G-3 AGE DIFFERENCES IN DECISIONAL STRATEGY INFLUENCE FRAMING EFFECTS IN MEDICAL DECISIONS

Tuesday, October 26, 2010: 10:45 AM
Grand Ballroom Centre (Sheraton Centre Toronto Hotel)
Erin L. Woodhead, PhD, VA Palo Alto GRECC, Palo Alto, CA, Elizabeth B. Lynch, PhD, Rush University Medical Center, Chicago, IL and Barry A. Edelstein, PhD, West Virginia University, Morgantown, WV

Purpose: The extent to which age impacts susceptibility to framing effects is unclear, particularly within the area of medical decision making. The current study used a think-aloud technique to determine whether the information used by participants in their decision making process varied by age and by frame.

Method: For Study 1, a think-aloud method was used to determine systematic patterns in type of information used by older and younger adults when making hypothetical treatment choices for lung cancer treatments. A within-subject design was used with 40 younger adults (M = 19.8, SD = 1.5) and 40 older adults (M = 77.4, SD = 5.9). Participants responded to both frames (survival and mortality). Logistic regression analyses were used to predict treatment choice (surgery or radiation). Qualitative data analysis software was used to analyze responses to the think-aloud portion of the study.

Result: Qualitative analysis revealed that two major decisional strategies were used by all participants: a strategy based on the presented data and one based on personal/vicarious experience. Older age predicted decreased use of a data strategy (O.R. = 0.97, p < 0.001). Frame, education, and treatment choice did not predict strategy. Frame interacted with decisional strategy to predict treatment choice (O.R. = 0.19, p < 0.001). Those using a data strategy were significantly more likely to demonstrate framing effects than those using an experience strategy. Age did not modify this effect. Age and education did not independently predict treatment choice. These results were replicated in Study 2, which employed a between-subjects design with 61 older adults (M = 76.8, SD = 7.1) and 63 younger adults (M = 18.6, SD = 1.9). In Study 2, age was the only significant predictor of strategy (O.R. = 0.96, p < 0.001), with older adults less likely to use a data strategy. Similar to Study 1, frame interacted with decisional strategy to predict treatment choice (O.R. = 0.32, p < 0.05).

Conclusion: These results suggest that age is not directly related to framing effects. Instead, age appears to influence adoption of a decisional strategy, which then impacts susceptibility to framing effects. Older adults may be less susceptible to framing effects due to their increased reliance on experience-based information processing.

Candidate for the Lee B. Lusted Student Prize Competition