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Michael I. Trenell

Researcher at Newcastle University

Publications -  167
Citations -  10868

Michael I. Trenell is an academic researcher from Newcastle University. The author has contributed to research in topics: Fatty liver & Randomized controlled trial. The author has an hindex of 46, co-authored 162 publications receiving 8342 citations. Previous affiliations of Michael I. Trenell include Leeds General Infirmary & University of Sydney.

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Journal ArticleDOI

Exploration of Sleep as a Specific Risk Factor for Poor Metabolic and Mental Health: A UK Biobank Study of 84,404 Participants.

TL;DR: In this paper, the authors investigated the impact of sleep duration and fragmentation on physical and mental health in older adults using accelerometry data collected from the UK Biobank (n=84,404).
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Exercise Induces Peripheral Muscle But Not Cardiac Adaptations After Stroke: A Randomized Controlled Pilot Trial

TL;DR: In this article, a single-center, single-blind, randomized controlled pilot trial was conducted to explore the physiological factors affecting exercise-induced changes in peak oxygen consumption and function post stroke.
Journal ArticleDOI

Supplemental oxygen and muscle metabolism in mitochondrial myopathy patients.

TL;DR: It is concluded that oxygen therapy is associated with significant improvements in muscle metabolism in patients with mitochondrial myopathy and suggested that patients with MM could benefit from therapies which improve the provision of oxygen.
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Liver and muscle glycogen repletion using 13C magnetic resonance spectroscopy following ingestion of maltodextrin, galactose, protein and amino acids

TL;DR: The present study evaluated whether the inclusion of protein (PRO) and amino acids (AA) within a maltodextrin (MD) and galactose (GAL) recovery drink enhanced post-exercise liver and muscle glycogen repletion and suggested that the increased postprandial insulinaemia only compensated for the lower MD content in the MD-GAL-PRO+AA treatment.
Proceedings ArticleDOI

Automating the Placement of Time Series Models for IoT Healthcare Applications

TL;DR: This work describes a method for automatically partitioning stream processing across a set of components in order to optimise for a range of factors including sensor battery life and communications bandwidth, and illustrates this using the implementation of a statistical model predicting the glucose levels of type II diabetes patients in order.