The Role of Behavioral Economic Incentive Design and Demographic Characteristics in Financial Incentive-Based Approaches to Changing Health Behaviors: A Meta-Analysis. Issue 5 (May 2015)

• Record Type: Journal Article
• Authors: Haff, Nancy; Patel, Mitesh S.; Lim, Raymond; Zhu, Jingsan; Troxel, Andrea B.; Asch, David A.; Volpp, Kevin G.
• Is Part Of: American journal of health promotion. Volume 29, Issue 5(2015)
• Page Start: 314
• Page End: 323
• Publication Date: 2015-05
• Subjects: Financial Incentives -- Behavioral Economics -- Health Behavior Change -- Demographics -- Income -- Gender -- Race -- Age -- Education -- Incentive Structure -- Manuscript format: research -- Research purpose: modeling/relationship testing -- Study design: meta-analysis -- Outcome measure: behavioral -- Setting: national -- Health focus: smoking control, weight control, medication adherence -- Strategy: incentives -- Target population: adults -- Target population circumstances: all education levels, all income levels, all races/ethnicities; Health promotion -- Periodicals; Health Promotion; Health promotion; Periodicals; Periodicals; 613.05
• DOI: 10.4278/ajhp.140714-LIT-333
• Languages: English
• ISSNs: 0890-1171

Abstract

Objective: To evaluate the use of behavioral economics to design financial incentives to promote health behavior change and to explore associations with demographic characteristics. Data Source: Studies performed by the Center for Health Incentives and Behavioral Economics at the University of Pennsylvania published between January 2006 and March 2014. Study Inclusion and Exclusion Criteria: Randomized, controlled trials with available participant-level data. Studies that did not use financial incentives to promote health behavior change were excluded. Data Extraction: Participant-level data from seven studies were pooled. Data Synthesis: Meta-analysis on the pooled sample using a random-effects model with interaction terms to examine treatment effects and whether they varied by incentive structure or demographic characteristics. Results: The pooled study sample comprised 1403 participants, of whom 35% were female, 70% were white, 24% were black, and the mean age was 48 years (standard deviation 11.2 years). In the fully adjusted model, participants offered financial incentives had higher odds of behavior change (odds ratio [OR]: 3.96; p < .01) when compared to control. There were no significant interactions between financial incentives and gender, age, race, income, or education. When further adjusting for incentive structure, blacks had higher odds than whites of achieving behavior change (OR: 1.67; p< .05) with a conditional payment. Compared to lower-income participants, higher-income participants had lower odds of behavior change (OR: 0.46; p = .01) with a regret lottery. Conclusion: Financial incentives designed using concepts from behavioral economics were effective for promoting health behavior change. There were no large and consistent relationships between the effectiveness of financial incentives and observable demographic characteristics. Second-order examinations of incentive structure suggest potential relationships among the effectiveness of financial incentives, incentive structure, and the demographic characteristics of race and income.