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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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.

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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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3.4K 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

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 ...

Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

The main goal of this study was to assess the usability of a tablet-computer-based application (EmoCogMeter) in investigating the effects of age on cognitive functions across the lifespan in a sample of 378 healthy subjects (age range 18-89 years). Consistent with previous findings we found an age-related cognitive decline across a wide range of neuropsychological domains (memory, attention, executive functions), thereby proving the usability of our tablet-based application. Regardless of prior computer experience, subjects of all age groups were able to perform the tasks without instruction or feedback from an experimenter. Increased motivation and compliance proved to be beneficial for task performance, thereby potentially increasing the validity of the results. Our promising findings underline the great clinical and practical potential of a tablet-based application for detection and monitoring of cognitive dysfunction.

We tested the usability of a tablet-computer-based application (EmoCogMeter) in investigating the effects of age on cognition. Results show an age-related cognitive decline, thereby proving the usability of our application. Findings underline the great clinical and practical potential of a tablet-based application for detection and monitoring of cognitive dysfunction.

The main goal of this study was to assess the usability of a tablet-computer-based application (EmoCogMeter) in investigating the effects of age on ...

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with schizophrenia who are at familial high risk (FHR) may show similar cognitive and emotional changes. However, the neurological changes underlying the emergence of these symptoms remain unclear. This study sought to identify differences in frontal, striatal, and limbic regions in children and adolescents with FHR using functional magnetic resonance imaging. Groups of 21 children and adolescents at FHR and 21 healthy controls completed an emotional oddball task that relied on selective attention and the suppression of task-irrelevant emotional information. The standard oddball task was modified to include aversive and neutral distractors in order to examine potential group differences in both emotional and executive processing. This task was designed specifically to allow for children and adolescents to complete by keeping the difficulty and emotional image content age-appropriate. Furthermore, we demonstrate a technique for suitable fMRI registration for children and adolescent participants. This paradigm may also be applied in future studies to measure changes in neural activity in other populations with hypothesized developmental changes in executive and emotional processing.

This paper describes how to use the emotional oddball task and fMRI to measure brain activation in children and adolescents at familial high risk for schizophrenia (FHR).&#160;FMRI was used to measure differences in fronto-striato-limbic regions during an emotional oddball task. Children with FHR exhibited abnormal functional activation during adolescence.

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with ...

Fat Preference: A Novel Model of Eating Behavior in Rats

Obesity is a growing problem in the United States of America, with more than a third of the population classified as obese. One factor contributing to this multifactorial disorder is the consumption of a high fat diet, a behavior that has been shown to increase both caloric intake and body fat content. However, the elements regulating preference for high fat food over other foods remain understudied.
To overcome this deficit, a model to quickly and easily test changes in the preference for dietary fat was developed. The Fat Preference model presents rats with a series of choices between foods with differing fat content. Like humans, rats have a natural bias toward consuming high fat food, making the rat model ideal for translational studies. Changes in preference can be ascribed to the effect of either genetic differences or pharmacological interventions. This model allows for the exploration of determinates of fat preference and screening pharmacotherapeutic agents that influence acquisition of obesity.

Dietary fat content influences both energy intake and body fat composition in mammals. By examining rats&#8217; preference for high fat food in a series of choice experiments, it is possible to test genetic differences and pharmacological interventions on their preference for high fat food.

Obesity is a growing problem in the United States of America, with more than a third of the population classified as obese. One factor contributing to ...

Signal Attenuation as a Rat Model of Obsessive Compulsive Disorder

In the signal attenuation rat model of obsessive-compulsive disorder (OCD), lever-pressing for food is followed by the presentation of a compound stimulus which serves as a feedback cue. This feedback is later attenuated by repeated presentations of the stimulus without food (without the rat emitting the lever-press response). In the next stage, lever-pressing is assessed under extinction conditions (i.e., no food is delivered). At this stage rats display two types of lever-presses, those that are followed by an attempt to collect a reward, and those that are not. The latter are the measure of compulsive-like behavior in the model. A control procedure in which rats do not experience the attenuation of the feedback cue serves to distinguish between the effects of signal attenuation and of extinction. The signal attenuation model is a highly validated model of OCD and differentiates between compulsive-like behaviors and behaviors that are repetitive but not compulsive. In addition the measures collected during the procedure eliminate alternative explanations for differences between the groups being tested, and are quantitative, unbiased and unaffected by inter-experimenter variability. The major disadvantages of this model are the costly equipment, the fact that it requires some technical know-how and the fact that it is time-consuming compared to other models of OCD (11 days). The model may be used for detecting the anti- or pro-compulsive effects of pharmacological and non-pharmacological manipulations and for studying the neural substrate of compulsive behavior.

The goal of the protocol described in this paper is to induce compulsive-like behavior in rats for the study of obsessive-compulsive disorder (OCD). This behavior is precipitated by attenuating a signal indicating that a lever-press response was effective in producing food.

In the signal attenuation rat model of obsessive-compulsive disorder (OCD), lever-pressing for food is followed by the presentation of a compound stimulus ...

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

In addition to impairments in social communication and the presence of restricted interests and repetitive behaviors, deficits in sensory processing are now recognized as a core symptom in autism spectrum disorder (ASD). Our ability to perceive and interact with the external world is rooted in sensory processing. For example, listening to a conversation entails processing the auditory cues coming from the speaker (speech content, prosody, syntax) as well as the associated visual information (facial expressions, gestures). Collectively, the &#8220;integration&#8221; of these multisensory (i.e., combined audiovisual) pieces of information results in better comprehension. Such multisensory integration has been shown to be strongly dependent upon the temporal relationship of the paired stimuli. Thus, stimuli that occur in close temporal proximity are highly likely to result in behavioral and perceptual benefits &#8211; gains believed to be reflective of the perceptual system&#39;s judgment of the likelihood that these two stimuli came from the same source. Changes in this temporal integration are expected to strongly alter perceptual processes, and are likely to diminish the ability to accurately perceive and interact with our world. Here, a battery of tasks designed to characterize various aspects of sensory and multisensory temporal processing in children with ASD is described. In addition to its utility in autism, this battery has great potential for characterizing changes in sensory function in other clinical populations, as well as being used to examine changes in these processes across the lifespan.

We describe how to implement a battery of behavioral tasks to examine the processing and integration of sensory stimuli in children with ASD. The goal is to characterize individual differences in temporal processing of simple auditory and visual stimuli and relate these to higher order perceptual skills like speech perception.

In addition to impairments in social communication and the presence of restricted interests and repetitive behaviors, deficits in sensory processing are ...

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis (AFEA) software. Additionally, we demonstrate a method for extracting relevant emotional data output and plotting the emotional response of a population over a specified time frame. By time pairing each participant's treatment response to a control stimulus (baseline), the overall emotional response over time and across multiple participants can be quantified. AFEA is a prospective analytical tool for assessing unbiased response to food and beverages. At present, most research has mainly focused on beverages. Methodologies and analyses have not yet been standardized for the application of AFEA to beverages and foods; however, a consistent standard methodology is needed. Optimizing video capture procedures and resulting video quality aids in a successful collection of emotional response to foods. Furthermore, the methodology of data analysis is novel for extracting the pertinent data relevant to the emotional response. The combinations of video capture optimization and data analysis will aid in standardizing the protocol for automated facial expression analysis and interpretation of emotional response data.

A protocol for capturing and statistically analyzing emotional response of a population to beverages and liquefied foods in a sensory evaluation laboratory using automated facial expression analysis software is described.

We demonstrate a method for capturing emotional response to beverages and liquefied foods in a sensory evaluation laboratory using automated facial ...

Use of a Psychophysiological Script-driven Imagery Experiment to Study Trauma-related Dissociation in Borderline Personality Disorder

This protocol offers a detailed description of a psychophysiological experiment using script-driven trauma-related imagery and standardized clinical instruments within a comparative design assessing physiological and psychopathological features of individuals with BPD. This method aims at studying the psychological and physiological effects of trauma-related dissociation. Since the psychodiagnostic classification of trauma-related disorders relies on the observation of evolutionarily determined responses to life-threat, an integrated assessment paradigm for the study of reactions to traumatic memories proposes a very appropriate methodological approach.
The employed script-driven imagery paradigm uses individual recall instructions to activate trauma-related memory networks and prompt associated emotional and physiological responses. These responses are measured by means of self-rating scales and physiological assessments. During the individual recall, participants are asked to vividly imagine traumatic and everyday experiences and other situations triggered by short personalized verbal scripts they authored beforehand together with the experimenters. A wide range of affective reactions and different physiological parameters can be measured. We used this paradigm to investigate dissociative states in BPD and to find physiological and affective correlates of dissociative states. Some of the participants were having severe traumatic antecedents.
To investigate different reaction patterns within the same diagnostic group, participants with different levels of traumatic histories, symptom severities, and co-morbidities should be included. By using short verbal scripts, the level of stress induced to participants is held as low as possible without affecting the validity of the object of investigation.

We present a protocol of personalized script-driven trauma-related imagery and clinical assessments within a comparison design for investigating peritraumatic dissociation (PD), psychophysiological reactions, i.e. heart rate (HR) and skin conductance (SC), and psychological features of often severely traumatized individuals with borderline personality disorder (BPD).

This protocol offers a detailed description of a psychophysiological experiment using script-driven trauma-related imagery and standardized clinical ...

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Three-dimensional gait analysis (3DGA) is shown to be a useful clinical tool for the evaluation of gait abnormality due to movement disorders. However, the use of 3DGA in actual clinics remains uncommon. Possible reasons could include the time-consuming measurement process and difficulties in understanding measurement results, which are often presented using a large number of graphs. Here we present a clinician-friendly 3DGA method developed to facilitate the clinical use of 3DGA. This method consists of simplified preparation and measurement processes that can be performed in a short time period in clinical settings and intuitive results presentation to facilitate clinicians&#39; understanding of results. The quick, simplified measurement procedure is achieved by the use of minimum markers and measurement of patients on a treadmill. To facilitate clinician understanding, results are presented in figures based on the clinicians&#39; perspective. A Lissajous overview picture (LOP), which shows the trajectories of all markers from a holistic viewpoint, is used to facilitate intuitive understanding of gait patterns. Abnormal gait pattern indices, which are based on clinicians&#39; perspectives in gait evaluation and standardized using the data of healthy subjects, are used to evaluate the extent of typical abnormal gait patterns in stroke patients. A graph depicting the analysis of the toe clearance strategy, which depicts how patients rely on normal and compensatory strategies to achieve toe clearance, is also presented. These methods could facilitate implementation of 3DGA in clinical settings and further encourage development of measurement strategies from the clinician&#39;s point of view.

In this study, a clinician-friendly three-dimensional gait analysis method, which was designed to be performed in the rehabilitation clinic, is presented. The method consists of a simplified measurement method and intuitive figures to facilitate clinicians&#39; understanding of the results.

Three-dimensional gait analysis (3DGA) is shown to be a useful clinical tool for the evaluation of gait abnormality due to movement disorders. However, ...

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 ...