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| Name | Class |
|---|---|
| University of Cambridge | OTHER |
| University of Glasgow | OTHER |
| Lancaster University | OTHER |
| University of Southampton |
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Fatigue is a common symptom and can be the most distressing symptom of a range of medical conditions. This Ecological Momentary Assessment study will investigate lived experiences of fatigue in detail in individuals with myeloma, long COVID, heart failure, and in healthy controls without fatigue.
Participants will wear ECG patches and wrist-worn sensors that measure heart rate variability, activity levels, posture, and other parameters. They will self-rate their levels of fatigue four times daily and on-demand (when fatigue levels are noticeably good or troublesome). They will participate in an end of study interview and will have an optional feedback session with a researcher to make sense of the data they have provided.
Fatigue can be the most disabling symptom experienced by patients with a wide range of diseases. In primary care, it is very challenging for clinicians to differentiate between physiological fatigue (i.e., "normal" tiredness associated with lifestyle factors) and fatigue caused by underlying pathology such as heart disease or cancer. Treatment of persistent fatigue is usually by trial-and-error without attention to personalized triggers or disparate fatigue mechanisms.
This feasibility study will investigate patient experiences of fatigue in depth, combining objective measures of sensed physiological parameters with patient reports and validated patient reported outcome measures. Patients will be recruited with three distinct clinical conditions: myeloma; long COVID; and heart failure. A healthy control group will also be recruited.
Participants will participate in a feasibility study with a longitudinal, Ecological Momentary Assessment (EMA) design, wearing sensors, and providing four times daily short self-reports of fatigue over a two-to-four week period (to be determined by the individual participant and their preferences and patterns of fatigue). They will complete validated fatigue, affect, and interoceptive awareness scores at baseline and at two weeks and participate in end of study telephone interviews with a Research Assistant.
Sensors will measure objective parameters including activity levels; heart rate; sleep; and posture (sitting/standing). Additional sensors ("beacons") will measure participant's movements and positioning within their own environment (position relative to the beacons - beacon location to be determined by participant placement); environmental temperature; noise and light levels.
Data will be analysed using multilevel modelling and Machine Learning to detect patterns in the fatigue experiences and to compare fatigue measurements within individuals; between individuals with the same clinical condition; and between groups of individuals with different clinical conditions/controls.
This feasibility study will provide data that helps to determine:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Myeloma | Individuals experiencing fatigue related to myeloma or its treatment |
| |
| Long COVID | Individuals experiencing fatigue related to Long COVID |
| |
| Heart failure | Individuals experiencing fatigue related to heart failure or its treatment |
| |
| Controls | Individuals who are not experiencing problematic fatigue and who do not have myeloma, long COVID, or heart failure |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Observational | Other | Sensed and lived experience data from all groups without a specific behavioural or drug intervention |
|
| Measure | Description | Time Frame |
|---|---|---|
| Cognitive and physical fatigue screen (zero to ten point rating) | Fatigue screen based on "state" items from the validated Mental and Physical State and Trait Energy and Fatigue Scale (O'Connor P. University of Georgia; 2006). Participants will self-report their fatigue at that moment (split into physical and cognitive/mental fatigue) on a 0-10 point numerical rating scale, anchored with "I feel no fatigue" = 0 and "strongest feeling of fatigue ever" = 10. Participants will be prompted to respond via an app four times daily and can also provide on-demand ratings when their fatigue levels are particularly problematic or when they are not experiencing problematic fatigue. Multilevel modelling will be conducted to identify changes in fatigue over time and to explore the relationships between self-reported fatigue scores and sleep, activity levels (step-count, posture, measured by wrist worn sensor), respiratory rate (measured by ECG patch), and heart rate variability (measured by ECG patch) | Two to four weeks (participant defined) |
| Lived experiences of fatigue interview | Qualitative data collected by an end of study interview according to a topic schedule | 1 day (End of study interview) |
| Views and opinions about the sensing technologies interview | Qualitative data collected by an end of study interview according to a topic schedule | 1 day (End of study interview) |
| Views and opinions about the trial methods and study participation interview | Qualitative data collected by an end of study interview according to a topic schedule | 1 day (End of study interview) |
| Measure | Description | Time Frame |
|---|---|---|
| Drop-out rate | The proportion of participants who drop out of the study before the end of study interview | Participant-led study end date - two to four weeks |
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Inclusion Criteria:
Inclusion Criteria for Group A, individuals with myeloma
Inclusion Criteria for Group B, heart failure
Inclusion Criteria for Group C, long COVID
Inclusion criteria for Group D, control group
• Individuals aged 18 years or over without the disease conditions specified in Groups A to C
Exclusion Criteria:
Exclusion Criteria Applying to all participants:
We will not exclude patient participants in groups A to C based on the type of prescribed medications that they are taking. Instead, this will be carefully documented.
For Group A, myeloma
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Participants will be selected from the community and from hospital haematology and cardiology clinics
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Aberdeen | Aberdeen | UK | AB25 2ZD | United Kingdom |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38802273 | Derived | Adam R, Lotankar Y, Sas C, Powell D, Martinez V, Green S, Cooper J, Bradbury K, Sive J, Hill DL. Understanding patterns of fatigue in health and disease: protocol for an ecological momentary assessment study using digital technologies. BMJ Open. 2024 May 27;14(5):e081416. doi: 10.1136/bmjopen-2023-081416. |
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This project has put in place a data sharing plan in accordance with the International Committee of Medical Journal Editors (ICMJE), as published in Annals.org on 6 June 2017.
Data will be shared with investigators whose proposed use of the data has been approved by the Research Ethics Committee and who are parties to the project data sharing agreement for the purposes of conducting analyses to achieve the aims in the approved protocol. The data will be made available as pseudonymised data shared using a University of Aberdeen approved data sharing platform.
Data sharing will start in December 2022 and data sharing may continue until the end of the retention period for research data generated by the study (current retention period 20 years).
Party to the project data sharing agreement Use approved by Research Ethics Committee.
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| ICF | No | No | Yes | Informed Consent Form | Oct 14, 2022 | Mar 29, 2024 | ICF_000.pdf |
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| ID | Term |
|---|---|
| D054219 | Neoplasms, Plasma Cell |
| D000094024 | Post-Acute COVID-19 Syndrome |
| D006333 | Heart Failure |
| ID | Term |
|---|---|
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D000086382 | COVID-19 |
| D011024 | Pneumonia, Viral |
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| ID | Term |
|---|---|
| D057832 | Watchful Waiting |
| ID | Term |
|---|---|
| D017063 | Outcome Assessment, Health Care |
| D010043 | Outcome and Process Assessment, Health Care |
| D011787 | Quality of Health Care |
| D006298 | Health Services Administration |
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| OTHER |
| University College, London | OTHER |
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| D011014 |
| Pneumonia |
| D012141 | Respiratory Tract Infections |
| D007239 | Infections |
| D014777 | Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D000094025 | Post-Infectious Disorders |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |