Jared M Bruce1,2, Amanda S Bruce3,4, Sharon Lynch5, Joanie Thelen6, Seung-Lark Lim6, Julia Smith6, Delwyn Catley3, Derek D Reed7,8, David P Jarmolowicz7,8. 1. Department of Psychology, University of Missouri - Kansas City, 5030 Cherry Hall, Kansas City, MO, 64110, USA. brucejm@umkc.edu. 2. Department of Biomedical and Health Informatics, University of Missouri - Kansas City, Kansas City, MO, USA. brucejm@umkc.edu. 3. Center for Healthy Lifestyles and Nutrition, Children's Mercy Hospital, Kansas City, MO, USA. 4. Department of Pediatrics, University of Kansas Medical Center, Kansas City, KS, USA. 5. Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA. 6. Department of Psychology, University of Missouri - Kansas City, 5030 Cherry Hall, Kansas City, MO, 64110, USA. 7. Department of Applied Behavior Science, University of Kansas, Lawrence, KS, USA. 8. Cofrin-Logan Center for Addiction Research and Treatment, Lawrence, KS, USA.
Abstract
RATIONALE: Patients weigh risks and benefits when making treatment decisions. Despite this, relatively few studies examine the behavioral patterns underpinning these decisions. Moreover, individual differences in these patterns remain largely unexplored. OBJECTIVES: The purpose of this study was to test a probability discounting model to explain the independent influences of risks and benefits when patients make hypothetical treatment decisions. Furthermore, we examine how individual differences in this probability discounting function are associated with patient demographics, clinical characteristics, disease knowledge, neuropsychiatric status, and adherence. METHODS: Two hundred eight participants with relapsing-remitting multiple sclerosis (MS) indicated their likelihood (0-100%) of taking a hypothetical medication as the probability of mild side effects (11 values from .1 to 99.9%) and reported medication efficacies (11 values from .1 to 99.9%) varied systematically. They also completed a series of questionnaires and cognitive tests. RESULTS: Individual components of medication treatment decision making were successfully described with a probability discounting model. High rates of discounting based on risks were associated with poor treatment adherence and less disease-specific knowledge. In contrast, high rates of discounting of benefits was associated with poorer cognitive functioning. Regression models indicated that risk discounting predicted unique variance in treatment adherence. CONCLUSIONS: Insights gained from the present study represent an important early step in understanding individual differences associated with medical decision making in MS. Future research may wish to use this knowledge to inform the development of empirically supported adherence interventions.
RATIONALE: Patients weigh risks and benefits when making treatment decisions. Despite this, relatively few studies examine the behavioral patterns underpinning these decisions. Moreover, individual differences in these patterns remain largely unexplored. OBJECTIVES: The purpose of this study was to test a probability discounting model to explain the independent influences of risks and benefits when patients make hypothetical treatment decisions. Furthermore, we examine how individual differences in this probability discounting function are associated with patient demographics, clinical characteristics, disease knowledge, neuropsychiatric status, and adherence. METHODS: Two hundred eight participants with relapsing-remitting multiple sclerosis (MS) indicated their likelihood (0-100%) of taking a hypothetical medication as the probability of mild side effects (11 values from .1 to 99.9%) and reported medication efficacies (11 values from .1 to 99.9%) varied systematically. They also completed a series of questionnaires and cognitive tests. RESULTS: Individual components of medication treatment decision making were successfully described with a probability discounting model. High rates of discounting based on risks were associated with poor treatment adherence and less disease-specific knowledge. In contrast, high rates of discounting of benefits was associated with poorer cognitive functioning. Regression models indicated that risk discounting predicted unique variance in treatment adherence. CONCLUSIONS: Insights gained from the present study represent an important early step in understanding individual differences associated with medical decision making in MS. Future research may wish to use this knowledge to inform the development of empirically supported adherence interventions.
Entities:
Keywords:
Adherence; Disease-modifying therapy; Medical decision making; Multiple sclerosis; Probability discounting
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