Real-time measurement of post-stroke fatigue in daily life and its relationship with the retrospective Fatigue Severity Scale

Improving our understanding of post-stroke fatigue is crucial to develop more effective interventions. This effort may be hampered by the methods used to assess fatigue, which usually rely on retrospective memory reports. However, such reports are prone to memory bias and may not capture variability in fatigue in daily life; thereby failing to adequately represent symptom experience. This study aimed to assess the strength of the relationship between real-time experience of post-stroke fatigue and the commonly used retrospective Fatigue Severity Scale (FSS). Thirty individuals with stroke completed 10 daily questionnaires about momentary (here-and-now) fatigue for six consecutive days using the mHealth application PsyMateTM (Experience Sampling Method). From these real-time fatigue ratings (N = 1012), we calculated three indices: total average, peak fatigue, and fatigue on the final day. Afterwards, participants rated their fatigue retrospectively with the FSS. Results showed weak to moderate and strong correlations (range: .334, .667), with retrospective reports capturing up to 44% of the variance in the indices of momentary fatigue. Exploratory analyses also revealed that even individuals with similar total FSS scores demonstrated highly different day-to-day fatigue patterns. We conclude that retrospective measures may provide an incomplete view of post-stroke fatigue and diurnal variation therei