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Digital Health: Innovative engineering technologies to improve the understanding and management of fatigue

Lead Research Organisation: University of Aberdeen
Department Name: Sch of Medicine, Medical Sci & Nutrition

Abstract

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.
 
Description Significant progress has been made in each of our work packages. In Work Package One, experiments using WiFi technology to make remote measurements from healthy volunteers has already resulted in several publications and the laboratory work is continuing and should yield additional results and papers.
We have completed our patient engagement work. Five successful focus groups were held on-line with individuals with long COVID, myeloma and heart failure. We were able to get key insights into patterns of fatigue and lived experiences of fatigue. Long COVID fatigue and myeloma fatigue are qualitatively different: long COVID is characterised by high levels of cognitive fatigue and post exertional malaise, whereas myeloma fatigue varies predictably based on treatment cycles. In heart disease, fatigue is often conflated with physical exertion and breathlessness. The results of our patient facing work support our hypothesis that fatigue is not a single entity and that we may be able to objectively quantify and categorise fatigue based on underlying mechanisms.
We have completed data collection in our Ecological Momentary Assessment study and will submit an end of study form shortly. The study involved the National Health Service and recruited people with long COVID, myeloma, heart failure, and controls without fatigue/a study condition. 40 people were consented into our study, 3 participants lost contact/did not provide data but we have usable data for 37 participants. We recruited11 control participants, 12 with long COVID, 10 with myeloma, and 4 with heart failure. The protocol for our study has been published in BMJ Open and is also registered at https://classic.clinicaltrials.gov/ct2/show/NCT05622669
We have generated a rich dataset (described separately) and analysis of the data are ongoing. The dataset is complex and we have engaged with an expert in time series analysis who is helping with the analysis. We are having fortnightly data analysis meetings and are exploring associations between steps, heart rate variability, sleep and fatigue. We will publish our results this year.
Our team in Cambridge have been developing artificial intelligence algorithms to enhance data analysis/recognition of temporal patterns within the fatigue data and this work is also ongoing.
Exploitation Route We have generated a rich dataset from patients with three different clinical conditions and high levels of fatigue. These include and are not limited to:
Data derived from wearable sensors - ECG data, activity data, location data, posture and sleep data
Repeated real-time fatigue self-rating scores
Qualitative interview data
Data from questionnaires at baseline and follow up
We are analysing the data to look for distinct patterns of fatigue within individuals over time, and between individuals with different study conditions. We expect this funding to lead to a larger project of research to identify fatigue classes/a classification system, and to ultimately help with the diagnosis and management of fatigue.
Sectors Digital/Communication/Information Technologies (including Software)

Healthcare
 
Title Fatigue Ecological Momentary Assessment Dataset 
Description This dataset is still accumulating data. Briefly, we have collected data from 11 control participants without problematic fatigue, 12 people with long COVID, 10 with myeloma, and 4 with heart failure. We have linked a dataset containing: Data from wearable sensors - ECG monitoring data, activity data, sleep data, location data (at home/out of the home/which room the participant is in). Data from repeated real-time self-reports for fatigue scores (mental and physical) and suspected triggers for fatigue For each participant we have also collected rich qualitative data in the form of interviews. We also collected qualitative data (transcriptions of audiorecordings) from "feedback" sessions with participants in which their data were fed back to them and they "sense-checked" it to confirm face validity. 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? No  
Impact The combination of qualitative and matched quantitative data, objective and subjective data will allow certain algorithms to be tested for validity (e.g. sleep algorithms). The data will also give insights into fatigue experiences in granular depth over time in individuals. 
 
Description Interview for national news 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact I gave an interview to the Guardian Newspaper about our fatigue study
Year(s) Of Engagement Activity 2023
URL https://www.theguardian.com/lifeandstyle/2023/may/19/exhausting-search-for-solutions-to-uk-rise-in-t...
 
Description Interview for national news 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact I gave an interview to the BBC which resulted in a "BBC Future" article on fatigue.
Year(s) Of Engagement Activity 2024
URL https://www.bbc.co.uk/future/article/20240125-why-do-some-people-feel-tired-all-the-time
 
Description Interview for national news 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact Professor Hill gave an interview to the Times national newspaper about our study
Year(s) Of Engagement Activity 2023
URL https://www.thetimes.co.uk/article/exhausted-you-may-have-hyper-fatigue-jc9dmvmbg
 
Description Patient group workshops x 6 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Patients, carers and/or patient groups
Results and Impact We conducted a series of formal focus groups to engage people with fatigue in our research project. We conducted:
Two focus groups with people with long COVID (2022)
A focus group with people with myeloma
Two focus groups with people with heart conditions in December 2022, however, we did not go on to use the data from these groups because we had suspicions that they were attended by people without heart disease.
We conducted a further heart focus group, avoiding social media methods of recruitment and using a third sector intermediary (Alliance) to reach genuine patients.
The focus groups were extremely valuable. They involved patients in helping to design our research study. We identified and dealt with concerns about the technology and data we were collecting. We re-designed our EMA study to encompass a patient feedback session. The feedback sessions have been popular and valued by patients, and have generated unique data for us about fatigue and the technologies we were using to assess fatigue.
Year(s) Of Engagement Activity 2022,2023