Description of chewing and food intake over the course of a meal

Rule-based systems to automatically count bites from meal videos

Eating behavior is a key factor for nutritional intake and plays a significant role in the development of eating disorders and obesity. The standard methods to detect eating behavior events (i.e., bites and chews) from video recordings rely on manual annotation, which lacks objective assessment and standardization. Yet, video recordings of eating episodes provide a non-invasive and scalable source for automation. Here, we present a rule-based system to count bites automatically from video recordings with 468 3D facial key points. We tested the performance against manual annotation in 164 videos from 15 participants. The system can count bites with 79% accuracy when annotation is available, and 71.4% when annotation is unavailable. The system showed consistent performance across varying food textures. Eating behavior researchers can use this automated and objective system to replace manual bite count annotation, provided the system's error is acceptable for the purpose of their study. Utilizing our approach enables real-time bite counting, thereby promoting interventions for healthy eating behaviors. Future studies in this area should explore rule-based systems and machine learning methods with 3D facial key points to extend the automated analysis to other eating events while providing accuracy, interpretability, generalizability, and low computational requirements.

Personalized lattice-structured prosthesis as a graftless solution for mandible reconstruction and prosthetic restoration: A finite element analysis

Oral cavity tumors are a prevalent cause of mandible reconstruction surgeries. The mandible is vital for functions like oralization, respiration, mastication, and deglutition. Current mandible reconstruction methods have low success rates due to complications like plate fracture or exposure, infections, and screw loosening. Autogenous bone grafts are commonly used but carry the risk of donor region morbidity. Despite technological advances, an ideal solution for mandible reconstruction remains elusive. Additive manufacturing in medicine offers personalized prosthetics from patient-specific medical images, allowing for the creation of porous structures with tailored mechanical properties that mimic bone properties. This study compared a commercial reconstruction plate with a lattice-structured personalized prosthesis under different biting and osseointegration conditions using Finite Element Analysis. Patient-specific images were obtained from an individual who underwent mandible reconstruction with a commercial plate and suffered from plate fracture by fatigue after 26 months. Compared to the commercial plate, the maximum von Mises equivalent stress was significantly lowered for the personalized prosthesis, hindering a possible fatigue fracture. The equivalent von Mises strains found in bone were within bone maintenance and remodeling intervals. This work introduces a design that doesn't require grafts for large bone defects and allows for dental prosthesis addition without the need for implants.

Capturing Eating Behavior from Video Analysis: A Systematic Review

Current methods to detect eating behavior events (i.e., bites, chews, and swallows) lack objective measurements, standard procedures, and automation. The video recordings of eating episodes provide a non-invasive and scalable source for automation. Here, we reviewed the current methods to automatically detect eating behavior events from video recordings. According to PRISMA guidelines, publications from 2010-2021 in PubMed, Scopus, ScienceDirect, and Google Scholar were screened through title and abstract, leading to the identification of 277 publications. We screened the full text of 52 publications and included 13 for analysis. We classified the methods in five distinct categories based on their similarities and analyzed their accuracy. Facial landmarks can count bites, chews, and food liking automatically (accuracy: 90%, 60%, 25%). Deep neural networks can detect bites and gesture intake (accuracy: 91%, 86%). The active appearance model can detect chewing (accuracy: 93%), and optical flow can count chews (accuracy: 88%). Video fluoroscopy can track swallows but is currently not suitable beyond clinical settings. The optimal method for automated counts of bites and chews is facial landmarks, although further improvements are required. Future methods should accurately predict bites, chews, and swallows using inexpensive hardware and limited computational capacity. Automatic eating behavior analysis will allow the study of eating behavior and real-time interventions to promote healthy eating behaviors.

Insights into the constellating drivers of satiety impacting dietary patterns and lifestyle

Food intake and body weight regulation are of special interest for meeting today's lifestyle essential requirements. Since balanced energy intake and expenditure are crucial for healthy living, high levels of energy intake are associated with obesity. Hence, regulation of energy intake occurs through short- and long-term signals as complex central and peripheral physiological signals control food intake. This work aims to explore and compile the main factors influencing satiating efficiency of foods by updating recent knowledge to point out new perspectives on the potential drivers of satiety interfering with food intake regulation. Human internal factors such as genetics, gender, age, nutritional status, gastrointestinal satiety signals, gut enzymes, gastric emptying rate, gut microbiota, individual behavioral response to foods, sleep and circadian rhythms are likely to be important in determining satiety. Besides, the external factors (environmental and behavioral) impacting satiety efficiency are highlighted. Based on mechanisms related to food consumption and dietary patterns several physical, physiological, and psychological factors affect satiety or satiation. A complex network of endocrine and neuroendocrine mechanisms controls the satiety pathways. In response to food intake and other behavioral cues, gut signals enable endocrine systems to target the brain. Intestinal and gastric signals interact with neural pathways in the central nervous system to halt eating or induce satiety. Moreover, complex food composition and structures result in considerable variation in satiety responses for different food groups. A better understanding of foods and factors impacting the efficiency of satiety could be helpful in making smart food choices and dietary recommendations for a healthy lifestyle based on updated scientific evidence.

Impact of Masticatory Behaviors Measured With Wearable Device on Metabolic Syndrome: Cross-sectional Study

Background

It has been widely recognized that mastication behaviors are related to the health of the whole body and to lifestyle-related diseases. However, many studies were based on subjective questionnaires or were limited to small-scale research in the laboratory due to the lack of a device for measuring mastication behaviors during the daily meal objectively. Recently, a small wearable masticatory counter device, called bitescan (Sharp Co), for measuring masticatory behavior was developed. This wearable device is designed to assess objective masticatory behavior by being worn on the ear in daily life.

Objective

This study aimed to investigate the relation between mastication behaviors in the laboratory and in daily meals and to clarify the difference in mastication behaviors between those with metabolic syndrome (MetS) and those without (non-MetS) measured using a wearable device.

Methods

A total of 99 healthy volunteers (50 men and 49 women, mean age 36.4 [SD 11.7] years) participated in this study. The mastication behaviors (ie, number of chews and bites, number of chews per bite, and chewing rate) were measured using a wearable ear-hung device. Mastication behaviors while eating a rice ball (100 g) in the laboratory and during usual meals for an entire day were monitored, and the daily energy intake was calculated. Participants’ abdominal circumference, fasting glucose concentration, blood pressure, and serum lipids were also measured. Mastication behaviors in the laboratory and during meals for 1 entire day were compared. The participants were divided into 2 groups using the Japanese criteria for MetS (positive/negative for MetS or each MetS component), and mastication behaviors were compared.

Results

Mastication behaviors in the laboratory and during daily meals were significantly correlated (number of chews r=0.36; P<.001; number of bites r=0.49; P<.001; number of chews per bite r=0.33; P=.001; and chewing rate r=0.51; P<.001). Although a positive correlation was observed between the number of chews during the 1-day meals and energy intake (r=0.26, P=.009), the number of chews per calorie ingested was negatively correlated with energy intake (r=–0.32, P=.002). Of the 99 participants, 8 fit the criteria for MetS and 14 for pre-MetS. The number of chews and bites for a rice ball in the pre-MetS(+) group was significantly lower than the pre-MetS(–) group (P=.02 and P=.04, respectively). Additionally, scores for the positive abdominal circumference and hypertension subgroups were also less than the counterpart groups (P=.004 and P=.01 for chews, P=.006 and P=.02 for bites, respectively). The number of chews and bites for an entire day in the hypertension subgroup were significantly lower than in the other groups (P=.02 and P=.006). Furthermore, the positive abdominal circumference and hypertension subgroups showed lower numbers of chews per calorie ingested for 1-day meals (P=.03 and P=.02, respectively).

Conclusions

These results suggest a relationship between masticatory behaviors in the laboratory and those during daily meals and that masticatory behaviors are associated with MetS and MetS components.

Trial Registration

University Hospital Medical Information Network Clinical Trials Registry R000034453; https://tinyurl.com/mwzrhrua

Relationship between body mass index and masticatory factors evaluated with a wearable device

Numerous studies have evaluated the relationship between eating behavior and obesity, however few studies have objectively assessed eating behavior. Additionally, the association of masticatory behaviors with masticatory performance remains unclear. This study aimed to verify the relationship between masticatory performance and behavior measured by a wearable masticatory counter, and BMI. 365 healthy adults participated. Mastication behaviors, i.e. number of chews and bites, chewing rate, and chewing time, were measured using wearable masticatory counter while consuming one rice ball (100 g). Masticatory performance was evaluated using testing gummy jelly. Lifestyle habits including exercise, walking, and breakfast, were surveyed by questionnaire. The correlation coefficients between masticatory behaviors and performance and BMI were analyzed. Furthermore, multiple regression analysis was performed. The number of chews showed positive correlation with chewing rate, number of bites and chewing time, but no correlation with masticatory performance. BMI had weak but significant negative correlation with number of chews, bites, chewing time, and masticatory performance, but had no correlation with chewing rate. Multiple regression analysis revealed that BMI was associated with sex, age, number of chews, bites, masticatory performance, and walking speed. In conclusion, masticatory behavior and performance were not interrelated, but both were independently associated with BMI weakly.

Short and Long-Term Innovations on Dietary Behavior Assessment and Coaching: Present Efforts and Vision of the Pride and Prejudice Consortium

Overweight, obesity and cardiometabolic diseases are major global health concerns. Lifestyle factors, including diet, have been acknowledged to play a key role in the solution of these health risks. However, as shown by numerous studies, and in clinical practice, it is extremely challenging to quantify dietary behaviors as well as influencing them via dietary interventions. As shown by the limited success of 'one-size-fits-all' nutritional campaigns catered to an entire population or subpopulation, the need for more personalized coaching approaches is evident. New technology-based innovations provide opportunities to further improve the accuracy of dietary assessment and develop approaches to coach individuals towards healthier dietary behaviors. Pride & Prejudice (P&P) is a unique multi-disciplinary consortium consisting of researchers in life, nutrition, ICT, design, behavioral and social sciences from all four Dutch Universities of Technology. P&P focuses on the development and integration of innovative technological techniques such as artificial intelligence (AI), machine learning, conversational agents, behavior change theory and personalized coaching to improve current practices and establish lasting dietary behavior change.

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.

Edograms: recording the microstructure of meal intake in humans-a window on appetite mechanisms

Early attempts at the objective measurement of food intake in humans followed many heuristic pioneer studies in laboratory animals, which revealed how homeostatic and hedonic factors interact to shape the daily eating patterns. Early studies in humans examined the characteristics of intake responses at discrete ingestive events. Described for the first time in 1969, the edogram consisted of a parallel recording of chewing and swallowing responses during standardized lunches, allowing parameters of the "microstructure of meals" to be quantified under varying conditions of deprivation or sensory stimulation, in parallel with overall meal size, meal duration, and eating rate. Edographic studies showed consistent changes in the microstructure of meals in response to palatability level (increased eating rate, decreased chewing time and number of chews per food unit, shorter intrameal pauses, and increased prandial drinking under improved palatability). Longer premeal deprivation affected the eating responses at the beginning of meals (decreased chewing time and number of chews per food unit) but not at the end. Eating rate decelerated during the course of meals in normal-weight participants but not in participants with obesity. These observations largely agreed with contemporary works using other objective measurement methods. They were confirmed and expanded in later studies, notably in the investigation of satiation mechanisms affecting weight control. Importantly, research has demonstrated that the parameters of the microstructure of meals not only reflect the influence of stimulatory/inhibitory factors but can, per se, exert a causal role in satiation and satiety. The early edographic recording instruments were improved over the years and taken out of laboratory settings in order to address the measurement of spontaneous intake responses in free-living individuals. Much remains to be done to make these instruments entirely reliable under the immense variety of situations where food consumption occurs.

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.

Association Between Self-Reported Eating Rate, Energy Intake, and Cardiovascular Risk Factors in a Multi-Ethnic Asian Population

Eating faster is associated with greater body mass index (BMI), but less is known about the relationships between eating rate, energy intake, body composition, and cardio-metabolic risk factors in different Asian ethnic groups. Using data from the Singapore Multi-Ethnic Cohort (n = 7011; 21-75 y), we investigated associations between self-reported eating rate (SRER), with energy intake, body composition, blood pressure, and blood lipids. SRER and lifestyle was assessed using interviewer-administered questionnaires. Multivariable models were used to examine the associations of SRER with energy intake, body composition, blood pressure, and blood lipids after adjusting for covariates. General and abdominal overweight were defined as BMI ≥ 23 kg/m² and waist circumference > 90cm (men) and > 80cm (women), respectively. On average, faster eaters (vs. slower eaters) consumed 105kcal/day more (p = 0.034), had ~5kg higher body weight (p < 0.001), 1.3 kg/m² higher BMI (p < 0.001), and 3.1cm larger waist-circumference (p < 0.001). Faster eaters had significantly higher blood pressure, circulating triglycerides, and total-to-high-density lipoprotein cholesterol ratio than slower eaters. Faster eaters were twice as likely to develop general (multivariable-OR: 2.2;95%CI,1.8-2.6; p < 0.001), and abdominal (OR:1.8;95%CI, 1.5-2.2; p < 0.001) overweight than slower eaters. This association was observed across all subgroups by age, sex, and ethnicity. Findings suggest that SRER is a robust behavioral marker for increased risk of higher energy intake, obesity, and poor cardio-metabolic health, and a modifiable behavioral risk-factor for obesity prevention.

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.

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.

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.

Effect of chewing rate on meal intake

Fast eating has been shown to increase the risk of overweight in both children and adults. The objectives of the present study were to investigate the correlation between chewing rate and the number of chews per mouthful and to evaluate if they were associated with the weight of meal intake. Thirty healthy subjects, aged 18-24 yr, ate a test lunch at their habitual speed until they felt satiated. The activities of masseter and suprahyoid muscles were recorded to determine the number of chews and the moment of swallowing. The weight of meal intake was recorded along with body mass index (BMI), chewing rate, number of chews per mouthful, meal duration, ingestion rate, hunger, and food preference levels. The mean weight (±SD) of meal intake, chewing rate, and number of chews per mouthful were 261.4 ± 78.9 g, 94.4 ± 13.5 chews min^-1 , 19.2 ± 6.4 chews per mouthful, respectively. Chewing rate was not correlated with the number of chews per mouthful. The multivariable linear regression showed that meal intake was significantly positively associated with chewing rate, meal duration, and BMI, but inversely associated with the number of chews per mouthful (adjusted R² = 0.42). It was concluded that the number of chews was not associated with chewing rate but meal intake was explained by both reduced number of chews and increased chewing rate.

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.

Control of Eating Behavior Using a Novel Feedback System

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

Automatic Analysis of Food Intake and Meal Microstructure Based on Continuous Weight Measurements

The structure of the cumulative food intake (CFI) curve has been associated with obesity and eating disorders. Scales that record the weight loss of a plate from which a subject eats food are used for capturing this curve; however, their measurements are contaminated by additive noise and are distorted by certain types of artifacts. This paper presents an algorithm for automatically processing continuous in-meal weight measurements in order to extract the clean CFI curve and in-meal eating indicators, such as total food intake and food intake rate. The algorithm relies on the representation of the weight-time series by a string of symbols that correspond to events such as bites or food additions. A context-free grammar is next used to model a meal as a sequence of such events. The selection of the most likely parse tree is finally used to determine the predicted eating sequence. The algorithm is evaluated on a dataset of 113 meals collected using the Mandometer, a scale that continuously samples plate weight during eating. We evaluate the effectiveness for seven indicators and for bite-instance detection. We compare our approach with three state-of-the-art algorithms, and achieve the lowest error rates for most indicators (24 g for total meal weight). The proposed algorithm extracts the parameters of the CFI curve automatically, eliminating the need for manual data processing, and thus facilitating large-scale studies of eating behavior.

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.

Fast or slow-foods? Describing natural variations in oral processing characteristics across a wide range of Asian foods

The structural properties of foods have a functional role to play in oral processing behaviours and sensory perception, and also impact on the meal size and the experience of fullness.

Variation in the Oral Processing of Everyday Meals Is Associated with Fullness and Meal Size; A Potential Nudge to Reduce Energy Intake?

Laboratory studies have demonstrated that experimental manipulations of oral processing can have a marked effect on energy intake. Here, we explored whether variations in oral processing across a range of unmodified everyday meals could affect post-meal fullness and meal size. In Study 1, female participants (N = 12) attended the laboratory over 20 lunchtime sessions to consume a 400-kcal portion of a different commercially available pre-packaged meal. Prior to consumption, expected satiation was assessed. During each meal, oral processing was characterised using: (i) video-recordings of the mouth and (ii) real-time measures of plate weight. Hunger and fullness ratings were elicited pre- and post-consumption, and for a further three hours. Foods that were eaten slowly had higher expected satiation and delivered more satiation and satiety. Building on these findings, in Study 2 we selected two meals (identical energy density) from Study 1 that were equally liked but maximised differences in oral processing. On separate days, male and female participants (N = 24) consumed a 400-kcal portion of either the "fast" or "slow" meal followed by an ad libitum meal (either the same food or a dessert). When continuing with the same food, participants consumed less of the slow meal. Further, differences in food intake during the ad libitum meal were not compensated at a subsequent snacking opportunity an hour later. Together, these findings suggest that variations in oral processing across a range of unmodified everyday meals can affect fullness after consuming a fixed portion and can also impact meal size. Modifying food form to encourage increased oral processing (albeit to a lesser extent than in experimental manipulations) might represent a viable target for food manufacturers to help to nudge consumers to manage their weight.

Effect of chewing speed on the detection of a foreign object in food

Accidentally biting hard on a piece of hard foreign object in food is among the causes of tooth fracturing and could be associated with oral sensibility. This study has investigated the effect of chewing speed on the ability to detect a foreign object in food in human. Fourteen healthy subjects were asked to randomly chew one of 10 cooked rice balls, five of which containing a foreign object made from a tiny uncooked rice grain, until they detected the rice grain. Each subject chewed the test foods both at 50 (slow) and 100 (fast) chews min(-1). The accuracy of detection and the number of chews before detection (CBD) were recorded and compared between the two chewing speeds using paired t-tests. The results showed that almost all subjects detected the foreign object by biting. The accuracy of detection was more than 90% and not significantly different between slow and fast chewing but the mean CBD in slow chewing (11·7 ± 1·3 chews) was significantly different from that in fast chewing (20·7 ± 1·9 chews; P < 0·001). The study showed that slow chewers required less number of chews before a foreign object in food could be detected and was, presumably, more effective in detecting the object compared to fast chewers. If each chew bears equal probability of teeth encountering the foreign object, slow chewing might also reduce the chance of accidentally biting hard on the foreign object and fracturing the tooth.

Effect of chewing speed on energy expenditure in healthy subjects

OBJECTIVE

The aim of the study was to investigate the effect of rate of chewing on energy expenditure in human subjects.

MATERIALS AND METHODS

Fourteen healthy subjects (aged 18-24 years) within the normal range of BMI participated in a cross-over experiment consisting of two 6-min sessions of gum chewing, slow (∼60 cycles/min) and fast (∼120 cycles/min) chewing. The resting energy expenditure (REE) and during gum chewing was measured using a ventilated hood connected to a gas analyzer system. The normality of data was explored using the Shapiro-Wilk test. The energy expenditure rate during chewing and the energy expenditure per chewing cycle were compared between the two chewing speeds using Wilcoxon signed ranks tests.

RESULTS

The energy expenditure per chewing cycle during slow chewing (median 1.4, range 5.2 cal; mean 2.1±1.6 cal) was significantly higher than that during fast chewing (median 0.9, range 2.2 cal; mean 1.0±0.7 cal) (p < 0.005). However, the energy expenditure rate was not significantly different between the two chewing speeds (p > 0.05).

CONCLUSIONS

The results of this study suggest that chewing at a slower speed could increase the energy expenditure per cycle and might affect the total daily energy expenditure.

Designing food structures for nutrition and health benefits

In addition to providing specific sensory properties (e.g., flavor or textures), there is a need to produce foods that also provide functionality within the gastrointestinal (GI) tract, over and above simple nutrition. As such, there is a need to understand the physical and chemical processes occurring in the mouth, stomach, small intestine, and large intestine, in addition to the food structure-physiology interactions. In vivo techniques and in vitro models have allowed us to study and simulate these processes, which aids us in the design of food microstructures that can provide functionality within the human body. Furthermore, it is important to be aware of the health or nutritional needs of different groups of consumers when designing food structures, to provide targeted functionality. Examples of three groups of consumers (elderly, obese, and athletes) are given to demonstrate their differing nutritional requirements and the formulation engineering approaches that can be utilized to improve the health of these individuals. Eating is a pleasurable process, but foods of the future will be required to provide much more in terms of functionality for health and nutrition.