Resumen de Effects on Strength, Power, and Flexibility in Adolescents of Nonperiodized Vs. Daily Nonlinear Periodized Weight Training
Eveline Moreira Moraes, Steven J. Fleck, Marcelo Ricardo Dias, Roberto Simão
The aim of this study was to compare 2 models of resistance training (RT) programs, nonperiodized (NP) training and daily nonlinear periodized (DNLP) training, on strength, power, and flexibility in untrained adolescents. Thirty-eight untrained male adolescents were randomly assigned to 1 of 3 groups: a control group, NP RT program, and DNLP program. The subjects were tested pretraining and after 4, 8, and 12 weeks for 1 repetition maximum (1RM) resistances in the bench press and 45[degrees] leg press, sit and reach test, countermovement vertical jump (CMVJ), and standing long jump (SLJ). Both training groups performed the same sequence of exercises 3 times a week for a total of 36 sessions. The NP RT consisted of 3 sets of 10-12RM throughout the training period. The DNLP training consisted of 3 sets using different training intensities for each of the 3 training sessions per week. The total volume of the training programs was not significantly different. Both the NP and DNLP groups exhibited a significant increase in the 1RM for the bench press and 45[degrees] leg press posttraining compared with that pretraining, but there were no significant differences between groups (p <= 0.05). The DNLP group's 1RM changes showed greater percentage improvements and effect sizes. Training intensity for the bench press and 45[degrees] leg press did not significantly change during the training. In the CMVJ and SLJ tests, NP and DNLP training showed no significant change. The DNLP group showed a significant increase in the sit and reach test after 8 and 12 weeks of training compared with pretraining; this did not occur with NP training. In summary, in untrained adolescents during a 12-week training period, a DNLP program can be used to elicit similar and possible superior maximal strength and flexibility gains compared with an NP multiset training model.
