Symptom Clusters and Social Support: Predicting Health Promotion and Quality of Life of Older Adults with Longstanding Multiple Sclerosis
Objectives: To identify symptom clusters that predict the health promotion behaviors and QOL of older people with longstanding MS.
Methods: Year 18 data from 215 older adults (ages 60 to 90, mean=68) with longstanding MS (years since diagnosis 18 to 57, mean=29) from a longitudinal study were used for analyses. 9.3% of the sample had benign sensory MS; 38.1% had relapsing-remitting MS; and 40.4% had progressive MS. MS symptoms were measured by the CESD, Perceived Stress Scale, PROMIS Measures of pain, fatigue, sleep and cognitive abilities. Health promotion behaviors, QOL, functional limitations and personal resource were measured by HPLP Lifestyle II, a 1-item global QOL measure, Incapacity Status Scale and Personal Resource Questionnaire. Bivariate correlations and factor analyses were used to identify symptom clusters and hierarchical multivariate regression analysis was performed to predict health promotion and QOL.
Results: The correlations between the symptoms ranged from 0.33 to 0.81. Two symptom clusters were found: the physical/psychological/cognitive cluster and pain cluster. After controlling the demographic factors, and functional limitations, the physical/psychological/cognitive cluster and social support predicted total HPLP score significantly. Social support and both symptom clusters predicted QOL significantly.
Conclusions: The symptoms of older people with longstanding MS tend to cluster and clusters are associated with health promotion and QOL. The co-occurrence of the symptoms should be considered for more comprehensive care and effective interventions.
Acknowledgements：This study was supported in part with grant funding from the National Institutes of Health, National Institute of Nursing Research (R01 NR003195) and by the James R. Dougherty Jr. Centennial Professorship in Nursing at the University of Texas at Austin. Editorial support was provided by the Cain Center for Nursing Research and the Center for Transdisciplinary Collaborative Research in Self-management Science (P30, NR015335) at The University of Texas at Austin School of Nursing. The authors would also like to acknowledge Vicki K. Kullberg for project management, and Ashley Henneghan, Lauren Culp, and Nicki Gloris for data entry and checking.