Individualized Monitoring of Muscle Recovery in Elite Badminton

Aetiology, epidemiology and treatment of musculoskeletal injuries in badminton players: a systematic review and meta-analysis

The present study has two main goals: to conduct a systematic review of musculoskeletal injuries experienced by badminton players, and to examine the management of such injuries. Searches were conducted of the PROSPERO, PubMed, Scopus, and SPORTDiscus databases, from their inception until March 2023. The papers analysed were all based on a study population consisting of individuals aged 18 years or more, diagnosed with badminton-related injuries. The methodological quality assessments was using the Newcastle-Ottawa Scale and REVMAN. A total of 28 studies were included in the systematic review. In total, the analysis included 2435 participants. Of these athletes, 35.6% (1012) were female and 64.4% (1503) were male. By type of injury, sprains were the most commonly studied and the most prevalent, accounting for 36.06% of the sample. These were followed by muscle injuries, representing 23.86% of the total. Injuries to the joints were the least prevalent, accounting for 4.97% of the sample. Lower limb injuries accounted for 52.15% of the total. Of these, ankle injuries were the most common. Despite the generally low quality of the studies considered, the evidence suggests that musculoskeletal injuries, especially to the lower limb, most commonly affect badminton players of all levels.

Blood-Based Markers for Skeletal and Cardiac Muscle Function in Eventing Horses before and after Cross-Country Rides and How They Are Influenced by Plasma Volume Shift

Horses competing in cross-country tests are subjected to high physical demands. Within the scope of this prospective longitudinal study, blood values of 20 elite eventing horses were examined before and after two- to four-star cross-country rides. The aim was to find out whether blood-based markers for skeletal muscle and cardiac muscle function change after cross-country exercise. Parameters that provide information about fluid balance, muscle enzymes, metabolites and cardiac muscle-specific markers were investigated. We developed an approach to eliminate the concentration changes caused by reduced plasma volume. Parameters were measured pre, 10 and 30 min post exercise and the next morning and were evaluated using a mixed model. Thirty minutes after exercise, most parameter concentrations changed in an exercise-dependent manner. The next morning, most exercise-related markers recovered rapidly, while creatine kinase (CK) (26% increase; p = 0.008) and lactate dehydrogenase (LDH) (15% increase; p < 0.001) showed a declining but sustained increase. Cardiac troponin I (cTnI) increased above the reference range in 40 of the 55 rides (73%) and in 18 of 20 horses in the morning after exercise.

Individualised reference ranges for markers of muscle recovery assessment in soccer

Recently an individualisation algorithm has been developed and shown to significantly improve the diagnostic accuracy of creatine kinase (CK) and urea in endurance sports and Badminton. In this study, the applicability and benefit of this algorithm was evaluated using repeated measures data from 161 professional German soccer players monitored during the 2015-2017 seasons. Venous blood samples were collected after a day off (recovered state) and after a minimum of two strenuous training sessions within 48 h (non-recovered state) and analysed for CK and urea. Group-based reference ranges were derived from that same dataset to ensure the best possible reference for comparison. A z-test was conducted to analyse differences in error rates between individualised and group-based classifications. CK values for the individualised approach showed significantly lower error rates in the assessment of muscle recovery compared to both a population-based (p < .001; z-value: -17.01; test-pass error rate: 21 vs. 67%; test-fail: 19 vs. 64%) and a group-based cut-off (p < .001; z-value: -15.29; test-pass error rate: 65%; test-fail: 67%). It could be concluded that the assessment of muscle recovery in soccer using individualised interpretations of blood-borne markers may offer higher diagnostic accuracy than a population-based and a sample-specific group-based approach.HighlightsAssessing muscle recovery via CK using individualised ranges seems to offer a higher diagnostic accuracy than a sample-specific group-based analysis.Using an individualised algorithm seems to be a promising approach to overcome diagnostic problems arising from large inter- and intraindividual variability in blood parameters as it significantly improved the diagnostic accuracy of CK as a recovery marker.As recovery assessment in elite soccer ultimately aims at the accurate detection of differences in the individual player this algorithm seems to offer coaches and sport scientists a more sensitive approach compared to group-specific evaluations.

Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle-consistent generative adversarial networks

In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation-dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. A total of 17 healthy adults (35.8 ± 11.1 years, eight women and nine men) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional cycle-consistent generative adversarial network (CycleGAN), conditioned on the participant's class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement was tested. The classification F1-score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross-movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross-movement analysis and the artificial generation of larger amounts of data.

Biomarkers of post-match recovery in semi-professional and professional football (soccer)

High-level football (soccer) players face intense physical demands that result in acute and residual fatigue, impairing their physical performance in subsequent matches. Further, top-class players are frequently exposed to match-congested periods where sufficient recovery times are not achievable. To evaluate training and recovery strategies, the monitoring of players' recovery profiles is crucial. Along with performance and neuro-mechanical impairments, match-induced fatigue causes metabolic disturbances denoted by changes in chemical analytes that can be quantified in different body fluids such as blood, saliva, and urine, thus acting as biomarkers. The monitoring of these molecules might supplement performance, neuromuscular and cognitive measurements to guide coaches and trainers during the recovery period. The present narrative review aims to comprehensively review the scientific literature on biomarkers of post-match recovery in semi-professional and professional football players as well as provide an outlook on the role that metabolomic studies might play in this field of research. Overall, no single gold-standard biomarker of match-induced fatigue exists, and a range of metabolites are available to assess different aspects of post-match recovery. The use of biomarker panels might be suitable to simultaneously monitoring these broad physiological processes, yet further research on fluctuations of different analytes throughout post-match recovery is warranted. Although important efforts have been made to address the high interindividual heterogeneity of available markers, limitations inherent to these markers might compromise the information they provide to guide recovery protocols. Further research on metabolomics might benefit from evaluating the long-term recovery period from a high-level football match to shed light upon new biomarkers of post-match recovery.

Recovery During a Congested Schedule and Injury in Professional Football

PURPOSE

To analyze the relationships between the recovery kinetics experienced by professional football players and noncontact injury.

METHODS

A cohort of 46 professional football players (age 24.2 [4.7] y) from the same team (French Ligue 1) was monitored each day between matches when the team played twice a week. The recovery monitoring procedure was implemented after 38 matches and included some questionnaires: duration of sleep, Hooper scale (quality of sleep, level of stress, fatigue, and muscle soreness), perceived recovery status scale, creatine kinase concentrations, a countermovement jump, and an isometric force test. Noncontact injuries were collected during these periods.

RESULTS

Noncontact injuries were associated with perceived fatigue and muscle soreness 2 days (relative risk [RR] = 1.89 and 1.48, respectively) and 3 days following the matches (RR = 2.08 and 2.08, respectively). An increase of sleep quantity during the 2 nights following a match was significantly associated with a lower RR (RR = 0.65), as well as a lower decrement score on the isometric force test on each of the 3 days after the matches (RR = 0.97, RR = 0.99, and RR = 0.97, respectively). No other association was reported for the variables sleep quality, stress, perceived recovery, creatine kinase concentrations, countermovement jump, and noncontact injuries.

CONCLUSION

During a congested schedule, implementing a recovery monitoring protocol including questionnaires about fatigue, muscle soreness, quantity of sleep, and a physical test of isometric force could help practitioners prevent injuries.

Forecasting football injuries by combining screening, monitoring and machine learning

Identifying players or circumstances associated with an increased risk of injury is fundamental for successful risk management in football. So far, time-constant and volatile risk factors are generally considered separately in either a screening (constant) or a monitoring (volatile) approach each resulting in a restricted set of explanatory variables. Consequently, improvements in predictive accuracy may be expected when screening and monitoring data are combined, especially when analysed with current machine learning (ML) techniques. This trial was designed as a prospective observational cohort study aiming to forecast non-contact time-loss injuries in male professional football (soccer). Injuries were registered according to the Fuller consensus. Gradient boosting with ROSE upsampling within a leave-one-out cross-validation was used for data analysis. The hierarchical data structure was considered throughout. Different splits of the original dataset were used to probe the robustness of results. Data of 88 players from 4 teams and 51 injuries could be analysed. The cross-validated performance of the gradient boosted model (ROC area under the curve 0.61) was promising and higher compared to models without integration of screening data. Importantly, holdout test set performance was similar (ROC area under the curve 0.62) indicating prospect of generalizability to new cases. However, the variation of predictive accuracy and feature importance with different splits of the original dataset reflects the relatively low number of events. It is concluded that ML-based injury forecasting based on the integration of screening and monitoring data is promising. However, external prospective verification and continued model development are required.

Dwarfs on the Shoulders of Giants: Bayesian Analysis With Informative Priors in Elite Sports Research and Decision Making

While sample sizes in elite sports are necessarily small, so are the effects that may be relevant. This conundrum is complicated by an understandable reluctance of athletes to comply with extensive study requirements. In Bayesian analyses, pre-existing knowledge (e.g., from sub-elite trials) can be formally included to supplement scarce data. Moreover, some design specifics for small sample research extend to the extreme case of a single subject. This provides the basis for actionable feedback (e.g., about individual responses) thereby incentivising participation. As a proof-of-concept, we conducted a replicated cross-over trial on the effect of cold-water immersion (CWI) on sprint performance recovery in soccer players. Times for 30 m linear sprint and the initial 5 m section, respectively, were measured by light gates before and 24 h after induction of fatigue. Data were analysed by Bayesian and by standard frequentist methods. Informative priors are based on a published metaanalysis. Seven players completed the trial. Sprint performance was 4.156 ± 0.193 s for 30 m linear sprint and 0.978 ± 0.064 s for the initial 5 m section. CWI improved recovery of sprint time for the initial 5 m section (difference to control: −0.060 ± 0.060 s, p = 0.004) but not for the full 30 m sprint (0.002 ± 0.115 s, p = 0.959), with general agreement between Bayesian and frequentist interval estimates. On the individual level, relevant differences between analytical approaches were present for most players. Changes in the two performance measures are correlated (p = 0.009) with a fairly good reproducibility of individual response patterns. Bayesian analyses with informative priors may be a practicable and meaningful option particularly for very small samples and when the analytical aim is decision making (use / don't use in the specific setting) rather than generalizable inference.

Biochemical Monitoring of Muscle Recovery in Elite Handball Using an Individualized Approach

Purpose: To evaluate the classification performance of a new method to individualize reference ranges for biomarkers of muscle recovery in elite handball. Methods: In a longitudinal observational study, creatine kinase (CK) and urea levels were assessed in 16 male elite handball players during the 2019–20 preseason. Blood samples were collected at prespecified time points when players were considered either recovered or nonrecovered. Initially, linear mixed-effects models were calculated, as outlined in the study design, to examine the effect of recovery status on CK and urea levels. Finally, a fixed-effect model was calculated for urea based on the law of parsimony. Individualized reference ranges were calculated using a recently published algorithm. To investigate whether the individualized approach resulted in a more accurate classification of recovery status (recovered or nonrecovered), it was compared with a group-based approach derived from the same data set, utilizing predefined error rates. Results: Linear mixed-effects models showed a large effect of recovery status on CK (P < .001, d = 3.49) and a small effect on urea (P = .018, d = 0.382). In contrast to CK (P = .017), urea had no significant interindividual variation. Hence, individualization was examined only for CK. The numerical decrease in both CK error rates by the individualized approach was significant for the test-pass error rate (P = .0196, ϕ = .19). Conclusions: Our findings underscore the critical role of CK for monitoring in team sports such as handball. The observed improvement in CK error rates suggests a promising opportunity to individualize biochemical monitoring, although further studies encompassing larger sample sizes are warranted.

Recovery-Stress Response of Blood-Based Biomarkers

The purpose of this study was to investigate blood-based biomarkers and their regulation with regard to different recovery-stress states. A total of 35 male elite athletes (13 badminton, 22 soccer players) were recruited, and two venous blood samples were taken: one in a ‘recovered’ state (REC) after a minimum of one-day rest from exercise and another one in a ‘non-recovered’ state (NOR) after a habitual loading microcycle. Overall, 23 blood-based biomarkers of different physiologic domains, which address inflammation, muscle damage, and tissue repair, were analyzed by Luminex assays. Across all athletes, only creatine kinase (CK), interleukin (IL-) 6, and IL-17A showed higher concentrations at NOR compared to REC time points. In badminton players, higher levels of CK and IL-17A at NOR were found. In contrast, a higher value for S100 calcium-binding protein A8 (S100A8) at REC was found in badminton players. Similar differences were found for BDNF in soccer players. Soccer players also showed increased levels of CK, and IL-6 at NOR compared to REC state. Several molecular markers were shown to be responsive to differing recovery-stress states, but their suitability as biomarkers in training must be further validated.

Monitoring training and recovery responses with heart rate measures during standardized warm-up in elite badminton players

Purpose

To investigate short-term training and recovery-related effects on heart rate during a standardized submaximal running test.

Methods

Ten elite badminton players (7 females and 3 males) were monitored during a 12-week training period in preparation for the World Championships. Exercise heart rate (HRex) and perceived exertion were measured in response to a 5-min submaximal shuttle-run test during the morning session warm-up. This test was repeatedly performed on Mondays after 1–2 days of pronounced recovery (‘recovered’ state; reference condition) and on Fridays following 4 consecutive days of training (‘strained’ state). In addition, the serum concentration of creatine kinase and urea, perceived recovery–stress states, and jump performance were assessed before warm-up.

Results

Creatine kinase increased in the strained compared to the recovered state and the perceived recovery–stress ratings decreased and increased, respectively (range of average effects sizes: |d| = 0.93–2.90). The overall HRex was 173 bpm and the observed within-player variability (i.e., standard deviation as a coefficient of variation [CV]) was 1.3% (90% confidence interval: 1.2% to 1.5%). A linear reduction of -1.4% (-3.0% to 0.3%) was observed in HRex over the 12-week observational period. HRex was -1.5% lower (-2.2% to -0.9%) in the strained compared to the recovered state, and the standard deviation (as a CV) representing interindividual variability in this response was 0.7% (-0.6% to 1.2%).

Conclusions

Our findings suggest that HRex measured during a standardized warm-up can be sensitive to short-term accumulation of training load, with HRex decreasing on average in response to consecutive days of training within repeated preparatory weekly microcycles. From a practical perspective, it seems advisable to determine intra-individual recovery–strain responses by repeated testing, as HRex responses may vary substantially between and within players.

Can Individual Movement Characteristics Across Different Throwing Disciplines Be Identified in High-Performance Decathletes?

Although the individuality of whole-body movements has been suspected for years, the scientific proof and systematic investigation that individuals possess unique movement patterns did not manifest until the introduction of the criteria of uniqueness and persistence from the field of forensic science. Applying the criteria of uniqueness and persistence to the individuality of motor learning processes requires complex strategies due to the problem of persistence in the learning processes. One approach is to examine the learning process of different movements. For this purpose, it is necessary to differentiate between two components of movement patterns: the individual-specific component and the discipline-specific component. To this end, a kinematic analysis of the shot put, discus, and javelin throwing movements of seven high-performance decathletes during a qualification competition was conducted. In total, joint angle waveforms of 57 throws formed the basis for the recognition task of individual- and discipline-specific throwing patterns using a support vector machine. The results reveal that the kinematic throwing patterns of the three disciplines could be distinguished across athletes with a prediction accuracy of up to 100% (57 of 57 throws). In addition, athlete-specific throwing characteristics could also be identified across the three disciplines. Prediction accuracies of up to 52.6% indicated that up to 10 out of 19 throws of a discipline could be assigned to the correct athletes, based on only knowing these athletes from the kinematic throwing patterns in the other two disciplines. The results further suggest that individual throwing characteristics across disciplines are more pronounced in shot put and discus throwing than in javelin throwing. Applications for training and learning practice in sports and therapy are discussed. In summary, the chosen approach offers a broad field of application related to the search of individualized optimal movement solutions in sports.