Monitoring Psychometric States of Recovery to Improve Performance in Soccer Players: A Brief Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Search Strategy
2.2. The Hooper Index (HI)
2.3. Total Quality of Recovery (TQR)
3. Results
3.1. Relationship between HI, TQR, and Internal Training Load
3.2. The Relationship of HI and TQR with the Physical, Physiological, and Technical Aspects
3.3. Influence of HI and TQR on the Feeling States
3.4. Relationship between HI, TQR, and Ramadan
4. Conclusions
5. Practical Applications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Selmi, O.; Ouergui, I.; Levitt, D.E.; Nikolaidis, P.T.; Knechtle, B.; Bouassida, A. Small-Sided Games are More Enjoyable Than High-Intensity Interval Training of Similar Exercise Intensity in Soccer. Open Access J. Sports Med. 2020, 11, 77–84. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Selmi, O.; Gonçalves, B.; Ouergui, I.; Levitt, D.E.; Sampaio, J.; Bouassida, A. Influence of well-being indices and recovery state on the technical and physiological aspects of play during small-sided games. J. Strength Cond. Res. 2021, 35, 2802–2809. [Google Scholar] [CrossRef] [PubMed]
- de Sire, A.; Marotta, N.; Lippi, L.; Scaturro, D.; Farì, G.; Liccardi, A.; Moggio, L.; Letizia Mauro, G.; Ammendolia, A.; Invernizzi, M. Pharmacological Treatment for Acute Traumatic Musculoskeletal Pain in Athletes. Medicina 2021, 57, 1208. [Google Scholar] [CrossRef]
- Clemente, F.M.; Silva, R.; Chen, Y.S.; Aquino, R.; Praça, G.M.; Castellano, J.; Nobari, H.; Mendes, B.; Rosemann, T.; Knechtle, B. Accelerometry-workload indices concerning different levels of participation during congested fixture periods in professional soccer: A pilot study conducted over a full season. Int. J. Environ. Res. Public Health. 2021, 18, 1137. [Google Scholar] [CrossRef] [PubMed]
- Selmi, O.; Gonçalves, B.; Ouergui, I.; Sampaio, J.; Bouassida, A. Influence of well-being variables and recovery state in physical enjoyment of professional soccer players during small-sided games. Res. Sports Med. 2018, 26, 199–210. [Google Scholar] [CrossRef]
- Clemente, F.M.; Rabbani, A.; Araújo, J.P. Ratings of perceived recovery and exertion in elite youth soccer players: Interchangeability of 10-point and 100-point scales. Physiol. Behav. 2019, 210, 112641. [Google Scholar] [CrossRef] [PubMed]
- Osiecki, R.; Rubio, T.B.G.; Coelho, R.L.; Novack, L.F.; Conde, J.H.S.; Alves, C.G.; Malfatti, C.R.M. The total quality recovery scale (TQR) as a proxy for determining athletes’ recovery state after a professional soccer match. J. Exerc. Physiol. 2015, 18, 27–32. [Google Scholar]
- Goulart, K.N.D.O.; Duffield, R.; Junior, G.O.C.; Passos Ramos, G.; Pimenta, E.M.; Couto, B.P. Recovery timeline following resistance training in professional female soccer players. Sci. Med. Footb. 2020, 4, 233–239. [Google Scholar] [CrossRef]
- Clemente, F.M.; Mendes, B.; Nikolaidis, P.T.; Calvete, F.; Carriço, S.; Owen, A.L. Internal training load and its longitudinal relationship with seasonal player wellness in elite professional soccer. Physiol. Behav. 2017, 179, 262–267. [Google Scholar] [CrossRef]
- Nobari, H.; Aquino, R.; Clemente, F.M.; Khalafi, M.; Adsuar, J.C.; Pérez-Gómez, J. Description of acute and chronic load, training monotony and strain over a season and its relationships with well-being status: A study in elite under-16 soccer players. Physiol. Behav. 2020, 225, 113117. [Google Scholar] [CrossRef]
- Nobari, H.; Alves, A.R.; Haghighi, H.; Clemente, F.M.; Carlos-Vivas, J.; Pérez-Gómez, J.; Ardigò, L.P. Association between training load and well-being measures in young soccer players during a season. Int. J. Environ. Res. Public Health 2021, 18, 4451. [Google Scholar] [CrossRef] [PubMed]
- Saidi, K.; Zouhal, H.; Boullosa, D.; Dupont, G.; Hackney, A.C.; Bideau, B.; Abderrahman, A.B. Biochemical Markers and Wellness Status During a Congested Match Play Period in Elite Soccer Players. Int. J. Sports Physiol. Perform. 2022, 17, 605–620. [Google Scholar] [CrossRef]
- Oliveira, R.; Brito, J.P.; Loureiro, N.; Padinha, V.; Ferreira, B.; Mendes, B. Does the distribution of the weekly training load account for the match results of elite professional soccer players? Physiol. Behav. 2020, 225, 113118. [Google Scholar] [CrossRef] [PubMed]
- Pereira, L.A.; Freitas, T.T.; Zanetti, V.; Loturco, I. Variations in Internal and External Training Load Measures and Neuromuscular Performance of Professional Soccer Players During a Preseason Training Period. J. Hum. Kinet. 2022, 81, 149–162. [Google Scholar] [CrossRef] [PubMed]
- Moalla, W.; Fessi, M.S.; Farhat, F.; Nouira, S.; Wong, D.P.; Dupont, G. Relationship between daily training load and psychometric status of professional soccer players. Res. Sports Med. (Print) 2016, 24, 387–394. [Google Scholar] [CrossRef]
- Selmi, O.; Marzouki, H.; Ouergui, I.; BenKhalifa, W.; Bouassida, A. Influence of intense training cycle and psychometric status on technical and physiological aspects performed during the small-sided games in soccer players. Res. Sports Med. 2018, 26, 401–412. [Google Scholar] [CrossRef]
- Buchheit, M.; Racinais, S.; Bilsborough, J.C. Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players. J. Sci. Med. Sport 2014, 16, 550–555. [Google Scholar] [CrossRef] [PubMed]
- Okholm Kryger, K.; Mutamba, K.; Mitchell, S.; Miller, S.C.; Forrester, S. Physical performance and perception of foot discomfort during a soccer-specific match simulation. A comparison of football boots. J. Sports Sci. 2021, 39, 1046–1054. [Google Scholar] [CrossRef]
- Kaya, M.H.; Adiguzel, T. Technology Integration Through Evidence-Based Multimodal Reflective Professional Training. Contemp. Educ. Technol. 2021, 13, ep323. [Google Scholar] [CrossRef]
- Haddad, M.; Chaouachi, A.; Wong, D.; Castagna, C.; Hambli, M.; Hue, O.; Chamari, K. Influence of fatigue, stress, muscle soreness and sleep on perceived exertion during submaximal effort. Physiol. Behav. 2013, 119, 185–189. [Google Scholar] [CrossRef]
- Hooper, S.L.; Mackinnon, L.T.; Howard, A.; Gordon, R.D.; Bachmann, A.W. Markers for monitoring overtraining and recovery. Med. Sci. Sports Exerc. 1995, 27, 106–112. [Google Scholar] [CrossRef] [PubMed]
- Ihsan, N. Development of speed measurement system for pencaksilat kick based on sensor technology. In IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2017; Volume 180, p. 012171. [Google Scholar]
- Kenttä, G.; Hassmén, P. Overtraining and recovery. Sports Med. 1998, 26, 1–16. [Google Scholar] [CrossRef] [PubMed]
- Ehsan, S.; Reinhold, S.; Bahram, S. Assessment of GPM-IMERG and other precipitation products against gauge data under different topographic and climatic conditions in Iran: Preliminary results. Remote Sens. 2016, 8, 135. [Google Scholar]
- Fessi, M.S.; Nouira, S.; Dellal, A.; Owen, A.; Elloumi, M.; Moalla, W. Changes of the psychophysical state and feeling of wellness of professional soccer players during pre-season and in-season periods. Res. Sport Med. 2016, 24, 375–386. [Google Scholar] [CrossRef] [PubMed]
- Fernandes, R.; Brito, J.P.; Vieira, L.H.P.; Martins, A.D.; Clemente, F.M.; Nobari, H.; Oliveira, R. In-Season Internal Load and Wellness Variations in Professional Women Soccer Players: Comparisons between Playing Positions and Status. Int. J. Environ. Res. Public Health 2021, 18, 12817. [Google Scholar] [CrossRef] [PubMed]
- Novack, L.F.; de Souza, G.C.; Conde, J.H.S.; de Souza, R.O.; Osiecki, R. Quantification of match internal load and its relationship with physical fitness and recovery state of professional soccer athletes during the competitive period. Hum. Mov. 2018, 19, 30–37. [Google Scholar] [CrossRef] [Green Version]
- Lockwood, C.; Munn, Z.; Porritt, K. Qualitative research synthesis: Methodological guidance for systematic reviewers utilizing meta-aggregation. Int. J. Evid.-Based Healthc. 2015, 13, 179–187. [Google Scholar] [CrossRef]
- Mäestu, J.; Jürimäe, J.; Kreegipuu, K.; Jürimäe, T. Changes in perceived stress and recovery during heavy training in highly trained male rowers. Sport Psychol. 2006, 20, 24–39. [Google Scholar] [CrossRef] [Green Version]
- Chamari, K.; Haddad, M.; Wong, D.P.; Dellal, A.; Chaouachi, A. Injury rates in professional soccer players during Ramadan. J. Sports Sci. 2012, 30 (Suppl. S1), S93–S102. [Google Scholar] [CrossRef]
- Gastin, P.B.; Meyer, D.; Robinson, D. Perceptions of wellness to monitor adaptive responses to training and competition in elite Australian football. J. Strength Cond. Res. 2013, 27, 2518–2526. [Google Scholar] [CrossRef]
- Thorpe, R.T.; Strudwick, A.J.; Buchheit, M.; Atkinson, G.; Drust, B.; Gregson, W. Monitoring fatigue during the in-season competitive phase in elite soccer players. Int. J. Sports Physiol. Perform. 2015, 10, 958–964. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Angeli, A.; Minetto, M.; Dovio, A.; Paccotti, P. The overtraining syndrome in athletes: A stress-related disorder. J. Endocrinol. Investig. 2004, 27, 603–612. [Google Scholar] [CrossRef] [PubMed]
- Mendes, B.; Clemente, F.M.; Calvete, F.; Carriço, S.; Owen, A. Seasonal Training Load Monitoring Among Elite Level Soccer Players: Perceived Exertion and Creatine Kinase Variations Between Microcycles. J. Hum. Kinet. 2022, 81, 85–95. [Google Scholar] [CrossRef] [PubMed]
- Bishop, J.L.; Dobrea, E.Z.N.; McKeown, N.K.; Parente, M.; Ehlmann, B.L.; Michalski, J.R.; Bibring, J.P. Phyllosilicate diversity and past aqueous activity revealed at Mawrth Vallis, Mars. Science 2008, 321, 830–833. [Google Scholar] [CrossRef] [Green Version]
- Debien, P.B.; Mancini, M.; Coimbra, D.R.; de Freitas, D.G.; Miranda, R.; Bara Filho, M.G. Monitoring training load, recovery, and performance of Brazilian professional volleyball players during a season. Int. J. Sports Physiol. Perform. 2018, 13, 1182–1189. [Google Scholar] [CrossRef] [PubMed]
- Montgomery, S.B.; Goode, D.L.; Kvikstad, E.; Albers, C.A.; Zhang, Z.D.; Mu, X.J.; 1000 Genomes Project Consortium. The origin, evolution, and functional impact of short insertion–deletion variants identified in 179 human genomes. Genome Res. 2013, 23, 749–761. [Google Scholar] [CrossRef] [Green Version]
- Nässi, A.; Ferrauti, A.; Meyer, T.; Pfeiffer, M.; Kellmann, M. Development of two short measures for recovery and stress in sport. Eur. J. Sport Sci. 2017, 17, 894–903. [Google Scholar] [CrossRef]
- Reddan, G.; Harrison, G. Restructuring the bachelor of exercise science degree to meet industry needs. Int. J. Work-Integr. Learn. 2010, 11, 13. [Google Scholar]
- Borg, I.; Elizur, D. Job insecurity: Correlates, moderators, and measurement. Int. J. Manpow. 1992, 13, 13–26. [Google Scholar] [CrossRef]
- Suzuki, M.; Omori, M.; Hatakeyama, M.; Yamada, S.; Matsushita, K.; Iijima, S. Predicting recovery of upper-body dressing ability after stroke. Arch. Phys. Med. Rehabil. 2006, 87, 1496–1502. [Google Scholar] [CrossRef]
- Kenttä, G.; Hassmén, P. Underrecovery and overtraining: A conceptual model. In Enhancing Recovery, Preventing Underperformance in Athletes; Kellmann, M., Ed.; Human Kinetics: Champaign, IL, USA, 2002; pp. 57–79. [Google Scholar]
- Brink, M.S.; Nederhof, E.; Visscher, C.; Schmikli, S.L.; Lemmink, K.A. Monitoring load, recovery, and performance in young elite soccer players. J. Strength Cond. Res. 2010, 24, 597–603. [Google Scholar] [CrossRef] [Green Version]
- Fernandes, R.; Oliveira, R.; Martins, A.D.; de Brito, J.M. Internal training, and match load quantification of one-match week schedules in female first league Portugal soccer team. Cuad. Psicol. Deporte 2021, 21, 126–138. [Google Scholar] [CrossRef]
- Perri, E.; Simonelli, C.; Rossi, A.; Trecroci, A.; Alberti, G.; Iaia, F.M. Relationship Between Wellness Index and Internal Training Load in Soccer: Application of a Machine Learning Model. Int. J. Sports Physiol. Perform. 2021, 16, 695–703. [Google Scholar] [CrossRef] [PubMed]
- Selmi, O.; Ouerghi, N.; Khalifa, W.B.; Amara, F.; Bouassida, A. The influence of total quality recovery in perceived enjoyment during football specific training. Iran. J. Public Health 2018, 47, 1211–1212. [Google Scholar] [PubMed]
- Selmi, O.; Ouerghi, N.; Khalifa, W.B.; Jebabli, N.; Feki, M.; Bouassida, A. Influence of Stress, Fatigue, Sleep and Delayed Onset Muscle Soreness on Perceived Physical Enjoyment Exertion during Small Sided Games. Iran. J. Public Health 2018, 47, 449. [Google Scholar] [PubMed]
- Lathlean, T.J.H.; Gastin, P.B.; Newstead, S.V.; Finch, C.F. A Prospective Cohort Study of Load and Wellness (Sleep, Fatigue, Soreness, Stress, and Mood) in Elite Junior Australian Football Players. Int. J. Sports Physiol. Perform. 2019, 14, 829–840. [Google Scholar] [CrossRef]
- Malone, S.; Owen, A.; Newton, M.; Mendes, B.; Tiernan, L.; Hughes, B.; Collins, K. Wellbeing. Perception and the Impact on External Training Output among Elite Soccer Players. J. Sci. Med. Sport 2018, 21, 29–34. [Google Scholar] [CrossRef]
- Smith, L.N. Cyclical learning rates for training neural networks. In Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA, 24–31 March 2017; pp. 464–472. [Google Scholar]
- Hammoudi-Nassib, S.; Chtara, M.; Nassib, S.; Briki, W.; Hammoudi-Riahi, S.; Tod, D.; Chamari, K. Time interval moderates the relationship between psyching-up and actual sprint performance. J. Strength Cond. Res. 2014, 28, 3245–3254. [Google Scholar] [CrossRef]
- Smith, M.R.; Zeuwts, L.; Lenoir, M.; Hens, N.; De Jong, L.M.; Coutts, A.J. Mental fatigue impairs soccer-specific decision-making skill. J. Sports Sci. 2016, 34, 1297–1304. [Google Scholar] [CrossRef]
- Ferraz, C.; Finan, F.; Moreira, D.B. Corrupting learning: Evidence from missing federal education funds in Brazil. J. Public Econ. 2012, 96, 712–726. [Google Scholar] [CrossRef]
- Fanchini, M.; Ghielmetti, R.; Coutts, A.J.; Schena, F.; Impellizzeri, F.M. Effect of training-session intensity distribution on session rating of perceived exertion in soccer players. Int. J. Sports Physiol. Perform. 2015, 10, 426–430. [Google Scholar] [CrossRef] [PubMed]
- Charlot, K.; Zongo, P.; Leicht, A.S.; Hue, O.; Galy, O. Intensity, recovery kinetics and well-being indices are not altered during an official FIFA futsal tournament in Oceanian players. J. Sports Sci. 2015, 34, 379–388. [Google Scholar] [CrossRef] [PubMed]
- Casamichana, D.; Castellano, J.; Castagna, C. Comparing the physical demands of friendly matches and small-sided games in semiprofessional soccer players. J. Strength Cond. Res. 2012, 26, 837–843. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Castellano, J.; Casamichana, D.; Lago, C. The use of match statistics that discriminate between successful and unsuccessful soccer teams. J. Hum. Kinet. 2012, 31, 139. [Google Scholar] [CrossRef] [PubMed]
- Nedelec, M.; McCall, A.; Carling, C.; Legall, F.; Berthoin, S.; Dupont, G. The influence of soccer playing actions on the recovery kinetics after a soccer match. J. Strength Cond. Res. 2014, 28, 1517–1523. [Google Scholar] [CrossRef]
- Sahli Costabal, F.; Yang, Y.; Perdikaris, P.; Hurtado, D.E.; Kuhl, E. Physics-informed neural networks for cardiac activation mapping. Front. Phys. 2020, 8, 42. [Google Scholar] [CrossRef] [Green Version]
- Boukhris, O.; Trabelsi, K.; Shephard, R.J.; Hsouna, H.; Abdessalem, R.; Chtourou, L.; Chtourou, H. Sleep patterns, alertness, dietary intake, muscle soreness, fatigue, and mental stress recorded before, during and after Ramadan observance. Sports 2019, 7, 118. [Google Scholar] [CrossRef] [Green Version]
- Bouzid, M.A.; Abaïdia, A.E.; Bouchiba, M.; Ghattassi, K.; Daab, W.; Engel, F.A.; Chtourou, H. Effects of Ramadan fasting on recovery following a simulated soccer match in professional soccer players: A pilot study. Front. Physiol. 2019, 10, 1480. [Google Scholar] [CrossRef] [Green Version]
- Meeusen, R.; Duclos, M.; Foster, C.; Fry, A.; Gleeson, M.; Nieman, D.; Raglin, J.; Rietjens, G.; Steinacker, J.; Urhausen, A. European College of Sport Science; American College of Sports Medicine. 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, 186–205. [Google Scholar] [CrossRef]
- Howatson, G.; van Someren, K.A. The prevention and treatment of exercise-induced muscle damage. Sports Med. 2008, 38, 483–503. [Google Scholar] [CrossRef]
- Fatouros, I.G.; Jamurtas, A.Z. Insights into the molecular etiology of exercise-induced inflammation: Opportunities for optimizing performance. J. Inflamm. Res. 2016, 9, 175–186. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- My, G.; Marsigliante, S.; Bianco, A.; Zangla, D.; Silva, C.M.D.; Muscella, A. Biological, Psychological, and Physical Performance Variations in Football Players during the COVID-19 Lockdown: A Prospective Cohort Study. Int. J. Environ. Res. Public Health 2022, 19, 2739. [Google Scholar] [CrossRef] [PubMed]
Sleep | Stress |
---|---|
1- very, very good | 1- very, very low |
2- very good | 2- very low |
3- good | 3- low |
4- medium | 4- medium |
5- bad | 5- high |
6- very bad | 6- very high |
7- very, very bad | 7- very, very high |
Fatigue | DOMS |
1- very, very low | 1- very, very low |
2- very low | 2- very low |
3- low | 3- low |
4- medium | 4- medium |
5- high | 5- high |
6- very high | 6- very high |
7- very, very high | 7- very, very high |
Total Quality of Recovery (TQR) | |
---|---|
6 | - |
7 | Very-very low recovery |
8 | - |
9 | Very-low recovery |
10 | - |
11 | 11 Low Recovery |
12 | - |
13 | 13 Reasonable recovery |
14 | - |
15 | 15 Good recovery |
16 | - |
17 | 17 Very good recovery |
18 | - |
19 | Very-very good recovery |
20 | - |
Study | Participant (Number, Sex, Level, Age) | Index Measure | Condition/Duration | Aim | Results | Findings | |
---|---|---|---|---|---|---|---|
HI | TQR | ||||||
Selmi et al. (2018b) [16] | 16, male, professional (25 ± 0.8) | Y | Y | Mid-season competitive period (4 weeks) | To investigate the effect of training load of early season preparation period on psychometric status | TL was associated to HI scores (sleep, stress, fatigue, and DOMS). | Sleep, stress, fatigue, and DOMS represent a useful strategy for coaches to control TL in soccer players during early season preparation period. |
Nobari et al. (2020) [10] | 29, male, professional (15 ± 0.2) | Y | From the beginning of competitive period for eighteen weeks and two weeks half a season | To analyze the associations between training load metrics and weekly (w) reports of HI scores (sleep, stress, fatigue, and DOMS) | There is a correlation between TL variables (TL, monotony, and strain) and weekly well-being indicators between the 20 weeks. | HI scores moderate-large related to acute load, monotony, and strain; however, overall weekly HI was the best predictor of the acute load. | |
Perri et al. (2021) [45] | 28, male, subelite (20.9 ± 2.4) | Y | Competitive season | To investigate the relationship between the daily training load and HI | A significant correlation was reported between daily TL and HI measured the day after; additionally, a similar weekly pattern seems to be repeating itself throughout the season in both TL and HI. | TL affects the HI in soccer players. | |
Clemente et al. (2017) [9] | 35, male, professional (25.7 ± 5.0) | Y | Entire competitive period | To examine the relationship between TL and HI (sleep, stress, fatigue, and DOMS) across two different training microcycles (1 vs. 2 competitive games) | DOMS, fatigue and HI were higher in 2-game weeks compared with 1-game weeks. ITL was negatively correlated to DOMS, sleep, fatigue, stress, and HI in 2-game weeks. From 1-game microcycle only TL negatively correlated to stress. | As a result, care should be taken when planning the lead into and out of a 2-game fixture microcycle, highlighting key specific recovery strategies to dampen the increased stress effect. | |
Nobari et al. (2021) [11] | 36, male, elite (15.5 ± 0.2) | Y | Entire season | To determine weekly (w) and daily variations of well-being ratings relative to HI (i.e., fatigue, stress, DOMS, and sleep quality) during a soccer season based on players’ positions | There were found: A significant increase in stress and sleep for all players’ positions from early- to end-season. A significant difference between well-being status 5 days before match day (MD) and 4 days before MD, compared to MD for all playing positions. The highest and lowest records occurred during end-season for fatigue (central midfielders, and for DOMS (strikers), early season (central defenders), and early season (wide defenders). | Coaches must use HI for monitoring their teams throughout the full season, to avoid overtraining and injuries. | |
Lathlean et al. (2019) [48] | 562, male, elite (17.7 ± 0.3) | Y | One competitive season | To investigate associations between TL (training and competition) and HI (sleep, stress, fatigue, and DOMS) | Season overall wellness had a significant linear negative association with 1 week TL and an inverse U-curve relationship with session TL. HI scores were identified to have associations with TL. | TL is important in managing the HI of players. Quantifying TL and HI helps to optimize player management and has the potential to avoid injury. | |
Malone et al. (2018) [49] | 48, male, professional (25.3 ± 3.1) | Y | Entire competitive season | To investigate the relationship between training and HI (sleep, stress, fatigue, and DOMS) in response to training and/or match load | Significant effects of HI scores on integrated and external TL measures. | Monitoring of player HI can offer coaches with information about the training program that can be usual from individual players during a training session. | |
Thorpe et al. (2015) [32] | 10, male, elite, (19.1 ± 0.6) | Y | In-season competitive period | To determine the sensitivity HI measures to daily TL accumulated over the previous 2, 3, and 4 days (d) during a short in-season competitive period | Correlations between variability HI were negligible and not statistically significant for all accumulation TLs. | The sensitivity of HI variables to changes in TL is generally not improved when compared with TLs. | |
Pereira et al. (2022) [14] | 18, male, professional (24.3 ± 4.8) | Y | Y | Entire season | To analyze TL and TQR and HI changes in professional soccer players after a 4 week pre-season | Higher TL values associated with reduced TQR and increased DOMS scores. | Strong, positive associations between TL and psychometric indices (TQR, DOMS) |
Fessi et al. (2016) [25] | 17, male, professional (23.7 ± 3.2) | Y | Competitive period | To explore changes in weekly TL, quality of sleep, quantity of stress, fatigue, DOMS, and affective valence between pre- and in-season periods of professional soccer players | Higher players’ TLs were recorded during pre-season when compared with in-season period. The ratings of sleep, stress, fatigue, and DOMS in pre-season were higher than those observed during in-season whereas the feeling score was lower. | Pre-season period of training induces significantly more strenuous and exhausting demands on professional soccer players compared with the in-season period at the elite level. | |
Moalla et al. (2016) [15] | 14, male, professional (25.7 ± 2.6) | Y | At the beginning of the 2013–2014 season (16 week training period) | To investigate the relationship between daily TL and the HI (sleep, fatigue, stress, and DOMS) | Significant relationships between TL and perceived fatigue, muscle soreness, sleep, and stress. | HI is both a simple and useful tool for monitoring perceived well-being and psychometric players’ status of professional soccer players. | |
Selmi et al. (2021) [3] | 15, male, professional (24 ± 1) | Y | Y | During the pre-season | To examine the perceived well-being, TQR, and psychological responses during an intensified training period (IT) | Significant relationships were found between TL and HI, TQR. | HI and TQR found to be sensitive measures and may provide coaches with information about wellness and psychological state of soccer players during IT. |
Nobari et al. (2020) [10] | 21, male, elite (under 16 years old) | Y | Competitive season | To analyze the associations between TL, monotony, strain, and HI | HI indicators were moderate-large related to TL, monotony, and strain. | HI was the best predictor of the acute load. | |
Selmi et al. (2020) [1] | 15, male, professional (24 ± 1) | Y | Y | Intensified training periods (IT) (2 weeks) | To examine the relationship between TL, HI, and TQR during intensified training period (IT) | TL, monotony, and strain increased during IT HI (stress, sleep quality, fatigue level, and DOMS) increased and TQR decreased during IT. TL related to HI, TQR. | Higher TL affect negatively, perceived well-being, recovery state of soccer players during IT |
Fernandes et al. (2021b) [26] | 19, female, professional (24.1 ± 2.7) | Y | In-season period (10 weeks) | To describe the association between weekly variations of TL, monotony, strain, and weekly variations of HI (stress, fatigue, DOMS, and sleep) | Some associations between HI categories (sleep, stress, fatigue, DOMS) and TL variables. | Higher TL associated with higher HI. | |
Fernandes et al. (2021a) [44] | 16, female, professional (24.0 ± 2.9) | Y | Competitive season | To compare session rated HI between training and match days (MD) from the same women’s Portuguese League team | DOMS revealed differences between MD-4 vs. MD-2; HI showed higher values on MD-5 vs. MD-4 vs. MD-2 vs. MD. | Results from HI showed that sleep, fatigue, stress, and DOMS were fairly well controlled by coaches and staff. |
Study | Participants (Number, Sex, Level, Age) | Index Measure | Condition/ Duration | Aim | Results | Findings | |
---|---|---|---|---|---|---|---|
HI | TQR | ||||||
Selmi et al. (2018b) [16] | 16, male, professional (25 ± 0.8) | Y | Y | Early season preparation period (4-weeks) | To investigate the influence of HI and TQR on physiological and technical aspects during an intense training cycle | Physiological variables did not change after IT and were not influenced by HI and TQR. HI and TQR were related to successful passes, interceptions, and lost passes measured after IT during soccer specific training test. | No relationship was recorded between psychometric state (HI and TQR) and physiological responses during soccer specific training and those technical aspects were affected by the TQR and the HI variability. |
Buchheit et al. (2014) [17] | 18, male, professional (21.9 ± 2.0) | Y | Pre-season training camp | To examine the usefulness of physiological and psychometric variables during high-intensity running performance | Changes in submaximal exercise HR (Hrex) and sleep, stress, fatigue level, DOMS, but not cortisol, were slightly to very largely correlated with changes in Yo-YoIR2 performance and HSR during the standardized training drills. | Sleep, stress, fatigue level, DOMS, and HRex, but not cortisol, are highly sensitive to subtle daily changes in TL and are well correlated with positive changes in high-intensity running performance | |
Selmi et al. (2021) [3] | 15, male, professional (25 ± 1) | Y | Y | Intensified session (2 weeks) | To examine the relationship between HI (sleep, stress, fatigue, and DOMS), TQR, countermovement jump, and biochemical markers of fatigue in response to an intensified training period | HI was positively correlated with cortisol, T/C ratio, and creatine kinase, and negatively correlated with CMJ. Furthermore, TQR was negatively correlated with T/C ratio, creatine kinase, and C-reactive protein, and positively correlated with CMJ. | Neuromuscular fatigue, muscle damage, and change in the anabolic/catabolic state induced by the IT were related to well-being and TQR among professional soccer players. |
Clemente et al. (2021) [4] | 25, male, professional (28.1 ± 4.6) | Pre-season period | To analyze the blood measures changes and their relationships with HI changes after pre-season training. | Correlations were found between HI, all derived RPE measures, hematological variables, and biochemical measures. | The results indicated the significant relationships between blood and well-being measures; monitoring hematological and biochemical measures allow coaches to minimize injury risk, overreaching, and overtraining. | ||
Mendes et al. (2022) [34] | 35, male, professional (25.7 ± 5.0) | Y | Entire season | To determine the relationships between the HI and CK levels over the weekly microcycles of the season | HI and CK were significantly higher in weekly microcycles with one match than with two. | HI would be a very useful approach to monitor the effects of TL in elite professional soccer players. | |
Thorpe et al. (2015) [32] | 10, male, elite players (19.1 ± 0.6) | Y | Competitive period (17 days) | To quantify the relationship between HI and total high intensity running distance (THIR), countermovement jump height (CMJ), and heart rate variability | Fluctuations in fatigue were significantly correlated with THIR distance. Correlations between variability in muscle soreness, sleep quality, heart rate variability, and THIR distance were negligible and not statistically significant. | Perceived ratings of fatigue were sensitive to daily fluctuations in THIR distance. | |
Saidi et al. (2022) [12] | 14, male, elite soccer players | Y | Congested period of match play (12 weeks) | To analyze HI, biochemical markers and physical fitness in relation to changes in training and match exposure | A significant increase was found in stress, fatigue, DOMS, and HI during the congested period of match play (CP) compared with the regular period of match play (RP). In CP, significant relationships were found between C-reactive protein and creatine kinase with the HI, and the fatigue score. In addition, the fatigue score and DOMS correlated with Yo-Yo intermittent recovery test and best of repeated shuttle sprint ability test. | Elite soccer players’ well-being status reflects declines in physical fitness during intensive period of congested match play, while biochemical changes do not. | |
Osiecki et al. (2015) [7] | 10, male, professional (26.6 ± 4.5) | Y | Competitive season | To indicate the relationship between TQR, RPE, and creatine kinase (CK) after an official professional soccer match | No significant associations were found between TQR and RPE; CK and RPE. However, we did find a statistically significant association between TQR and CK. | The findings indicate that TQR could be used in the evaluation of professional soccer players to determine recovery state after an official game. | |
Brink et al. (2010) [43] | 18, male, elite (17 ± 0.5) | Y | Full competitive season | To investigate the relation between TL, TQR, and monthly field test performance | Session RPE and TQR scores did not contribute to the prediction of performance. The duration of training and game play in the week before field test performance is most strongly related to interval endurance capacity. | Coaches should focus on training duration to improve interval endurance capacity in elite soccer players. | |
Nedelec et al. (2014) [58] | 10, male, professional (21.8 ± 3.2) | Y | Y | From mid- to end-season. | To examine the relationship between, HI, TQR, CMJ, isometric maximum voluntary contraction (MVC) of the hamstring muscles, peak speed (PS) and playing actions completed during the match (PACM) | Correlations between CMJ, MVC, PS, fatigue, muscle soreness, TQR, and PACM were assessed. Significant correlations were observed between the DOMS and the number of sprints <5 m performed during the match at 48 and 72 h. | Fatigue, DOMS, and TQR affect neuromuscular fatigue and physical aspects for up to 72 h. |
Study | Participant: (Number, Sex, Level, Age | Index Measure | Condition/Duration | Psychological Variable Measured | Aim | Results | Findings | |
---|---|---|---|---|---|---|---|---|
HI | TQR | |||||||
Selmi et al. (2018a) [5] | 16, male, professional (25 ± 0.8) | Y | Y | Mid-season competitive period (4 weeks) | Physical enjoyment (PACES) | To assess the effects of the HI on physical enjoyment (PE) and RPE during soccer specific training sessions | PE was not related to HI variables (sleep, stress, level of fatigue, and DOMS) and RPE. | Rating of PE and RPE does not seem to be influenced by the variability of HI during SSG with young players |
Selmi et al. (2018d) [47] | 16, male, professional (16,5 ± 0,6) | Y | Last 3 weeks of the competitive season | Physical enjoyment (PE) | To examine the effects of the HI on physical enjoyment (PE) during SSG | The rating of PE does not seem to be influenced by the variability of the HI during SSG with young players. Stress, fatigue, sleep, and DOMS are not contributing signals to altered PE. | The PE induced by a training method might vary according to modality of exercise, outcomes, and desire of the players. | |
Selmi et al. (2018c) [46] | 16, male, young soccer players (16.5 ± 0.6) | Y | Competitive season | Physical enjoyment (PE) | To investigate the effects of the TQR on physical enjoyment (PE) rating during SSG | No significant correlation found between TQR and PE. | The PE induced by a training method might vary according to types of exercise, motivation, and encouragement of the players. PE does not seem to be affected by the variability of TQR during SSG. | |
Selmi et al. (2020) [1] | 15, male, professional (24 ± 1) | Y | Y | Intensified training periods (IT) (2 weeks) | Profile of mood states (POMS) | To examine the perceived well-being, recovery quality and psychological responses during intensified training period (IT) | Significant relationships were found between TL and HI, TQR and mood state. | HI?, TQR, and mood were found to be sensitive measures and may provide coaches with information about psychological state of soccer players during IT. |
Fessi et al. (2016) [25] | 17, male, professional (23.7 ± 3.2) | Y | Competitive period | Feeling scale | To examine the association between HI and affective valence during pre- and in-season | Affective valence associated with sleep, stress, fatigue, and DOMS during pre- and in-season. | PE seem to be affected by the variability of HI |
Study | Participant: (Number, Sex, Level, Age) | Condition/ Duration | Aim | Results | Findings | |
---|---|---|---|---|---|---|
HI | TQR | |||||
Chamari et al. 2012 [30] | 42, male, professional | Y | Ramadan (during two consecutive seasons) | To determine the effects of Ramadan’s monthly workout on injury rates and HI in professional players | This study showed no significant differences between the three periods (4 weeks before Ramadan, during the month of Ramadan, and 4 weeks after Ramadan) were observed in sleep quality, stress, fatigue level, and DOMS | HI was not affected by the month of Ramadan. |
Bouzid et al. 2019 [61] | 8, male, elite soccer players (21.0 ± 0.4) | Y | Ramadan month | To examine the effects of Ramadan fasting on HI following soccer matches simulation | Stress increased only at 0 h on end-Ramadan, while fatigue level increased at 24 h at before-Ramadan and at 0, 24, and 48 h at end-Ramadan, and DOMS increased throughout the recovery period at both occasions, with a higher level at end-Ramadan. | Subjective ratings parameters were higher at the end of Ramadan in soccer players. |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Selmi, O.; Ouergui, I.; Muscella, A.; My, G.; Marsigliante, S.; Nobari, H.; Suzuki, K.; Bouassida, A. Monitoring Psychometric States of Recovery to Improve Performance in Soccer Players: A Brief Review. Int. J. Environ. Res. Public Health 2022, 19, 9385. https://doi.org/10.3390/ijerph19159385
Selmi O, Ouergui I, Muscella A, My G, Marsigliante S, Nobari H, Suzuki K, Bouassida A. Monitoring Psychometric States of Recovery to Improve Performance in Soccer Players: A Brief Review. International Journal of Environmental Research and Public Health. 2022; 19(15):9385. https://doi.org/10.3390/ijerph19159385
Chicago/Turabian StyleSelmi, Okba, Ibrahim Ouergui, Antonella Muscella, Giulia My, Santo Marsigliante, Hadi Nobari, Katsuhiko Suzuki, and Anissa Bouassida. 2022. "Monitoring Psychometric States of Recovery to Improve Performance in Soccer Players: A Brief Review" International Journal of Environmental Research and Public Health 19, no. 15: 9385. https://doi.org/10.3390/ijerph19159385
APA StyleSelmi, O., Ouergui, I., Muscella, A., My, G., Marsigliante, S., Nobari, H., Suzuki, K., & Bouassida, A. (2022). Monitoring Psychometric States of Recovery to Improve Performance in Soccer Players: A Brief Review. International Journal of Environmental Research and Public Health, 19(15), 9385. https://doi.org/10.3390/ijerph19159385