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Abstract: PO1212

Merits of Using mHealth Experience Sampling Methodology to Assess Fatigue in Chronic Hemodialysis Patients

Session Information

Category: Dialysis

  • 701 Dialysis: Hemodialysis and Frequent Dialysis


  • Brys, Astrid, Universiteit Maastricht, Maastricht, Limburg, Netherlands
  • Bossola, Maurizio, Universita Cattolica del Sacro Cuore Facolta di Medicina e Chirurgia, Roma, Lazio, Italy
  • Gambaro, Giovanni, Azienda Ospedaliera Universitaria Integrata Verona Unita Operativa Nefrologia and Dialisi, Verona, Veneto, Italy
  • Lenaert, Bert, Universiteit Maastricht, Maastricht, Limburg, Netherlands

Understanding the development of fatigue and related behavioral, social and psychological factors in hemodialysis (HD) patients is crucial for the development of effective treatment. However, conventional fatigue measures provide limited insight in diurnal variations in fatigue and related factors in daily life and are prone to memory bias. The Experience Sampling Methodology (ESM) overcomes these limitations by repeatedly assessing ‘real-time’ symptoms in patients’ natural environments.
We aimed to gain in-depth understanding of HD patients’ diurnal fatigue patterns and related variables using a mobile Health ESM application (mHealth app) and to investigate how retrospective fatigue reports correspond to real-time symptom experiences.


Forty HD patients used the mHealth app for seven days to assess real-time fatigue and potentially related variables, including daily activities, social company, location and mood. In addition, they retrospectively evaluated their experienced fatigue over the preceding week on an end-of-week questionnaire and the conventional Fatigue Severity Scale (FSS).


Analyses of momentary observations (N=1778) revealed that fatigue as well as mood varied between and within individuals. Real-time fatigue significantly related to concurrent type of daily activity and mood. Interestingly, time-lagged analyses showed that HD treatment predicted more fatigue later in time (β=.22, p=.013), and that higher fatigue levels predicted lower mood later in time (β=.05, p=.019). Retrospective fatigue evaluation was significantly higher than the mean of real-time fatigue score, t(38)=3.54, p=.001. FSS-scores correlated moderately with mean real-time fatigue score, r=.63.


This study demonstrates that fatigue in chronic HD patients varies over time and increases after HD treatment, corroborating the occurrence of post-dialysis fatigue. Furthermore, our findings suggest that depressed mood may be secondary to fatigue in HD patients. Finally, retrospective fatigue evaluation overestimated real-time assessments, suggesting memory bias when using conventional fatigue measurement instruments. ESM provides novel insights and personalized information about fatigue symptoms in patients daily life, paving the way toward personalized interventions.