts from data points taken before and after data collection period. †Percentage of adult height calculated for gymnast aged 19 from predicted height calculated at aged 17.9 years.

Descriptive characteristics of training load & injury

During the 8-week build-up to competition, the weekly average (±SD) training load (combined sRPE of trampoline and non-trampoline training) and trampoline only training load was 3310 AU (± 1347 AU) and 2220 AU (± 1216 AU), respectively. The daily average (±SD) differential load and EWMA_7day over the 8-weeks was 30 AU (812 ± AU) and 335 AU (193 ± AU), respectively. Total weekly training load and the observed pattern of training load over the 8-weeks leading up to a competition varied between gymnasts (Figure 1). Injury was reported on 127 of 1240 (10%) recorded training sessions (trampoline and non-trampoline). Of these, 28 (22%) training sessions were modified as a result of injury (2% of all recorded training sessions). The average weekly prevalence of reported injury (Table 2). The majority of injury (weekly prevalence) was described to occur in the lower limbs (68.4%) [foot/ankle (29.8%), shin (19.3%), knee (12.3%), hip (7.0%), leg (3.5%), Achilles tendon (1.8%), calf (1.8%)], followed by the back (21.1%). There were no observable differences in injury location between male and female gymnasts.

Table 2. Average weekly prevalence of an ‘all-complaints‘ injury

	All	Pre	Circa	Post
All complaints	30% (27 to 34%)	25% (2 to 48%)	38% (24 to 52%)	28% (17 to 39%)
All complaints that resulted in modification of training	10% (8 to 12%)	10% (−3 to 23%)	13% (4 to 21%)	6% (1 to 12%)

Average Weekly Prevalence (95 CI). Pre, Circa and Post growth-spurt status.

Figure 1. Training load (sRPE) of individual trampoline gymnasts over an 8-week period. Red line represents 7-day exponentially weighted moving average

Interactions of growth, training load and injury

There was a significant non-linear association between %PAH and the probability of injury when adjusting for either training load metric (controlling for differential load, P = 0.015 [Figure 2a), controlling for EWMA_7day; P = 0.008 (Figure 2(b)), with the highest injury risk estimated at 90.2% and 89.9% PAH, respectively (circa growth spurt). The probability of injury was not significantly associated with differential loads (P = 0.856) (Figure 2(c)). However, the probability of injury significantly increased with increases in EWMA_7day training load (RR: 1.88 95% CI: 1.21–2.91, P = 0.005) (Figure 2(d)). There was no significant interaction between %PAH, training load and the probability of injury (differential load; P = 0.620 (Figure 2(e)), EWMA_7day; P = 0.088 (Figure 2(f)). There was no main effect of sex (P = 0.800).

Figure 2. Interactions of growth (%PAH), training load and probability of injury. Shaded areas represent 95% CI. * Significant interactions P < 0.05. 80% (Red), 90% (Blue) & 100% (Green) PAH for graphs e & f

Discussion

The aim of the study was to understand the main and interactive effects between growth and training load on injury risk in competitive, trampoline gymnasts. Previous research monitoring training load in trampoline gymnasts is limited (Gao et al., 2009; Van Der et al., 2007). This is the first study to monitor training load using sRPE in trampoline gymnasts. Session-RPE is a simple and non-invasive tool that does not rely on any equipment to monitor internal training load. It also has been proven to be reliable, valid and ecologically useful in multiple sports (Haddad et al., 2017), and has been used in other gymnastics disciplines (Debien et al., 2020; Dumortier et al., 2018; Minganti et al., 2010). The total weekly training load and pattern of training load over the 8-week build up varied amongst individual gymnasts. These variations are due to a combination of individual training programmes, individual perceptions of exertion to a given training session (Haddad et al., 2017) and other additional influences (illness/injury, holiday, training camps, etc). Interestingly, in some gymnasts, the observed pattern of training load leading up to competition did not show a traditional taper phase towards competition (Le Meur et al., 2012). Typically, training load is reduced towards a major competition to elicit optimal sports performance (Le Meur et al., 2012), whereas the observed pattern of training load in some gymnasts showed an increase in training load towards competition. This may partly be explained by a discrepancy in coaches and athlete’s perception of training load, where coaches have been reported to underestimate training load in other youth sports (Brink et al., 2014; Murphy et al., 2014).

This is also the first study to report the prevalence of injury in trampoline gymnasts. The average weekly prevalence of reported injury was lower than previously reported in competitive, rhythmic gymnasts (Gram et al., 2020). When gymnasts were grouped into growth spurt status (pre, circa & post growth spurt), an injury was more prevalent in gymnasts circa-growth spurt compared to those pre- and post-growth spurt. This aligns to research in other youth athletes, where the risk of injury is reported to be greater in those experiencing rapid growth (Bergeron et al., 2015; Difiori et al., 2014; Johnson et al., 2019; Van der Sluis et al., 2015). In line with previous research in trampoline gymnastics (Grapton et al., 2013), the location of injuries reported were predominantly in the lower limbs and back. Injuries in these locations corresponds to the body parts predominantly used in trampoline gymnastics and should therefore be a priority for injury prevention efforts in this setting.

The interaction between growth (%PAH) and probability of injury suggests that there is a peak in injury risk around 90% PAH. The peak in injury risk parallels the peak in height velocity during the adolescent growth spurt, where peak height velocity (PHV) occurs between 85% and 96% of PAH (average at 90–91% of PAH) (Parr et al., 2020; Sanders et al., 2017). The window used to identify the adolescent growth spurt has been found to correctly predict PHV for 96% of academy football players (Parr et al., 2020). Findings in this study are similar to those of Johnson et al. (2019), who found that in a sample of academy football players, those between 88% and 95% of PAH were at a greater risk of injury. Both findings in this study and those by Johnson et al. (2019) suggest that youth athletes are at an increased risk of injury during the adolescent growth spurt, identified by 85–96% of PAH. However, Monasterio et al. (2020) recently found more injuries to occur post-PHV (>96% of PAH) in Spanish academy football players. It should however be noted that injuries in this study were only considered if it caused more than 7 days of absence (categorised as moderate to severe) (Monasterio et al., 2020), and therefore may account for the discrepancies in injuries during different periods of growth. Consequently, although more research is warranted in this area, calculating a youth athlete’s %PAH may be a useful tool for identifying those who are at an increased risk of all types of injury as a result of the adolescent growth spurt. During this period (85–96% PAH), response to prescribed training should be carefully monitored and managed, to reduce the risk of injury.

Two training load metrics (week-to-week changes and 7-day exponentially weighted moving average) were used to analyse the training load during the 8-week build up to competition. The lack of interaction between week-to-week changes in training load and the probability of injury suggests that any week-to-week changes in training load, i.e. spikes or troughs in training load, did not increase the risk of injury. This differs from other youth sports such as handball, Gaelic football and football, where spikes in acute training load were linked to an increased risk of injury (Bowen et al., 2017; Malisoux et al., 2013; Møller et al., 2017; O’keeffe et al., 2020; Watson et al., 2017). In comparison to this study, the majority of previous studies used an acute-chronic workload ratio (ACWR) to identify spikes in training load associated with the risk of injury. Many methodological issues have, however, been raised regarding ACWR and its association with injury risk (Impellizzeri et al., 2020; Wang et al., 2020). Alternatively, measuring week-to-week changes in training load appears to be a useful tool for capturing spikes in acute loads, whilst eliminating some of the methodological issues associated with using the ACWR. O’keeffe et al. (2020) recently found that greater absolute week-to-week changes in training load were linked to injury risk in youth Gaelic football players. However, it should be highlighted that absolute week-to-week changes in training load do not account for the decaying effects of load over time. Week-to-week changes in training load are better represented when the rate of change in load from one week to another is smoothed using an exponentially weighted moving average (Lazarus et al., 2017). Subsequently, the difference in findings regarding spikes in training load and injury risk in youth athletes may partly be explained by the differences in methodologies employed.

Conversely, the interaction between EWMA_7day and probability of injury suggest that the risk of injury increases with higher weekly training loads. An association between high weekly loads and increased risk of injury has also been found in youth football (Bowen et al., 2017; Watson et al., 2017) and Gaelic football (O’keeffe et al., 2020). High weekly loads have also been linked to an increased risk of injury in an adult population of professional rugby union players (Cross et al., 2016). It is likely that the risk of injury increases with higher weekly loads as athletes are less likely to have the required fitness to cope with the demands of training. Consequently, weekly training loads should be monitored to reduce the risk of injury in youth athletes.

This is the first study to look at the interaction between growth, training load and injury risk. No significant interaction was found between growth (%PAH), training load (week-to-week changes or EWMA_7day) and the probability of injury. However, the observed relationship between growth, high weekly training loads (EWMA_7day) and the risk of injury (P = 0.089) suggests an interaction may exist but could be limited by the number of gymnasts in this study. The relationship suggests that pre-pubescent gymnasts, about to enter the growth spurt period are at greater risk of injury when exposed to higher weekly loads. However, further research with a larger cohort of gymnasts is required to understand whether there are any associations between growth, training load and risk of injury in young gymnasts.

The data in this study suggests that the risk of injury increases during the adolescent growth spurt and with higher weekly training loads in competitive, trampoline gymnasts. Sadly, due to the COVID-19 pandemic, data collection was unable to be completed in Men’s artistic and Women’s artistic gymnasts, and therefore the number of gymnasts in this study were low and only representative of trampoline gymnasts. Although the number of gymnasts in the study are low, the number of injury events match those required to make moderate to strong correlations (Bahr & Holme, 2003). The data is limited in that there were no female gymnasts grouped as pre-growth spurt and therefore should be considered whilst interpreting the relationship between growth and injury risk. It is, however, likely the findings pertaining to growth and injury could be extended to other gymnastics disciplines.

Limitations also exist regarding data collection. As the diary was self-reported by gymnasts (with coach assistance), it is unknown whether any training days were not reported, whether gymnasts took part in other sports or physical activities outside of gymnastics including physical education at school and at what time point the diaries were completed in respect to training. In terms of RPE, gymnasts were not provided with any RPE verbal anchors, but were told to “Rate out of 10 how hard you felt the session was (10 being the hardest session)”. Additionally, gymnasts were not advised to wait the recommended 10–30 minutes before selecting an RPE rating for a training session (Foster et al., 2001; Uchida et al., 2014) as a means of reducing missing data. As a result, there may be some bias to activities taking place at the latter end of a training session if gymnasts completed their dairies immediately after a training session ended.

In respect to injury, the definition of injury employed in this study encompassed all self-reported complaints (pain and or injury). As a result, the individual perceptions/reporting of pain or injury, and whether or not the reported pain is linked to an injury is a limitation of this study. Similarly, it is unknown whether the perception of pain is altered in youth athletes during puberty. Although a “time-loss” injury definition will definitively capture injuries, it fails to incorporate those injuries that have not resulted in time-loss, i.e. injuries that have resulted in modified training (Clarsen & Bahr, 2014) and or influences performance (Bolling et al., 2019). An injury definition incorporating all complaints was therefore chosen for this study. The method of injury surveillance in this study also means that the severity of an injury is limited to whether training was modified or not and therefore it is difficult to understand the full severity of an injury.

Outcomes from this study highlight the need for coaches to monitor training load and identify periods of rapid growth to reduce the risk of injury in gymnasts. It is recommended that coaches are educated and encouraged to identify periods of rapid growth in gymnasts, particularly by identifying growth status using %PAH and flagging gymnasts between 85% and 96% of PAH as circa-growth spurt. In addition, coaches should be open to modifying gymnastics training where necessary throughout the adolescent growth spurt to minimise the risk of injury during this time. Similarly, coaches and gymnasts should be educated and encouraged to monitor training load, including internal loads (sRPE) and responses (e.g., soreness) to training to reduce the risk of injury. Reducing the risk of injuries in young gymnasts is important as injuries may impact on long-term development, future participation in the sport and could consequently lead to long-term health problems, both physical and psychological.

Building from this study, further research should aim to look at the interactions between growth, training load and injury in different gymnastics disciplines, in larger cohorts and over a longer period of time. Further research could also look to explore pain in youth athletes during the adolescent growth spurt and its association with injury risk. From an applied standpoint, further research should be based on practical solutions to reduce the risk of injury in gymnasts during the adolescent growth spurt. A possible avenue may be based on the specificities of modifying training. This may include, but not limited to, exploring the role of physical preparation/strength and conditioning for injury prevention, recovery, and managing repetitions of the same movement patterns (Difiori et al., 2014). Future research should also aim to further understand the relationship between training load and the risk of injury in gymnastics to help support coaches with planning and adapting training.

In conclusion, the risk of injury increases with the adolescent growth spurt and with higher weekly loads in trampoline gymnasts. However, in this study the risk of injury did not increase with spikes in training load or specifically in pre, circa or post-growth spurt gymnasts with higher weekly loads. Coaches and gymnasts should be educated and encouraged to monitor growth and training load to reduce the risk of injuries in gymnasts.

Acknowledgments

The authors would like to thank the gymnasts and coaches for their time in taking part in the study. The authors would like to also thank Tracy Whittaker–Smith and Gary Short for their logistical support throughout.

Disclosure statement

The following authors, Tejal Sarika Patel, Alex McGregor and Karen Williams are employees of British Gymnastics.

Additional information

Funding

This work was supported by British Gymnastics and University of Bath

Appendix 1

Trampoline Week 1.

Table

Date	Duration (min)	No. Contacts	Total DD	RPE (1-10)	Any pain or injury (Location and Type)	Modified training (Y/N)	Reason for modification	Coaching Comments
Total

Total DD = Total difficulty

Non- trampoline Week 1.

Table

Date	Duration (min)	Non-TRA session type	RPE (1-10)	Any pain or injury (Location and Type)	Modified training (Y/N)	Reason for modification
Total