Depression and Cancer-Related Fatigue: A Cross-Lagged Panel Analysis of Causal Effects

Abstract

Fatigue is one of the most common and debilitating symptoms reported by cancer patients, yet it is infrequently diagnosed or treated. Relatively little is understood about its etiology in the cancer context. Recently, as researchers have begun to focus attention on cancer-related fatigue (CRF), depression has emerged as its strongest correlate. Few longitudinal studies have been done, however, to determine whether causal influences between the two symptoms exist. The aim of the current study was to determine whether depression has a causal influence on CRF and whether reciprocal effects exist. The study used a single-group cohort design of longitudinal data from a randomized controlled trial (N = 405) of an intervention for pain and depression in a heterogeneous sample of cancer patients. To be eligible, participants met criteria for clinically significant pain or depression. A hypothesis that depression would influence change in fatigue after 3 months was tested using latent variable cross-lagged panel analysis, a structural equation modeling technique. A second hypothesis was that fatigue would also influence change in depression over time but at a lesser magnitude. Depression and fatigue were strongly correlated in the sample (i.e., baseline correlation of latent variables was 0.72). Although the model showed good fit to the data, χ2 (66, N = 329) = 88.16, p = 0.04, SRMR = 0.030, RMSEA = 0.032, and CFI = 1, neither cross-lagged structural path was significant. The findings suggest that depression had no causal influence on changes in fatigue in this sample, and fatigue did not influence change in depression. The clinical implication is that depression treatment may not be helpful as a treatment for CRF and therefore interventions specifically targeting fatigue may be needed. Future research should include additional waves of data and larger sample sizes.