Background: Cognitive fatigue after childhood cancer is frequently overlooked despite guidelines recommending follow-up, and might be mistaken for depression due to overlapping symptoms. Our objectives were: 1) to examine ratings of fatigue in survivors of paediatric brain tumours (BT) and acute lymphoblastic leukaemia (ALL) compared to healthy controls, 2) to examine the relationship between symptoms of depression and cognitive fatigue, and 3) to evaluate parent-child concordance in ratings of cognitive fatigue. Methods: Survivors of BT (n = 30), survivors of ALL (n = 30), and healthy controls (n = 60) aged 8-18 years completed the Pediatric Quality of Life Multidimensional Fatigue Scale and the Beck Youth Inventories. Associations between cognitive fatigue, diagnosis and depression were assessed with general linear modelling. Group differences were analysed using the Kruskal-Wallis test. Parent-child concordance was investigated with internal consistency reliability. Results: Cognitive fatigue was prevalent in 70% of survivors of BT survivors and in 30% of survivors of ALL. Diagnosis was the main predictor of cognitive fatigue (p < .001, η p 2 = 0.178), followed by depression (p = .010, η p 2 = 0.080). Survivors of BT reported significantly more fatigue than healthy controls on all fatigue subscales. While they also expressed more symptoms of depression, we found no evidence for an interaction effect. Parent-child concordance was moderate to good among survivors, but poor for controls. Conclusions: Survivors of BT and ALL suffer from cognitive fatigue, with survivors of BT expressing more problems. Cognitive fatigue and depression should be assessed in survivors of childhood cancer using both self-rated and proxy-rated measures, and appropriate interventions offered.
Objective Fatigue is one of the most prevalent and distressing symptoms reported by survivors of childhood cancer. There is currently a lack of longitudinal studies on cancer-related fatigue, and especially on the relationship between the course of fatigue during treatment and fatigue at follow-up. The purpose of the current study was therefore to investigate if the course of fatigue during treatment, treatment intensity, serious adverse events, sex, or age at diagnosis are associated with cancer-related fatigue after treatment. Methods Participants were 92 children and adolescents diagnosed with acute lymphoblastic leukemia (mean age at diagnosis was 6.26 years). Fatigue was measured with PedsQL multidimensional fatigue scale proxy reports 5 months after diagnosis, 12 months after diagnosis, 24 months after diagnosis, and at follow-up 12 months after end of treatment. The effect of patient and treatment characteristics on fatigue reported at follow-up was tested through logistic regression analyses. Results The course of fatigue during treatment significantly predicted fatigue reported at follow-up for general fatigue (p = .038, OR = 9.20), sleep/rest fatigue (p = .011, OR = 15.48), and cognitive fatigue (p < .001, OR = 10.78). None of the other variables were associated with fatigue at follow-up for any of the subscales. Conclusions The findings demonstrate that fatigue reported during treatment can predict fatigue at follow-up. These results stress the need for longitudinal assessments. Healthcare professionals need to be aware that pediatric patients who are fatigued during treatment need to receive additional attention and timely interventions since cancer-related fatigue will not resolve by itself in the first year after end of treatment.
Objective: The relationship between fatigue and cognition has not been fully elucidated in children and adolescent survivors of brain tumours. The aim of the present study was to investigate the potential relationship between fatigue and cognitive impairments in these survivors, as this group is at risk for both types of deficits. Methods: Survivors of paediatric brain tumours (n = 45) underwent a neuropsychological testing on average 4 years after diagnosis. Mean age at follow-up was 13.41 years. Cognition was assessed with neuropsychological tests, and fatigue with the Pediatric Quality of Life (PedsQL™) Multidimensional Fatigue Scale. Regression analysis, adjusted for cranial radiotherapy and age at diagnosis, was used to investigate the associations between cognitive variables and fatigue subscales. Cognitive variables associated with fatigue were subsequently exploratively assessed. Results: Significant associations were found for cognitive fatigue and measures of cognitive processing speed; Coding: p = .003, r = .583, 95% CI [9.61; 22.83] and Symbol Search: p = .001, r = .585, 95% CI [10.54; 24.87]. Slower processing speed was associated with poorer results for cognitive fatigue. Survivors with the largest decrease in processing speed from baseline to follow-up also experienced the most cognitive fatigue. Survivors expressed more cognitive fatigue compared to other types of fatigue. Conclusions: The association between cognitive fatigue and cognitive processing speed in children and adolescents treated for brain tumours is in concordance with the results previously reported in adults. Some survivors experience fatigue without impairment in processing speed, indicating the need for comprehensive assessments. Moreover, the study supports that fatigue is a multidimensional concept which should be measured accordingly.
According to the World Health Organization (WHO), about 50 million people worldwide are currently affected by dementia; every year, about 10 million people develop dementia. These figures will double or even triple by 2050. One of the most important modifiable risk factors of dementia is lack of cognitive activity. Neuropsychology makes very important contributions to the diagnosis (screening/case-finding, diagnosis-specific cognitive profiles) and to the evaluation of new treatment attempts. In this interactive workshop these aspects will be critically reviewed and new possibilities for further development will be discussed. Learning Objectives: This workshop is designed to help you: 1) Discuss cognitive stimulation as a possible preventive measure of dementia using the technique of Motivational Interviewing; 2) Rate the contribution of neuropsychology to the screening/case-finding of neurodegenerative brain disorders and the differential diagnosis of dementia; 3) Identify the great potential of an optimized new neuropsychology for improving the early detection of neurodegenerative brain disorders and the evaluation of therapeutic efforts.
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