Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-23T07:16:01.624Z Has data issue: false hasContentIssue false

A brief review of salient factors influencing adult eating behaviour

Published online by Cambridge University Press:  19 June 2017

Christine Emilien
Affiliation:
Department of Food Science and Human Nutrition, Iowa State University, Ames, IA 50010, USA
James H. Hollis*
Affiliation:
Department of Food Science and Human Nutrition, Iowa State University, Ames, IA 50010, USA
*
*Corresponding author: Associate Professor James Hollis, email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

A better understanding of the factors that influence eating behaviour is of importance as our food choices are associated with the risk of developing chronic diseases such as obesity, CVD, type 2 diabetes or some forms of cancer. In addition, accumulating evidence suggests that the industrial food production system is a major contributor to greenhouse gas emission and may be unsustainable. Therefore, our food choices may also contribute to climate change. By identifying the factors that influence eating behaviour new interventions may be developed, at the individual or population level, to modify eating behaviour and contribute to society’s health and environmental goals. Research indicates that eating behaviour is dictated by a complex interaction between physiology, environment, psychology, culture, socio-economics and genetics that is not fully understood. While a growing body of research has identified how several single factors influence eating behaviour, a better understanding of how these factors interact is required to facilitate the developing new models of eating behaviour. Due to the diversity of influences on eating behaviour this would probably necessitate a greater focus on multi-disciplinary research. In the present review, the influence of several salient physiological and environmental factors (largely related to food characteristics) on meal initiation, satiation (meal size) and satiety (inter-meal interval) are briefly discussed. Due to the large literature this review is not exhaustive but illustrates the complexity of eating behaviour. The present review will also highlight several limitations that apply to eating behaviour research.

Type
Review Article
Copyright
© The Authors 2017 

Introduction

Eating behaviour is a broad term that encompasses several decisions regarding what to eat, when to eat and how much to eat. Understanding eating behaviour is important as our food choices have significant implications for the individual and society. For instance, overweight and obesity are leading public health problems throughout the world( Reference Hurt, Kulisek and Buchanan 1 ). While the causes of the obesity epidemic are being debated and divergent views are held( Reference Blair, Archer and Hand 2 Reference Hill, Wyatt and Peters 5 ), these conditions are ultimately caused by a chronic positive energy balance( Reference Hall, Heymsfield and Kemnitz 6 ). This simple statement suggests that weight management is simply a matter of balancing energy in and energy out. From a thermodynamic perspective this is correct but this simple statement masks a complex and poorly understood relationship between energy in and out and energy balance. For instance, foods are not merely vehicles for energy. Foods differ in their macronutrient content, energy density or physical form. Each of these factors influences the rate of enzymic reactions or the postprandial metabolic and endocrine response and could alter the processes of satiation and satiety( Reference Feinman and Fine 7 ). Moreover, factors such as eating rate, social facilitation or the environment in which a food is eaten also influence satiation or satiety. Consequently, while thermodynamically speaking, energy is energy, energy from different foods may have different effects on appetite, food intake and ultimately body weight. A consequence of this knowledge is that foods can be chosen or developed that reduce appetite and aid weight management. Moreover, food environments that augment food intake can be modified or avoided to reduce the risk of overeating. Differences in the effect on appetite raise the possibility that some diets will offer a metabolic advantage over what would be predicted by the energy content of a food( Reference Feinman and Fine 8 ). However, the magnitude of a metabolic advantage in practice has yet to be fully established and may be modest( Reference Buchholz and Schoeller 9 , Reference Hollis and Mattes 10 ).

In addition to obesity, our food decisions have the potential to influence the risk of developing other chronic diseases such as type 2 diabetes, CVD and some types of cancer( Reference McCullough, Feskanich and Stampfer 11 , Reference Dietz, Douglas and Brownson 12 ). Besides health concerns, climate change is seen as the biggest global threat of the 21st century( Reference Costello, Abbas and Allen 13 ) and its mitigation is a leading societal goal. Accumulating evidence indicates that our dietary choices are a significant contributor to climate change and that the modern food system is unsustainable( Reference McMichael, Powles and Butler 14 , Reference Carlsson-Kanyama and Gonzáles 15 ). Consequently, policies or dietary interventions that manipulate eating behaviour to achieve one societal goal should recognise that these may adversely influence other societal goals.

Considering that eating behaviour has the potential to exacerbate or mitigate several of the leading societal problems it is important that we understand the factors that influence eating behaviour and determine how it can be manipulated to suit societal goals. The aim of the present review is to provide a brief overview of several salient factors that influence several aspects of eating behaviour. In particular, this review focuses on factors that influence the amount eaten. Due to the large number of studies that have been conducted, this review is not exhaustive but seeks to illustrate that eating behaviour is determined by a number of interacting systems. These systems are dynamic and may respond to perturbations in energy balance by a robust physiological response to counteract these changes. Consequently, developing effective approaches to manipulate eating behaviour may not be a straightforward undertaking. This review will also include a brief discussion of several limitations of eating behaviour research. It is important to note these limitations when evaluating the literature relating to eating behaviour.

Eating behaviour

Eating behaviour is influenced by a cacophony of internal and external signals that influence our eating decisions( Reference Bilman, van Kleef and van Trijp 16 , Reference Cohen 17 ). While the primary goal of eating is to ingest sufficient nutrients to satisfy biological requirements, the types of food and the amount eaten to meet this goal are shaped by a multitude of factors including physiology, environmental( Reference Larson and Story 18 ), cultural( Reference Novotny, Williams and Vinoya 19 ), emotional( Reference Nicholls, Devonport and Blake 20 ), social( Reference Redd and Decastro 21 ), self-actualisation( Reference Satter 22 ), economic and access pressures. Many of these will have a profound effect on eating behaviour. For instance, while physiological signals may signal the requirement to eat due to fluctuations in energy stores( Reference Cummings, Purnell and Frayo 23 ), the behavioural response will be strongly influenced by food accessibility or the types of available foods( Reference Wansink, Painter and Lee 24 ). An example of this is provided by food-insecure individuals who may have limited food choices because of poor access to supermarkets or grocery stores( Reference Oemichen and Smith 25 , Reference Ma, Liese and Bell 26 ) or food costs( Reference Jablonski, McFadden and Colpaart 27 ).

It would appear that physiology provides few absolute rules for eating and a vast array of eating choices are possible. For instance, there is no physiological reason why foods that are typically eaten at dinner cannot be eaten at breakfast. Moreover, there is no physiological reason why nutrition cannot be obtained from eating potential foods such as insects or worms. That these behaviours rarely occur is due to food culture or customs that strongly influence the types of foods that are eaten or the time of day that they are eaten( Reference Cleveland, Rojas-Mendez and Laroche 28 , Reference Axelson 29 ). While food culture may appear stable it can change markedly due to immigrants introducing new foods or methods of preparing foods, changes in lifestyles (for example, reduction in cooking skills, reduction in time to prepare foods at home), or changing ethical considerations (for example, desire to eat organic foods or vegetarian diets)( Reference Slater 30 Reference Fox and Ward 32 ). Political factors can also change the food culture. For instance, it has also been proposed that Chinese food culture was influenced by the taste preferences of Mao Zedong for sweet potatoes( Reference Lu 33 ).

At this time, significant progress has been made in understanding how physiology, culture, custom or socio-economic factors individually influence food decisions but how these factors interact to shape eating behaviour is less well understood. Due to the myriad of factors that influence eating each individual factor will probably only explain a small amount of the variation in eating behaviour. Consequently, it is possible that developing multidisciplinary approaches to understanding eating behaviour may yield new insights regarding individuals’ food decisions and provide models that better explain decision making related to food.

Terminology

Blundell et al. ( Reference Blundell, Rogers and Hill 34 ) described a satiety cascade which integrated psychological and biological signals into a framework that integrates the processes of satiation and satiety. The satiety cascade model has been reviewed by other authors( Reference Halford and Harrold 35 , Reference Van Kleef, Van Trijp and Van Den Borne 36 ) and provides a useful model for understanding the various factors that influence eating behaviour and their temporal relationship.

It is important to note that the colloquial use of the terms hunger, satiation and satiety is frequently different from their scientific use. For the present review, the terminology proposed by Blundell et al. ( Reference Blundell, de Graaf and Hulshof 37 ) will be used. Hunger is a ‘construct or intervening variable that connotes the drive to eat. Not directly measurable but can be inferred from objective conditions’. Satiation is the ‘process that leads to the termination of eating; therefore controls meal size’, while satiety is the ‘process that leads to inhibition of further eating, decline in hunger, increase in fullness after a meal has finished’. It should be noted that for logistical reasons most studies of satiety do not generally measure the length of the inter-meal interval but measure appetite sensations or biomarkers of appetite over a fixed period (typically 3–4 h) (for example, Zhu et al. ( Reference Zhu and Hollis 38 , Reference Zhu, Hsu and Hollis 39 ) and Emilien et al. ( Reference Emilien, West and Hollis 40 )). After this period, food intake at a test meal is measured and used as a marker of satiety( Reference Blundell, de Graaf and Hulshof 37 ).

Measurement of eating behaviour and food intake

When evaluating the literature, it is important to understand the various limitations of experimental approaches to investigating human eating behaviour. While a full discussion of experimental methodology is beyond the scope of this review, a recent paper provides an interesting discussion of the limitations of appetite research( Reference Caudwell, Gibbons and Hopkins 41 ).

Humans can provide a subjective assessment of their motivation to eat which can be captured using questionnaires( Reference Blundell, de Graaf and Hulshof 37 ). These appetite questionnaires predict meal initiation and food intake, and are sensitive to experimental manipulations( Reference Stubbs, Hughes and Johnstone 42 ). However, it has been argued that the ability of questionnaires to predict meal size is modest, limiting their usefulness( Reference Mattes 43 ). For instance, in a study of free-living individuals the correlation coefficient between subjective hunger and energy intake was 0·27( Reference de Castro and Elmore 44 ). Mattes( Reference Mattes 43 ) found that over a 7 d period the correlation coefficient between hunger and energy intake was 0·5. A major weakness of these studies is that food intake was self-reported rather than observed. Considering the well-documented problems with measuring food intake in free-living individuals( Reference Archer, Hand and Blair 45 ), it is debatable that these studies provide an accurate assessment of the association between appetite ratings and meal size. Laboratory studies may provide a more accurate assessment of the association between appetite ratings and energy intake as food intake can be measured accurately. However, a meta-analysis of four short-term laboratory studies found that the correlation coefficient between hunger and energy intake was 0·16 and between fullness and energy intake was only –0·20( Reference Parker, Sturm and MacIntosh 46 ).

From these studies it may be argued that the ability of questionnaires to predict meal size is modest and that directly measuring food intake is a more useful and relevant measure of appetite. This may be misguided as there are several reasons why food intake should not be viewed as an ‘objective’ or uncontaminated marker of appetite( Reference Booth 47 ). First, eating behaviour studies are generally conducted in highly contrived situations that do not reflect the environment in which study participants typically ingest food. Second, these studies frequently provide food portion servings in excess of what might usually be eaten which may augment consumption beyond what would usually be eaten( Reference Rolls, Morris and Roe 48 ). Moreover, other factors may serve to promote overconsumption at a test meal including the availability of free food or so that a later meal does not need to be purchased. Third, while foods used in studies are generally palatable (the participants rating of the palatability of foods is typically measured during screening), there is a difference between ‘liking’ and ‘wanting’ and participants may not want to eat the test food at that particular occasion and reduce food intake( Reference Finlayson, King and Blundell 49 ). Fourth, if participants are aware that they are being observed they may adjust their eating behaviour to meet social norms( Reference Vartanian, Sokol and Herman 50 ).

However, laboratory studies have several strengths and have undoubtedly made a substantial contribution to the understanding of human eating behaviour. Through the use of appropriate controls, a specific factor can be isolated so that its effect on appetite or food intake can be determined free from the influence of extraneous variables. In addition, the laboratory provides the opportunity to make precise and accurate measures of food intake or other appetite measures. It is also possible to collect biological samples or make physiological measurements that may provide mechanistic explanations for the observed results. Laboratory studies provide strong internal validity (for example, the ability to draw causal inferences). However, a key limitation of laboratory studies is that they do not reflect the environment in which human eating behaviour is expressed, which limits the generalisability of the data. Humans typically eat in environments that include features, noticed or unnoticed, that influence decision making (for example, food choices or energy intake) such as atmospherics, salience or social norms( Reference Wansink 51 ). These features may potentiate or limit internal physiological appetite signals or weaken/strengthen self-control to influence eating behaviour. Moreover, evidence suggests that when participants are aware that their food intake is being monitored they consumer smaller meals( Reference Robinson, Hardman and Halford 52 ). Consequently, the results from laboratory studies may not reflect eating behaviour in typical eating situations.

An alternative approach to investigating eating behaviour is the field study. In this type of study, human subjects are observed in a typical eating environment. These studies have the advantage of having high ecological validity (for example, the results can be generalised to real-life settings) although participants in studies generally know that they are being observed which may influence their behaviour( Reference Herman, Polivy and Silver 53 ). Moreover, field studies are frequently limited by poor control over experimental conditions. Perhaps the most significant drawback of field studies is the difficulty measuring food intake in free-living individuals( Reference Archer, Hand and Blair 45 ).

Another key limitation of many studies of eating behaviour is that they are typically short term and are frequently less than 24 h in duration. They typically only observe behaviour at one or two meals. It should not be assumed that the results from short-term studies will persist over the medium to long term and lead to changes in body-weight change, as physiological changes will probably occur to oppose changes in body weight. A possible example of the body adapting to changes in food intake is provided by studies of energy-yielding beverages. Some short-term laboratory studies report that that there is no dietary compensation (i.e. reduction in the consumption of other energy sources to compensate for the energy provided by the test food) for energy consumed in liquid form( Reference DiMeglio and Mattes 54 ). Consequently, the increased consumption of energy-yielding beverages should lead to an amount of weight gain that would be predicted by the amount of energy provided by the beverage being regularly consumed. However, a longer-term study found that when energy-yielding beverages are added to the diet, weight gain is not as high as expected, suggesting that dietary compensation does occur( Reference Kaiser, Shikany and Keating 55 ). Consequently, short-term studies may overestimate the influence of energy-yielding beverages on energy intake and exaggerate the potential impact on body weight. Still, short-term studies provide an opportunity to screen potential anti-obesity agents or strategies before conducting expensive and logistically challenging long-term studies. However, this may also mean that a potentially useful approach to reducing body weight may be abandoned prematurely if its effect on eating behaviour does not manifest itself in the short term. For instance, changes in the gut microbiota may influence the appetitive response due to a potential effect on satiety-related hormones( Reference Fetissov 56 , Reference Breton, Tennoune and Lucas 57 ). Consequently, it may take several weeks for an intervention to alter the gut microbiota so that an effect on appetite can manifest itself.

The physiological regulation of eating behaviour

It has been proposed that multiple biological mechanisms act to regulate body fat( Reference Guyenet and Schwartz 58 ). This is known as the set-point theory( Reference Harris 59 ). In this model, perturbations in body fat are corrected for by changes in appetite that lead to a change in energy intake so that the perturbation is corrected( Reference Speakman, Levitsky and Allison 60 ). For instance, leptin is secreted by the adipose tissue in direct proportion to adiposity( Reference Maffei, Halaas and Ravussin 61 ). Central administration of leptin has several metabolic and behavioural effects including increased energy expenditure, increased lipolysis and reduced food intake( Reference Friedman and Halaas 62 ). It is likely that these effects are mediated by hypothalamic neuropeptides including melonocortin-4 and neuropeptide Y( Reference Sohn 63 ). Consequently, if an individual gains weight, circulating leptin levels would increase which would increase energy expenditure and reduce appetite until the perturbation in body weight is corrected. Conversely, if weight is lost, leptin levels are reduced leading to reduced energy expenditure and increased appetite until the perturbation in body weight is corrected. This set-point model accounts for observations that body weight remains remarkably constant over a long period of time( Reference Van Wye, Dubin and Blair 64 ) and for observations that periods of underfeeding are followed by periods of hyperphagia( Reference Keys, Brožek and Henschel 65 ). It has been argued that the discrepancy between energy intake and expenditure may be as little as 74 kJ/d and such precision points to a physiological regulatory system( Reference Speakman, Levitsky and Allison 60 ). It should be noted that the set-point model is not universally accepted and others argue that body weight is not tightly regulated( Reference Speakman, Levitsky and Allison 60 ). While it is acknowledged that body weight remains constant for extended periods and that physiological mechanisms contribute to this stability, there are non-physiological factors that also influence body weight( Reference Wirtshafter and Davis 66 Reference Payne and Dugdale 68 ). That is, if environmental factors remain constant over a period of time this will result in a stable body weight.

A resolution to this debate is required because the approach to reducing the number of overweight or obese individuals would differ depending on the correct model. The set-point theory essentially denies a role for environment or social factors in determining body weight and subsumes everything to physiology. Consequently, efforts to alter the food environment would have little effect on body weight. However, if non-physiological models are correct, changes to the environment may be a useful strategy for reducing obesity. As neither model fully explains human eating behaviour and body-weight changes, the development of new models is required. The development of new models may be facilitated by an increased focus on multi-disciplinary research that integrates the most salient impacts on eating behaviour (both physiological and environmental).

Hunger and meal initiation

In the 1950s, Mayer( Reference Mayer 69 ) proposed that hunger sensations were due to a decrease in glucose utilisation which was detected by glucose-sensitive sites in the brain. The increase in hunger sensations would lead to meal initiation. This became known as the glucostatic theory. Later studies found that the administration of exogenous insulin or pharmacological agents that prevent the cellular oxidation of glucose (2-deoxy-d-glucose) causes animal to eat( Reference Grossman 70 , Reference Smith and Epstein 71 ). However, these studies reduce blood glucose to levels that are not normally encountered and their relevance to meal initiation in normal situations is limited. In studies where blood glucose levels have been continuously monitored it has been demonstrated that meal initiation is preceded by a fall in blood glucose concentration shortly before eating begins( Reference Louis-Sylvestre and Le Magnen 72 , Reference Campfield and Smith 73 ). In humans, it has also been found that meal requests correlate with a transient drop in blood glucose( Reference Campfield, Smith and Rosenbaum 74 ). However, when individuals are in negative energy balance there was no correlation between meal initiation and a drop in blood glucose( Reference Kovacs, Westerterp-Plantenga and Saris 75 ). Furthermore, in a study that used a euglycaemic clamp to keep blood glucose levels steady it was found that human subjects still spontaneously requested food, indicating that a decline in glucose utilisation rates was not a necessary precondition for meal initiation( Reference Chapman, Goble and Wittert 76 ). Moreover, the current data supporting a link between blood glucose and meal initiation are based on correlation and do not prove a causal link.

It has been hypothesised that ghrelin, a hormone secreted by the stomach, is a factor in meal initiation( Reference Cummings, Purnell and Frayo 23 ). To date, ghrelin is the only peripheral orexigenic hormone that has been identified. Studies have shown that ghrelin rises before a meal is initiated( Reference Cummings, Purnell and Frayo 23 , Reference Cummings, Frayo and Marmonier 77 ) and decreases after feeding( Reference Tschop, Wawarta and Riepl 78 ). Moreover, the intravenous administration of ghrelin to rodents results in the stimulation of food intake( Reference Wren, Small and Ward 79 ). Plasma ghrelin concentration has also been found to correlate with hunger ratings( Reference Cummings, Frayo and Marmonier 77 ). However, mice lacking ghrelin receptors do not exhibit altered meal patterns, suggesting that that ghrelin does not have an essential role in meal initiation( Reference Wortley, Anderson and Garcia 80 ). Other research has raised the possibility that ghrelin rises in anticipation of a meal rather than as a stimulus for a meal( Reference Frecka and Mattes 81 ).

Indeed, while an argument can be made that each of these metabolic factors are causally related to meal initiation it may also be argued that these are anticipatory reflexes initiated because of the expectation of food intake. It has been proposed that while under certain circumstances physiological signals can initiate a meal, these are rarely encountered during normal life and under most situations it is environmental factors (for example, access to food, habitual meal times based on work schedule) that cause an eating episode to be initiated( Reference Woods 82 ).

Satiation

Before food is ingested a collection of responses, known as the cephalic phase response (CPR), prepares the body for the ingestion of food( Reference Power and Schulkin 83 ). While the CPR is small and transient it may have implications for satiation( Reference Woods 84 ) and it has been proposed that a stronger CPR will lead to reduced meal size( Reference Smeets, Erkner and de Graaf 85 ). While it has been demonstrated that bypassing the CPR by introducing food directly into the stomach results in larger meal sizes( Reference Stratton, Stubbs and Elia 86 ), further research is required to understand the role of the CPR in satiation. Still, the importance of taste in the correct metabolic response to nutrient intake has also been demonstrated by Spetter et al. ( Reference Spetter, Mars and Viergever 87 ) and this may lead to changes in satiation. Another study has found modest effects of the CPR on hormones related to appetite( Reference Zhu, Hsu and Hollis 88 ). Little is currently known about how the CPR influences satiation and a better understanding of what factors influence the CPR and how this makes an impact on meal size is required.

Another key contributor to meal termination is sensory-specifc satiety (SSS). SSS describes the reduction in the pleasantness of a food due to its continued consumption while the pleasantness of uneaten foods remains unchanged( Reference Rolls, Rolls and Rowe 89 ). One study found that SSS is a key influence in meal termination( Reference Hetherington 90 ) and that providing a variety of foods in a meal can delay satiation and increase meal size( Reference Hollis and Henry 91 , Reference Rolls, Rowe and Rolls 92 ). It is not clear how palatability influences SSS although consuming foods with stronger flavours does not influence the process of SSS( Reference Hollis and Henry 93 ). At this time, the physiological basis for SSS is poorly understood although changes in neuron firing rates in areas of the brain associated with the hedonic evaluation of foods may be involved( Reference Rolls, Murzi and Yaxley 94 ). It is interesting to note that older adults do not develop SSS( Reference Rolls and McDermott 95 ), possibly due to the effect of sensory losses( Reference Schiffman 96 ). The effect of this on food intake in older adults has not been fully established. While it may be predicted that the reduction in SSS would reduce the stimulatory effect of food variety on food intake in older adults, this does not appear to be the case( Reference Hollis and Henry 91 ).

Gastric distention has been found to contribute to meal termination. In a study by Geliebter( Reference Geliebter, Westreich and Gage 97 ), participants were asked to swallow a balloon that was then filled with varying amounts of water( Reference Geliebter, Westreich and Gage 97 ). As the balloon volume increased, causing gastric distention, meal size was reduced, suggesting that gastric distention is involved in satiation. Further studies have shown that causing gastric distention is related to sensations of hunger and fullness and the response may be mediated by cholecystokinin (CCK)-8( Reference Melton, Kissileff and Pi-Sunyer 98 ).

Recent studies have raised the possibility that RMR and fat-free mass are key drivers of energy intake( Reference Blundell, Finlayson and Gibbons 99 ). Caudwell et al. ( Reference Caudwell, Gibbons and Hopkins 41 ) found that meal size was correlated with fat-free mass but not fat mass. An implication of this finding that these data are not consistent with adipocentric models of appetite control was noted by the authors. A subsequent study reported a correlation between RMR and energy intake( Reference Caudwell, Gibbons and Hopkins 41 ). The authors propose that RMR could be a useful marker for energy intake and possibly represents a physiological marker for hunger. However, further research is required to demonstrate a causal relationship between fat-free mass, RMR and meal size.

Other studies have found a correlation between body temperature( Reference Devries, Strubbe and Wildering 100 ) or metabolic rate( Reference Even and Nicolaidis 101 ) and meal size. It has also been reported that lower ambient temperatures are associated with increased meal size( Reference Westerterp-Plantenga, Lichtenbelt and Strobbe 102 ). Further research is required to clarify the role of these and other potential influences on food intake that have gained little attention( Reference McAllister, Dhurandhar and Keith 103 ).

Satiety

Many physiological factors contribute to satiety. A growing body of research has identified several hormones that are secreted by the gastrointestinal tract and influence eating behaviour. This raises the possibility that pharmacological agents or dietary supplements could be developed that increase the secretion of the hormones to increase satiety. Multiple gut hormones have been linked to the expression of satiety( Reference Perry and Wang 104 ) and a full discussion of these is beyond the scope of the present review. Only three hormones, CCK, glucagon-like-peptide-1 (GLP-1) and peptide YY (PYY)3–36, will be discussed due to their widespread measurement in appetite studies.

CCK is a hormone secreted by I-cells located predominantly in the proximal duodenum( Reference Little, Horowitz and Feinle-Bisset 105 ). CCK has an effect on meal size or early-stage satiety although its role in eating behaviour may be dispensable( Reference Bergh, Sjostedt and Hellers 106 ). Studies have generally shown that infusing CCK into dogs, rodents or human subjects reduces food intake or suppresses appetite in a dose-dependent manner( Reference Schick, Schusdziarra and Mössner 107 , Reference Reidelberger, Kalogeris and Solomon 108 ). Moreover, when a CCK-1 antagonist is administered before a meal is eaten this leads to increased meal sizes( Reference Beglinger, Degen and Matzinger 109 , Reference Hewson, Leighton and Hill 110 ). In human subjects, studies have been conducted where CCK has been infused and the effect on food intake or appetite measured. These studies generally support a role for CCK on meal size. Schick et al. ( Reference Schick, Schusdziarra and Mössner 107 ) found that infusing CCK reduced food intake but only at supra-physiological levels. These results were mirrored by a study that infused CCK at physiological levels and had no statistically significant effect on food intake or appetite sensations( Reference Lieverse, Jansen and van de Zwan 111 ). Different results were obtained by Ballinger et al. ( Reference Ballinger, McLoughlin and Medbak 112 ) who found that a physiological dose of CCK reduced food intake by approximately 1350 kJ. Similar findings to Ballinger et al. ( Reference Ballinger, McLoughlin and Medbak 112 ) were reported by Lieverse et al. ( Reference Lieverse, Jansen and Masclee 113 ), Gurtzwiller( Reference Gutzwiller, Degen and Matzinger 114 , Reference Gutzwiller, Drewe and Ketterer 115 ) and Brennan et al. ( Reference Brennan, Feltrin and Horowitz 116 ). Fatty acids and protein appear to be potent stimulators of CCK secretion while carbohydrate has a minor effect( Reference Liddle 117 ). While there is evidence that CCK is causally involved in satiation it is not clear that its administration or increasing its plasma concentration will lead to a change in body weight. Rodent studies report that while the repeated administration of CCK reduced meal size, the rodents ate more frequently, meaning there was little effect on overall food intake( Reference West, Fey and Woods 118 ).

GLP-1 is a hormone secreted by cells in the ileum. After secretion, the active form (GLP-17–36) is rapidly converted to the inactive form (GLP-19–36) by dipeptidyl peptidase-4. GLP-1 is thought to have a role in the ‘ileal break’ mechanism which slows the entry of nutrients into the large intestine to facilitate absorption in the small intestine( Reference Degen, Oesch and Matzinger 119 ). It has been proposed that GLP-1 may influence satiety by slowing gastric emptying and prolonging gastric distention( Reference Little, Pilichiewicz and Russo 120 ). GLP-1 may also directly interact with the brain and GLP-1 receptors have been found in the hypothalamus( Reference Alvarez, Martinez and Roncero 121 ). There is a correlation between postprandial GLP-1 concentration and activation of areas in the hypothalamus associated with satiety( Reference Pannacciulli, Le and Salbe 122 ). While this evidence suggests a role for GLP-1 in satiety this has still to be confirmed. Several studies have infused GLP-1 and examined the effect on food intake in appetite but have provided mixed results. While some studies report that infusing GLP-1 reduces food intake and/or appetite( Reference Gutzwiller, Degen and Matzinger 114 , Reference Flint, Raben and Astrup 123 Reference Nagell, Wettergren and Pedersen 125 ), other studies have found no effect on the same outcome measures( Reference Brennan, Feltrin and Horowitz 116 , Reference Little, Pilichiewicz and Russo 120 , Reference Long, Sutton and Amaee 126 , Reference Neary, Small and Druce 127 ). It has also been argued that the studies demonstrating an effect of GLP-1 on food intake or appetite used supra-physiological doses of GLP-1 and its relevance to satiety under normal conditions remains unclear( Reference Mars, Stafleu and de Graaf 128 ).

PYY3–36 is released primarily in the distal gastrointestinal tract and acts as a agonist on the Y2 receptor in the hypothalamus( Reference Batterham, Cowley and Small 129 , Reference Nonaka, Shioda and Niehoff 130 ). A potential effect of PYY3–36 on food intake was first reported in 2002 by Batterham et al. ( Reference Batterham, Cowley and Small 129 ) who found that food intake was reduced by 33 % in the 24 h after PYY3–36 was infused for 2 h. While subsequent studies report that infusing PYY3–36 reduces food intake or increases satiety this only occurs at higher doses or when infused with GLP-1( Reference Neary, Small and Druce 127 , Reference Degen, Oesch and Casanova 131 Reference le Roux, Borg and Murphy 134 ). Some authors have suggested that the effect of PYY3–36 on food intake may be due to feelings of nausea rather than an effect on satiety( Reference Degen, Oesch and Casanova 131 , Reference le Roux, Borg and Murphy 134 ). In a review, it was concluded that there was no overlap between the circulating concentration of PYY3–36 following a meal and following infusion of PYY3–36 ( Reference Mars, Stafleu and de Graaf 128 ). This raises questions about the role of PYY3–36 in the normal satiety process. Further research is required to fully characterise the role of PYY3–36 in the satiety process.

Environmental factors that influence eating behaviour

Hunger and meal initiation

It is likely that under normal circumstances, where food is reliably available, meal initiation is largely influenced by our schedules or the opportunistic access to foods( Reference Woods 82 , Reference Strubbe and Woods 135 ). In a study that asked obese participants why they initiated a meal the most common response (32·7 % of respondents) was that it was a meal time( Reference Tuomisto, Tuomisto and Hetherington 136 ) and only 20 % of meals were initiated in response to hunger. It appears that hunger is not a necessary stimulus for meal initiation, with a study finding that the exposure to a palatable food was sufficient to cause meal initiation even when the individual was sated( Reference Cornell, Rodin and Weingarten 137 ). Moreover, the sight and proximity of food have also been found to stimulate food intake independent of hunger( Reference Wansink, Painter and Lee 24 ).

Another potentially key contributor to meal initiation is food cravings. Food cravings are experienced by 21–97 % of the population and are defined as an intense desire to consume a specific food independent of hunger( Reference Gendall, Joyce and Abbott 138 ). Food cravings typically involve the desire to eat energy-dense, high-fat foods( Reference Gilhooly, Das and Golden 139 ). Data indicate that obese people experience more frequent food cravings than their lean counterparts( Reference Abiles, Rodriguez-Ruiz and Abiles 140 , Reference Franken and Muris 141 ). A study that used diet records has reported an association between food cravings and energy intake( Reference Hill, Weaver and Blundell 142 ) while a laboratory study found that specific food cravings were associated with intake of a corresponding food( Reference Martin, O’Neil and Tollefson 143 ). It has been suggested that cravings and other forms of food cue reactivity should lead to increased food intake and consequently weight gain. However, studies provide inconsistent results and further study is required. For instance, while several studies have found that food cue exposure can increase eating in adults( Reference van den Akker, Jansen and Frentz 144 Reference Ferriday and Brunstrom 146 ) and is associated with weight gain( Reference Demos, Heatherton and Kelley 147 , Reference Yokum, Gearhardt and Harris 148 ), other studies have failed to show any associations( Reference Jansen, Nederkoorn and van Baak 149 Reference Zoon, He and de Wijk 151 ). A recent meta-analysis combined the results from forty-five studies and found a statistically significant but moderate effect of food cue reactivity and craving on eating (r 0·33)( Reference Boswell and Kober 152 ).

Satiation

In general, it seems that in the short term meal size is determined by environmental factors, although physiology clearly places limits on the amount that is eaten in a meal. However, it must be remembered that long-term body-weight regulatory systems may exert an increasing influence on meal size if body weight is being gained or lost. Therefore, caution should be used when interpreting the results gained from the short-term studies that are discussed in this section.

It has been suggested that the role of physiology in determining meal size has been overstated and that the largest influence on meal size is the pre-ingestive decisions regarding meal size( Reference Brunstrom 153 ). Observational studies suggest that humans plan the amount of food that they are going to eat in advance( Reference Pilgrim and Kamen 154 , Reference Vermeer, Steenhuis and Seidell 155 ). Other studies have shown that memory of a recent meal can reduce food intake at the subsequent meal( Reference Higgs 156 , Reference Rozin, Dow and Moscovitch 157 ). Disrupting memory through the use of distractors increases food intake at that meal but also increases food intake at subsequent meals( Reference Oldham-Cooper, Hardman and Nicoll 158 ). This suggests that the memory of how much was eaten at a meal has consequences for food intake over the short term. In a series of studies by Brunstrom et al. ( Reference Brunstrom, Shakeshaft and Scott-Samuel 159 , Reference Brunstrom, Collingwood and Rogers 160 ) it was reported that individuals gauge the expected satiation of a meal and the amount of energy served correlates with this expectation. It is potentially interesting to note that expected satiation of a product remains stable over time and repeated exposure to lower-energy-density alternatives has no effect on expected satiety( Reference O’Sullivan, Alexander and Ferriday 161 ). This information may have implications for the creation of new dietary products to aid weight management.

Evidence suggests that the portion size of foods has increased over the past three decades( Reference Young and Nestle 162 Reference Piernas and Popkin 164 ). These observations are of interest, as serving a larger portion size increases food intake( Reference Rolls, Morris and Roe 48 , Reference Diliberti, Bordi and Conklin 165 , Reference Levitsky and Youn 166 ). A noteworthy observation by Rolls et al. ( Reference Rolls, Morris and Roe 48 ) was that when a large portion of food was served participants consumed 30 % more energy compared with the small portion. However, appetite ratings were similar following both meals and only 45 % of the participants noticed that the portion sizes differed. These observations have public health implications as people frequently eat away from home and are not able to control the portion size served to them.

The palatability of the meal can also increase meal size. A cross-sectional study found that meals in the highest palatability rating were 40 % higher than at the lowest rating( Reference de Castro, Bellisle and Dalix 167 ). Intervention studies have generally supported this observation, showing that palatable versions of a food are eaten in higher amounts than bland or unpalatable versions( Reference Bellisle, Lucas and Amrani 168 , Reference Spiegel, Shrager and Stellar 169 ). The relevance of this information to normal eating behaviour may be questioned, as few people choose to eat unpalatable foods. However, it has been reported that food deprivation increases the rated palatability of foods, which results in larger meal sizes( Reference Spiegel, Shrager and Stellar 169 ). It has been proposed that we live in an obesogenic environment that provides access to cheap, energy-dense and palatable foods( Reference Swinburn, Egger and Raza 170 ). Therefore, the frequent exposure to palatable foods may provide a strong incentive to consume food, leading to the overconsumption of foods that potentially have low satiating potential( Reference Blundell and Macdiarmid 171 ).

Accumulating evidence suggests that eating rate may influence satiation. In a meta-analysis it was found that slowing eating rate by any method reduced meal size( Reference Robinson, Almiron-Roig and Rutters 172 ). This included studies that slowed eating rate by eating slowly( Reference Scisco, Muth and Dong 173 ), increasing the number of masticatory cycles before swallowing( Reference Zhu and Hollis 174 , Reference Zhu, Hsu and Hollis 175 ), or manipulating food form( Reference Viskaal-van Dongen, Kok and de Graaf 176 ). A precise mechanism has not yet been identified but could involve increased sensory exposure( Reference Bolhuis, Lakemond and de Wijk 177 ) or a longer meal time which allows nutrients to enter the gastrointestinal tract and stimulate cells that secrete CCK or other satiety-related hormones( Reference Zhu, Hsu and Hollis 178 , Reference Cassady, Hollis and Fulford 179 ). It is also possible that mastication has an independent effect on satiation. Studies using rodent models suggest that mastication activates areas of the brain associated with satiation through an increase in histamine production( Reference Sakata, Yoshimatsu and Masaki 180 ). This is initiated by stimulation of the periodontal ligaments( Reference Inagaki, Yamatodani and Ando-Yamamoto 181 ). At this time, it is not clear that this pathway has an important role in human eating behaviour. An answer to this question would be useful as it may provide an explanation for observations that tooth loss (which would result in lower stimulation of periodontal ligaments even with dentures) is associated with higher body weight( Reference Zhu and Hollis 182 ).

The form in which a food is consumed has also been found to influence meal size. In particular, foods that can be ingested rapidly (such as liquids or low-fibre foods) are associated with increased energy intake( Reference Viskaal-van Dongen, Kok and de Graaf 176 ). This observation is important, as the modern food supply is characterised by an ample supply of highly processed, low-fibre foods. Other aspects of food rheology such as viscosity have also been found to influence satiation( Reference de Wijk, Zijlstra and Mars 183 , Reference Zijlstra, Mars and de Wijk 184 ). One potential explanation for these results is that foods that require greater eating effort and are eaten more slowly increase oral exposure time which augments satiation( Reference Zijlstra, de Wijk and Mars 185 ).

Studies have shown that humans eat a constant weight of food( Reference Bell, Castellanos and Pelkman 186 Reference Stubbs, Harbron and Murgatroyd 189 ). Consequently, altering the energy density of the meal (kJ/g) would enable people to eat the same weight of food but consume less energy. Several studies have altered the energy density of the diet by increasing the water content of the food( Reference Rolls, Bell and Thorwart 190 ), increasing the air content of food( Reference Osterholt, Roe and Rolls 191 ) or increasing the fibre content of food( Reference Bell, Castellanos and Pelkman 186 ). These effects of reducing the energy density of food on energy intake persists over a 48 h period( Reference Bell, Castellanos and Pelkman 186 ) and may lead to weight loss over the long term( Reference Ledikwe, Rolls and Smiciklas-Wright 192 ). Another potential method to reduce the energy density of the diet is to drink water with a meal; however, there is little supporting evidence to suggest this influences food intake( Reference Rolls, Bell and Thorwart 190 ).

Satiety

A number of studies have investigated the influence of various interventions on the processes of satiety. A large number of studies have investigated the role of nutrients on satiety. It is generally thought that there is a macronutrient hierarchy, with protein being more satiating than carbohydrate which is more satiating than fat( Reference Stubbs, Ferres and Horgan 193 ). However, a recent systematic review and meta-analysis found that when high-protein meals were compared with low-protein meals only 35 % reported a reduction in hunger while 55 % showed an increase in postprandial fullness( Reference Leidy, Clifton and Astrup 194 ). Only 18 % of studies found that a high-protein test food reduced food intake at the next meal compared with a low-protein meal. The inconsistent results may be due to differences in the protein quality used in the test meals or the form of the test meal. It has also been proposed that to obtain an effect on satiety a protein threshold must be crossed. This has been estimated to be approximately 25–30 g( Reference Paddon-Jones and Leidy 195 ) although insufficient data exist to specify a minimum amount of protein that may have an effect on appetite.

Much recent effort has focused on the effect of energy-yielding beverages on satiety. Several studies have found that beverages are less satiating than their solid equivalents( Reference DiMeglio and Mattes 54 , Reference Tournier and Louis-Sylvestre 196 Reference Stull, Apolzan and Thalacker-Mercer 199 ), although conflicting data exist( Reference Almiron-Roig, Flores and Drewnowski 200 ). It has been hypothesised that it is the liquid medium that is responsible for the poor satiating effect and the macronutrient content of the beverage is not relevant( Reference Mattes 201 ). Supporting this hypothesis is a well-controlled study where it was found that solid versions of high-carbohydrate, -fat and -protein foods was more satiating than the liquid equivalents( Reference Mourao, Bressan and Campbell 202 ). However, other studies have demonstrated that beverages containing fibre produce satiety( Reference Lyly, Liukkonen and Salmenkallio-Marttila 203 ). Moreover, studies have shown that beverages that are consumed as part of weight-loss programmes induce satiation, which may suggest that expectation has a role( Reference Frestedt, Young and Bell 204 ). Supporting a role of expectation is a study that led participants to believe that a beverage or solid food would change form in the stomach to become a solid or liquid; perceptions of satiety and gastric emptying rate were altered( Reference Cassady, Considine and Mattes 205 ). Moreover, soup provides energy in a liquid form but studies have shown it to cause a robust satiety response( Reference Mattes 206 , Reference Zhu and Hollis 207 ). It has also been found that a liquid soup may be more satiating than a soup containing solid pieces of food( Reference Zhu, Hsu and Hollis 39 ). This may be due to an increased satiety hormone response due to the greater availability of nutrients.

Food form has also been shown to influence satiety. Some studies have found that increasing the viscosity of a semi-liquid food increases postprandial satiety( Reference Marciani, Gowland and Spiller 208 , Reference Zhu, Hsu and Hollis 209 ). This may be due in part to a slower gastric emptying rate( Reference Marciani, Gowland and Spiller 208 , Reference Zhu, Hsu and Hollis 209 ) although gastric dilution may rapidly reduce the viscosity of a meal leading to minimal effects on gastric emptying rate( Reference Marciani, Gowland and Spiller 210 ). Changing the form of a solid food using a food processer to form a purée has been shown to increase satiety compared with eating a solid equivalent( Reference Zhu, Hsu and Hollis 39 ) or induce reduced satiety compared with eating a solid equivalent( Reference Flood-Obbagy and Rolls 211 ). The breaking of the plant cell walls may increase the availability of nutrients from the food( Reference Ellis, Kendall and Ren 212 ) and increase altering the postprandial endocrine response in a manner that increases satiety( Reference Cassady, Hollis and Fulford 179 ). However, processing foods also increased the postprandial insulin response( Reference Zhu, Hsu and Hollis 39 ). In light of the widespread consumption of processed food, further research is warranted to fully understand the impact of processing on appetite and markers of chronic disease.

Emerging evidence suggests that in addition to an effect on satiation, eating rate or mastication may also influence satiety. However, data are inconsistent. A meta-analysis relating to eating rate did not find evidence that slowing eating rate has a robust effect on satiety( Reference Robinson, Almiron-Roig and Rutters 172 ). Another meta-analysis found an association between eating fast and obesity( Reference Ohkuma, Hirakawa and Nakamura 213 ). However, three studies that have investigated the effect of mastication on satiety found that increased chewing activity increased postprandial satiety( Reference Zhu, Hsu and Hollis 178 , Reference Cassady, Hollis and Fulford 179 , Reference Li, Zhang and Hu 214 ). It is possible that increasing masticatory effort means that the swallowed bolus contains smaller particles, increasing the surface area available for digestive enzymes to act on. As many satiety-related hormones are secreted in response to nutrients in the small intestine, these changed digestion kinetics may be sufficient to cause an altered endocrine profile that is associated with increased satiety.

Discussion

The present review has focused on several salient physiological and environmental factors that have been found to influence eating behaviour. This list is not exhaustive but illustrates that there are several potential points of intervention, either at the individual or population level, to modify eating behaviour and contribute to societal public health or environmental goals. Further research is required to understand how multiple factors interact to determine eating behaviour. This will involve a greater focus on multidisciplinary research. However, there are considerable barriers to this approach that have been previously discussed( Reference Pellmar 215 ). An interesting review that discusses the multidisciplinary aspects of developing consumer products that augment satiety has been published( Reference Van Kleef, Van Trijp and Van Den Borne 36 ). It is likely that new approaches to training scientists will be required to overcome these barriers.

Several physiological mechanisms have been identified that influence eating behaviour and body weight. While physiological explanations are insufficient on their own, a key insight from this research is that body-weight regulation is strongly asymmetric in that it strongly resists weight loss but only weakly protects against overeating or weight gain. This would provide an explanation for why weight gain is relatively easy whereas attempts to lose weight frequently end in failure. Another key insight is that there are multiple levels of redundancy in the physiological system. Whole parts of the system can be made inoperative without significant effect, as other systems appear to take over, causing changes in behaviour to counteract the loss (for example, increases in feeding frequency). This suggests that pharmaceutical or functional food approaches that aim to aid weight loss by targeting a single mechanism (for example, augmenting CCK secretion to increase satiety) may have limited success. Further research is required to fully understand the physiological basis of eating behaviour and whether greater success in manipulating it may be achieved by targeting multiple systems.

As the physiological systems that influence eating behaviour do not appear to strongly oppose overeating, it could be argued that environmental changes that promote food intake are the key driver of overeating and weight gain. This means that there should be a strong focus on gaining a better understanding of how environmental factors interact to influence eating behaviour. A better understanding of how environmental factors interact to influence eating behaviour may require new approaches to research. For instance, a large percentage of laboratory research seeks to isolate one factor so that its effect on eating behaviour can be unambiguously determined. This approach has several strengths and has strong internal validity, but as people do not generally eat in such environments this type of research lacks external validity. Individuals eat in environments where multiple factors combine to influence eating behaviour. An individual’s eating decisions may also be constrained by cultural, socio-economic or accessibility issues. It is not clear how all these factors interact. They may cancel each other out, have an additive effect to promote food intake or satiety, or there may be a synergistic effect which potentiates the influence of individual factors. Consequently, it is not possible to predict how many of the observed environmental influences on eating behaviour will operate in commonly encountered eating environments. It is possible that new technologies, such as virtual reality, could be employed to combine the advantages of laboratory studies and field studies( Reference Parsons 216 ) to better understand eating behaviour in realistic environments. Research is required to develop and test the validity of new approaches to eating behaviour research.

If environmental factors are a key influence on eating behaviour, another challenge will be keeping pace with societal and technological advances. We are in a period where technological and societal changes are occurring at an unprecedented pace. The effect of these changes on eating behaviour is poorly understood. For instance, societal changes such as increased urbanisation will influence the environment in which most people make eating decisions (for example, greater access to convenience stores, restaurants or supermarkets). Changes in social inequality also have the potential to influence eating decisions( Reference Ng, Poti and Popkin 217 ). How socio-economic inequality interacts with the food environment to influence eating behaviour requires further study. Technological changes may also have a profound effect on our eating behaviour. Artificial intelligence approaches have been developed to create recipes (for example, IBM and Chef Watson). This technology could be potentially developed to aid healthy eating decisions, especially as advances in personalised nutrition are made. The degree to which our eating decisions could be made by artificial intelligence, its acceptability to the consumer and the effect on health is worthy of research. As it stands, it would appear that some people are willing to outsource some of their food decisions by using meal services that ship ‘healthy’ meals to their home. Moreover, as more people buy their foods online, research is required to determine how this approach influences food decisions and whether this provides new avenues to promote healthier eating choices. Of course, health advantages provided by technological advances have the potential to further entrench health inequalities unless there is widespread access to the relevant technology.

The wider effects of changes in eating behaviour should be considered when policy or dietary advice is being formulated. For instance, short-term appetite studies suggest that protein is the most satiating macronutrient. Dietary advice to increase protein consumption to aid weight management would therefore seem prudent as obesity is a leading public health problem. However, studies suggest that diets high in animal protein contribute to climate change and are unsustainable( Reference Westhoek, Lesschen and Rood 218 , Reference Pimentel and Pimentel 219 ). While more research is required to understand this complex problem, consideration to other societal problems should be made.

Eating behaviour is a complex yet fascinating area of study where much remains to be discovered. Considering that eating behaviour contributes to several of society’s pressing problems, more focus should be placed on understanding eating behaviour and how it may be manipulated so that societal goals can be achieved.

Acknowledgements

This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

C. E. and J. H. H. contributed equally to the concept and writing of this paper.

There are no conflicts of interest.

References

1. Hurt, RT, Kulisek, C, Buchanan, LA, et al. (2010) The obesity epidemic: challenges, health initiatives, and implications for gastroenterologists. Gastroenterol Hepatol 6, 780792.Google Scholar
2. Blair, SN, Archer, E & Hand, GA (2013) Commentary: Luke and Cooper are wrong: physical activity has a crucial role in weight management and determinants of obesity. Int J Epidemiol 42, 18361838.Google Scholar
3. Swinburn, B, Sacks, G & Ravussin, E (2009) Increased food energy supply is more than sufficient to explain the US epidemic of obesity. Am J Clin Nutr 90, 14531456.CrossRefGoogle ScholarPubMed
4. Luke, A & Cooper, RS (2013) Physical activity does not influence obesity risk: time to clarify the public health message. Int J Epidemiol 42, 18311836.CrossRefGoogle ScholarPubMed
5. Hill, JO, Wyatt, HR & Peters, JC (2012) Energy balance and obesity. Circulation 126, 126132.CrossRefGoogle ScholarPubMed
6. Hall, KD, Heymsfield, SB, Kemnitz, JW, et al. (2012) Energy balance and its components: implications for body weight regulation. Am J Clin Nutr 95, 989994.CrossRefGoogle ScholarPubMed
7. Feinman, RD & Fine, EJ (2004) “A calorie is a calorie” violates the second law of thermodynamics. Nutr J 3, 9.Google Scholar
8. Feinman, RD & Fine, EJ (2003) Thermodynamics and metabolic advantage of weight loss diets. Metab Syndr Relat Disord 1, 209219.Google Scholar
9. Buchholz, AC & Schoeller, DA (2004) Is a calorie a calorie? Am J Clin Nutr 79, 899906.CrossRefGoogle ScholarPubMed
10. Hollis, JH & Mattes, RD (2005) Are all calories created equal? Emerging issues in weight management. Curr Diab Rep 5, 374378.Google Scholar
11. McCullough, ML, Feskanich, D, Stampfer, MJ, et al. (2002) Diet quality and major chronic disease risk in men and women: moving toward improved dietary guidance. Am J Clin Nutr 76, 12611271.CrossRefGoogle ScholarPubMed
12. Dietz, WH, Douglas, CE & Brownson, RC (2016) Chronic disease prevention: tobacco avoidance, physical activity, and nutrition for a healthy start. JAMA 316, 16451646.Google Scholar
13. Costello, A, Abbas, M, Allen, A, et al. (2009) Managing the health effects of climate change. Lancet 373, 16931733.Google Scholar
14. McMichael, AJ, Powles, JW, Butler, CD, et al. (2007) Food, livestock production, energy, climate change, and health. Lancet 370, 12531263.CrossRefGoogle ScholarPubMed
15. Carlsson-Kanyama, A & Gonzáles, A (2009) Potential contributions of food consumption patterns to climate change. Epidemiology 89, 1704S1709S.Google Scholar
16. Bilman, E, van Kleef, E & van Trijp, H (2015) External cues challenging the internal appetite control system – overview and practical implications. Crit Rev Food Sci Nutr (epublication ahead of print version 13 October 2015).Google Scholar
17. Cohen, DA (2008) Obesity and the built environment: changes in environmental cues cause energy imbalances. Int J Obes (Lond) 32, Suppl. 7, S137S142.Google Scholar
18. Larson, N & Story, M (2009) A review of environmental influences on food choices. Ann Behav Med 38, Suppl. 1, S56S73.Google Scholar
19. Novotny, R, Williams, AE, Vinoya, AC, et al. (2009) US acculturation, food intake, and obesity among Asian-Pacific hotel workers. J Am Diet Assoc 109, 17121718.Google Scholar
20. Nicholls, W, Devonport, TJ & Blake, M (2016) The association between emotions and eating behaviour in an obese population with binge eating disorder. Obes Rev 17, 3042.CrossRefGoogle Scholar
21. Redd, M & Decastro, JM (1992) Social facilitation of eating – effects of social instruction on food intake. Physiol Behav 52, 749754.Google Scholar
22. Satter, E (2007) Hierarchy of food needs. J Nutr Educ Behav 39, S187S188.Google Scholar
23. Cummings, DE, Purnell, JQ, Frayo, RS, et al. (2001) A preprandial rise in plasma ghrelin levels suggests a role in meal initiation in humans. Diabetes 50, 17141719.Google Scholar
24. Wansink, B, Painter, JE & Lee, YK (2006) The office candy dish: proximity’s influence on estimated and actual consumption. Int J Obes (Lond) 30, 871875.Google Scholar
25. Oemichen, M & Smith, C (2016) Investigation of the food choice, promoters and barriers to food access issues, and food insecurity among low-income, free-living Minnesotan seniors. J Nutr Educ Behav 48, 397404.e1.Google Scholar
26. Ma, X, Liese, AD, Bell, BA, et al. (2016) Perceived and geographic food access and food security status among households with children. Public Health Nutr 19, 27812788.Google Scholar
27. Jablonski, BB, McFadden, DT & Colpaart, A (2016) Analyzing the role of community and individual factors in food insecurity: identifying diverse barriers across clustered community members. J Community Health 41, 910923.Google Scholar
28. Cleveland, M, Rojas-Mendez, JI, Laroche, M, et al. (2016) Identity, culture, dispositions and behavior: a cross-national examination of globalization and culture change. J Bus Res 69, 10901102.Google Scholar
29. Axelson, ML (1986) The impact of culture on food-related behavior. Annu Rev Nutr 6, 345363.Google Scholar
30. Slater, J (2013) Is cooking dead? The state of Home Economics Food and Nutrition education in a Canadian province. Int J Consum Stud 37, 617624.Google Scholar
31. Gatley, A, Caraher, M & Lang, T (2014) A qualitative, cross cultural examination of attitudes and behaviour in relation to cooking habits in France and Britain. Appetite 75, 7181.Google Scholar
32. Fox, N & Ward, K (2008) Health, ethics and environment: a qualitative study of vegetarian motivations. Appetite 50, 422429.Google Scholar
33. Lu, HC (2015) The tastes of Chairman Mao: the quotidian as statecraft in the great leap forward and its aftermath. Mod China 41, 539572.Google Scholar
34. Blundell, J, Rogers, PJ & Hill, AJ (1987) Evaluating the satiating power of foods: implications for acceptance and consumption. In Food Acceptance and Nutrition, pp. 205219 [J Colms, DA Booth, RM Pangborn and O Raunhardt, editors]. London: Academic Press.Google Scholar
35. Halford, JC & Harrold, JA (2012) Satiety-enhancing products for appetite control: science and regulation of functional foods for weight management. Proc Nutr Soc 71, 350362.Google Scholar
36. Van Kleef, E, Van Trijp, JC, Van Den Borne, JJ, et al. (2012) Successful development of satiety enhancing food products: towards a multidisciplinary agenda of research challenges. Crit Rev Food Sci Nutr 52, 611628.Google Scholar
37. Blundell, J, de Graaf, C, Hulshof, T, et al. (2010) Appetite control: methodological aspects of the evaluation of foods. Obes Rev 11, 251270.Google Scholar
38. Zhu, Y & Hollis, JH (2014) Gastric emptying rate, glycemic and appetite response to a liquid meal in lean and overweight males. Int J Food Sci Nutr 65, 615620.Google Scholar
39. Zhu, Y, Hsu, WH & Hollis, JH (2013) The effect of food form on satiety. Int J Food Sci Nutr 64, 385391.Google Scholar
40. Emilien, CH, West, R & Hollis, JH (2016) The effect of the macronutrient composition of breakfast on satiety and cognitive function in undergraduate students. Eur J Nutr (epublication ahead of print version 5 July 2016).Google Scholar
41. Caudwell, P, Gibbons, C, Hopkins, M, et al. (2011) The influence of physical activity on appetite control: an experimental system to understand the relationship between exercise-induced energy expenditure and energy intake. Proc Nutr Soc 70, 171180.Google Scholar
42. Stubbs, RJ, Hughes, DA, Johnstone, AM, et al. (2000) The use of visual analogue scales to assess motivation to eat in human subjects: a review of their reliability and validity with an evaluation of new hand-held computerized systems for temporal tracking of appetite ratings. Br J Nutr 84, 405415.Google Scholar
43. Mattes, R (1990) Hunger ratings are not a valid proxy measure of reported food intake in humans. Appetite 15, 103113.Google Scholar
44. de Castro, JM & Elmore, DK (1988) Subjective hunger relationships with meal patterns in the spontaneous feeding behavior of humans: evidence for a causal connection. Physiol Behav 43, 159165.Google Scholar
45. Archer, E, Hand, GA & Blair, SN (2013) Validity of U.S. nutritional surveillance: National Health and Nutrition Examination Survey caloric energy intake data, 1971–2010. PLOS ONE 8, e76632.Google Scholar
46. Parker, BA, Sturm, K, MacIntosh, C, et al. (2004) Relation between food intake and visual analogue scale ratings of appetite and other sensations in healthy older and young subjects. Eur J Clin Nutr 58, 212218.CrossRefGoogle ScholarPubMed
47. Booth, DA (2009) Lines, dashed lines and “scale” ex-tricks. Objective measurements of appetite versus subjective tests of intake. Appetite 53, 434437.Google Scholar
48. Rolls, BJ, Morris, EL & Roe, LS (2002) Portion size of food affects energy intake in normal-weight and overweight men and women. Am J Clin Nutr 76, 12071213.CrossRefGoogle ScholarPubMed
49. Finlayson, G, King, N & Blundell, JE (2007) Liking vs. wanting food: importance for human appetite control and weight regulation. Neurosci Biobehav Rev 31, 9871002.CrossRefGoogle ScholarPubMed
50. Vartanian, LR, Sokol, N, Herman, CP, et al. (2013) Social models provide a norm of appropriate food intake for young women. PLOS ONE 8, e79268.Google Scholar
51. Wansink, B (2004) Environmental factors that increase the food intake and consumption volume of unknowing consumers. Annu Rev Nutr 24, 455479.CrossRefGoogle ScholarPubMed
52. Robinson, E, Hardman, CA, Halford, JCG, et al. (2015) Eating under observation: a systematic review and meta-analysis of the effect that heightened awareness of observation has on laboratory measured energy intake. Am J Clin Nutr 102, 324337.Google Scholar
53. Herman, CP, Polivy, J & Silver, R (1979) Effects of an observer on eating behavior: the induction of “sensible” eating. J Pers 47, 8599.Google Scholar
54. DiMeglio, DP & Mattes, RD (2000) Liquid versus solid carbohydrate: effects on food intake and body weight. Int J Obes 24, 794800.Google Scholar
55. Kaiser, KA, Shikany, JM, Keating, KD, et al. (2013) Will reducing sugar-sweetened beverage consumption reduce obesity? Evidence supporting conjecture is strong, but evidence when testing effect is weak. Obes Rev 14, 620633.CrossRefGoogle ScholarPubMed
56. Fetissov, SO (2017) Role of the gut microbiota in host appetite control: bacterial growth to animal feeding behaviour. Nat Rev Endocrinol 13, 1125.Google Scholar
57. Breton, J, Tennoune, N, Lucas, N, et al. (2016) Gut commensal E. coli proteins activate host satiety pathways following nutrient-induced bacterial growth. Cell Metab 23, 324334.Google Scholar
58. Guyenet, SJ & Schwartz, MW (2012) Regulation of food intake, energy balance, and body fat mass: implications for the pathogenesis and treatment of obesity. J Clin Endocrinol Metab 97, 745755.Google Scholar
59. Harris, RB (1990) Role of set-point theory in regulation of body weight. FASEB J 4, 33103318.Google Scholar
60. Speakman, JR, Levitsky, DA, Allison, DB, et al. (2011) Set points, settling points and some alternative models: theoretical options to understand how genes and environments combine to regulate body adiposity. Dis Model Mech 4, 733745.Google Scholar
61. Maffei, M, Halaas, J, Ravussin, E, et al. (1995) Leptin levels in human and rodent: measurement of plasma leptin and ob RNA in obese and weight-reduced subjects. Nat Med 1, 11551161.CrossRefGoogle ScholarPubMed
62. Friedman, JM & Halaas, JL (1998) Leptin and the regulation of body weight in mammals. Nature 395, 763770.Google Scholar
63. Sohn, JW (2015) Network of hypothalamic neurons that control appetite. BMB Rep 48, 229233.Google Scholar
64. Van Wye, G, Dubin, JA, Blair, SN, et al. (2007) Adult obesity does not predict 6-year weight gain in men: The Aerobics Center Longitudinal Study. Obesity 15, 15711577.Google Scholar
65. Keys, A, Brožek, J, Henschel, A, et al. (1950) The Biology of Human Starvation (2 volumes). St Paul, MN: University of Minnesota Press.CrossRefGoogle Scholar
66. Wirtshafter, D & Davis, JD (1977) Set points, settling points, and the control of body weight. Physiol Behav 19, 7578.Google Scholar
67. Levitsky, DA (2005) The non-regulation of food intake in humans: hope for reversing the epidemic of obesity. Physiol Behav 86, 623632.Google Scholar
68. Payne, PR & Dugdale, AE (1977) Mechanisms for the control of body-weight. Lancet i, 583586.Google Scholar
69. Mayer, J (1953) Glucostatic mechanism of regulation of food intake. N Engl J Med 249, 1316.Google Scholar
70. Grossman, SP (1986) The role of glucose, insulin and glucagon in the regulation of food intake and body weight. Neurosci Biobehav Rev 10, 295315.Google Scholar
71. Smith, GP & Epstein, AN (1969) Increased feeding in response to decreased glucose utilization in the rat and monkey. Am J Physiol 217, 10831087.Google Scholar
72. Louis-Sylvestre, J & Le Magnen, J (1980) Fall in blood glucose level precedes meal onset in free-feeding rats. Neurosci Biobehav Rev 4, Suppl. 1, 1315.Google Scholar
73. Campfield, LA & Smith, FJ (1990) Transient declines in blood glucose signal meal initiation. Int J Obes 14, Suppl. 3, 1531; discussion 31–34.Google Scholar
74. Campfield, LA, Smith, FJ, Rosenbaum, M, et al. (1996) Human eating: evidence for a physiological basis using a modified paradigm. Neurosci Biobehav Rev 20, 133137.Google Scholar
75. Kovacs, EMR, Westerterp-Plantenga, MS, Saris, WHM, et al. (2002) Associations between spontaneous meal initiations and blood glucose dynamics in overweight men in negative energy balance. Br J Nutr 87, 3945.CrossRefGoogle ScholarPubMed
76. Chapman, IM, Goble, EA, Wittert, GA, et al. (1998) Effect of intravenous glucose and euglycemic insulin infusions on short-term appetite and food intake. Am J Physiol 274, R596R603.Google Scholar
77. Cummings, DE, Frayo, RS, Marmonier, C, et al. (2004) Plasma ghrelin levels and hunger scores in humans initiating meals voluntarily without time- and food-related cues. Am J Physiol Endocrinol Metab 287, E297E304.Google Scholar
78. Tschop, M, Wawarta, R, Riepl, RL, et al. (2001) Post-prandial decrease of circulating human ghrelin levels. J Endocrinol Invest 24, RC19RC21.Google Scholar
79. Wren, AM, Small, CJ, Ward, HL, et al. (2000) The novel hypothalamic peptide ghrelin stimulates food intake and growth hormone secretion. Endocrinology 141, 43254328.Google Scholar
80. Wortley, KE, Anderson, KD, Garcia, K, et al. (2004) Genetic deletion of ghrelin does not decrease food intake but influences metabolic fuel preference. Proc Natl Acad Sci U S A 101, 82278232.Google Scholar
81. Frecka, JM & Mattes, RD (2008) Possible entrainment of ghrelin to habitual meal patterns in humans. Am J Physiol Gastrointest Liver Physiol 294, G699G707.Google Scholar
82. Woods, SC (2009) The control of food intake: behavioral versus molecular perspectives. Cell Metab 9, 489498.Google Scholar
83. Power, ML & Schulkin, J (2008) Anticipatory physiological regulation in feeding biology: cephalic phase responses. Appetite 50, 194206.Google Scholar
84. Woods, SC (1991) The eating paradox: how we tolerate food. Psychol Rev 98, 488505.Google Scholar
85. Smeets, PAM, Erkner, A & de Graaf, C (2010) Cephalic phase responses and appetite. Nutr Rev 68, 643655.Google Scholar
86. Stratton, RJ, Stubbs, RJ & Elia, M (2003) Short-term continuous enteral tube feeding schedules did not suppress appetite and food intake in healthy men in a placebo-controlled trial. J Nutr 133, 25702576.Google Scholar
87. Spetter, MS, Mars, M, Viergever, MA, et al. (2014) Taste matters – effects of bypassing oral stimulation on hormone and appetite responses. Physiol Behav 137, 917.Google Scholar
88. Zhu, Y, Hsu, WH & Hollis, JH (2014) Modified sham feeding of foods with different macronutrient compositions differentially influences cephalic change of insulin, ghrelin, and NMR-based metabolomic profiles. Physiol Behav 135, 135142.Google Scholar
89. Rolls, BJ, Rolls, ET, Rowe, EA, et al. (1981) Sensory specific satiety in man. Physiol Behav 27, 137142.Google Scholar
90. Hetherington, MM (1996) Sensory-specific satiety and its importance in meal termination. Neurosci Biobehav Rev 20, 113117.Google Scholar
91. Hollis, JH & Henry, CJ (2007) Dietary variety and its effect on food intake of elderly adults. J Hum Nutr Diet 20, 345351.Google Scholar
92. Rolls, BJ, Rowe, EA, Rolls, ET, et al. (1981) Variety in a meal enhances food intake in man. Physiol Behav 26, 215221.Google Scholar
93. Hollis, JH & Henry, CJK (2007) Sensory-specific satiety and flavor amplification of foods. J Sens Stud 22, 367376.Google Scholar
94. Rolls, ET, Murzi, E, Yaxley, S, et al. (1986) Sensory-specific satiety: food-specific reduction in responsiveness of ventral forebrain neurons after feeding in the monkey. Brain Res 368, 7986.Google Scholar
95. Rolls, BJ & McDermott, TM (1991) Effects of age on sensory-specific satiety. Am J Clin Nutr 54, 988996.Google Scholar
96. Schiffman, SS (1997) Taste and smell losses in normal aging and disease. JAMA 278, 13571362.Google Scholar
97. Geliebter, A, Westreich, S & Gage, D (1988) Gastric distention by balloon and test-meal intake in obese and lean subjects. Am J Clin Nutr 48, 592594.Google Scholar
98. Melton, PM, Kissileff, HR & Pi-Sunyer, FX (1992) Cholecystokinin (CCK-8) affects gastric pressure and ratings of hunger and fullness in women. Am J Physiol Regul Integr Comp Physiol 263, R452R456.Google Scholar
99. Blundell, JE, Finlayson, G, Gibbons, C, et al. (2015) The biology of appetite control: do resting metabolic rate and fat-free mass drive energy intake? Physiol Behav 152, 473478.Google Scholar
100. Devries, J, Strubbe, JH, Wildering, WC, et al. (1993) Patterns of body temperature during feeding in rats under varying ambient temperatures. Physiol Behav 53, 229235.Google Scholar
101. Even, P & Nicolaidis, S (1985) Spontaneous and 2DG induced metabolic changes and feeding: the ischymetric hypothesis. Brain Res Bull 15, 429435.Google Scholar
102. Westerterp-Plantenga, MS, Lichtenbelt, WDV, Strobbe, H, et al. (2002) Energy metabolism in humans at a lowered ambient temperature. Eur J Clin Nutr 56, 288296.CrossRefGoogle Scholar
103. McAllister, EJ, Dhurandhar, NV, Keith, SW, et al. (2009) Ten putative contributors to the obesity epidemic. Crit Rev Food Sci Nutr 49, 868913.Google Scholar
104. Perry, B & Wang, Y (2012) Appetite regulation and weight control: the role of gut hormones. Nutr Diabetes 2, e26.Google Scholar
105. Little, TJ, Horowitz, M & Feinle-Bisset, C (2005) Role of cholecystokinin in appetite control and body weight regulation. Obes Rev 6, 297306.Google Scholar
106. Bergh, C, Sjostedt, S, Hellers, G, et al. (2003) Meal size, satiety and cholecystokinin in gastrectomized humans. Physiol Behav 78, 143147.Google Scholar
107. Schick, RR, Schusdziarra, V, Mössner, J, et al. (1991) Effect of CCK on food intake in man: physiological or pharmacological effect? Z Gastroenterol 29, 5358.Google Scholar
108. Reidelberger, RD, Kalogeris, TJ & Solomon, TE (1989) Plasma CCK levels after food intake and infusion of CCK analogs that inhibit feeding in dogs. Am J Physiol Regul Integr Comp Physiol 256, R1148R1154.CrossRefGoogle ScholarPubMed
109. Beglinger, C, Degen, L, Matzinger, D, et al. (2001) Loxiglumide, a CCK-A receptor antagonist, stimulates calorie intake and hunger feelings in humans. Am J Physiol Regul Integr Comp Physiol 280, R1149R1154.Google Scholar
110. Hewson, G, Leighton, GE, Hill, RG, et al. (1988) The cholecystokinin receptor antagonist L364,718 increases food intake in the rat by attenuation of the action of endogenous cholecystokinin. Br J Pharmacol 93, 7984.Google Scholar
111. Lieverse, RJ, Jansen, JB, van de Zwan, A, et al. (1993) Effects of a physiological dose of cholecystokinin on food intake and postprandial satiation in man. Regul Peptides 43, 8389.Google Scholar
112. Ballinger, A, McLoughlin, L, Medbak, S, et al. (1995) Cholecystokinin is a satiety hormone in humans at physiological postprandial plasma concentrations. Clin Sci 89, 375381.Google Scholar
113. Lieverse, RJ, Jansen, JBM, Masclee, AAM, et al. (1995) Satiety effects of a physiological dose of cholecystokinin in humans. Gut 36, 176179.Google Scholar
114. Gutzwiller, JP, Degen, L, Matzinger, D, et al. (2004) Interaction between GLP-1 and CCK-33 in inhibiting food intake and appetite in men. Am J Physiol Regul Integr Comp Physiol 287, R562R567.Google Scholar
115. Gutzwiller, JP, Drewe, J, Ketterer, S, et al. (2000) Interaction between CCK and a preload on reduction of food intake is mediated by CCK-A receptors in humans. Am J Physiol Regul Integr Comp Physiol 279, R189R195.Google Scholar
116. Brennan, IM, Feltrin, KL, Horowitz, M, et al. (2005) Evaluation of interactions between CCK and GLP-1 in their effects on appetite, energy intake, and antropyloroduodenal motility in healthy men. Am J Physiol Regul Integr Comp Physiol 288, R1477R1485.Google Scholar
117. Liddle, RA (1995) Regulation of cholecystokinin secretion by intraluminal releasing factors. Am J Physiol Gastrointest Liver Physiol 269, G319G327.Google Scholar
118. West, DB, Fey, D & Woods, SC (1984) Cholecystokinin persistently suppresses meal size but not food intake in free-feeding rats. Am J Physiol 246, R776R787.Google Scholar
119. Degen, L, Oesch, S, Matzinger, D, et al. (2006) Effects of a preload on reduction of food intake by GLP-1 in healthy subjects. Digestion 74, 7884.Google Scholar
120. Little, TJ, Pilichiewicz, AN, Russo, A, et al. (2006) Effects of intravenous glucagon-like peptide-1 on gastric emptying and intragastric distribution in healthy subjects: relationships with postprandial glycemic and insulinemic responses. J Clin Endocrinol Metab 91, 19161923.Google Scholar
121. Alvarez, E, Martinez, MD, Roncero, I, et al. (2005) The expression of GLP-1 receptor mRNA and protein allows the effect of GLP-1 on glucose metabolism in the human hypothalamus and brainstem. J Neurochem 92, 798806.Google Scholar
122. Pannacciulli, N, Le, DSNT, Salbe, AD, et al. (2007) Postprandial glucagon-like peptide-1 (GLP-1) response is positively associated with changes in neuronal activity of brain areas implicated in satiety and food intake regulation in humans. NeuroImage 35, 511517.Google Scholar
123. Flint, A, Raben, A, Astrup, A, et al. (1998) Glucagon-like peptide 1 promotes satiety and suppresses energy intake in humans. J Clin Invest 101, 515520.Google Scholar
124. Gutzwiller, JP, Goke, B, Drewe, J, et al. (1999) Glucagon-like peptide-1: a potent regulator of food intake in humans. Gut 44, 8186.Google Scholar
125. Nagell, CF, Wettergren, A, Pedersen, JF, et al. (2004) Glucagon-like peptide-2 inhibits antral emptying in man, but is not as potent as glucagon-like peptide-1. Scand J Gastroenterol 39, 353358.Google Scholar
126. Long, SJ, Sutton, JA, Amaee, WB, et al. (1999) No effect of glucagon-like peptide-1 on short-term satiety and energy intake in man. Br J Nutr 81, 273279.Google Scholar
127. Neary, NM, Small, CJ, Druce, MR, et al. (2005) Peptide YY3–36 and glucagon-like peptide-17–36 inhibit food intake additively. Endocrinology 146, 51205127.Google Scholar
128. Mars, M, Stafleu, A & de Graaf, C (2012) Use of satiety peptides in assessing the satiating capacity of foods. Physiol Behav 105, 483488.Google Scholar
129. Batterham, RL, Cowley, MA, Small, CJ, et al. (2002) Gut hormone PYY3–36 physiologically inhibits food intake. Nature 418, 650654.Google Scholar
130. Nonaka, N, Shioda, S, Niehoff, ML, et al. (2003) Characterization of blood–brain barrier permeability to PYY3–36 in the mouse. J Pharmacol Exp Ther 306, 948953.Google Scholar
131. Degen, L, Oesch, S, Casanova, M, et al. (2005) Effect of peptide YY3–36 on food intake in humans. Gastroenterology 129, 14301436.Google Scholar
132. le Roux, CW, Batterham, RL, Aylwin, SJB, et al. (2006) Attenuated peptide YY release in obese subjects is associated with reduced satiety. Endocrinology 147, 38.Google Scholar
133. Sloth, B, Holst, JJ, Flint, A, et al. (2007) Effects of PYY1–36 and PYY3–36 on appetite, energy intake, energy expenditure, glucose and fat metabolism in obese and lean subjects. Am J Physiol Endocrinol Metab 292, E1062E1068.Google Scholar
134. le Roux, CW, Borg, CM, Murphy, KG, et al. (2008) Supraphysiological doses of intravenous PYY3–36 cause nausea, but no additional reduction in food intake. Ann Clin Biochem 45, 9395.Google Scholar
135. Strubbe, JH & Woods, SC (2004) The timing of meals. Psychol Rev 111, 128141.Google Scholar
136. Tuomisto, T, Tuomisto, MT, Hetherington, M, et al. (1998) Reasons for initiation and cessation of eating in obese men and women and the affective consequences of eating in everyday situations. Appetite 30, 211222.CrossRefGoogle ScholarPubMed
137. Cornell, CE, Rodin, J & Weingarten, H (1989) Stimulus-induced eating when satiated. Physiol Behav 45, 695704.Google Scholar
138. Gendall, KA, Joyce, PR & Abbott, RM (1999) The effects of meal composition on subsequent craving and binge eating. Addict Behav 24, 305315.Google Scholar
139. Gilhooly, CH, Das, SK, Golden, JK, et al. (2007) Food cravings and energy regulation: the characteristics of craved foods and their relationship with eating behaviors and weight change during 6 months of dietary energy restriction. Int J Obes 31, 18491858.Google Scholar
140. Abiles, V, Rodriguez-Ruiz, S, Abiles, J, et al. (2010) Psychological characteristics of morbidly obese candidates for bariatric surgery. Obes Surg 20, 161167.Google Scholar
141. Franken, IHA & Muris, P (2005) Individual differences in reward sensitivity are related to food craving and relative body weight in healthy women. Appetite 45, 198201.Google Scholar
142. Hill, AJ, Weaver, CFL & Blundell, JE (1991) Food craving, dietary restraint and mood. Appetite 17, 187197.Google Scholar
143. Martin, CK, O’Neil, PM, Tollefson, G, et al. (2008) The association between food cravings and consumption of specific foods in a laboratory taste test. Appetite 51, 324326.Google Scholar
144. van den Akker, K, Jansen, A, Frentz, F, et al. (2013) Impulsivity makes more susceptible to overeating after contextual appetitive conditioning. Appetite 70, 7380.Google Scholar
145. Coelho, JS, Polivy, J, Herman, CP, et al. (2009) Wake up and smell the cookies. Effects of olfactory food-cue exposure in restrained and unrestrained eaters. Appetite 52, 517520.Google Scholar
146. Ferriday, D & Brunstrom, JM (2008) How does food-cue exposure lead to larger meal sizes? Br J Nutr 100, 13251332.Google Scholar
147. Demos, KE, Heatherton, TF & Kelley, WM (2012) Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci 32, 55495552.Google Scholar
148. Yokum, S, Gearhardt, AN, Harris, JL, et al. (2014) Individual differences in striatum activity to food commercials predict weight gain in adolescents. Obesity 22, 2544–2451.Google Scholar
149. Jansen, A, Nederkoorn, C, van Baak, L, et al. (2009) High-restrained eaters only overeat when they are also impulsive. Behav Res Ther 47, 105110.Google Scholar
150. Larsen, JK, Hermans, RCJ & Engels, RCME (2012) Food intake in response to food-cue exposure. Examining the influence of duration of the cue exposure and trait impulsivity. Appetite 58, 907913.Google Scholar
151. Zoon, HFA, He, W, de Wijk, RA, et al. (2014) Food preference and intake in response to ambient odours in overweight and normal-weight females. Physiol Behav 133, 190196.Google Scholar
152. Boswell, RG & Kober, H (2016) Food cue reactivity and craving predict eating and weight gain: a meta-analytic review. Obes Rev 17, 159177.Google Scholar
153. Brunstrom, JM (2014) Mind over platter: pre-meal planning and the control of meal size in humans. Int J Obes 38, S9S12.Google Scholar
154. Pilgrim, FJ & Kamen, JM (1963) Predictors of human food consumption. Science 139, 501502.Google Scholar
155. Vermeer, WM, Steenhuis, IHM & Seidell, JC (2009) From the point-of-purchase perspective: a qualitative study of the feasibility of interventions aimed at portion-size. Health Policy 90, 7380.Google Scholar
156. Higgs, S (2002) Memory for recent eating and its influence on subsequent food intake. Appetite 39, 159166.CrossRefGoogle ScholarPubMed
157. Rozin, P, Dow, S, Moscovitch, M, et al. (1998) What causes humans to begin and end a meal? A role for memory for what has been eaten, as evidenced by a study of multiple meal eating in amnesic patients. Psychol Sci 9, 392396.Google Scholar
158. Oldham-Cooper, RE, Hardman, CA, Nicoll, CE, et al. (2011) Playing a computer game during lunch affects fullness, memory for lunch, and later snack intake. Am J Clin Nutr 93, 308313.Google Scholar
159. Brunstrom, JM, Shakeshaft, NG & Scott-Samuel, NE (2008) Measuring ‘expected satiety’ in a range of common foods using a method of constant stimuli. Appetite 51, 604614.Google Scholar
160. Brunstrom, JM, Collingwood, J & Rogers, PJ (2010) Perceived volume, expected satiation, and the energy content of self-selected meals. Appetite 55, 2529.Google Scholar
161. O’Sullivan, HL, Alexander, E, Ferriday, D, et al. (2010) Effects of repeated exposure on liking for a reduced-energy-dense food. Am J Clin Nutr 91, 15841589.Google Scholar
162. Young, LR & Nestle, M (2002) The contribution of expanding portion sizes to the US obesity epidemic. Am J Public Health 92, 246249.Google Scholar
163. Nielsen, SJ & Popkin, BM (2003) Patterns and trends in food portion sizes, 1977–1998. JAMA 289, 450453.Google Scholar
164. Piernas, C & Popkin, BM (2011) Increased portion sizes from energy-dense foods affect total energy intake at eating occasions in US children and adolescents: patterns and trends by age group and sociodemographic characteristics, 1977–2006. Am J Clin Nutr 94, 13241332.Google Scholar
165. Diliberti, N, Bordi, PL, Conklin, MT, et al. (2004) Increased portion size leads to increased energy intake in a restaurant meal. Obes Res 12, 562568.Google Scholar
166. Levitsky, DA & Youn, T (2004) The more food young adults are served, the more they overeat. J Nutr 134, 25462549.Google Scholar
167. de Castro, JM, Bellisle, F, Dalix, AM, et al. (2000) Palatability and intake relationships in free-living humans: characterization and independence of influence in North Americans. Physiol Behav 70, 343350.Google Scholar
168. Bellisle, F, Lucas, F, Amrani, R, et al. (1984) Deprivation, palatability and the micro-structure of meals in human subjects. Appetite 5, 8594.Google Scholar
169. Spiegel, TA, Shrager, EE & Stellar, E (1989) Responses of lean and obese subjects to preloads, deprivation, and palatability. Appetite 13, 4569.Google Scholar
170. Swinburn, B, Egger, G & Raza, F (1999) Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med 29, 563570.Google Scholar
171. Blundell, JE & Macdiarmid, JI (1997) Passive overconsumption. Fat intake and short-term energy balance. Ann N Y Acad Sci 827, 392407.Google Scholar
172. Robinson, E, Almiron-Roig, E, Rutters, F, et al. (2014) A systematic review and meta-analysis examining the effect of eating rate on energy intake and hunger. Am J Clin Nutr 100, 123151.Google Scholar
173. Scisco, JL, Muth, ER, Dong, YJ, et al. (2011) Slowing bite-rate reduces energy intake: an application of the bite counter device. J Am Diet Assoc 111, 12311235.Google Scholar
174. Zhu, Y & Hollis, JH (2014) Increasing the number of chews before swallowing reduces meal size in normal-weight, overweight, and obese adults. J Acad Nutr Diet 114, 926931.Google Scholar
175. Zhu, Y, Hsu, WH & Hollis, JH (2014) Increased number of chews during a fixed-amount meal suppresses postprandial appetite and modulates glycemic response in older males. Physiol Behav 133, 136140.Google Scholar
176. Viskaal-van Dongen, M, Kok, FJ & de Graaf, C (2011) Eating rate of commonly consumed foods promotes food and energy intake. Appetite 56, 2531.Google Scholar
177. Bolhuis, DP, Lakemond, CMM, de Wijk, RA, et al. (2011) Both longer oral sensory exposure to and higher intensity of saltiness decrease ad libitum food intake in healthy normal-weight men. J Nutr 141, 22422248.Google Scholar
178. Zhu, Y, Hsu, WH & Hollis, JH (2013) Increasing the number of masticatory cycles is associated with reduced appetite and altered postprandial plasma concentrations of gut hormones, insulin and glucose. Br J Nutr 110, 384390.Google Scholar
179. Cassady, BA, Hollis, JH, Fulford, AD, et al. (2009) Mastication of almonds: effects of lipid bioaccessibility, appetite, and hormone response. Am J Clin Nutr 89, 794800.Google Scholar
180. Sakata, T, Yoshimatsu, H, Masaki, T, et al. (2003) Anti-obesity actions of mastication driven by histamine neurons in rats. Exp Biol Med 228, 11061110.Google Scholar
181. Inagaki, N, Yamatodani, A, Ando-Yamamoto, M, et al. (1988) Organization of histaminergic fibers in the rat brain. J Comp Neurol 273, 283300.Google Scholar
182. Zhu, Y & Hollis, JH (2015) Associations between the number of natural teeth and metabolic syndrome in adults. J Clin Periodontol 42, 113120.Google Scholar
183. de Wijk, RA, Zijlstra, N, Mars, M, et al. (2008) The effects of food viscosity on bite size, bite effort and food intake. Physiol Behav 95, 527532.Google Scholar
184. Zijlstra, N, Mars, M, de Wijk, RA, et al. (2008) The effect of viscosity on ad libitum food intake. Int J Obes 32, 676683.Google Scholar
185. Zijlstra, N, de Wijk, RA, Mars, M, et al. (2009) Effect of bite size and oral processing time of a semisolid food on satiation. Am J Clin Nutr 90, 269275.Google Scholar
186. Bell, EA, Castellanos, VH, Pelkman, CL, et al. (1998) Energy density of foods affects energy intake in normal-weight women. Am J Clin Nutr 67, 412420.Google Scholar
187. Kendall, A, Levitsky, DA, Strupp, BJ, et al. (1991) Weight loss on a low-fat diet: consequence of the imprecision of the control of food intake in humans. Am J Clin Nutr 53, 11241129.Google Scholar
188. Rolls, BJ, Castellanos, VH, Halford, JC, et al. (1998) Volume of food consumed affects satiety in men. Am J Clin Nutr 67, 11701177.Google Scholar
189. Stubbs, RJ, Harbron, CG, Murgatroyd, PR, et al. (1995) Covert manipulation of dietary fat and energy density: effect on substrate flux and food-intake in men eating ad libitum . Am J Clin Nutr 62, 316329.Google Scholar
190. Rolls, BJ, Bell, EA & Thorwart, ML (1999) Water incorporated into a food but not served with a food decreases energy intake in lean women. Am J Clin Nutr 70, 448455.Google Scholar
191. Osterholt, KM, Roe, DS & Rolls, BJ (2007) Incorporation of air into a snack food reduces energy intake. Appetite 48, 351358.Google Scholar
192. Ledikwe, JH, Rolls, BJ, Smiciklas-Wright, H, et al. (2007) Reductions in dietary energy density are associated with weight loss in overweight and obese participants in the PREMIER trial. Am J Clin Nutr 85, 12121221.Google Scholar
193. Stubbs, J, Ferres, S & Horgan, G (2000) Energy density of foods: effects on energy intake. Crit Rev Food Sci 40, 481515.Google Scholar
194. Leidy, HJ, Clifton, PM, Astrup, A, et al. (2015) The role of protein in weight loss and maintenance. Am J Clin Nutr 101, 1320s1329s.Google Scholar
195. Paddon-Jones, D & Leidy, H (2014) Dietary protein and muscle in older persons. Curr Opin Clin Nutr 17, 511.Google Scholar
196. Tournier, A & Louis-Sylvestre, J (1991) Effect of the physical state of a food on subsequent intake in human subjects. Appetite 16, 1724.Google Scholar
197. Hulshof, T, Degraaf, C & Weststrate, JA (1993) The effects of preloads varying in physical state and fat content on satiety and energy intake. Appetite 21, 273286.Google Scholar
198. Porrini, M, Crovetti, R, Riso, P, et al. (1995) Effects of physical and chemical characteristics of food on specific and general satiety. Physiol Behav 57, 461468.Google Scholar
199. Stull, AJ, Apolzan, JW, Thalacker-Mercer, AE, et al. (2008) Liquid and solid meal replacement products differentially affect postprandial appetite and food intake in older adults. J Am Diet Assoc 108, 12261230.Google Scholar
200. Almiron-Roig, E, Flores, SY & Drewnowski, A (2004) No difference in satiety or in subsequent energy intakes between a beverage and a solid food. Physiol Behav 82, 671677.Google Scholar
201. Mattes, RD (2006) Beverages and positive energy balance: the menace is the medium. Int J Obes 30, S60S65.Google Scholar
202. Mourao, DM, Bressan, J, Campbell, WW, et al. (2007) Effects of food form on appetite and energy intake in lean and obese young adults. Int J Obes 31, 16881695.Google Scholar
203. Lyly, M, Liukkonen, KH, Salmenkallio-Marttila, M, et al. (2009) Fibre in beverages can enhance perceived satiety. Eur J Nutr 48, 251258.Google Scholar
204. Frestedt, JL, Young, LR & Bell, M (2012) Meal replacement beverage twice a day in overweight and obese adults (MDRC2012-001). Curr Nutr Food Sci 8, 320329.Google Scholar
205. Cassady, BA, Considine, RV & Mattes, RD (2012) Beverage consumption, appetite, and energy intake: what did you expect? Am J Clin Nutr 95, 587593.Google Scholar
206. Mattes, R (2005) Soup and satiety. Physiol Behav 83, 739747.Google Scholar
207. Zhu, Y & Hollis, JH (2013) Soup consumption is associated with a reduced risk of overweight and obesity but not metabolic syndrome in US adults: NHANES 2003–2006. PLOS ONE 8, e75630.Google Scholar
208. Marciani, L, Gowland, PA, Spiller, RC, et al. (2001) Effect of meal viscosity and nutrients on satiety, intragastric dilution, and emptying assessed by MRI. Am J Physiol Gastrointest Liver Physiol 280, G1227G1233.Google Scholar
209. Zhu, Y, Hsu, WH & Hollis, JH (2013) The impact of food viscosity on eating rate, subjective appetite, glycemic response and gastric emptying rate. PLOS ONE 8, e67482.Google Scholar
210. Marciani, L, Gowland, PA, Spiller, RC, et al. (2000) Gastric response to increased meal viscosity assessed by echo-planar magnetic resonance imaging in humans. J Nutr 130, 122127.Google Scholar
211. Flood-Obbagy, JE & Rolls, BJ (2009) The effect of fruit in different forms on energy intake and satiety at a meal. Appetite 52, 416422.Google Scholar
212. Ellis, PR, Kendall, CWC, Ren, YL, et al. (2004) Role of cell walls in the bioaccessibility of lipids in almond seeds. Am J Clin Nutr 80, 604613.Google Scholar
213. Ohkuma, T, Hirakawa, Y, Nakamura, U, et al. (2015) Association between eating rate and obesity: a systematic review and meta-analysis. Int J Obes 39, 15891596.Google Scholar
214. Li, J, Zhang, N, Hu, L, et al. (2011) Improvement in chewing activity reduces energy intake in one meal and modulates plasma gut hormone concentrations in obese and lean young Chinese men. Am J Clin Nutr 94, 709716.Google Scholar
215. Pellmar, TC (2000) Barriers to interdisciplinary research and training. In Bridging Disciplines in the Brain, Behavioral, and Clinical Sciences, pp. 41–53 [TC Pellmar and L Eisenberg, editors]. Washington, DC: National Academies Press.Google Scholar
216. Parsons, TD (2015) Virtual reality for enhanced ecological validity and experimental control in the clinical, affective and social neurosciences. Front Hum Neurosci 9, 660.Google Scholar
217. Ng, SW, Poti, JM & Popkin, BM (2016) Trends in racial/ethnic and income disparities in foods and beverages consumed and purchased from stores among US households with children, 2000–2013. Am J Clin Nutr 104, 750759.Google Scholar
218. Westhoek, H, Lesschen, JP, Rood, T, et al. (2014) Food choices, health and environment: effects of cutting Europe’s meat and dairy intake. Global Environ Chang 26, 196205.Google Scholar
219. Pimentel, D & Pimentel, M (2003) Sustainability of meat-based and plant-based diets and the environment. Am J Clin Nutr 78, 3 Suppl., 660S663S.Google Scholar