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Behavioral Choice Theory and Obesity: From Molecular Genetics to Public Policy Leonard H. Epstein, Ph.D. Distinguished Professor of Pediatrics and Social and Preventive Medicine University at Buffalo School of Medicine and Biomedical Sciences 1 Behavioral Choice Theory Behavioral choice theory, or behavioral economics, involves understanding factors that influence choice between two or more alternatives 2 Choice and behavioral interventions Task of intervention or treatment is to shift choice from an unhealthy, highly reinforcing behavior to less reinforcing, but more healthy alternatives – Choosing to be more active rather than sedentary – Choosing to eat healthier, rather than less healthy eating – Choosing to play sports rather than use drugs 3 Choice: now or later? Concurrent choices – Eating junk food or healthy food now – Being sedentary or active now – Relative reinforcing value; substitutes or complements Delayed choices – Eating healthy now to lose weight later – Being active now to be stronger and faster later – Delay discounting or behavioral impulsivity 4 Topics to be addressed from perspective of behavioral economic theory Basic science of choice, using drug selfadministration and eating as examples What happens to energy balance (the relationship between energy intake and energy expenditure) or body composition when you modify sedentary behaviors? What is the role of food pricing on choice? 5 Experimental Design: Alcohol Dependence Rats randomized to control, intermittent free choice (once per week), continuous free choice, or forced intake of ethanol Became dependent on ethanol over 32 weeks of drug administration Animals kept ethanol-abstinent for 3 months, during which time they showed withdrawal (change in pain threshold, hyperthermia, inactivity) Animals then provided self-administration test Wolffgramm, J., Heyne, A. (1995). From controlled drug intake to loss of control: the irreversible development of drug addiction in the rat. Behavioural Brain Research, 6 70, 77-94. 3 2.5 2 g/kg/day ethanol 1.5 1 0.5 0 Controls Forced Intermittent Free Choice Continuous Free Choice Wolffgramm, J., Heyne, A. (1995). From controlled drug intake to loss of control: the irreversible development of drug addiction in the rat Behavioural Brain Research, 70, 77-94. 7 Experimental Design : Opiate Dependence Rats randomized to control, choice or forced intake of etonitazene Became dependent on opiate over 30 weeks of drug administration Animals kept opiate-abstinent for 19 weeks, during which time they showed withdrawal (change in pain threshold) Animals then provided self-administration test Wolffgramm, J., Heyne, A. (1995). From controlled drug intake to loss of control: the irreversible development of drug addiction in the rat. Behavioural Brain Research, 8 70, 77-94. 90 80 70 60 50 ug/kg/day Etonitazene 40 30 20 10 0 Controls Forced Free Choice Wolffgramm, J., Heyne, A. (1995). From controlled drug intake to loss of control: the irreversible development of drug addiction in the rat Behavioural Brain Research, 70, 77-94. 9 Choices for cocaine or placebo 10 9 8 7 6 Choices 5 4 3 2 1 0 Drug Placebo Higgins, Bickel & Hughes. (1994). Influence of an alternative reinforcer on human cocaine self-administration. Life Sciences, 55, 179-187. 10 Choices for cocaine or placebo 12 10 8 First Exposure Second Exposure 6 4 2 0 $0.00 $0.50 $1.00 $2.00 Higgins, Bickel & Hughes. (1994). Influence of an alternative reinforcer on human cocaine self-administration. Life Sciences, 55, 179-187. 11 Fulton, Woodside & Shizgal (2000). Modulation of brain reward circuitry by leptin. Science, 287, 125-128. 12 13 14 Do genetic factors influence choice of food as a reinforcer? Food reinforcement is mediated in part by dopaminergic activity in the brain Dopamine has multiple functions in the brain, including: – Locomotor activity – Feeding and drinking – Reinforcement – Appetitive motivation 15 Dopamine, Eating, and Obesity Dopamine is released in the striatum during eating in rodents (Heffner et al., 1980; Hernandez & Hoebel, 1988) and humans (Small et al., 2003) In genetically obese mice (ob/ob), administration of dopamine normalizes body weight (Bina & Cincotta, 2000) In obese humans, dopamine agonists reduce energy intake (Leddy, et al, 2004) Chronic treatment with antipsychotic drugs, which block the dopamine receptors, increases obesity risk (American Diabetes Association, 2004) 16 Dopamine D2 Receptor DRD2 may be a common neurobiological substrate underlying drug addiction and obesity (Volkow & Wise, 2005) Imaging studies show that obese individuals have reduced expression of DRD2 similar in magnitude to reductions reported in drug-addicted subjects (Wang et al., 2001) Density of the D2 receptor is related to polymorphisms of the A1 and A2 alleles, with 3 genotypes: A1/A1, A1/A2, A2/A2. 17 Taq 1 Al Allele of the DRD2 D2 genotype provides an estimate of DRD2 density and, thus, an estimate of dopaminergic system activity – The presence of at least one copy of the Taq 1 A1 allele of the DRD2 is associated with a 30-40% reduction in the density of DRD2 (Noble et al., 1994; Jonsson et al., 1999). A current hypothesis is that reduced sensitivity to dopamine in individuals with the Taq 1 A1 leads to a need for excess dopamine in the synapse (Bowirrat & Oscar-Berman, 2005). 18 DRD2 genotypes, food reinforcement, obesity and energy intake 18 – 40 year olds (n=45 non-obese and n=29 obese) Not currently on a diet Not currently on medications that affect appetite or eating (methylphenidate, etc) No eating disorders Must like study foods Epstein, Temple, Neaderhiser, Salis, Erbe, & Leddy, (2007). Food reinforcement, the dopamine D2 receptor genotype and energy intake in obese and non-obese humans. Behavioral Neuroscience, 121, 877-886. 19 Study Design Two visits to the laboratory – separated by 1 week First visit = “taste test” – Measure energy intake of snack foods – Take DNA sample Second visit = food reinforcement – Participants complete reinforcement task – Height/weight obtained Epstein, Temple, Neaderhiser, Salis, Erbe, & Leddy, (2007). Food reinforcement, the dopamine D2 receptor genotype and energy intake in obese and non-obese humans. Behavioral Neuroscience, 121, 877-886. 20 0 Non-Obese Obese Individuals with the Taq 1 A1 allele have a higher BMI 35 Body Mass Index (kg/m2) 30 * 25 20 15 10 5 0 A2/A2 A1/A1 or A1/A2 Epstein, Temple, Neaderhiser, Salis, Erbe, & Leddy, (2007). Food reinforcement, the dopamine D2 receptor genotype and energy intake in obese and non-obese humans. Behavioral Neuroscience, 121, 877-886. 21 Obese Individuals Find Food More Reinforcing than Non-Obese 500 400 300 200 100 * 2000 Non-obese (n=45) Obese (n=29) Total Button Presses Number of Button Presses 600 1500 1000 500 0 0 4 8 16 32 64 128 256 512 1024 Non-Obese Obese Epstein, Temple, Neaderhiser, Salis, Erbe, & Leddy, (2007). Food reinforcement, the dopamine D2 receptor genotype and energy intake in obese and non-obese humans. Behavioral Neuroscience, 121, 877-886. 22 Obese Individuals with the Taq 1 A1 Allele Have the Highest Levels of Food Reinforcement Non-Obese/A2/A2 (n=25) Non-Obese/A1/A1 or A1/A2 (n=20) Obese/A2/A2 (n=12) Obese/A1/A1 or A1/A2 (n=17) 600 3000 * * * 400 200 Total Button Presses Number of Button Presses 800 2500 A2/A2 A1/A1 or A1/A2 * 2000 1500 1000 500 0 0 4 8 16 32 64 128 256 512 1024 Non-Obese Obese Epstein, Temple, Neaderhiser, Salis, Erbe, & Leddy, (2007). Food reinforcement, the dopamine D2 receptor genotype and energy intake in obese and non-obese humans. Behavioral Neuroscience, 121, 877-886. 23 Taq 1 A1 Interacts with Food Reinforcement to Increase Energy Intake A2/A2 A1/A2 or A1/A1 Energy Consumed (KCal) 1000 800 600 400 200 0 Low High Food Reinforcement Level Epstein, Temple, Neaderhiser, Salis, Erbe, & Leddy, (2007). Food reinforcement, the dopamine D2 receptor genotype and energy intake in obese and non-obese humans. Behavioral Neuroscience, 121, 877-886. 24 Summary of basic science studies Choice is important to establish drugs as rewards Relative reinforcing value depends on behavioral cost and alternatives There are genetic and neurobiological factors that influence choice 25 26 Reinforcing value of physical activity: Design Children in three categories: – Non-obese (<20% overweight) – Moderately obese (20-80% overweight) – Very obese (>80% overweight) Provided access to a moderately liked vigorous activity at a constant variable ratio (VR2) reinforcement schedule versus a highly liked sedentary activity with the schedule varied from VR2 to VR32. Epstein, Smith, Vara, & Rodefer. (1991). Behavioral economic analysis of activity choice in obese children. Health Psychology, 10, 311-316. 27 200 Active Time (sec) 175 150 Non-obese Moderately Obese Very Obese 125 100 75 50 25 0 2/8 2/32 2/16 2/2 2/4 Vigorous/Sedentary VR Schedules of Reinforcement Epstein, Smith, Vara, & Rodefer. (1991). Behavioral economic analysis of activity choice in obese children. Health Psychology, 10, 311-316. 28 How can reducing sedentary behavior influence energy balance? Sedentary behaviors can reduce the opportunity to be physically active – Reducing sedentary behaviors may increase physical activity Sedentary behaviors can increase the opportunities to eat – Reducing sedentary behaviors can reduce energy intake and dietary fat intake 29 30 31 32 33 Effects of decreasing sedentary behavior versus increasing activity on weight change in obese children Obese 8-12 year-old children from 61 families randomized to groups that targeted: – Increased physical activity – Decreased sedentary behavior – Combined increased physical activity, decreased sedentary behavior Provided 4 month intensive treatment, followed at 12 month post randomization 34 Effects of decreasing sedentary behavior versus increasing activity on weight change in obese children Traffic light diet used for reducing calories, increasing nutrient density and balancing nutrient intake Increase activity goals from 30-150 points/week – 300-1500 kcal/week for a 150 lb person Decrease sedentary from 35 hours/week to goal of 15 hours/week – decreases in 5 hour increments 35 Epstein et al. (1995). Effects of decreasing sedentary behavior and increasing physical activity on weight change in obese children. Health Psychology, 14, 109-115. 36 Epstein et al. (1995). Effects of decreasing sedentary behavior and increasing physical activity on weight change in obese children. Health Psychology, 14, 109-115. 50 45 40 35 30 Exercise Combined Reduce sedentary 25 20 15 10 5 0 Change in liking for physical activity 37 What happens to eating and activity when you change sedentary behavior? 58 families with non-obese and obese 12-15 year-old children – Participated in a 9 week experiment » Baseline: maintain usual pattern of sedentary behavior, physical activity and energy intake. » Increase sedentary behavior by 25% to 50% of baseline » Decrease sedentary behavior by 25% to 50% of baseline – Targeted sedentary behaviors were watching TV or VCR, playing computer games, or using the internet. Activity measured using tri-axial accelerometer Dietary intake from repeated 24-hour phone dietary recalls Epstein LH, Paluch RA, Consalvi A, Riordan K, Scholl T. Effects of manipulating38 sedentary behavior on physical activity and food intake. J Pediatr. 2002; 140:334-339. Changes in targeted sedentary behaviors from baseline during increase and decrease phases Change in minutes of targeted sedentary behavior/ day from baseline 150 *** 100 50 0 - 50 - 100 *** - 150 Increase Decrease Epstein, L. H., Roemmich, J. N., Paluch, R. A., & Raynor, H. A. (2005). Physical activity as a substitute for sedentary behavior in youth. Annals of Behavioral Medicine, 29, 200-209. 39 a 600 a 500 400 b 300 200 100 Diet ar y f at int ak e in associat ion wit h t ar get ed sed ent ar y b ehavior Ener gy int ak e (k cal/ d ay) associat ed wit h t ar get ed sed ent ar y b ehavior s Energy intake in association with watching television influenced more by reducing than increasing sedentary behaviors 240 a 220 200 a 180 160 140 b 120 100 80 60 Design: 16 of the nonoverweight 12-16 year-old youth who did not underreport energy Intake were studied in the same 3phase within subjects design 40 Baseline Increase Decrease Baseline Increase Decrease Phases Phases Epstein, L. H., Roemmich, J. N., Paluch, R. A., & Raynor, H. A. (2005). The influence of changes in sedentary behavior on energy and macronutrient intake in 40 youth. American Journal of Clinical Nutrition, 81, 361-366. 41 Can reducing television watching modify z-BMI in 4-7 year-old youth? Most programs to change body weight involve combinations of programs to decrease energy intake and increase physical activity – Reducing television watching may have an impact on body weight since the effects on reducing energy intake are potentially so strong – Even if the effects on physical activity are more limited 42 Can reducing television watching modify z-BMI in 4-7 year-old youth? 70 4-7 year-old children at the 75th BMI percentile or greater were randomized to: – A TV and computer game reduction group » Goal of reducing these targeted sedentary behaviors by 50% » Provided TV Allowance monitors as well as monthly newsletters. Newsletter provided ideas for substitutes for watching television and ideas for parenting – Monitoring control group » TV Allowance devices but no reduction goals » Received newsletter without ideas for substitutes or parenting – Activity measured using accelerometers – Dietary intake measured using food frequency questionnaire (Rockett) 43 Hours of television and computer garmes/week 30 Control group TV reduction group 25 20 15 10 5 -6 0 6 12 18 24 Months Epstein, L.H., Roemmich, J.N., Robinson, J.L., Paluch, R.A., Winiewicz, D.D., Fuerch, J.H. & Robinson, T.N. (2008). A randomized trial on the effects of reducing television on BMI in young children. Archives of Pediatrics and Adolescent Medicine, 162, 239-245. 44 1.70 Control group TV reduction group 1.65 1.60 zBMI 1.55 1.50 1.45 1.40 1.35 1.30 -6 0 6 12 18 24 X Data Epstein, L.H., Roemmich, J.N., Robinson, J.L., Paluch, R.A., Winiewicz, D.D., Fuerch, J.H. & Robinson, T.N. (2008). A randomized trial on the effects of reducing television on BMI in young children. Archives of Pediatrics and Adolescent Medicine, 162, 239-245. 45 1600 Control group TV reduction group Energy intake/day 1500 1400 1300 1200 1100 -6 0 6 12 18 24 Months Epstein, L.H., Roemmich, J.N., Robinson, J.L., Paluch, R.A., Winiewicz, D.D., Fuerch, J.H. & Robinson, T.N. (2008). A randomized trial on the effects of reducing television on BMI in young children. Archives of Pediatrics and Adolescent Medicine, 2008, 239-245. 46 Background on pricing research The first step in consuming food is purchasing food • Over the past three decades less healthy foods have become much cheaper in comparison to healthier alternatives. – Increases in energy intake are due in part to purchasing increased energy. Pricing interventions are powerful ways to modify purchasing and thus consumption Own-price elasticity Price of A is negatively related to purchases of A – Price of coffee increases, demand for coffee decreases Experimental studies have shown increases in purchases of healthier foods as their price is reduced (subsides) and reductions in purchases of less healthy foods as their price is increased (taxes) – – – – French et. al. (1997) Pricing strategy to promote fruit and vegetable purchases in high school cafeteria. Journal of American Dietetic Association, 97, 1008-10. Horgen K., Brownell, K. (2002). Comparison of price change interventions in promoting healthy food choices. Health Psychology, 21, 505-12. Epstein, LH, Handley, EA, Dearing, KK, Cho, DD, Roemmich, JN, Paluch, RA et. al (2006). Purchases of food in youth: Influence of price and income. Psychological Science, 17,82-89. Epstein, LH, Dearing, KK, Paluch, RA, Roemmich, JN & Cho, D (2007). Price and maternal obesity influence purchasing of low and high energy dense foods. American Journal of Clinical Nutrition, 86, 914-922. Cross-price elasticity Price of A is positively related to purchases of B – Price of coffee increases, purchases of tea increase Experimental studies have shown increases in purchases of healthier foods as the price of less healthy foods is increased Individual differences may moderate cross-price elasticity – Moderated by amount of money available » Epstein, LH, Handley, EA, Dearing, KK, Cho, DD, Roemmich, JN, Paluch, RA et. al (2006). Purchases of food in youth: Influence of price and income. Psychological Science, 17,82-89. Children with less money are more likely to substitute healthy foods for less healthy foods when the price of less healthy foods increase – Moderated by maternal weight » Epstein, LH, Dearing, KK, Paluch, RA, Roemmich, JN & Cho, D (2007). Price and maternal obesity influence purchasing of low and high energy dense foods. American Journal of Clinical Nutrition, 86, 914-922. Lean mothers were more likely to substitute healthy foods for less healthy foods when the price of less healthy foods increase Effect of Weight on Effectiveness of Pricing Interventions BMI=20 BMI=40 BMI=20 BMI=40 9.0 Purchases of Low Energy Dense Foods Purchases of High Energy Dense Foods 7 6 5 4 3 8.5 8.0 7.5 7.0 6.5 6.0 2 50 75 100 125 Percentage of HED Reference Price Figure 1: Number of Purchases of HED foods when the price of HED foods was changing in individuals with high and low BMI. 150 50 75 100 125 Percentage of HED Reference Price Figure 2: Number of Purchases of LED foods when the price of HED foods was changing in individuals with high and low BMI. Epstein, LH, Dearing, KK, Paluch, RA, Roemmich, JN & Cho, D (2007). Price and maternal obesity influence purchasing of low and high energy dense foods. American Journal of Clinical Nutrition, 86, 914-922. 150 Taxes or Subsides? Is it better to subsidize the cost of healthy foods or tax unhealthy foods? Using an experimental analog food purchasing task, we examined the effects of increasing the prices of high calorie for nutrient foods (HCFN) by 12.5% and 25% (TAX) or reducing the prices of low calorie for nutrient foods (LCFN) by 12.5% and 25% (SUBSIDY) on mother’s purchases of 68 common foods and beverages. Methods Participants were 42 mothers, 20 were lower income (< $50,000) and 22 were higher income(≥ $50,000). 45% were obese (BMI ≥ 30) Each participant was given a study income of $22.50/family member for each of 5 purchasing trials In addition to number of items purchased, we also analyzed changes in calories and macronutrients purchased, as we thought this would provide a better index of how price changes influence health – A family could buy more fruits and vegetables, but still purchase the same amount of high-energy density, low nutrient density foods. If they do not substitute healthier for less healthy foods, buying more fruits and vegetables may have limited effects on weight and health. Data Analysis Mixed effects regression models were used to assess the effects of price change on calories and macronutrients purchased as well as the number of HCFN and LCFN foods purchased – Log values of the prices and the number of calories purchased after adjusting for family size were used – Covariates such as age, BMI, family income and hunger were added to the models and they were tested to examine whether they moderated the influence of price on purchases Total calories purchased in relationship to taxes and subsidies 11000 Epstein, Dearing , Roba & Finkelstein. In press. The influence of taxes and subsidies on energy purchased in an experimental purchasing study. Psychological Science. 10000 Total calories purchased Fig.1. Total calories purchased (A), changes in macronutrients (B) and proportion of calories as macronutrents (C) purchased. Mixed regression models showed subsidies increased calories purchased, due to increases in fat, carbohydrates and protein purchased. Taxes reduced calories purchased, with significant reductions in fat and carbohydrates. Subsides resulted in no changes in the quality of the diet as calories increased, while taxes resulted in reductions in fat and increases in protein. 9000 8000 7000 6000 -25.0 -12.5 0.0 12.5 25.0 Price Changes Reduction in price from reference price - Subsidies Increase in price from reference price - Taxes Macronutrients purchased in relationship to taxes and subsidies Fig.1. Total calories purchased (A), changes in macronutrients (B) and proportion of calories as macronutrents (C) purchased. Mixed regression models showed subsidies increased calories purchased, due to increases in fat, carbohydrates and protein purchased. Taxes reduced calories purchased, with significant reductions in fat and carbohydrates. Subsides resulted in no changes in the quality of the diet as calories increased, while taxes resulted in reductions in fat and increases in protein. Epstein, Dearing , Roba & Finkelstein. In press. The influence of taxes and subsidies on energy purchased in an experimental purchasing study. Psychological Science. Change in macronutrients from reference price 1200 1000 800 600 Fat Carbohydrates Protein 400 200 0 -200 -400 -25% -12.5% 0% 12.5% 25% Price Changes Epstein, Dearing , Roba & Finkelstein. In press. The influence of taxes and subsidies on energy purchased in an experimental purchasing study. Psychological Science. 20 18 16 Proportion of calories as fat Fig.1. Total calories purchased (A), changes in macronutrients (B) and proportion of calories as macronutrents (C) purchased. Mixed regression models showed subsidies increased calories purchased, due to increases in fat, carbohydrates and protein purchased. Taxes reduced calories purchased, with significant reductions in fat and carbohydrates. Subsides resulted in no changes in the quality of the diet as calories increased, while taxes resulted in reductions in fat and increases in protein. 22 32 Proportion of calories as carbohydrates Proportion of macronutrients purchased in relationship to taxes and subsidies Proportion of calories as protein C 58 30 28 26 56 54 52 50 -25.0 -12.5 0.0 12.5 Price changes 25.0 57 58 59 60 61 62 Summary Behavioral economics is a theoretical approach that can be used to study how people make choices about health behaviors Behavioral economics can be studied at the genetic, physiological, environmental or behavioral levels Choice is important to understand factors that influence the motivation to engage in healthier behaviors 63 Acknowledgements Brian Saelens, Ph.D. Holly Raynor, Ph.D. James Roemmich, Ph.D. Rocco Paluch, M.S. Jenn Temple, Ph.D. John Leddy, M.D. Tom Robinson, M.D. 64