Ultra-processed food advertisements dominate the food advertising landscape in two Stockholm areas with low vs high socioeconomic status. Is it time for regulatory action?

BACKGROUND: Ultra-processed food consumption is a risk factor for obesity and has a negative environmental impact. Food companies spend billions of dollars on advertisements each year to increase the consumption of ultra-processed food. In Australia, USA, and New Zealand, most food advertisements around schools and in train stations promote ultra-processed food, but no similar studies have been conducted in Sweden. The aim of this study was to explore the proportion of ultra-processed food advertisements in two districts of Stockholm, Sweden with low vs. high socioeconomic status (SES). METHODS: Two independent researchers (per area) mapped all advertisements, including storefronts, in two Stockholm districts. During consecutive days, all advertisements were photographed in Skärholmen (low SES district), and Östermalmstorg (high SES district), on the streets inside and outside the subway stations, as well as inside and outside of local shopping malls. Advertisements promoting food products were identified and a trained dietician categorized whether they promoted ultra-processed foods. Chi-Square test was conducted to test for differences in the proportion of ultra-processed food advertisements between the two study areas. RESULTS: In total, 4092 advertisements were photographed in Skärholmen (n = 1935) and Östermalm (n = 2157). 32.8% of all advertisements promoted food, while 65.4% of food advertisements promoted ultra-processed foods. A significantly higher proportion of ultra-processed food advertisements out of total food advertisements was identified in the low SES area, irrespective of the researcher taking the pictures (74.6% vs. 61.8%, p < 0.001 and 70.4% vs. 54.8%, p = 0.001). There was no significant difference in the proportion of food advertisements out of total advertisements between the two areas. CONCLUSIONS: This study provides initial evidence about the scale and the differences in exposure to food advertisements across areas in Stockholm. The observed high proportion of ultra-processed food advertisements is concerning and is in sharp contrast to the Swedish dietary guidelines that recommend reduced consumption of such foods. Based on our results, residents in low SES areas might be more exposed to ultra-processed food advertisements than those in high SES areas in Stockholm. If such findings are confirmed in additional areas, they should be considered during the deployment of food advertisement regulatory actions.

The clinicians' view of food-related obstacles for treating eating disorders: A qualitative study.

Background: Good health requires healthy eating. However, individuals with eating disorders, such as anorexia nervosa, require treatment to modify their dietary behaviours and prevent health complications. There is no consensus on the best treatment practices and treatment outcomes are usually poor. While normalising eating behaviour is a cornerstone in treatment, few studies have focused on eating and food-related obstacles to treatment. Objective: The aim of the study was to investigate clinicians' perceived food-related obstacles to treatment of eating disorders (EDs). Design: Qualitative focus group discussions were conducted with clinicians involved in eating disorder treatment to get an understanding of their perceptions and beliefs regarding food and eating among eating disorder patients. Thematic analysis was used to find common patterns in the collected material. Results: From the thematic analysis the following five themes were identified: (1) ideas about healthy and unhealthy food, (2) calculating with calories, (3) taste, texture, and temperature as an excuse, (4) the problems with hidden ingredients and (5) the challenges of extra food. Discussion: All identified themes showed not only connections to each other but also some overlap. All themes were associated with a requirement of control, where food may be perceived as a threat, with the effects of food consumption resulting in a perceived net loss, rather than a gain. This mindset can greatly influence decision making. Conclusions: The results of this study are based on experience and practical knowledge that could improve future ED treatments by enhancing our understanding the challenges certain foods pose for patients. The results may also help to improve dietary plans by including and explaining challenges for patients at different stages of treatment. Future studies could further investigate the causes and best treatment practices for people suffering from EDs and other eating disturbances.

Eating Behavior and Satiety With Virtual Reality Meals Compared With Real Meals: Randomized Crossover Study.

BACKGROUND: Eating disorders and obesity are serious health problems with poor treatment outcomes and high relapse rates despite well-established treatments. Several studies have suggested that virtual reality technology could enhance the current treatment outcomes and could be used as an adjunctive tool in their treatment. OBJECTIVE: This study aims to investigate the differences between eating virtual and real-life meals and test the hypothesis that eating a virtual meal can reduce hunger among healthy women. METHODS: The study included 20 healthy women and used a randomized crossover design. The participants were asked to eat 1 introduction meal, 2 real meals, and 2 virtual meals, all containing real or virtual meatballs and potatoes. The real meals were eaten on a plate that had been placed on a scale that communicated with analytical software on a computer. The virtual meals were eaten in a room where participants were seated on a real chair in front of a real table and fitted with the virtual reality equipment. The eating behavior for both the real and virtual meals was filmed. Hunger was measured before and after the meals using questionnaires. RESULTS: There was a significant difference in hunger from baseline to after the real meal (mean difference=61.8, P<.001) but no significant change in hunger from before to after the virtual meal (mean difference=6.9, P=.10). There was no significant difference in food intake between the virtual and real meals (mean difference=36.8, P=.07). Meal duration was significantly shorter in the virtual meal (mean difference=-5.4, P<.001), which led to a higher eating rate (mean difference=82.9, P<.001). Some participants took bites and chewed during the virtual meal, but the number of bites and chews was lower than in the real meal. The meal duration was reduced from the first virtual meal to the second virtual meal, but no significant difference was observed between the 2 real meals. CONCLUSIONS: Eating a virtual meal does not appear to significantly reduce hunger in healthy individuals. Also, this methodology does not significantly result in eating behaviors identical to real-life conditions but does evoke chewing and bite behavior in certain individuals. TRIAL REGISTRATION: ClinicalTrials.gov NCT05734209, https://clinicaltrials.gov/ct2/show/NCT05734209.

Limited effect of over-the-counter doses of ibuprofen on mechanisms regulating muscle hypertrophy during resistance training in young adults.

We have previously shown that maximal over-the-counter doses of ibuprofen, compared with low doses of acetylsalicylic acid, reduce muscle hypertrophy in young individuals after 8 wk of resistance training. Because the mechanism behind this effect has not been fully elucidated, we here investigated skeletal muscle molecular responses and myofiber adaptations in response to acute and chronic resistance training with concomitant drug intake. Thirty-one young (aged 18-35 yr) healthy men (n = 17) and women (n = 14) were randomized to receive either ibuprofen (IBU; 1,200 mg daily; n = 15) or acetylsalicylic acid (ASA; 75 mg daily; n = 16) while undergoing 8 wk of knee extension training. Muscle biopsies from the vastus lateralis were obtained before, at week 4 after an acute exercise session, and after 8 wk of resistance training and analyzed for mRNA markers and mTOR signaling, as well as quantification of total RNA content (marker of ribosome biogenesis) and immunohistochemical analysis of muscle fiber size, satellite cell content, myonuclear accretion, and capillarization. There were only two treatment × time interaction in selected molecular markers after acute exercise (atrogin-1 and MuRF1 mRNA), but several exercise effects. Muscle fiber size, satellite cell and myonuclear accretion, and capillarization were not affected by chronic training or drug intake. RNA content increased comparably (∼14%) in both groups. Collectively, these data suggest that established acute and chronic hypertrophy regulators (including mTOR signaling, ribosome biogenesis, satellite cell content, myonuclear accretion, and angiogenesis) were not differentially affected between groups and therefore do not explain the deleterious effects of ibuprofen on muscle hypertrophy in young adults.NEW & NOTEWORTHY Here we show that mTOR signaling, fiber size, ribosome biogenesis, satellite cell content, myonuclear accretion, and angiogenesis were not differentially affected between groups undergoing 8 wk of resistance training with concomitant anti-inflammatory medication (ibuprofen versus low-dose aspirin). Atrogin-1 and MuRF-1 mRNA were more downregulated after acute exercise in the low-dose aspirin group than in the ibuprofen group. Taken together it appears that these established hypertrophy regulators do not explain the previously reported deleterious effects of high doses of ibuprofen on muscle hypertrophy in young adults.

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

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

Correction: Virtual Reality App for Treating Eating Behavior in Eating Disorders: Development and Usability Study.

[This corrects the article DOI: 10.2196/24998.].

Virtual Reality App for Treating Eating Behavior in Eating Disorders: Development and Usability Study.

BACKGROUND: Anorexia nervosa is one of the more severe eating disorders, which is characterized by reduced food intake, leading to emaciation and psychological maladjustment. Treatment outcomes are often discouraging, with most interventions displaying a recovery rate below 50%, a dropout rate from 20% to 50%, and a high risk of relapse. Patients with anorexia nervosa often display anxiety and aversive behaviors toward food. Virtual reality has been successful in treating vertigo, anxiety disorder, and posttraumatic stress syndrome, and could potentially be used as an aid in treating eating disorders. OBJECTIVE: The aim of this study was to evaluate the feasibility and usability of an immersive virtual reality technology administered through an app for use by patients with eating disorders. METHODS: Twenty-six participants, including 19 eating disorder clinic personnel and 5 information technology personnel, were recruited through emails and personal invitations. Participants handled virtual food and utensils on an app using immersive virtual reality technology comprising a headset and two hand controllers. In the app, the participants learned about the available actions through a tutorial and they were introduced to a food challenge. The challenge consisted of a meal type (meatballs, potatoes, sauce, and lingonberries) that is typically difficult for patients with anorexia nervosa to eat in real life. Participants were instructed, via visual feedback from the app, to eat at a healthy rate, which is also a challenge for patients. Participants rated the feasibility and usability of the app by responding to the mHealth Evidence Reporting and Assessment checklist, the 10-item System Usability Scale, and the 20-point heuristic evaluation questionnaire. A cognitive walkthrough was performed using video recordings of participant interactions in the virtual environment. RESULTS: The mean age of participants was 37.9 (SD 9.7) years. Half of the participants had previous experience with virtual reality. Answers to the mHealth Evidence Reporting and Assessment checklist suggested that implementation of the app would face minor infrastructural, technological, interoperability, financial, and adoption problems. There was some disagreement on intervention delivery, specifically regarding frequency of use; however, most of the participants agreed that the app should be used at least once per week. The app received a mean score of 73.4 (range 55-90), earning an overall "good" rating. The mean score of single items of the heuristic evaluation questionnaire was 3.6 out of 5. The lowest score (2.6) was given to the "accuracy" item. During the cognitive walkthrough, 32% of the participants displayed difficulty in understanding what to do at the initial selection screen. However, after passing the selection screen, all participants understood how to progress through the tasks. CONCLUSIONS: Participants found the app to be usable and eating disorder personnel were positive regarding its fit with current treatment methods. Along with the food item challenges in the current app, participants considered that the app requires improvement to offer environmental and social (eg, crowded room vs eating alone) challenges.

Physical activity and sleep during the first week of anorexia nervosa inpatient care.

BACKGROUND: Individuals with Anorexia Nervosa are often described as restless, hyperactive and having disturbed sleep. The result reproducibility and generalisability of these results are low due to the use of unreliable methods, different measurement methods and outcome measures. A reliable method to measure both physical activity and sleep is through accelerometry. The main purpose of the study was to quantify the physical activity and sleeping behaviour of anorexia nervosa patients. Another purpose was to increase result reproducibility and generalisability of the study. MATERIAL AND METHODS: Accelerometer data were collected from the first week of treatment of anorexia nervosa at an inpatient ward. Raw data from the Axivity AX3© accelerometer was used with the open-source package GGIR for analysis, in the free statistical software R. Accelerometer measurements were transformed into euclidean norm minus one with negative values rounded to zero (ENMO). Physical activity measurements of interest were 24h average ENMO, daytime average ENMO, inactivity, light activity, moderate activity, and vigorous activity. Sleep parameters of interest were sleep duration, sleep efficiency, awakenings, and wake after sleep onset. The sleep duration of different age groups was compared to recommendations by the National Sleep Foundation using a Fisher's exact test. RESULTS: Of 67 patients, due to data quality 58 (93% female) were included in the analysis. Average age of participants was 17.8 (±6.9) years and body mass index was 15.5 (±1.9) kg/m2. Daytime average ENMO was 17.4 (±5.1) mg. Participants spent 862.6 (±66.2) min per day inactive, 88.4 (±22.6) min with light activities, 25.8 (±16.7) min with moderate activities and 0.5 (±1.8) min with vigorous activities. Participants slept for 461.0 (±68.4) min, waking up 1.45 (±1.25) times per night for 54.6 (±35.8) min, having an average sleep quality of 0.88 (±0.10). 31% of participants met sleep recommendations, with a significantly higher number of 6-13 year old patients failing to reach recommendations compared to 14-25 year old patients. CONCLUSION: The patient group spent most of their time inactive at the beginning of treatment. Most patients failed to reach sleep recommendations. The use of raw data and opensource software should ensure result reproducibility, enable comparison across points in treatment and comparison with healthy individuals.

Validation of a Deep Learning System for the Full Automation of Bite and Meal Duration Analysis of Experimental Meal Videos.

Eating behavior can have an important effect on, and be correlated with, obesity and eating disorders. Eating behavior is usually estimated through self-reporting measures, despite their limitations in reliability, based on ease of collection and analysis. A better and widely used alternative is the objective analysis of eating during meals based on human annotations of in-meal behavioral events (e.g., bites). However, this methodology is time-consuming and often affected by human error, limiting its scalability and cost-effectiveness for large-scale research. To remedy the latter, a novel "Rapid Automatic Bite Detection" (RABiD) algorithm that extracts and processes skeletal features from videos was trained in a video meal dataset (59 individuals; 85 meals; three different foods) to automatically measure meal duration and bites. In these settings, RABiD achieved near perfect agreement between algorithmic and human annotations (Cohen's kappa κ = 0.894; F1-score: 0.948). Moreover, RABiD was used to analyze an independent eating behavior experiment (18 female participants; 45 meals; three different foods) and results showed excellent correlation between algorithmic and human annotations. The analyses revealed that, despite the changes in food (hash vs. meatballs), the total meal duration remained the same, while the number of bites were significantly reduced. Finally, a descriptive meal-progress analysis revealed that different types of food affect bite frequency, although overall bite patterns remain similar (the outcomes were the same for RABiD and manual). Subjects took bites more frequently at the beginning and the end of meals but were slower in-between. On a methodological level, RABiD offers a valid, fully automatic alternative to human meal-video annotations for the experimental analysis of human eating behavior, at a fraction of the cost and the required time, without any loss of information and data fidelity.

Lower Energy Intake among Advanced vs. Early Parkinson's Disease Patients and Healthy Controls in a Clinical Lunch Setting: A Cross-Sectional Study.

Unintentional weight loss has been observed among Parkinson's disease (PD) patients. Changes in energy intake (EI) and eating behavior, potentially caused by fine motor dysfunction and eating-related symptoms, might contribute to this. The primary aim of this study was to investigate differences in objectively measured EI between groups of healthy controls (HC), early (ESPD) and advanced stage PD patients (ASPD) during a standardized lunch in a clinical setting. The secondary aim was to identify clinical features and eating behavior abnormalities that explain EI differences. All participants (n = 23 HC, n = 20 ESPD, and n = 21 ASPD) went through clinical evaluations and were eating a standardized meal (200 g sausages, 400 g potato salad, 200 g apple purée and 500 mL water) in front of two video cameras. Participants ate freely, and the food was weighed pre- and post-meal to calculate EI (kcal). Multiple linear regression was used to explain group differences in EI. ASPD had a significantly lower EI vs. HC (-162 kcal, p < 0.05) and vs. ESPD (-203 kcal, p < 0.01) when controlling for sex. The number of spoonfuls, eating problems, dysphagia and upper extremity tremor could explain most (86%) of the lower EI vs. HC, while the first three could explain ~50% vs. ESPD. Food component intake analysis revealed significantly lower potato salad and sausage intakes among ASPD vs. both HC and ESPD, while water intake was lower vs. HC. EI is an important clinical target for PD patients with an increased risk of weight loss. Our results suggest that interventions targeting upper extremity tremor, spoonfuls, dysphagia and eating problems might be clinically useful in the prevention of unintentional weight loss in PD. Since EI was lower in ASPD, EI might be a useful marker of disease progression in PD.

Formative Evaluation of a Smartphone App for Monitoring Daily Meal Distribution and Food Selection in Adolescents: Acceptability and Usability Study.

BACKGROUND: Obesity interventions face the problem of weight regain after treatment as a result of low compliance. Mobile health (mHealth) technologies could potentially increase compliance and aid both health care providers and patients. OBJECTIVE: This study aimed to evaluate the acceptability and usability and define system constraints of an mHealth system used to monitor dietary habits of adolescents in real life, as a first step in the development of a self-monitoring and lifestyle management system against adolescent obesity. METHODS: We recruited 26 students from a high school in Stockholm, Sweden. After a 30-minute information meeting and 5-minute individual instruction on how to use an mHealth system (smartphone with app and two external sensors), participants used it for 2-3 weeks to objectively collect dietary habits. The app and sensors were used by the participants, without supervision, to record as many main meals and snacks as possible in real life. Feasibility was assessed following the "mHealth evidence reporting and assessment checklist," and usability was assessed by questionnaires. Compliance was estimated based on system use, where a registration frequency of 3 main meals (breakfast, lunch, and dinner) per day for the period of the experiment, constituted 100% compliance. RESULTS: Participants included in the analysis had a mean age of 16.8 years (SD 0.7 years) and BMI of 21.9 kg/m2 (SD 4.1 kg/m2). Due to deviations from study instructions, 2 participants were excluded from the analysis. During the study, 6 participants required additional information on system use. The system received a 'Good' grade (77.1 of 100 points) on the System Usability Scale, with most participants reporting that they were comfortable using the smartphone app. Participants expressed a willingness to use the app mostly at home, but also at school; most of their improvement suggestions concerned design choices for the app. Of all main meals, the registration frequency increased from 70% the first week to 76% the second week. Participants reported that 40% of the registered meals were home-prepared, while 34% of the reported drinks contained sugar. On average, breakfasts took place at 8:30 AM (from 5:00 AM to 2:00 PM), lunches took place at 12:15 PM (from 10:15 AM to 6:15 PM), and dinners took place at 7:30 PM (from 3:00 PM to 11:45 PM). When comparing meal occurrence during weekdays vs weekends, breakfasts and lunches were eaten 3 hours later during weekends, while dinner timing was unaffected. CONCLUSIONS: From an infrastructural and functional perspective, system use was feasible in the current context. The smartphone app appears to have high acceptability and usability in high school students, which are the intended end-users. The system appears promising as a relatively low-effort method to provide real-life dietary habit measurements associated with overweight and obesity risk.

A smartphone application for semi-controlled collection of objective eating behavior data from multiple subjects.

BACKGROUND & OBJECTIVE: The study of eating behavior has made significant progress towards understanding the association of specific eating behavioral patterns with medical problems, such as obesity and eating disorders. Smartphones have shown promise in monitoring and modifying unhealthy eating behavior patterns, often with the help of sensors for behavior data recording. However, when it comes to semi-controlled deployment settings, smartphone apps that facilitate eating behavior data collection are missing. To fill this gap, the present work introduces ASApp, one of the first smartphone apps to support researchers in the collection of heterogeneous objective (sensor-acquired) and subjective (self-reported) eating behavior data in an integrated manner from large-scale, naturalistic human subject research (HSR) studies. METHODS: This work presents the overarching and deployment-specific requirements that have driven the design of ASApp, followed by the heterogeneous eating behavior dataset that is collected and the employed data collection protocol. The collected dataset combines objective and subjective behavior information, namely (a) dietary self-assessment information, (b) the food weight timeseries throughout an entire meal (using a portable weight scale connected wirelessly), (c) a photograph of the meal, and (d) a series of quantitative eating behavior indicators, mainly calculated from the food weight timeseries. The designed data collection protocol is quick, straightforward, robust and capable of satisfying the requirement of semi-controlled HSR deployment. RESULTS: The implemented functionalities of ASApp for research assistants and study participants are presented in detail along with the corresponding user interfaces. ASApp has been successfully deployed for data collection in an in-house testing study and the SPLENDID study, i.e., a real-life semi-controlled HSR study conducted in the cafeteria of a Swedish high-school in the context of an EC-funded research project. The two deployment studies are described and the promising results from the evaluation of the app with respect to attractiveness, usability, and technical soundness are discussed. Access details for ASApp are also provided. CONCLUSIONS: This work presents the requirement elucidation, design, implementation and evaluation of a novel smartphone application that supports researchers in the integrated collection of a concise yet rich set of heterogeneous eating behavior data for semi-controlled HSR.

Food Intake during School Lunch Is Better Explained by Objectively Measured Eating Behaviors than by Subjectively Rated Food Taste and Fullness: A Cross-Sectional Study.

School lunches contribute significantly to students' food intake (FI) and are important to their long-term health. Objective quantification of FI is needed in this context. The primary aim of this study was to investigate how much eating rate (g/min), number of food additions, number of spoonfuls, change in fullness, food taste, body mass index (BMI), and sex explain variations in school lunch FI. The secondary aim was to assess the reliability of repeated FI measures. One hundred and three (60 females) students (15⁻18 years old) were monitored while eating lunch in their normal school canteen environment, following their usual school schedules. A subgroup of students (n = 50) participated in a repeated lunch (~3 months later). Linear regression was used to explain variations in FI. The reliability of repeated FI measurements was assessed by change in mean, coefficient of variation (CV), and intraclass correlation (ICC). The regression model was significant and explained 76.6% of the variation in FI. Eating rate was the strongest explanatory variable, followed by spoonfuls, sex, food additions, food taste, BMI, and change in fullness. All explanatory variables were significant in the model except BMI and change in fullness. No systematic bias was observed in FI (-7.5 g (95% CI = -43.1⁻28 g)) while individual students changed their FI from -417 to +349 g in the repeated meal (CV 26.1% (95% CI = 21.4⁻33.5%), ICC 0.74 (95% CI = 0.58⁻0.84)). The results highlight the importance of objective eating behaviors for explaining FI in a school lunch setting. Furthermore, our methods show promise for large-scale quantification of objectively measured FI and eating behaviors in schools.

Predicting Real-Life Eating Behaviours Using Single School Lunches in Adolescents.

Large portion sizes and a high eating rate are associated with high energy intake and obesity. Most individuals maintain their food intake weight (g) and eating rate (g/min) rank in relation to their peers, despite food and environmental manipulations. Single meal measures may enable identification of "large portion eaters" and "fast eaters," finding individuals at risk of developing obesity. The aim of this study was to predict real-life food intake weight and eating rate based on one school lunch. Twenty-four high-school students with a mean (±SD) age of 16.8 yr (±0.7) and body mass index of 21.9 (±4.1) were recruited, using no exclusion criteria. Food intake weight and eating rate was first self-rated ("Less," "Average" or "More than peers"), then objectively recorded during one school lunch (absolute weight of consumed food in grams). Afterwards, subjects recorded as many main meals (breakfasts, lunches and dinners) as possible in real-life for a period of at least two weeks, using a Bluetooth connected weight scale and a smartphone application. On average participants recorded 18.9 (7.3) meals during the study. Real-life food intake weight was 327.4 g (±110.6), which was significantly lower (p = 0.027) than the single school lunch, at 367.4 g (±167.2). When the intra-class correlation of food weight intake between the objectively recorded real-life and school lunch meals was compared, the correlation was excellent (R = 0.91). Real-life eating rate was 33.5 g/min (±14.8), which was significantly higher (p = 0.010) than the single school lunch, at 27.7 g/min (±13.3). The intra-class correlation of the recorded eating rate between real-life and school lunch meals was very large (R = 0.74). The participants' recorded food intake weights and eating rates were divided into terciles and compared between school lunches and real-life, with moderate or higher agreement (κ = 0.75 and κ = 0.54, respectively). In contrast, almost no agreement was observed between self-rated and real-life recorded rankings of food intake weight and eating rate (κ = 0.09 and κ = 0.08, respectively). The current study provides evidence that both food intake weight and eating rates per meal vary considerably in real-life per individual. However, based on these behaviours, most students can be correctly classified in regard to their peers based on single school lunches. In contrast, self-reported food intake weight and eating rate are poor predictors of real-life measures. Finally, based on the recorded individual variability of real-life food intake weight and eating rate, it is not advised to rank individuals based on single recordings collected in real-life settings.

The Effect of Food Unit Sizes and Meal Serving Occasions on Eating Behaviour Characteristics: Within Person Randomised Crossover Studies on Healthy Women.

Manipulating food properties and serving environment during a meal can significantly change food intake at group level. However, the evaluation of the usefulness of such manipulations requires an understanding of individual behavioural changes. Three studies were conducted to explore the effect of unit size and meal occasion on eating behaviour characteristics (food intake, meal duration, number of bites and chews). All studies used a randomised crossover design, with a one-week wash-out period, starting with a familiarisation meal, with the participation of healthy, normal weight females between the ages of 18⁻35 years. In Study 1 (n = 19) three cube sizes (0.5, 1.0 and 1.5 cm³) of vegetable hash and chicken were compared. In Study 2 (n = 18) mashed potatoes and mincemeat were compared to whole potatoes and meatballs. In Study 3 (n = 29) meals served at lunch time (11:00⁻13:00) were compared to identical meals served at dinner time (17:00⁻19:00). The largest food unit size lead to significantly increased meal duration in Study 2 (mean difference 0.9 min, 95% confidence interval (CI) 0.0⁻1.8), but not in Study 1 (mean difference 1 min, 95% CI 0.1⁻2.0). There was a significant increase in number of chews in the large unit size condition of both Study 1 (mean difference 88, 95% CI 12⁻158) and Study 2 (mean difference 95, 95% CI 12⁻179). Different serving occasions did not significantly change any of the eating behaviours measured. Except for number of bites in Study 2 (R² = 0.60), most individuals maintained their eating behaviour relative to the group across unit sizes and serving occasions conditions (R² > 0.75), which suggests single meal testing can provide information about the behavioural characteristics of individual eating styles under different conditions.

Objective quantification of the food proximity effect on grapes, chocolate and cracker consumption in a Swedish high school. A temporal analysis.

Close food proximity leads to increased short-term energy intake, potentially contributing to the long-term development of obesity. However, its precise effects on eating behaviour are still unclear, especially with food available for extended periods of time. This study involved two similar high school student groups (15-17 years old), which had ad libitum access to grapes, chocolates and crackers during an hour-long experimental session. In the distal condition the foods were placed 6 meters away from the students (n = 24), in contrast to the proximal condition (n = 17) were the food was placed near the students. The identification of the type and the quantification of the amount of each food selected, for each individual serving, was facilitated through use of food scales and video recording. In the proximal condition individuals served themselves grapes and crackers more often and consumed more chocolate than in the distal condition. In total, participants in the proximal condition ingested significantly more energy (726 kcal vs. 504 kcal; p = 0.029), without reporting higher fullness. Food proximity also affected the temporal distribution of servings, with the first five minutes of the sessions corresponding to 53.1% and 45.6% of the total energy intake for the distal and proximal conditions, respectively. After the first five minutes, the servings in the distal condition were strongly clustered in time, with many students getting food together. In the proximal condition however, students displayed an unstructured pattern of servings over time. In conclusion, this study strengthens past evidence regarding the important role of food proximity on individual energy intake and, for the first time, it associates continuous food proximity to the emergence of unstructured eating over time. These conclusions, expanded upon by future studies, could support the creation of meaningful intervention strategies based on spatially and temporally controlled food availability.

A parametric Probabilistic Context-Free Grammar for food intake analysis based on continuous meal weight measurements.

Monitoring and modification of eating behaviour through continuous meal weight measurements has been successfully applied in clinical practice to treat obesity and eating disorders. For this purpose, the Mandometer, a plate scale, along with video recordings of subjects during the course of single meals, has been used to assist clinicians in measuring relevant food intake parameters. In this work, we present a novel algorithm for automatically constructing a subject's food intake curve using only the Mandometer weight measurements. This eliminates the need for direct clinical observation or video recordings, thus significantly reducing the manual effort required for analysis. The proposed algorithm aims at identifying specific meal related events (e.g. bites, food additions, artifacts), by applying an adaptive pre-processing stage using Delta coefficients, followed by event detection based on a parametric Probabilistic Context-Free Grammar on the derivative of the recorded sequence. Experimental results on a dataset of 114 meals from individuals suffering from obesity or eating disorders, as well as from individuals with normal BMI, demonstrate the effectiveness of the proposed approach.