Fatigue in Advanced Cancer Patients: Congruence Between Patients and Their Informal Caregivers About Patients' Fatigue Severity During Cancer Treatment With Palliative Intent and Predictors of Agreement
SourceJournal of Pain and Symptom Management, 52, 3, (2016), pp. 336-344
Article / Letter to editor
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Journal of Pain and Symptom Management
SubjectRadboudumc 18: Healthcare improvement science RIHS: Radboud Institute for Health Sciences; Radboudumc 3: Disorders of movement DCMN: Donders Center for Medical Neuroscience; Radboudumc 9: Rare cancers RIHS: Radboud Institute for Health Sciences
CONTEXT: Informal caregivers (ICs) are increasingly involved in the monitoring of symptoms during advanced cancer patients' treatment with palliative intent. A common but subjective symptom during this extended treatment phase is fatigue. OBJECTIVES: This exploratory longitudinal study aimed to determine agreement between patients and ICs about patients' fatigue severity. In addition, predictors of agreement over time were studied. METHODS: A sample of 107 patients with advanced cancer (life expectancy >/= six months) and their ICs completed the subscale fatigue severity of the Checklist Individual Strength based on the patient's status at baseline and six months later. This eight-item subscale has a validated cutoff to determine the presence of clinically relevant levels of fatigue. ICs' own fatigue severity, strain, self-esteem, and relationship satisfaction were examined as predictors of agreement. RESULTS: A total of 107 dyads completed measures at baseline and 69 dyads six months later. At baseline, ICs' significantly overestimated patients' fatigue severity (P < 0.001) with a moderate amount of bias (Cohen's d = 0.48). In 81 of the 107 dyads (76%), there was congruence about the presence or absence of severe fatigue. On a group level, congruence did not significantly change over time. On a dyad level, there was a tendency to either remain congruent or reach congruence. Next to baseline congruence, ICs' fatigue severity and strain predicted ICs' fatigue ratings (R(2) = 0.22). CONCLUSION: Most ICs accurately predict presence or absence of clinically relevant levels of patients' fatigue. ICs' own fatigue severity and strain should be taken into account as they influence agreement.
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