Characterizing Patient-Reported Fatigue Using Electronic Diaries in Neurodegenerative and Immune-Mediated Inflammatory Diseases: Observational Study

Fatigue is a common and debilitating symptom in chronic conditions such as immune-mediated inflammatory diseases (IMIDs) and neurodegenerative diseases (NDDs). Although fatigue frequently fluctuates within and between days, traditional patient-reported outcomes (PROs) rely on weekly recall periods and may miss short-term variability. High-frequency electronic diaries (eDiaries) enable real-time, granular data collection, but their feasibility and comparability with established PROs need further evaluation.
This study assessed the feasibility and acceptability of a high-frequency eDiary to capture intraday fatigue variability and examined its relationship with the Functional Assessment of Chronic Illness Therapy–Fatigue (FACIT-F), a validated weekly recall PRO. Data were collected from 159 participants in the 4-week IDEA-FAST feasibility study conducted at four European centers, including individuals with NDDs (n=39), IMIDs (n=78), and healthy volunteers (n=42). Participants reported physical and mental fatigue up to four times daily using a 7-point Likert scale. Adherence was defined as the proportion of completed entries, and correlations between averaged eDiary scores and weekly FACIT-F scores were analyzed.
Overall adherence was 61.99%, with marked differences between cohorts. Adherence was higher in IMID cohorts (73.84%) than in NDD cohorts (43.45%), with the highest adherence observed in primary Sjögren syndrome and the lowest in Parkinson and Huntington disease cohorts. Fatigue showed significant diurnal variation, with higher levels reported in the evening than the morning (P<.001). A moderate correlation was found between eDiary and FACIT-F scores (Spearman=0.46, P<.001), with stronger associations for specific FACIT-F items in some cohorts.
These findings demonstrate that high-frequency eDiaries are feasible and valid for assessing fatigue and complement weekly PROs by capturing intraday fatigue fluctuations.

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