Unraveling Fatigue in Hemodialysis Patients: comparing retrospective reports to real-time assessments with an mHealth Experienced Sampling Method

CONTEXT: Fatigue is prevalent among hemodialysis (HD) patients and associated with depressive mood. To advance our understanding of its etiology and develop appropriate treatments, reliable measurement instruments are needed. However, conventional fatigue and mood questionnaires are prone to bias due to their retrospective nature and may misrepresent or overestimate actual symptom experience (i.e., the so-called 'memory-experience' gap). Experience sampling Methodology (ESM) overcomes this limitation through repeated real-time assessments in patients' natural environment, thereby providing reliable and ecologically valid data.

OBJECTIVES: We investigated to what extent retrospective symptom reporting accurately represents real-time experiences of fatigue and mood in HD patients using an ESM mobile Health application (PsyMateTM).

METHODS: Forty HD patients used the PsyMateTM for one week to assess real-time fatigue and mood. Additionally, they retrospectively evaluated their symptom experience completing end-of-day and end-of-week questionnaires; and the conventional Fatigue Severity Scale (FSS) and Hospital Anxiety and Depression Scale (HADS).

RESULTS: Results of real-time observations (N=1777) showed that fatigue and mood varied between and within individuals. Retrospective end-of-week fatigue evaluation was significantly higher than the average real-time fatigue score, t(38)=3.54, p=0.001, d=0.57. FSS and HADS correlated moderately to strong with the average ESM score for fatigue and mood: r=0.66 and r=0.77, respectively.

CONCLUSION: Retrospective fatigue assessment may lead to overestimation of real-time symptom experience. ESM provides detailed insight and personalized information about symptom experiences which may be crucial for the design of more targeted and personalized interventions for fatigue and mood problems in HD patients.