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Does prolonged chewing reduce food intake? Fletcherism revisited
Appetite 57 (2011) 295–298
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Does prolonged chewing reduce food intake? Fletcherism revisited Hendrik Jan Smit a,*, E. Katherine Kemsley b, Henri S. Tapp b, C. Jeya K. Henry a a b
Functional Food Centre, School of Life Sciences, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, United Kingdom Bioinformatics and Statistics Group, Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, United Kingdom
A R T I C L E I N F O
A B S T R A C T
Article history: Received 17 August 2010 Received in revised form 25 January 2011 Accepted 4 February 2011 Available online 21 February 2011
Horace Fletcher (1849–1919) spread his doctrine to chew each mouthful thoroughly in order to prevent gaining weight. We sought to test this idea by manipulating chewing instructions whilst using electromyography to monitor chewing behaviour. Comparing 35 with 10 chews per mouthful, we showed that higher chewing counts reduced food intake despite increasing chewing speed, and despite doubling meal duration for achieving a subjective reference point for feeling ‘comfortably full’. Although limited by a low sample size, our preliminary findings confirm Mr. Fletcher’s doctrine, and provide a basis for further research in this area. Outcomes and implications are discussed. ß 2011 Elsevier Ltd. All rights reserved.
Keywords: Mastication Energy intake Electromyography Satiation
Introduction In view of the obesity epidemic, the development of safe and effective strategies to combat this condition is of principal importance. For a long time, obese people were thought to exhibit a different ‘eating style’ to normal weight people as defined by what is also referred to as the ‘microstructure of eating’ (i.e., bite size, ingestion rate, number of chews per bite, chewing speed, etc.), an idea possibly initiated by Ferster, Nurnberger, & Levitt (1962), who also argued that by reducing ingestion rate and taking smaller bites, obese people would reduce their food intake. The idea of ‘eating slowly’ has consequently been implemented by eating behaviour therapists (e.g., see Stuart, 1967). Subsequent studies have either confirmed such an eating style by comparing obese and normal weight people (e.g., Maruyama et al., 2008; Otsuka et al., 2006; Sasaki, Katagiri, Tsuji, Shimoda, & Amano, 2003; Okuma, Yoshimatsu, Sakata, & Adachi, 2000; Spiegel, 2000 – 3rd study; Marston, London, Cohen, & Cooper, 1977; Gaul, Craighead, & Mahoney, 1975), or not found any differences (e.g., Spiegel et al., 1993; Kaplan, 1980). Nevertheless, the general consensus in the literature appears to be that lower ingestion rates reduce energy intake (e.g., Andrade, Greene, & Melanson, 2008; Kaplan, 1980; Spiegel & Jordan, 1978) and weight loss (Spiegel, Wadden, & Foster, 1991), and that higher ingestion rates are associated with higher BMIs and/or obesity (e.g., Maruyama et al., 2008; Otsuka et al., 2006; Sasaki et al., 2003; Okuma et al., 2000; Marston et al., 1977. Indeed, because peak plasma times for various satiety hormones
* Corresponding author. E-mail address: [email protected] (H.J. Smit). 0195-6663/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2011.02.003
have been reported in the literature (e.g., CCK + 30 min: Liddle et al., 1985; in: Moran & Kinzig, 2004); insulin +30 min: Parra, Martinez de Morentin, & Martinez, 2005; GLP-1 + 60 min: Blundell & Naslund, 1999), theoretical support for the idea that ‘eating faster increases energy intake’ is in part provided by the notion that if satiety signals have certain post-ingestion peak plasma times, ‘fast’ eaters will logically have consumed more energy compared to ‘slow’ eaters when satiety occurs. Note that the majority of behavioural studies highlighted above have focused on ingestion rate as the main aspect of eating behaviour. However, a variety of individual factors can affect food intake, some of which have been identified as likely to affect chewing parameters, including chews per mouthful (CPM). Factors affecting food intake include: portion size (Rolls, Roe, & Meengs, 2004); palatability (de Castro, Bellisle, Dalix, & Pearcey, 2000); viscosity (de Wijk, Zijlstra, Mars, de Graaf, & Prinz, 2008); attention to the food consumed (Weijzen, Liem, Zandstra, & de Graaf, 2008); distraction from the food consumed (Bellisle & Dalix, 2001); and memory for previous meal (Higgs, 2002). Factors likely to affect chewing parameters include palatability (Bellisle, Guy-Grand, & Le Magnen, 2000) and bite size (Spiegel, 2000; Spiegel, Kaplan, Tomassini, & Stellar, 1993). However, the literature appears to lack reports of the effects of chewing rates on food intake, especially where CPM is manipulated, whilst ingestion rate and CPM may relate to energy intake differentially. Although the general idea that chewing food thoroughly is good for one’s health is – in itself – nothing new, it is nowadays often considered an ‘‘old-wives’ tale’’. Nevertheless, in order to preclude overweight and save money in the process, Horace Fletcher (1849– 1919, also named ‘‘The great masticator’’) advocated chewing food 50–100 times until it turned to liquid or until it ‘swallows itself’: an
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idea borrowed from the British Prime Minister William Gladstone (who advised 32 chews per mouthful – once for every tooth) and the English physician William Kitchener (30–40 chews per mouthful when eating meat) at the time. Fletcher’s popularity at the time even produced a colloquialism, as expressed by: ‘‘Don’t gobble your food, but ‘Fletcherize’’’ (Christen & Christen, 1997). Our aim was to test if Mr Fletcher’s doctrine would result in what it claimed to do, i.e., reduce food intake. We therefore performed a pilot study to provide a basis for future work in this area, measuring food intake, meal duration and CPM, following different chewing instructions in a standardised laboratory setting. Methods Participants Thirteen volunteers (5 males and 8 females) were recruited from Oxford Brookes University students and staff, and a participant database. Recruitment criteria were: 20–50 years of age and generally healthy; fluent English speaking; full dental record; not habitually skipping any meals; BMI = 18.5–25 or 30– 40; not actively dieting; not exercising any more than moderate physical activity; not suffering from sleep complaints, food allergies or food intolerances. Ethical approval was obtained from the University Research Ethics Committee, and participants provided their fully informed consent prior to taking part in the study. They were told the study was ‘‘An investigation into the effects of chewing on mood’’ in an attempt to avoid awareness of the actual purpose of the study, as this could have induced attitudedriven behavioural change, thereby creating a potentially major confounding factor. Experimental design Effects of the number of chews per mouthful (CPM) on food intake, and differential aspects of chewing behaviour between normal weight and obese people, were investigated by three experimental conditions applied to the consumption of a standard lunch in the laboratory: ad libitum chewing (session 1), 10 CPM and 35 CPM (session 2 and 3 in randomised counterbalanced order). Food served for the test lunches consisted of 250 g penne pasta, cooked for exactly 10 min in 5l water containing 5 g salt, then drained to make approx. 500 g cooked pasta, to which 50 g pesto was added. Estimated serving size was 530 g (pesto sticks to inside of pan) containing 4.3 MJ and equating to approximately 820 kJ/ 100 g. Participants were asked to eat until they felt ‘comfortably full’ whilst taking two pieces of pasta per mouthful. CPM and meal duration were measured by electromyography (EMG) using a Biometrics DataLog P3X8. Food/energy intake was measured using a Sartorius B4100 precision balance, whilst measures of appetite and mood were collected immediately before and immediately after each meal using paper-based visual-analogue scales (pVAS). Procedure Volunteers arrived at the laboratory for a mid-afternoon familiarisation session, during which all procedures were explained, and pictures were shown of the meal as served, and of a person with the EMG electrodes attached. They were explained the importance of keeping sleep, diet and exercise levels similar between testing days in order to minimise confounding factors, upon which they were asked to fill in a questionnaire recording date of birth, gender, dieting behaviour, sleeping patterns, breakfast meal composition and mode of transport to get to the laboratory. They were asked to repeat the recorded behaviours for
each visit to the laboratory. They then returned for three test lunches, each separated by 47 h to one week. Test lunches were scheduled for either 12:15 or 13:15 h for each participant. Upon arrival, surface-mounted bipolar electrodes (Biometrics SX230 pre-amplifiers, connected to the DataLog) were taped (Biometrics sensor tape) to the skin on both left and right masseter muscles, and the anterior portion of both left and right temporalis muscles. Additionally, a Ground Strap (Biometrics R206) was connected to the inner side of the wrist of the non-feeding hand. Next, whilst the pVAS were filled out, food was prepared for serving. Just before serving, and by means of standardising some aspects of food intake, participants were reminded to: (1) ‘‘Take 2 bits of pasta on your fork at a time’’ (standardised bite size); (2) ‘‘Swallow properly before starting next mouthful’’ (no ‘leapfrogging’); (3) ‘‘Lean forward above plate to avoid having to swallow during chewing’’ (assist with previous instruction); (4) ‘‘Eat until you feel ‘comfortably full’. You need to decide where that level of fullness is, how it feels, and remember it for next time’’ (to compare meal sizes and durations between 10 and 35 CPM); (5) ‘‘Don’t feel obliged to finish your plate’’. Next, food was brought to the participant on a large pre-warmed plate with napkin and fork, whilst they received one condition-specific instruction, either: ‘‘Chew your food as you normally would’’ (session 1); ‘‘Chew each mouthful 10 times’’ or ‘‘Chew each mouthful 35 times’’ (session 2 and 3 in randomized counterbalanced order). All eating instructions were displayed before the participant during the meal, with clear emphasis on the condition-specific instruction. Across the test lunches, water consumption was kept constant, and was determined by the amount of water consumed from a full glass served at the start of session 1. During this first session, participants were asked to drink whenever they felt they wanted to do so, but only in small sips. In subsequent sessions, participants were again asked to drink their water in small sips, but to finish the glass before reaching the end of their meal. At the end of the final testing session, participants were asked by questionnaire if, during one of the testing days, they thought they ate more or less, or felt more or less satisfied after the meal, compared to the other days. Additionally, they were asked: ‘‘Do you think you could make 35 chews per mouthful a general habit?’’ as both closed-question (‘‘No, I would not be able to do that’’; ‘‘I guess I could if I wanted to’’; or ‘‘Yes, I don’t think that would be a problem’’) and as an open-ended question (‘‘please explain below’’). Finally, any other comments were invited (‘‘Is there anything else you’d like to tell us regarding the study?’’). Analysis EMG data were exported from the Biometrics DataLog software as ‘.wav’ files and analysed for CPM and meal duration using Matlab (The Mathworks, Inc), for which scripts were developed inhouse (Kemsley, Sprunt, Defernez, & Smith, 2002). All statistical analyses were performed using the statistical software SPSS. Analyses employed were paired samples, independent samples Student t-tests and GLM repeated measures. Additionally, equality in gender ratios between the two weight groups, and uniformity for responses to the willingness to make 35 CPM habitual chewing behaviour were investigated using a chi-square test. The degrees of freedom associated with statistical tests are presented in parentheses. Means are reported (S.E.). Results Participants Eleven participants (4 males and 7 females) finished the study. They comprised 6 normal weight (2 M, 4F; BMI = 22.0 2.0; range:
H.J. Smit et al. / Appetite 57 (2011) 295–298
20.7–25.2) and 5 obese (2 M, 3F; BMI = 33.6 2.1; range: 30.7–35.6) participants. Main test measures Participants ate 12% less when chewing 35 CPM compared to 10 CPM (313 17 g vs. 358 19; t(10) = 3.43; P = 0.006), as also indicated by the number of mouthfuls (61.2 3.8 vs. 69.6 4.0; t(10) = 3.18; P = 0.010) and ingestion rate (11.1 0.5 g/min vs. 23.7 0.8; t(10) = 14.01; P < 0.001). Interestingly, instructing participants to chew 35 CPM resulted in faster chewing (35 CPM: 1.10 0.03 chews/sec vs. 10 CPM: 0.78 0.03; t(10) = 7.83; P < 0.001). Despite this, 35 CPM resulted in a substantially longer meal duration (35 CPM: 28.6 1.8 min vs. 10 CPM: 15.1 0.6; t(10) = 9.68; P < 0.001). Postmeal fullness ratings did not differ between the two conditions (35 CPM: 72 7 mm vs. 10 CPM: 71 8), as was also confirmed by poststudy participant feedback: Despite stronger efforts required to perform 35 CPM, 82% (9 out of 11) of participants claimed they manage to reach the same level of fullness at the end of each test meal. When comparing habitual chewing data from normal weight participants with obese participants we found that, whilst habitual CPM did not differ between the two groups (obese: 15.4 1.0 vs. normal weight: 14.9 3.6; F(1,9) = 0.02; P = 0.893), the obese group ate 32% less than the normal weight group (240 25 g vs. 355 29 respectively; F(1,9) = 8.33; P = 0.018), also reflected in a marginally lower ingestion rate in the same group (15.8 1.6 g/min vs. 21.3 2.5 respectively; F(1,9) = 3.09; P = 0.112). The latter findings are substantiated by marginally lower pre-meal ratings for hunger across conditions in our obese participants (obese: 51.5 5.7 vs. normal weight: 67.8 5.2; F(1,9) = 4.42; P = 0.065) (see Table 1). Discussion Fletcher’s doctrine has been confirmed in this pilot study, as more chews per mouthful reduced food/energy intake despite prolonged meal duration. The results of this study tie in with research investigating factors that affect ingestion rate, which show that slower eating reduces food or energy intake (e.g., Zandian, Ioakimidis, Bergh, Brodin, & So¨dersten, 2009; Andrade et al., 2008; Spiegel et al., 1993; Kaplan, 1980; Spiegel & Jordan, 1978), whilst higher ingestion rates are predictive of higher BMIs in some studies (e.g., Sasaki et al., 2003; Otsuka et al., 2006) but not in others (e.g., our data shown here; Spiegel et al., 1993; Kaplan, 1980). Moreover, a public health message that promotes ‘slower eating’ may not be conceptually clear, or combine a variety of instructions, and may therefore more difficult to implement in motivating behavioural change, whereas a message instructing on how to chew may be less problematic, although further research involving behavioural change may be able to confirm this. Other findings in support of our results include acute reductions in hunger following higher chewing rates (40 CPM vs. 10 or 25 CPM: Cassady, Hollis, Fulford, Considine, & Mattes, 2009). Although
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these findings predict the observed reduction in food intake for the higher CPM condition in the current study, they do not explain the near-100% increase in meal duration time we recorded between 10 and 35 CPM. Moreover, although a higher CPM may intuitively predict an increased meal duration, peak plasma times for various satiety hormones do not suggest a delayed satiety response (see introduction). Nevertheless, a reduced ingestion rate produces a stronger satiety response (Kokkinos et al., 2010), as do higher chewing counts (Cassady et al., 2009), although a significant difference in the fastest responding hormone reported (GLP-1) did not occur until 60 min (Kokkinos et al., 2010) or even 3 h (Cassady et al., 2009), after the meal. However, neither publication assessed plasma CCK levels. Additionally, the food consumed in our study was pasta, which, broken down into sugars, predicts a blunted plasma glucose curve with a blunted insulin response curve at decreased CPM (see below). Note that this was confirmed by Kokkinos et al. (2010) but not by (Cassady et al., 2009), although both studies used test foods (ice cream and almonds respectively) with very different macronutrient profiles to our pasta. Another possible cause for the difference in meal durations between the two conditions in our study could be a faster satiety response due to stomach distension following increased ingestion rates in the 10 CPM condition, whilst this may have been further amplified by slower gastric emptying due to larger undigested food particles after mastication (e.g., Vincent et al., 1995; Urbain et al., 1989). Note that Spiegel et al. (1993) found that lower ingestion rates were offset by longer meal durations, showing no difference in meal size between obese and normal weight people. In our study, food intake was reduced by 12% at 35 CPM compared to 10 CPM, and was therefore only partially offset by the 1.4 higher chewing speeds and 1.9 longer meal duration in the 35 CPM condition. It does, however, emphasise the complexity of the topic, and the need to simultaneously measure several – often related – variables within the microstructure of eating. Despite the fact that our low sample size may not allow for reliable comparisons between normal weight and obese participants, we need to highlight one aspect. Despite the longstanding assumption that obese people eat faster than non-obese people, the obese people in our study did not show a higher ingestion rate compared to the normal weight participants. Instead, they showed a 32% lower food/energy intake compared to the normal weight participants. Although marginally higher pre-meal fullness ratings may explain this difference, this was not supported by ratings for, for example, hunger. Naturally, it is feasible that people with disturbed eating behaviour will change this behaviour to conform to expected social norms when this behaviour is under investigation in a laboratory setting. Because we did not test for this, it is unclear if our obese participants showed signs of abnormal eating behaviour, e.g., on one or more factors of the Dutch Eating Behaviour Questionnaire. Nevertheless, it may be that contingency awareness provides a complicating factor when comparing obese with normal weight participants in an experimental setting.
Table 1 Means (standard error) of the main data collected. Measure
Mastication/experimental 10 CPM
Food intake (g) Number of mouthfuls Meal duration (min) Ingestion rate (g/min) CPM Chewing speed (s 1)
358 19 69.6 4.0 15.1 0.6 23.7 0.8 10.5 0.4 0.78 0.03
Weight group/habitual 35 CPM **
313 17 61.2 3.8** 28.6 1.8*** 11.1 0.5*** 31.4 1.3*** 1.10 0.03***
Normal weight
Obese
355 29 67.3 4.3 17.5 1.8 21.3 2.5 14.9 3.6 0.88 0.12
240 25* 48.4 5.3* 15.2 1.0 15.8 1.6 15.4 1.0 0.79 0.07
Notes: Significant differences were tested either within-group (‘Mastication’, columns 2 and 3) or between-group (‘Weight group’, columns 4 and 5). CPM: chews per mouthful. * p < .05., **p < .01., ***p < .001.
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When asked if they could make the 35 CPM a habit, most participants replied negatively. Although one participant’s response that she was ‘‘chewing faster to get to the next mouthful’’, as also indicative of faster chewing (time/chew) in the 35 CPM condition compared to 10 CPM, others explained that this was because in the 35 CPM condition ‘‘there was not much to chew on towards the end’’. Moreover, aversion to the 35 CPM instructions was only expressed by one participant. Because post-test levels of hunger and fullness did not differ between the two conditions, a possible aversion to the 35 CPM condition can therefore not explain the reduced energy intake in this condition. It may, however, question the potential efficacy of a ‘Public Health Message’ that aims to implement behavioural change regarding eating behaviour. As much of our behaviour is ‘entrenched’ at an early age, parents of young children may constitute the most effective target group. Sceptics of health benefits of Mr Fletcher’s doctrine may wish to uphold these findings: When chewing food more thoroughly, glycaemic response peaks and insulin response peaks are reportedly higher (Read et al., 1986), and/or have areas under the curve visibly larger. Moreover, less thoroughly chewed food results in higher energy losses through the stool (Cassady et al., 2009). Although this argues against a notion that thorough chewing constitutes a healthier behaviour compared to less thorough chewing (and therefore slower eating), we have clearly shown that more thorough chewing results in reduced energy intake, and this should more than compensate for these seemingly controversial counter-effects. Although prolonged chewing as advocated by Mr Fletcher was already in use as part of an array of measures to reduce ingestion rate almost 50 years ago (Ferster et al., 1962), its efficacy has – to our knowledge – not previously been tested directly. Nevertheless, our findings need to be validated and be clarified in terms of biomarkers (e.g., satiety hormone levels) and prolonged chewing of ordinary foods. Additionally, the efficacy of prolonged chewing compared to other measures employed to reduce ingestion rate (e.g., smaller bite size, pauses between mouthfuls) need to be investigated, especially in terms of behavioural change. Moreover, the methodology could be improved by employing the Universal Eating Monitor (Kissileff, Klingsberg, & Van Itallie, 1980), allowing for analysis of cumulative intake data (see Dovey, Clark-Carter, Boyland, & Halford, 2009 for a review) and the identification of linear and non-linear eaters. Additionally, a more reliable identification of the point of swallowing in the EMG data should be implemented, maybe inspired by the small balloon used in Bellisle et al. (2000). To advance our understanding in this field is of paramount importance, and this paper aims to initiate a contribution to this area of research by focusing on the role of mastication in food intake rather than on food intake per se. Concluding, we have confirmed Mr Fletcher’s doctrine by showing that higher chewing counts per bite or mouthful reduce food intake despite faster chewing and longer meal duration. This emphasises the legitimacy of prolonged chewing as part of a dietary strategy for weight loss, although further research is needed to validate the findings and compare this technique with other methods used to reduce ingestion rate. References Andrade, A. M., Greene, G. W., & Melanson, K. J. (2008). Eating slowly led to decreases in energy intake within meals in healthy women. Journal of the American Dietetic Association, 108, 1186–1191. Bellisle, F., & Dalix, A.-M. (2001). Cognitive restraint can be offset by distraction, leading to increased meal intake in women. The American Journal of Clinical Nutrition, 74, 197–200. Bellisle, F., Guy-Grand, B., & Le Magnen, J. (2000). Chewing and swallowing as indices of the stimulation to eat during meals in humans. Effects revealed by the edogram
method and video recordings. Neuroscience & Biobehavioral Reviews, 24, 223–228. Blundell, J. E., & Naslund, E. (1999). Glucagon-like peptide-1, satiety and appetite control. The British Journal of Nutrition, 81, 259–260. Cassady, B. A., Hollis, J. H., Fulford, A. D., Considine, R. V., & Mattes, R. D. (2009). Mastication of almonds. Effects of lipid bioaccessibility, appetite, and hormone response. American Journal of Clinical Nutrition, 89, 794–800. de Castro, J. M., Bellisle, F., Dalix, A.-M., & Pearcey, S. M. (2000). Palatability and intake relationships in free-living humans. Characterization and independence of influence in North Americans. Physiology and Behavior, 70, 343–350. Christen, A. G., & Christen, J. A. (1997). Horace Fletcher (1849–1919). ‘‘The Great Masticator’’. Journal of the History of Dentistry, 45, 95–100. Dovey, T. M, Clark-Carter, D., Boyland, E. J., & Halford, J. C. (2009). A guide to analysing Universal Eating Monitor data. Assessing the impact of different analysis techniques. Physiology & Behavior, 96, 78–84. Ferster, C. B., Nurnberger, J. I., & Levitt, E. E. (1962). The control of eating. Journal of Mathematics, 1, 87–109. Gaul, D. J., Craighead, W. E., & Mahoney, M. J. (1975). Relationship between eating rates and obesity. Journal of Consulting and Clinical Psychology, 43, 123–125. Higgs, S. (2002). Memory for recent eating and its influence on subsequent food intake. Appetite, 39, 159–166. Kaplan, D. L. (1980). Eating style of obese and nonobese males. Psychosomatic Medicine, 42, 529–538. Kemsley, E. K., Sprunt, J. C., Defernez, M., & Smith, A. C. (2002). Multivariate analysis of electromyographic (EMG) frequency spectra to characterise mastication. Journal of Texture Studies, 33, 15–34. Kissileff, H. R., Klingsberg, G., & Van Itallie, T. B. (1980). Universal eating monitor for continuous recording of solid or liquid consumption in man. American Journal of Physiology, 238, R14–R22. Kokkinos, A., le Roux, C. W., Alexiadou, K., Tentolouris, N., Vincent, R. P., Kyriaki, D., et al. (2010). Eating slowly increases the postprandial response of the anorexigenic gut hormones, peptide YY and glucagon-like peptide-1. The Journal of Clinical Endocrinology & Metabolism, 95, 333–337. Marston, A. R., London, P., Cohen, N., & Cooper, L. M. (1977). In vivo observation of the eating behavior of obese and nonobese subjects. Journal of Consulting and Clinical Psychology, 45, 335–336. Maruyama, K., Sato, S., Ohira, T., Maeda, K., Noda, H., Kubota, Y., et al. (2008). The joint impact on being overweight of self reported behaviours of eating quickly and eating until full. Cross sectional survey. British Medical Journal, 337, a2002. Moran, T. H., & Kinzig, K. P. (2004). Gastrointestinal satiety signals II. Cholecystokinin. The American Journal of Physiology. Gastrointestinal and Liver Physiology, 286, G183–188. Okuma, K., Yoshimatsu, H., Sakata, T., & Adachi, K. (2000). The effect of chewing chart recording in the treatment of obesity. Japanese Journal of Psychosomatic Medicine, 40, 247–253. Otsuka, R., Tamakoshi, K., Yatsuya, H., Murata, C., Sekiya, A., Wada, K., et al. (2006). Eating fast leads to obesity. Findings based on self-administered questionnaires among middle-aged Japanese men and women. Journal of Epidemiology, 16, 117–124. Parra, M. D., Martinez de Morentin, B. E., & Martinez, J. A. (2005). Postprandial insulin response and mitochondrial oxidation in obese men nutritionally treated to lose weight. European Journal of Clinical Nutrition, 59, 334–340. Read, N. W., Welch, I. M., Austen, C. J., Barnish, C., Bartlett, C. E., Baxter, A. J., et al. (1986). Swallowing food without chewing; a simple way to reduce postprandial glycaemia. British Journal of Nutrition, 55, 43–47. Rolls, B. J., Roe, L. S., & Meengs, J. S. (2004). Salad and satiety. Energy density and portion size of a first-course salad affect energy intake at lunch. Journal of the American Dietetic Association, 104, 1570–1576. Sasaki, S., Katagiri, A., Tsuji, T., Shimoda, T., & Amano, K. (2003). Self-reported rate of eating correlates with body mass index in 18-y-old Japanese women. International Journal of Obesity, 27, 1405–1410. Spiegel, T. A. (2000). Rate of intake, bites, and chews. The interpretation of lean–obese differences. Neuroscience and Biobehavioral Reviews, 24, 229–237. Spiegel, T. A., Kaplan, J. M., Tomassini, A., & Stellar, E. (1993). Bite size, ingestion rate, and meal size in lean and obese women. Appetite, 21, 131–145. Spiegel, T. A., Wadden, T. A., & Foster, G. D. (1991). Objetive measurement of eating rate during behavioral treatment of obesity. Behaviour Therapy, 22, 61–67. Spiegel, T. A., & Jordan, H. A. (1978). Effects of simultaneous oral-intragastric ingestion on meal patterns and satiety in humans. Journal of Comparative and Physiological Psychology, 92, 133–141. Stuart, R. B. (1967). Behavioral control of eating. Behavior Research and Therapy, 5, 357–365. Urbain, J. L. C., Siegel, J. A., Charkes, N. D., Maurer, A. H., Malmud, L. S., & Fisher, R. S. (1989). The two-component stomach. Effects of meal particle size on fundal and antral emptying. European Journal of Nuclear Medicine, 15, 254–259. Vincent, R., Roberts, A., Frier, M., Perkins, A. C., MacDonald, I. A., & Spiller, R. C. (1995). Effect of bran particle size on gastric emptying and small bowel transit in humans. A scintigraphic study. Gut, 37, 216–219. Weijzen, P. L., Liem, D. G., Zandstra, E. H., & de Graaf, C. (2008). Sensory specific satiety and intake. The difference between nibble- and bar-size snacks. Appetite, 50, 435–442. de Wijk, R. A., Zijlstra, N., Mars, M., de Graaf, C., & Prinz, J. F. (2008). The effects of food viscosity on bite size, bite effort and food intake. Physiology and Behavior, 95, 527–532. Zandian, M., Ioakimidis, I., Bergh, C., Brodin, U., & So¨dersten, P. (2009). Decelerated and linear eaters. Effect of eating rate on food intake and satiety. Physiology and Behavior, 96, 270–275.
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Appetite journal homepage: www.elsevier.com/locate/appet
Short communication
Does prolonged chewing reduce food intake? Fletcherism revisited Hendrik Jan Smit a,*, E. Katherine Kemsley b, Henri S. Tapp b, C. Jeya K. Henry a a b
Functional Food Centre, School of Life Sciences, Oxford Brookes University, Gipsy Lane, Headington, Oxford OX3 0BP, United Kingdom Bioinformatics and Statistics Group, Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, United Kingdom
A R T I C L E I N F O
A B S T R A C T
Article history: Received 17 August 2010 Received in revised form 25 January 2011 Accepted 4 February 2011 Available online 21 February 2011
Horace Fletcher (1849–1919) spread his doctrine to chew each mouthful thoroughly in order to prevent gaining weight. We sought to test this idea by manipulating chewing instructions whilst using electromyography to monitor chewing behaviour. Comparing 35 with 10 chews per mouthful, we showed that higher chewing counts reduced food intake despite increasing chewing speed, and despite doubling meal duration for achieving a subjective reference point for feeling ‘comfortably full’. Although limited by a low sample size, our preliminary findings confirm Mr. Fletcher’s doctrine, and provide a basis for further research in this area. Outcomes and implications are discussed. ß 2011 Elsevier Ltd. All rights reserved.
Keywords: Mastication Energy intake Electromyography Satiation
Introduction In view of the obesity epidemic, the development of safe and effective strategies to combat this condition is of principal importance. For a long time, obese people were thought to exhibit a different ‘eating style’ to normal weight people as defined by what is also referred to as the ‘microstructure of eating’ (i.e., bite size, ingestion rate, number of chews per bite, chewing speed, etc.), an idea possibly initiated by Ferster, Nurnberger, & Levitt (1962), who also argued that by reducing ingestion rate and taking smaller bites, obese people would reduce their food intake. The idea of ‘eating slowly’ has consequently been implemented by eating behaviour therapists (e.g., see Stuart, 1967). Subsequent studies have either confirmed such an eating style by comparing obese and normal weight people (e.g., Maruyama et al., 2008; Otsuka et al., 2006; Sasaki, Katagiri, Tsuji, Shimoda, & Amano, 2003; Okuma, Yoshimatsu, Sakata, & Adachi, 2000; Spiegel, 2000 – 3rd study; Marston, London, Cohen, & Cooper, 1977; Gaul, Craighead, & Mahoney, 1975), or not found any differences (e.g., Spiegel et al., 1993; Kaplan, 1980). Nevertheless, the general consensus in the literature appears to be that lower ingestion rates reduce energy intake (e.g., Andrade, Greene, & Melanson, 2008; Kaplan, 1980; Spiegel & Jordan, 1978) and weight loss (Spiegel, Wadden, & Foster, 1991), and that higher ingestion rates are associated with higher BMIs and/or obesity (e.g., Maruyama et al., 2008; Otsuka et al., 2006; Sasaki et al., 2003; Okuma et al., 2000; Marston et al., 1977. Indeed, because peak plasma times for various satiety hormones
* Corresponding author. E-mail address: [email protected] (H.J. Smit). 0195-6663/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.appet.2011.02.003
have been reported in the literature (e.g., CCK + 30 min: Liddle et al., 1985; in: Moran & Kinzig, 2004); insulin +30 min: Parra, Martinez de Morentin, & Martinez, 2005; GLP-1 + 60 min: Blundell & Naslund, 1999), theoretical support for the idea that ‘eating faster increases energy intake’ is in part provided by the notion that if satiety signals have certain post-ingestion peak plasma times, ‘fast’ eaters will logically have consumed more energy compared to ‘slow’ eaters when satiety occurs. Note that the majority of behavioural studies highlighted above have focused on ingestion rate as the main aspect of eating behaviour. However, a variety of individual factors can affect food intake, some of which have been identified as likely to affect chewing parameters, including chews per mouthful (CPM). Factors affecting food intake include: portion size (Rolls, Roe, & Meengs, 2004); palatability (de Castro, Bellisle, Dalix, & Pearcey, 2000); viscosity (de Wijk, Zijlstra, Mars, de Graaf, & Prinz, 2008); attention to the food consumed (Weijzen, Liem, Zandstra, & de Graaf, 2008); distraction from the food consumed (Bellisle & Dalix, 2001); and memory for previous meal (Higgs, 2002). Factors likely to affect chewing parameters include palatability (Bellisle, Guy-Grand, & Le Magnen, 2000) and bite size (Spiegel, 2000; Spiegel, Kaplan, Tomassini, & Stellar, 1993). However, the literature appears to lack reports of the effects of chewing rates on food intake, especially where CPM is manipulated, whilst ingestion rate and CPM may relate to energy intake differentially. Although the general idea that chewing food thoroughly is good for one’s health is – in itself – nothing new, it is nowadays often considered an ‘‘old-wives’ tale’’. Nevertheless, in order to preclude overweight and save money in the process, Horace Fletcher (1849– 1919, also named ‘‘The great masticator’’) advocated chewing food 50–100 times until it turned to liquid or until it ‘swallows itself’: an
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idea borrowed from the British Prime Minister William Gladstone (who advised 32 chews per mouthful – once for every tooth) and the English physician William Kitchener (30–40 chews per mouthful when eating meat) at the time. Fletcher’s popularity at the time even produced a colloquialism, as expressed by: ‘‘Don’t gobble your food, but ‘Fletcherize’’’ (Christen & Christen, 1997). Our aim was to test if Mr Fletcher’s doctrine would result in what it claimed to do, i.e., reduce food intake. We therefore performed a pilot study to provide a basis for future work in this area, measuring food intake, meal duration and CPM, following different chewing instructions in a standardised laboratory setting. Methods Participants Thirteen volunteers (5 males and 8 females) were recruited from Oxford Brookes University students and staff, and a participant database. Recruitment criteria were: 20–50 years of age and generally healthy; fluent English speaking; full dental record; not habitually skipping any meals; BMI = 18.5–25 or 30– 40; not actively dieting; not exercising any more than moderate physical activity; not suffering from sleep complaints, food allergies or food intolerances. Ethical approval was obtained from the University Research Ethics Committee, and participants provided their fully informed consent prior to taking part in the study. They were told the study was ‘‘An investigation into the effects of chewing on mood’’ in an attempt to avoid awareness of the actual purpose of the study, as this could have induced attitudedriven behavioural change, thereby creating a potentially major confounding factor. Experimental design Effects of the number of chews per mouthful (CPM) on food intake, and differential aspects of chewing behaviour between normal weight and obese people, were investigated by three experimental conditions applied to the consumption of a standard lunch in the laboratory: ad libitum chewing (session 1), 10 CPM and 35 CPM (session 2 and 3 in randomised counterbalanced order). Food served for the test lunches consisted of 250 g penne pasta, cooked for exactly 10 min in 5l water containing 5 g salt, then drained to make approx. 500 g cooked pasta, to which 50 g pesto was added. Estimated serving size was 530 g (pesto sticks to inside of pan) containing 4.3 MJ and equating to approximately 820 kJ/ 100 g. Participants were asked to eat until they felt ‘comfortably full’ whilst taking two pieces of pasta per mouthful. CPM and meal duration were measured by electromyography (EMG) using a Biometrics DataLog P3X8. Food/energy intake was measured using a Sartorius B4100 precision balance, whilst measures of appetite and mood were collected immediately before and immediately after each meal using paper-based visual-analogue scales (pVAS). Procedure Volunteers arrived at the laboratory for a mid-afternoon familiarisation session, during which all procedures were explained, and pictures were shown of the meal as served, and of a person with the EMG electrodes attached. They were explained the importance of keeping sleep, diet and exercise levels similar between testing days in order to minimise confounding factors, upon which they were asked to fill in a questionnaire recording date of birth, gender, dieting behaviour, sleeping patterns, breakfast meal composition and mode of transport to get to the laboratory. They were asked to repeat the recorded behaviours for
each visit to the laboratory. They then returned for three test lunches, each separated by 47 h to one week. Test lunches were scheduled for either 12:15 or 13:15 h for each participant. Upon arrival, surface-mounted bipolar electrodes (Biometrics SX230 pre-amplifiers, connected to the DataLog) were taped (Biometrics sensor tape) to the skin on both left and right masseter muscles, and the anterior portion of both left and right temporalis muscles. Additionally, a Ground Strap (Biometrics R206) was connected to the inner side of the wrist of the non-feeding hand. Next, whilst the pVAS were filled out, food was prepared for serving. Just before serving, and by means of standardising some aspects of food intake, participants were reminded to: (1) ‘‘Take 2 bits of pasta on your fork at a time’’ (standardised bite size); (2) ‘‘Swallow properly before starting next mouthful’’ (no ‘leapfrogging’); (3) ‘‘Lean forward above plate to avoid having to swallow during chewing’’ (assist with previous instruction); (4) ‘‘Eat until you feel ‘comfortably full’. You need to decide where that level of fullness is, how it feels, and remember it for next time’’ (to compare meal sizes and durations between 10 and 35 CPM); (5) ‘‘Don’t feel obliged to finish your plate’’. Next, food was brought to the participant on a large pre-warmed plate with napkin and fork, whilst they received one condition-specific instruction, either: ‘‘Chew your food as you normally would’’ (session 1); ‘‘Chew each mouthful 10 times’’ or ‘‘Chew each mouthful 35 times’’ (session 2 and 3 in randomized counterbalanced order). All eating instructions were displayed before the participant during the meal, with clear emphasis on the condition-specific instruction. Across the test lunches, water consumption was kept constant, and was determined by the amount of water consumed from a full glass served at the start of session 1. During this first session, participants were asked to drink whenever they felt they wanted to do so, but only in small sips. In subsequent sessions, participants were again asked to drink their water in small sips, but to finish the glass before reaching the end of their meal. At the end of the final testing session, participants were asked by questionnaire if, during one of the testing days, they thought they ate more or less, or felt more or less satisfied after the meal, compared to the other days. Additionally, they were asked: ‘‘Do you think you could make 35 chews per mouthful a general habit?’’ as both closed-question (‘‘No, I would not be able to do that’’; ‘‘I guess I could if I wanted to’’; or ‘‘Yes, I don’t think that would be a problem’’) and as an open-ended question (‘‘please explain below’’). Finally, any other comments were invited (‘‘Is there anything else you’d like to tell us regarding the study?’’). Analysis EMG data were exported from the Biometrics DataLog software as ‘.wav’ files and analysed for CPM and meal duration using Matlab (The Mathworks, Inc), for which scripts were developed inhouse (Kemsley, Sprunt, Defernez, & Smith, 2002). All statistical analyses were performed using the statistical software SPSS. Analyses employed were paired samples, independent samples Student t-tests and GLM repeated measures. Additionally, equality in gender ratios between the two weight groups, and uniformity for responses to the willingness to make 35 CPM habitual chewing behaviour were investigated using a chi-square test. The degrees of freedom associated with statistical tests are presented in parentheses. Means are reported (S.E.). Results Participants Eleven participants (4 males and 7 females) finished the study. They comprised 6 normal weight (2 M, 4F; BMI = 22.0 2.0; range:
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20.7–25.2) and 5 obese (2 M, 3F; BMI = 33.6 2.1; range: 30.7–35.6) participants. Main test measures Participants ate 12% less when chewing 35 CPM compared to 10 CPM (313 17 g vs. 358 19; t(10) = 3.43; P = 0.006), as also indicated by the number of mouthfuls (61.2 3.8 vs. 69.6 4.0; t(10) = 3.18; P = 0.010) and ingestion rate (11.1 0.5 g/min vs. 23.7 0.8; t(10) = 14.01; P < 0.001). Interestingly, instructing participants to chew 35 CPM resulted in faster chewing (35 CPM: 1.10 0.03 chews/sec vs. 10 CPM: 0.78 0.03; t(10) = 7.83; P < 0.001). Despite this, 35 CPM resulted in a substantially longer meal duration (35 CPM: 28.6 1.8 min vs. 10 CPM: 15.1 0.6; t(10) = 9.68; P < 0.001). Postmeal fullness ratings did not differ between the two conditions (35 CPM: 72 7 mm vs. 10 CPM: 71 8), as was also confirmed by poststudy participant feedback: Despite stronger efforts required to perform 35 CPM, 82% (9 out of 11) of participants claimed they manage to reach the same level of fullness at the end of each test meal. When comparing habitual chewing data from normal weight participants with obese participants we found that, whilst habitual CPM did not differ between the two groups (obese: 15.4 1.0 vs. normal weight: 14.9 3.6; F(1,9) = 0.02; P = 0.893), the obese group ate 32% less than the normal weight group (240 25 g vs. 355 29 respectively; F(1,9) = 8.33; P = 0.018), also reflected in a marginally lower ingestion rate in the same group (15.8 1.6 g/min vs. 21.3 2.5 respectively; F(1,9) = 3.09; P = 0.112). The latter findings are substantiated by marginally lower pre-meal ratings for hunger across conditions in our obese participants (obese: 51.5 5.7 vs. normal weight: 67.8 5.2; F(1,9) = 4.42; P = 0.065) (see Table 1). Discussion Fletcher’s doctrine has been confirmed in this pilot study, as more chews per mouthful reduced food/energy intake despite prolonged meal duration. The results of this study tie in with research investigating factors that affect ingestion rate, which show that slower eating reduces food or energy intake (e.g., Zandian, Ioakimidis, Bergh, Brodin, & So¨dersten, 2009; Andrade et al., 2008; Spiegel et al., 1993; Kaplan, 1980; Spiegel & Jordan, 1978), whilst higher ingestion rates are predictive of higher BMIs in some studies (e.g., Sasaki et al., 2003; Otsuka et al., 2006) but not in others (e.g., our data shown here; Spiegel et al., 1993; Kaplan, 1980). Moreover, a public health message that promotes ‘slower eating’ may not be conceptually clear, or combine a variety of instructions, and may therefore more difficult to implement in motivating behavioural change, whereas a message instructing on how to chew may be less problematic, although further research involving behavioural change may be able to confirm this. Other findings in support of our results include acute reductions in hunger following higher chewing rates (40 CPM vs. 10 or 25 CPM: Cassady, Hollis, Fulford, Considine, & Mattes, 2009). Although
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these findings predict the observed reduction in food intake for the higher CPM condition in the current study, they do not explain the near-100% increase in meal duration time we recorded between 10 and 35 CPM. Moreover, although a higher CPM may intuitively predict an increased meal duration, peak plasma times for various satiety hormones do not suggest a delayed satiety response (see introduction). Nevertheless, a reduced ingestion rate produces a stronger satiety response (Kokkinos et al., 2010), as do higher chewing counts (Cassady et al., 2009), although a significant difference in the fastest responding hormone reported (GLP-1) did not occur until 60 min (Kokkinos et al., 2010) or even 3 h (Cassady et al., 2009), after the meal. However, neither publication assessed plasma CCK levels. Additionally, the food consumed in our study was pasta, which, broken down into sugars, predicts a blunted plasma glucose curve with a blunted insulin response curve at decreased CPM (see below). Note that this was confirmed by Kokkinos et al. (2010) but not by (Cassady et al., 2009), although both studies used test foods (ice cream and almonds respectively) with very different macronutrient profiles to our pasta. Another possible cause for the difference in meal durations between the two conditions in our study could be a faster satiety response due to stomach distension following increased ingestion rates in the 10 CPM condition, whilst this may have been further amplified by slower gastric emptying due to larger undigested food particles after mastication (e.g., Vincent et al., 1995; Urbain et al., 1989). Note that Spiegel et al. (1993) found that lower ingestion rates were offset by longer meal durations, showing no difference in meal size between obese and normal weight people. In our study, food intake was reduced by 12% at 35 CPM compared to 10 CPM, and was therefore only partially offset by the 1.4 higher chewing speeds and 1.9 longer meal duration in the 35 CPM condition. It does, however, emphasise the complexity of the topic, and the need to simultaneously measure several – often related – variables within the microstructure of eating. Despite the fact that our low sample size may not allow for reliable comparisons between normal weight and obese participants, we need to highlight one aspect. Despite the longstanding assumption that obese people eat faster than non-obese people, the obese people in our study did not show a higher ingestion rate compared to the normal weight participants. Instead, they showed a 32% lower food/energy intake compared to the normal weight participants. Although marginally higher pre-meal fullness ratings may explain this difference, this was not supported by ratings for, for example, hunger. Naturally, it is feasible that people with disturbed eating behaviour will change this behaviour to conform to expected social norms when this behaviour is under investigation in a laboratory setting. Because we did not test for this, it is unclear if our obese participants showed signs of abnormal eating behaviour, e.g., on one or more factors of the Dutch Eating Behaviour Questionnaire. Nevertheless, it may be that contingency awareness provides a complicating factor when comparing obese with normal weight participants in an experimental setting.
Table 1 Means (standard error) of the main data collected. Measure
Mastication/experimental 10 CPM
Food intake (g) Number of mouthfuls Meal duration (min) Ingestion rate (g/min) CPM Chewing speed (s 1)
358 19 69.6 4.0 15.1 0.6 23.7 0.8 10.5 0.4 0.78 0.03
Weight group/habitual 35 CPM **
313 17 61.2 3.8** 28.6 1.8*** 11.1 0.5*** 31.4 1.3*** 1.10 0.03***
Normal weight
Obese
355 29 67.3 4.3 17.5 1.8 21.3 2.5 14.9 3.6 0.88 0.12
240 25* 48.4 5.3* 15.2 1.0 15.8 1.6 15.4 1.0 0.79 0.07
Notes: Significant differences were tested either within-group (‘Mastication’, columns 2 and 3) or between-group (‘Weight group’, columns 4 and 5). CPM: chews per mouthful. * p < .05., **p < .01., ***p < .001.
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When asked if they could make the 35 CPM a habit, most participants replied negatively. Although one participant’s response that she was ‘‘chewing faster to get to the next mouthful’’, as also indicative of faster chewing (time/chew) in the 35 CPM condition compared to 10 CPM, others explained that this was because in the 35 CPM condition ‘‘there was not much to chew on towards the end’’. Moreover, aversion to the 35 CPM instructions was only expressed by one participant. Because post-test levels of hunger and fullness did not differ between the two conditions, a possible aversion to the 35 CPM condition can therefore not explain the reduced energy intake in this condition. It may, however, question the potential efficacy of a ‘Public Health Message’ that aims to implement behavioural change regarding eating behaviour. As much of our behaviour is ‘entrenched’ at an early age, parents of young children may constitute the most effective target group. Sceptics of health benefits of Mr Fletcher’s doctrine may wish to uphold these findings: When chewing food more thoroughly, glycaemic response peaks and insulin response peaks are reportedly higher (Read et al., 1986), and/or have areas under the curve visibly larger. Moreover, less thoroughly chewed food results in higher energy losses through the stool (Cassady et al., 2009). Although this argues against a notion that thorough chewing constitutes a healthier behaviour compared to less thorough chewing (and therefore slower eating), we have clearly shown that more thorough chewing results in reduced energy intake, and this should more than compensate for these seemingly controversial counter-effects. Although prolonged chewing as advocated by Mr Fletcher was already in use as part of an array of measures to reduce ingestion rate almost 50 years ago (Ferster et al., 1962), its efficacy has – to our knowledge – not previously been tested directly. Nevertheless, our findings need to be validated and be clarified in terms of biomarkers (e.g., satiety hormone levels) and prolonged chewing of ordinary foods. Additionally, the efficacy of prolonged chewing compared to other measures employed to reduce ingestion rate (e.g., smaller bite size, pauses between mouthfuls) need to be investigated, especially in terms of behavioural change. Moreover, the methodology could be improved by employing the Universal Eating Monitor (Kissileff, Klingsberg, & Van Itallie, 1980), allowing for analysis of cumulative intake data (see Dovey, Clark-Carter, Boyland, & Halford, 2009 for a review) and the identification of linear and non-linear eaters. Additionally, a more reliable identification of the point of swallowing in the EMG data should be implemented, maybe inspired by the small balloon used in Bellisle et al. (2000). To advance our understanding in this field is of paramount importance, and this paper aims to initiate a contribution to this area of research by focusing on the role of mastication in food intake rather than on food intake per se. Concluding, we have confirmed Mr Fletcher’s doctrine by showing that higher chewing counts per bite or mouthful reduce food intake despite faster chewing and longer meal duration. This emphasises the legitimacy of prolonged chewing as part of a dietary strategy for weight loss, although further research is needed to validate the findings and compare this technique with other methods used to reduce ingestion rate. References Andrade, A. M., Greene, G. W., & Melanson, K. J. (2008). Eating slowly led to decreases in energy intake within meals in healthy women. Journal of the American Dietetic Association, 108, 1186–1191. Bellisle, F., & Dalix, A.-M. (2001). Cognitive restraint can be offset by distraction, leading to increased meal intake in women. The American Journal of Clinical Nutrition, 74, 197–200. Bellisle, F., Guy-Grand, B., & Le Magnen, J. (2000). Chewing and swallowing as indices of the stimulation to eat during meals in humans. Effects revealed by the edogram
method and video recordings. Neuroscience & Biobehavioral Reviews, 24, 223–228. Blundell, J. E., & Naslund, E. (1999). Glucagon-like peptide-1, satiety and appetite control. The British Journal of Nutrition, 81, 259–260. Cassady, B. A., Hollis, J. H., Fulford, A. D., Considine, R. V., & Mattes, R. D. (2009). Mastication of almonds. Effects of lipid bioaccessibility, appetite, and hormone response. American Journal of Clinical Nutrition, 89, 794–800. de Castro, J. M., Bellisle, F., Dalix, A.-M., & Pearcey, S. M. (2000). Palatability and intake relationships in free-living humans. Characterization and independence of influence in North Americans. Physiology and Behavior, 70, 343–350. Christen, A. G., & Christen, J. A. (1997). Horace Fletcher (1849–1919). ‘‘The Great Masticator’’. Journal of the History of Dentistry, 45, 95–100. Dovey, T. M, Clark-Carter, D., Boyland, E. J., & Halford, J. C. (2009). A guide to analysing Universal Eating Monitor data. Assessing the impact of different analysis techniques. Physiology & Behavior, 96, 78–84. Ferster, C. B., Nurnberger, J. I., & Levitt, E. E. (1962). The control of eating. Journal of Mathematics, 1, 87–109. Gaul, D. J., Craighead, W. E., & Mahoney, M. J. (1975). Relationship between eating rates and obesity. Journal of Consulting and Clinical Psychology, 43, 123–125. Higgs, S. (2002). Memory for recent eating and its influence on subsequent food intake. Appetite, 39, 159–166. Kaplan, D. L. (1980). Eating style of obese and nonobese males. Psychosomatic Medicine, 42, 529–538. Kemsley, E. K., Sprunt, J. C., Defernez, M., & Smith, A. C. (2002). Multivariate analysis of electromyographic (EMG) frequency spectra to characterise mastication. Journal of Texture Studies, 33, 15–34. Kissileff, H. R., Klingsberg, G., & Van Itallie, T. B. (1980). Universal eating monitor for continuous recording of solid or liquid consumption in man. American Journal of Physiology, 238, R14–R22. Kokkinos, A., le Roux, C. W., Alexiadou, K., Tentolouris, N., Vincent, R. P., Kyriaki, D., et al. (2010). Eating slowly increases the postprandial response of the anorexigenic gut hormones, peptide YY and glucagon-like peptide-1. The Journal of Clinical Endocrinology & Metabolism, 95, 333–337. Marston, A. R., London, P., Cohen, N., & Cooper, L. M. (1977). In vivo observation of the eating behavior of obese and nonobese subjects. Journal of Consulting and Clinical Psychology, 45, 335–336. Maruyama, K., Sato, S., Ohira, T., Maeda, K., Noda, H., Kubota, Y., et al. (2008). The joint impact on being overweight of self reported behaviours of eating quickly and eating until full. Cross sectional survey. British Medical Journal, 337, a2002. Moran, T. H., & Kinzig, K. P. (2004). Gastrointestinal satiety signals II. Cholecystokinin. The American Journal of Physiology. Gastrointestinal and Liver Physiology, 286, G183–188. Okuma, K., Yoshimatsu, H., Sakata, T., & Adachi, K. (2000). The effect of chewing chart recording in the treatment of obesity. Japanese Journal of Psychosomatic Medicine, 40, 247–253. Otsuka, R., Tamakoshi, K., Yatsuya, H., Murata, C., Sekiya, A., Wada, K., et al. (2006). Eating fast leads to obesity. Findings based on self-administered questionnaires among middle-aged Japanese men and women. Journal of Epidemiology, 16, 117–124. Parra, M. D., Martinez de Morentin, B. E., & Martinez, J. A. (2005). Postprandial insulin response and mitochondrial oxidation in obese men nutritionally treated to lose weight. European Journal of Clinical Nutrition, 59, 334–340. Read, N. W., Welch, I. M., Austen, C. J., Barnish, C., Bartlett, C. E., Baxter, A. J., et al. (1986). Swallowing food without chewing; a simple way to reduce postprandial glycaemia. British Journal of Nutrition, 55, 43–47. Rolls, B. J., Roe, L. S., & Meengs, J. S. (2004). Salad and satiety. Energy density and portion size of a first-course salad affect energy intake at lunch. Journal of the American Dietetic Association, 104, 1570–1576. Sasaki, S., Katagiri, A., Tsuji, T., Shimoda, T., & Amano, K. (2003). Self-reported rate of eating correlates with body mass index in 18-y-old Japanese women. International Journal of Obesity, 27, 1405–1410. Spiegel, T. A. (2000). Rate of intake, bites, and chews. The interpretation of lean–obese differences. Neuroscience and Biobehavioral Reviews, 24, 229–237. Spiegel, T. A., Kaplan, J. M., Tomassini, A., & Stellar, E. (1993). Bite size, ingestion rate, and meal size in lean and obese women. Appetite, 21, 131–145. Spiegel, T. A., Wadden, T. A., & Foster, G. D. (1991). Objetive measurement of eating rate during behavioral treatment of obesity. Behaviour Therapy, 22, 61–67. Spiegel, T. A., & Jordan, H. A. (1978). Effects of simultaneous oral-intragastric ingestion on meal patterns and satiety in humans. Journal of Comparative and Physiological Psychology, 92, 133–141. Stuart, R. B. (1967). Behavioral control of eating. Behavior Research and Therapy, 5, 357–365. Urbain, J. L. C., Siegel, J. A., Charkes, N. D., Maurer, A. H., Malmud, L. S., & Fisher, R. S. (1989). The two-component stomach. Effects of meal particle size on fundal and antral emptying. European Journal of Nuclear Medicine, 15, 254–259. Vincent, R., Roberts, A., Frier, M., Perkins, A. C., MacDonald, I. A., & Spiller, R. C. (1995). Effect of bran particle size on gastric emptying and small bowel transit in humans. A scintigraphic study. Gut, 37, 216–219. Weijzen, P. L., Liem, D. G., Zandstra, E. H., & de Graaf, C. (2008). Sensory specific satiety and intake. The difference between nibble- and bar-size snacks. Appetite, 50, 435–442. de Wijk, R. A., Zijlstra, N., Mars, M., de Graaf, C., & Prinz, J. F. (2008). The effects of food viscosity on bite size, bite effort and food intake. Physiology and Behavior, 95, 527–532. Zandian, M., Ioakimidis, I., Bergh, C., Brodin, U., & So¨dersten, P. (2009). Decelerated and linear eaters. Effect of eating rate on food intake and satiety. Physiology and Behavior, 96, 270–275.