Context

Rhythmic gymnastics requires a high level of complexity and perfection of technical gestures, associated with well-developed physical and artistic capacities. The training-load and recovery profiles of rhythmic gymnasts across a season are unknown.

Objective

To analyze the training load and recovery of professional rhythmic gymnasts during 1 season.

Design

Cohort study.

Setting

Brazilian National Training Center of Rhythmic Gymnastics and competition facilities.

Patients or Other Participants

Eight gymnasts from the Brazilian national senior rhythmic gymnastics group.

Main Outcome Measure(s)

Session rating of perceived exertion (session-RPE) and total quality recovery (TQR) scores were collected daily for 43 weeks. We obtained the session-RPE after each session and TQR score before the first session of the day. Performances during 5 competitions were also recorded. The season was divided into 8 periods. Total weekly internal training load (wITL), training intensity, frequency, duration, recovery, and acute : chronic workload ratio were calculated for analysis.

Results

The season mean wITL was 10 381 ± 4894 arbitrary units, mean session-RPE score was 5.0 ± 1.6, and mean TQR score was 12.8 ± 1.3. The gymnasts trained an average of 8.7 ± 2.9 sessions per week, with a mean duration of 219 ± 36 minutes. Each competitive period showed increased wITL compared with the previous period. Training-load variables (wITL and session-RPE) and recovery were inversely correlated. Gymnasts were poorly recovered (TQR < 13) during 50.9% of the season (n = 167 times), especially during competitive weeks. Spikes in load (acute : chronic workload ratio ≥ 1.5) occurred across 18.1% of the season (n = 55 times).

Conclusions

The training-load variables and recovery changed throughout a professional rhythmic gymnastics group season, mainly during competitive periods. The correct distribution of training load is critical to ensure that gymnasts are entering competitions in a recovered state.

Key Points
  • Most high weekly training loads, high-intensity training, and spikes in load occurred during competitive periods.

  • Training-load variables increased during competitive periods.

  • During half of the season, the gymnasts were not adequately recovered, especially during competition weeks.

  • The periods of underrecovery were more frequent when associated with high-intensity training and an acute : chronic workload ratio ≥1.5, reinforcing the negative association between total quality recovery and internal training-load variables.

  • Despite the negative relationship, high training loads alone did not cause underrecovery.

The challenge of sport training is to promote appropriate stimuli for each athlete to achieve specific adaptations and the best performance at the right moments.1  Therefore, a balance between the stressor stimuli (load) and recovery is necessary to promote positive psychophysiological changes in athletes.13  However, the relationship among load, recovery, and performance is complex, with a fine line between the achievement of training goals and the occurrence of maladaptation.35  A better understanding of the relationship between the training load and consequent athlete response is possible via an individual, accurate, and longitudinal monitoring process during different periods of the season.2 

To improve this understanding, several methods of quantifying internal and external training loads have been described.2  Among these, the session rating of perceived exertion (session-RPE)6  stands out as a method of monitoring internal training load (ITL) because it involves a simple, noninvasive, and low-cost application tool. Moreover, it has been a reliable and ecologically valid method of monitoring ITL in athletes from multiple sports.7 

Holistic monitoring of training requires an integrated analysis of several variables (eg, physiological, psychological, sociological, and mechanical) measured with different tools (eg, objective and subjective) to transform data into real-time action on the field.2,3  Several instruments have been used to measure and monitor athletes' perceptions of recovery and wellbeing.3,5,8  Subjective tools have greater sensitivity and responsivity to variations in external training load than do other, more objective tools.3,8  Given its simplicity and practicality, the total quality recovery (TQR) scale9  offers a viable method of monitoring athlete recovery.1013 

Rhythmic gymnastics requires a high level of complexity and perfection of technical gestures (with the body and manual apparatus), associated with well-developed physical and artistic capacities.14  Gymnasts are subjected to high training loads from a very young age,1416  which can result in overuse injuries and maladaptation as a consequence of such training.1719  Elite gymnasts perceived that these high or inadequate training loads were the main causes of their injuries20  and impaired sleep and performance.21  Furthermore, researchers have recently identified relationships between injury risk and training-load distribution4,22,23  and perceived recovery,24  which reaffirm the importance of understanding the behavior and relationships of these variables in high-level sports, such as rhythmic gymnastics. Despite the many training-load studies that have been conducted, few investigators16,25  have discussed the distribution of training load and responses to it in rhythmic gymnasts.

Research involving longitudinal monitoring of training in rhythmic gymnasts is lacking, reinforcing the need to better understand the training dose-response relationship in these athletes. The training-load and recovery profiles of rhythmic gymnasts across an entire season are unknown. Therefore, the purpose of our study was to analyze the training load and recovery of professional rhythmic gymnasts during 1 season.

METHODS

Participants

Eight gymnasts (age = 20.5 ± 2.5 years, height = 165 ± 4 cm, mass = 53.38 ± 3.93 kg, experience in rhythmic gymnastics = 14.3 ± 2.4 years) from the Brazilian senior rhythmic gymnastics group participated. In the last 2 decades, Brazil has developed a tradition of accomplishment in rhythmic gymnastics group exercises (5-time Pan American champions and 2-time Olympic finalists between 1999 and 2015) such that the best Brazilian gymnasts are invited to join the national group each season. The present sample of gymnasts were the Pan American champions and ranked 16th in the 2015 World Championship. They were in good health at the beginning of the study, although some had minor lower limb overuse injuries (eg, tendinopathy, fasciitis). They were familiarized with the monitoring tools. All participants provided written informed consent, and the study was approved by the Ethics Committee in Research with Humans of the Federal University of Juiz de Fora (CAAE 41423314.7.0000.5147).

Design

The group was monitored across 363 training sessions and 16 competition sessions during a 43-week period between February and December 2015. The training program was planned exclusively by the technical staff (C.F.) without interference from the researchers. Training sessions started with a light warm-up, followed by classical ballet, conditioning (strength and flexibility), and technical training (repetitions of isolated movements, parts, and the whole routine). The technical staff divided the season into 8 periods based on the model proposed by Laffranchi26 : basic preparatory, specific preparatory, precompetitive, competitive 1, varied, competitive 2, competitive 3, and transitional (Table 1). During the monitored season, the group participated in 5 international competitions: Grand Prix Berlin Masters, Pan American Games (first main competition), World Cup, World Championship (second main competition), and Meeting Brazil.

Table 1

Description of Season Periods of Professional Rhythmic Gymnasts

Description of Season Periods of Professional Rhythmic Gymnasts
Description of Season Periods of Professional Rhythmic Gymnasts

Training Load

Duration and frequency of the training and competition sessions were captured. The ITL was determined by the session-RPE method.6  The session ITL was calculated as the product of the duration of the training session (in minutes) and session-RPE score and reported in arbitrary units (AUs). The ITL was described using the total weekly ITL (wITL), which was the sum of all session ITLs during that week. The wITL was classified according to the range of mean values observed throughout the 43 weeks: high (≥75%), moderate-high (≥50% to <75%), moderate-low (≥25% to <50%), or low (<25%).12,27  The session-RPE score of each session (training intensity) was classified as high (≥7), moderate (>4 to <7), or low (≤4).28,29  From the wITL values, we computed the acute : chronic workload ratio (ACWR). This ratio describes the acute (1-week) workload in relation to the chronic (4-week rolling average) workload.22,23  The ACWR was calculated using coupled acute and chronic workload data.30  A spike, or rapid increase, in training load was defined as an ACWR ≥1.5. On days off, the training load was considered zero, and this value was included in the general analysis.

Recovery

The TQR scale9  was used to monitor recovery. Before the first session of each day, athletes answered the question, “How do you feel about your recovery?” by pointing to a value on the scale from 6 to 20. Daily TQR values from a given week were used to calculate the weekly average TQR score for each athlete. The TQR score was not assessed on days off. A score of ≥13 (reasonable recovery) indicated a minimally adequate recovery state.9 

Performance

Performance was assessed via competition scores25  and rankings21  obtained over the season. The gymnasts presented 2 routines in each competition (mix: 6 clubs and 2 hoops; simple: 5 ribbons). The judges evaluated each routine independently, and the maximal possible score was 20 points.

Statistical Analysis

The weekly descriptive analysis of training-load variables and recovery was reported throughout the season. To test differences among the wITL, session-RPE, training duration, recovery, and ACWR of the season periods, we used generalized estimating equations with a γ distribution. When differences were present, we compared the means of each period (except for the last) with the mean of the subsequent periods using the post hoc Bonferroni test. Effect sizes were calculated using Cohen d, adopting the following classification for data interpretation: trivial (<0.2), small (0.2–0.6), moderate (0.6–1.2), large (1.2–2.0), or very large (2.0–4.0).31  Pearson product moment correlation coefficients and corresponding 90% confidence intervals (CIs) were used to analyze the relationships between the ITL variables and TQR score over the season. The magnitude of correlations was determined using the modified scale of Hopkins31 : trivial (r < 0.1), small (r = 0.1–0.3), moderate (r = 0.3–0.5), large (r = 0.5–0.7), very large (r = 0.7–0.9), nearly perfect (r > 0.9), or perfect (r = 1). We also described the proportions of classifications of weekly wITL, training intensity, recovery state, and spikes in load completed by each gymnast during the season. Data were analyzed using SPSS (version 24; IBM Corp, Armonk, NY). The α level was set at .05.

RESULTS

The distributions of wITL, frequency and intensity (session-RPE) of sessions, recovery, and ACWR over the season are shown in Figure 1. The wITL mean was 10 381 ± 4894 AU, and the highest value was 21 012 ± 2122 AU (week 38). The mean weekly session-RPE score was 5.0 ± 1.6, and the highest was 8.1 ± 0.4 (week 38). The average number of sessions per week was 8.7 ± 2.9. Mean session duration was 219 ± 36 minutes, and mean total weekly duration was 1878 ± 671 minutes. The mean TQR score was 12.8 ± 1.3, the lowest was 9.9 ± 2.9 (week 40), and the highest was 15.3 ± 2.8 (week 41). The mean ACWR across the season was 1.09 ± 0.52, reaching 2.69 ± 0.25 in week 34.

Figure 1

Distribution of A, weekly internal training load; B, number and intensity of sessions per week; C, total quality recovery score; and D, acute : chronic workload ratio throughout a season in a professional rhythmic gymnastics group.

Figure 1

Distribution of A, weekly internal training load; B, number and intensity of sessions per week; C, total quality recovery score; and D, acute : chronic workload ratio throughout a season in a professional rhythmic gymnastics group.

Training load and recovery variables during each period of the season are presented in Table 2. Sequential comparison showed mainly variations of training-load variables across the second half of the season, especially in competitive 2. For recovery scores, we observed a small reduction in the precompetitive period and a moderate increase in the transitional period. The ACWR displayed very large increases in competitive 2 and 3 and a moderate reduction in the transitional period. Performance during the 5 competitions by the judges' scores (total score of each routine in qualification and final), all-around (sum of scores of qualification) ranking position, and number of national group participants is provided in Table 3.

Table 2

Weekly Internal Training Load, Session Rating of Perceived Exertion, Total Weekly Training Duration, Recovery Score, and Acute : chronic Workload Ratio of Each Period of the Season

Weekly Internal Training Load, Session Rating of Perceived Exertion, Total Weekly Training Duration, Recovery Score, and Acute : chronic Workload Ratio of Each Period of the Season
Weekly Internal Training Load, Session Rating of Perceived Exertion, Total Weekly Training Duration, Recovery Score, and Acute : chronic Workload Ratio of Each Period of the Season
Table 3

Scores and All-Around Rankings From the 5 Competitions During the Season

Scores and All-Around Rankings From the 5 Competitions During the Season
Scores and All-Around Rankings From the 5 Competitions During the Season

We noted correlations, albeit they were small to moderate, between TQR score and wITL (N = 328; r = −0.17; 90% CI = −0.26, −0.08; P = .002) and session-RPE (N = 328; r = −0.32; 90% CI = −0.40, −0.23; P < .001). No correlation existed between TQR score and duration (N = 328; r = 0.01; 90% CI = −0.08, 0.10; P = .90) and ACWR (N = 304; r = 0.02; 90% CI = −0.08, 0.11; P = .80).

Across the season, 12.8% (n = 44) of individual wITL magnitudes were classified as high, 30.2% (n = 104) as moderate-high, 43% (n = 148) as moderate-low, and 14% (n = 48) as low. Of the session-RPE classifications, 9.0% (n = 31) were high, 64.8% (n = 223) were moderate, and 26.2% (n = 90) were low intensity. The TQR score was <13 (underrecovery) in 50.9% (n = 167) of individual weekly occurrences. Across the 5 competitions (weeks 15, 22, 26, 30, and 40), the proportions of underrecovered gymnasts were 75% (n = 6), 50% (n = 4), 100% (n = 8), 75% (n = 6), and 87.5% (n = 7), respectively. Considering only the training intensity and recovery state, across 70.3% (n = 52/74) of low-intensity weeks, the gymnasts were recovered. In contrast, across 74.2% (n = 23/31) of high-intensity weeks, the athletes were in an underrecovered state. Individual spikes in load were observed 55 times (18.1%). Moreover, 80% of high wITL, 74% of high-intensity training, 41% of low-intensity training, and 67% of spikes occurred during competitive periods. The distribution and proportions of 304 individual measures of training load, spikes in load, and recovery state are illustrated in Figure 2. The proportion of gymnasts who had either a moderate-high or high training load, underrecovery state, or spikes in load during each week of the season is given in Figure 3.

Figure 2

Number and proportion of gymnasts in >1 category of training load, underrecovery (total quality recovery score < 13) or recovery (total quality recovery score ≥ 13) state, or spikes (acute : chronic workload ratio [ACWR] ≥ 1.5) in load during each week of the season (N = 304). Low training load indicates a weekly internal training load <25%; moderate-low, 25% ≤ weekly internal training load <50%; moderate-high, 50% ≤ weekly internal training load <75%; and high, weekly internal training load ≥75%.

Figure 2

Number and proportion of gymnasts in >1 category of training load, underrecovery (total quality recovery score < 13) or recovery (total quality recovery score ≥ 13) state, or spikes (acute : chronic workload ratio [ACWR] ≥ 1.5) in load during each week of the season (N = 304). Low training load indicates a weekly internal training load <25%; moderate-low, 25% ≤ weekly internal training load <50%; moderate-high, 50% ≤ weekly internal training load <75%; and high, weekly internal training load ≥75%.

Figure 3

Proportion of gymnasts demonstrating underrecovery state (total quality recovery score < 13), moderate-high (50% ≤ weekly internal training load <75%) or high (weekly internal training load ≥75%) training load or spikes (acute : chronic workload ratio ≥1.5) in load during each week of the season (N = 304).

Figure 3

Proportion of gymnasts demonstrating underrecovery state (total quality recovery score < 13), moderate-high (50% ≤ weekly internal training load <75%) or high (weekly internal training load ≥75%) training load or spikes (acute : chronic workload ratio ≥1.5) in load during each week of the season (N = 304).

DISCUSSION

We analyzed the training load and recovery of professional rhythmic gymnasts during 1 season. Training load and recovery changed across the season, particularly during competitive periods. The gymnasts were poorly recovered during half of the season, with negative correlations between recovery and training load. To ensure optimal recovery of rhythmic gymnasts preparing for international competitions, distribution of the training load may require modification.

Compared with values previously described in professional athletes,10,12,27  the wITLs we observed in the rhythmic gymnasts were considerably higher. This was a consequence of the long duration and high frequency of training sessions per week.15,19,20  Despite the higher absolute magnitude of wITL, the high-load weeks were less frequent across the season. Authors of similar studies of professional volleyball12  and futsal27  players have shown frequencies of 64% and 27% high wITL during the season, respectively, contrasted with 12.8% in our study. The literature22,23  has demonstrated that reaching high (and appropriate) chronic loads over the season is important, but the type, content, and progression of these loads are also relevant to minimize the risk of injury and optimize performance. Team sports usually present long competitive periods (months), with 1 or 2 matches per week,4,12  which makes gradual increases in wITL across the in-season period difficult. Conversely, given the frequency and duration of competitions (2–4 days), the rhythmic gymnastics calendar may benefit from a safer wITL progression over the season. Along with what (type and content) and how (progression) high loads are achieved, it is also important to manage when they occur across the season for each athlete. In our study, 80% of high wITL, 74.2% of high-intensity training, and 67% of spikes occurred during competitive periods. This loading profile may impair gymnasts' recovery and performance during competitions, as well as expose them to maladaptation.

Corroborating our results, researchers19,20,21  have also reported long training durations for rhythmic gymnasts. This finding may be related to the number of interventions and the feedback given by coaches during training sessions due to the highly technical demands of the sport. However, the concept of training load is not exclusively related to physical load.4  In this respect, these moments are an inherent part of training in aesthetic sports and represent the cognitive load that would still contribute to the ITL. Despite the validity and reliability of the session-RPE method, it is possible that a more specific tool capable of measuring these nuances could provide more accurate training-load information and avoid overestimations.

In rhythmic gymnastics, greater focus on technical training and routine repetition is expected as a competition draws closer.17,26  Law et al19  found that technical training and routine repetition were the most demanding parts of training for elite rhythmic gymnasts. Furthermore, Fernandez-Villarino et al25  observed session-RPE scores between 7 and 9 (high intensity) during 10 sessions in the competitive period. Our results showed an increase in session-RPE during the competitive 2 period and reduced frequency of low-intensity training in competitive periods (41%). Considering this scenario and the negative association between session-RPE and TQR score, we suggest a better distribution of training intensity across the professional rhythmic gymnastics season to allow more recovery during competitive periods.

Kenttä and Hassmén9  suggested a TQR score of 13 as the minimal level of recovery that athletes must attain, even after days of light training. Based on this approach, our gymnasts were poorly recovered during 50.9% of the season, and at least half of the group was underrecovered during all 5 competition weeks. In contrast to this result, Debien et al12  reported that the lowest TQR score of professional volleyball players over a season was 13.8 ± 1.4, which occurred during the week with the highest difficulty match score. Despite the use of strategies for optimizing recovery, the process depends on time to adequately repair tissue and reestablish performance.3  Therefore, the long duration (approximately 3.7 hours per session) and high frequency (8.7 ± 2.9 sessions per week) of training sessions in rhythmic gymnastics21  disturb this restorative process, making it difficult for athletes to recover appropriately across the season.

In addition to high training load and intensity, other factors that may have impaired recovery in the rhythmic gymnasts were the concentrated high wITLs,13  spikes in loads,23  or even a mismatch between coaches' and athletes' perceptions of recovery.11,17  Moreover, our results showed that the athletes were not appropriately recovered during 74.2% of the high-intensity weeks and the TQR score was >13 during 70.3% of the low-intensity weeks. Certainly, the multifactorial and individual nature of recovery reflects more than simple training loads; other aspects, such as sleep, social life, and nutrition, also affect the athletes' perceived recovery,3,5  although we did not analyze them. The complexity of the relationship among training, recovery, and performance increases the importance of frequent, individual, and multivariate management of training and its responses. Furthermore, recovery should also be carefully planned, with the best individual strategies chosen to ensure better performance and less maladaptation during critical periods.3 

The ACWR model has been used to safely progress training loads and manage injury risk in several team sports. This variable captures the training load performed in a short time period (ie, acute load) relative to the training load over a longer time (ie, chronic load).22,23  Small fluctuations in training load (within an ACWR range of approximately 0.8 to 1.3) have been associated with a low injury risk, whereas higher ACWRs (≥1.5) have been associated with an increased injury risk.22  We observed no correlations between ACWR and recovery, yet our results revealed 55 individual occurrences of spikes in load (ACWR ≥ 1.5) and increases in ACWR during competitive periods 2 and 3. Several authors3234  have encouraged practitioners to use the ACWR in combination with other variables when interpreting athlete-monitoring data. How the ACWR could be used in conjunction with other monitoring tools as a multidimensional athlete management system to contribute to decision making in the practical environment is highlighted in Figures 2 and 3.

In addition to ACWR, other training-load–derived metrics, such as monotony and strain, could provide relevant information related to training outcomes. Monotony represents the variability in the training stimulus, whereas strain is the product of monotony and training load.6  In a study of female collegiate basketball athletes, Anderson et al35  found a higher injury incidence when rapid increases in load occurred (ie, spikes) at the beginning of the season and after a week off. However, no conclusive results were demonstrated for monotony and strain.35  The only study16  in which researchers investigated ACWR among rhythmic gymnastics was conducted in young amateur athletes. Even though they recognized the need for further research, the authors16  suggested that an ACWR between 1.2 and 1.4 might be a safe strategy to control training intensification (4-week period) in this population without impairment of mucosal immunity. Given the paucity of research in rhythmic gymnastics, further investigation is needed to better understand the interactions of training-load metrics, such as ACWR, monotony, and strain, as well as recovery, injury, and performance.

Our study had possible limitations. Our findings may be considered novel, but other national senior rhythmic gymnastics groups may present different training-load and recovery profiles over the season. Researchers should examine different training-load methods and other national rhythmic gymnastics groups and individuals, thoroughly analyze rhythmic gymnasts' daily training and competition demands, and assess the specific requirements of rhythmic gymnastics coaches and practitioners to improve their outcomes in the field.

CONCLUSIONS

The season of a professional senior rhythmic gymnastics group presents a particular and varied training-load distribution. Despite the high absolute magnitude of wITL, most wITL and session-RPE intensities across the season were moderate. Training-load variables increased during competitive periods. During half of the season, gymnasts were not adequately recovered, especially in competition weeks. The periods of underrecovery were more frequent when associated with high-intensity training and ACWRs ≥1.5, reinforcing the negative association between ITL variables and TQR score. Despite this negative relationship, high training loads alone did not cause underrecovery; it is also essential to manage the what (type and content), how (progression), and when (period) of these workloads applied across a professional rhythmic gymnastics season.

ACKNOWLEDGMENTS

We thank the Brazilian Gymnastics Confederation, technical staff, and gymnasts for their contributions. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001 and by the Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG).

REFERENCES

REFERENCES
1. 
Meeusen
R,
Duclos
M,
Foster
C,
et al
Prevention, diagnosis, and treatment of the overtraining syndrome: joint consensus statement of the European College of Sport Science and the American College of Sports Medicine
.
Med Sci Sports Exerc
.
2013
;
45
(1)
:
186
205
.
2. 
Bourdon
PC,
Cardinale
M,
Murray
A,
et al
Monitoring athlete training loads: consensus statement
.
Int J Sports Physiol Perform
.
2017
;
12
(suppl 2)
:
S2161
S2170
.
3. 
Kellmann
M,
Bertollo
M,
Bosquet
L,
et al
Recovery and performance in sport: consensus statement
.
Int J Sports Physiol Perform
.
2018
;
13
(2)
:
240
245
.
4. 
Soligard
T,
Schwellnus
M,
Alonso
JM,
et al
How much is too much? (part 1) International Olympic Committee consensus statement on load in sport and risk of injury
.
Br J Sports Med
.
2016
;
50
(17)
:
1030
1041
.
5. 
Heidari
J,
Beckmann
J,
Bertollo
M,
et al
Multidimensional monitoring of recovery status and implications for performance
.
Int J Sports Physiol Perform
.
2019
;
14
(1)
:
2
8
.
6. 
Foster
C,
Florhaug
JA,
Franklin
J,
et al
A new approach to monitoring exercise training
.
J Strength Cond Res
.
2001
;
15
(1)
:
109
115
.
7. 
Haddad
M,
Stylianides
G,
Djaoui
L,
Dellal
A,
Chamari
K.
Session-RPE method for training load monitoring: validity, ecological usefulness, and influencing factors
.
Front Neurosci
.
2017
;
11
:
612
.
8. 
Saw
AE,
Main
LC,
Gastin
PB.
Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review
.
Br J Sports Med
.
2016
;
50
(5)
:
281
291
.
9. 
Kenttä
G,
Hassmen
P.
Overtraining and recovery: a conceptual model
.
Sports Med
.
1998
;
26
(1)
:
1
16
.
10. 
Timoteo
TF,
Debien
PB,
Miloski
B,
Werneck
FZ,
Gabbett
T,
Bara Filho
MG.
Influence of workload and recovery on injuries in elite male volleyball players
[published online ahead of print August 15,
2018]
.
J Strength Cond Res.
11. 
Doeven
SH,
Brink
MS,
Frencken
WGP,
Lemmink
KAPM.
Impaired player-coach perceptions of exertion and recovery during match congestion
.
Int J Sports Physiol Perform
.
2017
;
12
(9)
:
1151
1156
.
12. 
Debien
PB,
Mancini
M,
Coimbra
DR,
de Freitas
DGS,
Miranda
R,
Bara Filho
MG.
Monitoring training load, recovery, and performance of Brazilian professional volleyball players during a season
.
Int J Sports Physiol Perform
.
2018
;
13
(9)
:
1182
1189
.
13. 
Freitas
VH,
Nakamura
FY,
Miloski
B,
Samulski
D,
Bara Filho
MG.
Sensitivity of physiological and psychological markers to training load intensification in volleyball players
.
J Sports Sci Med
.
2014
;
13
(3)
:
571
579
.
14. 
Douda
HT,
Toubekis
AG,
Avloniti
AA,
Tokmakidis
SP.
Physiological and anthropometric determinants of rhythmic gymnastics performance
.
Int J Sports Physiol Perform
.
2008
;
3
(1)
:
41
54
.
15. 
Bobo-Arce
M,
Méndez-Rial
B.
Determinants of competitive performance in rhythmic gymnastics: a review
.
J Hum Sport Exerc
.
2013
;
8
(proc 3)
:
S711
S727
.
16. 
Antualpa
K,
Aoki
MS,
Moreira
A.
Intensified training period increases salivary IgA responses but does not affect the severity of upper respiratory tract infection symptoms in prepuberal rhythmic gymnasts
.
Pediatr Exerc Sci
.
2018
;
30
(2)
:
189
197
.
17. 
Cavallerio
F,
Wadey
R,
Wagstaff
CRD.
Understanding overuse injuries in rhythmic gymnastics: a 12-month ethnographic study
.
Psychol Sport Exerc
.
2016
;
25
:
100
109
.
18. 
Edouard
P,
Steffen
K,
Junge
A,
Leglise
M,
Soligard
T,
Engebretsen
L.
Gymnastics injury incidence during the 2008, 2012 and 2016 Olympic Games: analysis of prospectively collected surveillance data from 963 registered gymnasts during Olympic Games
.
Br J Sports Med
.
2018
;
52
(7)
:
475
481
.
19. 
Law
MP,
Côté
J,
Ericsson
KA.
Characteristics of expert development in rhythmic gymnastics: a retrospective study
.
Int J Sport Exerc Psychol
.
2007
;
5
(1)
:
82
103
.
20. 
Kolar
E,
Pavletič
MS,
Smrdu
M,
Atiković
A.
Athletes' perception of the causes of injury in gymnastics
.
J Sports Med Phys Fitness
.
2017
;
57
(5)
:
703
710
.
21. 
Silva
MR,
Paiva
T.
Poor precompetitive sleep habits, nutrients' deficiencies, inappropriate body composition and athletic performance in elite gymnasts
.
Eur J Sport Sci
.
2016
;
16
(6)
:
726
735
.
22. 
Gabbett
TJ.
The training-injury prevention paradox: should athletes be training smarter and harder?
Br J Sports Med
.
2016
;
50
(5)
:
273
280
.
23. 
Gabbett
TJ,
Hulin
BT,
Blanch
P,
Whiteley
R.
High training workloads alone do not cause sports injuries: how you get there is the real issue
.
Br J Sports Med
.
2016
;
50
(8)
:
444
445
.
24. 
van der Does
HT,
Brink
MS,
Otter
RT,
Visscher
C,
Lemmink
KA.
Injury risk is increased by changes in perceived recovery of team sport players
.
Clin J Sport Med
.
2017
;
27
(1)
:
46
51
.
25. 
Fernandez-Villarino
MA,
Sierra-Palmeiro
E,
Bobo-Arce
M,
Lago-Peñas
C.
Analysis of the training load during the competitive period in individual rhythmic gymnastics
.
Int J Perform Anal Sport
.
2015
;
15
(2)
:
660
667
.
26. 
Laffranchi
B.
Treinamento Desportivo Aplicado À Ginástica Rítmica
.
Londrina, Paraná, Brazil
:
UNOPAR;
2001
.
27. 
Miloski
B,
de Freitas
VH,
Nakamura
FY,
de A Nogueira
FC,
Bara-Filho
MG.
Seasonal training load distribution of professional futsal players: effects on physical fitness, muscle damage and hormonal status
.
J Strength Cond Res
.
2016
;
30
(6)
:
1525
1533
.
28. 
Lovell
TW,
Sirotic
AC,
Impellizzeri
FM,
Coutts
AJ.
Factors affecting perception of effort (session rating of perceived exertion) during rugby league training
.
Int J Sports Physiol Perform
.
2013
;
8
(1)
:
62
69
.
29. 
Moreira
A,
Bilsborough
JC,
Sullivan
CJ,
Cianciosi
M,
Aoki
MS,
Coutts
AJ.
Training periodization of professional Australian football players during an entire Australian Football League season
.
Int J Sports Physiol Perform
.
2015
;
10
(5)
:
566
571
.
30. 
Gabbett
TJ,
Hulin
B,
Blanch
P,
Chapman
P,
Bailey
D.
To couple or not to couple?
For acute:chronic workload ratios and injury risk, does it really matter? Int J Sports Med
.
2019
;
40
(9)
:
597
600
.
31. 
Hopkins
WG.
A new view of statistics
.
Sportscience Web site
.
2020
.
32. 
Hulin
BT,
Gabbett
TJ.
Indeed association does not equal prediction: the never-ending search for the perfect acute:chronic workload ratio
.
Br J Sports Med
.
2019
;
53
(3)
:
144
145
.
33. 
Gabbett
TJ,
Nassis
GP,
Oetter
E,
et al
The athlete monitoring cycle: a practical guide to interpreting and applying training monitoring data
.
Br J Sports Med
.
2017
;
51
(20)
:
1451
1452
.
34. 
Gabbett
TJ.
Debunking the myths about training load, injury and performance: empirical evidence, hot topics and recommendations for practitioners
.
Br J Sports Med
.
2020
;
54
(1)
:
58
66
.
35. 
Anderson
L,
Triplett-McBride
T,
Foster
C,
Doberstein
S,
Brice
G.
Impact of training patterns on incidence of illness and injury during a women's collegiate basketball season
.
J Strength Cond Res
.
2003
;
17
(4)
:
734
738
.

Author notes

Portions of the Methods section were adapted with permission from Debien PB, Mancini M, Coimbra DR, de Freitas DGS, Miranda R, Bara Filho MG. Monitoring training load, recovery, and performance of Brazilian professional volleyball players during a season. Int J Sports Physiol Perform. 2018;13(9):1182–1189. Copyright 2020 Human Kinetics, Inc.