Objective: Cancer-related fatigue (CRF) is among the most distressing symptoms reported by cancer survivors as compromising their quality of life. This study investigates the complex interplay between CRF and functional health (FH) in survivors of Hodgkin lymphoma by using longitudinal data to clarify the etiology of CRF. Methods: Data from N = 3596 survivors (HD13-15) from the German Hodgkin Study Group was analyzed using bivariate latent curve models with structured residuals to model how the interplay between CRF and FH unfolds over time across and within individuals. CRF and FH were measured with the EORTC QLQ-C30. Assessed FH domains were physical, cognitive, emotional, social, and role functioning. Age at diagnosis, gender, country, baseline fatigue, and cancer stage were included as covariates. Results: The latent curve models with structured residuals had an adequate model fit (χ2 = 416.63–548.28, df = 114, p < .001, root mean square error of approximation = .03, comparative fit index = .98–.99, Tucker-Lewis index = .97–.98). On the between-person level, CRF and all FH domains were strongly negatively correlated (r = −.72 to r = −.84). On the within-person level, earlier CRF (ρF = −.05 to ρF = −.12) and FH deviations (ρFH = −.05 to ρFH = −.13) negatively predicted subsequent deviations of the respective other variable. Time-specific within-person improvements in physical, cognitive, and emotional functioning reduced CRF more than vice versa, whereas the effect of CRF was stronger for social functioning. Role functioning had a balanced relation with CRF. Conclusions: This analysis reveals a complex reciprocal relation between CRF and FH with distinct between- and within-person effects. The results contribute to a better understanding of CRF in survivors of Hodgkin lymphoma and could inform the development of much-needed targeted interventions.

Understanding the longitudinal dynamics of behavior, their stability and change over time, are of great interest in the social and behavioral sciences. Researchers investigate the degree to which an observed measure reflects stable components of the construct, situational fluctuations, method effects, or just random measurement error. An important question in such models is whether autoregressive effects occur between the residuals, as in the trait-state occasion model (TSO model), or between the state variables, as in the latent state-trait model with autoregression (LST-AR model). In this article, we compare the two approaches by applying revised latent state-trait theory (LST-R theory). Similarly to Eid et al. ( 2017) regarding the TSO model, we show how to formulate the LST-AR model using definitions from LST-R theory, and we discuss the practical implications. We demonstrate that the two models are equivalent when the trait loadings are allowed to vary over time. This is also true for bivariate model versions. The different but same approaches to modeling latent states and traits with autoregressive effects are illustrated with a longitudinal study of cancer-related fatigue in Hodgkin lymphoma patients.

No abstract