This work was funded by Region Stockholm.

All work on experimental subjects and patients was approved by the Swedish Ethical Review Authority, Sweden (D. nr: 2019-05505). Before registration in the system, all individuals provided written consent to the storage, handling, and analysis of their data. Patients were referred to the clinic for specialized treatment for eating disorders either by physician&#39;s referral or self-referral. Suffering from an eating disorder was the inclusion criteria for the patients.
1. Patient registration by clinicians
NOTE: Patient registration (Figure 1) is completed by a clinician using a developed custom web tool (see Table of Materials).
<ol>
	<li>Navigate to the web landing page using any modern browser upon patient referral.</li>
	<li>Use an existing account associated with a clinician to log in to the web tool.</li>
	<li>Fill in the patient registration form, including patient ID, social security number, birth date, age, and sex.</li>
	<li>Press the Save button to register a new patient. At this point, the procedure can be paused and then restarted later.</li>
</ol>
2. Questionnaire for patients
NOTE: The patient fills in the questionnaire on a smart device using a custom app developed in a web tool (see Table of Materials). If the patient is a minor, the questionnaire is filled in by a parent or guardian. Their data is organized in thematic blocks. After the answer to each question is completed, the system presents the next question (Figure 2).
<ol>
	<li>Open the questionnaire application on a smart device.</li>
	<li>Fill in the social security number for the patients (Figure 3).</li>
	<li>Fill in the first visit date; the current date is used by default.</li>
	<li>Fill in the information corresponding to the patients&#39; weight, height, and age.</li>
	<li>Fill in the information corresponding to behavior such as induced vomiting, snack frequency, and eating rate.</li>
	<li>Fill in the particulars corresponding to cognitive and emotional items such as fear of gaining weight and feelings of body dysmorphia.</li>
	<li>Press the Done button to finish the questionnaire. At this point, the questionnaire can be paused and then restarted later.</li>
</ol>
3. Risk assessment by clinicians
NOTE: The risk assessment (Figure 4) is retrieved and used by the clinician using a custom web tool (see Table of Materials).
<ol>
	<li>Navigate to the web landing page using any web browser. Use an existing account to log in to the web tool.</li>
	<li>Search for the patient using the patient&#39;s social security number or patient ID.</li>
	<li>Add measured weight and height to the system.</li>
	<li>Press the Result tab to get the algorithmic decision of whether the patient has an ED and, if so, what type of ED.</li>
	<li>Press tab Questions 1-20, or Questions 21-34, to display the questions where the patients&#39; responses deviate from answers by healthy individuals.</li>
	<li>Select a final diagnosis under the Result tab, based on the algorithm and the clinician&#39;s expertise.</li>
</ol>

<ol>
	<li>Treasure, J., Duarte, T. A., Schmidt, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eating+disorders.">Eating disorders.</a> Lancet. 395 (10227), 899-911 (2020).</li><li>American Psychiatric Association. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. , (2013).</li><li>Smink, F. R. E., van Hoeken, D., Hoek, H. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epidemiology+of+eating+disorders:+incidence,+prevalence+and+mortality+rates.">Epidemiology of eating disorders: incidence, prevalence and mortality rates.</a> Current Psychiatry Reports. 14 (4), 406-414 (2012).</li><li>Allen, K. L., Byrne, S. M., Oddy, W. H., Crosby, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DSM-IV-TR+and+DSM-5+eating+disorders+in+adolescents:+prevalence,+stability,+and+psychosocial+correlates+in+a+population-based+sample+of+male+and+female+adolescents.">DSM-IV-TR and DSM-5 eating disorders in adolescents: prevalence, stability, and psychosocial correlates in a population-based sample of male and female adolescents.</a> Journal of Abnormal Psychology. 122 (3), 720-732 (2013).</li><li>Preti, A., 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=The+epidemiology+of+eating+disorders+in+six+European+countries:+results+of+the+ESEMeD-WMH+project.">The epidemiology of eating disorders in six European countries: results of the ESEMeD-WMH project.</a> Journal of Psychiatric Research. 43 (14), 1125-1132 (2009).</li><li>Stice, E., Gau, J. M., Rohde, P., Shaw, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Risk+factors+that+predict+future+onset+of+each+DSM-5+eating+disorder:+predictive+specificity+in+high-risk+adolescent+females.">Risk factors that predict future onset of each DSM-5 eating disorder: predictive specificity in high-risk adolescent females.</a> Journal of Abnormal Psychology. 126 (1), 38-51 (2017).</li><li>S&#246;dersten, P., Brodin, U., Zandian, M., Bergh, C. E. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Verifying+Feighner's+hypothesis;+anorexia+nervosa+is+not+a+psychiatric+disorder.">Verifying Feighner's hypothesis; anorexia nervosa is not a psychiatric disorder.</a> Frontiers in Psychology. 10, 2110 (2019).</li><li>Lucas, G. M., Gratch, J., King, A., Morency, L. -. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=It's+only+a+computer:+Virtual+humans+increase+willingness+to+disclose.">It's only a computer: Virtual humans increase willingness to disclose.</a> Computers in Human Behavior. 37, 94-100 (2014).</li><li>Hendler, N., Spurgeon, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+Clinical+Diagnoses+Versus+Computerized+Test+Diagnoses+using+the+Maryland+Clinical+Diagnostics+Diagnostic+Paradigm+(Expert+System)+for+Diagnosing+Chronic+Pain+in+the+Neck,+Back+and+Limbs.">Comparison of Clinical Diagnoses Versus Computerized Test Diagnoses using the Maryland Clinical Diagnostics Diagnostic Paradigm (Expert System) for Diagnosing Chronic Pain in the Neck, Back and Limbs.</a> Journal of Anesthesia and Critical Care: Open Access. 6 (5), (2016).</li><li>Richens, J. G., Lee, C. M., Johri, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improving+the+accuracy+of+medical+diagnosis+with+causal+machine+learning.">Improving the accuracy of medical diagnosis with causal machine learning.</a> Nature Communications. 11 (1), 3923 (2020).</li><li>Dahlgren, C. L., Walsh, B. T., Vrabel, K., Siegwarth, C., R&#248;, &. #. 2. 1. 6. ;. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eating+disorder+diagnostics+in+the+digital+era:+validation+of+the+Norwegian+version+of+the+Eating+Disorder+Assessment+for+DSM-5+(EDA-5).">Eating disorder diagnostics in the digital era: validation of the Norwegian version of the Eating Disorder Assessment for DSM-5 (EDA-5).</a> Journal of Eating Disorders. 8 (1), 1-7 (2020).</li></ol>

Complete openness concerning financial arrangements is intended here. Brodin declares that he has no financial interests related to this study. Our research is carried out at the Karolinska Institute, where S&#246;dersten is a professor emeritus. 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 the remaining 5%. Mando Group AB contracts with Region Stockholm every fifth year to treat patients with eating disorders. Mando Group AB signed its first contract in 1997 with Region Stockholm and, since then, its treatment is one of the standards of care offered to the citizens of Stockholm. Mando Group AB won the latest tender and there are now Mandometer Clinics in four Stockholm hospitals. This arrangement is the same as when Region Stockholm contracts with its own clinics to treat patients with all kinds of disease, including eating disorders. That is to say, Region Stockholm provides eating disorders services to the citizens of Stockholm both through a clinic of its own and through Mando Group AB. All healthcare in Sweden is funded through the tax system; private pay is extremely uncommon. It should be added, first, that Mando Group AB is in compliance with the recommendation of the International Committee of Medical Journal Editors on &#8217;Author&#160;Responsibilities-Conflicts of Interest&#39;, http://www.icmje.org/recommendations/browse/rolesand-responsibilities/. Second, it should also be added that all profit that Mando Group AB has made has been reinvested in research and development and there have been no dividends to stock owners. All of the above is declared in all manuscript submissions and thus far, journals have judged it necessary to publish only some of the details. It seems, however, that&#160;the potential ethical problem when scientists translate their 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 his or her 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 &#8217;problem&#39; is similar in both cases. However, the more important incentive is the improvement of the treatment of patients with eating disorders. We are researchers working in an academic setting and like many other medical research institutes today, the Karolinska Institute encourages scientists to translate their research into the clinic in companies that aim to generate financial profits to be used for research and development&#160;(https://issuu.com/karolinska_institutet/docs/ki_strategy2030_eng).

Early and accurate diagnosis of eating disorders is critical to initiating appropriate treatment, improving treatment outcomes, and reducing poor health outcomes1. To determine a diagnosis, clinicians must handle large amounts of psychological and physiological information, and a large amount of data makes diagnosis a time-consuming task with a high risk of misdiagnosis.
The system described here speeds up the decision-making process by providing an automatic questionnaire-based diagnosis. In addition, it allows clinicians to view the specific responses that deviate from the expected responses of a healthy individual. The system was developed with clinicians' needs in mind, which is why the questionnaire is simple enough to be filled in by the patient alone, further reducing the time required by clinicians when considering a diagnosis. For the same reason, responses are saved on input, ensuring the process can be paused at any time. Computer connection issues and other interruptions do not require repeating the entire process.
A limitation to the protocol is that a diagnosis suggestion cannot be calculated unless all questionnaire items have been filled in. Another limitation is that medical measurements, such as blood pressure and peripheral temperature, are not included in the system but must be evaluated by clinicians or medical doctors.
There are several digital tools for eating disorder diagnoses, such as semi-structured electronic interviews11, but there are currently no diagnostic algorithms based on the DSM-5 or the International Classification of Disease, 11th Revision (ICD-11). The primary problem with available approaches is that they do not provide a simple way for clinicians to receive aid in the diagnosis or communicate what constitutes unhealthy responses. The current system is intended for use both in primary care, by health professionals with little knowledge of eating disorders, and in specialist clinics for discussing more complex cases, aiding clinicians in the decision-making process during diagnosis of eating disorders. This system results in improved quality of care, a reduction of time and effort by clinicians, and provides improved efficiency for the clinician in their daily practice.
The diagnostic algorithm is currently based on one questionnaire and allows the system to teach clinicians to diagnose patients better and consult other health professionals in difficult cases. Future development of the system must also include medical data. In addition, the prognostic ability of the algorithm can be improved by refining the questionnaire, replacing redundant, non-informative items with more relevant ones. A longitudinal approach also needs to be considered. If the patient receives appropriate treatment, it is essential to follow their health progression over time. Many items in the questionnaire are still valid for a follow-up approach. However, the questionnaire and algorithm need to be reformulated to create an index to measure health progression.

Dieting and an associated increase in physical activity are the known causes of anorexia nervosa and other eating disorders1. The most common eating disorders mentioned in the diagnostic manual of mental disorders (DSM-5) are anorexia nervosa (AN), bulimia nervosa (BN), binge-eating disorder (BED), and other specified feeding or eating disorder (OSFED)2. These disorders primarily affect females and are accompanied by severe physical and/or psychosocial health complications and distress3. Approximately 13% of females suffer from eating disorders4, and the prevalence of AN in women is estimated at 0.3%-1% throughout their lives, with an even higher percentage of women suffering from BN5.
A multitude of risk factors is associated with specific eating disorders. Dieting during early adolescence and a low body mass index (BMI) increases the risk of AN in females, but early puberty, thin-ideal internalization, body dissatisfaction, negative affect, and social support deficits do not6. Among the factors that predict the onset of BN are weight concerns, body dissatisfaction, drive for thinness, ineffectiveness, low interoceptive awareness, and dieting, but not perfectionism, maturity fears, interpersonal distrust, or BMI6. While there are symptomatic differences between the various types of eating disorders, there is a similarity in the risk factors. This suggests that eating pathology and maladaptive eating behavior (dieting) are common risk factors across all eating disorders.
Indeed, eating pathology is conspicuous in eating disorders. However, the difficulty of defining and quantifying pathological eating behavior, combined with the fact that diagnosis primarily relies on the subjective description of the symptom dimensions, can make the boundaries between diagnoses appear to be unclear7. This issue makes the diagnosis of eating disorders difficult, especially for health practitioners unfamiliar with eating disorder patients, such as primary care physicians.
Health professionals in primary care are often the first to be approached by individuals suffering from an eating disorder. Given the importance of early detection and intervention for a favorable prognosis, care providers must have the tools to help them recognize these disorders. Therefore, a diagnosis must be determined quickly and accurately to prevent delays in their treatment by specialists.
One way of achieving this diagnostic goal is to digitalize and automate questionnaires regarding their symptoms. An added benefit of this method could be that the responses are more truthful since studies suggest patients trust virtual therapists more than human clinicians for discussing mental health issues8. Another potential benefit is increased diagnostic reliability, with some studies suggesting that computer diagnoses can have higher reliability than in-person diagnoses9,10.
In the present protocol, an algorithm has been developed based on the responses to open-end and closed-end questions on physical condition, behavior, emotions, and thoughts by 949 consecutively referred patients (for demographic data, see Table 1). Of the 949 participants, 91.6% (869) were female, 18.0% had AN, 19.0% BN, 13.5% BED, 36.8% OSFED, 6.8% obesity (OB), and 5.9% had no eating disorder (No ED). The algorithm estimates both the probability of having an eating disorder and the conclusion regarding which type of eating disorder the individual has. The questionnaire items are based on DSM-5 criteria for Feeding and Eating Disorders and the diagnostic features of AN, BN, BED, and OSFED. OB (excess body fat) is not included in DSM-5 as a mental disorder. However, there are robust associations between OB and BED2. The questionnaire items are grouped into three categories: (1) Conditions, such as BMI, weight loss/gain during the last year, and self-induced vomiting. (2) Behaviors including eating patterns, dieting, weighing oneself, self-induced vomiting, isolation from friends and family, and avoiding activities. (3) Cognitions/thoughts, such as desired weight, being afraid of losing control, overeating, thoughts about food, believing oneself to be fat when others say you are too thin, and reaction to weight gain. The algorithm is based on an unconditional discriminant analysis that assigns weights to items stepwise, identifying the most discriminating items for each of the five diagnoses.&#160;The diagnostic information is displayed on an easy-to-use web-based interface.

<table><tbody><tr><td>Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis</td><td>Mando</td><td>Pending assignment</td><td></td></tr><tr><td>Claris FileMaker Go 19</td><td>Claris</td><td>-</td><td>For patient registration, custom app for questionnaire, risk assessment</td></tr><tr><td>iPad 7th generation (2019)</td><td>Apple</td><td>A2197</td><td></td></tr></tbody></table>

a computer-based platform for aiding clinicians in eating disorder analysis and diagnosis

Eating disorders (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified eating or feeding disorders) have a combined prevalence of 13% and are associated with severe physical and psychosocial problems. Early diagnosis, which is important for effective treatment and prevention of undesirable long-term health consequences, imposes problems among non-specialist clinicians unfamiliar with these patients, such as those working in primary care. Early, accurate diagnosis, particularly in primary care, allows expert interventions early enough in the disorder to facilitate positive treatment outcomes. Computer-assisted diagnostic procedures offer a possible solution to this problem by providing expertise via an algorithm that has been developed from a large number of cases that have been diagnosed in person by expert diagnosticians and expert caregivers. A web-based system for determining an accurate diagnosis for patients suspected to suffer from an eating disorder was developed based on these data. The process is automated using an algorithm that estimates the respondent's probability of having an eating disorder and the type of eating disorder the individual has. The system provides a report that works as an aid for clinicians during the diagnostic process and serves as an educational tool for new clinicians.

The patient registration described in step 1 is carried out by a clinician completing the form presented in Figure 1 in the computerized medical records. Once the clinician registers a new patient, the application moves to step 2, which allows the patient to complete the questionnaire. To start the questionnaire, the patient or clinician first needs to input the social security number (or ID) of the patient into the app on a smart device (Figure 3), after which the app displays the first questionnaire item. Figure 2 shows a screenshot of one item from the diagnostic questionnaire. Once a response is selected to a questionnaire item, the application moves to the next item. Patients cannot move back to alter the response of previous questions, and if the questionnaire is prematurely terminated, the responses are still saved, and the user can return to fill in the missing items. Once all questions have been answered, the app is closed automatically.
After the questionnaire has been completed, the risk assessment page presented in Figure 4 is available for the clinician via a web interface. Clinicians can view the recommended diagnosis on the &#39;Result&#39; page, along with the estimated probability of accuracy from 0-1 (i.e., a 100% range), based on an automatic computation of risk factors of the questionnaire. By pressing either the tab &#39;Questions 1-20&#39; or &#39;Questions 21-34&#39;, clinicians can view the healthy responses (color turquoise) and the deviating responses (color red) that resulted in the suggested diagnosis (Figure 5). The algorithm (see Table of Materials) estimates both the probability of having an eating disorder and the conclusion regarding which type of eating disorder the individual has. The accuracy of the model is 97.1% for having an ED and 82.8% for ED diagnoses (Table 2). The diagnostic information is displayed on an easy-to-use web-based interface. This information provides clinicians with confidence in their decision if the algorithmic diagnosis agrees with the clinician&#39;s own evaluation. If the algorithmic diagnosis does not agree with the clinician&#39;s evaluation, the clinician is encouraged to seek a second opinion from other health professionals. The system also allows clinicians to train themselves to become better at diagnosing patients by viewing the specific questions expected to deviate from a healthy individual in the question tabs and by reviewing difficult cases with other health professionals.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63848/63848fig01.jpg" /> 
Figure 1: An illustration of the patient registration form in the web tool. <a href="https://www.jove.com/files/ftp_upload/63848/63848fig01large.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/63848/63848fig02.jpg" /> 
Figure 2: Sample of a single questionnaire item displayed on a smart tablet. <a href="https://www.jove.com/files/ftp_upload/63848/63848fig02large.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/63848/63848fig03.jpg" /> 
Figure 3: Snapshot of the page asking for the required social security number before completing the diagnosis questionnaire. <a href="https://www.jove.com/files/ftp_upload/63848/63848fig03large.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/63848/63848fig04.jpg" /> 
Figure 4: Example of result page with recommended diagnosis and estimated accuracy (in this case 100%) as displayed using the web tool. The result page also shows the date, sex, age, and BMI of the patient. At the top, clinicians can select a diagnosis. <a href="https://www.jove.com/files/ftp_upload/63848/63848fig04large.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/63848/63848fig05.jpg" /> 
Figure 5: Questionnaire responses and their association with the suggested diagnosis. Red indicates a high association, and teal suggests a low association. <a href="https://www.jove.com/files/ftp_upload/63848/63848fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Female (n = 869)</td>
			<td>Male (n = 80)</td>
			<td>Total (n = 949)</td>
		</tr>
		<tr>
			<td>Age</td>
			<td>21.0 (17.0 - 30.0)</td>
			<td>21.0 (15.0 - 33.5)</td>
			<td>21.0 (17.0 - 30.0)</td>
		</tr>
		<tr>
			<td>Height</td>
			<td>167.0 (162.0 - 170.0)</td>
			<td>176.5 (169.0 - 183.0)</td>
			<td>167.0 (162.0 - 171.0)</td>
		</tr>
		<tr>
			<td>Weight</td>
			<td>58.2 (50.0 - 75.0)</td>
			<td>71.5 (57.0 - 97.0)</td>
			<td>59.1 (50.0 - 76.5)</td>
		</tr>
		<tr>
			<td>BMI</td>
			<td>20.9 (17.8 - 26.7)</td>
			<td>21.3 (18.0 - 31.2)</td>
			<td>21 (17.9 - 27.1)</td>
		</tr>
	</tbody>
</table>
Table 1: Demographic data of the 949 patients included for the diagnostic algorithm development. Values are expressed as median (lower quartile - upper quartile).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Diagnosis</td>
			<td>Clinician diagnosis</td>
			<td>Algorithm diagnosis</td>
			<td>Accuracy (%)</td>
		</tr>
		<tr>
			<td>AN</td>
			<td>171</td>
			<td>172</td>
			<td>87.1</td>
		</tr>
		<tr>
			<td>BN</td>
			<td>180</td>
			<td>181</td>
			<td>82.2</td>
		</tr>
		<tr>
			<td>BED</td>
			<td>128</td>
			<td>138</td>
			<td>81.3</td>
		</tr>
		<tr>
			<td>OSFED</td>
			<td>349</td>
			<td>328</td>
			<td>79.9</td>
		</tr>
		<tr>
			<td>OB</td>
			<td>65</td>
			<td>74</td>
			<td>90.8</td>
		</tr>
	</tbody>
</table>
Table 2: Number of patients in each ED category diagnosed by a trained clinician and the decision and accuracy of the algorithm.

Watch this Scientific Journal Video about A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis at JoVE.com

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Diagnosing eating disorders in healthcare is challenging. Therefore, the present protocol develops an algorithm based on 949 patient responses to a questionnaire, with the diagnosis displayed on an easy-to-use web-based interface. This system facilitates the accurate diagnosis of eating disorders while excluding those believed to have an eating disorder.

Eating disorders (anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified eating or feeding disorders) have a combined prevalence of ...

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3.5K Views.  Karolinska Institutet. Diagnosing eating disorders in healthcare is challenging. Therefore, the present protocol develops an algorithm based on 949 patient responses to a questionnaire, with the diagnosis displayed on an easy-to-use web-based interface. This system facilitates the accurate diagnosis of eating disorders while excluding those believed to have an eating disorder. 

Video: A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Handwriting Analysis Indicates Spontaneous Dyskinesias in Neuroleptic Na&#239;ve Adolescents at High Risk for Psychosis

Growing evidence suggests that movement abnormalities are a core feature of psychosis. One marker of movement abnormality, dyskinesia, is a result of impaired neuromodulation of dopamine in fronto-striatal pathways. The traditional methods for identifying movement abnormalities include observer-based reports and force stability gauges. The drawbacks of these methods are long training times for raters, experimenter bias, large site differences in instrumental apparatus, and suboptimal reliability. Taking these drawbacks into account has guided the development of better&#160;standardized and more efficient procedures to examine movement abnormalities through handwriting analysis software and tablet. Individuals at&#160;risk for psychosis showed significantly more dysfluent pen movements (a proximal measure for dyskinesia) in a handwriting task. Handwriting kinematics offers a great advance over previous methods of assessing dyskinesia, which could clearly be beneficial for understanding the etiology of psychosis.

Handwriting analysis software significantly improves upon existing instruments measuring movement disorders. Individuals at&#160;risk for psychosis and healthy controls completed handwriting tasks to test for dyskinesia. Results suggest that youth at&#160;risk for psychosis exhibit dyskinesia and that handwriting analysis could significantly contribute to wider dissemination of early identification efforts

Growing evidence suggests that movement abnormalities are a core feature of psychosis. One marker of movement abnormality, dyskinesia, is a result of ...

Contextual and Cued Fear Conditioning Test Using a Video Analyzing System in Mice

The contextual and cued fear conditioning test is one of the behavioral tests that assesses the ability of mice to learn and remember an association between environmental cues and aversive experiences. In this test, mice are placed into a conditioning chamber and are given parings of a conditioned stimulus (an auditory cue) and an aversive unconditioned stimulus (an electric footshock). After a delay time, the mice are exposed to the same conditioning chamber and a differently shaped chamber with presentation of the auditory cue. Freezing behavior during the test is measured as an index of fear memory. To analyze the behavior automatically, we have developed a video analyzing system using the ImageFZ application software program, which is available as a free download at http://www.mouse-phenotype.org/. Here, to show the details of our protocol, we demonstrate our procedure for the contextual and cued fear conditioning test in C57BL/6J mice using the ImageFZ system. In addition, we validated our protocol and the video analyzing system performance by comparing freezing time measured by the ImageFZ system or a photobeam-based computer measurement system with that scored by a human observer. As shown in our representative results, the data obtained by ImageFZ were similar to those analyzed by a human observer, indicating that the behavioral analysis using the ImageFZ system is highly reliable. The present movie article provides detailed information regarding the test procedures and will promote understanding of the experimental situation.

This article presents a protocol for a contextual and cued fear conditioning test using a video analyzing system to assess fear learning and memory in mice.

The contextual and cued fear conditioning test is one of the behavioral tests that assesses the ability of mice to learn and remember an association ...

Using the Activity-based Anorexia Rodent Model to Study the Neurobiological Basis of Anorexia Nervosa

Anorexia nervosa (AN) is a psychiatric illness characterized by excessively restricted caloric intake and abnormally high levels of physical activity. A challenging illness to treat, due to the lack of understanding of the underlying neurobiology, AN has the highest mortality rate among psychiatric illnesses. To address this need, neuroscientists are using an animal model to study how neural circuits may contribute toward vulnerability to AN and may be affected by AN. Activity-based anorexia (ABA) is a bio-behavioral phenomenon described in rodents that models the key symptoms of anorexia nervosa. When rodents with free access to voluntary exercise on a running wheel experience food restriction, they become hyperactive &#8211; running more than animals with free access to food. Here, we describe the procedures by which ABA is induced in adolescent female C57BL/6 mice. On postnatal day 36 (P36), the animal is housed with access to voluntary exercise on a running wheel. After 4 days of acclimation to the running wheel, on P40, all food is removed from the cage. For the next 3 days, food is returned to the cage (allowing animals free food access) for 2 hr daily. After the fourth day of food restriction, free access to food is returned and the running wheel is removed from the cage to allow the animals to recover. Continuous multi-day analysis of running wheel activity shows that mice become hyperactive within 24 hr following the onset of food restriction. The mice run even during the limited time during which they have access to food. Additionally, the circadian pattern of wheel running becomes disrupted by the experience of food restriction. We have been able to correlate neurobiological changes with various aspects of the animals&#8217; wheel running behavior to implicate particular brain regions and neurochemical changes with resilience and vulnerability to food-restriction induced hyperactivity.

Here we present a protocol to induce activity-based anorexia (ABA) in female adolescent mice. ABA is a condition of hyperactivity evoked by imposing food restriction on rodents with access to a running wheel. This phenomenon is being used as a model to study the underlying neurobiology of anorexia nervosa.

Anorexia nervosa (AN) is a psychiatric illness characterized by excessively restricted caloric intake and abnormally high levels of physical activity. A ...

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.

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 ...

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

The analysis of functional abilities and their development in early childhood (0-6 years old) are fundamental aspects among young children with certain types of developmental difficulties that can facilitate prevention, through programmed interventions adapted to the needs of each user (student or patient). There are, however, few investigations to date, that have analyzed the use of automated tools for recording and interpreting the results of the initial assessment. Here, a protocol is presented to examine the functional abilities in early childhood in young children, aged between 3-6 years old, with intellectual disabilities, but the protocol can also be used for ages 0 to 6 years. The protocol makes use of a computer application, eEarlyCare, that facilitates the interpretation of the results of systematic observations, which are recorded in natural environments by professionals trained in early intervention. The software can be used to analyze 11 functional areas (Food Autonomy, Personal Care and Hygiene, Dressing and Undressing Independently, Sphincter Control, Functional Mobility, Communication and Language, Daily Life Routines, Adaptive Behavior and Attention) and a total of 114 different behaviors. Its use facilitates the analysis of the observed abilities and greatly assists early intervention. Compared to other observational methods, it allows a more efficient use of personal and material resources. The use of the computer application facilitates the recording of the observation results, which helps with organization and reflection on the observations. The software displays the observation results on-screen compared to normal developmental parameters. This information can be referred to for decision-making about the most suitable intervention program for each user (student or patient). Likewise, clustering techniques are applied to analyze the relation between the type of intellectual disabilities and functional development identified with the software, a relation that is intended to serve as a guide for early-care professional intervention.

We present a protocol to use a computational tool to record and analyze the functional abilities of children aged 3-6 years old. The protocol facilitates the comparison of these abilities throughout their development and can be used to assess developmental difficulties.

The analysis of functional abilities and their development in early childhood (0-6 years old) are fundamental aspects among young children with certain ...

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed (iOS/Android). This app can be used as a food record and&#160;24-h recall (or shorter recall periods). Different sampling schemes can be created on either prespecified or random days/times within a predetermined period for both methods, with&#160;push notifications to urge the participants to register their food intake. In case of non-response, notifications are automatically rescheduled to ensure complete data collection. For use as a food record, respondents can access the app and log their food intake throughout the day. Food records close automatically at the end of the day; recalls close after submission of the consumed items. The recall as well as the food record module provide access to an extensive food list based on the Dutch food composition database (FCDB), which can be accustomed to fit different research purposes. When selecting a food item, respondents are simultaneously prompted to insert portion size, i.e., in household measures (e.g., cups, spoons, glasses), standard portion sizes (e.g., small, medium, large), or weight in grams, and eating occasion/time of consumption. Portion size options can be adjusted, e.g., only entry in grams in case of a weighed food record or time of consumption instead of eating occasion). The app also includes a My Dishes function, which allows the respondent to create their own recipes or product combinations (e.g., a daily breakfast) and only report the total quantity consumed. Subsequently, the app accounts for yield and retention factors. The data are stored on a secure server. If desired, additional questions, i.e., in general or those related to specific food items or eating occasions can be incorporated. This paper describes the development of the system (app and backend), including expert evaluations and usability testing.

This article describes the protocol for the development of an innovative smartphone-based dietary assessment application Traqq, including expert evaluations and usability testing.

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed ...

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. ...

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Different methods can be used in research to assess dietary intake, many of which are still paper-based. Written estimated food diaries are often utilized in clinical trials, despite being a burden for both study participants and researchers. This method requires participant literacy, it is time consuming, labor intensive, and can easily lead to under-reporting. With advancements in technology, there is a growing interest in electronic diaries that automate the dietary assessment process. These are focused on improving accuracy, reducing both time and cost and providing users with a visual and more enjoyable experience. The methodology presented here aimed to validate the &#39;Boden Food Plate&#39;, a novel web-based platform for self-recording of food and drink items, compared to a traditional estimated food diary. The application was also rated on a satisfaction scale by study participants using a paper-based questionnaire. Sixty-seven participants completed the dietary measures on both the three-day electronic and paper food diaries. For the analysis, only dietary data completed at both study time points (baseline and week six) was utilized. Despite small mean differences between dietary data collection methods, Bland Altman analysis showed fairly wide 95% limits of agreement between the electronic platform and the written estimated food diary and there were few cases which did not fall within the 95% confidence intervals. Overall, participants found the electronic food diary to be more fun than the paper method and as easy to use as hard copy diaries. The new platform has potential as a self-recording tool for the collection of dietary data, particularly when utilized in clinical trial settings. However, further validation studies are needed to improve the validity of this novel electronic dietary data collection tool.

The &#39;Boden Food Plate&#39; is an electronic food diary designed to be an interactive and fun method to collect dietary intake using visual depictions. The purpose of this study was to validate the web-based application against a traditional three-day estimated food diary method.

Different methods can be used in research to assess dietary intake, many of which are still paper-based. Written estimated food diaries are often utilized ...

Medicine

Mobile Device-assisted Dietary Ecological Momentary Assessments for the Evaluation of the Adherence to the Mediterranean Diet in a Continuous Manner

Mobile device-assisted dietary ecological momentary assessments (EMAs) have emerged as a new tool allowing the evaluation of dietary intake in real time, in a real-world setting and in a continuous manner. They have the potential to minimize recall bias, participant, and investigator burden, and decrease economic and time investment while maximizing ecological validity.
We developed a set of EMAs aimed at evaluating continuous adherence to the MedDiet. Four multiple-choice EMAs are sent daily in a randomized manner from a total of eight questions. The EMAs enquire about the consumption of 11 key food groups of the Mediterranean diet in the last 24-48 h in a semi-quantitative way. EMAs capture the daily frequency of consumption of fruits, vegetables, and extra virgin olive oil on different days of the week. Additionally, EMAs capture the weekly frequency of consumption of whole grain products, sugary drinks, nuts, legumes, sweets, fish and seafood, and red and processed meats. A designed scoring system behind the EMAs extracts the percentage of adherence to the MedDiet recommendations and calculates a quality index of the diet every week. Individualized reports are sent periodically to the volunteers highlighting the strengths and weaknesses of their diet. EMAs are also expected to have a behavioral effect, reinforcing the choice of Mediterranean foods.

Presented here is a study protocol aimed at monitoring continuous adherence to the Mediterranean diet (MedDiet) by means of ecological momentary assessments. The method evaluates the intake of key food groups of the MedDiet and calculates an index of adherence.

Mobile device-assisted dietary ecological momentary assessments (EMAs) have emerged as a new tool allowing the evaluation of dietary intake in real time, ...

Integrated Spreadsheets for Nutritional Analysis of Population Diet Surveys

It can be challenging to calculate nutrient intakes in population diet surveys because existing nutritional analysis software is generally oriented toward analyzing intakes of individuals and may not allow users to input or easily modify the food composition data used in the analysis. These are drawbacks that are more problematic in low- and middle-income country settings. While there are numerous software-assisted dietary assessment platforms that conduct onboard nutritional analysis and are appropriate for use in large surveys, they are often similarly limited, and further restrict users to specific assessment modalities. This paper presents a multifunctional system of integrated spreadsheets for nutritional analysis of population diet surveys (ISNAPDS) that provides a solution for situations in which data have been collected but cannot be adequately analyzed with existing software. The protocol involves supplying the system with fully customizable data on food composition, food group classifications, and food intake (food intake in g/day may be entered directly or calculated based on user-supplied intake frequencies and either standard or variable serving sizes). Following data entry, the user modifies a set of simple pre-populated formulas to match them to the structure of the input data and the system applies these formulas to calculate nutrient and food group intakes, and the contributions of food groups to nutrient intakes for all members of the survey population. The flexibility of the ISNAPDS system allows it to accommodate the global diversity of foods consumed and analyze quantitative, semiquantitative, and nonquantitative food consumption data collected using prospective and retrospective assessment methods employing different reference periods and portion size estimation methods. To date, the system has been applied in published and ongoing analyses of 24 h recall, diet record, food frequency, and disaggregated household consumption data from population surveys in China, Ethiopia, India, Mongolia, Thailand, and a multi-country analysis of 10 sub-Saharan African countries.

This paper presents a system of integrated spreadsheets using simple formulas to calculate nutrient and food group intakes and the contributions of food groups to nutrient intakes for analysis of population diet survey data. The system accommodates quantitative, semiquantitative, and nonquantitative food intake data and a user-supplied food composition table.

It can be challenging to calculate nutrient intakes in population diet surveys because existing nutritional analysis software is generally oriented toward ...