Maryam Esfandiari shows how the device works and Vasileios Papapanagiotou describes how the data generated are filtered and fitted to the CFI curve. Per S&#246;dersten describes the research on which the clinical intervention relies and Cecilia Bergh discusses its use in the clinic. The work is supported by Mando Group AB, the European Community's ICT Programme SPLENDID (610746) and the European Community's Health, demographic change and well-being Programme (727688).

All work on experimental subjects and patients has been approved by the Central Ethical Review Board of Stockholm, Sweden.
1. Control of Eating Rate and Food Intake by Visual Feedback
<ol>
	<li>Download the device application.</li>
	<li>Turn on the scale by pressing the red button on the bottom of the scale (Figure 1).</li>
	<li>Open the application and select a meal by pressing Control to eat without visual feedback or by pressing Training to eat with visual feedback (Figure 2).</li>
	<li>Connect the scale to the smartphone by selecting one of the scales found and pressing CONNECT (Figure 3).</li>
	<li>Place a plate on the scale. Press Done to continue (Figure 4).</li>
	<li>Put food on the plate (0%).
	<ol>
		<li>Put more food on the plate (72%).</li>
		<li>Press Start when ready to eat (100%) (Figure 5).</li>
	</ol>
	</li>
	<li>Adapt rate of eating to the dashed blue reference curve (Figure 6).</li>
	<li>Rate feeling of fullness when the rating scale appears on the screen. Adapt rating of fullness to the s-shaped dashed purple reference curve. Press Done to continue eating (Figure 7).</li>
	<li>Slow down if eating too quickly (Figure 8).</li>
	<li>With no food left on plate, press No when finished eating (Figure 9).</li>
	<li>As the meal duration is shown when meal is finished, press Done to exit (Figure 10).</li>
</ol>

<ol>
	<li>Zandian, M., Ioakimidis, I., Bergh, C., Brodin, U., S&#246;dersten, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Decelerated+and+linear+eaters:+Effect+of+eating+rate+on+food+intake+and+satiety.">Decelerated and linear eaters: Effect of eating rate on food intake and satiety.</a> Physiol. Behav. 96, 270-275 (2009).</li><li>. Elements of the Theory of Computation Available from: <a target="_blank" href="https://dl.acm.org/citation.cfm?id=549820">https://dl.acm.org/citation.cfm?id=549820</a> (1997)</li><li>Lieberman, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Evolution of the Human Head. , (2011).</li><li>Ungar, P. 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Ed. 296, 521 (1988).</li><li>S&#246;dersten, P., Bergh, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effective+treatment+of+eating+disorders:+results+at+multiple+sites.">Effective treatment of eating disorders: results at multiple sites.</a> Behav. Neurosci. 127, 878-889 (2013).</li><li>Yamagishi, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+speed-eating+habit+on+subsequent+body+mass+index+and+blood+pressure+among+schoolchildren+-+The+Ibaraki+Children's+Cohort+Study+(IBACHIL).">Impact of speed-eating habit on subsequent body mass index and blood pressure among schoolchildren - The Ibaraki Children's Cohort Study (IBACHIL).</a> Circ. J. Off. J. Jpn. Circ. Soc. , (2017).</li><li>Ford, A. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Treatment+of+childhood+obesity+by+retraining+eating+behaviour:+randomised+controlled+trial.">Treatment of childhood obesity by retraining eating behaviour: randomised controlled trial.</a> BMJ. 340, b5388 (2010).</li><li>Jordan, H. A., Wieland, W. F., Zebley, S. P., Stellar, E., Stunkard, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Direct+measurement+of+food+intake+in+man:+a+method+for+the+objectiive+study+of+eating+behavior.">Direct measurement of food intake in man: a method for the objectiive study of eating behavior.</a> Psychosom. Med. 28, 836-842 (1966).</li><li>Meyer, J. -. E., Pudel, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+studies+on+food-intake+in+obese+and+normal+weight+subjects.">Experimental studies on food-intake in obese and normal weight subjects.</a> J. Psychosom. Res. 16, 305-308 (1972).</li><li>Kissileff, H. R., Klingsberg, G., Itallie, T. B. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Universal+eating+monitor+for+continuous+recording+of+solid+or+liquid+consumption+in+man.">Universal eating monitor for continuous recording of solid or liquid consumption in man.</a> Am. J. Physiol. - Regul. Integr. Comp. Physiol. 238, R14-R22 (1980).</li><li>Zandian, M., Ioakimidis, I., Bergh, C., S&#246;dersten, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Linear+eaters+turned+decelerated:+reduction+of+a+risk+for+disordered+eating?.">Linear eaters turned decelerated: reduction of a risk for disordered eating?.</a> Physiol. Behav. 96, 518-521 (2009).</li><li>Zandian, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Control+of+Body+Weight+by+Eating+Behavior+in+Children.">Control of Body Weight by Eating Behavior in Children.</a> Front. 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Our research is carried out at the Karolinska Institute, where S&#246;dersten is a professor. The research is translated clinically by Mando Group AB, a company started by S&#246;dersten and Bergh, who have 47.5% of the stock each. Professor Michael Leon of the University of California at Irvine has 5%. Mando Group AB contracts with the County Council of Stockholm every fifth year to treat patients with eating disorders. Mando Groups AB signed its first contract in 1997 with the County Council of Stockholm and, since then, its treatment is one standard of care offered to the citizens of Stockholm. This arrangement is the same as when the County Council of Stockholm contracts with its own clinics to treat patients with all kinds of disease, including eating disorders. That is to say, the County Council of Stockholm provides eating disorders services to the citizens of Stockholm both through a clinic of its own and through Mando Group AB. There is a third provider of care for patients with eating disorders in Stockholm, which is a private clinic. All health care in Sweden is funded through the tax system, private pay is extremely uncommon. However, patients can pay privately for treatment by Mando Group AB and patients from Australia have done that because insurers in Australia have denied reimbursement. It should be added, firstly, that Mando Group AB is in compliance with the recommendation of the International Committee of Medical Journal Editors on &#8220;Author Responsibilities-Conflicts of Interest&#8221; http://www.icmje.org/recommendations/browse/roles-and-responsibilities/author-responsibilities--conflicts-of-interest.html. Secondly, it should also be added that all profit that Mando Group AB has made has been re-invested in research and development and that there have been no dividends to stock owners. This policy will not be changed. All of the above is declared in all manuscript submissions and so far, journals have judged it necessary to publish only some of the details. It seems, however, that the potential ethical problem when scientists translate research findings into the clinic in a company is not unlike that which arises when any scientist, in an academic setting, is developing a theory and needs further economic funding for her/his work and may receive recognition and financial benefits for the work. The incentive is, in part, economic in this case as well and the ethical &#8220;problem&#8221; is similar in both cases. However, the more important incentive is the improvement of the treatment of patients with eating disorders. Medical institutes, including the Karolinska Institute, encourage the translation of research into the clinic in companies that aim to generate financial profits to be used for research and development (see: http://ki.se.proxy.kib.ki.se/sites/default/files/summary_strategy2018.pdf).

While the method described here is a major intervention in our treatment of patients with problems of body weight, the treatment has additional interventions, which have been described before, including criteria for admission and remission34 and outcome at three months intervals in each of 1428 patients26. The device, not these additional interventions, is the focus of the present report.
The Control mode of the device is used to record eating behavior in healthy subjects and in under- and overweight patients. The Training mode is subsequently used to change eating behavior for experimental or clinical purposes. Using visual feedback in the treatment of anorexic patients, 100% as indicated on the computer screen, is the amount of food the patient should eat determined individually on the basis of three preliminary tests in the Control mode. 100% is a small amount of food in an anorexic patient, but more food than s/he ate in the three preliminary tests. An anorexic patient pratices eating progressively more food, and s/he is asked to add food until 100% is indicated on the screen. The shape of the dashed blue reference curve, which remains the same over the course of treatment, is based on the finding that normal weight healthy people eat at a progessively decreasing rate, i.e., in a decelerated manner1. The dashed purple reference curve for the feeling of fullness is based on the finding that the cumulative satiety curve is best fitted to a sigmoid curve1. There are no numbers on the axes. The treatment continues until the patient is able to eat about 300-350 g of food in about 10-15 min without a reference curve. These data were obtained from normal weight, healthy subjects. Both under- and overweight patients have a choice between three dishes of ordinary Swedish food in clinical pratice and they are offered water to drink. Experimental manipulation of eating behavior is done in the same manner in healthy subjects1,6,7,32,33. Because re-establishing the normal feeling of fullness using the visual feedback provided by a reference curve is done in the same manner it is not discussed here. Clinically, however, this is an important intervention as anorexic patients rate the feeling of fullness much higher than do healthy subjects.
While patients follow the visual feedback when eating in treatment, they are allowed to add food to the plate during the meal when they eat without feedback. Food additions and other events, including pressure exterted on the plate during sectioning of the food using forks and knives, affect the recording by the device creating artifacts and errors that were previously dealt with manually as described in35. The data generated from eating using the device is collected in a custom made database, from which the clinician can retrieve the results for providing patients with feeback on how treatment progresses and the researcher can use for analysing experimental results.
Anorexic patients eat only little food at a slow rate34. Interestingly, however, they are able to consume normal sized meals even at very low BMI18. For example, the patient reported was able to eat 300 g very early in treatment and long before normalization of her BMI and a mere three days after eating in a disordered manner, i.e., consuming only little food at an accelerating rate. Anorexic patients who start eating at an extremely low rate are likely to increase the rate of eating over the course of the initial meals, as did three of the patients reported here. When patients approach remission, however, the inverse relationship among the rate of deceleration and the initial rate of eating emerges1. Several hundred patients with eating disorders have been treated to remission and the protocol has proven user friendly with no adverse side effects26. Severely obese patients have been similarly treated to lose weight, albeit not to a normal weight28,33. At present there is no method that normalizes the body weight of severely overweight patients.
A limitation of the method is that subjects are likely affected by the device even in the absence of visual feedback. That hypothesis can be tested by comparing data obtained with the device with data obtained from video-recorded meals. Paradoxically, the same limitation may be an advantage in treatment, which aims at normalizing food intake.
While the present method has long been used to record eating behavior, the addition of visual feedback for experimental and clinical purposes has not been used outside our research and clinic. For clinicians aiming at restoring the eating behavior of the patients and for scientists interested in using eating behavior as an experimental variable, the device offers a method. The analysis of the results can now be automated using the present algorithm for computing the CFI curve, alleviating the need for time-consuming manual processing. This makes monitoring and analysis of eating behavior in large groups of subjects possible.
The effectiveness of the algortihm will be further improved. At present it is based on estimations of deviations from true values of bites as dissociated from artifacts.
The device will be used among children to prevent problems of body weight in adulthood. Most of the hardware and software discussed in this report are ready for such large scale use. Possible improvements, including optimizing the visual design of feedback on the screen of the device and expanding the CFG with additional kinds of in-meal actions and the analysis of partially completed meals, must await the outcome of the intended large scale use.

The device presented here is used to restore the body weight and the health of under- and overweight patients by controlling the rate of eating and the intake of food via visual feedback provided on a computer screen during the meal. It consists of a custom made electronic scale and a computer, e.g., a smartphone. An app allows a subject to connect the smartphone to the scale via Bluetooth. Once the smartphone is connected to the scale, the subject puts a plate on the scale and food on the plate and starts eating. At regular intervals, a rating scale appears on the screen and the subject is asked to rate her/his feeling of fullness. The rating scale can be disconnected, depending upon the aim of the experiment or the clinical intervention. For experimental and clinical purposes a reference curve for eating rate and a reference curve for the feeling of fullness are displayed on the screen of the smartphone. The subject can adapt to the reference curves because her/his rate of eating and ratings of fullness appear on the screen during the meal.
The device records the decrease of the weight of the plate as food is consumed and the ratings of fullness over the course of the meal and stores the recordings. The weight loss data are used to yield a quadratic model of the cumulative food intake (CFI) curve: y=kx2+lx, where y=amount of food consumed, k=change in the rate of eating over the course of the meal and l=initial rate of eating1. The CFI curve is based on modelling the following three actions during the meal: bite, food addition, and artifact (weight changes unrelated to food consumption). These actions are mapped on non-terminal symbols of a context-free grammar (CFG)2. The recording, after pre-processing, is partitioned into time intervals corresponding to weight changes (based on forward derivative and delta coefficients), which are then mapped to CFG terminal symbols. The CFG then allows estimation of the most likely interpretation of the meal assuming independence for each event.
The device is used in research as well as in clinical practice. It was first used to treat patients with anorexia nervosa and other eating disorders and subsequently to treat severely overweight patients.
Eating assisted by visual feedback has the following scientific basis. Animals, including humans, have evolved to eat many different foods, but they select what to eat under &#34;buffet&#34; conditions3,4. However, they are equipped, anatomically and behaviorally, to eat whatever foods are available if conditions get compromised3,4 and eating behavior, chewing in particular, has therefore been a main driver of the evolution of the human head, including the brain and the masticatory apparatus3. The pattern of eating may therefore be more important for the control of body weight than the kind of food eaten. In support, diet intervention has minor, if any, effect on normalizing body weight5 and the device described here can e.g., assist school children in eating a normal amount of food when challenged to eat quickly during the school lunch6 and reverse the effect of a brief period of fasting on food intake in young women and men7.
Neuroscience hypothesizes that the cause of weight problems is lodged in the brain8. However, it is unlikely that the hundreds of millions of people in the world, who now weigh too much9, developed the problem because of a neural abnormality before the weight gain. It is more likely that neural abnormalities10, as well as medical abnormalities11, develop as an effect of eating too much12, save, perhaps in some rare genotypes13.
A possible reason why neuroscience fails to explain the problems of body weight is because its basic assumption is incorrect, i.e., that body weight is kept nearly constant via excitatory/inhibitory neural controls exerted over eating behavior8. Whatever inhibition the brain may exert has obviously not prevented hundreds of millions of people to become seriously overweight recently9. A better understanding of the role of brain in eating emerged from the discovery that hypothalamic peptides once thought to stimulate eating act instead to permit the search for food, even at the expense of food intake14,15. These results, which were confirmed recently16,17, support the view that body weight is kept at a healthy level only when the physical price of food is high, a condition referred to as &#34;the human homeostatic phenoptype&#34;18. Considering the multiplicity of brain and genetic networks engaged in eating behavior and other behaviors also19, it would be difficult, if at all possible, to decrease or increase body weight by manipulating a neurotransmitter system or two. Given these consideration, it comes as no surprise that pharmacological intervention has minimal, or no effects on body weight in the seriously overweight20, as well as in the underweight21.
It is ironic that some of these recent findings merely confirm the predictions concerning the role of diets, genes, brain, and physical activity in body weight regulation made by Mayer in 1953 already22. As an alternative, eating behavior may have a causal role in the control of body weight and neuroscience and genetics may have mistaken mechanism for cause. In support, daily eating over the year strives to maximize food intake in humans, pushing body weight upwards to counteract any influence of food shortage23. In the human homeostatic phenotype, the influence of this kind of physiological overeating is counterbalanced by the physical effort needed to obtain food18. Because that effort is close to zero today humans need external support to maintain a healthy low body weight. That support is provided by the present device.
Rather than targeting eating behavior, standard treatments for eating disorders target the patients&#180; psychological symptoms. On average 30% of the patients drop out of these treatments, fewer than 50% go into remission, although they remain symptomatic, and at least 30% relapse within one year of discharge24. However, it was found long ago that treating eating behavior has a better effect than treating the psychological symptoms25. This finding was replicated more recently by using the device described in this report. By this method, an estimated 75% of patients with eating disorders go into remission and 10% relapse over five years of follow-up26, an improvement over standards of care24.
Although the rate of eating has increased in seriously overweight patients, the cause-effect relationship among rate of eating and body weight has not been clarified and the rate of eating has been determined by questionnaires27. The device described here makes objective measurement of eating rate and food intake possible and when used to reduce these two measures, it has been demonstrated to be more effective in decreasing body weight and improving health than standard diet and exercise intervention in severely overweight adolescents28.
The principle of recording eating behavior utilized in the present device was described long ago29,30,31, and it has since then been used in experiments to record and change the rate of eating1,6,7,32,33. However, it has not been used to treat under- and overweight patients outside of clinical trials and in clinical practice. Part of the clinical program involves using the device at home and in everyday life. It has proven to be user friendly in both laboratory research and in clinical practice.

<table><tbody><tr><td>Scale</td><td>T&amp;Uuml;M ELEKTRONIK</td><td>no catalogue number</td><td>csutm made not comercially available</td></tr></tbody></table>

control of eating behavior using a novel feedback system

Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve of food intake, meal duration and rate of eating modeled by a quadratic equation. The purpose of the method is to change eating behavior by providing visual feedback on the computer screen that the subject can adapt to because her/his own rate of eating appears on the screen during the meal. The data generated by the method is automatically analyzed and fitted to the quadratic equation using a custom made algorithm. The method has the advantage of recording eating behavior objectively and offers the possibility of changing eating behavior both in experiments and in clinical practice. A limitation may be that experimental subjects are affected by the method. The same limitation may be an advantage in clinical practice, as eating behavior is more easily stabilized by the method. A treatment that uses this method has normalized body weight and restored the health of several hundred patients with anorexia nervosa and other eating disorders and has reduced the weight and improved the health of severely overweight patients.

Five women, who were 17.5 (15-24) (median, min-max) years old, were treated to remission from anorexia nervosa. Figure 11, Figure 12, Figure 13 show the data generated by the device, the appearance of the CFI curve and the quadratic model in two recordings in the early part of treatment and a recording at the time of remission. Note the accelerating rate of eating in the first test without visual feedback, intake of a large meal already three days later with reference curve providing visual feedback and doubling of the rate of eating at the time of remission in comparison with the two recordings in the early part of treatment in one of the patients.
Figure 14A shows that the time to remission was very variable in the five patients. The figure also shows that with a normal Body Mass Index (BMI) at the time of remission (Figure 14B), the patients consumed more food than they did at admission (Figure 14C), and that the duration of the meal, although not reduced, was less variable (Figure 14D). Note that anorexics can consume a relatively large meal at admission with a low BMI (Figure 14C). It is also noteworthy that while three patients ate at an accelerating rate at admission, all but one ate at a decelerated rate when in remission (Figure 14E). Correlatively, the intitial rate of eating had increased at the time of remission (Figure 14F).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57432/57432fig1.jpg" /> 
Figure 1: The scale. Press button to turn on the scale. <a href="/files/ftp_upload/57432/57432fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57432/57432fig2.jpg" /> 
Figure 2: Start meal. Open app to start meal. Press Control to eat without visual feedback. Press Training to eat with visual feedback. <a href="/files/ftp_upload/57432/57432fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57432/57432fig3.jpg" /> 
Figure 3: Connect scale and smartphone. Select scale using app. Press Connect to proceed. <a href="/files/ftp_upload/57432/57432fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57432/57432fig4.jpg" /> 
Figure 4: Put plate on scale. Put plate on the scale and press Done to proceed. <a href="/files/ftp_upload/57432/57432fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/57432/57432fig5.jpg" /> 
Figure 5: Put food on plate. Put food on the plate until 100% is indicated and press Start when ready to eat. <a href="/files/ftp_upload/57432/57432fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/57432/57432fig6.jpg" /> 
Figure 6: Eating with visual feedback. Adapt rate of eating to dashed blue reference curve. <a href="/files/ftp_upload/57432/57432fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/57432/57432fig7.jpg" /> 
Figure 7: Rating of fullness. Indicate feeling of fullness when rating scale appears. Adapt rating to dashed purple reference curve and press Done to continue eating. <a href="/files/ftp_upload/57432/57432fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/57432/57432fig8.jpg" /> 
Figure 8: Adapting rate of eating. Modify rate of eating if deviating from dashed blue reference curve. <a href="/files/ftp_upload/57432/57432fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/57432/57432fig9.jpg" /> 
Figure 9: Meal completed. Press No if there is no food on the plate. <a href="/files/ftp_upload/57432/57432fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/57432/57432fig10.jpg" /> 
Figure 10: Meal finished. Compliance to references curves and duration of meal displayed. Press Done to exit. <a href="/files/ftp_upload/57432/57432fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/57432/57432fig11.jpg" /> 
Figure 11: Early cumulative food intake (CFI) curve in anorexia nervosa. Recording (blue line), artifacts removed (red line) and CFI curve modeled by quadratic equation. Data from an anorexic patient on the second day of treatment eating without visual feedback. <a href="/files/ftp_upload/57432/57432fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/57432/57432fig12.jpg" /> 
Figure 12: Early response to treatment in anorexia nervosa. Patient adapts to reference curve on the fifth day of treatment. Data from the same patient. <a href="/files/ftp_upload/57432/57432fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 13" class="xfigimg" src="/files/ftp_upload/57432/57432fig13.jpg" /> 
Figure 13: Remission from anorexia nervosa. Increase in the rate of eating at time of remission after 175 days of treatment. Data from the same patient eating without visual feedback. <a href="/files/ftp_upload/57432/57432fig13large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 14" class="xfigimg" src="/files/ftp_upload/57432/57432fig14.jpg" /> 
Figure 14: Effect of treating eating behavior in anorexia nervosa. Time to remission highly variable (A) as Body Mass Index (BMI) normalizes (B) in five patients treated to remission from anorexia nervosa. Note variability in food intake (C), meal duration (D), deceleration of the rate of eating (E) and the initial rate of eating (F) at admission (Adm) and normalization of these measures at remission (Rem). Patients eating without reference curve. Data are medians (min-max). <a href="/files/ftp_upload/57432/57432fig14large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Control of Eating Behavior Using a Novel Feedback System at JoVE.com

Control of Eating Behavior Using a Novel Feedback System

Subjects eat food from a plate placed on a scale connected to a computer that records the weight loss of the plate during the meal. Feedback on the computer screen allows the subject to adapt her/his eating behavior to reference curves thus normalizing body weight.

Subjects eat food from a plate that sits on a scale connected to a computer that records the weight loss of the plate during the meal and makes up a curve ...

control-of-eating-behavior-using-a-novel-feedback-system

Research

JoVE Journal

Behavior

10.9K Views.  Karolinska Institutet. Subjects eat food from a plate placed on a scale connected to a computer that records the weight loss of the plate during the meal. Feedback on the computer screen allows the subject to adapt her/his eating behavior to reference curves thus normalizing body weight.

Video: Control of Eating Behavior Using a Novel Feedback System

A Novel Procedure for Evaluating the Reinforcing Properties of Tastants in Laboratory Rats: Operant Intraoral Self-administration

This paper describes a novel method for studying the bio-behavioral basis of addiction to food. This method combines the surgical component of taste reactivity with the behavioral aspects of operant self-administration of drugs. Under very brief general anaesthesia, rats are implanted with an intraoral (IO) cannula that allows delivery of test solutions directly in the oral cavity. Animals are then tested in operant self-administration chambers whereby they can press a lever to receive IO infusions of test solutions. IO self-administration has several advantages over experimental procedures that involve drinking a solution from a spout&#160;or operant responding for solid pellets or solutions delivered in a receptacle. Here, we show that IO self-administration can be employed to study self-administration of high fructose corn syrup (HFCS). Rats were first tested for self-administration on a progressive ratio (PR) schedule, which assesses the maximum amount of operant behavior that will be emitted for different concentrations of HFCS (i.e.&#160;8%, 25%, and 50%). Following this test, rats self-administered these concentrations on a continuous schedule of reinforcement (i.e.&#160;one infusion for each lever press) for 10 consecutive days (1 session/day; each lasting 3 hr), and then they were retested on the PR schedule. On the continuous reinforcement schedule, rats took fewer infusions of higher concentrations, although the lowest concentration of HFCS (8%) maintained more variable self-administration. Furthermore, the PR tests revealed that 8% had lower reinforcing value than 25% and 50%. These results indicate that IO self-administration can be employed to study acquisition and maintenance of responding for sweet solutions. The sensitivity of the operant response to differences in concentration and schedule of reinforcement makes IO self-administration an ideal procedure to investigate the neurobiology of voluntary intake of sweets.

The current study evaluates a novel procedure for assessing the reinforcing effects of palatable solutions in laboratory rats: intraoral self-administration. To this end, operant responding (i.e.&#160;lever pressing) for intraoral infusions of sweet solutions at different concentrations was measured on continuous and progressive ratios schedules of reinforcement.

This paper describes a novel method for studying the bio-behavioral basis of addiction to food. This method combines the surgical component of taste ...

Errors as a Means of Reducing Impulsive Food Choice

Nowadays, the increasing incidence of eating disorders due to poor self-control has given rise to increased obesity and other chronic weight problems, and ultimately, to reduced life expectancy. The capacity to refrain from automatic responses is usually high in situations in which making errors is highly likely. The protocol described here aims at reducing imprudent preference in women during hypothetical intertemporal choices about appetitive food by associating it with errors. First, participants undergo an error task where two different edible stimuli are associated with two different error likelihoods (high and low). Second, they make intertemporal choices about the two edible stimuli, separately. As a result, this method decreases the discount rate for future amounts of the edible reward that cued higher error likelihood, selectively. This effect is under the influence of the self-reported hunger level. The present protocol demonstrates that errors, well known as motivationally salient events, can induce the recruitment of cognitive control, thus being ultimately useful in reducing impatient choices for edible commodities.

Giving in to temptation of tasty food may result in long-term overweight problems. This protocol describes how to reduce imprudent preference for edible commodities during hypothetical intertemporal choices in women by associating them with errors.

Nowadays, the increasing incidence of eating disorders due to poor self-control has given rise to increased obesity and other chronic weight problems, and ...

Force and Position Control in Humans - The Role of Augmented Feedback

During motor behaviour, humans interact with the environment by for example manipulating objects and this is only possible because sensory feedback is constantly integrated into the central nervous system and these sensory inputs need to be weighted in order meet the task specific goals. Additional feedback presented as augmented feedback was shown to have an impact on motor control and motor learning. A number of studies investigated whether force or position feedback has an influence on motor control and neural activation. However, as in the previous studies the presentation of the force and position feedback was always identical, a recent study assessed whether not only the content but also the interpretation of the feedback has an influence on the time to fatigue of a sustained submaximal contraction and the (inhibitory) activity of the primary motor cortex using subthreshold transcranial magnetic stimulation. This paper describes one possible way to investigate the influence of the interpretation of feedback on motor behaviour by investigating the time to fatigue of submaximal sustained contractions together with the neuromuscular adaptations that can be investigated using surface EMG. Furthermore, the current protocol also describes how motor cortical (inhibitory) activity can be investigated using subthreshold TMS, a method known to act solely on the cortical level. The results show that when participants interpret the feedback as position feedback, they display a significantly shorter time to fatigue of a submaximal sustained contraction. Furthermore, subjects also displayed an increased inhibitory activity of the primary cortex when they believed to receive position feedback compared when they believed to receive force feedback. Accordingly, the results show that interpretation of feedback results in differences on a behavioural level (time to fatigue) that is also reflected in interpretation-specific differences in the amount of inhibitory M1 activity.

Controlling an identical movement with position or force feedback results in different neural activation and motor behavior. This protocol describes how to investigate behavioral changes by looking at neuromuscular fatigue and how to evaluate motor cortical (inhibitory) activity using subthreshold TMS with respect to the interpretation of augmented feedback.

During motor behaviour, humans interact with the environment by for example manipulating objects and this is only possible because sensory feedback is ...

Behavioral Disturbances: An Innovative Approach to Monitor the Modulatory Effects of a Nutraceutical Diet

In dogs, diets are often used to modulate behavioral disturbances related to chronic anxiety and stress caused by intense and restless activity. However, the traditional ways to monitor behavioral changes in dogs are complicated and not efficient. In the current clinical evaluation, a new, simple monitoring system was used to assess the effectiveness of a specific diet in positively modulating the intense and restless activity of 24 dogs of different ages and breeds. This protocol describes how to easily and rapidly evaluate improvement in a set of symptoms related to generalized anxiety by using a specific sensor, a mobile phone app, a wireless router, and a computer. The results showed that dogs treated with specific diets showed significant improvement in the times spent active and at rest after 10 days (p &lt; 0.01 and p &lt; 0.05, respectively). These dogs also showed an overall significant improvement in clinical and behavioral symptoms. A specific sensor, along with its related hardware, was demonstrated to successfully monitor behavioral changes relating to movement in dogs.

We report a simple approach to evaluate the effectiveness of a specific diet in positively modulating the daily activity and clinical and behavioral symptoms of dogs with evident behavioral disturbances.

In dogs, diets are often used to modulate behavioral disturbances related to chronic anxiety and stress caused by intense and restless activity. However, ...

Feeding Experimentation Device (FED): Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents

Food intake measurements are essential for many research studies. Here, we provide a detailed description of a novel solution for measuring food intake in mice: the Feeding Experimentation Device (FED). FED is an open-source system that was designed to facilitate flexibility in food intake studies. Due to its compact and battery powered design, FED can be placed within standard home cages or other experimental equipment. Food intake measurements can also be synchronized with other equipment in real-time via FED's transistor-transistor logic (TTL) digital output, or in post-acquisition processing as FED timestamps every event with a real-time clock. When in use, a food pellet sits within FED's food well where it is monitored via an infrared beam. When the pellet is removed by the mouse, FED logs the timestamp onto its internal secure digital (SD) card and dispenses another pellet. FED can run for up to 5 days before it is necessary to charge the battery and refill the pellet hopper, minimizing human interference in data collection. Assembly of FED requires minimal engineering background, and off-the-shelf materials and electronics were prioritized in its construction. We also provide scripts for analysis of food intake and meal patterns. Finally, FED is open-source and all design and construction files are online, to facilitate modifications and improvements by other researchers.

Feeding Experimentation Device FED: Construction and Validation of an Open-source Device for Measuring Food Intake in Rodents

Feeding Experimentation Device (FED) is an open-source device for measuring food intake in mice. FED can also synchronize food intake measurements with other techniques via a real-time digital output. Here, we provide a step-by-step tutorial for the construction, validation, and usage of FED.

Food intake measurements are essential for many research studies. Here, we provide a detailed description of a novel solution for measuring food intake in ...

Progressive-ratio Responding for Palatable High-fat and High-sugar Food in Mice

Foods that are rich in fat and sugar significantly contribute to over-eating and escalating rates of obesity. The consumption of palatable foods can produce a rewarding effect that strengthens action-outcome associations and reinforces future behavior directed at obtaining these foods. Increasing evidence that the rewarding effects of energy-dense foods play a profound role in overeating and the development of obesity has heightened interest in studying the genes, molecules and neural circuitry that modulate food reward1,2. The rewarding impact of different stimuli can be studied by measuring the willingness to work to obtain them, such as in operant conditioning tasks3. Operant models of food reward measure acquired and voluntary behavioral responses that are directed at obtaining food. A commonly used measure of reward strength is an operant procedure known as the progressive ratio (PR) schedule of reinforcement.4,5 In the PR task, the subject is required to make an increasing number of operant responses for each successive reward. The pioneering study of Hodos (1961) demonstrated that the number of responses made to obtain the last reward, termed the breakpoint, serves as an index of reward strength4. While operant procedures that measure changes in response rate alone cannot separate changes in reward strength from alterations in performance capacity, the breakpoint derived from the PR schedule is a well-validated measure of the rewarding effects of food. The PR task has been used extensively to assess the rewarding impact of drugs of abuse and food in rats (e.g.,6-8), but to a lesser extent in mice9. The increased use of genetically engineered mice and diet-induced obese mouse models has heightened demands for behavioral measures of food reward in mice. In the present article we detail the materials and procedures used to train mice to respond (lever-press) for a high-fat and high-sugar food pellets on a PR schedule of reinforcement. We show that breakpoint response thresholds increase following acute food deprivation and decrease with peripheral administration of the anorectic hormone leptin and thereby validate the use of this food-operant paradigm in mice.

The present report details the protocol employed to measure the rewarding effects of high-fat food in mice using a progressive ratio operant conditioning task.

Foods that are rich in fat and sugar significantly contribute to over-eating and escalating rates of obesity. The consumption of palatable foods can ...

Neuroscience

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. Recent technological innovations allow for the development of more accurate and precise dietary and eating behavior assessment tools that require less effort for both the user and the researcher. Therefore, a new sensor-based device to assess food intake and eating behavior was developed. The device is a regular dining tray equipped with a video camera and three separate built-in weighing stations. The weighing stations measure the weight of the bowl, plate, and drinking cup continuously over the course of a meal. The video camera positioned to the face records eating behavior characteristics (chews, bites), which are analyzed using artificial intelligence (AI)-based automatic facial expression software. The tray weight and the video data are transported at real-time to a personal computer (PC) using a wireless receiver. The outcomes of interest, such as the amount eaten, eating rate and bite size, can be calculated by subtracting the data of these measures at the timepoints of interest. The information obtained by the current version of the tray can be used for research purposes, an upgraded version of the device would also facilitate the provision of more personalized advice on dietary intake and eating behavior. Contrary to the conventional dietary assessment methods, this dietary assessment device measures food intake directly within a meal and is not dependent on memory or the portion size estimation. Ultimately, this device is therefore suited for daily main meal food intake and eating behavior measures. In the future, this technology based dietary assessment method can be linked to health applications or smart watches to obtain a complete overview of exercise, energy intake, and eating behavior.

This protocol shows and explains a new technology-based dietary assessment method. The method consists of a dining tray with multiple built-in weighing scales and a video camera. The device is unique in the sense that it incorporates automated measures of food and drink intake and eating behavior over the course of a meal.

The vast majority of dietary and eating behavior assessment methods are based on self-reports. They are burdensome and also prone to measurement errors. ...

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control neuroplastic processes and correct abnormal function, or even shift functions from damaged tissue to physiologically healthy brain regions, hold the potential to dramatically improve overall health. Of the current neuroplastic interventions in development, neurofeedback training (NFT) from functional Magnetic Resonance Imaging (fMRI) has the advantages of being completely non-invasive, non-pharmacologic, and spatially localized to target brain regions, as well as having no known side effects. Furthermore, NFT techniques, initially developed using fMRI, can often be translated to exercises that can be performed outside of the scanner without the aid of medical professionals or sophisticated medical equipment. In fMRI NFT, the fMRI signal is measured from specific regions of the brain, processed, and presented to the participant in real-time. Through training, self-directed mental processing techniques, that regulate this signal and its underlying neurophysiologic correlates, are developed. FMRI NFT has been used to train volitional control over a wide range of brain regions with implications for several different cognitive, behavioral, and motor systems. Additionally, fMRI NFT has shown promise in a broad range of applications such as the treatment of neurologic disorders and the augmentation of baseline human performance. In this article, we present an fMRI NFT protocol developed at our institution for modulation of both healthy and abnormal brain function, as well as examples of using the method to target both cognitive and auditory regions of the brain.

The ability to induce and/or control neural plasticity may be critical in future treatments for neurologic disorders and the recovery from brain injury. In this paper, we present a protocol on the use of neurofeedback training with functional magnetic resonance imaging to modulate human brain function.

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control ...

Use of Real-Time Functional Magnetic Resonance Imaging-Based Neurofeedback to Downregulate Insular Cortex in Nicotine-Addicted Smokers

It has been more than a decade since the first functional magnetic resonance imaging (fMRI)-based neurofeedback approach was successfully implemented. Since then, various studies have demonstrated that participants can learn to voluntarily control a circumscribed brain region. Consequently, real-time fMRI (rtfMRI) provided a novel opportunity to study modifications of behavior due to manipulation of brain activity. Hence, reports of rtfMRI applications to train self-regulation of brain activity and the concomitant modifications in behavioral and clinical conditions such as neurological and psychiatric disorders [e.g., schizophrenia, obsessive compulsive Disorder (OCD), stroke] have rapidly increased.
Neuroimaging studies in addiction research have shown that the anterior cingulate cortex, orbitofrontal cortex, and insular cortex are activated during the presentation of drug-associated cues. Also, activity in both left and right insular cortices have been shown to be highly correlated with drug urges when participants are exposed to craving-eliciting cues. Hence, the bilateral insula is of particular importance in researching drug urges and addiction due to its role in the representation of bodily (interoceptive) states. This study explores the use of rtfMRI neurofeedback for the reduction in blood oxygen-level dependent (BOLD) activity in bilateral insular cortices of nicotine-addicted participants. The study also tests if there are neurofeedback training-associated modifications in the implicit attitudes of participants towards nicotine-craving cues and explicit-craving behavior.

In real-time functional magnetic resonance imaging (rtfMRI), brain activity is experimentally manipulated as an independent variable, and behavior is measured as a dependent variable. The protocol presented here focuses on the practical use of rtfMRI as a therapeutic tool for psychiatric disorders such as nicotine addiction.

It has been more than a decade since the first functional magnetic resonance imaging (fMRI)-based neurofeedback approach was successfully implemented. ...

Sucrose Preference and Novelty-Induced Hypophagia Tests in Rats using an Automated Food Intake Monitoring System

The prevalence and incidence of depressive disorders are rising worldwide, affecting about 322 million individuals, underlining the need for behavioral studies in animal models. In this protocol, to study depression-like and anhedonic behavior in rats, the established sucrose preference and novelty-induced hypophagia tests are combined with an automated food and liquid intake monitoring system. Prior to testing, in the sucrose preference paradigm, male rats are trained for at least 2 days to consume a sucrose solution in addition to tap water. During the test, rats are again exposed to water and sucrose solution. Consumption is registered every second by the automated system. The ratio of sucrose to total water intake (sucrose preference ratio) is a surrogate parameter for anhedonia. In the novelty-induced hypophagia test, male rats undergo a training period in which they are exposed to a palatable snack. During training, rodents show a stable baseline snack intake. On test day, the animals are transferred from home cages into a fresh, empty cage representing a novel unknown environment with access to the known palatable snack. The automated system records the total intake and its underlying microstructure (e.g., latency to approaching the snack), providing insight into anhedonic and anxious behaviors. The combination of these paradigms with an automated measuring system provides more detailed information, along with higher accuracy by reducing measuring errors. However, the tests use surrogate parameters and only depict depression and anhedonia in an indirect manner.

Presented here is a protocol to study depression-like and anhedonic behavior in rats. It combines two well-established behavioral methods, the sucrose preference and novelty-induced hypophagia tests, with an automated food and liquid intake monitoring system, to indirectly investigate rodent behavior using surrogate parameters.

The prevalence and incidence of depressive disorders are rising worldwide, affecting about 322 million individuals, underlining the need for behavioral ...