Pilot Study: PORTION-O-MAT - A Mixed Reality Solution for Investigating Perceptual and Behavioural Abnormalities During Food Portioning in Adolescents with Anorexia Nervosa

Pilot Study: PORTION-O-MAT - A Mixed Reality Solution for Investigating Perceptual and Behavioural Abnormalities During Food Portioning in Adolescents with Anorexia Nervosa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pilot Study: PORTION-O-MAT - A Mixed Reality Solution for Investigating Perceptual and Behavioural Abnormalities During Food Portioning in Adolescents with Anorexia Nervosa Jessica Gutheil, Oliver Kratz, Martin Diruf, Stefanie Horndasch This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6997366/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2025 Read the published version in Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity → Version 1 posted 9 You are reading this latest preprint version Abstract Objective . Anorexia nervosa (AN) is a severe eating disorder characterized by perceptual distortions and restrictive eating behaviours. This pilot study examines portion size estimation in adolescent AN patients using a mixed-reality (MR) approach. The objective is to identify systematic biases in portion perception compared to healthy controls and to analyse cognitive distortions affecting portion selection. Methods. A total of 30 female participants were recruited: 15 adolescent AN patients and 15 healthy adults as pretest. Participants engaged in a simulated meal assembly task within an MR environment, adjusting portion sizes of virtual food components to match a "typical" meal size (100%). Decision-making patterns and self-reported eating disorder symptoms were recorded. Statistical analyses included descriptive statistics, group comparisons and correlation analysis to examine associations between clinical variables and portion sizes, decision-making time and other decision parameters. Results. AN patients consistently selected significantly smaller portion sizes than healthy adults, particularly for high-calorie foods. No significant differences were observed in decision-making time or uncertainty indicators. Discussion. The findings support the hypothesis that AN patients exhibit altered food perception in the sense that they tend to overestimate the size of visually presented food portions. The MR approach proved effective in simulating meal selection, Future studies should include larger and more diverse samples and incorporate real food intake to further validate these results. Anorexia nervosa mixed reality adolescent portion size estimation food stimuli perception bias Figures Figure 1 Figure 2 Figure 3 1 Introduction Anorexia nervosa (AN) is a severe mental disorder with significant risks for those affected (Davison, Neale & Hautzinger, 2016 ; Fairburn & Harrison, 2003 ; Jacobi & Beintner, 2021 ). an increase in AN diagnoses has been observed among young people in recent years (Federal Ministry of Health, 2022; Jacobi & Beintner, 2021 ). The treatment of AN involving refeeding and consequently the training of adequate meal intake is a critical focus in clinical psychology and medicine. Altered perception of portion sizes is considered a key aspect of the disorder (Dörsam et al., 2020 ; Milos et al., 2013 ; Pasi et al., 2022 ; Robinson et al., 2016 ). Adolescents with AN face the daily challenge of confronting food or food-related stimuli, which may be perceived as aversive and contrary to their disorder-related intentions. Appropriate food portioning can thus become a complex and burdensome process. In the context of Eating Disorders (ED), changes in attention, information processing, memory, learning, and executive functions are particularly common. Patients exhibit attention biases, indicating altered attentional allocation (Lloyd & Steinglass, 2018 ). This can involve intensified focus on a specific stimulus (orientation), reduced ability to shift attention away (distraction), and avoidance of attending to certain stimuli (attention avoidance; Cisler & Koster, 2010 ; Lloyd & Steinglass, 2018 ). Due to limited cognitive flexibility, a strong cognitive bias often makes it difficult for ED patients to respond selectively and flexibly to anxiety-inducing stimuli (Schulte-Rüther & Konrad, 2015). While conclusive data is lacking, it is suggested that food-related cues may trigger similar attentional distortions in AN patients as typically anxiety-provoking stimuli do (Aspen, Darcy & Lock, 2013 ; Cowdrey et al., 2011 ). Exposure to food stimuli is often associated with negative emotions in individuals with AN, as they generally rate food images as less pleasant compared to healthy individuals (Horndasch et al., 2018 ; Joos et al., 2011 ; Santel et al., 2006 ; Uher et al., 2004 ). Studies on anxiety have demonstrated a positive correlation between food portion size and both physiological responses (e.g., heart rate, skin conductance) and self-reported anxiety levels (Kissileff, 2016). Adolescents with AN exhibit increased pre-meal anxiety and heightened autonomic nervous system activation when exposed to images of larger portion sizes, suggesting that food-related anxiety may play a crucial role in the development and maintenance of AN. Furthermore, research supports the use of computer-based methods to objectively and systematically assess responses to food stimuli (Kissileff et al., 2016 ). Additional studies have confirmed a positive relationship between portion size and anxiety (Herzog et al., 2017 ; Dörsam et al., 2020 ), with findings indicating that the impact of food energy density on anxiety is moderated by portion size (Herzog et al., 2017 ). Additionally, ED patients display a heightened focus on details of specific body parts and food attributes, resulting in reduced central coherence. This means they struggle to view acquired information in an overall context (Kappel et al., 2014 ; Lopez et al., 2008 ). It is unclear whether overall perception of food portions in individuals with ED is also impaired. Notably, AN patients often report a high caloric intake even when they objectively consume little (Lloyd & Steinglass, 2018 ). Adolescents and adults with AN overestimate food size, process food analytically, and resist the height-width illusion - perceptual biases that may impact treatment approaches (Zitron-Emanuel et al., 2022 ). They also tend to overestimate portion sizes compared to healthy individuals, though seemingly only for small meals (e.g., Milos et al., 2013 ; Yellowlees, Walker & Ben-Tovim, 1988). These overestimations are even greater when patients imagine consuming the meals later (Lloyd & Steinglass, 2018 ). However, body size estimations in non-self-referential contexts are comparable to those in control groups, suggesting emotional rather than visual perceptual influences on attention (Klos et al., 2023 ). AN patients also show no systematic sensory-perceptual deficits (Goldzak-Kunik et al., 2012 ) These findings largely rule out a general perceptual disorder (Lloyd & Steinglass, 2018 ), yet provide consistent evidence of nonspecific neuropsychological impairments possibly linked to food-related fear (Goldzak-Kunik et al., 2012 ; Manuel & Wade, 2013 ; Lauer, 2010 ; Murphy, 2004). Recent studies have expanded on these findings by investigating the perceptual accuracy of food portions in individuals with AN. Research suggests that AN patients consistently overestimate portion sizes and caloric content compared to healthy controls. However, the magnitude of these misperceptions varies depending on the context of presentation. When portion sizes were visually presented without immediate consumption, overestimations were more pronounced. In contrast, real-time portioning tasks yielded slightly more accurate assessments, though distortions remained present. These results underscore the importance of considering both perceptual and cognitive factors when evaluating food-related behaviours in AN patients (Williamson et al., 1999 ). The reward system plays a key role in eating disorders and consists of two components: liking (hedonic pleasure) and wanting (motivation to obtain a reward) (Berridge et al., 2009). Studies suggest that these aspects are altered in anorexia nervosa (AN). AN patients report reduced liking and wanting, especially for high-calorie foods (Cowdrey et al., 2013; Scaife et al., 2016 ). They also rate neutral images more negatively after food exposure and show shorter reaction times when choosing between high-calorie foods (Spring & Bulik, 2014 ). The approach-avoidance paradigm suggests that motivationally appealing stimuli are easier to approach than to avoid (Lloyd & Steinglass, 2018 ). However, AN patients demonstrate a higher accuracy in avoiding food-related stimuli, particularly high-calorie foods after treatment (Veenstra & de Jong, 2011 ; Neimeijer, de Jong & Roefs, 2015 ). Findings remain mixed, but evidence suggests a diminished reward response to food, especially high-calorie foods, in AN patients (Lloyd & Steinglass, 2018 ). A central aspect of this investigation is the confrontation of adolescent AN patients with food stimuli. Several studies have utilized photographs of food for this purpose. Forster and colleagues (2017) attempted to develop food images for use with children and adolescents aged 18 months to 16 years, aiming to use these as an alternative to weighed food diaries. This led to the creation of the Young Person’s Food Atlas (YPFA), which showed strong agreement with weighed food diaries, with mostly accurate portion estimates. (Forster et al., 2017). Due to a lack of studies accurately assessing food portion perception in AN a need for more research in this area has been claimed (Dörsam et al., 2020 ). Traditional methods for assessing portion size perception, such as questionnaires and photographic images, have limitations due to their lack of interactivity. These static approaches fail to fully capture the complexities of real-world food evaluation, where individuals engage with food in a dynamic and multisensory manner. To address this gap, our study employs a MR- technique that allows participants to interact with virtual food stimuli in a more immersive and ecologically valid environment. MR technologies are characterized by the combination of virtual and real elements (Ronsdorf, 2020 ). This approach enhances the accuracy of portion size assessment by integrating perceptual and cognitive factors in real-time, offering new insights into food-related behaviours. In the context of studies on the treatment of anxiety disorders and phobias, promising results have already been achieved by creating immersive and controlled environments (e.g., Andersen et al., 2023 ; Donnelly et al., 2021 ). To evaluate the potential of this method for the assessment and treatment of AN, we conducted a pilot study and asked healthy young adults and adolescents with AN to evaluate our MR device for realism and to estimate portion size. We hypothesized that adolescent patients with AN would estimate significantly smaller portion sizes compared to objective 100% meal standards. Furthermore, in a more exploratory way due to the small sample size we looked for correlations between this underestimation with eating disorder symptom severity and the duration of treatment at the time of assessment. We also hypothesized that the decision-making process of adolescent AN patients would differ significantly from that of healthy adults in the pretest. Specifically, we assumed that the overall process would take significantly longer in the AN group and that these patients would show significantly more frequent changes in their selections, whereas pretest participants would more often try out larger portions than their final choice. 2 Materials and Methods 2.1 Participants This study included 30 female participants, divided into two groups: an experimental group of 15 adolescent patients diagnosed with anorexia nervosa (AN) and a pretest of 15 healthy adult women. Participants who failed to complete all questionnaire items or did not adjust all of the food components in the simulation were excluded from specific analyses. Participants in the experimental group met the diagnostic criteria for AN or atypical AN according to ICD-10. Recruitment took place at our clinic, with diagnoses confirmed by experienced psychiatrists and psychologists specializing in child and adolescent psychiatry. The German version of CASCAP (Clinical Assessment Schedule for Children and Adolescents with Psychopathology, Döpfner et al., 1999 ) interview was used for diagnostic assessment. It is a structured interview used to assess a wide range of psychological disorders in children and adolescents. Exclusion criteria included comorbid active psychosis, severe obsessive-compulsive symptoms that could significantly influence decision-making, acute infectious diseases, pervasive developmental disorders, intellectual disabilities, and pregnancy. Participants in the control group were recruited from clinic staff and were required to have no history of an ED. The study was conducted on-site at our clinic. Written informed consent was obtained from all participants and, in the case of minors, from their legal guardians. The study received approval from the Ethics Committee of Friedrich-Alexander-Universität Erlangen-Nürnberg. 2.2 Hardware The study employed a custom-built MR setup (“Portion-O-Mat”) designed to create an immersive dining simulation. The system consisted of a reconstructed dining table with real tableware, integrated push buttons concealed beneath a tablecloth, and a centrally mounted projector connected to a computer. The computer was operated using a mouse and keyboard, with a display toggle function allowing seamless switching between the monitor and projector. An overview of the hardware can be seen in Fig. 1 and Fig. 2 . The projector displayed meal components onto a plate or salad bowl, alongside meal descriptions and instructions presented on a menu-like card. Participants selected meal components using the push buttons, while portion sizes and quantities were adjusted using a rotary sensor embedded in a pepper mill. To enhance immersion, the push buttons’ functions were dynamically visualized via projected icons. All user interactions, including button presses and rotation inputs, were logged with corresponding timestamps in a database. 2.3 Software Software development and testing were conducted using XAMPP (Version 8.2.12; Apache Friends), a cross-platform software package providing a local web development environment. XAMPP includes the Apache HTTP (hypertext transfer protocol) Server, the MariaDB database system, and the PHP and Perl programming languages, enabling seamless integration of all required components. The Apache server was accessed via Google Chrome at " http://localhost" . The Web Bluetooth API (application programming interface) in Google Chrome enabled real-time data acquisition from the Polar OH1 pulse sensor. The display application was implemented in PHP, with meal projection based on a preloaded image series. Each image was isolated against a transparent background, allowing individual food components to be layered for realistic composite meal presentations (e.g., spaghetti as a base layer, sauce above it, and Parmesan cheese on top). The portion size of each component could be adjusted independently without affecting the others. 2.4 Stimuli Five different meals, each composed of three main components and a side salad, were used as experimental stimuli. To control for potential dietary biases, only vegetarian ingredients were included, as individuals with AN often exhibit a preference for vegetarian diets. The selected meals and their components are presented in Table 1 . Table 1 Meals with their components Component 1 Component 2 Component 3 Component 4 Meal 0: Schnitzel* with French fries Ketchup French fries Schnitzel* Salad0 Meal 1: Spaghetti Arrabbiata Spaghetti Red Sauce Parmesan Salad1 Meal 2: Gnocchi con Funghi Mushrooms Vegetables Gnocchi Salad2 Meal 3: Fish sticks* with Potatoes Remoulade Potatoes Fish sticks * Salad3 Meal 4: Kaiserschmarrn with Apple Sauce Kaiserschmarrn Apple Sauce Powdered Sugar Salad4 * To accommodate alternative diets, vegan options were used and explicitly labelled as such. Food components were photographed in incremental portion sizes to prevent participants from inferring a "correct" portion based on image count alone. The increments were standardized within each component, using either weight-based or count-based measurements. The photography setup included a matte blue-painted plate to facilitate image editing, with a camera mounted on a tripod and triggered remotely to ensure image consistency. Image processing was performed using GIMP (Gnu Image Manipulation Program, Version 2.10.38). Images were edited into layered files, masked to remove background elements, and converted to a transparent format. All images were resized to 1500x1500 pixels. An example of a projected meal component (pizza) is shown in Fig. 3 . Image metadata was stored in a MariaDB database for later retrieval in the web application. To enhance the visual realism of the projected components, a CSS-based shadow effect was implemented. Reference portion sizes for each meal component were determined using established nutritional guidelines, including those provided by the German Nutrition Society (Deutsche Gesellschaft für Ernährung, DGE). In addition, the definition of portion sizes in our study was based on the recommendations provided by the Nutrition Therapy Department of the University Hospital Erlangen, as well as on portion size information indicated on product packaging. The DGE recommends an average daily energy intake of approximately 2,000 kcal for a balanced diet, distributed across five meals (three main meals and two snacks). The guidelines include specific portion sizes for different food groups. Adolescents aged 15 and older should consume approximately 0.8 grams of protein per kilogram of body weight per day, distributed across these five meals. In addition, 50% of the daily energy intake should come from carbohydrates, and 30% should be derived from fats. Meal components were arranged on a plate matching the dimensions of the experimental setup, and photographs were taken at predefined portion size increments. Each image was assigned a percentage relative to the established 100% standard portion. The assignment of variable increments and portion endpoints minimized the risk of participants inferring standard portion sizes based on stepwise progression. 2.5 Procedure The experimental sessions were scheduled at approximately 10:30 AM, between breakfast and lunch, to minimize the influence of hunger and satiety states on decision-making. Participants were first briefed on the study procedures, after which they completed digital forms and visual analogue scales (VAS) on a tablet as described below. The collected data included demographic information, treatment history, self-reported stress levels, mood, and hunger state. The following instruction was provided both verbally and in written form: "Welcome to the PORTION-O-MAT. You will encounter five meal tasks in random order. Your goal is to assemble a complete meal using the provided components. The portion size should correspond to a typical adult restaurant serving (100%). You may practice with a trial meal before beginning the actual test." Participants completed five meal assembly tasks, selecting portion sizes they deemed appropriate for a full meal (100% portion). The order of meals was randomized. A practice trial was provided before the main experiment. To minimize social stressors, participants completed the main task alone. The program recorded the total session duration, the time to first component selection, and the rotation speed of the pepper mill during portion adjustments. Upon completing the active session, participants filled out additional self-report measures assessing eating disorder symptoms, perceived realism of the simulation, and food cravings using the short version of the Food Craving Questionnaire (FCQ-T-r; Meule, Hermann, & Kübler, 2014 ). The FCQ-T-r assesses trait food cravings across multiple dimensions and has demonstrated excellent internal consistency (Cronbach’s α ≥ .93; Meule, Hermann, & Kübler, 2014 ) and good test-retest reliability over a four-week period. The Eating Disorder Examination Questionnaire (EDE-Q; Fairburn & Beglin, 1994 ) was used to assess eating disorder psychopathology, including restraint, eating concern, weight concern, and shape concern. The EDE-Q has demonstrated high internal consistency (Cronbach’s α = .85–.97; Hilbert & Tuschen-Caffier, 2016 ) and good test-retest reliability in clinical and non-clinical populations. Additionally, the Eating Attitudes Test (EAT-26; Garner, Olmsted & Garfinkel, 1982) was administered to screen for disordered eating behaviours, with subscales measuring dieting, bulimia, and oral control. The German version (EAT-26D, Meermann & Vandereycken, 1987) has shown a satisfactory internal consistency (Cronbach’s α = .83; Berger et al., 2012 ) and is widely used as a screening tool for eating disorder risk. These assessments were placed at the end of the session to prevent biasing performance during the experiment. Participants did not receive immediate feedback on their portion selections to avoid learning effects in potential follow-up trials. However, feedback was provided at the end of the study, supporting the potential application of the MR method for future training interventions. The assessment of subjectively perceived stress was conducted before the exercise, immediately afterward, and at the end of the study using a 10-point visual analogue scale (“At this moment, I feel …”; 0 = “not stressed at all”; 9 = “extremely stressed”). Patients' mood was also measured using a 10-point visual analogue scale (“My current mood is …”; 0 = “extremely bad”; 9 = “extremely good”). Similarly, hunger levels were assessed via a 10-point visual analogue scale (“How hungry are you right now?”; 0 = “not hungry at all”; 9 = “very hungry”). To evaluate the subjective realism of the presented food stimuli, another 10-point visual analogue scale was used (“The meals seemed to me …”; 0 = “extremely unrealistic”; 9 = “very realistic”). Particularly regarding the mood VAS, previous findings have shown that its use is meaningful, as the scale—despite its simplicity—yields reliable values, especially in assessing changes over time (Fähndrich & Linden, 1982 ). 2.6 Data Analysis Questionnaire data were collected via SosciSurvey® (SosciSurvey GmbH, Munich) and linked to experimental data using participant ID codes. During testing, all interaction were continuously logged in a database. Data were subsequently exported to Excel for pre-processing and renamed for consistency. Statistical analyses were performed using IBM SPSS Statistics (Version 29.0.1.0; IBM Corporation, New York, USA). Total questionnaire scores (EDE-Q, EAT-26D, FCQ-T-r) were calculated, and selected variables were recoded where necessary. BMI was computed based on height and weight extracted from patient files. BMI age percentiles were computed according to KiGGS (Rosario, Stolzenberg & Neuhauser, 2010). Interaction times with meal components were aggregated into total session duration, and portion size percentages were averaged across meals for comparative analyses. To investigate decision-making patterns and potential biases, various measures were analysed and compared with results of the pretest data: Total Meal Configuration Time: The overall duration required to finalize a complete meal. Interaction Duration Per Component: The time spent adjusting individual meal components. Decision Uncertainty: Assessed through the number of directional changes and switches between components during portion adjustments. Larger Portion Trials: Examined whether participants tested larger portions before selecting their final meal size. The hypotheses for this study were formulated prior to data collection. Additionally, the analytic plan was pre-specified, and any analyses that were conducted in exploratory manner are clearly identified and discussed. Descriptive statistics were computed for categorical variables (n, %) and continuous variables (M, SD). The Shapiro-Wilk test was used to assess normality. Group comparisons and comparisons to reference values were conducted using t-tests. The one-tailed hypothesis test was applied to assess the predicted selection of smaller portion sizes by AN patients. Pearson correlations were computed for normally distributed variables, while Spearman correlations were used for non-normally distributed data. The significance threshold was set at α = .05. Given the small and unequal sample sizes, effect sizes were calculated using Hedge’s g (Hedges, 1981 ). 3 Results 3.1 Participant Characteristics Fifteen female adolescents with AN were included in this pilot study. All participants completed the investigation, and no exclusions were necessary. At the time of testing, 73.3% were undergoing inpatient treatment, while 26.7% were in a day clinic. A summary of the sample characteristics is provided in Table 2 . Table 2 Participant Characteristics Variable Mean SD Age (years) 14.80 1.57 BMI (kg/m²) 16.93 1.93 BMI age percentile 10.42 10.42 Treatment duration (days) 48.14 30.52 Weight gain (kg) 1.95 1.77 EAT-26D Total Score 31.43 18.41 EDE-Q Total Score 3.30 1.86 EDE-Q Restraint 2.97 2.18 EDE-Q Eating Concern 2.59 1.76 EDE-Q Weight Concern 3.59 1.97 EDE-Q Shape Concern 4.06 2.05 FCQ-T-r Total Score 29.50 13.63 Note. BMI – Body Mass Index, EAT-26 - Eating Attitudes Test, EDE-Q - Eating Disorder Examination Questionnaire, FCQ-T-r - Food Craving Questionnaire (reduced trait version) 3.2 Preliminary Analyses The evaluation of the difference in the portion size estimates from an objectively defined target of 100% in a pretest with healthy female adults showed that nine out of 20 components significantly differed from the target value. Moreover, significant differences were observed in mean portion sizes across entire meals as seen in Table 3 . Meal 2 and 3 did not differ significantly from 100%. Table 3 Difference in portion size estimates from an objectively defined target of 100% (pretest) Meal Mean Portion Size (%) SD Mean Difference Effect Size (g) t-Value p-Value Meal 0 126.32 37.24 26.32 0.67 2.65 .020 Meal 1 83.54 16.13 -16.46 -0.96 -3.82 .002 Meal 4 79.71 18.58 -20.29 -1.02 -3.94 < .001 Deviation from the 100% target was assessed using a one-sample t-test with a test value of 100 . To assess the realism of the food stimuli used in the experimental condition, participants with AN rated the stimuli on a scale from 1 to 10, yielding a mean realism score of 6.93 (SD = 1.87). Before the portion selection task, participants also rated their mood and hunger levels (M = 5.00, SD = 1.31 and M = 2.33, SD = 2.41). 3.3 Portion Size selection by Participants with AN One participant was excluded from specific analyses due to missing data. 16 out of 20 components significantly deviated from the test value, demonstrating reduced portion sizes. Significant differences from the test value of 100 were observed in the mean portion sizes averaged across entire meals as you can see in Table 4 . Table 4 Difference in portion size estimates from an objectively defined target of 100% (experimental group) Meal Mean Portion Size SD Mean Difference Effect Size (g) t-Value p-Value Meal0 77.98 14.49 -22.02 -1.43 -5.68 < .001 Meal1 46.95 14.80 -53.05 -3.39 -13.89 < .001 Meal2 59.46 16.03 -40.54 -2.38 -9.46 < .001 Meal3 79.75 19.37 -20.25 -0.98 -3.91 < .001 Meal4 52.84 14.45 -47.16 -3.07 -12.22 < .001 Deviation from the 100% target was assessed using a one-sample t-test with a test value of 100 . In addition to this analysis, a group comparison was conducted comparing portion selection between the pretest and the experimental group. It revealed significant differences for all meals as seen in Table 5 . Table 5 Comparison of Portion Choices Between Pretest and Experimental Group Meal t-Value Effect Size (g) p-Value Meal0 -4.53 -1.66 < .001 Meal1 -6.37 -2.30 < .001 Meal2 -4.00 -1.49 < .001 Meal3 -2.82 -1.03 .005 Meal4 -4.21 -1.57 < .001 Group comparisons were performed using t-test 3.4 Influence of Participant Characteristics Correlation analyses examined the relationship between treatment-related factors and portion size selection. A significant positive correlation was found between treatment duration and portion size selection for Meal0 (r(12) = .60, p = .023). However, no significant associations were observed for other meals or for total scores of the EAT-26D, EDE-Q, and FCQ-T-R questionnaires. Furthermore, BMI, mood, hunger levels, perception of realism, and prior treatment duration did not significantly predict portion size selection. Subgroup comparisons based on treatment setting (inpatient vs. day clinic) also yielded no significant differences. 3.5 Decision-Making Characteristics The duration of participant interactions with the portion selection interface was recorded for each component and aggregated across entire meals. Three participants were excluded from some analyses due to missing data. No significant group differences between AN patients and the pretest sample were found regarding total meal selection time. However, significant differences emerged only for the specific component Ketchup (t(16.07) = -2.81, p = .013). An overview can be seen in supplement A. To assess decision-making behaviour, the frequency of directional changes (increases or decreases in portion size) and the number of component switches were analysed (Table 6 ). These variables did not follow a normal distribution, and revealed no significant differences between groups regarding total directional changes (U = 65.50, p = .220) or total component switches (U = 86.50, p = .830). An analysis of the number of times participants selected a portion size larger than their final choice revealed no significant difference between the groups (experimental group: M = 4.93, SD = 3.41; pretest group: M = 6.08, SD = 3.73; U = 74.00, p = .430). Table 6 Comparison in Decision Making Behaviour between Pretest and Experimental Group AN Pretest Comparison AN x Pretest* Mean SD Mean SD U p Directional changes 10.64 7,29 18.31 15.00 65,50 .220 Component switches 3.93 4.30 4.54 4.74 86.50 .830 Larger Portion Sizes 4.93 3.41 6.08 3.73 74.00 .430 Group comparisons were performed using Mann-Whitney-U-Test 4 Discussion The present study examined portioning decisions among adolescents with AN compared to a pretest in healthy participants using a MR approach. Over a period of two months, 16 patients with anorexia nervosa (AN) from our clinic were informed about the “Portion-O-Mat” study. Of these, 15 agreed to participate and obtained consent from their legal guardians. Upon providing consent, all participants completed the full procedure. However, some components were left unprocessed by individual participants, resulting in their partial exclusion from specific analyses. The high rate of consent and low discontinuation rate during the study indicate a high degree of acceptance among patients with AN in spite of the possibly aversive food-related content. Similarly, all female adult participants from the pilot study, recruited simultaneously with the AN group, who were approached agreed to participate. One healthy participant was excluded from the analysis due to repeated interruptions during meal size configuration. All others completed the study without any issues which indicates a high degree of feasibility. The integration of MR technology into this study represents a key methodological advancement. A recent review observed a “highly experimental character and a certain laboratory atmosphere” in most studies on food portioning in AN (Dörsam et al., 2020 ). Participants in the current experiment rated the visual stimuli as sufficiently realistic, indicating that the MR environment effectively simulated relevant features of real meal scenarios. This realism is crucial for external validity, as it enhances the ecological relevance of the findings. Nonetheless, it remains unclear whether virtual meal presentation elicits the same emotional, physiological, and motivational responses as actual food exposure. Further research is needed to directly compare virtual and real-life food interactions in terms of neural and behavioural outcomes. The results indicate significant differences in portion selection between groups, with AN patients consistently choosing smaller portions than healthy adults. These findings align with previous research suggesting that individuals with AN systematically underestimate portion sizes and overestimate caloric content (Robinson et al., 2016 ; Lloyd & Steinglass, 2018 ; Milos 2013; Pasi, 2022). A key finding of the study is that AN patients demonstrated a significant reduction in portion size selection compared to the control group. This could be explained by increased anxiety towards specific foods and a distorted cognitive evaluation of food intake (Cowdrey et al., 2013; Scaife et al., 2016 ). These results align with neurobiological models that attribute altered food-related decision-making in AN to dysfunctions in the reward circuitry (Berridge et al., 2009), which may skew food evaluation processes and reduce the hedonic value of eating. Interestingly, the study found no significant differences in decision times between the groups. This suggests that the selection of reduced portion sizes by AN patients is not due to increased hesitation or uncertainty but rather a consistent, albeit distorted, food evaluation. It should nevertheless be considered that the reasons for the duration of meal configuration may differ between groups. The healthy participants showed a noticeable playful interest in the “Portion-o-Mat”, meaning that the total duration does not accurately reflect decision speed, whereas this assumption is more applicable to the AN group. Additionally, the analysis of decision patterns (e.g., changes in portion size during selection) did not reveal significant differences, indicating that the selection process itself may not be primarily driven by impulsive factors but rather by deeply ingrained cognitive biases. The observed correlation between treatment duration and increased portion size in one meal condition suggests a potential normalization effect with therapeutic progress. Although tentative, this points to the value of longitudinal monitoring of food evaluation behaviour. Therapeutic interventions may, over time, help recalibrate distorted food perceptions—particularly when coupled with exposure to realistic meal situations, as enabled by the MR setup. The results confirm that the visual stimuli in the MR setup were perceived as realistic, strengthening the external validity of the method. However, it remains unclear whether virtual representations of meals evoke the same emotional and physiological responses as the confrontation with real food. Based on our findings and findings of previous studies (Dörsam, 2020; Pasi, 2022; Robinson, 2016), a potential therapeutic approach could involve regular visual exposure to progressively larger food portions, with ongoing monitoring of changes in portion size estimation throughout treatment, aiming to recalibrate patients’ visual perception of what constitutes a "normal" portion size. Despite the study's strengths, several limitations must be acknowledged. First, the relatively small sample size—constrained by the clinical context and the novelty of the technology—limits generalizability. Future studies should recruit larger and more diverse samples to enhance statistical power and ensure a broader representation of the AN population. This would also allow for more robust outlier detection and subgroup analyses. Moreover, stress-related responses to the task were not systematically assessed. Since food exposure may trigger anxiety in AN patients (Milos et al., 2013 ), future studies should incorporate physiological (e.g., heart rate, salivary cortisol) and subjective measures of stress. These data would provide valuable insights into the emotional underpinnings of portion selection behaviour. The timing of assessment is also a relevant factor. Some participants were near the end of their treatment and may have already undergone therapeutic interventions that influenced their behaviour. Future research should prioritize earlier stages of treatment and include repeated measures across the treatment timeline to examine intra-individual change. A longitudinal design would be particularly beneficial in assessing whether perceptual biases shift as patients progress through therapy. Recent findings by Pasi et al. ( 2022 ) emphasize that perceptual misjudgements of portion size in individuals with AN persist across various stages of illness and recovery, suggesting a relatively stable cognitive bias rather than a transient symptom. This highlights the importance of studying a broader spectrum of illness severity and recovery stages in future research, in order to better capture the persistence and variability of these distortions. Regarding the control group, the current pretest was conducted with healthy adults. To enhance developmental comparability, future research should include healthy adolescents matched in age and educational background to the clinical group. These control participants should also complete standardized measures of ED symptomatology to rule out subclinical pathology. Some pretest participants experienced technical difficulties and approached the task with a more playful attitude. This highlights the importance of standardizing instructions and improving technical reliability. As MR technology continues to evolve, improvements in interface responsiveness, visual quality, and environmental realism are expected. Enhancing the immersive quality of the task—for example, through the inclusion of food odors, ambient kitchen sounds, or lighting adjustments—could further simulate real-life eating scenarios and deepen emotional engagement. Even though an influence of intent-to-eat could not be demonstrated by Pasi and colleagues ( 2022 ), it remains another important factor that should be re-examined in future studies. Improvements to the food database are also warranted. Expanding the range of meal components—particularly desserts and mixed dishes— and exact determination of caloric content would allow for a more comprehensive analysis of food-related decision-making. As Dörsam and colleagues ( 2020 ) have already demonstrated, different food characteristics have varying impacts on the resulting perceptual distortion. Accordingly future work should also again investigate how variables such as energy density, palatability, and individual hunger levels influence portioning behaviour. Furthermore, creating a broader spectrum of meal choices for the Portion-O-Mat could allow researchers to compare decisions involving sweet vs. savoury meals and shed light on food-type-specific avoidance tendencies. In addition, correlating portioning behaviour with subscale scores from validated eating disorder questionnaires (e.g., drive for thinness, fear of weight gain) could help identify symptom-specific cognitive distortions. More detailed metrics—such as time to first interaction, speed of increasing or decreasing portions, and the number of exploratory actions—may provide a nuanced understanding of the decision process. Finally, post-task evaluations in which participants are asked whether they could realistically consume the portioned meal—or what percentage of it they believe they could eat—could bridge the gap between simulated decisions and real-world behaviour. A particularly promising avenue would be to examine whether portioning behaviour changes when participants expect to eat the selected meal after the task, thereby introducing motivational relevance and accountability into the decision-making process. 5 Conclusion This pilot evaluation of a MR technology shows a high degree of feasibility and acceptability of this method in young female participants. Its preliminary results contribute novel insights into the food-related decision-making of individuals with anorexia nervosa. The findings underscore the role of cognitive distortions in portion selection and highlight the potential of MR technology to provide ecologically valid, yet controlled, assessment environments. Continued refinement of this approach, paired with larger and longitudinal studies, will be essential for understanding the mechanisms underlying maladaptive eating behaviours and developing targeted, evidence-based interventions. Declarations This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The study has characteristics of a pilot case-control design (Level 3-4 evidence), but due to the small sample, lack of matching, and exploratory design, it is considered Level 4. Author Contribution J.G. Wrote the main manuscript text, prepared figures, did formal analysis, and was part of testing and methodology.M.D. had the idea for the project and took care of the technical implementation, data curation, methodology and conceptualisation.S.H. Was project administration, supervision, and took care oft main review.O.K. - Resources and supervision.All authors reviewed the manuscript. Acknowledgements The authors would like to thank all participants for their valuable time and contribution to this study. We also acknowledge the support of our colleagues and advisors who provided helpful feedback throughout the research process. 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M., & Jong, P. J. de (2011). Reduced automatic motivational orientation towards food in restricting anorexia nervosa. Journal of Abnormal Psychology , 120 (3), 708–718. https://doi.org/10.1037/a0023926 Williamson, D. A., Muller, S. L., Reas, D. L., & Thaw, J. M. (1999). Cognitive bias in eating disorders: Implications for theory and treatment. Behavior Modification , 23 (4), 556–577. https://doi.org/10.1177/0145445599234003 Yellowlees, P. M., Roe, M., Walker, M. K., & Ben-Tovim, D. I. (1988). Abnormal perception of food size in anorexia nervosa. British Medical Journal (Clinical Research Ed.) , 296 (6638), 1689–1690. https://doi.org/10.1136/bmj.296.6638.1689 Zitron-Emanuel, N., Ganel, T., Albini, E., Abbate-Daga, G., & Marzola, E. (2022). The perception of food size and food shape in anorexia nervosa. Appetite , 169 , 105858. https://doi.org/10.1016/j.appet.2021.105858 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6997366","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480345053,"identity":"b228d31d-3b37-4599-8276-50f2b5778041","order_by":0,"name":"Jessica Gutheil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACxgYgkQBhszEwGFjIgZk8xGlhBmmRMCaoBQmAtDBIJDYQ0sLc3v74w4MaOwb5iPxjDz4USKTPDztjwPCmAo/Des6YSSQcS2YwvJHMbjjDQCJ34+20BMY5Z/BomZHDxpDYwMxgOCOZTZoHpGV28gFm3jY8WuY/f/whsaEeouWPgUS64WygCbz/8NkCDKXEhsMM8hJALUB2grw0yJYGfH7JAfnlOI8Bz2MzyR4DCcMN0mkJB+ccw63FsP34448/aqrl5NsTn0n8+GMjLz87x/DBmxo8WqAu4DE4ABUBMQ5gVQsF8nBGAzpjFIyCUTAKRgEUAADkU0zLKXhzaAAAAABJRU5ErkJggg==","orcid":"","institution":"University Hospital Erlangen","correspondingAuthor":true,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Gutheil","suffix":""},{"id":480345058,"identity":"251fc1ef-7bbd-4b8d-a6f1-3d106ef0d580","order_by":1,"name":"Oliver Kratz","email":"","orcid":"","institution":"University Hospital Erlangen","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Kratz","suffix":""},{"id":480345059,"identity":"5667f458-e626-46d4-9fd2-f9e43f69b4ff","order_by":2,"name":"Martin Diruf","email":"","orcid":"","institution":"University Hospital Erlangen","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Diruf","suffix":""},{"id":480345060,"identity":"b3ecc468-1920-4b09-94e9-0e963d5ab168","order_by":3,"name":"Stefanie Horndasch","email":"","orcid":"","institution":"Klinikum Bielefeld","correspondingAuthor":false,"prefix":"","firstName":"Stefanie","middleName":"","lastName":"Horndasch","suffix":""}],"badges":[],"createdAt":"2025-06-28 11:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6997366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6997366/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40519-025-01797-2","type":"published","date":"2025-11-06T15:57:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86131514,"identity":"01aab0d1-9182-44e8-bb2f-4f099b7cb271","added_by":"auto","created_at":"2025-07-07 06:50:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170603,"visible":true,"origin":"","legend":"\u003cp\u003eOverview image of the experimental setup.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6997366/v1/e62d1ca712cb67ae0dddc339.jpeg"},{"id":86131511,"identity":"0a94c33e-2c62-4386-bbeb-fa10ca2eee1e","added_by":"auto","created_at":"2025-07-07 06:50:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":286629,"visible":true,"origin":"","legend":"\u003cp\u003eTop view of the \"Portion-O-Mat\" workspace\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6997366/v1/610064383a259003bf0e5434.jpeg"},{"id":86132298,"identity":"5b2069f3-0b63-467b-8bc2-ace81ea725a5","added_by":"auto","created_at":"2025-07-07 06:58:36","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89875,"visible":true,"origin":"","legend":"\u003cp\u003eFood stimuli – Practice trial. These are the food stimuli we created for the trial run. Portion sizes: 13%, 25%, 38%, 50%, 63%, 75%, 88%, 100%.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6997366/v1/51fa6077de7bc6be5ee6b40d.jpeg"},{"id":95564723,"identity":"584fc0e7-77ab-4bbe-8ce9-596dbd94573e","added_by":"auto","created_at":"2025-11-10 16:10:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1476613,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6997366/v1/103980d6-3bdc-4425-ad7b-edc357d4a819.pdf"},{"id":86131509,"identity":"d394204f-dc03-484b-bd5a-ebae2ce1b032","added_by":"auto","created_at":"2025-07-07 06:50:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22431,"visible":true,"origin":"","legend":"","description":"","filename":"Supplements.docx","url":"https://assets-eu.researchsquare.com/files/rs-6997366/v1/240dc434dacda8fea44e3174.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pilot Study: PORTION-O-MAT - A Mixed Reality Solution for Investigating Perceptual and Behavioural Abnormalities During Food Portioning in Adolescents with Anorexia Nervosa","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAnorexia nervosa (AN) is a severe mental disorder with significant risks for those affected (Davison, Neale \u0026amp; Hautzinger, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fairburn \u0026amp; Harrison, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Jacobi \u0026amp; Beintner, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). an increase in AN diagnoses has been observed among young people in recent years (Federal Ministry of Health, 2022; Jacobi \u0026amp; Beintner, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The treatment of AN involving refeeding and consequently the training of adequate meal intake is a critical focus in clinical psychology and medicine. Altered perception of portion sizes is considered a key aspect of the disorder (D\u0026ouml;rsam et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Milos et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pasi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Robinson et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Adolescents with AN face the daily challenge of confronting food or food-related stimuli, which may be perceived as aversive and contrary to their disorder-related intentions. Appropriate food portioning can thus become a complex and burdensome process.\u003c/p\u003e \u003cp\u003eIn the context of Eating Disorders (ED), changes in attention, information processing, memory, learning, and executive functions are particularly common. Patients exhibit attention biases, indicating altered attentional allocation (Lloyd \u0026amp; Steinglass, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This can involve intensified focus on a specific stimulus (orientation), reduced ability to shift attention away (distraction), and avoidance of attending to certain stimuli (attention avoidance; Cisler \u0026amp; Koster, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lloyd \u0026amp; Steinglass, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Due to limited cognitive flexibility, a strong cognitive bias often makes it difficult for ED patients to respond selectively and flexibly to anxiety-inducing stimuli (Schulte-R\u0026uuml;ther \u0026amp; Konrad, 2015). While conclusive data is lacking, it is suggested that food-related cues may trigger similar attentional distortions in AN patients as typically anxiety-provoking stimuli do (Aspen, Darcy \u0026amp; Lock, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cowdrey et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Exposure to food stimuli is often associated with negative emotions in individuals with AN, as they generally rate food images as less pleasant compared to healthy individuals (Horndasch et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Joos et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Santel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Uher et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Studies on anxiety have demonstrated a positive correlation between food portion size and both physiological responses (e.g., heart rate, skin conductance) and self-reported anxiety levels (Kissileff, 2016). Adolescents with AN exhibit increased pre-meal anxiety and heightened autonomic nervous system activation when exposed to images of larger portion sizes, suggesting that food-related anxiety may play a crucial role in the development and maintenance of AN. Furthermore, research supports the use of computer-based methods to objectively and systematically assess responses to food stimuli (Kissileff et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additional studies have confirmed a positive relationship between portion size and anxiety (Herzog et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; D\u0026ouml;rsam et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with findings indicating that the impact of food energy density on anxiety is moderated by portion size (Herzog et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, ED patients display a heightened focus on details of specific body parts and food attributes, resulting in reduced central coherence. This means they struggle to view acquired information in an overall context (Kappel et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lopez et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It is unclear whether overall perception of food portions in individuals with ED is also impaired. Notably, AN patients often report a high caloric intake even when they objectively consume little (Lloyd \u0026amp; Steinglass, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Adolescents and adults with AN overestimate food size, process food analytically, and resist the height-width illusion - perceptual biases that may impact treatment approaches (Zitron-Emanuel et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). They also tend to overestimate portion sizes compared to healthy individuals, though seemingly only for small meals (e.g., Milos et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yellowlees, Walker \u0026amp; Ben-Tovim, 1988). These overestimations are even greater when patients imagine consuming the meals later (Lloyd \u0026amp; Steinglass, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, body size estimations in non-self-referential contexts are comparable to those in control groups, suggesting emotional rather than visual perceptual influences on attention (Klos et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). AN patients also show no systematic sensory-perceptual deficits (Goldzak-Kunik et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) These findings largely rule out a general perceptual disorder (Lloyd \u0026amp; Steinglass, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), yet provide consistent evidence of nonspecific neuropsychological impairments possibly linked to food-related fear (Goldzak-Kunik et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Manuel \u0026amp; Wade, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lauer, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Murphy, 2004). Recent studies have expanded on these findings by investigating the perceptual accuracy of food portions in individuals with AN. Research suggests that AN patients consistently overestimate portion sizes and caloric content compared to healthy controls. However, the magnitude of these misperceptions varies depending on the context of presentation. When portion sizes were visually presented without immediate consumption, overestimations were more pronounced. In contrast, real-time portioning tasks yielded slightly more accurate assessments, though distortions remained present. These results underscore the importance of considering both perceptual and cognitive factors when evaluating food-related behaviours in AN patients (Williamson et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The reward system plays a key role in eating disorders and consists of two components: liking (hedonic pleasure) and wanting (motivation to obtain a reward) (Berridge et al., 2009). Studies suggest that these aspects are altered in anorexia nervosa (AN). AN patients report reduced liking and wanting, especially for high-calorie foods (Cowdrey et al., 2013; Scaife et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). They also rate neutral images more negatively after food exposure and show shorter reaction times when choosing between high-calorie foods (Spring \u0026amp; Bulik, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The approach-avoidance paradigm suggests that motivationally appealing stimuli are easier to approach than to avoid (Lloyd \u0026amp; Steinglass, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, AN patients demonstrate a higher accuracy in avoiding food-related stimuli, particularly high-calorie foods after treatment (Veenstra \u0026amp; de Jong, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Neimeijer, de Jong \u0026amp; Roefs, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Findings remain mixed, but evidence suggests a diminished reward response to food, especially high-calorie foods, in AN patients (Lloyd \u0026amp; Steinglass, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA central aspect of this investigation is the confrontation of adolescent AN patients with food stimuli. Several studies have utilized photographs of food for this purpose. Forster and colleagues (2017) attempted to develop food images for use with children and adolescents aged 18 months to 16 years, aiming to use these as an alternative to weighed food diaries. This led to the creation of the Young Person\u0026rsquo;s Food Atlas (YPFA), which showed strong agreement with weighed food diaries, with mostly accurate portion estimates. (Forster et al., 2017). Due to a lack of studies accurately assessing food portion perception in AN a need for more research in this area has been claimed (D\u0026ouml;rsam et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Traditional methods for assessing portion size perception, such as questionnaires and photographic images, have limitations due to their lack of interactivity. These static approaches fail to fully capture the complexities of real-world food evaluation, where individuals engage with food in a dynamic and multisensory manner. To address this gap, our study employs a MR- technique that allows participants to interact with virtual food stimuli in a more immersive and ecologically valid environment. MR technologies are characterized by the combination of virtual and real elements (Ronsdorf, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This approach enhances the accuracy of portion size assessment by integrating perceptual and cognitive factors in real-time, offering new insights into food-related behaviours.\u003c/p\u003e \u003cp\u003eIn the context of studies on the treatment of anxiety disorders and phobias, promising results have already been achieved by creating immersive and controlled environments (e.g., Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Donnelly et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To evaluate the potential of this method for the assessment and treatment of AN, we conducted a pilot study and asked healthy young adults and adolescents with AN to evaluate our MR device for realism and to estimate portion size. We hypothesized that adolescent patients with AN would estimate significantly smaller portion sizes compared to objective 100% meal standards. Furthermore, in a more exploratory way due to the small sample size we looked for correlations between this underestimation with eating disorder symptom severity and the duration of treatment at the time of assessment. We also hypothesized that the decision-making process of adolescent AN patients would differ significantly from that of healthy adults in the pretest. Specifically, we assumed that the overall process would take significantly longer in the AN group and that these patients would show significantly more frequent changes in their selections, whereas pretest participants would more often try out larger portions than their final choice.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eThis study included 30 female participants, divided into two groups: an experimental group of 15 adolescent patients diagnosed with anorexia nervosa (AN) and a pretest of 15 healthy adult women. Participants who failed to complete all questionnaire items or did not adjust all of the food components in the simulation were excluded from specific analyses.\u003c/p\u003e \u003cp\u003eParticipants in the experimental group met the diagnostic criteria for AN or atypical AN according to ICD-10. Recruitment took place at our clinic, with diagnoses confirmed by experienced psychiatrists and psychologists specializing in child and adolescent psychiatry. The German version of CASCAP (Clinical Assessment Schedule for Children and Adolescents with Psychopathology, D\u0026ouml;pfner et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) interview was used for diagnostic assessment. It is a structured interview used to assess a wide range of psychological disorders in children and adolescents.\u003c/p\u003e \u003cp\u003eExclusion criteria included comorbid active psychosis, severe obsessive-compulsive symptoms that could significantly influence decision-making, acute infectious diseases, pervasive developmental disorders, intellectual disabilities, and pregnancy. Participants in the control group were recruited from clinic staff and were required to have no history of an ED.\u003c/p\u003e \u003cp\u003eThe study was conducted on-site at our clinic. Written informed consent was obtained from all participants and, in the case of minors, from their legal guardians. The study received approval from the Ethics Committee of Friedrich-Alexander-Universit\u0026auml;t Erlangen-N\u0026uuml;rnberg.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Hardware\u003c/h2\u003e \u003cp\u003eThe study employed a custom-built MR setup (\u0026ldquo;Portion-O-Mat\u0026rdquo;) designed to create an immersive dining simulation. The system consisted of a reconstructed dining table with real tableware, integrated push buttons concealed beneath a tablecloth, and a centrally mounted projector connected to a computer. The computer was operated using a mouse and keyboard, with a display toggle function allowing seamless switching between the monitor and projector. An overview of the hardware can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe projector displayed meal components onto a plate or salad bowl, alongside meal descriptions and instructions presented on a menu-like card. Participants selected meal components using the push buttons, while portion sizes and quantities were adjusted using a rotary sensor embedded in a pepper mill. To enhance immersion, the push buttons\u0026rsquo; functions were dynamically visualized via projected icons. All user interactions, including button presses and rotation inputs, were logged with corresponding timestamps in a database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Software\u003c/h2\u003e \u003cp\u003eSoftware development and testing were conducted using XAMPP (Version 8.2.12; Apache Friends), a cross-platform software package providing a local web development environment. XAMPP includes the Apache HTTP (hypertext transfer protocol) Server, the MariaDB database system, and the PHP and Perl programming languages, enabling seamless integration of all required components. The Apache server was accessed via Google Chrome at \"\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://localhost\"\u003c/span\u003e\u003cspan address=\"http://localhost\u0026quot;\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe Web Bluetooth API (application programming interface) in Google Chrome enabled real-time data acquisition from the Polar OH1 pulse sensor. The display application was implemented in PHP, with meal projection based on a preloaded image series. Each image was isolated against a transparent background, allowing individual food components to be layered for realistic composite meal presentations (e.g., spaghetti as a base layer, sauce above it, and Parmesan cheese on top). The portion size of each component could be adjusted independently without affecting the others.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Stimuli\u003c/h2\u003e \u003cp\u003eFive different meals, each composed of three main components and a side salad, were used as experimental stimuli. To control for potential dietary biases, only vegetarian ingredients were included, as individuals with AN often exhibit a preference for vegetarian diets. The selected meals and their components are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeals with their components\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComponent 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComponent 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComponent 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComponent 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal 0:\u003c/p\u003e \u003cp\u003eSchnitzel* with French fries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKetchup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrench fries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSchnitzel*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSalad0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal 1:\u003c/p\u003e \u003cp\u003eSpaghetti Arrabbiata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpaghetti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRed Sauce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParmesan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSalad1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal 2:\u003c/p\u003e \u003cp\u003eGnocchi con Funghi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMushrooms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGnocchi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSalad2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal 3:\u003c/p\u003e \u003cp\u003eFish sticks* with Potatoes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemoulade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotatoes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFish sticks *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSalad3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal 4:\u003c/p\u003e \u003cp\u003eKaiserschmarrn with Apple Sauce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKaiserschmarrn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApple Sauce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePowdered Sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSalad4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* To accommodate alternative diets, vegan options were used and explicitly labelled as such.\u003c/p\u003e \u003cp\u003e Food components were photographed in incremental portion sizes to prevent participants from inferring a \"correct\" portion based on image count alone. The increments were standardized within each component, using either weight-based or count-based measurements. The photography setup included a matte blue-painted plate to facilitate image editing, with a camera mounted on a tripod and triggered remotely to ensure image consistency.\u003c/p\u003e \u003cp\u003eImage processing was performed using GIMP (Gnu Image Manipulation Program, Version 2.10.38). Images were edited into layered files, masked to remove background elements, and converted to a transparent format. All images were resized to 1500x1500 pixels. An example of a projected meal component (pizza) is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Image metadata was stored in a MariaDB database for later retrieval in the web application. To enhance the visual realism of the projected components, a CSS-based shadow effect was implemented.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e Reference portion sizes for each meal component were determined using established nutritional guidelines, including those provided by the German Nutrition Society (Deutsche Gesellschaft f\u0026uuml;r Ern\u0026auml;hrung, DGE). In addition, the definition of portion sizes in our study was based on the recommendations provided by the Nutrition Therapy Department of the University Hospital Erlangen, as well as on portion size information indicated on product packaging. The DGE recommends an average daily energy intake of approximately 2,000 kcal for a balanced diet, distributed across five meals (three main meals and two snacks). The guidelines include specific portion sizes for different food groups. Adolescents aged 15 and older should consume approximately 0.8 grams of protein per kilogram of body weight per day, distributed across these five meals. In addition, 50% of the daily energy intake should come from carbohydrates, and 30% should be derived from fats. Meal components were arranged on a plate matching the dimensions of the experimental setup, and photographs were taken at predefined portion size increments. Each image was assigned a percentage relative to the established 100% standard portion. The assignment of variable increments and portion endpoints minimized the risk of participants inferring standard portion sizes based on stepwise progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Procedure\u003c/h2\u003e \u003cp\u003eThe experimental sessions were scheduled at approximately 10:30 AM, between breakfast and lunch, to minimize the influence of hunger and satiety states on decision-making. Participants were first briefed on the study procedures, after which they completed digital forms and visual analogue scales (VAS) on a tablet as described below. The collected data included demographic information, treatment history, self-reported stress levels, mood, and hunger state.\u003c/p\u003e \u003cp\u003eThe following instruction was provided both verbally and in written form:\u003cem\u003e\"Welcome to the PORTION-O-MAT. You will encounter five meal tasks in random order. Your goal is to assemble a complete meal using the provided components. The portion size should correspond to a typical adult restaurant serving (100%). You may practice with a trial meal before beginning the actual test.\"\u003c/em\u003e\u003c/p\u003e \u003cp\u003eParticipants completed five meal assembly tasks, selecting portion sizes they deemed appropriate for a full meal (100% portion). The order of meals was randomized. A practice trial was provided before the main experiment. To minimize social stressors, participants completed the main task alone. The program recorded the total session duration, the time to first component selection, and the rotation speed of the pepper mill during portion adjustments.\u003c/p\u003e \u003cp\u003eUpon completing the active session, participants filled out additional self-report measures assessing eating disorder symptoms, perceived realism of the simulation, and food cravings using the short version of the Food Craving Questionnaire (FCQ-T-r; Meule, Hermann, \u0026amp; K\u0026uuml;bler, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The FCQ-T-r assesses trait food cravings across multiple dimensions and has demonstrated excellent internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;\u0026ge;\u0026thinsp;.93; Meule, Hermann, \u0026amp; K\u0026uuml;bler, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and good test-retest reliability over a four-week period. The Eating Disorder Examination Questionnaire (EDE-Q; Fairburn \u0026amp; Beglin, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) was used to assess eating disorder psychopathology, including restraint, eating concern, weight concern, and shape concern. The EDE-Q has demonstrated high internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.85\u0026ndash;.97; Hilbert \u0026amp; Tuschen-Caffier, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and good test-retest reliability in clinical and non-clinical populations. Additionally, the Eating Attitudes Test (EAT-26; Garner, Olmsted \u0026amp; Garfinkel, 1982) was administered to screen for disordered eating behaviours, with subscales measuring dieting, bulimia, and oral control. The German version (EAT-26D, Meermann \u0026amp; Vandereycken, 1987) has shown a satisfactory internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.83; Berger et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and is widely used as a screening tool for eating disorder risk. These assessments were placed at the end of the session to prevent biasing performance during the experiment. Participants did not receive immediate feedback on their portion selections to avoid learning effects in potential follow-up trials. However, feedback was provided at the end of the study, supporting the potential application of the MR method for future training interventions.\u003c/p\u003e \u003cp\u003eThe assessment of subjectively perceived stress was conducted before the exercise, immediately afterward, and at the end of the study using a 10-point visual analogue scale (\u0026ldquo;At this moment, I feel \u0026hellip;\u0026rdquo;; 0 = \u0026ldquo;not stressed at all\u0026rdquo;; 9 = \u0026ldquo;extremely stressed\u0026rdquo;). Patients' mood was also measured using a 10-point visual analogue scale (\u0026ldquo;My current mood is \u0026hellip;\u0026rdquo;; 0 = \u0026ldquo;extremely bad\u0026rdquo;; 9 = \u0026ldquo;extremely good\u0026rdquo;). Similarly, hunger levels were assessed via a 10-point visual analogue scale (\u0026ldquo;How hungry are you right now?\u0026rdquo;; 0 = \u0026ldquo;not hungry at all\u0026rdquo;; 9 = \u0026ldquo;very hungry\u0026rdquo;). To evaluate the subjective realism of the presented food stimuli, another 10-point visual analogue scale was used (\u0026ldquo;The meals seemed to me \u0026hellip;\u0026rdquo;; 0 = \u0026ldquo;extremely unrealistic\u0026rdquo;; 9 = \u0026ldquo;very realistic\u0026rdquo;). Particularly regarding the mood VAS, previous findings have shown that its use is meaningful, as the scale\u0026mdash;despite its simplicity\u0026mdash;yields reliable values, especially in assessing changes over time (F\u0026auml;hndrich \u0026amp; Linden, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data Analysis\u003c/h2\u003e \u003cp\u003eQuestionnaire data were collected via SosciSurvey\u0026reg; (SosciSurvey GmbH, Munich) and linked to experimental data using participant ID codes. During testing, all interaction were continuously logged in a database. Data were subsequently exported to Excel for pre-processing and renamed for consistency. Statistical analyses were performed using IBM SPSS Statistics (Version 29.0.1.0; IBM Corporation, New York, USA).\u003c/p\u003e \u003cp\u003eTotal questionnaire scores (EDE-Q, EAT-26D, FCQ-T-r) were calculated, and selected variables were recoded where necessary. BMI was computed based on height and weight extracted from patient files. BMI age percentiles were computed according to KiGGS (Rosario, Stolzenberg \u0026amp; Neuhauser, 2010). Interaction times with meal components were aggregated into total session duration, and portion size percentages were averaged across meals for comparative analyses.\u003c/p\u003e \u003cp\u003eTo investigate decision-making patterns and potential biases, various measures were analysed and compared with results of the pretest data:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTotal Meal Configuration Time: The overall duration required to finalize a complete meal.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInteraction Duration Per Component: The time spent adjusting individual meal components.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDecision Uncertainty: Assessed through the number of directional changes and switches between components during portion adjustments.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLarger Portion Trials: Examined whether participants tested larger portions before selecting their final meal size.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe hypotheses for this study were formulated prior to data collection. Additionally, the analytic plan was pre-specified, and any analyses that were conducted in exploratory manner are clearly identified and discussed. Descriptive statistics were computed for categorical variables (n, %) and continuous variables (M, SD). The Shapiro-Wilk test was used to assess normality. Group comparisons and comparisons to reference values were conducted using t-tests. The one-tailed hypothesis test was applied to assess the predicted selection of smaller portion sizes by AN patients. Pearson correlations were computed for normally distributed variables, while Spearman correlations were used for non-normally distributed data. The significance threshold was set at α\u0026thinsp;=\u0026thinsp;.05. Given the small and unequal sample sizes, effect sizes were calculated using Hedge\u0026rsquo;s g (Hedges, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1981\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participant Characteristics\u003c/h2\u003e \u003cp\u003eFifteen female adolescents with AN were included in this pilot study. All participants completed the investigation, and no exclusions were necessary. At the time of testing, 73.3% were undergoing inpatient treatment, while 26.7% were in a day clinic. A summary of the sample characteristics is provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI age percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment duration (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight gain (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAT-26D Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDE-Q Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDE-Q Restraint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDE-Q Eating Concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDE-Q Weight Concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDE-Q Shape Concern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCQ-T-r Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote. BMI \u0026ndash; Body Mass Index, EAT-26 - Eating Attitudes Test, EDE-Q - Eating Disorder Examination Questionnaire, FCQ-T-r - Food Craving Questionnaire (reduced trait version)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Preliminary Analyses\u003c/h2\u003e \u003cp\u003eThe evaluation of the difference in the portion size estimates from an objectively defined target of 100% in a pretest with healthy female adults showed that nine out of 20 components significantly differed from the target value. Moreover, significant differences were observed in mean portion sizes across entire meals as seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Meal 2 and 3 did not differ significantly from 100%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference in portion size estimates from an objectively defined target of 100% (pretest)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Portion Size (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect Size (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-16.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-20.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eDeviation from the 100% target was assessed using a one-sample t-test with a test value of 100\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTo assess the realism of the food stimuli used in the experimental condition, participants with AN rated the stimuli on a scale from 1 to 10, yielding a mean realism score of 6.93 (SD\u0026thinsp;=\u0026thinsp;1.87). Before the portion selection task, participants also rated their mood and hunger levels (M\u0026thinsp;=\u0026thinsp;5.00, SD\u0026thinsp;=\u0026thinsp;1.31 and M\u0026thinsp;=\u0026thinsp;2.33, SD\u0026thinsp;=\u0026thinsp;2.41).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Portion Size selection by Participants with AN\u003c/h2\u003e \u003cp\u003eOne participant was excluded from specific analyses due to missing data. 16 out of 20 components significantly deviated from the test value, demonstrating reduced portion sizes. Significant differences from the test value of 100 were observed in the mean portion sizes averaged across entire meals as you can see in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifference in portion size estimates from an objectively defined target of 100% (experimental group)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMean Portion Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect Size (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003et-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e14.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-22.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e14.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-53.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-13.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-40.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-9.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e19.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-20.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e14.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-47.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-12.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eDeviation from the 100% target was assessed using a one-sample t-test with a test value of 100\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn addition to this analysis, a group comparison was conducted comparing portion selection between the pretest and the experimental group. It revealed significant differences for all meals as seen in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Portion Choices Between Pretest and Experimental Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect Size (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eGroup comparisons were performed using t-test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Influence of Participant Characteristics\u003c/h2\u003e \u003cp\u003eCorrelation analyses examined the relationship between treatment-related factors and portion size selection. A significant positive correlation was found between treatment duration and portion size selection for Meal0 (r(12)\u0026thinsp;=\u0026thinsp;.60, p\u0026thinsp;=\u0026thinsp;.023). However, no significant associations were observed for other meals or for total scores of the EAT-26D, EDE-Q, and FCQ-T-R questionnaires. Furthermore, BMI, mood, hunger levels, perception of realism, and prior treatment duration did not significantly predict portion size selection. Subgroup comparisons based on treatment setting (inpatient vs. day clinic) also yielded no significant differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Decision-Making Characteristics\u003c/h2\u003e \u003cp\u003eThe duration of participant interactions with the portion selection interface was recorded for each component and aggregated across entire meals. Three participants were excluded from some analyses due to missing data. No significant group differences between AN patients and the pretest sample were found regarding total meal selection time. However, significant differences emerged only for the specific component Ketchup (t(16.07) = -2.81, p\u0026thinsp;=\u0026thinsp;.013). An overview can be seen in supplement A.\u003c/p\u003e \u003cp\u003eTo assess decision-making behaviour, the frequency of directional changes (increases or decreases in portion size) and the number of component switches were analysed (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These variables did not follow a normal distribution, and revealed no significant differences between groups regarding total directional changes (U\u0026thinsp;=\u0026thinsp;65.50, p\u0026thinsp;=\u0026thinsp;.220) or total component switches (U\u0026thinsp;=\u0026thinsp;86.50, p\u0026thinsp;=\u0026thinsp;.830).\u003c/p\u003e \u003cp\u003eAn analysis of the number of times participants selected a portion size larger than their final choice revealed no significant difference between the groups (experimental group: M\u0026thinsp;=\u0026thinsp;4.93, SD\u0026thinsp;=\u0026thinsp;3.41; pretest group: M\u0026thinsp;=\u0026thinsp;6.08, SD\u0026thinsp;=\u0026thinsp;3.73; U\u0026thinsp;=\u0026thinsp;74.00, p\u0026thinsp;=\u0026thinsp;.430).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison in Decision Making Behaviour between Pretest and Experimental Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePretest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eComparison AN x Pretest*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirectional changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent switches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarger Portion Sizes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eGroup comparisons were performed using Mann-Whitney-U-Test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe present study examined portioning decisions among adolescents with AN compared to a pretest in healthy participants using a MR approach. Over a period of two months, 16 patients with anorexia nervosa (AN) from our clinic were informed about the \u0026ldquo;Portion-O-Mat\u0026rdquo; study. Of these, 15 agreed to participate and obtained consent from their legal guardians. Upon providing consent, all participants completed the full procedure. However, some components were left unprocessed by individual participants, resulting in their partial exclusion from specific analyses. The high rate of consent and low discontinuation rate during the study indicate a high degree of acceptance among patients with AN in spite of the possibly aversive food-related content. Similarly, all female adult participants from the pilot study, recruited simultaneously with the AN group, who were approached agreed to participate. One healthy participant was excluded from the analysis due to repeated interruptions during meal size configuration. All others completed the study without any issues which indicates a high degree of feasibility. The integration of MR technology into this study represents a key methodological advancement. A recent review observed a \u0026ldquo;highly experimental character and a certain laboratory atmosphere\u0026rdquo; in most studies on food portioning in AN (D\u0026ouml;rsam et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Participants in the current experiment rated the visual stimuli as sufficiently realistic, indicating that the MR environment effectively simulated relevant features of real meal scenarios. This realism is crucial for external validity, as it enhances the ecological relevance of the findings. Nonetheless, it remains unclear whether virtual meal presentation elicits the same emotional, physiological, and motivational responses as actual food exposure. Further research is needed to directly compare virtual and real-life food interactions in terms of neural and behavioural outcomes.\u003c/p\u003e \u003cp\u003eThe results indicate significant differences in portion selection between groups, with AN patients consistently choosing smaller portions than healthy adults. These findings align with previous research suggesting that individuals with AN systematically underestimate portion sizes and overestimate caloric content (Robinson et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lloyd \u0026amp; Steinglass, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Milos 2013; Pasi, 2022).\u003c/p\u003e \u003cp\u003eA key finding of the study is that AN patients demonstrated a significant reduction in portion size selection compared to the control group. This could be explained by increased anxiety towards specific foods and a distorted cognitive evaluation of food intake (Cowdrey et al., 2013; Scaife et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These results align with neurobiological models that attribute altered food-related decision-making in AN to dysfunctions in the reward circuitry (Berridge et al., 2009), which may skew food evaluation processes and reduce the hedonic value of eating.\u003c/p\u003e \u003cp\u003eInterestingly, the study found no significant differences in decision times between the groups. This suggests that the selection of reduced portion sizes by AN patients is not due to increased hesitation or uncertainty but rather a consistent, albeit distorted, food evaluation. It should nevertheless be considered that the reasons for the duration of meal configuration may differ between groups. The healthy participants showed a noticeable playful interest in the \u0026ldquo;Portion-o-Mat\u0026rdquo;, meaning that the total duration does not accurately reflect decision speed, whereas this assumption is more applicable to the AN group. Additionally, the analysis of decision patterns (e.g., changes in portion size during selection) did not reveal significant differences, indicating that the selection process itself may not be primarily driven by impulsive factors but rather by deeply ingrained cognitive biases.\u003c/p\u003e \u003cp\u003eThe observed correlation between treatment duration and increased portion size in one meal condition suggests a potential normalization effect with therapeutic progress. Although tentative, this points to the value of longitudinal monitoring of food evaluation behaviour. Therapeutic interventions may, over time, help recalibrate distorted food perceptions\u0026mdash;particularly when coupled with exposure to realistic meal situations, as enabled by the MR setup. The results confirm that the visual stimuli in the MR setup were perceived as realistic, strengthening the external validity of the method. However, it remains unclear whether virtual representations of meals evoke the same emotional and physiological responses as the confrontation with real food.\u003c/p\u003e \u003cp\u003eBased on our findings and findings of previous studies (D\u0026ouml;rsam, 2020; Pasi, 2022; Robinson, 2016), a potential therapeutic approach could involve regular visual exposure to progressively larger food portions, with ongoing monitoring of changes in portion size estimation throughout treatment, aiming to recalibrate patients\u0026rsquo; visual perception of what constitutes a \"normal\" portion size.\u003c/p\u003e \u003cp\u003eDespite the study's strengths, several limitations must be acknowledged. First, the relatively small sample size\u0026mdash;constrained by the clinical context and the novelty of the technology\u0026mdash;limits generalizability. Future studies should recruit larger and more diverse samples to enhance statistical power and ensure a broader representation of the AN population. This would also allow for more robust outlier detection and subgroup analyses.\u003c/p\u003e \u003cp\u003eMoreover, stress-related responses to the task were not systematically assessed. Since food exposure may trigger anxiety in AN patients (Milos et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), future studies should incorporate physiological (e.g., heart rate, salivary cortisol) and subjective measures of stress. These data would provide valuable insights into the emotional underpinnings of portion selection behaviour.\u003c/p\u003e \u003cp\u003eThe timing of assessment is also a relevant factor. Some participants were near the end of their treatment and may have already undergone therapeutic interventions that influenced their behaviour. Future research should prioritize earlier stages of treatment and include repeated measures across the treatment timeline to examine intra-individual change. A longitudinal design would be particularly beneficial in assessing whether perceptual biases shift as patients progress through therapy. Recent findings by Pasi et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) emphasize that perceptual misjudgements of portion size in individuals with AN persist across various stages of illness and recovery, suggesting a relatively stable cognitive bias rather than a transient symptom. This highlights the importance of studying a broader spectrum of illness severity and recovery stages in future research, in order to better capture the persistence and variability of these distortions.\u003c/p\u003e \u003cp\u003eRegarding the control group, the current pretest was conducted with healthy adults. To enhance developmental comparability, future research should include healthy adolescents matched in age and educational background to the clinical group. These control participants should also complete standardized measures of ED symptomatology to rule out subclinical pathology.\u003c/p\u003e \u003cp\u003e Some pretest participants experienced technical difficulties and approached the task with a more playful attitude. This highlights the importance of standardizing instructions and improving technical reliability. As MR technology continues to evolve, improvements in interface responsiveness, visual quality, and environmental realism are expected. Enhancing the immersive quality of the task\u0026mdash;for example, through the inclusion of food odors, ambient kitchen sounds, or lighting adjustments\u0026mdash;could further simulate real-life eating scenarios and deepen emotional engagement. Even though an influence of intent-to-eat could not be demonstrated by Pasi and colleagues (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), it remains another important factor that should be re-examined in future studies.\u003c/p\u003e \u003cp\u003eImprovements to the food database are also warranted. Expanding the range of meal components\u0026mdash;particularly desserts and mixed dishes\u0026mdash; and exact determination of caloric content would allow for a more comprehensive analysis of food-related decision-making. As D\u0026ouml;rsam and colleagues (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) have already demonstrated, different food characteristics have varying impacts on the resulting perceptual distortion. Accordingly future work should also again investigate how variables such as energy density, palatability, and individual hunger levels influence portioning behaviour. Furthermore, creating a broader spectrum of meal choices for the Portion-O-Mat could allow researchers to compare decisions involving sweet vs. savoury meals and shed light on food-type-specific avoidance tendencies.\u003c/p\u003e \u003cp\u003eIn addition, correlating portioning behaviour with subscale scores from validated eating disorder questionnaires (e.g., drive for thinness, fear of weight gain) could help identify symptom-specific cognitive distortions. More detailed metrics\u0026mdash;such as time to first interaction, speed of increasing or decreasing portions, and the number of exploratory actions\u0026mdash;may provide a nuanced understanding of the decision process.\u003c/p\u003e \u003cp\u003eFinally, post-task evaluations in which participants are asked whether they could realistically consume the portioned meal\u0026mdash;or what percentage of it they believe they could eat\u0026mdash;could bridge the gap between simulated decisions and real-world behaviour. A particularly promising avenue would be to examine whether portioning behaviour changes when participants expect to eat the selected meal after the task, thereby introducing motivational relevance and accountability into the decision-making process.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis pilot evaluation of a MR technology shows a high degree of feasibility and acceptability of this method in young female participants. Its preliminary results contribute novel insights into the food-related decision-making of individuals with anorexia nervosa. The findings underscore the role of cognitive distortions in portion selection and highlight the potential of MR technology to provide ecologically valid, yet controlled, assessment environments. Continued refinement of this approach, paired with larger and longitudinal studies, will be essential for understanding the mechanisms underlying maladaptive eating behaviours and developing targeted, evidence-based interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study has characteristics of a pilot case-control design (Level 3-4 evidence), but due to the small sample, lack of matching, and exploratory design, it is considered Level 4.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.G. Wrote the main manuscript text, prepared figures, did formal analysis, and was part of testing and methodology.M.D. had the idea for the project and took care of the technical implementation, data curation, methodology and conceptualisation.S.H. Was project administration, supervision, and took care oft main review.O.K. - Resources and supervision.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to thank all participants for their valuable time and contribution to this study. We also acknowledge the support of our colleagues and advisors who provided helpful feedback throughout the research process.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe collected data will be shared upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndersen, N. J., Schwartzman, D., Martinez, C., Cormier, G., \u0026amp; Drapeau, M. (2023). 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The perception of food size and food shape in anorexia nervosa. \u003cem\u003eAppetite\u003c/em\u003e, \u003cem\u003e169\u003c/em\u003e, 105858. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.appet.2021.105858\u003c/span\u003e\u003cspan address=\"10.1016/j.appet.2021.105858\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"eating-and-weight-disorders-studies-on-anorexia-bulimia-and-obesity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eawd","sideBox":"Learn more about [Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity](https://www.springer.com/journal/40519)","snPcode":"40519","submissionUrl":"https://submission.nature.com/new-submission/40519/3","title":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Anorexia nervosa, mixed reality, adolescent, portion size estimation, food stimuli, perception bias","lastPublishedDoi":"10.21203/rs.3.rs-6997366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6997366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e. Anorexia nervosa (AN) is a severe eating disorder characterized by perceptual distortions and restrictive eating behaviours. This pilot study examines portion size estimation in adolescent AN patients using a mixed-reality (MR) approach. The objective is to identify systematic biases in portion perception compared to healthy controls and to analyse cognitive distortions affecting portion selection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods. \u003c/strong\u003eA total of 30 female participants were recruited: 15 adolescent AN patients and 15 healthy adults as pretest. Participants engaged in a simulated meal assembly task within an MR environment, adjusting portion sizes of virtual food components to match a \"typical\" meal size (100%). Decision-making patterns and self-reported eating disorder symptoms were recorded. Statistical analyses included descriptive statistics, group comparisons and correlation analysis to examine associations between clinical variables and portion sizes, decision-making time and other decision parameters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e AN patients consistently selected significantly smaller portion sizes than healthy adults, particularly for high-calorie foods. No significant differences were observed in decision-making time or uncertainty indicators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion.\u003c/strong\u003e The findings support the hypothesis that AN patients exhibit altered food perception in the sense that they tend to overestimate the size of visually presented food portions. The MR approach proved effective in simulating meal selection, Future studies should include larger and more diverse samples and incorporate real food intake to further validate these results.\u003c/p\u003e","manuscriptTitle":"Pilot Study: PORTION-O-MAT - A Mixed Reality Solution for Investigating Perceptual and Behavioural Abnormalities During Food Portioning in Adolescents with Anorexia Nervosa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 06:50:32","doi":"10.21203/rs.3.rs-6997366/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-24T07:14:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T09:49:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-08T09:34:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207215215971994989950897956130507277871","date":"2025-07-23T12:14:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266474555749776388406347556458260458946","date":"2025-07-23T11:05:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-02T13:48:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-02T13:46:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-02T11:51:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","date":"2025-06-28T10:58:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"eating-and-weight-disorders-studies-on-anorexia-bulimia-and-obesity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eawd","sideBox":"Learn more about [Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity](https://www.springer.com/journal/40519)","snPcode":"40519","submissionUrl":"https://submission.nature.com/new-submission/40519/3","title":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1e23da96-0c60-4b1d-935f-ea7238ead15b","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:09:12+00:00","versionOfRecord":{"articleIdentity":"rs-6997366","link":"https://doi.org/10.1007/s40519-025-01797-2","journal":{"identity":"eating-and-weight-disorders-studies-on-anorexia-bulimia-and-obesity","isVorOnly":false,"title":"Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity"},"publishedOn":"2025-11-06 15:57:14","publishedOnDateReadable":"November 6th, 2025"},"versionCreatedAt":"2025-07-07 06:50:32","video":"","vorDoi":"10.1007/s40519-025-01797-2","vorDoiUrl":"https://doi.org/10.1007/s40519-025-01797-2","workflowStages":[]},"version":"v1","identity":"rs-6997366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6997366","identity":"rs-6997366","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}