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De Larochelambert Q, Hamri I, Chassard T, Meignié A, Storme F, Dupuit M, Diry A, Toussaint JF, Louis PY, Coulmy N, Antero JDS. Exploring the effect of the menstrual cycle or oral contraception on elite athletes' training responses when workload is not objectively quantifiable: the MILS approach and findings from female Olympians. BMJ Open Sport Exerc Med 2024; 10:e001810. [PMID: 38882205 PMCID: PMC11177701 DOI: 10.1136/bmjsem-2023-001810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 06/18/2024] Open
Abstract
Objectives Develop the Markov Index Load State (MILS) model, based on hidden Markov chains, to assess athletes' workload responses and investigate the effects of menstrual cycle (MC)/oral contraception (OC), sex steroids hormones and wellness on elite athletes' training. Methods On a 7-month longitudinal follow-up, daily training (volume and perceived effort, n=2200) and wellness (reported sleep quality and quantity, fitness, mood, menstrual symptoms, n=2509) data were collected from 24 female rowers and skiers preparing for the Olympics. 51 MC and 54 OC full cycles relying on 214 salivary hormone samples were analysed. MC/OC cycles were normalised, converted in % from 0% (first bleeding/pill withdrawal day) to 100% (end). Results MILS identified three chronic workload response states: 'easy', 'moderate' and 'hard'. A cyclic training response linked to MC or OC (95% CI) was observed, primarily related to progesterone level (p=8.23e-03 and 5.72e-03 for the easy and hard state, respectively). MC athletes predominantly exhibited the 'easy' state during the cycle's first half (8%-53%), transitioning to the 'hard' state post-estimated ovulation (63%-96%). OC users had an increased 'hard' state (4%-32%) during pill withdrawal, transitioning to 'easy' (50%-60%) when on the pill. Wellness metrics influenced the training load response: better sleep quality (p=5.20e-04), mood (p=8.94e-06) and fitness (p=6.29e-03) increased the likelihood of the 'easy' state. Menstrual symptoms increased the 'hard' state probability (p=5.92e-02). Conclusion The MILS model, leveraging hidden Markov chains, effectively analyses cumulative training load responses. The model identified cyclic training responses linked to MC/OC in elite female athletes.
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Affiliation(s)
- Quentin De Larochelambert
- Institut de Recherche bioMédicale et d'Epidemiologie du Sport (IRMES), Institut National du Sport de l'Expertise et de la Performance (INSEP), Paris, France
- French Rowing Federation, Nogent-sur-Marne, France
- Scientific Department, Fédération Française de Ski, Annecy, France
- Institut de Mathématiques de Bourgogne, UMR 5584, CNRS & Université de Bourgogne, F-21000 Dijon, France, Dijon, France
| | - Imad Hamri
- Institut de Recherche bioMédicale et d'Epidemiologie du Sport (IRMES), Institut National du Sport de l'Expertise et de la Performance (INSEP), Paris, France
| | - Tom Chassard
- Institut de Recherche bioMédicale et d'Epidemiologie du Sport (IRMES), Institut National du Sport de l'Expertise et de la Performance (INSEP), Paris, France
| | - Alice Meignié
- Institut de Recherche bioMédicale et d'Epidemiologie du Sport (IRMES), Institut National du Sport de l'Expertise et de la Performance (INSEP), Paris, France
| | - Florent Storme
- Institut de Recherche bioMédicale et d'Epidemiologie du Sport (IRMES), Institut National du Sport de l'Expertise et de la Performance (INSEP), Paris, France
| | - Marine Dupuit
- Institut de Recherche bioMédicale et d'Epidemiologie du Sport (IRMES), Institut National du Sport de l'Expertise et de la Performance (INSEP), Paris, France
| | - Allison Diry
- French Rowing Federation, Nogent-sur-Marne, France
| | - Jean-François Toussaint
- Institut de Recherche bioMédicale et d'Epidemiologie du Sport (IRMES), Institut National du Sport de l'Expertise et de la Performance (INSEP), Paris, France
- CIMS, Hôtel-Dieu, AP-HP, Paris, France
| | - Pierre Yves Louis
- Institut de Mathématiques de Bourgogne, UMR 5584, CNRS & Université de Bourgogne, F-21000 Dijon, France, Dijon, France
- Université Bourgogne Franche-Comté, Institut Agro, Université de Bourgogne, INRAE, UMR PAM 1517, 21000 Dijon, France
| | - Nicolas Coulmy
- Scientific Department, Fédération Française de Ski, Annecy, France
| | - Juliana da Silva Antero
- Institut de Recherche bioMédicale et d'Epidemiologie du Sport (IRMES), Institut National du Sport de l'Expertise et de la Performance (INSEP), Paris, France
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Taber CB, Sharma S, Raval MS, Senbel S, Keefe A, Shah J, Patterson E, Nolan J, Sertac Artan N, Kaya T. A holistic approach to performance prediction in collegiate athletics: player, team, and conference perspectives. Sci Rep 2024; 14:1162. [PMID: 38216641 PMCID: PMC10786827 DOI: 10.1038/s41598-024-51658-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 01/08/2024] [Indexed: 01/14/2024] Open
Abstract
Predictive sports data analytics can be revolutionary for sports performance. Existing literature discusses players' or teams' performance, independently or in tandem. Using Machine Learning (ML), this paper aims to holistically evaluate player-, team-, and conference (season)-level performances in Division-1 Women's basketball. The players were monitored and tested through a full competitive year. The performance was quantified at the player level using the reactive strength index modified (RSImod), at the team level by the game score (GS) metric, and finally at the conference level through Player Efficiency Rating (PER). The data includes parameters from training, subjective stress, sleep, and recovery (WHOOP straps), in-game statistics (Polar monitors), and countermovement jumps. We used data balancing techniques and an Extreme Gradient Boosting (XGB) classifier to predict RSI and GS with greater than 90% accuracy and a 0.9 F1 score. The XGB regressor predicted PER with an MSE of 0.026 and an R2 of 0.680. Ensemble of Random Forest, XGB, and correlation finds feature importance at all levels. We used Partial Dependence Plots to understand the impact of each feature on the target variable. Quantifying and predicting performance at all levels will allow coaches to monitor athlete readiness and help improve training.
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Affiliation(s)
- Christopher B Taber
- Department of Physical Therapy and Human Movement Science, Sacred Heart University, Fairfield, CT, USA
| | - Srishti Sharma
- School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India
| | - Mehul S Raval
- School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India
| | - Samah Senbel
- School of Computer Science and Engineering, Sacred Heart University, Fairfield, CT, USA
| | - Allison Keefe
- Department of Physical Therapy and Human Movement Science, Sacred Heart University, Fairfield, CT, USA
| | - Jui Shah
- Department of Physical Therapy and Human Movement Science, Sacred Heart University, Fairfield, CT, USA
| | - Emma Patterson
- Department of Physical Therapy and Human Movement Science, Sacred Heart University, Fairfield, CT, USA
| | - Julie Nolan
- Department of Physical Therapy and Human Movement Science, Sacred Heart University, Fairfield, CT, USA
| | - N Sertac Artan
- College of Engineering and Computing Sciences, New York Institute of Technology, New York, NY, USA
| | - Tolga Kaya
- School of Computer Science and Engineering, Sacred Heart University, Fairfield, CT, USA.
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Bache-Mathiesen LK, Andersen TE, Dalen-Lorentsen T, Clarsen B, Fagerland MW. Assessing the cumulative effect of long-term training load on the risk of injury in team sports. BMJ Open Sport Exerc Med 2022; 8:e001342. [PMID: 35722043 PMCID: PMC9152939 DOI: 10.1136/bmjsem-2022-001342] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/16/2022] [Indexed: 11/30/2022] Open
Abstract
Objectives Determine how to assess the cumulative effect of training load on the risk of injury or health problems in team sports. Methods First, we performed a simulation based on a Norwegian Premier League male football dataset (n players=36). Training load was sampled from daily session rating of perceived exertion (sRPE). Different scenarios of the effect of sRPE on injury risk and the effect of relative sRPE on injury risk were simulated. These scenarios assumed that the probability of injury was the result of training load exposures over the previous 4 weeks. We compared seven different methods of modelling training load in their ability to model the simulated relationship. We then used the most accurate method, the distributed lag non-linear model (DLNM), to analyse data from Norwegian youth elite handball players (no. of players=205, no. of health problems=471) to illustrate how assessing the cumulative effect of training load can be done in practice. Results DLNM was the only method that accurately modelled the simulated relationships between training load and injury risk. In the handball example, DLNM could show the cumulative effect of training load and how much training load affected health problem risk depending on the distance in time since the training load exposure. Conclusion DLNM can be used to assess the cumulative effect of training load on injury risk.
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Affiliation(s)
| | - Thor Einar Andersen
- Department of Sports Medicine, Oslo Sports Trauma Research Centre, Norwegian School of Sports Sciences, Oslo, Norway
| | - Torstein Dalen-Lorentsen
- Department of Sports Medicine, Oslo Sports Trauma Research Centre, Norwegian School of Sports Sciences, Oslo, Norway.,Department of Smart Sensors and Microsystems, SINTEF Digital, Oslo, Norway
| | - Benjamin Clarsen
- Department of Sports Medicine, Oslo Sports Trauma Research Centre, Norwegian School of Sports Sciences, Oslo, Norway.,Centre for Disease Burden, Norwegian Institute of Public Health, Bergen, Norway
| | - Morten Wang Fagerland
- Department of Sports Medicine, Oslo Sports Trauma Research Centre, Norwegian School of Sports Sciences, Oslo, Norway.,Research Support Services, Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
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Coyne JOC, Coutts AJ, Newton RU, Haff GG. The Current State of Subjective Training Load Monitoring: Follow-Up and Future Directions. SPORTS MEDICINE - OPEN 2022; 8:53. [PMID: 35426569 PMCID: PMC9012875 DOI: 10.1186/s40798-022-00433-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/13/2022] [Indexed: 01/11/2023]
Abstract
This article addresses several key issues that have been raised related to subjective training load (TL) monitoring. These key issues include how TL is calculated if subjective TL can be used to model sports performance and where subjective TL monitoring fits into an overall decision-making framework for practitioners. Regarding how TL is calculated, there is conjecture over the most appropriate (1) acute and chronic period lengths, (2) smoothing methods for TL data and (3) change in TL measures (e.g., training stress balance (TSB), differential load, acute-to-chronic workload ratio). Variable selection procedures with measures of model-fit, like the Akaike Information Criterion, are suggested as a potential answer to these calculation issues with examples provided using datasets from two different groups of elite athletes prior to and during competition at the 2016 Olympic Games. Regarding using subjective TL to model sports performance, further examples using linear mixed models and the previously mentioned datasets are provided to illustrate possible practical interpretations of model results for coaches (e.g., ensuring TSB increases during a taper for improved performance). An overall decision-making framework for determining training interventions is also provided with context given to where subjective TL measures may fit within this framework and the determination if subjective measures are needed with TL monitoring for different sporting situations. Lastly, relevant practical recommendations (e.g., using validated scales and training coaches and athletes in their use) are provided to ensure subjective TL monitoring is used as effectively as possible along with recommendations for future research.
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Affiliation(s)
- Joseph O C Coyne
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, 6027, Australia. .,, 18 Bondi Pl, Kingscliff, NSW, 2487, Australia.
| | - Aaron J Coutts
- Human Performance Research Centre, University of Technology Sydney (UTS), Moore Park Rd, Moore Park, NSW, 2021, Australia.,School of Sport, Exercise and Rehabilitation, University of Technology Sydney (UTS), Moore Park Rd, Moore Park, NSW, 2021, Australia
| | - Robert U Newton
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, 6027, Australia
| | - G Gregory Haff
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, 6027, Australia.,Directorate of Psychology and Sport, University of Salford, Salford, Greater Manchester, UK
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Impellizzeri FM, McCall A, Ward P, Bornn L, Coutts AJ. Training Load and Its Role in Injury Prevention, Part 2: Conceptual and Methodologic Pitfalls. J Athl Train 2021; 55:893-901. [PMID: 32991699 DOI: 10.4085/1062-6050-501-19] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In part 2 of this clinical commentary, we highlight the conceptual and methodologic pitfalls evident in current training-load-injury research. These limitations make these studies unsuitable for determining how to use new metrics such as acute workload, chronic workload, and their ratio for reducing injury risk. The main overarching concerns are the lack of a conceptual framework and reference models that do not allow for appropriate interpretation of the results to define a causal structure. The lack of any conceptual framework also gives investigators too many degrees of freedom, which can dramatically increase the risk of false discoveries and confirmation bias by forcing the interpretation of results toward common beliefs and accepted training principles. Specifically, we underline methodologic concerns relating to (1) measure of exposures, (2) pitfalls of using ratios, (3) training-load measures, (4) time windows, (5) discretization and reference category, (6) injury definitions, (7) unclear analyses, (8) sample size and generalizability, (9) missing data, and (10) standards and quality of reporting. Given the pitfalls of previous studies, we need to return to our practices before this research influx began, when practitioners relied on traditional training principles (eg, overload progression) and adjusted training loads based on athletes' responses. Training-load measures cannot tell us whether the variations are increasing or decreasing the injury risk; we recommend that practitioners still rely on their expert knowledge and experience.
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Affiliation(s)
- Franco M Impellizzeri
- Faculty of Health, Human Performance Research Centre and School of Sport, Exercise and Rehabilitation, University of Technology Sydney, Australia
| | - Alan McCall
- Arsenal Football Club, London, United Kingdom
| | | | | | - Aaron J Coutts
- Faculty of Health, Human Performance Research Centre and School of Sport, Exercise and Rehabilitation, University of Technology Sydney, Australia
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Gabbett TJ. The Training-Performance Puzzle: How Can the Past Inform Future Training Directions? J Athl Train 2021; 55:874-884. [PMID: 32991700 DOI: 10.4085/1062/6050.422.19] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Over the past 20 years, research on the training-load-injury relationship has grown exponentially. With the benefit of more data, our understanding of the training-performance puzzle has improved. What were we thinking 20 years ago, and how has our thinking changed over time? Although early investigators attributed overuse injuries to excessive training loads, it has become clear that rapid spikes in training load, above what an athlete is accustomed, explain (at least in part) a large proportion of injuries. In this respect, it appears that overuse injuries may arise from athletes being underprepared for the load they are about to perform. However, a question of interest to both athletic trainers (ATs) and researchers is why some athletes sustain injury at low training loads, while others can tolerate much greater training loads? A higher chronic training load and well-developed aerobic fitness and lower body strength appear to moderate the training-injury relationship and provide a protective effect against spikes in load. The training-performance puzzle is complex and dynamic-at any given time, multiple inputs to injury and performance exist. The challenge facing researchers is obtaining large enough longitudinal data sets to capture the time-varying nature of physiological and musculoskeletal capacities and training-load data to adequately inform injury-prevention efforts. The training-performance puzzle can be solved, but it will take collaboration between researchers and clinicians as well as an understanding that efficacy (ie, how training load affects performance and injury in an idealized or controlled setting) does not equate to effectiveness (ie, how training load affects performance and injury in the real-world setting, where many variables cannot be controlled).
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Affiliation(s)
- Tim J Gabbett
- Gabbett Performance Solutions, Brisbane, and Centre for Health Research, University of Southern Queensland, Ipswich, Australia
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7
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Sedeaud A, De Larochelambert Q, Moussa I, Brasse D, Berrou JM, Duncombe S, Antero J, Orhant E, Carling C, Toussaint JF. Does an Optimal Relationship Between Injury Risk and Workload Represented by the "Sweet Spot" Really Exist? An Example From Elite French Soccer Players and Pentathletes. Front Physiol 2020; 11:1034. [PMID: 32982781 PMCID: PMC7485291 DOI: 10.3389/fphys.2020.01034] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 07/28/2020] [Indexed: 01/12/2023] Open
Abstract
Objective To examine the relationships between the occurrence and severity of injuries using three workload ratios (ACWR, EWMA, REDI) in elite female soccer players and international male and female pentathletes. Materials and Methods Female soccer players in the U16 to U18 national French teams (n = 24) and international athletes (n = 12, 4 women and 8 men) in the French modern pentathlon team were monitored throughout an entire season. The Acute Chronic Workload Ratio (ACWR), the Exponentially Weighted Moving Averages (EWMA), and the Robust Exponential Decreasing Index (REDI) were calculated for internal load by the ROE method in soccer and external load in pentathlon. The occurrence and severity of injuries (determined according to time-loss) were quantified in the sweet spot zone [0.8; 1.3] and compared to the other zones of load variation: [0; 0.8], [1.3; 1.5], [1.5; +8], using the three ratios. Results Over the study period, a total of sixty-six injuries (2.75 per athlete) were reported in the soccer players and twelve in pentathletes (1 per athlete). The cumulative severity of all injuries was 788 days lost in soccer and 36 in pentathlon: respectively, 11.9 days lost per injury in soccer player and 3.0 per pentathlete. The mean values across the three methods in soccer showed a higher number of injuries detected in the [0; 0.8] workload ratio zone: 22.3 ± 6.4. They were 17.3 ± 3.5 in the sweet spot ([0.8-1.3] zone) and 17.6 ± 5.5 in the [1.5; +8] zone. In comparison to the [1.5; +8] zone, soccer players reported a higher number of days lost to injuries in the presumed sweet spot and in the [0-0.8] zone: 204.7 ± 28.7 and 275.0 ± 120.7 days, respectively. In pentathletes, ten of the twelve injuries (83.3%) occurred in the presumed sweet spot. REDI was the only method capable of tracking workloads over all-time series. Conclusion In the present cohort of elite soccer players and pentathletes, acute chronic workload calculations showed an association with injury occurrence and severity but did not provide evidence supporting existence of a sweet spot diminishing injury risk.
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Affiliation(s)
- Adrien Sedeaud
- Institut de Recherche Biomédicale et d'Épidémiologie du Sport (IRMES), Institut National du Sport, de l'Expertise et de la Performance (INSEP), Paris, France.,EA7329 Institut de Recherche BioMédicale et d'Épidémiologie du Sport (IRMES), Paris, France
| | - Quentin De Larochelambert
- Institut de Recherche Biomédicale et d'Épidémiologie du Sport (IRMES), Institut National du Sport, de l'Expertise et de la Performance (INSEP), Paris, France.,EA7329 Institut de Recherche BioMédicale et d'Épidémiologie du Sport (IRMES), Paris, France
| | - Issa Moussa
- Institut de Recherche Biomédicale et d'Épidémiologie du Sport (IRMES), Institut National du Sport, de l'Expertise et de la Performance (INSEP), Paris, France.,EA7329 Institut de Recherche BioMédicale et d'Épidémiologie du Sport (IRMES), Paris, France
| | | | | | - Stephanie Duncombe
- School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Juliana Antero
- Institut de Recherche Biomédicale et d'Épidémiologie du Sport (IRMES), Institut National du Sport, de l'Expertise et de la Performance (INSEP), Paris, France.,EA7329 Institut de Recherche BioMédicale et d'Épidémiologie du Sport (IRMES), Paris, France
| | | | | | - Jean-Francois Toussaint
- Institut de Recherche Biomédicale et d'Épidémiologie du Sport (IRMES), Institut National du Sport, de l'Expertise et de la Performance (INSEP), Paris, France.,EA7329 Institut de Recherche BioMédicale et d'Épidémiologie du Sport (IRMES), Paris, France.,Centre d'Investigation en Médecine du Sport, Paris, France
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