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Details of Grant 

EPSRC Reference: EP/W003228/1
Title: Digital Health: Innovative engineering technologies to improve the understanding and management of fatigue
Principal Investigator: Adam, Dr R
Other Investigators:
Bradbury, Dr K Sas, Professor C Hill, Professor DL
Martinez, Dr V Cooper, Professor J
Researcher Co-Investigators:
Project Partners:
Department: Sch of Medicine, Medical Sci & Nutrition
Organisation: University of Aberdeen
Scheme: Standard Research - NR1
Starts: 10 January 2022 Ends: 09 January 2024 Value (£): 404,491
EPSRC Research Topic Classifications:
Artificial Intelligence Med.Instrument.Device& Equip.
RF & Microwave Technology
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
26 Jan 2021 Digital Health Sandpit Full Proposals Announced
Summary on Grant Application Form
Fatigue is considered as a "final common pathway": a vague clinical symptom that can result from many different diseases and mechanisms. Our limited understanding of fatigue stems, in part, from its subjective and fluctuating nature and its complex interplay of parameters associated with "tiredness" such as sleep, exercise, and mood. This project will investigate sensory technologies to objectively, accurately and unobtrusively measure fatiguability, as an indicator of fatigue. These measurements will be correlated to sensed data (activity levels, sleep, heart rate, and others) and individuals' self-reports. Granular details will be obtained about patterns in the human fatigue experience. The results will reveal whether there could be distinct, clinically relevant fatigue phenotypes. We will also use longitudinal research (studying participants closely over a several week period). To date, longitudinal fatigue research been limited by statistical analysis methods (such as multilevel modelling) which are unable to detect subtle or complex relationships between fatigue and related life-style factors over time. We will use artificial intelligence algorithms to help analyse and classify these correlations within the sensed data, self-reports and qualitative data.
Key Findings
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Potential use in non-academic contexts
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Summary
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Organisation Website: http://www.abdn.ac.uk