PACE: a Novel Eating Behavior Phenotype to Assess Risk for Obesity in Middle Childhood.

BACKGROUND: Behavioral phenotypes that predict future weight gain are needed to identify children susceptible to obesity. OBJECTIVES: This prospective study developed an eating behavior risk score to predict change in adiposity over 1 y in children. METHODS: Data from 6 baseline visits (Time 1, T1) and a 1-y follow-up visit (Time 2, T2) were collected from 76, 7- to 8-y-old healthy children recruited from Central Pennsylvania. At T1, children had body mass index (BMI) percentiles <90 and were classified with either high (n = 33; maternal BMI ≥30 kg/m2) or low (n = 43; maternal BMI ≤25 kg/m2) familial risk for obesity. Appetitive traits and eating behaviors were assessed at T1. Adiposity was measured at T1 and T2 using dual-energy x-ray absorptiometry, with a main outcome of fat mass index (FMI; total body fat mass divided by height in meters squared). Hierarchical linear regressions determined which eating measures improved prediction of T2 FMI after adjustment for covariates in the baseline model (T1 FMI, sex, income, familial risk, and Tanner stage). RESULTS: Four eating measures-Portion susceptibility, Appetitive traits, loss of control eating, and eating rate-were combined into a standardized summary score called PACE. PACE improved the baseline model to predict 80% variance in T2 FMI. PACE was positively associated with the increase in FMI in children from T1 to T2, independent of familial risk (r = 0.58, P < 0.001). Although PACE was higher in girls than boys (P < 0.05), it did not differ by familial risk, income, or education. CONCLUSIONS: PACE represents a cumulative eating behavior risk score that predicts adiposity gain over 1 y in middle childhood. If PACE similarly predicts adiposity gain in a cohort with greater racial and socioeconomic diversity, it will inform the development of interventions to prevent obesity. This trial was registered at clinicaltrials.gov as NCT03341247.

Beyond ultra-processed: considering the future role of food processing in human health.

Food-based dietary guidelines have been the basis of public health recommendations for over half a century, but more recently, there has been a trend to classify the health properties of food not by its nutrient composition, but by the degree to which it has been processed. This concept has been supported by many association studies, narrative reviews and the findings from one randomised controlled feeding trial, which demonstrated the sustained effect of ultra-processed diets on increasing both energy intake and body weight. This has led to widespread speculation as to specific features of ultra-processed foods that promote increased energy intakes. Rising interest in the ultra-processed topic has led to proposals to include guidance and restrictions on the consumption of processed foods in national dietary guidelines, with some countries encouraging consumers to avoid highly processed foods completely, and only choose minimally processed foods. However, there remains a lack of consensus on the role of processed foods in human health when faced with the challenges of securing the food supply for a growing global population, that is, healthy, affordable and sustainable. There has also been criticism of the subjective nature of definitions used to differentiate foods by their degree of processing, and there is currently a lack of empirical data to support a clear mechanism by which highly processed foods promote greater energy intakes. Recommendations to avoid all highly processed foods are potentially harmful if they remove affordable sources of nutrients and will be impractical for most when an estimated two-thirds of current energy purchased are from processed or ultra-processed foods. The current review highlights some considerations when interpreting the dietary association studies that link processed food intake to health and offers a critique on some of the mechanisms proposed to explain the link between ultra-processed food and poor health. Recent research suggests a combination of higher energy density and faster meal eating rates are likely to influence meal size and energy intakes from processed foods and offers new perspectives on how to manage this in the future. In going beyond the ultra-processed debate, the aim is to summarise some important considerations when interpreting existing data and identify the important gaps for future research on the role of processed food in health.

Factors associated with eating rate: a systematic review and narrative synthesis informed by socio-ecological model.

Accumulating evidence shows associations between rapid eating and overweight. Modifying eating rate might be a potential weight management strategy without imposing additional dietary restrictions. A comprehensive understanding of factors associated with eating speed will help with designing effective interventions. The aim of this review was to synthesise the current state of knowledge on the factors associated with eating rate. The socio-ecological model (SEM) was utilised to scaffold the identified factors. A comprehensive literature search of eleven databases was conducted to identify factors associated with eating rate. The 104 studies that met the inclusion criteria were heterogeneous in design and methods of eating rate measurement. We identified thirty-nine factors that were independently linked to eating speed and mapped them onto the individual, social and environmental levels of the SEM. The majority of the reported factors pertained to the individual characteristics (n = 20) including demographics, cognitive/psychological factors and habitual food oral processing behaviours. Social factors (n = 11) included eating companions, social and cultural norms, and family structure. Environmental factors (n = 8) included food texture and presentation, methods of consumption or background sounds. Measures of body weight, food form and characteristics, food oral processing behaviours and gender, age and ethnicity were the most researched and consistent factors associated with eating rate. A number of other novel and underresearched factors emerged, but these require replication and further research. We highlight directions for further research in this space and potential evidence-based candidates for interventions targeting eating rate.

Validation of computational models to characterize cumulative intake curves from video-coded meals.

Introduction: Observational coding of eating behaviors (e.g., bites, eating rate) captures behavioral characteristics but is limited in its ability to capture dynamic patterns (e.g., temporal changes) across a meal. While the Universal Eating Monitor captures dynamic patterns of eating through cumulative intake curves, it is not commonly used in children due to strict behavioral protocols. Therefore, the objective of this study was to test the ability of computational models to characterize cumulative intake curves from video-coded meals without the use of continuous meal weight measurement. Methods: Cumulative intake curves were estimated using Kisslieff's Quadratic model and Thomas's logistic ordinary differential equation (LODE) model. To test if cumulative intake curves could be characterized from video-coded meals, three different types of data were simulated: (1) Constant Bite: simplified cumulative intake data; (2) Variable Bite: continuously measured meal weight data; and (3) Bite Measurement Error: video-coded meals that require the use of average bite size rather than measured bite size. Results: Performance did not differ by condition, which was assessed by examining model parameter recovery, goodness of fit, and prediction error. Therefore, the additional error incurred by using average bite size as one would with video-coded meals did not impact the ability to accurately estimate cumulative intake curves. While the Quadratic and LODE models were comparable in their ability to characterize cumulative intake curves, the LODE model parameters were more distinct than the Quadradic model. Greater distinctness suggests the LODE model may be more sensitive to individual differences in cumulative intake curves. Discussion: Characterizing cumulative intake curves from video-coded meals expands our ability to capture dynamic patterns of eating behaviors in populations that are less amenable to strict protocols such as children and individuals with disordered eating. This will improve our ability to identify patterns of eating behavior associated with overconsumption and provide new opportunities for treatment.

A framework to estimate the environmentally attainable intake of dairy cows in constraining environments.

Most intake models for dairy cows have been developed to make predictions under normal conditions, in which animals can meet their nutritional requirements. To estimate intake under constraining conditions, i.e. when intake is defined by the environment and not by the animal's requirements, it is necessary to develop models that take into account environmentally driven effects. The aim of this work was to develop a framework to represent the links between environmental variables (food quality and quantity, as well as ambient temperature, season, and farm type) and intake. The framework integrates time as the major constraint on intake and proposes the environmentally attainable intake (EAI) as the product of the Eating Rate (ER) and the Eating Time (ET). ER is the maximum sustainable rate (gr DM/min) at which animals bite the food, and ET is the daily time (min/d) that animals have to eat. The architecture of the framework is easily extensible to add constraints such as predation pressure, reproductive costs, competition, parasitism, or diseases. Data from grazing and indoor dairy farms were used to test the usability of the framework. The results show that a time use-based framework is a reliable approach to estimate intake considering environmental variables with minimum use of animals' characteristics. In conclusion, a high-level framework of feeding behaviour, that captures the main underlying mechanisms of intake in constrained environments, can be used to predict the EAI and the effects of the environment on animal performance.

Food oral processing and eating behavior from infancy to childhood: evidence on the role of food texture in the development of healthy eating behavior.

Eating behaviors develop in early life and refine during childhood, shaping long-term food choice and dietary habits, which underpin optimum growth and health. The development of Food Oral Processing (FOP) is of major importance in the establishment of eating behaviors at two scale levels: for the initial acceptance of food texture and for the longer-term development of eating behaviors associated to food intake. To date, both processes have been studied as independent topics and the current review proposes a parallel vision on their development from the onset of complementary feeding to later childhood. Individual factors affecting these FOP-related behaviors as they relate to food texture acceptance are discussed, alongside examples of interventions aiming at modifying them. Opportunity to better consider food textures when designing foods for children is addressed. Altogether, the review demonstrates the critical role of food texture in the development of a child's FOP skills, eating habits, and dietary patterns. These scientific knowledges need to be considered for the development of healthier eating behavior. We identify research gaps that need to be addressed and highlight the need to design foods that can support the development of healthy oral processing and eating behaviors among infants and children.

The Association Between Eating Quickly and Excessive Gestational Weight Gain.

Objectives: Maintaining an appropriate gestational weight gain (GWG) is essential for a safe pregnancy and delivery. This study aimed to determine the association between the habit of eating quickly and the risk of excessive GWG. Methods: We administered a questionnaire on eating habits to 1246 pregnant Japanese women in their second to third trimesters. We categorized the participants into three groups according to their answers to the question "Do you eat quickly?" Group 1, "always" or "usually"; Group 2, "sometimes"; and Group 3, "rarely" or "never." We assessed GWG according to the "The Optimal Weight Gain Chart" (Ministry of Health, Labor and Welfare, Japan), and those who exceeded the criteria were considered "excessive." Logistic regression analysis was performed with the risk of excess GWG as the dependent variable and quick food intake as the independent variable, to obtain relevant odds ratios (ORs) and 95% confidence intervals (CIs). Model 1 was unadjusted, and Model 2 was adjusted for age, prepregnancy body mass index, energy intake, mother's educational attainment, household income, exercise habits, and childbearing experience. Results: The OR (95% CI) for Groups 2 and 3 in Model 1, compared with Group 1, was 0.80 (0.62-1.05) and 0.61 (0.43-0.88), respectively (p for trend = 0.047). In Model 2, the OR (95% CI) for Groups 2 and 3 were 0.73 (0.55-0.96) and 0.59 (0.40-0.86), respectively (p for trend = 0.003). Conclusion: These results suggest that quick food ingestion increases the risk of excessive GWG.

Ultraprocessed Foods and Obesity Risk: A Critical Review of Reported Mechanisms.

Epidemiologic evidence supports a positive association between ultraprocessed food (UPF) consumption and body mass index. This has led to recommendations to avoid UPFs despite very limited evidence establishing causality. Many mechanisms have been proposed, and this review critically aimed to evaluate selected possibilities for specificity, clarity, and consistency related to food choice (i.e., low cost, shelf-life, food packaging, hyperpalatability, and stimulation of hunger/suppression of fullness); food composition (i.e., macronutrients, food texture, added sugar, fat and salt, energy density, low-calorie sweeteners, and additives); and digestive processes (i.e., oral processing/eating rate, gastric emptying time, gastrointestinal transit time, and microbiome). For some purported mechanisms (e.g., fiber content, texture, gastric emptying, and intestinal transit time), data directly contrasting the effects of UPF and non-UPF intake on the indices of appetite, food intake, and adiposity are available and do not support a unique contribution of UPFs. In other instances, data are not available (e.g., microbiome and food additives) or are insufficient (e.g., packaging, food cost, shelf-life, macronutrient intake, and appetite stimulation) to judge the benefits versus the risks of UPF avoidance. There are yet other evoked mechanisms in which the preponderance of evidence indicates ingredients in UPFs actually moderate body weight (e.g., low-calorie sweetener use for weight management; beverage consumption as it dilutes energy density; and higher fat content because it reduces glycemic responses). Because avoidance of UPFs holds potential adverse effects (e.g., reduced diet quality, increased risk of food poisoning, and food wastage), it is imprudent to make recommendations regarding their role in diets before causality and plausible mechanisms have been verified.

Portion control tableware differentially impacts eating behaviour in women with and without overweight.

Portion control tableware has been described as a potentially effective approach for weight management, however the mechanisms by which these tools work remain unknown. We explored the processes by which a portion control (calibrated) plate with visual stimuli for starch, protein and vegetable amounts modulates food intake, satiety and meal eating behaviour. Sixty-five women (34 with overweight/obesity) participated in a counterbalanced cross-over trial in the laboratory, where they self-served and ate a hot meal including rice, meatballs and vegetables, once with a calibrated plate and once with a conventional (control) plate. A sub-sample of 31 women provided blood samples to measure the cephalic phase response to the meal. Effects of plate type were tested through linear mixed-effect models. Meal portion sizes (mean ± SD) were smaller for the calibrated compared with the control plate (served: 296 ± 69 vs 317 ± 78 g; consumed: 287 ± 71 vs 309 ± 79 g respectively), especially consumed rice (69 ± 24 vs 88 ± 30 g) (p < 0.05 for all comparisons). The calibrated plate significantly reduced bite size (3.4 ± 1.0 vs 3.7 ± 1.0 g; p < 0.01) in all women and eating rate (32.9 ± 9.5 vs 33.7 ± 9.2 g/min; p < 0.05), in lean women. Despite this, some women compensated for the reduced intake over the 8 h following the meal. Pancreatic polypeptide and ghrelin levels increased post-prandially with the calibrated plate but changes were not robust. Plate type had no influence on insulin, glucose levels, or memory for portion size. Meal size was reduced by a portion control plate with visual stimuli for appropriate amounts of starch, protein and vegetables, potentially because of the reduced self-served portion size and the resulting reduced bite size. Sustained effects may require the continued use of the plate for long-term impact.

Combined effect of eating speed instructions and food texture modification on eating rate, appetite and later food intake.

Modifying food texture and eating slowly each reduce appetite and energy intake. No study has evaluated the effect of combining these measures to slow eating speed and determine the effect on appetite. The aim of this study was to investigate whether there is a combined effect of manipulating oral processing behaviours (OPBs) in this manner on self-reported satiety and subsequent food intake. A 2 × 2 design was used with four breakfast conditions in total. Twenty-four participants attended four study visits where they were asked to consume one of two isocaloric fixed-portion breakfasts differing in texture: 1) granola with milk and 2) yogurt with muesli and conserve. Participants consumed each breakfast twice, with verbal instructions to chew slowly at one visit and at a normal rate at another. Consumption was video-recorded to behaviourally code OPBs. Participants completed visual analogue scales of self-reported appetite measures at the beginning of the test session, immediately prior to and immediately after breakfast consumption. They also completed a food diary documenting food intake for the remainder of the day. The breakfast designed to be eaten slowest (the harder-textured meal with instructions to eat slowly) was eaten at a slower rate, with a greater number of chews per bite and a slower bite rate (p < 0.001) compared to the other meals. No differences were observed between the breakfast conditions on subjective measures of post-prandial satiety, or subsequent energy or macronutrient consumption. Results of this study highlight that combined effects of texture and instructions are most effective at reducing eating rate, though eating slower was not shown to enhance post-meal satiety. Reduced eating speed has previously been shown to reduce ad-libitum energy intake. Future research should consider combined approaches to reduce eating speed, to mitigate the risk of overconsumption within meals.

Eating rate and bite size were related to food intake across meals varying in portion size: A randomized crossover trial in adults.

Serving larger portions leads to increased food intake, but behavioral factors that influence the magnitude of this portion size effect have not been well characterized. We investigated whether measures of eating microstructure such as eating rate and bite size moderated the portion size effect. We also explored how sensory-specific satiety (SSS; the relative hedonic decline of a food as it is eaten) was affected by eating microstructure and larger portions. In a randomized crossover design, 44 adults aged 18-68 y (66% women; 46% with overweight and obesity) ate lunch in the laboratory once a week for 4 weeks. The meal consisted of pasta that was varied in portion size (400, 500, 600, or 700 g) and 700 g of water. Meals were video-recorded to assess bite count and meal duration, which were used to calculate mean eating rate (g/min) and mean bite size (g/bite). At each meal participants also completed an assessment of SSS. The results showed that as larger portions were served, meal intake increased in a curvilinear manner (p < 0.0001). Measures of eating microstructure did not moderate the portion size effect but were related to intake across all portions; faster eating rate, larger bite size, higher bite count, and longer meal duration were associated with greater consumption at all meals (all p < 0.0001). SSS was not influenced by any measure of eating microstructure or by portion size (all p > 0.10). In summary, the portion size effect was not moderated by eating microstructure, but relatively faster eating rates and larger bite sizes at meals, along with large portions, combined to increase food intake.

Effect of chewing behavior modification on food intake, appetite and satiety-related hormones: A Systematic Review

Purpose: Obesity has become a growing public health issue worldwide. Studies have shown that eating rate is one of the most important factors to consider in the strategies to prevent and/or treat obesity. Eating rate can be reduced through different strategies, such as an increase in oro-sensory exposure, the modification of food texture, and an increase in the number of chewing cycles. The aim of this systematic review was to analyze the available evidence regarding the effect of chewing behavior modification on the parameters that contribute to obesity. Methods: A systematic search was done on the electronic databases Pubmed, Cochrane Central Register of Controlled Trials, and Scopus, using the terms "mastication", "chewing", "chewing speed", "prolonged chewing", "number of chews", "masticatory cycles" "satiety" "satiety response" "appetite", "appetite regulation", "nutritional status" and "obesity". Results: A total of 23 intervention studies were selected that intervened in the participants' chewing behavior, either by reducing the eating rate, increasing oro-sensory exposure, food hardness or the number of chewing cycles. In most studies these interventions were effective at reducing food intake, subjective appetite and improving the plasma levels of satiety-related hormones and metabolites; moreover, they reduced body mass index in the long term. Conclusion: The currently available evidence seems to indicate that modifications to chewing behavior can bring with it a myriad of benefits for the treatment of obesity.

Propósito: La obesidad se ha convertido en un problema de salud pública creciente a nivel mundial. Investigaciones han demostrado que la tasa de ingesta es uno de los factores importantes a considerar en las estrategias para prevenir o tratar la obesidad. La tasa de ingesta puede reducirse a través de diferentes estrategias; el aumento de la exposición oro-sensorial, la modificación de la textura de los alimentos y el aumento en el número de ciclos masticatorios. El objetivo de esta revisión sistemática fue analizar la evidencia disponible sobre el efecto de la modificación de la conducta masticatoria sobre los parámetros que contribuyen a la obesidad. Métodos: Se realizó una búsqueda sistemática en las bases de datos electrónicas Pubmed, Cochrane Central Register of Controlled Trials y Scopus, con los términos "mastication", "chewing", "chewing speed", "prolonged chewing", "number of chews", "masticatory cycles" "satiety" "satiety response" "appetite", "appetite regulation", "nutritional status" y "obesity". Resultados: Se seleccionaron 23 estudios que intervenían en el comportamiento masticatorio de los participantes, ya sea reduciendo de la tasa de ingesta, aumentando la exposición oro-sensorial, dureza de los alimentos y número de ciclos masticatorios. Estas intervenciones resultaron ser efectivas para reducir la ingesta de alimentos, el apetito subjetivo y mejorar los niveles plasmáticos de las hormonas y metabolitos relacionados con la saciedad, además, a largo plazo, permitieron reducciones en el índice de masa corporal. Conclusión: La evidencia disponible actualmente parece señalar que las modificaciones en el comportamiento masticatorio pueden traer consigo múltiples beneficios para el tratamiento de la obesidad.

Body mass index and variability in meal duration and association with rate of eating.

Background: A fast rate of eating is associated with a higher risk for obesity but existing studies are limited by reliance on self-report and the consistency of eating rate has not been examined across all meals in a day. The goal of the current analysis was to examine associations between meal duration, rate of eating, and body mass index (BMI) and to assess the variance of meal duration and eating rate across different meals during the day. Methods: Using an observational cross-sectional study design, non-smoking participants aged 18-45 years (N = 29) consumed all meals (breakfast, lunch, and dinner) on a single day in a pseudo free-living environment. Participants were allowed to choose any food and beverages from a University food court and consume their desired amount with no time restrictions. Weighed food records and a log of meal start and end times, to calculate duration, were obtained by a trained research assistant. Spearman's correlations and multiple linear regressions examined associations between BMI and meal duration and rate of eating. Results: Participants were 65% male and 48% white. A shorter meal duration was associated with a higher BMI at breakfast but not lunch or dinner, after adjusting for age and sex (p = 0.03). Faster rate of eating was associated with higher BMI across all meals (p = 0.04) and higher energy intake for all meals (p < 0.001). Intra-individual rates of eating were not significantly different across breakfast, lunch, and dinner (p = 0.96). Conclusion: Shorter beakfast and a faster rate of eating across all meals were associated with higher BMI in a pseudo free-living environment. An individual's rate of eating is constant over all meals in a day. These data support weight reduction interventions focusing on the rate of eating at all meals throughout the day and provide evidence for specifically directing attention to breakfast eating behaviors.

Interrelations Between Food Form, Texture, and Matrix Influence Energy Intake and Metabolic Responses.

PURPOSE OF REVIEW: Nutrition often focuses on food composition, yet differences in food form, texture, and matrix influence energy intake and metabolism. This review outlines how these attributes of food impact oral processing, energy intake, and metabolism. RECENT FINDINGS: Food form has a well-established impact on intake, where liquids are consumed more than solids and semi-solids. For solids, texture properties like thickness, hardness, and lubrication, and geometrical properties like size and shape influence oral processing, eating rate, and intake. Food matrix integrity can influence nutrient and energy absorption and is strongly influenced by food processing. Food texture and matrix play important roles in modulating energy intake and absorption. Future research needs to consider the often overlooked role of texture and matrix effects on energy and metabolic responses to composite foods and meals. Research is needed to understand how processing impacts macro- and micro-structure of food and its long-term impact on energy balance and health.

Texture-based differences in eating rate influence energy intake for minimally processed and ultra-processed meals.

BACKGROUND: Consumption of ultra-processed foods has been linked with higher energy intakes. Food texture is known to influence eating rate (ER) and energy intake to satiation, yet it remains unclear whether food texture influences energy intakes from minimally processed and ultra-processed meals. OBJECTIVES: We examined the independent and combined effects of food texture and degree of processing on ad libitum food intake. We also investigated whether differences in energy intake during lunch influenced postmeal feelings of satiety and later food intake. METHODS: In this crossover study, 50 healthy-weight participants [n = 50 (24 men); mean ± SD age: 24.4 ± 3.1 y; BMI: 21.3 ± 1.9 kg/m2] consumed 4 ad libitum lunch meals consisting of "soft minimally processed," "hard minimally processed," "soft ultra-processed," and "hard ultra-processed" components. Meals were matched for total energy served, with some variation in meal energy density (±0.20 kcal/g). Ad libitum food intake (kcal and g) was measured and ER derived using behavioral coding of videos. Subsequent food intake was self-reported by food diary. RESULTS: There was a main effect of food texture on intake, whereby "hard minimally processed" and "hard ultra-processed" meals were consumed slower overall, produced a 21% and 26% reduction in food weight (g) and energy (kcal) consumed, respectively. Intakes were higher for "soft ultra-processed" and "soft minimally processed" meals (P < 0.001), after correcting for meal pleasantness. The effect of texture on food weight consumed was not influenced by processing levels (weight of food: texture*processing-effect, P = 0.376), but the effect of food texture on energy intake was (energy consumed: texture*processing-effect, P = 0.015). The least energy was consumed from the "hard minimally processed" meal (482.9 kcal; 95% CI: 431.9, 531.0 kcal) and the most from the "soft ultra-processed" meal (789.4 kcal; 95% CI: 725.9, 852.8 kcal; Δ=↓∼300 kcal). Energy intake was lowest when harder texture was combined with the "minimally processed" meals. Total energy intake across the day varied directly with energy intakes of the test meals (Δ15%, P < 0.001). CONCLUSIONS: Findings suggest that food texture-based differences in ER and meal energy density contribute to observed differences in energy intake between minimally processed and ultra-processed meals.This trial was registered at clinicaltrials.gov as NCT04589221.

The feasibility, acceptability, and benefit of interventions that target eating speed in the clinical treatment of children and adolescents with overweight or obesity: A systematic review and meta-analysis.

Eating at a faster speed is positively correlated with having a higher BMI. Modifying eating speed may offer a treatment opportunity for those with overweight and obesity. This review sought to understand the feasibility, acceptability, and benefit to using eating speed interventions in paediatric clinical weight-management settings. The PICO Framework was used. Clinical studies of eating speed interventions as a treatment for paediatric patients with overweight or obesity were included. No limits to search date were implemented. A systematic search of MEDLINE, PsychINFO and EMBASE via OVID, Web of Science and JBI, Database of systematic reviews and Implementation reports, along with trial registers NICE, ClinicalTrials.gov and Cochrane Central Register of Controlled Trials was conducted. Two authors were responsible for screening, extraction, and evaluation of the risk of bias. Fifteen papers reporting twelve interventions addressing eating-speed were identified, involving a total of 486 active participants (range 7-297). Study design was weak with only one full RCT and there were some concerns over quality and risk of bias (Cochrane RoB 2.0). Limited sample sizes and different measured outcomes did not allow powered evaluations of effect for all outcomes. There is some indication, overall, that addressing eating speed has the potential to be a beneficial adjunct to clinical obesity treatment, although the pooled effect estimate did not demonstrate a difference in BMISDS status following eating speed interventions compared to control [pooled mean difference (0.04, 95% CI -0.39 to 0.46, N = 3)]. Developments to improve the engagement to, and acceptability of, interventions are required, alongside rigorous high-quality trials to evaluate effectiveness.

Development of a Video-Based Tool for the Self-Assessment of Eating Rates in Young Adult Females.

Eating speed assessments are often based on self-reports, and establishing objective "ratings" is required to improve accuracy. However, no relevant simple assessment tool incorporating "ratings" is currently available. This study aimed to develop a group-adaptable eating rate assessment tool for young females using smartphones. Fifty female college students were directly observed while eating, and a self-assessment tool for the eating rate was created using video. Using the directly observed eating rate of a test food A as the gold standard (GS), we compared the eating rate self-assessment findings between those obtained using a conventional questionnaire and those obtained using an assessment tool. The validity and reproducibility of the assessment tool were verified. In terms of validity, the correlation coefficient for the GS questionnaire (r=0.442, p<0.001) was similar to that for the self-assessment tool (r=0.491, p<0.001). The reproducibility of repeated measurements of the self-assessment tool was inferior to that of the questionnaire (weighted kappa coefficients; 0.393 vs. 0.804). This may be explained in part by participants selecting the same items with specific words such as "fast" or "slow" on two occasions. As for the validity of test food A, additional measurements for test food C on a subset of subjects (n=16) showed a strong positive correlation (r=0.845, p<0.001) between A and C. The present study suggests that a video-based self-assessment tool we developed for young adult females is straightforward, and allows the subjects to observe specific and visual ratings in a manner that is less burdensome and time-effective than conventional questionnaire methods.

Impact of Individual Differences in Eating Rate on Oral Processing, Bolus Properties and Post-Meal Glucose Responses.

PURPOSE: Modifying food texture has been shown to influence oral processing behaviour. We explored the impact of food texture on oral processing, bolus formation and post-prandial glucose responses (PPG) among fast and slow eaters. METHODS: Male participants (N=39) were split into fast or slow eaters based on natural differences in eating rate when consuming two carbohydrate-equivalent test-meals differing in texture (white rice and rice cake). PPG and satiety responses were compared for fast and slow eaters over 120-min for each test-meal. Each groups test-meal PPG was compared for bolus and saliva properties at the point of swallow. RESULTS: White rice displayed lower instrumental hardness, chewiness and Young's modulus and was perceived less chewy, springy and sticky than rice cake. Slow eaters (n=24, white rice: 13.3 g/min; rice cake: 15.1 g/min) required an average 42% more chews per bite (p < 0.001), had 60% longer oral exposure time (OET), and consumed both test-meals (p < 0.001) at half the eating rate of fast eaters (n=15). Slow eaters had higher PPG following the rice cake meal at 15 (p = 0.046) and 45 min (p = 0.034) than fast eaters. A longer OET was a positive predictor of early PPG at 30-min after the white rice meal (ß = 0.178, p = 0.041) and saliva uptake was a significant predictor (ß = 0.458, p = 0.045) of PPG for slow eaters when consuming rice cake. Increasing food hardness and stiffness (Young's modulus) had a greater impact on eating rate for slow eaters than fast eaters. CONCLUSIONS: Eating rate, oral exposure time and bolus saliva uptake were the predictors of an individual's post-prandial glycaemic response amongst slow eaters. Increasing the number of chews per bite with a longer oral exposure time increased saliva uptake in the bolus at the moment of swallowing and enhanced temporal changes in PPG, leading to greater glycaemic peaks in rice cake meal. Differences in eating rate between slow and fast eaters when consuming rice cake meal influenced temporal changes in PPG but not total PPG, and bolus properties did not differ between eating rate groups.

Eating Speed, Eating Frequency, and Their Relationships with Diet Quality, Adiposity, and Metabolic Syndrome, or Its Components.

Excess body weight is a major global health concern, particularly due to its associated increased health risks. Several strategies have been proposed to prevent overweight and obesity onset. In the past decade, it has been suggested that eating speed/rate and eating frequency might be related to obesity. The main aim of this narrative review was to summarize existing evidence regarding the impact of eating speed/rate and eating frequency on adiposity, metabolic syndrome (MetS), or diet quality (DQ). For this purpose, a literature search of observational and interventional trials was conducted between June and September 2020 in PubMed and Web of Sciences databases, without any data filters and no limitations for publication date. Results suggest that children and adults with a faster eating speed/rate may be associated with a higher risk of developing adiposity, MetS or its components. Furthermore, a higher eating frequency could be associated with diet quality improvement, lower adiposity, and lower risk of developing MetS or its components. Further interventional trials are warranted to clarify the mechanism by which these eating behaviors might have a potential impact on health.

Fast Eating Is Associated with Increased BMI among High-School Students.

Fast self-reported eating rate (SRER) has been associated with increased adiposity in children and adults. No studies have been conducted among high-school students, and SRER has not been validated vs. objective eating rate (OBER) in such populations. The objectives were to investigate (among high-school student populations) the association between OBER and BMI z-scores (BMIz), the validity of SRER vs. OBER, and potential differences in BMIz between SRER categories. Three studies were conducted. Study 1 included 116 Swedish students (mean ± SD age: 16.5 ± 0.8, 59% females) who were eating school lunch. Food intake and meal duration were objectively recorded, and OBER was calculated. Additionally, students provided SRER. Study 2 included students (n = 50, mean ± SD age: 16.7 ± 0.6, 58% females) from Study 1 who ate another objectively recorded school lunch. Study 3 included 1832 high-school students (mean ± SD age: 15.8 ± 0.9, 51% females) from Sweden (n = 748) and Greece (n = 1084) who provided SRER. In Study 1, students with BMIz ≥ 0 had faster OBER vs. students with BMIz < 0 (mean difference: +7.7 g/min or +27%, p = 0.012), while students with fast SRER had higher OBER vs. students with slow SRER (mean difference: +13.7 g/min or +56%, p = 0.001). However, there was "minimal" agreement between SRER and OBER categories (κ = 0.31, p < 0.001). In Study 2, OBER during lunch 1 had a "large" correlation with OBER during lunch 2 (r = 0.75, p < 0.001). In Study 3, fast SRER students had higher BMIz vs. slow SRER students (mean difference: 0.37, p < 0.001). Similar observations were found among both Swedish and Greek students. For the first time in high-school students, we confirm the association between fast eating and increased adiposity. Our validation analysis suggests that SRER could be used as a proxy for OBER in studies with large sample sizes on a group level. With smaller samples, OBER should be used instead. To assess eating rate on an individual level, OBER can be used while SRER should be avoided.