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Development of a Theory Based Dynamic Model on Demand for Obesity Preventive Goods

Morro M.L. Touray

Research Fellow, Health Economics and Policy Research Unit, Faculty of Life Sciences and Education, University of South Wales, Pontypridd, Wales, CF37 1DL, United Kingdom

Abstract: Introduction: Obesity is one of the leading causes of preventable morbidity and mortality world-wide. The behavioural nature of the condition has been highlighted by the fact that it is largely the result of an energy imbalance between calories consumed and calories expended. In that respect, obesity related morbidity and mortality can be reduced through preventive behaviours. As behavioural scientists, economists have done little to date to explain and understand why the demand for obesity preventing activities is low. The aim of this paper is to develop an economic theory-based dynamic model to gain better understanding of people’s obesity preventive behaviours. Methods: A literature search using a PICO approach was developed to identify the relevant variables considered to influence the demand for obesity preventive goods. To inform the model, a framework was developed to group variables and help determine appropriate linkages between them. Results: Anchors, anxiety and anxiety driven variables are fundamental influences of people’s risk reduction actions. The anchors, which are environmental as well as personal in character, serve as references and stimulate anxieties. However, anxiety levels are driven by many other variables including stigma and perceived health outcomes. In response to one’s anxiety an individual will take actions which can be explained, at least in part, by conventional economic theories particularly in terms of costs and utilities. Conclusions: Conventional economic theories of consumer behaviour cannot fully explain the demand for obesity preventive goods. The model demonstrates that many factors have to be considered including health economic, psychological and behavioural economic theories. The model should be tested through a well designed questionnaire before using it in a general adult population.

Keywords: obesity, Behavioural Economics, preventive goods, demand, modelling

Received May 28, 2014; Revised July 10, 2014; Accepted July 18, 2014

Citation: Morro M.L. Touray, Development of a Theory Based Dynamic Model on Demand for Obesity Preventive Goods. Journal of Behavioural Economics Finance Entrepreneurship Accounting and Transport, 2 (3). 70-76. January 2014. doi:10.12691/jbe-2-3-3

1. Introduction

The World Health Organisation has defined overweight and obesity as “abnormal or excessive fat accumulation that presents a risk to health [65].” For present purposes the term obesity is used to cover both ‘overweight’ (conventionally defined as a Body Mass Index (BMI) ≥25) and ‘obese’ (BMI ≥30). The high prevalence of obesity in the United Kingdom (UK) [42,54,55,56] and worldwide [15,31] is having profound impact on preventable morbidity, mortality [64] and reduced health related quality of life [3,22,28,33,43,61,66,68] with dire ramifications on the limited healthcare resources [8,10,11,14,16,18,20,37,41, 47,51,52].

The behavioural aspects of obesity are evident from the fact that obesity is mainly the result of an energy imbalance between calories consumed and calories expended. In that regard, obesity related morbidity and mortality can be reduced through preventive behaviours aimed at preventing or reducing obesity [57].

Economics is a behavioural science which explains consumer behaviour mainly through utility theory which, at its simplest, states that a consumer will demand a unit of a good if the utility (satisfaction) they get from consuming it exceeds its price. It has long been recognised within economics that the demand for healthcare does not quite fit within that paradigm since the utility which patients get from consuming healthcare (e.g. being examined, receiving injections, taking medicine, etc.) is clearly different from that derived from goods whose consumption they enjoy [12,26]. This led to a conceptualisation of the demand for healthcare as being derived from a more fundamental demand for health [21,26].

A “preventive good” has been defined as any good (or service) whose consumption reduces the risk of future ill health [12]. As obesity is associated with health risk, any good whose consumption reduces obesity can be regarded as a preventive good. Some obesity preventive goods, for example gastric band surgery or prescribed weight loss drugs are provided by health professionals, and economists have undertaken many evaluations to assess the cost effectiveness of these sorts of interventions [6,38,40,44,58,62]. However, most of the ways in which people can prevent or reduce obesity do not involve formal health care systems but there is a paucity of economic studies on what influences people’s obesity affecting behaviours [57]. It has been recognised, though, that demand for non-medical preventive goods is unlikely to be explained through conventional utility theory for the same reason as for medical care i.e. many obesity preventive goods such as exercise or low calorie foods, may not yield utility in the conventional sense [12].

The aim of this paper is to develop a theory-based dynamic model using conventional economic theory but also including elements of psycho-social behavioural theory to gain better understanding of people’s preventive behaviours in the context of obesity.

2. Method

A systematic literature search using a Population, Intervention, Comparison and Outcome (PICO) [50,53] approach was followed to identify the relevant variables considered to influence the demand for obesity preventive goods. The PICO approach was used not only to define the research questions, the target population, identify the various interventions needed to prevent the development of the condition and the expected results, but importantly also the creation of a logical structure for the literature search and search terms.

The search terms included but were not limited to “obesity prevention, overweight prevention, weight gain prevention, demand for weight gain prevention, obesity/weight gain prevention factors, modelling preventive behaviours, factors for abnormal weight gain, demand for weight loss interventions, obesity/overweight – cultural factors, obesity/overweight – environmental factors, obesity/overweight – behavioural factors, obesity – diet and nutrition, overweight/obesity – risk factors, obesity prevention and lifestyle, obesity/overweight – economic factors, preventive behaviour – utility models, preventive behaviour – economic models.” The main repositories used include PubMed Central, EcoBiz, EcoLit, Global Health, JSTOR, SSRN, NBER, Wiley Online Library, Elsevier, Science Direct and Anthropology online.

In order to produce the model, a framework was developed through which the identified variables which are hypothesised to affect the demand for obesity preventive goods were grouped into three categories; anchors, anxiety/anxiety driven variables and action oriented variables. The anchors serve as reference points for decisions and may ultimately trigger anxiety. However, the extent of one’s anxiety is also influenced by a number of other variables (anxiety driven variables). Action oriented variables concern actions one will take as efforts to overcome the anxiety. These groupings, based on behavioural, health and conventional economics and psychological paradigms enable the linking of the variables based on their practicable and logical connections.

3. The Background Paradigms

3.1. Economic Theory of Demand

Traditional economic theory expresses demand as a two-dimensional function – a behavioural relationship between quantity demanded and the individual’s maximum willingness to pay for incremental increases in quantity [21]. However, price is not the only determinant of quantity demanded. The individual’s income, expectations, preferences, price of substitutes [32,35] and a myriad of other factors can be recognised as well.

The behavioural hypothesis suggests the consumer chooses a bundle of goods that is preferred to all other bundles that the individual can afford and maximises his/her utility [21]. This makes preference, choice and affordability (budget) essential elements in demand analysis. The theory of demand anchors on certain neoclassical or standard economic theories. Central to such theories is Pareto’s concept of homo economicus (economic man)[69] which assumes that consumers are rational and that they make rational choices; the motivating factor is utility maximisation; utilities are purely governed by selfish interests and they assume that all income and assets are completely interchangeable [67].

However, the extent and nature of behavioural anomalies within the concept of homo economicus have been well documented [59,60], demonstrating deviations between neoclassical theories of human behaviours and actual human behaviours. In real world scenarios, individuals exhibit characteristics of altruism as well as spiteful tendencies [7,69] in decision making and social relationships. These include the considerations people have for others and their environment, having some sense of fairness and justice, being kind to other people they may not know, or the tendency to inflict harm on other people, for example, as a form of revenge or punishment.

In this respect, it is possible to criticise the neoclassical theorem on a number of grounds one of which is its assumption that consumer’s preferences for current over future consumption are constant. A basic tenet of economics is that utility is not independent of when it arises with consumers putting a lower value on future, as compared with current utility. Thus consumers are willing to pay less (i.e. put a lower value) today for a good if they will have to wait one year to gain the utility, less still if they have to wait two years, and so on even if the utility will have the same nominal value at the time it’s received. In the jargon they discount future utility at a rate determined by their own personal rate of time preference. Some of the relevant sets of neoclassical economic axioms and their limitations considered in the construction of this model are briefly discussed below.

3.2. Intertemporal Choice and the Case for Discounted Utility Model (DUM)

The outcomes of decisions people make between goods or bundle of goods occur at different points in time. Actions relating to obesity are no exceptions of such decisions. Intertemporal choice describes any decision that requires trade-offs involving outcomes that will have their effects at different times [46]. Conventional economic analysis of intertemporal choice is based on the perception that the effect of delay on the value of the outcomes happening in the future can be illustrated by a discount function [48].

As with other healthcare goods, the primary objective for the consumption of goods for the prevention of obesity and associated risks is the anticipated future health gains. The Discounted Utility Model (DUM) is one tool used by economists to analyse intertemporal choices [67] of trade-offs involving benefits that will accrue over time.

The use of DUM is based on the assumption that consumers evaluate the utilities resulting from decisions in similar ways that financial markets evaluate losses and profits from investments. It is based on the tenets that people are time consistent in their preferences, meaning they would feel just as favourable toward a consumption trade-off now as in one year or two years time or even more [48,67].

3.3. Models of Prevention

Outside the economic paradigms, many psychological based theories and models of prevention have been applied in public health.

The Health Belief Model (HBM) [12,25] is the most widely used psychosocial model. It is based on the understanding that individuals will take a health-related action only if they feel that the ill-health can be avoided; have positive expectations that by taking a recommended action, they will avoid the ill-health; and believe that they can take the recommended action successfully. It centres on four main constructs namely perceived susceptibility, perceived severity, perceived benefits, and perceived barriers.

3.4. Limitations of DUM and Psychological Models of Prevention

Obesity is largely a result of complex interactions between behavioural, socio-cultural and environmental factors [23,57]. These factors centre on sedentary lifestyles and consumption of unhealthy foods. It is widely accepted that those confronted with weight related problems have followed unhealthy lifestyles which implies that individuals may not necessarily be acting in a purely rational way. People also have emotions, prejudices and biases which influence their behaviour in many respects.

Experiments undertaken mainly by behavioural economists on intertemporal choice, suggest that most of the assumptions in discounted utility do not necessarily hold with regard to consumer behaviour. The first anomaly to contradict discounted utility was that the observed discount rates appear to decline over time. The experimental observation [9] was that people tend to consider delaying consumption by a bigger sacrifice when the benefit is closer in time rather than when it is farther in the future. This means the closer (in time) the benefit is, the bigger the sacrifice people are willing to take. Such declining discounted rates revealing ‘decreasing impatience’ [45] became known as hyperbolic discounting [13,24,39]. The discovery of Hyperbolic discounting not only proved to be a direct challenge to discounted utility, but paved the way for more empirical testing which discovered a host of other anomalies now present in the intertemporal choice theory literature. Behavioural Economists also showed that individual decisions are contextual [9] and influenced by many other factors not necessarily utility only. Those factors on which decisions are based are referred to within the model presented here, as anchors [67].

HBM also has several limitations that can limit its use in public health. First, HBM does not account for other factors that influence people’s acceptance or otherwise of a health behaviour such as the person’s attitudes, habitual behaviours (e.g. addictions), issues of social acceptability (hence stigma), cultural factors and beliefs, environmental and socio-economic factors which may promote or otherwise the required action. It also does not factor issues of information asymmetry as it appears to assume that everyone has access to equal amounts of information on risks. Second, it also assumes that health outcome is the main objective in the decision making process. This may not necessarily be the case in the demand for goods for prevention of obesity. For instance, many regular gym users do so for intrinsic motivations which is the enjoyment they get hence utility in use and not necessarily based on the premise that they want to lose weight [2,17,29]. Third, HBM has descriptions of its various constructs but fails to provide explanations or suggestions of strategies with regards to changing health behaviours.

4. The Model

Upon considerations of and factoring the limitations outlined above, the model was developed (Figure 1) using psychological, behavioural economic and standard economic concepts. The model has three essential components – the anchors, anxiety and anxiety driven variables, and action oriented variables and is generally based on Cohen’s [12] utility model of prevention.

Figure 1. A theory based dynamic model on demand for obesity preventive goods

4.1. The Model Explained

The definitions of the identified variables have been provided in Table 1. This section aims to provide a simple explanation of the logical relationship between the variables.

The correlation between socio-economic status and educational achievements has been well established [63]. Education plays a fundamental role in the behavioural make-up of an individual. It serves as a platform for the individual’s understanding of not just their own physical and health attributes but, most importantly, the environment and culture and the acceptance or otherwise of the cultural factors particularly those that influence weight gain. Education also helps individuals to be aware of risks and helps in their assessments of the risks.

Table 1. Definitions of the identified variables

Cultural influences can at times be significant and includes how people perceive risk and their risk prevention attitudes (i.e. level of risk aversion). In some communities, excess weight is often associated with affluence [34,49]. What is eaten and how it is eaten (food habits) are also greatly influenced by culture [1]. In some cultural settings there is dependence on certain types of food and eating habits are dictated by certain cultural beliefs. High dependence on some of these foods does not only pose balanced diet threats, but are also sources of obesity [4,30]. Religious rules also affect food choices.

Many cultures are shaped around their environments in many respects. Cultures can therefore be seen as means to the adaptation to the environment to a certain extent and these environments have therefore influenced our understanding of ourselves, life and how we interact with it. In the perspective of food production what people grow ordinarily are those that are supported by their natural environment. However, as humans understand this natural environment they have not only devised means of adaptation but taming it for his/her developmental needs. Such a built environment (i.e. the human-made surroundings which provide settings for human activities including recreational facilities particularly those that support physical exercise), in turn affects health and wellbeing.

These built environments as well as use of the natural environments, what people grow and how they are grown are to very large extent results of local/national policies. The available land for food production is competing not just with the need for settlements but as well the need to preserve the ecosystem and flora and fauna. As a result of these challenges, food and agricultural policies are under scrutiny. The world has witnessed not only the use chemicals to boost production, but there are increasing policy arguments for the use of gene modification innovations. Some research have in fact observed correlations between the use of chemicals in agricultural activities [19] and incidence of obesity hence the use of the term “obesogens” [27,36].

While it is clear that education puts individuals in positions to be aware of and understand risks; that culture influences one’s level of risk aversion, it is however not necessarily the case that being risk aware makes one risk averse. Some people are generally risk takers not to mention the effects of addiction on the individual. There are also some cultural beliefs that mitigate against risk notwithstanding the fact that they are aware of the risks. Nonetheless, it is likely that one’s level of risk aversion does not only affect his/her preference (time) with regards to both economic/health outcome and their timings, but most essentially his/her anxiety.

Anxiety also anchors on both education and culture. However, the extent of one’s anxiety is driven by a number of variables such as perceived health outcome, perceived economic impact, perceived social impact and stigma. It is anxiety which serves as an impulse to take actions deemed appropriate to address the worries [12].

Nonetheless, taking action depends on income, the perceived effectiveness of the intervention and the effects in the consumption of the good. The latter can affect one’s action positively or negatively. For example some people enjoy going to the gym and to be involved in strenuous exercises. Others who dislike going to the gym let alone to be involved in strenuous exercises will get no enjoyment from the activity. Such positive/negative experiences in the process of taking the good is described as utility in use [12].

With regards to the perceived effectiveness, as in the case of most health interventions, the effects are not necessary instantaneous and it is mainly what the individual anticipates. In that connection the satisfaction gained therein is ultimately in anticipation that the desired objective will be met. Whereas utility in use can be negative, the utility in anticipation can only be positive and must outweigh the former if consumption is to take place. The combination of utility in use and in anticipation results in final utility which is a key economic determinant of demand in the standard economic principles of consumer theory. As in the case of utility, cost is a key factor of demand. However, how much one commits to spend on an intervention at a particular price depends on his/her income level. Where the preventive good demanded fails to satisfy one’s anxiety, the process will then follow a cycle through the feedback effect until the anxiety level eventually diminishes.

5. Conclusions and what next

The analysis of demand for obesity prevention goods is not necessarily fully explained by the principles of standard economic models of consumer behaviour. Instead behavioural economic, psychological and health economic theories will have to be considered to understand people’s preventive behaviours with regards to obesity risk reduction goods.

Anchors serve as decision references and stimulate anxieties which in turn trigger necessary actions. Levels of anxiety are driven by many other factors including concerns for socio-economic, socio-cultural and health outcomes. Notwithstanding this, utility and costs are ultimately crucial variables that influence the determination of demand.

Models are by no means full representations of the complex human behaviours in real life situations. This model demonstrates a possible combination of health economic, neoclassical and behavioural economic principles to explain complex human behaviours with regards to health risk reduction in the context of obesity.

The model needs to be tested and validated by a process of empirical investigation. A robust and well designed questionnaire must be developed for such investigations. It should target three key areas amongst others – the individual’s personal characteristics, socio-economic and cultural background/characteristics, and measuring of the relationships between the concerned variables and how they influence each other. The sample size for the empirical study must be representative of the target population. In this respect since preventive actions will come from normal weight as well as obese individuals, the two populations must be adequately represented in the target population.

Acknowledgement

I would like to thank my supervising team (namely Drs. Simon Williams, Sam Groves, Fasihul Alam and Prof. David Cohen) for my PhD. A special thanks to Professor David Cohen for the great welcome he gave me and my family upon arriving in Wales, and his continued support in guiding my studies and helping me to settle in a new country – Wales. My family and I now consider Wales as our home and I am now learning Welsh. Diolch i chi unwaith eto David a dymunaf ymddeoliad hapus a phob yr ydych yn dymuno i chi eich hun i chi.

Statement on Competing Interests

I have no conflicts of interest to declare.

References

[1]   | Bisogni CA, Connors M, Devine CM, Sobal J: Who We Are and How We Eat: A Qualitative Study of Identities in Food Choice. Journal of Nutrition Education and Behavior 34 (3): 128-139. 2002.
[2]   | Blais MR: Toward a New Measure of Intrinsic Motivation, Extrinsic Motivation, and Amotivation in Sports: The Sport Motivation Scale (SMS). Journal of Sport & Exercise Psychology, 17: 35-53. 1995.

[3]  
Blissmer B, Riebe D, Dye G, Ruggiero L, Greene G, Caldwell M: Health-related quality of life following a clinical weight loss intervention among overweight and obese adults: intervention and 24 month follow-up effects; Health and Quality of Life Outcomes. Health Qual Life Outcomes, 4 (43): 1-8.Jul 2006.

[4]   | Blundell JE, Cooling J: Routes to obesity: phenotypes, food choices and activity. British Journal of Nutrition, 83 (Suppl. 1): S33-S38. 2000
[5]   | Broome J: Utility. Economics and Philosophy, 7 (01): 1-12. April 1991
[6]   | Buchwald H, Avidor Y, Braunwald E, Jensen MD, Pories W, Fahrbach K, Schoelles K: Bariatric surgery: a systematic review and meta-analysis. JAMA, 292 (14): 1724-1737. Oct 2004
[7]   | Burnham CT: Altruism and spite in a selfish gene model of endogenous preferences. Journal of Bioeconomics, 3 (2-3): 123-148. May 2001.
[8]   | Burton WN, Chen CY, Schultz AB, Edington DW: The economic costs associated with body mass index in a workplace. J Occup Environ Med, 40 (9): 786-792. Sep 1998.
[9]   | Camerer C: Behavioral economics: Reunifying psychology and economics. Proc Natl Acad Sci USA, 96 (19): 10575-10577. Sep 1999.
[10]   | Cawley J: An economic framework for understanding physical activity and eating behaviors. Am J Prev Med, 27 (3 Suppl): 117-125. Oct 2004.
[11]   | Clegg A, Colquitt J, Sidhu M, Royle P, Walker A: Clinical and cost effectiveness of surgery for morbid obesity: a systematic review and economic evaluation. Int J Obes Relat Metab Disord, 27 (10): 1167-1177. Oct 2003.
[12]   | Cohen D: Utility model of preventative behaviour. J Epidemiol Community Health, 38 (1):61-65.Mar 1984.
[13]   | Dasgupta P, Maskin E: Uncertainty and Hyperbolic Discounting. The American Economic Review, 95 (4): 1290-1299. Sep 2005.
[14]   | Daviglus ML, Liu K, Yan LL, Pirzada A, Manheim L, Manning W, Carside DB, Wang R, Dyer AR, Greenland P et al: Relation of body mass index in young adulthood and middle age to Medicare expenditures in older age. JAMA, 292 (22):2743-2749. Dec 2004.
[15]   | Deitel M: Overweight and obesity worldwide now estimated to involve 1.7 billion people. Obes Surg, 13 (3):329-330. Jun 2003.
[16]   | Dent M: The economic burden of obesity. Oxford: National Obesity Observatory, UK; 2010.
[17]   | Dishman RK, Motl RW, Saunders R, Felton G, Ward DS, Dowda M, Pate RR: Enjoyment mediates effects of a school-based physical-activity intervention. Med Sci Sports Exerc, 37 (3): 478-487. Mar 2005.
[18]   | Durden ED, Huse D, Ben-Joseph R, Chu BC: Economic costs of obesity to self-insured employers. J Occup Environ Med, 50 (9): 991-997. Sep 2008.
[19]   | Egger G, Swinburn B: An “ecological” approach to the obesity pandemic. BMJ, 315 (7106): 477-480. Aug 1997.
[20]   | Finkelstein EA, Fiebelkorn C, Wang G: The costs of obesity among full-time employees Am J Health Promot, 20 (1): 45-51. Oct 2005.
[21]   | Folland S, Goodman AC, Stano M: The economics of health and health care. 5th edition. Upper Saddle River, New Jersey: Pearson Education Inc.; 2007
[22]   | Fontaine KR, Barofsky I: Obesity and health-related quality of life. Obes Rev, 2 (3): 173-182. Aug 2001.
[23]   | Foresight: Tackling obesities: future choices-project report (2nd Ed). London: Government Office for Science; 2007.
[24]   | Frederick S, Loewenstein G, O’Donoghue T: Time Discounting and Time Preference: A Critical Review. Journal of Economic Literature, XL: 351-401. Jun 2002.
[25]   | Green EC, Murphy E: Health belief model. In The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society. John Wiley & Sons, Ltd; 2014: 766-769.
[26]   | Grossman M: On the concept of health capital and the demand for health. Journal of Political Economy, 80 (2): 223-255.Mar-Apr 1972.
[27]   | Grün F, Blumberg B: Environmental obesogens: organotins and endocrine disruption via nuclear receptor signalling. Endocrinology, 147 (6 Suppl): S50-S55. Jun 2006.
[28]   | Hassan MK, Joshi AV, Madhavan SS, Amonkar MM: Obesity and health-related quality of life: a cross-sectional analysis of the US population. Int J Obes Relat Metab Disord, 27 (10): 1227-1232. Oct 2003.
[29]   | Hodgins M, Fuller R: 'Come for the weight loss, stay for the enjoyment'-exploring attributions for initiating and maintaining exercise. The Irish Journal of Psychology, 22 (2): 51-58.May 2001.
[30]   | James DC: Factors Influencing Food Choices, Dietary Intake, and Nutrition-Related Attitudes among African Americans: Application of a Culturally Sensitive Model. Ethn Health, 9 (4): 349-367.Nov 2004.
[31]   | James PT, Leach R, Kalamara E, Shayeghi M: The worldwide obesity epidemic. Obes Res, 9 (Suppl. 4): 228S-233S. Nov 2001.
[32]   | Jehle GA, Reny PJ: Advanced Microeconomic Theory. 3rd edition. Harlow, UK: Pearson Education Limited; 2000
[33]   | Kaukua J, Pekkarinen T, Sane T, Mustajoki P: Health-related quality of life in obese outpatients losing weight with very-low-energy diet and behaviour modification: a 2-y follow-up study. Int J Obes Relat Metab Disord, 27 (10): 1233-1241. Oct 2003.
[34]   | Kiawi E, Edwards R, Shu J, Unwin N, Kamadjeu R, Mbanya JC: Knowledge, attitudes, and behavior relating to diabetes and its factors among urban residents in Cameroon: A qualitative survey. Ethn Dis, 16 (2): 503-509. Spring 2006.
[35]   | Kreps DM: A course in microeconomic theory. Harlow, UK: Pearson Education Limited; 1990.
[36]   | Lake A, Townshend T: Obesogenic environments: exploring the built and food environments. J R Soc Promot Health, 126 (6): 262-267 Nov 2006.
[37]   | Long DA, Reed R, Lehman G: The cost of lifestyle health risks: obesity. J Occup Environ Med, 48 (3): 244-251. Mar 2006.
[38]   | MacLean LD, Rhode BM, Forse RA, Nohr C: Surgery for obesity-an update of a randomized trial. Obes Surg, 5 (2): 145-150. May 1995.
[39]   | Madden GJ, Bickel WK, Jacobs EA: Discounting of delayed rewards in opioid-dependent outpatients: Exponential or hyperbolic discounting functions? Experimental and Clinical Psychopharmacology, 7 (3): 284-293. Aug 1999.
[40]   | Martin LF, White S, Lindstrom Jr. W: Cost-benefit analysis for the treatment of severe obesity World J Surg, 22 (9): 1008-1017. Sep 1998.
[41]   | McCormick B, Stone I: Economic costs of obesity and the case for government intervention. Obes Rev, 8 (Suppl 1): 161-164. Mar 2007.
[42]   | NHS Health and Social Care Information Centre Lifestyles Statistics: Statistics on obesity, physical activity and diet: England, 2013. Leeds: National Health Service, UK; 2013.
[43]   | Ni Mhurchu C, Bennett D, Lin R, Hackett M, Jull A, Rodgers A: Obesity and health-related quality of life: results from a weight loss trial. N Z Med J, 117 (1207): U1211. Dec 2004.
[44]   | Pratt M, Macera CA, Sallis JF, O’Donnell M, Frank L: Economic interventions to promote physical activity: applications of the SLOTH model. Am J Prev Med, 27 (3 Suppl): 136-145. Oct 2004.
[45]   | Prelec D: Decreasing Impatience: A Criterion for Non-stationary Time Preference and “Hyperbolic” Discounting. The Scandinavian Journal of Economics, 106 (3): 511-532. Sep 2004.
[46]   | Pyone JS, Isen AM: Positive Affect, Intertemporal Choice, and Levels of Thinking: Increasing Consumers' Willingness to Wait. Journal of Marketing Research, 48 (3): 532-543. June 2011.
[47]   | Quesenberry CP, Caan B, Jacobson A: Obesity, health services use, and health care costs among members of a health maintenance organization. Arch Intern Med, 158 (5): 466-472. Mar 1998.
[48]   | Read D: Intertemporal choice-Working Paper LSEOR 03.58. London: The London School of Economics and Political Science; 2003.
[49]   | Renzaho AMN: Fat, rich and beautiful: changing socio-cultural paradigms associated with obesity risk, nutritional status and refugee children from sub-Saharan Africa. Health Place, 10 (1): 105-113. Mar 2004.
[50]   | Santos CMC, Pimenta CAM, Nobre MRC: The PICO strategy for the research question construction and evidence search. Rev Latino-am Enfermagem, 15 (3): 508-511. May-Jun 2007.
[51]   | Sassi F: Obesity and the economics of prevention: fit not fat. France: OECD Publishing; 2010.
[52]   | Sassi F, Cecchini M, Lauer JA, Lee YY, Guajardo-Barron V, Chisholm D: Tackling of unhealthy diets, physical inactivity, and obesity: health eff ects and cost-eff ectiveness. Lancet, 376 (9754): 1775-1784. Nov 2010.
[53]   | Schardt C, Adams MB, Owens T, Keitz S, Fontelo P: Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Medical Informatics and Decision Making, 7 (16).Jun 2007
[54]   | The NHS Information Centre Lifestyle Statistics: Statistics on obesity, physical activity and diet: England. Leeds: The Information Centre for Health and Social Care, NHS, UK; Jan 2008.
[55]   | The NHS Information Centre Lifestyle Statistics: Statistics on obesity, physical activity and diet: England, February 2009. Version 1 edition.: The Health and Social Care Information Centre, NHS, UK 2009.
[56]   | The NHS Information Centre Lifestyles Statistics: Statistics on obesity, physical activity and diet: England, 2010. Version 1 edition. Leeds, UK: The Health and Social Care Information Centre, NHS, UK; 2010.
[57]   | Touray MML: Research on obesity-time to think about the next agendas. Prog Health Sci, 3 (2): 40-52. Dec 2013.
[58]   | Tran BX, Nair AV, Kuhle S, Ohinmaa A, Veugelers PJ: Cost analyses of obesity in Canada: scope, quality, and implications. Cost Eff Resour Alloc, 11 (1): 3. Feb 2013.
[59]   | Tversky A, Kahneman D: Advances in prospect theory: cumulatve representation of uncertanty. J Risk Uncertainty 5 (4): 297-323. Oct 1992.
[60]   | Tversky A, Kahneman D: Judgment under uncertainty: heuristics and biases. Science, 185 (4157): 1124-1131. Sep 1974.
[61]   | van Nunen AM, Wouters EJ, Vingerhoets AJ, Hox JJ, Geenen R: The health-related quality of life of obese persons seeking or not seeking surgical or non-surgical treatment: a meta-analysis. Obes Surg, 17 (10): 1357-1366. Oct 2007.
[62]   | Wang LY, Yang Q, Lowry R, Wechsler H: Economic analysis of a school-based obesity prevention program. Obes Res, 11 (11): 1313-1324. Nov 2003.
[63]   | White KR: The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91 (3): 461-481. May 1982
[64]   | WHO: Mortality and Global Burden of Disease (GBD). World Health Organisation; 2012.
[65]   | Obesity and overweight [http://www.who.int/topics/obesity/en/]. 09/05/2014
[66]   | Wiczinski E, Do¨ ring A, John J, von Lengerke T: Obesity and health-related quality of life: Does social support moderate existing associations? Br J Health Psychol, 14 (Pt 4): 717-734. Nov 2009.
[67]   | Wilkinson N: An introduction to behavioral economics. Besingstoke, Hampshire: Palgrave Macmillan; 2008.
[68]   | Wille N, Bullinger M, Holl R, Hoffmeister U, Mann R, Goldapp C, Reinehr T, Westenhöfer J, Egmond-Froehlich A, Ravens-Sieberer U: Health-related quality of life in overweight and obese youths: Results of a multicenter study. Health Qual Life Outcomes, 8 (36). Apr 2010.
[69]   | Xin Z, Liu G: Homo economicus belief inhibits trust. PLOS ONE, 8 (10):e76671.Oct 2013.