Understanding Patterns of Fatigue in Health and Disease

Overview

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: * The utility and usefulness of different sensed parameters in understanding the fatigue experience * The practicality and acceptability of collecting the proposed sensed and self-reported data and recruitment and retention rates (to inform a larger study) * Whether there might be meaningful differences in fatigue between individuals and groups of individuals with distinct medical conditions * Whether there is scope for a larger study into clinical "phenotypes" of fatigue (distinct classes of fatigue that might vary according to different combinations of physiological signatures, different patient descriptions/language used to describe the experience, diurnal variation, etc.)