Dataset Information

Ability to predict repetitions to momentary failure is not perfectly accurate, though improves with resistance training experience.

ABSTRACT: 'Repetitions in Reserve' (RIR) scales in resistance training (RT) are used to control effort but assume people accurately predict performance a priori (i.e. the number of possible repetitions to momentary failure (MF)). This study examined the ability of trainees with different experience levels to predict number of repetitions to MF. One hundred and forty-one participants underwent a full body RT session involving single sets to MF and were asked to predict the number of repetitions they could complete before reaching MF on each exercise. Participants underpredicted the number of repetitions they could perform to MF (Standard error of measurements [95% confidence intervals] for combined sample ranged between 2.64 [2.36-2.99] and 3.38 [3.02-3.83]). There was a tendency towards improved accuracy with greater experience. Ability to predict repetitions to MF is not perfectly accurate among most trainees though may improve with experience. Thus, RIR should be used cautiously in prescription of RT. Trainers and trainees should be aware of this as it may have implications for the attainment of training goals, particularly muscular hypertrophy.

SUBMITTER: Steele J

PROVIDER: S-EPMC5712461 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Ability to predict repetitions to momentary failure is not perfectly accurate, though improves with resistance training experience.

Steele James J Endres Andreas A Fisher James J Gentil Paulo P Giessing Jürgen J

PeerJ 20171130

'Repetitions in Reserve' (RIR) scales in resistance training (RT) are used to control effort but assume people accurately predict performance <i>a priori</i> (i.e. the number of possible repetitions to momentary failure (MF)). This study examined the ability of trainees with different experience levels to predict number of repetitions to MF. One hundred and forty-one participants underwent a full body RT session involving single sets to MF and were asked to predict the number of repetitions they ...[more]

PMID: 29204323

Similar Datasets

Project description:Virtual reality (VR) can create safe, cost-effective, and engaging learning environments. It is commonly assumed that improvements in simulation fidelity lead to better learning outcomes. Some aspects of real environments, for example vestibular or haptic cues, are difficult to recreate in VR, but VR offers a wealth of opportunities to provide additional sensory cues in arbitrary modalities that provide task relevant information. The aim of this study was to investigate whether these cues improve user experience and learning outcomes, and, specifically, whether learning using augmented sensory cues translates into performance improvements in real environments. Participants were randomly allocated into three matched groups: Group 1 (control) was asked to perform a real tyre change only. The remaining two groups were trained in VR before performance was evaluated on the same, real tyre change task. Group 2 was trained using a conventional VR system, while Group 3 was trained in VR with augmented, task relevant, multisensory cues. Objective performance, time to completion and error number, subjective ratings of presence, perceived workload, and discomfort were recorded. The results show that both VR training paradigms improved performance for the real task. Providing additional, task-relevant cues during VR training resulted in higher objective performance during the real task. We propose a novel method to quantify the relative performance gains between training paradigms that estimates the relative gain in terms of training time. Systematic differences in subjective ratings that show comparable workload ratings, higher presence ratings and lower discomfort ratings, mirroring objective performance measures, were also observed. These findings further support the use of augmented multisensory cues in VR environments as an efficient method to enhance performance, user experience and, critically, the transfer of training from virtual to real environment scenarios.

Dataset Information

Ability to predict repetitions to momentary failure is not perfectly accurate, though improves with resistance training experience.

Publications

Ability to predict repetitions to momentary failure is not perfectly accurate, though improves with resistance training experience.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets