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Background and Aims Fatigue is one of the most prioritized outcomes among haemodialyis (HD) patients with great impact on health-related quality of life. However, evidence-based therapies are lacking. A better understanding of fatigue symptoms and related behavioral, social and psychological factors in HD patients is of primary importance. Conventionally used fatigue measurement instruments, such as the Fatigue Severity Scale (FSS), only provide a general picture of fatigue severity and are prone to memory bias due to their retrospective nature. However, they cannot provide detailed insight in diurnal variations in fatigue and related factors in daily life. The Experience Sampling Methodology (ESM) overcomes these limitations by repeated “real-time” assessments in patients’ natural environments using digital questionnaires. The aims of this study were (i) to gain in-depth understanding of HD patients’ diurnal fatigue patterns and related variables using a mobile Health (mHealth) ESM application, (ii) to better understand the nature of their interrelationships, and (iii) to explore the relationship between real-time experience of fatigue and its retrospective assessment. Method Forty chronic HD patients used the mHealth ESM application for seven consecutive days to assess momentary fatigue and potentially related variables, including daily activities, self-reported physical activity, social company, location and mood patterns. In addition, patients retrospectively evaluated their fatigue experience over the preceding day and week by means of end-of-day, and end-of-week questionnaires and the FSS. Results Multilevel regression analyses of momentary observations (N=1778) revealed that fatigue varied between and within individuals (Fig.1). Fatigue was significantly related to type of daily activity and mood. Time-lagged analyses showed that HD treatment predicted higher fatigue scores at a later time point, β = 0.22, p = 0.013. Interestingly, higher momentary fatigue also significantly predicted more depressed feelings at a later time point, β = 0.05, p = 0.019, but not the other way around. Retrospective evaluation of fatigue experience over the preceding week was significantly higher than the average of momentary fatigue scores, t(38) = 3.54, p = 0.001. The FSS correlated moderately with the average of momentary fatigue scores, r = 0.63. Conclusion This study demonstrates diurnal variability of fatigue in chronic HD patients. It also corroborates a previous result from our research group showing that fatigue increases as a response to HD treatment (i.e. post-dialysis fatigue) and should, at least partially, be distinguished from a more general fatigue experience. Furthermore, our findings may suggest that depressed mood is secondary to fatigue in HD patients given their temporal relationship. Finally, retrospective fatigue assessment led to overestimation of the real-time fatigue experience. ESM offers novel insights in fatigue in HD patients by capturing informative symptom variability in the flow of daily life, which is not provided by conventional fatigue measures. Moreover, ESM provides personalized information about fatigue symptoms and their relationship with other variables in daily life, paving the way towards personalized interventions for HD patients.

P0194UNRAVELING FATIGUE IN HAEMODIALYSIS PATIENTS: A PLEA FOR THE USE OF MOBILE HEALTH EXPERIENCE SAMPLING METHODOLOGY