A Longitudinal Approach to Understanding the Relationship Between Symptom Status and QOL.
This study demonstrates how a variant of growth curve modeling known as longitudinal parallel-process modeling can yield an understanding of the effect of symptoms on quality of life (QOL). A two-level hierarchical linear model with random intercepts and slopes was implemented within a structural equation modeling approach. The data (N = 367) comes from a large database of persons with HIV-associated illness. Twenty-three symptoms based on the Sign and Symptom Checklist for Persons with HIV disease and items measuring QOL from the general health status scales were used. Each respondent completed from 1 to 11 questionnaires. The number of reported symptoms had a significant association with patient QOL over time. These findings suggest that appropriate symptom management has the potential to improve patient QOL. This study demonstrates how a state-of-the-art longitudinal modeling technique evaluates the relationship between concurrent rates of change in measurements of two relevant variables.
University of Colorado Denver, Aurora, USA karen.sousa@ucdenver.edu.
Karen H
KH
Sousa
Texas A&M University, College Station, USA.
Oi-Man
OM
Kwok
University of Colorado Denver, Aurora, USA.
Sarah J
SJ
Schmiege
Arizona State University, Tempe, USA.
Stephen G
SG
West
D000328:Adult; D000368:Aged; D015658:HIV Infections / Q000150:complications / Q000401:mortality; D006304:Health Status; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D011788:Quality of Life / Q000523:psychology*; D063189:Symptom Assessment / Q000401:mortality*
longitudinal analysis; symptoms and quality of life