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01 August 2005

Framing health messages based on anomalies in time preference.

Monica Ortendahl, James Fries

Med Sci Monit 2005; 11(8): RA253-256 :: ID: 202641

Abstract

Time discounting processes and their effects are increasingly taken intoaccount in health-related decisions. Because these effects have a potentially large impact the characteristicsof discounting should also be taken into consideration when framing health messages. Research on therelationship between time and health is discussed with a special focus on discounting biases. The criteriafor selection of articles were potential practical application when formulating health messages. Timediscounting processes vary with individuals and contexts. Therefore, no single model is expected to describediscounting processes completely. Discounting biases appear more prevalent in health decisions than ineconomic decisions, even when health and monetary outcomes are matched for utility. Research on decision-makingunder conditions of uncertainty has documented numerous anomalies of expected utility. Analysis on theanomalies related to intertemporal choice and discounted utility (DU) include the magnitude effect, dynamicinconsistency effect, instant endowment, status quo bias, and sequence effect. Discounting biases inthe formulation of preventive health messages are important. The desire for behavioral change in theseprograms would benefit from considering the psychological factor of discounting. Framing health messagesin terms of large, important outcomes or long delays should induce lower implicit discount rates. Framinghealth messages as losses rather than gains, or as involving a series of outcomes rather than individualoutcomes, might similarly lower the implicit discount rate used.

Keywords: Costs and Cost Analysis, Decision Making, Disease, Health, Time Factors, Uncertainty, Weight Loss, Costs and Cost Analysis, Decision Making, Disease, Health, Time Factors, Uncertainty, Weight Loss

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750