Self-reported eating rate is associated with weight status in a Dutch population: a validation study and a cross-sectional study

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.

Consistent effect of eating rate on food and energy intake across twenty-four ad libitum meals

Foods consumed at lower eating rates (ER) lead to reductions in energy intake. Previous research has shown that texture-based differences in eating rateER can reduce meal size. The effect size and consistency of these effects across a wide range of composite and complex meals differing considerably in texture and varying in meal occasion have not been reported. We determined how consistently texture-based differences in ER can influence food and energy intake across a wide variety of meals. In a crossover design, healthy participants consumed twelve breakfast and twelve lunch meals that differed in texture to produce a fast or slow ER. A breakfast group (n = 15) and lunch group (n = 15) completed twelve ad libitum meal sessions each (six 'fast' and six 'slow' meals), where intake was measured and behavioural video annotation was used to characterise eating behaviour. Liking did not differ significantly between fast and slow breakfasts (P = 0·44) or lunches (P = 0·76). The slow meals were consumed on average 39 % ± 9 % (breakfast) and 45 % ± 7 % (lunch) slower than the fast meals (both P < 0·001). Participants consumed on average 22 % ± 5 % less food (84 g) and 13 % ± 6 % less energy (71 kcal) from slow compared with fast meals (mean ± SE; P < 0·001). Consuming meals with a slower ER led to a reduction in food intake, where an average decrease of 20 % in ER produced an 11 % ± 1 % decrease in food intake (mean ± SE). These findings add to the growing body of evidence showing that ER can be manipulated using food texture and that this has aits consistent effect on food and energy intake across a wide variety of Hedonically equivalent meals.

Evaluation of body composition and bone mineral density in patients with and without idiopathic condylar resorption of temporomandibular disorders by dual energy X-ray absorptiometry

Background/purpose

The impact of temporomandibular joint (TMJ) osseous destruction on bone mineral density (BMD) remains unclear due to controversial findings. Besides, no previous study has explored the relationship between idiopathic condylar resorption (ICR) and body composition. This study aimed to investigate the relationship between ICR and BMD or body composition.

Materials and methods

Between July 2018 and August 2022, patients evaluated by an experienced dentist and diagnosed with temporomandibular disorders (TMDs) were referred to our center. They were recruited while they received the magnetic resonance image (MRI) examination, BMD and body composition completely. Patients were further categorized into TMDs with or without ICR groups according to MRI findings. One-way analysis of variance was used to compare the variables of BMD and body composition in the two groups.

Results

In total, 67 patients were included in the analysis, with 42 categorized as TMDs with ICR and 25 as TMDs without ICR. Patients with ICR had a significantly higher lean mass percentage and lower fat mass percentage; lower android/gynoid fat ratio, and visceral adipose tissue area than those without ICR (P < 0.05). Besides, patients above age 30 with ICR had lower Z scores (P = 0.017) compared with subjects without ICR.

Conclusion

TMDs patients with ICR show a relationship with body composition and affect the lean and fat mass distribution, especially android/gynoid fat ratio. The pathophysiological mechanism remains unclear. Further researches to investigate teeth binding, malocclusion and dietary habits are important to understand the association of ICR, BMD and body composition.

Unit size influences ad libitum intake in a snacking context via eating rate

Geometric and textural properties of food, like unit size, have previously been shown to influence energy intake. While mechanism(s) driving this effect are unclear, unit size may relate to intake by affecting eating microstructure (e.g., eating rate, bite size). In a randomized crossover study, we investigated relationships between unit size, eating microstructure, and intake. Adults (n = 75, 75% women) consumed an ad libitum snack three times in our laboratory. This snack was a 70-g portion (∼2.5 servings) of one of three sizes of pretzel (small, medium, large). Intake was measured in grams by difference in weight before and after the snack. Each session was video recorded to measure eating microstructure; snack duration (min) and number of bites were annotated and used to calculate mean eating rate (g/min) and mean bite size (g/bite). Results revealed unit size influenced intake (grams and kcal; both p's ≤ 0.001), such that participants consumed 31% and 22% more of the large pretzels (16.9 ± 2.3 g) compared to the small (12.9 ± 2.3 g) and medium sizes (13.8 ± 2.3 g), respectively. Unit size also influenced eating rate and bite size (both p's < 0.001); the largest pretzel size yielded the fastest eating rate and largest mean bite size. Further analysis revealed that after accounting for eating microstructure, the effects of unit size on intake were no longer significant, suggesting eating microstructure was driving these effects. Together, these findings indicate that unit size influences intake by affecting eating microstructure and that food properties like unit size can be leveraged to moderate snack intake.

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.

Sex differences in waist circumference obesity and eating speed: a cross-sectional study of Japanese people with normal body mass index

Background

Fast eating has been positively associated with visceral fat accumulation in normal-weight individuals according to body mass index (BMI). However, previous studies have not examined energy and nutrients, or adjusted for food intake. We examined the relationship between eating speed and visceral fat accumulation, using waist circumference as an index, in middle-aged participants who were considered to be of standard weight according to BMI, with nutrient intake added as an adjustment factor.

Methods

We included 6,548 Japanese participants (3,875 men and 2,673 women) aged 40-74 years with BMI 18.5-25.0 kg/m2 who were enrolled in the Yamagata Cohort Study. Participants were divided into "fast," "normal," and "slow" eaters according to self-reported eating speed. Nutrient and food intake were evaluated using a food frequency questionnaire, and the difference in intake by eating speed and sex was compared. Logistic regression analysis was used to examine the relationship between waist circumference obesity (men ≥85 cm, women ≥90 cm, according to Japanese criteria) and eating speed, adjusted for nutrient intake and other lifestyle habits.

Results

In men, slow eaters had greater intakes of dietary protein, fat, polyunsaturated fatty acids (PUFA), omega-3 PUFA, total dietary fiber, soluble dietary fiber, insoluble dietary fiber, soybean products, fish, green and yellow vegetables, other vegetables, mushrooms, and seaweed in comparison with normal-speed eaters. In men, waist circumference obesity was significantly lower among slow eaters than in the group with normal eating speed. In women, waist circumference obesity was not significantly associated with eating speed and was not also associated with nutrient/food consumption except omega-6 PUFA.

Conclusion

Eating slowly was associated with healthy dietary habits. Our results could help prevent waist circumference obesity in men with a BMI between 18.5 and 25.0 kg/m2. However, similar findings were not observed in women, suggesting a sex difference.

Association of health behaviors, dietary habits, and oral health with weight gain after 20 years of age in community-dwelling Japanese individuals aged 40 years and older: a cross-sectional study

OBJECTIVES

This cross-sectional study investigated the factors associated with weight gain ≥ 10 kg after 20 years of age in the general Japanese population, with a focus on the number of teeth.

MATERIALS AND METHODS

We included individuals aged ≥ 40 years from Yamagata prefecture, Japan from 2017-2021. A postal survey was conducted using a self-administered questionnaire; 5,940 participants were included in the final analysis. The questionnaire included items on lifestyle factors, medical history, physical and mental conditions, oral health, and dietary intake. A multivariate logistic regression analysis was used to identify independent associations between weight gain ≥ 10 kg after 20 years of age and various parameters; adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were estimated.

RESULTS

Less than 20 teeth, male sex, drinking habit frequency, eating very fast or fast, and a higher frequency of eating-away-from-home were significant factors associated with weight gain ≥ 10 kg after 20 years of age; individuals with < 20 versus > 20 teeth exhibited a 1.35-fold higher OR (95% CI 1.15-1.59; p < 0.01).

CONCLUSIONS

Our results suggest that having < 20 teeth may affect weight gain ≥ 10 kg after 20 years of age. However, owing to the cross-sectional study design, causality could not be determined. Therefore, maintaining healthy lifestyle behaviors to avoid tooth loss may also affect weight gain ≥ 10 kg after 20 years of age.

CLINICAL RELEVANCE

Having < 20 teeth has the potential to affect long-term weight gain after 20 years of age.

Reasons individuals stop eating questionnaire (RISE-Q) among adults in the United Arab Emirates

The relationship between obesity and satiation is complex and bidirectional. Understanding differences in reasons for meal termination may enhance our understanding of overeating risks and susceptibility to overconsumption. This study aimed to investigate the reasons why individuals in the UAE stop eating. A cross-sectional web-based study was conducted among adults ≥18 years in the UAE (n = 1482). Using a self-administered online questionnaire, we collected information on sociodemographic characteristics, lifestyle habits, and eating behavior using the Reasons Individuals Stop Eating-Questionnaire (RISE-Q-15) used twice for breakfast/main meal. The items were categorized into five scales; decreased food appeal (DFA), physical satisfaction (PS), planned amount (PA), self-consciousness (SC), and decreased priority of eating (DPE). All items were scored from 1 to 7 ranging between 3 to 21 on each scale. A paired t-test was used to evaluate the difference between the RISE-Q scores on each scale concerning the two meals. The main reason why participants stopped eating breakfast was under the PS scale (14.91 ± 3.72), followed by the PA scale (14.58 ± 3.00). The main reason why participants stopped eating main meals was under the PS scale (14.78 ± 3.56), followed by the PA scale (14.77 ± 43.81). The mean score of the DPE scale was significantly higher for breakfast than the main meal (p = 0.038). More than half of the participants reported an average eating rate (58.7%). Pearson's chi-square analysis revealed that the eating rate was dependent on BMI (p<0.001). Considering individual mealtimes and addressing factors related to PS and PA of food is crucial when designing nutrition interventions aiming to promote healthy eating habits among adults in the UAE.

Relationships of rapid eating with visceral and subcutaneous fat mass and plasma adiponectin concentration

Rapid eating has been demonstrated to be associated with obesity and overweight. However, few studies have characterized the separate relationships of eating speed with visceral and subcutaneous fat mass or circulating adiponectin concentration. We hypothesized that rapid eating is associated with the larger visceral fat tissue (VFT) area and lower adiponectin concentration, but not with the subcutaneous fat tissue (SFT) area in men and women. We performed a cross-sectional study of 712 adults aged 20-86 years (528 men and 184 women; mean ± SD age 59.36 ± 13.61 years). The participants completed a self-reported questionnaire, and underwent anthropometric and laboratory measurements and computed tomographic imaging of the abdomen as a part of annual medical check-ups. Multivariate linear regression analyses revealed that rapid eating was associated with larger visceral (B = 24.74; 95% CI 8.87-40.61, p = 0.002) and subcutaneous fat areas (B = 31.31; 95% CI 12.23-50.38, p = 0.001), lower adiponectin concentration (B =  - 2.92; 95% CI - 4.39- - 1.46, p < 0.001), higher body mass index (BMI) (B = 2.13; 95% CI 1.02-3.25, p < 0.001), and larger waist circumference (B = 5.23; 95% CI 2.16-8.30, p < 0.001) in men, which is partially consistent with the hypothesis. In contrast, rapid eating was found to be associated only with BMI, and not with abdominal adipose area or adiponectin concentration in women, which is a result that is not consistent with the hypothesis. These results suggest that there is no difference in the association of rapid eating with VFT and SFT areas.

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.

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.

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.

Prevalence of tachyphagia at lunch and associated factors in a population of workers

PURPOSE

Obesity is a public health problem worldwide. The eating habits of French workers need to be clarified. In particular, tachyphagia (meal lasting < 15 min) promotes weight gain. The present study aimed to investigate the presence of tachyphagia at lunchtime, the factors associated with tachyphagia, and the relationship between tachyphagia and obesity among workers.

METHODS

This cross-sectional study was conducted between January and May 2016 in five departments of occupational medicine. An anonymous self-administered questionnaire was used to gather general declarative data, such as weight and height (to calculate body mass index [BMI]) and information about lunchtime habits, including the time spent eating.

RESULTS

A total of 415 workers with a mean age of 41.1 ± 12.6 years were included. The mean BMI was 23.8 ± 4.6 kg/m², and the prevalence of obesity was 15.9%. Tachyphagia occurred in 20.3% of the cases and was more frequent in younger workers. Multivariate analysis revealed that skipping meals, eating standing up, and eating fast food were positively associated with tachyphagia (p = 0.015, p = 0.028, and p = 0.027, respectively). Older age and eating with colleagues/friends were negatively associated with tachyphagia (p = 0.003 and p < 0.0001, respectively). No significant association was observed between tachyphagia and obesity.

CONCLUSION

Our study provides important information about the lunchtime habits of workers. Maintaining commensality is crucial, particularly in young workers. Companies should play a role in organising their employees' lunch breaks. Level III Evidence obtained from well-designed cohort or case-control analytic studies.

Chewing increases postprandial diet-induced thermogenesis

Slow eating, which involves chewing food slowly and thoroughly, is an effective strategy for controlling appetite in order to avoid being overweight or obese. Slow eating also has the effect of increasing postprandial energy expenditure (diet-induced thermogenesis). It is still unclear whether this is due to oral stimuli; that is, the duration of tasting food in the mouth and the duration of chewing. To investigate the effects of oral stimuli on diet-induced thermogenesis in 11 healthy normal weight males, we conducted a randomized crossover study comprising three trials: (1) drinking liquid food normally, (2) drinking liquid food after tasting, and (3) adding chewing while tasting. Oral stimuli (i.e., the duration of tasting liquid food in the mouth and the duration of chewing) significantly increased diet-induced thermogenesis after drinking liquid food. This result demonstrates that the increase in diet-induced thermogenesis is due to oral stimuli rather than the influence of the food bolus. Increased diet-induced thermogenesis induced by chewing and taste stimuli may help to prevent overweight and obesity.

Association of number of bites and eating speed with energy intake: Wearable technology results under free-living conditions

Personalized weight management strategies are gaining interest. However, knowledge is limited regarding eating habits and association with energy intake, and current technologies limit assessment in free-living situations. We assessed associations between eating behavior and time of day with energy intake using a wearable camera under free-living conditions and explored if obesity modifies the associations. Sixteen participants (50% with obesity) recorded free-living eating behaviors using a wearable fish-eye camera for 14 days. Videos were viewed by trained annotators who confirmed number of bites, eating speed, and time of day for each eating episode. Energy intake was determined by a trained dietitian performing 24-h diet recalls. Greater number of bites, reduced eating speed, and increased BMI significantly predicted higher energy intake among all participants (P < 0.05, each). There were no significant interactions between obesity and number of bites, eating speed, or time of day (p > 0.05). Greater number of bites and reduced eating speed were significantly associated with higher energy intake in participants without obesity. Results show that under free-living conditions, more bites and slower eating speed predicted higher energy intake when examining consumption of foods with beverages. Obesity did not modify these associations. Findings highlight how eating behaviors can impact energy balance and can inform weight management interventions using wearable technology.

Eating Behaviors Are Associated With Physical Fitness and Body Composition Among US Army Soldiers

OBJECTIVE

Examine associations between soldiers' eating behaviors, compliance with body composition and fitness standards, and physical performance.

DESIGN

Cross-sectional study.

SETTING

Eight Army installations.

PARTICIPANTS

US Army Soldiers (n = 1,591; 84% male).

MAIN OUTCOME MEASURES

Characteristics, eating behaviors, compliance with body composition and physical fitness standards, and fitness level were assessed via questionnaire.

ANALYSIS

Bivariate and multivariable logistic regression.

RESULTS

Eating mostly at a dining facility was associated with lower odds of body composition failure (odds ratio [OR], 0.44; 95% confidence interval [CI], 0.26-0.73); whereas, eating at a fast rate (OR, 1.51; 95% CI, 1.05-2.17) or often/always ignoring satiety cues (OR, 2.12; 95% CI, 1.06-4.27) was associated with higher odds of body composition failure. Eating mostly fast-food/convenience meals (OR, 1.75; 95% CI, 1.19-2.59) and eating at a fast rate (OR, 1.42; 95% CI, 1.04-1.93) was associated with higher odds of physical fitness failure. Skipping breakfast was associated with lower odds of high physical performance (OR, 0.41; 95% CI, 0.23-0.74); whereas, nutrition education was associated with higher odds of high physical performance (OR, 1.02; 95% CI, 1.01-1.04).

CONCLUSIONS AND IMPLICATIONS

As eating behaviors are modifiable, findings suggest opportunities for improving the specificity of Army health promotion and education programs.

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.

Consumption of Foods With Higher Energy Intake Rates is Associated With Greater Energy Intake, Adiposity, and Cardiovascular Risk Factors in Adults

BACKGROUND

Both high energy density and fast eating rates contribute to excess energy intakes. The energy intake rate (EIR; kcal/min) combines both the energy density (kcal/g) and eating rate (g/min) of a food to quantify the typical rate at which calories of different foods are ingested.

OBJECTIVES

We describe the EIRs of diets in a multi-ethnic Asian population, and examine relationships between the consumption of high-EIR foods and total energy intake, body composition, and cardio-metabolic risk factors.

METHODS

Diet and lifestyle data from the Singapore Multi-Ethnic Cohort 2 (n = 7011; 21-75 y), were collected through interviewer-administrated questionnaires. The EIR for each of the 269 foods was calculated as the product of its eating rate and energy density. Multivariable models were used to examine associations between the relative consumption of foods with higher and lower EIRs and energy intake, body composition, and cardio-metabolic risks, after adjusting for age, sex, ethnicity, education level, physical activity, smoking status, and alcohol drinking status.

RESULTS

Individuals with higher daily energy intakes and with obesity consumed a significantly larger percentage of their energy from high-EIR foods, with a smaller relative intake of lower-EIR foods. Individuals with raised serum cholesterol also consumed a significantly higher proportion of high-EIR foods, whereas those without hypertension consumed a larger percentage of energy intake from low-EIR foods. Individuals classified as having a "very high" dietary EIR had a significantly 1.3 kg higher body weight (95% CI, 0.2-1.5; P = 0.013), 0.4 kg/m2 higher BMI (95% CI, 0.03-0.8; P = 0.037), and 1.2 cm larger waist circumference (95% CI, 0.2-2.2; P = 0.010), and were more likely to have abdominal overweight (OR, 1.3; 95% CI, 1.1-1.5; P < 0.001) than those with a "low" dietary EIR.

CONCLUSIONS

Comparing foods by their EIRs summarizes the combined impact of energy density and eating rate, and may identify foods and dietary patterns that are associated with obesogenic eating styles and higher diet-related cardiovascular disease risk in an Asian population.

Reliability of a novel wearable device to measure chewing frequency

Purpose In recent years, the chewing frequency, i.e., the number of chewing cycles, has decreased owing to changes in dietary habits. Although these changes may be related to complete body health, there is no evidence-based tool to measure the dietary habits. We developed a small ear-hung wearable device for monitoring mastication behavior. The device, worn on the ear pinna, allows the counting of the number of chewing cycles, and data are collected on a smartphone via Bluetooth. In this study, the reliability of the novel device was verified.Methods A total of 22 healthy volunteers participated in the study. During measurement, the subjects wore the novel wearable device on their right ear pinna and were asked to chew gum, gummy jellies, and rice balls. The number of chewing cycles was counted by the device. A mandibular kinesiograph (MKG) was also recorded, and the chewing activity was recorded as a video. The accuracy, precision, and recall of the ear-hung device were calculated by comparing the data obtained from the MKG and the video recording. Additionally, the factors affecting reliability were examined.Results The accuracy, precision, and recall of the novel device were 101.6 ± 13.6%, 85.3 ± 11.0%, and 84.5 ± 9.5%, respectively. Although the accuracy was not affected by any factor, precision and recall of the novel device for women were significantly worse than that for men, and were greatest when the subjects were chewing gum.Conclusions Our findings suggest that the newly developed ear-hung wearable device for counting the number of chewing cycles was sufficiently reliable.

Virtual Reality as a Tool to Study the Influence of the Eating Environment on Eating Behavior: A Feasibility Study

In this manuscript, we describe a new approach to study the effect of the eating environment on food intake and eating behavior using virtual reality technology. Fifteen adults consumed pizza rolls in two virtual reality (VR) environments: a restaurant and a table in an empty room. The participants' food intake, eating parameters (e.g., masticatory parameters and eating rate), and their sensory evaluation of the test food was measured. The participants' sense of presence (the feeling of being in the virtual environment) and markers of arousal were also measured. There was no statistical significant difference in food intake or the sensory evaluation of the test food. In the restaurant condition, participants used fewer masticatory cycles before swallowing but there was no effect on eating rate or maximum bite force. Participants experienced a greater sense of presence when they were in the pizza restaurant scene. Moreover, their heart rate and skin temperature were higher in the restaurant condition. This study suggests that VR could be developed as a new tool to study the effect of the eating environment on food intake and eating behavior.

Combined Impact of a Faster Self-Reported Eating Rate and Higher Dietary Energy Intake Rate on Energy Intake and Adiposity

Eating more quickly and consuming foods with a higher energy-intake-rate (EIR: kcal/min) is associated with greater energy intake and adiposity. However, it remains unclear whether individuals who eat more quickly are more likely to consume foods with higher EIR. We investigated the overlap between self-reported eating rate (SRER) and the consumption of higher EIR foods, and their combined impact on daily energy intake and adiposity in a population-based Asian cohort (n = 7011; 21-75y). Food consumption was assessed using a validated Food Frequency Questionnaire. Moderated regression with simple slope analysis was conducted to evaluate whether SRER modified the association between dietary EIR and total dietary energy intakes. Faster eaters consumed a significantly higher proportion of energy from higher EIR foods among overweight individuals, but not among normal-weight individuals. Associations between dietary EIR and total energy intake were stronger among medium (β = 15.04, 95%CI: 13.00-17.08) and fast (β = 15.69, 95%CI: 12.61-18.78) eaters, compared with slower eaters (β = 9.89, 95%CI: 5.11-14.67; p-interaction = 0.032). Higher dietary EIR also tended to be more strongly associated with BMI in fast eaters (β = 0.025, 95%CI: 0.011-0.038) than in slow eaters (β = 0.017, 95%CI: -0.007-0.040). These findings suggest that the combination of eating more quickly and selecting a greater proportion of energy from higher EIR foods (i.e., softly textured, energy dense), promoted higher dietary energy intakes and adiposity.

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.

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.

Ultra-Processing or Oral Processing? A Role for Energy Density and Eating Rate in Moderating Energy Intake from Processed Foods

BACKGROUND

Recent observational data and a controlled in-patient crossover feeding trial show that consumption of "ultra-processed foods" (UPFs), as defined by the NOVA classification system, is associated with higher energy intake, adiposity, and at a population level, higher prevalence of obesity. A drawback of the NOVA classification is the lack of evidence supporting a causal mechanism for why UPFs lead to overconsumption of energy. In a recent study by Hall the energy intake rate in the UPF condition (48 kcal/min) was >50% higher than in the unprocessed condition (31 kcal/min). Extensive empirical evidence has shown the impact that higher energy density has on increasing ad libitum energy intake and body weight. A significant body of research has shown that consuming foods at higher eating rates is related to higher energy intake and a higher prevalence of obesity. Energy density can be combined with eating rate to create a measure of energy intake rate (kcal/min), providing an index of a food's potential to promote increased energy intake.

OBJECTIVE

The current paper compared the association between measured energy intake rate and level of processing as defined by the NOVA classification.

METHODS

Data were pooled from 5 published studies that measured energy intake rates across a total sample of 327 foods.

RESULTS

We show that going from unprocessed, to processed, to UPFs that the average energy intake rate increases from 35.5 ± 4.4, to 53.7 ± 4.3, to 69.4 ± 3.1 kcal/min (P < 0.05). However, within each processing category there is wide variability in the energy intake rate.

CONCLUSIONS

We conclude that reported relations between UPF consumption and obesity should account for differences in energy intake rates when comparing unprocessed and ultra-processed diets. Future research requires well-controlled human feeding trials to establish the causal mechanisms for why certain UPFs can promote higher energy intake.

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.

Association between Eating Speed and Classical Cardiovascular Risk Factors: A Cross-Sectional Study

Cardiovascular disease (CVD) is one of the main causes of morbidity and mortality around the world. Lifestyle is recognized as a key factor in the development of metabolic disorders and CVD. Recently, eating speed has been of particular interest since some studies have associated it with the development of obesity and other cardiometabolic disorders. We aimed to assess the association between eating speed and various cardiovascular risk factors. We conducted a cross-sectional analysis within the framework of the PREDIMED (Prevención con Dieta Mediterránea) study with 792 participants from the Reus-Tarragona center. Eating speed was self-reported according to participant perception and categorized as slow, medium, or fast. The association between eating speed and cardiovascular risk factors was assessed using Cox regression models with constant time of follow-up for all individuals. Compared to participants in the slow eating speed category, those in the faster eating speed category were 59% more likely to have the hypertriglyceridemia component of the metabolic syndrome (MetS) (Hazard Ratio, (HR) 1.59; 95% Confidence Interval (CI) 1.16–2.17), even after adjustment for potential confounders (HR 1.47; 95% CI 1.08–2.02). No other significant differences were observed. Eating speed was positively associated with the prevalence of the hypertriglyceridemia component of the MetS in a senior population at high cardiovascular risk.

The SPLENDID Eating Detection Sensor: Development and Feasibility Study

Background

The available methods for monitoring food intake—which for a great part rely on self-report—often provide biased and incomplete data. Currently, no good technological solutions are available. Hence, the SPLENDID eating detection sensor (an ear-worn device with an air microphone and a photoplethysmogram [PPG] sensor) was developed to enable complete and objective measurements of eating events. The technical performance of this device has been described before. To date, literature is lacking a description of how such a device is perceived and experienced by potential users.

Objective

The objective of our study was to explore how potential users perceive and experience the SPLENDID eating detection sensor.

Methods

Potential users evaluated the eating detection sensor at different stages of its development: (1) At the start, 12 health professionals (eg, dieticians, personal trainers) were interviewed and a focus group was held with 5 potential end users to find out their thoughts on the concept of the eating detection sensor. (2) Then, preliminary prototypes of the eating detection sensor were tested in a laboratory setting where 23 young adults reported their experiences. (3) Next, the first wearable version of the eating detection sensor was tested in a semicontrolled study where 22 young, overweight adults used the sensor on 2 separate days (from lunch till dinner) and reported their experiences. (4) The final version of the sensor was tested in a 4-week feasibility study by 20 young, overweight adults who reported their experiences.

Results

Throughout all the development stages, most individuals were enthusiastic about the eating detection sensor. However, it was stressed multiple times that it was critical that the device be discreet and comfortable to wear for a longer period. In the final study, the eating detection sensor received an average grade of 3.7 for wearer comfort on a scale of 1 to 10. Moreover, experienced discomfort was the main reason for wearing the eating detection sensor <2 hours a day. The participants reported having used the eating detection sensor on 19/28 instructed days on average.

Conclusions

The SPLENDID eating detection sensor, which uses an air microphone and a PPG sensor, is a promising new device that can facilitate the collection of reliable food intake data, as shown by its technical potential. Potential users are enthusiastic, but to be successful wearer comfort and discreetness of the device need to be improved.

Exacting Responses: Lack of Endocrine Cephalic Phase Responses Upon Oro-Sensory Exposure

Oro-sensory exposure (OSE) to food plays an important role in the regulation of food intake. One proposed underlying mechanism is the occurrence of cephalic phase responses (CPRs). CPRs include the pre-digestive endocrine responses induced by food-related sensory input. Yet, whether OSE duration or sweetness intensity affects CPRs is unknown. The objective of this study was to determine the independent and interactive effects of oro-sensory duration (chewing) and stimulation intensity (sweetness) on endocrine CPRs and satiation. Eighteen males (22 ± 2 years, BMI 22 ± 2 kg/m2) participated in a 2 × 2 randomized study with a control condition. Each session participants performed modified sham feeding (MSF) with one of the four gel-based model foods. During the control session no MSF was performed. Model foods differed in chewing duration (hard or soft texture) and sweetness (low or high intensity). During each session, eight blood samples were collected up till 25 min after MSF onset. Subsequently, food intake from an ad libitum lunch was measured. No typical CPR was found for insulin, pancreatic polypeptide (PP), and ghrelin. However, the overall PP response was 1.1 times greater for the hard sweet MSF condition compared to control (p = 0.02). Overall ghrelin responses were 1.1 times greater for the hard model food compared to the soft model food conditions (p = 0.003). These differences in endocrine response were not associated with differences in food intake at the subsequent meal. Exploratory sub-analysis of the responsive insulin curves showed that after 2.5 min of MSF the hard texture model foods insulin concentrations were 1.2 greater compared to the soft texture. These findings indicate that texture hardness and sweetness increase the overall PP response and that MSF on hard texture increases the overall ghrelin response compared to soft texture model foods. However, MSF on model foods does not lead to a typical CPR. This study, among others, shows that there are major dissimilarities in the endocrine responses to food stimulation between individuals. This emphasizes the importance of considering cephalic responders and non-responders. More research is needed to understand CPRs in relation to food texture and taste properties.

The effect of mastication on food intake, satiety and body weight

As mastication is the major component of the oral processing of solid foods a better understanding of its influence on ingestion, digestion and metabolism may lead to new approaches to improve health. A growing number of studies provide evidence that mastication may influence energy balance through several routes: activation of histaminergic neurons, reducing eating rate, altered digestion kinetics, and changes in macronutrient availability. Indeed, accumulating evidence indicates that increasing the number of masticatory cycles before swallowing reduces food intake and increases satiety. However, while slowing eating rate has been shown to limit weight gain in children and adolescents it is not clear that slowing eating rate by increasing the number of masticatory cycles or slowing mastication rate is a viable method to aid weight management ([10], [15]). Further research is required to determine the influence of mastication on energy balance and how it could be manipulated to aid weight management. This narrative review will provide a brief overview of the effect of mastication on food intake, satiety and body weight.