Set configuration refers to the repetitions performed with regard to the maximum possible number of repetitions, a factor affecting RPE that has not been previously studied. This study analyzed the effect of set configuration and muscle mass on RPE. Eight students ( M age = 23.7 yr. ± 1.7) completed four sessions corresponding to types of exercise with different amount of muscle mass (bench press and parallel squat) and two set configurations: a session with five sets of repetitions to failure and a cluster session. The cluster session involved the same intensity, volume, and rest than the failure session, guaranteeing the same work-to-rest ratio. RPE was higher in Failure vs Cluster sessions and higher in parallel squat vs bench press. This suggests that set configuration influences RPE. Similarly, RPE can be affected by the muscle mass of the exercise performed.

The purpose of this study was to compare acute mechanical and metabolic effects of 2 sessions of resistance training equated by volume and total resting time but with different set configuration: sets to failure (FS) vs. distribution of rest between each repetition (NFS). 10 male judoists completed a session consisting of 3 sets to failure of parallel back squat with 4 repetitions at maximum load, and a rest of 3 min between the sets. At least 72 h later subjects developed the same volume, but total resting time was distributed among individual repetitions. Before and after sessions isometric force and mean propulsive velocity with load corresponding to maximum propulsive power were assessed. Results showed that in respect to FS, NFS showed an 18.94% (±17.98) higher average mean propulsive velocity during session (0.42±0.04 vs. 0.35±0.08 m.s^− 1; p=0.009), lower blood lactate concentration after session (maximum average value 1.52±0.77 vs. 3.95±1.82; session effect: p=0.001) and higher mean propulsive velocity with load corresponding to maximum propulsive power (mean propulsive velocity immediately after session 0.64±0.09 vs. 0.59±0.12 m.s^− 1; session effect: p=0.019). These data show that distribution of rest in sessions equated for volume and total resting time determines differences in performance during sessions and mechanical or metabolic acute effects.

Purpose: To analyze performance during the execution of a maximum number of repetitions (MNR) in a cluster-set configuration.Method: Nine judokas performed 2 sessions of parallel squats with a load corresponding to 4-repetition maximum (4RM) with a traditional-training (TT) and cluster-training (CT) set configuration. The TT consisted of 3 sets of repetitions leading to failure and 3 min of rest between sets. In the CT the MNR was performed with a rest interval between repetitions (45.44 ± 11.89 s). The work-to-rest ratio was similar for CT and TT.Results: MNR in CT was 45.5 ± 32 repetitions and was 9.33 ± 1.87 times the volume in TT. There was a tendency for the average mean propulsive velocity (MPV) to be higher in CT (0.39 ± 0.04 vs 0.36 ± 0.04 m/s for CT and TT, respectively, P = .054, standardized mean difference [d] = 0.57). The average MPV was higher in CT for a similar number of repetitions (0.44 ± 0.08 vs 0.36 ± 0.04 m/s for CT and TT, respectively, P = .006, d = 1.33). The number of repetitions in TT was correlated with absolute 4RM load (r = –.719, P = .031) but not in CT (r = –.273, P = .477).Conclusions: A cluster-set configuration allows for a higher number of repetitions and improved sustainability of mechanical performance. CT, unlike TT, was not affected by absolute load, suggesting an improvement of training volume with high absolute loads.

This study compared the functional and neural effects of two strength training programmes differing in set configuration. Thirteen participants performed 10 sessions, over a period of 5 weeks, of unilateral leg extensions with different set configurations but with identical work-to-rest ratios for each limb: a traditional configuration (4 sets of 8 repetitions, 10RM load, 3-min pause between sets) and an inter-repetition rest configuration (32 repetitions, 10RM load, 17.4 s of rest between each repetition). Mean propulsive velocity of the traditional sessions was lower than for inter-repetition rest sessions (0.48 ± 0.06 vs. 0.54 ± 0.06 m · s⁻¹; P < 0.001), while perceived exertion was higher (8.3 ± 0.9 and 6.56 ± 1.6 for traditional training and IRT; P = 0.002). One repetition maximum (RM), work with 10RM load, maximum mean propulsive power, maximum voluntary contraction and time to failure with 50% of maximum isometric force improved similarly in both legs (time effect, P < 0.001; effect size range, 0.451–1.190). Time and set configuration did not show significant main effects or interactions for cortical adaptations (motor-evoked potentials, short-interval intracortical inhibition, intracortical facilitation). There were no significant correlations between changes in cortical and peripheral neural adaptations and strength improvement. In conclusion, inter-repetition rest configuration was as effective as traditional training in improving muscle performance.

Research question: The increase of physical exercise in fitness centres has turned these facilities in important active lifestyle promoters. However, only between 30% and 60% of subscribers still linked to the sports centre after a year. The aim of this study is to design a model to predict the drop out in fitness centres. Research methods: Monthly data corresponding to the actual behaviour of 14,522 members of three sport centres in Spain were recorded over the course of one year. In order to calculate the likelihood of drop out, logistic regressions were used to create predictive models for each centre. Results and findings: It was possible to predict abandonment of users of sport centres from their historical behaviour, although the predictive models obtained for each centre were not completely coincident. The effectiveness of the models was around 70%. Implications: The analysis of users’ behaviour in a fitness centre can allow to avoid the drop out and therefore the abandonment of physical activity. Segmenting the customers based on the likelihood of drop out may be useful for improving the effectiveness of the strategies of loyalty and for optimizing the organization of human and material resources.