Relationship Between the Total Quality Recovery Scale and Race Performance in Competitive College Swimmers over Two Seasons
Abstract
1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Training and Race Schedule
2.3. TQR Scores and TQR-Related Variables
2.4. Performance
2.5. Statistical Analysis
3. Results
3.1. Training Volume and TQR Distribution
3.2. TQR-Related Variables and Race Performance
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
TQR | Total quality recovery |
TQRra | TQR rolling averages |
TQRewma | TQR exponentially weighted moving averages |
sRPE | Session Rating of Perceived Exertion |
ACWR | Acute Chronic Workload Ratio |
LOCF | Last Observation Carried Forward |
CI | Confidence Interval |
IQR | Interquartile Range |
References
- Amara, S.; Hammami, R.; Zacca, R.; Mota, J.; Negra, Y.; Chortane, S. The effect of combining HIIT and dry-land training on strength, technique, and 100-m butterfly swimming performance in age-group swimmers: A randomized controlled trial. Biol. Sport 2023, 40, 85–91. [Google Scholar] [CrossRef] [PubMed]
- Chortane, O.G.; Amara, S.; Barbosa, T.M.; Hammami, R.; Khalifa, R.; Chortane, S.G.; van den Tillaar, R. Effect of high-volume training on psychological state and performance in competitive swimmers. Int. J. Environ. Res. Public Health 2022, 19, 7619. [Google Scholar] [CrossRef] [PubMed]
- Aspenes, S.; Kjendlie, P.L.; Hoff, J.; Helgerud, J. Combined strength and endurance training in competitive swimmers. J. Sports Sci. Med. 2009, 8, 357–365. [Google Scholar] [PubMed]
- Houmard, J.A.; Scott, B.K.; Justice, C.L.; Chenier, T.C. The effects of taper on performance in distance runners. Med. Sci. Sports Exerc. 1994, 26, 624–631. [Google Scholar]
- Mujika, I.; Padilla, S.; Pyne, D. Swimming performance changes during the final 3 weeks of training leading to the Sydney 2000 Olympic Games. Int. J. Sports Med. 2002, 23, 582–587. [Google Scholar] [CrossRef]
- Neary, J.P.; Bhambhani, Y.N.; McKenzie, D.C. Effects of different stepwise reduction taper protocols on cycling performance. Can. J. Appl. Physiol. 2003, 28, 576–587. [Google Scholar] [CrossRef]
- Neary, J.P.; Martin, T.P.; Reid, D.C.; Burnham, R.; Quinney, H.A. The effects of a reduced exercise duration taper programme on performance and muscle enzymes of endurance cyclists. Eur. J. Appl. Physiol. Occup. Physiol. 1992, 65, 30–36. [Google Scholar] [CrossRef]
- Houmard, J.A.; Johns, R.A. Effects of taper on swim performance. Practical implications. Sports Med. 1994, 17, 224–232. [Google Scholar] [CrossRef]
- Mujika, I.; Padilla, S. Detraining: Loss of training-induced physiological and performance adaptations. Part I: Short term insufficient training stimulus. Sports Med. 2000, 30, 79–87. [Google Scholar] [CrossRef]
- Thomas, L.; Busso, T. A theoretical study of taper characteristics to optimize performance. Med. Sci. Sports Exerc. 2005, 37, 1615–1621. [Google Scholar] [CrossRef]
- Bosquet, L.; Montpetit, J.; Arvisais, D.; Mujika, I. Effects of tapering on performance: A meta-analysis. Med. Sci. Sports Exerc. 2007, 39, 1358–1365. [Google Scholar] [CrossRef] [PubMed]
- Aouani, H.; Amara, S.; Rebai, H.; Barbosa, T.M.; van den Tillaar, R. Optimizing performance and mood state in competitive swimmers through tapering strategies. Front. Psychol. 2024, 15, 1307675. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.; Wang, Y.T.; Gao, W.; Zhong, Y. Effects of tapering on performance in endurance athletes: A systematic review and meta-analysis. PLoS ONE 2023, 18, e0282838. [Google Scholar] [CrossRef] [PubMed]
- Travis, S.K.; Mujika, I.; Gentles, J.A.; Stone, M.H.; Bazyler, C.D. Tapering and peaking maximal strength for powerlifting performance: A review. Sports 2020, 8, 125. [Google Scholar] [CrossRef]
- Thomas, L.; Mujika, I.; Busso, T. A model study of optimal training reduction during pre-event taper in elite swimmers. J. Sports Sci. 2008, 26, 643–652. [Google Scholar] [CrossRef]
- Banister, E.W.; Calvert, T.W.; Savage, M.V.; Bach, A. A system model of training for athletic performance. Aust. J. Sports Med. 1975, 7, 170–176. [Google Scholar]
- Vermeire, K.; Ghijs, M.; Bourgois, J.G.; Boone, J. The fitness-fatigue model: What’s in the numbers? Int. J. Sports Physiol. Perform. 2022, 17, 810–813. [Google Scholar] [CrossRef]
- Surała, O.; Malczewska-Lenczowska, J.; Sitkowski, D.; Witek, K.; Słomiński, P.; Certa, M.; Madej, D. Effect of training load on sleep parameters and biochemical fatigue markers in elite swimmers. Biol. Sport 2023, 40, 1229–1237. [Google Scholar] [CrossRef]
- Makni, E.; Bouaziz, T.; Chamari, K.; Tarwneh, R.; Moalla, W.; Elloumi, M. Fatigue score as a promising complementary training monitoring tool: A pilot study in elite rugby sevens players. Biol. Sport 2023, 40, 513–520. [Google Scholar] [CrossRef]
- Aubry, A.; Hausswirth, C.; Louis, J.; Coutts, A.J.; LE Meur, Y. Functional overreaching: The key to peak performance during the taper? Med. Sci. Sports Exerc. 2014, 46, 1769–1777. [Google Scholar] [CrossRef]
- Kenttä, G.; Hassmén, P. Overtraining and recovery. A conceptual model. Sports Med. 1998, 26, 1–16. [Google Scholar] [CrossRef] [PubMed]
- Saw, A.E.; Main, L.C.; Gastin, P.B. Monitoring the athlete training response: Subjective self-reported measures trump commonly used objective measures: A systematic review. Br. J. Sports Med. 2016, 50, 281–291. [Google Scholar] [CrossRef] [PubMed]
- Saw, A.E.; Kellmann, M.; Main, L.C.; Gastin, P.B. Athlete self-report measures in research and practice: Considerations for the discerning reader and fastidious practitioner. Int. J. Sports Physiol. Perform. 2017, 12, S2127–S2135. [Google Scholar] [CrossRef] [PubMed]
- Brink, M.S.; Nederhof, E.; Visscher, C.; Schmikli, S.L.; Lemmink, K.A.P.M. Monitoring load, recovery, and performance in young elite soccer players. J. Strength Cond. Res. 2010, 24, 597–603. [Google Scholar] [CrossRef]
- 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]
- Freitas, V.H.; Nakamura, F.Y.; Miloski, B.; Samulski, D.; Bara-Filho, M.G. Sensitivity of physiological and psychological markers to training load intensification in volleyball players. J. Sports Sci. Med. 2014, 13, 571–579. [Google Scholar]
- Debien, P.B.; Mancini, M.; Coimbra, D.R.; de Freitas, D.G.S.; Miranda, R.; Bara Filho, M.G.B. 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]
- Timoteo, T.F.; Debien, P.B.; Miloski, B.; Werneck, F.Z.; Gabbett, T.; Bara Filho, M.G. Influence of workload and recovery on injuries in elite male volleyball players. J. Strength Cond. Res. 2021, 35, 791–796. [Google Scholar] [CrossRef]
- Sansone, P.; Tschan, H.; Foster, C.; Tessitore, A. Monitoring training load and perceived recovery in female basketball: Implications for training design. J. Strength Cond. Res. 2020, 34, 2929–2936. [Google Scholar] [CrossRef]
- Crowcroft, S.; McCleave, E.; Slattery, K.; Coutts, A.J. Assessing the measurement sensitivity and diagnostic characteristics of athlete-monitoring tools in national swimmers. Int. J. Sports Physiol. Perform. 2017, 12, S295–S2100. [Google Scholar] [CrossRef]
- Collette, R.; Kellmann, M.; Ferrauti, A.; Meyer, T.; Pfeiffer, M. Relation between training load and recovery-stress state in high-performance swimming. Front. Physiol. 2018, 9, 845. [Google Scholar] [CrossRef] [PubMed]
- Avalos, M.; Hellard, P.; Chatard, J.C. Modeling the training–performance relationship using a mixed model in elite swimmers. Med. Sci. Sports Exerc. 2003, 35, 838–846. [Google Scholar] [CrossRef] [PubMed]
- Ruiz-Navarro, J.J.; López-Belmonte, Ó.; Gay, A.; Cuenca-Fernández, F.; Arellano, R. A new model of performance classification to standardize the research results in swimming. Eur. J. Sport Sci. 2023, 23, 478–488. [Google Scholar] [CrossRef] [PubMed]
- JATI-AATI. Japan Association of training Instructors-Advanced Accredited Training instructor. Available online: https://jati.jp/license/ (accessed on 6 November 2024). (In Japanese).
- Williams, S.; West, S.; Cross, M.J.; Stokes, K.A. Better way to determine the acute:chronic workload ratio? Br. J. Sports Med. 2017, 51, 209–210. [Google Scholar] [CrossRef]
- Stone, M.J.; Knight, C.J.; Hall, R.; Shearer, C.; Nicholas, R.; Shearer, D.A. The psychology of athletic tapering in sport: A scoping review. Sports Med. 2023, 53, 777–801. [Google Scholar] [CrossRef]
- Kellmann, M. Preventing overtraining in athletes in high-intensity sports and stress/recovery monitoring. Scand. J. Med. Sci. Sports 2010, 20, 95–102. [Google Scholar] [CrossRef]
- Howle, K.; Waterson, A.; Duffield, R. Recovery profiles following single and multiple matches per week in professional football. Eur. J. Sport Sci. 2019, 19, 1303–1311. [Google Scholar] [CrossRef]
- Inoue, A.; Dos Santos Bunn, P.D.S.; do Carmo, E.C.; Lattari, E.; da Silva, E.B. Internal training load perceived by athletes and planned by coaches: A systematic review and meta-analysis. Sports Med. Open 2022, 8, 35. [Google Scholar] [CrossRef]
- Doeven, S.H.; Brink, M.S.; Frencken, W.G.P.; Lemmink, K.A.P.M. Impaired player-coach perceptions of exertion and recovery during match congestion. Int. J. Sports Physiol. Perform. 2017, 12, 1151–1156. [Google Scholar] [CrossRef]
- Gabbett, T.J.; Hulin, B.T.; 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, 444–445. [Google Scholar] [CrossRef]
- Menaspà, P. Are rolling averages a good way to assess training load for injury prevention? Br. J. Sports Med. 2017, 51, 618–619. [Google Scholar] [CrossRef] [PubMed]
- Overall, J.E.; Tonidandel, S.; Starbuck, R.R. Last-observation-carried-forward (LOCF) and tests for difference in mean rates of change in controlled repeated measurements designs with dropouts. Soc. Sci. Res. 2009, 38, 492–503. [Google Scholar] [CrossRef]
- Benson, L.C.; Stilling, C.; Owoeye, O.B.A.; Emery, C.A. Evaluating methods for imputing missing data from longitudinal monitoring of athlete workload. J. Sports Sci. Med. 2021, 20, 188–196. [Google Scholar] [CrossRef] [PubMed]
- Impellizzeri, F.M.; Woodcock, S.; Coutts, A.J.; Fanchini, M.; McCall, A.; Vigotsky, A.D. What role do chronic workloads play in the acute to chronic workload ratio? Time to dismiss ACWR and its underlying theory. Sports Med. 2021, 51, 581–592. [Google Scholar] [CrossRef]
- Mujika, I.; Padilla, S. Scientific bases for precompetition tapering strategies. Med. Sci. Sports Exerc. 2003, 35, 1182–1187. [Google Scholar] [CrossRef]
- Mujika, I.; Chatard, J.C.; Busso, T.; Geyssant, A.; Barale, F.; Lacoste, L. Effects of training on performance in competitive swimming. Can. J. Appl. Physiol. 1995, 20, 395–406. [Google Scholar] [CrossRef]
- Lätt, E.; Jürimäe, J.; Mäestu, J.; Purge, P.; Rämson, R.; Haljaste, K.; Keskinen, K.L.; Rodriguez, F.A.; Jürimäe, T. Physiological, biomechanical and anthropometrical predictors of sprint swimming performance in adolescent swimmers. J. Sports Sci. Med. 2010, 9, 398–404. [Google Scholar]
- Saavedra, J.M.; Escalante, Y.; Rodríguez, F.A. A multivariate analysis of performance in young swimmers. Pediatr. Exerc. Sci. 2010, 22, 135–151. [Google Scholar] [CrossRef]
- Osiecki, R.; Rubio, G.; Coelho, R.; Novack, L.; Conde, S.; Alves, C.; Malfatti, M. The total quality recovery scale (TQR) as a proxy for determining athletes’ recovery state after a professional soccer match. J. Exerc. Physiol. Online 2015, 18, 27–32. [Google Scholar]
TQR | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | Total |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Total instances during observation period | 359 | 322 | 868 | 1284 | 1251 | 1758 | 1689 | 1961 | 883 | 599 | 207 | 153 | 50 | 23 | 22 | 11,429 |
% | 3.1 | 2.8 | 7.6 | 11.2 | 10.9 | 15.4 | 14.8 | 17.2 | 7.7 | 5.2 | 1.8 | 1.3 | 0.4 | 0.2 | 0.2 | 100.0 |
Instances during race days | 11 | 11 | 31 | 39 | 54 | 63 | 94 | 115 | 58 | 44 | 13 | 11 | 3 | 2 | 1 | 550 |
% | 2.0 | 2.0 | 5.6 | 7.1 | 9.8 | 11.5 | 17.1 | 20.9 | 10.5 | 8.0 | 2.4 | 2.0 | 0.5 | 0.4 | 0.2 | 100.0 |
Q1 | Q2 | Q3 | Q4 | p for Trend | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
n | n | OR | 95%CI | n | OR | 95%CI | n | OR | 95%CI | |||
(Lower–Upper) | (Lower–Upper) | (Lower–Upper) | ||||||||||
TQR | ||||||||||||
Raw | 146 | reference | 157 | 1.46 | (0.86–2.49) | 115 | 2.22 † | (1.28–3.87) | 132 | 3.13 ‡ | (1.84–5.30) | <0.001 |
TQRra | ||||||||||||
7d:14d | 136 | reference | 139 | 1.40 | (0.82–2.38) | 137 | 1.79 | (1.06–3.03) | 138 | 1.66 | (0.98–2.82) | 0.037 |
7d:21d | 140 | reference | 136 | 1.24 | (0.74–2.10) | 136 | 1.29 | (0.76–2.17) | 138 | 1.68 | (1.01–2.80) | 0.051 |
7d:28d | 138 | reference | 138 | 1.04 | (0.61–1.76) | 137 | 1.38 | (0.82–2.31) | 137 | 1.72 | (1.03–2.87) | 0.019 |
14d:21d | 137 | reference | 139 | 1.12 | (0.67–1.88) | 137 | 1.04 | (0.62–1.74) | 137 | 1.35 | (0.81–2.24) | 0.312 |
14d:28s | 137 | reference | 139 | 1.21 | (0.72–2.02) | 137 | 1.11 | (0.66–1.88) | 137 | 1.54 | (0.93–2.57) | 0.133 |
21d:28d | 136 | reference | 145 | 1.33 | (0.81–2.18) | 127 | 0.89 | (0.52–1.50) | 142 | 0.94 | (0.56–1.57) | 0.463 |
TQRewma | ||||||||||||
7d:14d | 137 | reference | 138 | 1.07 | (0.62–1.84) | 140 | 1.58 | (0.94–2.66) | 135 | 2.15 † | (1.28–3.60) | 0.001 |
7d:21d | 138 | reference | 138 | 1.30 | (0.76–2.24) | 136 | 1.48 | (0.86–2.53) | 138 | 2.62 ‡ | (1.56–4.41) | <0.001 |
7d:28d | 137 | reference | 140 | 1.37 | (0.79–2.35) | 135 | 1.71 | (1.00–2.92) | 138 | 2.48 ‡ | (1.47–4.19) | <0.001 |
14d:21d | 134 | reference | 142 | 1.29 | (0.76–2.22) | 138 | 1.49 | (0.87–2.55) | 136 | 2.30 † | (1.36–3.88) | 0.001 |
14d:28s | 142 | reference | 133 | 1.13 | (0.66–1.96) | 138 | 1.80 | (1.07–3.03) | 137 | 2.13 † | (1.27–3.57) | 0.001 |
21d:28d | 134 | reference | 142 | 1.20 | (0.70–2.05) | 142 | 1.47 | (0.87–2.48) | 132 | 2.19 † | (1.30–3.69) | 0.002 |
Q1 | Q2 | Q3 | Q4 | p for Trend | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
n | n | OR | 95% CI | n | OR | 95% CI | n | OR | 95% CI | |||
(Lower Upper) | (Lower Upper) | (Lower Upper) | ||||||||||
z-TQR | ||||||||||||
Raw | 140 | Reference | 136 | 0.95 | (0.53–1.70) | 145 | 1.70 | (1.00–2.91) | 129 | 4.35 ‡ | (2.56–7.41) | <0.001 |
z-TQRra | ||||||||||||
7d:14d | 137 | Reference | 138 | 1.11 | (0.65–1.88) | 138 | 1.45 | (0.86–2.43) | 137 | 1.72 | (1.03–2.87) | 0.022 |
7d:21d | 137 | Reference | 138 | 1.06 | (0.63–1.80) | 138 | 1.10 | (0.65–1.86) | 137 | 1.75 | (1.05–2.90) | 0.031 |
7d:28d | 137 | Reference | 138 | 1.11 | (0.65–1.88) | 138 | 1.45 | (0.86–2.43) | 137 | 1.72 | (1.03–2.87) | 0.022 |
14d:21d | 137 | Reference | 138 | 0.89 | (0.53–1.49) | 138 | 0.86 | (0.51–1.44) | 137 | 1.34 | (0.81–2.20) | 0.286 |
14d:28s | 137 | Reference | 138 | 1.22 | (0.73–2.03) | 138 | 1.10 | (0.65–1.85) | 137 | 1.36 | (0.81–2.26) | 0.325 |
21d:28d | 137 | Reference | 138 | 0.99 | (0.60–1.63) | 138 | 0.87 | (0.52–1.44) | 137 | 0.82 | (0.49–1.36) | 0.367 |
z-TQRewma | ||||||||||||
7d:14d | 137 | Reference | 138 | 0.95 | (0.55–1.65) | 138 | 1.78 | (1.06–2.99) | 137 | 2.09 † | (1.25–3.51) | 0.001 |
7d:21d | 137 | Reference | 138 | 1.16 | (0.67–2.00) | 138 | 1.69 | (1.00–2.88) | 137 | 2.48 ‡ | (1.47–4.18) | <0.001 |
7d:28d | 137 | Reference | 138 | 0.95 | (0.55–1.65) | 138 | 1.67 | (0.99–2.81) | 137 | 2.22 † | (1.33–3.72) | <0.001 |
14d:21d | 137 | reference | 138 | 1.03 | (0.59–1.79) | 138 | 1.85 | (1.10–3.11) | 137 | 2.18 † | (1.30–3.66) | <0.001 |
14d:28s | 137 | reference | 138 | 0.99 | (0.57–1.71) | 138 | 1.78 | (1.06–2.99) | 137 | 2.03 | (1.21–3.41) | 0.001 |
21d:28d | 137 | reference | 138 | 0.99 | (0.58–1.70) | 138 | 1.31 | (0.78–2.21) | 137 | 2.06 | (1.24–3.43) | 0.002 |
Q1 | Q2 | Q3 | Q4 | p | |||||
---|---|---|---|---|---|---|---|---|---|
Median | (IQR) | Median | (IQR) | Median | (IQR) | Median | (IQR) | ||
TQR | |||||||||
race day | 101.0 | (100.1–101.9) b | 100.9 | (99.9–101.8) b | 100.2 | (99.7–101.4) a | 100.3 | (99.6–101.0) a | <0.001 |
TQRra | – | ||||||||
7d:14d | 101.2 | (100.0–102.3) b | 100.6 | (99.8–101.3) a | 100.4 | (99.8–101.3) a | 100.4 | (99.6–101.4) a | 0.001 |
7d:21d | 101.1 | (100.0–102.1) b | 100.5 | (99.8–101.4) ab | 100.6 | (99.8–101.7) ab | 100.3 | (99.6–101.2) a | 0.002 |
7d:28d | 101.0 | (100.0–102.0) b | 100.8 | (99.8–101.7) ab | 100.5 | (99.8–101.6) ab | 100.3 | (99.6–101.2) a | 0.003 |
14d:21d | 100.9 | (99.9–101.9) b | 100.6 | (99.8–101.7) ab | 100.7 | (99.8–101.6) ab | 100.3 | (99.8–101.2) a | 0.039 |
14d:28s | 100.8 | (99.9–101.8) n.s. | 100.5 | (99.8–101.5) | 100.8 | (99.9–101.7) | 100.3 | (99.7–101.2) | 0.032 |
21d:28d | 100.6 | (99.8–101.7) n.s. | 100.5 | (99.6–101.3) | 100.7 | (100.0–101.6) | 100.7 | (99.9–101.7) | 0.103 |
TQRewma | – | – | |||||||
7d:14d | 101.1 | (100.0–101.9) c | 100.7 | (99.8–101.7) bc | 100.5 | (99.8–101.4) ab | 100.3 | (99.6–101.1) a | <0.001 |
7d:21d | 101.1 | (100.0–101.9) b | 100.6 | (99.8–101.5) b | 100.7 | (99.9–101.6) b | 100.2 | (99.5–100.9) a | 0.001 |
7d:28d | 101.1 | (100.1–101.9) b | 100.6 | (99.8–101.5) ab | 100.5 | (99.8–101.7) ab | 100.3 | (99.6–101.2) a | <0.001 |
14d:21d | 101.1 | (100.0–102.0) b | 100.6 | (99.8–101.5) ab | 100.6 | (99.9–101.6) ab | 100.3 | (99.6–101.2) a | <0.001 |
14d:28s | 101.1 | (100.0–102.0) b | 100.6 | (99.9–101.5) ab | 100.5 | (99.8–101.5) ab | 100.3 | (99.6–101.2) a | 0.001 |
21d:28d | 100.9 | (100.0–101.9) b | 100.7 | (99.8–101.8) ab | 100.6 | (99.8–101.5) ab | 100.3 | (99.6–101.2) a | 0.002 |
Q1 | Q2 | Q3 | Q4 | p | |||||
---|---|---|---|---|---|---|---|---|---|
Median | (IQR) | Median | (IQR) | Median | (IQR) | Median | (IQR) | ||
z-TQR | |||||||||
race day | 101.0 | (100.1–101.9) c | 100.9 | (99.9–101.8) b | 100.2 | (99.7–101.4) a | 100.3 | (99.6–101) a | <0.001 |
z-TQRra | |||||||||
7d:14d | 101.1 | (99.9–102.1) b | 100.6 | (99.9–101.4) ab | 100.5 | (99.8–101.3) a | 100.4 | (99.6–101.4) a | 0.003 |
7d:21d | 101.0 | (99.9–101.9) b | 100.7 | (99.9–101.4) ab | 100.7 | (99.9–101.7) b | 100.3 | (99.6–101.1) a | 0.001 |
7d:28d | 101.1 | (99.9–101.9) b | 100.7 | (99.9–101.7) ab | 100.5 | (99.8–101.5) ab | 100.3 | (99.6–101.3) a | 0.003 |
14d:21d | 100.8 | (99.8–102.0) b | 100.8 | (99.9–101.7) ab | 100.7 | (99.9–101.6) ab | 100.3 | (99.7–101.1) a | 0.027 |
14d:28s | 100.8 | (99.9–101.8) n.s. | 100.5 | (99.8–101.7) | 100.9 | (99.8–101.8) | 100.4 | (99.8–101.2) | 0.098 |
21d:28d | 100.4 | (99.8–101.5) n.s. | 100.6 | (99.7–101.4) | 100.7 | (99.9–101.6) | 100.7 | (99.9–101.7) | 0.477 |
z-TQRewma | – | ||||||||
7d:14d | 101.1 | (100.0–102.0) c | 100.8 | (100.0–101.6) bc | 100.4 | (99.8–101.3) ab | 100.3 | (99.6–101.3) a | <0.001 |
7d:21d | 101.1 | (100.0–101.9) c | 100.6 | (99.9–101.6) bc | 100.5 | (99.8–101.4) b | 100.2 | (99.6–101.2) a | <0.001 |
7d:28d | 101.0 | (100.0–101.9) b | 100.7 | (100.0–101.9) b | 100.5 | (99.8–101.4) ab | 100.3 | (99.6–101.1) a | <0.001 |
14d:21d | 101.0 | (100.0–101.9) c | 100.8 | (100.0–101.9) bc | 100.5 | (99.8–101.2) ab | 100.3 | (99.6–101.3) a | <0.001 |
14d:28s | 101.0 | (100.0–101.9) c | 100.7 | (100.0–101.6) bc | 100.5 | (99.8–101.4) ab | 100.3 | (99.6–101.2) a | 0.001 |
21d:28d | 100.9 | (100.0–102.0) b | 100.7 | (99.9–101.7) b | 100.6 | (99.8–101.5) ab | 100.3 | (99.6–101.2) a | 0.002 |
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Kato, T.; Kasugai, R.; Sakai, K. Relationship Between the Total Quality Recovery Scale and Race Performance in Competitive College Swimmers over Two Seasons. Sports 2025, 13, 139. https://doi.org/10.3390/sports13050139
Kato T, Kasugai R, Sakai K. Relationship Between the Total Quality Recovery Scale and Race Performance in Competitive College Swimmers over Two Seasons. Sports. 2025; 13(5):139. https://doi.org/10.3390/sports13050139
Chicago/Turabian StyleKato, Tsuyoshi, Ryota Kasugai, and Kensuke Sakai. 2025. "Relationship Between the Total Quality Recovery Scale and Race Performance in Competitive College Swimmers over Two Seasons" Sports 13, no. 5: 139. https://doi.org/10.3390/sports13050139
APA StyleKato, T., Kasugai, R., & Sakai, K. (2025). Relationship Between the Total Quality Recovery Scale and Race Performance in Competitive College Swimmers over Two Seasons. Sports, 13(5), 139. https://doi.org/10.3390/sports13050139