Philippe Hellard, Marta Avalos-Fernandes, Gaelle Lefort, Robin Pla, Inigo Mujika, Jean-François Toussaint, David B. Pyne, Fédération Française de Natation (FFN), CREPS Bordeaux Aquitaine, Centre d’études des transformations des activités physiques et sportives (CETAPS), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut de Recherche Interdisciplinaire Homme et Société (IRIHS), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU), Université de Bordeaux (UB), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI), University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), Universidad Finis Terrae, Institut de recherche biomédicale et d’épidémiologie du sport (IRMES - URP_7329), Institut national du sport, de l'expertise et de la performance (INSEP)-Université de Paris (UP), Centre d'Investigation en Médecine du sport (CIMS), Hôtel-Dieu-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), University of Canberra, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), French Institute of Sport (INSEP), Research Department, Laboratory Sport, Expertise and Performance (EA7370) (SEP (EA7370)), Institut national du sport, de l'expertise et de la performance (INSEP), Institut de recherche biomédicale et d’épidémiologie du sport (IRMES - EA 7329), Université Paris Descartes - Paris 5 (UPD5)-Institut national du sport, de l'expertise et de la performance (INSEP), Université Paris Descartes - Paris 5 (UPD5), Université Sorbonne Paris Cité (USPC), Hôpital Hôtel-Dieu [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôtel-Dieu, Institut national du sport, de l'expertise et de la performance (INSEP)-Université Paris Cité (UPCité), and Avalos, Marta
Background: This study investigated the periodization of elite swimmers' training over the 25 weeks preceding the major competition of the season. Methods: We conducted a retrospective observational study of elite male (n = 60) and female (n = 67) swimmers (46 sprint, 81 middle-distance) over 20 competitive seasons (1992-2012). The following variables were monitored: training corresponding to blood lactate 4-6 mmol.L-1, >6 mmol.L-1, and maximal swimming speed; general conditioning and maximal strength training hours; total training load (TTL); and the mean normalized volumes for both in-water and dryland workouts. Latent class mixed modeling was used to identify various TTL pattern groups. The associations between pattern groups and sex, age, competition event, Olympic quadrennial year, training contents, and relative performance were quantified. Results: For the entire cohort, similar to 86-90% of the training was swum at an intensity of [La](b) 4-6 mmol.L-1, and 3.5-4.5% at > 6 mmol.L-1. Three sprint TTL patterns were identified: a pattern with two long similar to 14-15-week macrocycles, one with two similar to 12-13 week macrocycles each composed of a balanced training load, and one with a single stable flat macrocycle. The long pattern elicited the fastest performances and was most prevalent in Olympic quadrennials (i.e., 4 seasons preceding the 2004, 2008, and 2012 Olympic Games). This pattern exhibited moderate week-to-week TTL variability (6 +/- 3%), progressive training load increases between macrocycles, and more training at 6 mmol.L-1. This fastest sprint pattern showed a waveform in the second macrocycle consisting of two progressive load peaks 10-11 and 4-6 weeks before competition. The stable flat pattern was the slowest and showed low TTL variability (4 +/- 3%), training load decreases between macrocycles (P < 0.01), and more training at 4-6 mmol.L-1 (P < 0.01). Conclusion: Progressive increases in training load, macrocycles lasting about 14-15 weeks, and substantial volume of training at intensities 6 mmol.L-1, were associated with peak performance in elite swimmers. This research was partially funded by the French Institute of Sport, Expertise and Performance (INSEP) and the French Ministry in charge of sports under grant no. 14r21.