Background: 
Physical inactivity, poor food choice and obesity are important risk factors for the development of Type 2 Diabetes (T2D). However, little is known about factors explaining why some individuals take more risks than others regarding their health behavior. 
Behavioral Economics (BE) is an approach that aims to identify and describe systematic patterns of cognitive biases to human decision making, when classic theories of utility maximization fail. Thereby, BE could also help to explain seemingly irrational health behavior and risk taking. 
Status Quo Bias (SQB) and Time Preference (TP) are concepts within the BE framework. SQB describes a preference for the current state of affairs. Closely related, TP is characterized by a higher weighting of immediate compared to later rewards. 
The identification of such patterns in health behavior would open up new points of action for the prevention of T2D.

Aims: 
The study intends to determine to which extent SQB and TP regulate physical inactivity, poor food choice and BMI. 

Method: 
This cross-sectional, population-based, study examined data of n = 4261 individuals from the KORA cohort in Germany. The sample included 51% female participants and ages between 24 and 75 years. The data was collected in the years 1999/2000. 
SQB was measured through a life related monetary question: ‘Imagine an alternative health insurance that offers the same benefits as your current insurance, but to a lower rate. How much Euro does the monthly rate need to decrease, to make you switch the insurance plan?’ This represents a monetary matching question, which assesses the financial stimulus that is needed for an individual to engage in short term efforts in favor of long term benefits. 
TP was assessed by a self-reported question: ‘If you like the taste of your meal - do you continue eating after you are saturated?’ Answer categories ranged from (1) always to (4) never.    
Physical inactivity was estimated by the sum-score of two separate questions on the time spent with physical activity during leisure time in summer and winter. Answer categories ranged from (1) > 2 hours to (4) none. A sum-score > 4 was categorized as physical inactive. Poor food choice was estimated by the frequency of consumption regarding 15 different comestibles. The cut-off score for an unhealthy diet was derived from the German Nutrition Society (DGE). 
Logistic and linear regression models were used to examine the association of SQB and TP with physical inactivity, poor food choice and BMI. The models were adjusted for age, sex and education, and in case of physical inactivity and poor food choice also for BMI.

Results: 
A high frequency of overeating was significantly associated with poor food choice (OR = 1.67, p < 0.001), and higher BMI (Estimate = 1.65, p < 0.001). The frequency of overeating was not associated with physical inactivity.  A high SQB predicted a higher chance of physical inactivity (OR = 1.47, p < 0.001). SQB was not associated to poor food choice but significantly correlated to BMI (Estimate = 0.67, p < 0.01).

Discussion: 
The results support existing evidence on the association between BE concepts and health decision making. Also, it introduces two life related tools, which could be used to identify individuals with higher SQB and TP that could profit from tailored interventions that approach these biases.
As described in the literature TP was associated with a higher BMI and poor food choice. However, there was no association with physical inactivity. Other studies show varying results for this association. In this study, the lack of correlation could be explained by the domain specificity of time preference, as observed in former studies. 
The found correlations between SQB, physical inactivity and BMI suggest further investigations on this concept and how it is related to health behavior. 
The study results are limited with respect to its characteristics as a cross-sectional study. Furthermore, the external validity of the questions on BE has to be tested in further research.
It is a major advantage of the questions on SQB and TP that they are self-reports on an actual behavior (SQB) and habit (TP) in participants’ everyday life. Additionally, the large sample size of the study adds valuable information to the discussion on the generalizability of BE concepts.

Can behavioral economic insights explain a lifestyle relevant for type-2-diabetes prevention?