Intertemporal discounting as a risk factor for high BMI: evidence from Australia, 2008

A Neuroeconomics Approach to Obesity

Obesity is a heterogeneous condition that is affected by physiological, behavioral, and environmental factors. Value-based decision making is a useful framework for integrating these factors at the individual level. The disciplines of behavioral economics and reinforcement learning provide tools for identifying specific cognitive and motivational processes that may contribute to the development and maintenance of obesity. Neuroeconomics complements these disciplines by studying the neural mechanisms underlying these processes. We surveyed recent literature on individual decision characteristics that are most frequently implicated in obesity: discounting the value of future outcomes, attitudes toward uncertainty, and learning from rewards and punishments. Our survey highlighted both consistent and inconsistent behavioral findings. These findings underscore the need to examine multiple processes within individuals to identify unique behavioral profiles associated with obesity. Such individual characterization will inform future studies on the neurobiology of obesity as well as the design of effective interventions that are individually tailored.

Temporal discounting as a candidate behavioral marker of obesity

Although obesity is a result of processes operating at multiple levels, most forms result from decision-making behavior. The aim of this review was to examine the candidacy of temporal discounting (TD) (i.e. the reduction in the value of a reinforcer as a function of the delay to its receipt) as a behavioral marker of obesity. For this purpose, we assessed whether TD has the ability to: identify risk for obesity development, diagnose obesity, track obesity progression, predict treatment prognosis/outcomes, and measure treatment effectiveness. Three databases (Pubmed, PsycINFO, and Web of Science) were searched using a combination of terms related to TD and obesity. A total of 153 papers were reviewed. Several areas show strong evidence of TD's predictive utility as a behavioral marker of obesity (e.g., distinguishing obese from non obese). However, other areas have limited and/or mixed evidence (e.g., predicting weight change). Given the positive relationship for TD in the majority of domains examined, further consideration for TD as a behavioral marker of obesity is warranted.

Time discounting, present biases, and health-related behaviors: Evidence from Japan

Human health is considered the outcome of intertemporal choices under tradeoffs between a small immediate reward and a larger delayed reward. Health-related behaviors are thus affected by personal time preferences. Based on an Internet-based survey conducted on Japanese adults, we contribute to the literature by incorporating the multifaceted nature of time discounting in an analysis of the associations between time preference and health-related behaviors. We find that, first, less patient respondents tend to exhibit worse health-related attributes. Second, present bias, which is measured by the degree of declining impatience, is positively associated with unhealthy behaviors for naïve respondents, who are unaware of their self-control problem. Third, such associations cannot be found in sophisticates, who are aware of that. As a policy implication, direct intervention policies, including "nudging," are more effective than a commitment device provision in correcting the unhealthy behaviors due to present bias. Fourth, the sign effect, wherein future losses are discounted at a lower rate than future gains, is negatively associated with unhealthy outcomes, although at weak statistical significance levels.

Obesity and health expenditures: evidence from Australia

Rising rates of obesity are a public health concern in every industrialized country. This study investigates the relationship between obesity and health care expenditure in Australia, where the rate of obesity has tripled in the last three decades. Now one in four Australians is considered obese, defined as having a body mass index (BMI, kg/m(2)) of 30 or over. The analysis is based on a random sample survey of over 240,000 adults aged 45 and over that is linked at the individual-level to comprehensive administrative health care claims for the period 2006-2009. This sub-population group has an obesity rate that is nearly 30% and is a major consumer of health services. Relative to the average annual health expenditures of those with normal weight, we find that the health expenditures of those with a BMI between 30 and 35 (obese type I) are 19% higher and expenditures of those with BMI greater than 35 (obese type II/III) are 51% higher. We find large and significant differences in all types of care: inpatient, emergency department, outpatient and prescription drugs. The obesity-related health expenditures are higher for obese type I women than men, but in the obese type II/III state, obesity-related expenditures are higher for men. When we stratify further by age groups, we find that obesity has the largest impact among men over age 75 and women aged 60-74 years old. In addition, we find that obesity impacts health expenditures not only through its link to chronic diseases, but also because it increases the cost of recovery from acute health shocks.