| 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: |
|
| 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: |
10 June 2024 |
Value (£): |
404,491
|
| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Med.Instrument.Device& Equip. |
| RF & Microwave Technology |
|
|
| EPSRC Industrial Sector Classifications: |
|
| Related Grants: |
|
| Panel History: |
| Panel Date | Panel Name | Outcome |
|
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 |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Potential use in non-academic contexts |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Impacts |
|
| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
|
| Date Materialised |
|
|
|
|
Sectors submitted by the Researcher |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
| Project URL: |
|
| Further Information: |
|
| Organisation Website: |
http://www.abdn.ac.uk |