| KEYWORD |
Can high-density surface EMG substantiate the prescription of personalized, resistance training?
Thesis abroad
keywords HIGH-DENSITY SURFACE EMG, MUSCLE FATIGUE, RESISTANCE TRAINING
Reference persons TAIAN MARTINS
External reference persons Prof Antonio Dello Iacono
University of the West of Scotland
Research Groups Laboratory for Engineering of the Neuromuscular System (LISiN)
Thesis type EXPERIMENTAL APPLIED
Description Muscle strengthening induces many structural and function benefits on the musculoskeletal system. Traditionally, strength training has been prescribed in the form of rigid, non-personalized training programs and constituent training variables (e.g., load, sets, repetitions, rest). Although this one-size-fits-all approach is promoted at group level, it fails to account for the variability of day-to-day performance, which is particularly hindered by within-session acute accrual of neuromuscular fatigue. In this regard, the Autoregulation Cluster Training (ACT) method has been recently conceptualized to prescribe strength training in a personalized manner. The ACT method combines the cluster-set training approach by accommodating changes to cluster-set structures in a dynamic manner during the ongoing training session based upon a specific autoregulation target (e.g., perception of effort, or mechanical outputs [task velocity, peak force, work]). Preliminary evidence demonstrated that the ACT method seems to exploit strength training performance outputs by mitigating neuromuscular and perceived fatigue.
In this research project, the student will investigate the comparative responses associated with training practices prescribed according to traditional- and ACT-based approaches. The expected findings will elucidate the neuromuscular mechanisms underpinning muscular fatiguability in strength training, thus providing further evidence to inform training prescription.
The activities proposed will be conducted in collaboration with Dr Antonio Dello Iacono at the Division of Sport and Exercise, School of Health and Life Sciences, University of the West of Scotland (UK)
Required skills Good understanding of neuromuscular physiology
Linear algebra
Advanced Matlab (or other coding languages) skills
High English proficiency
Background in resistance training is an asset
Notes Students willing to undertake this proposal are requested to contact the supervisor and submit:
1. A letter of interest for the proposed project
2. Elements motivating the decision to pursue this thesis (5-10 lines of text)
Deadline 31/12/2024
PROPONI LA TUA CANDIDATURA