Abstract

Management of Cancer-related Fatigue and Sleep Quality

PI: S.L. Beck

An important shift is now occurring in symptom management research to examine symptom clusters rather than single symptoms. The overall aim of the proposed research is to extend our scientific understanding of symptom management by examining the effect of an intervention for two target symptoms commonly reported during cancer treatment, fatigue and sleep disturbance. The specific aims of the proposed research are to (1) test the effectiveness of the combined fatigue/sleep intervention on the target symptoms of fatigue and sleep quality and (2) to expand the test of intervention effectiveness by examining hypotheses about mediating processes that could explain the clustering of the target symptoms (fatigue and sleep). A secondary aim is to examine the relationships among fatigue, sleep, pain, and depression to generate hypotheses about underlying processes that lead to clustering of these commonly reported symptoms during cancer treatment. The proposed research will use an experimental design to compare an individual management plan for fatigue/sleep with a control group that equates for time and attention. The study will be implemented in two clinical sites including an academic health science center (Barrett Cancer Center of the University of Cincinnati) and a comprehensive cancer center (Fox Chase Cancer Center in Philadelphia, PA). Participants will be randomized using a computer-generated assignment to the intervention or control group. The major outcomes of interest include the target symptoms of fatigue and sleep quality and the related symptoms of pain and depression; in addition, functional status, a dimension of quality of life also will be evaluated. The general analytic approach will be to use repeated measures ANOVA and multiple regression to test a mediation model in which the relationship between the intervention and fatigue is partially mediated through improvement in sleep due to the intervention. A strength of this model is that it provides an incremental approach to hypothesis testing.