Background: Fatigue in haemodialysis (HD) patients is a prevalent but complex symptom impacted by biological, behavioural, psychological and social variables. Conventional retrospective fatigue questionnaires cannot provide detailed insights into symptom variability in daily life and related factors. The experience sampling methodology (ESM) overcomes these limitations through repeated momentary assessments in patients' natural environments using digital questionnaires. This study aimed to gain in-depth understanding of HD patients' diurnal fatigue patterns and related variables using a mobile Health (mHealth) ESM application and sought to better understand the nature of their interrelationships.Methods: Forty HD patients used the mHealth ESM application for 7days to assess momentary fatigue and potentially related variables, including daily activities, self-reported physical activity, social company, location and mood.Results: Multilevel regression analyses of momentary observations (n=1777) revealed that fatigue varied between and within individuals. Fatigue was significantly related to HD treatment days, type of daily activity, mood and sleep quality. Time-lagged analyses showed that HD predicted higher fatigue scores at a later time point (beta =0.22, P=0.013). Interestingly, higher momentary fatigue also significantly predicted more depressed feelings at a later time point (beta =0.05, P=0.019) but not the other way around.Conclusions: ESM offers novel insights into fatigue in chronic HD patients by capturing informative symptom variability in the flow of daily life. Electronic ESM as a clinical application may help us better understand fatigue in HD patients by providing personalized information about its course and relationship with other variables in daily life, paving the way towards personalized interventions.
mHealth-based experience sampling method to identify fatigue in the context of daily life in haemodialysis patients
Gambaro, Giovanni;
2021-01-01
Abstract
Background: Fatigue in haemodialysis (HD) patients is a prevalent but complex symptom impacted by biological, behavioural, psychological and social variables. Conventional retrospective fatigue questionnaires cannot provide detailed insights into symptom variability in daily life and related factors. The experience sampling methodology (ESM) overcomes these limitations through repeated momentary assessments in patients' natural environments using digital questionnaires. This study aimed to gain in-depth understanding of HD patients' diurnal fatigue patterns and related variables using a mobile Health (mHealth) ESM application and sought to better understand the nature of their interrelationships.Methods: Forty HD patients used the mHealth ESM application for 7days to assess momentary fatigue and potentially related variables, including daily activities, self-reported physical activity, social company, location and mood.Results: Multilevel regression analyses of momentary observations (n=1777) revealed that fatigue varied between and within individuals. Fatigue was significantly related to HD treatment days, type of daily activity, mood and sleep quality. Time-lagged analyses showed that HD predicted higher fatigue scores at a later time point (beta =0.22, P=0.013). Interestingly, higher momentary fatigue also significantly predicted more depressed feelings at a later time point (beta =0.05, P=0.019) but not the other way around.Conclusions: ESM offers novel insights into fatigue in chronic HD patients by capturing informative symptom variability in the flow of daily life. Electronic ESM as a clinical application may help us better understand fatigue in HD patients by providing personalized information about its course and relationship with other variables in daily life, paving the way towards personalized interventions.
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