Objective quantification and analysis of eating behaviour associated with obesity development - from lab to real-life

Introduction: The last four decades have seen a marked increase in childhood and adult obesity prevalence, attributed to an “obesogenic” environment. Several genetical, environmental and behavioural factors have been identified that increase the risk of obesity, but treatment outcomes are usually modest and the risk of relapse high. One limitation responsible for these moderate results could be methodological, with researchers questioning both the external validity of eating behaviour measures in the laboratory (controlled) and the internal validity of eating behaviour measures in free-living (real-life) settings. Technological advances could solve some of these issues, allowing for accurate methods, similar to those used in controlled settings, to be used in real- life. Deploying accurate methods in both controlled and real-life settings would in turn enable the estimation of external validity, determining the limits of generalization between settings. In turn enabling the deployment of these methods in settings which allow large scale screening, for early identification of individuals at risk of becoming obese. Aim: The overarching aim of the thesis was to: i) evaluate the stability of human eating behaviour and ii) investigate the usability and feasibility of methods developed for controlled settings, when deployed in semi-controlled and real-life settings. Paper I – Determine if individuals maintain their eating behaviour, in relation to the group, despite experimental manipulations to meal conditions (i.e., unit sizes and serving occasion). Paper II – Feasibility of employing novel technology for baseline eating behaviour collection in adolescents eating school lunches in a school cafeteria setting (semi-controlled). Paper III – Feasibility of employing novel technology in an experimental manipulation study, to determine the effect of proximity in a semi-controlled school setting. Paper IV – By use of novel technology, examine the maintenance of eating behaviours in adolescents, from semi-controlled to real-life settings, both at group- and individual-level. Methods: Paper I – Three randomised crossover studies, of which two compared eating behaviour across different unit sizes, while one compared eating behaviour between lunch and dinner in healthy young adults. Performed in a controlled setting, employing traditional laboratory methods. Paper II – An observational study of healthy adolescents, performed at lunch in a school cafeteria, employing traditional laboratory methods in a semi-controlled setting. Paper III – A randomised experimental study of healthy adolescents, performed in a semi- controlled, comparing the eating behaviour between two groups seated at different proximity to food items. Paper IV – An observational study on eating behaviour of healthy adolescents, divided into two parts; i) collection of eating behaviour data, performed at lunch in a school cafeteria, using a similar protocol to that of Paper II and ii) collection of eating behaviour data by the participants in real-life settings, using the same devices as in the controlled setting. Results: In all papers the distribution of eating behaviour values between individuals were large. In Paper I, the largest increase in unit size significantly increased meal duration and chews and while there was a trend for both increased meal duration and number of chews the larger the food unit sizes were, it did not lead to a significant reduction in food intake. Meanwhile, the correlation coefficient of all eating behaviours across all conditions was high (except for number of bites between the largest and smallest food unit size condition). In Paper II, male participants ate significantly more, mediated by significantly larger bites. The bite sizes of both men and women were reduced as the meal progressed. In Paper III, increased distance to food led to a significant reduction in intake, caused by individuals taking less chocolate. In Paper IV, there was no significant difference in eating behaviour characteristics between the semi- controlled and real-life meals. In addition, the correlation coefficient of food intake and eating rate was high between settings, while the correlation of meal duration was low. Also, on an individual level, 50%, 32% and 27% of the food intake, eating rate and meal duration measures, respectively, from the semi-controlled meal fell within the confidence interval of the real-life meals. In the semi-controlled and real-life settings (Papers II-IV), the agreement between subjective and objective eating behaviour measures were very low. Meanwhile, in both semi- controlled and real-life settings the method could be deployed within the time schedule imposed by the school, with high data retention. Also, participants rated the comfortability participating in the semi-controlled and real-life settings very high and the usability of the system as “Good” or higher. Conclusions: Human eating behaviour appears stable in comparison to the group when unit size and serving occasion is manipulated in a controlled setting and when eating in different settings (semi- controlled and real-life). Suggesting generalisations can be made between settings and conditions and that risk behaviours may be measured in settings other than real-life, at least on group level. However, although individual prediction rates of eating behaviour characteristics from semi-controlled setting to real-life settings appears higher than subjective ratings, they are still too low for use in the design of tailored interventions. In addition, compared to controlled studies, the method allowed recruitment of a younger age group, since it enabled measurements in a different location. The thesis also provides evidence that the employed methods are usable, feasible and acceptable, with high data retention in adolescent users, in semi-controlled and real-life settings. Methods similar to the ones used in this thesis can provide previously unattainable information (primarily temporal) in settings that are less controlled than the laboratory, such as semi-controlled and real-life

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

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

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

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

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

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

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

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

Serving event characteristics per participant in the two experimental conditions.

<p>Serving event characteristics per participant in the two experimental conditions.</p

Group characteristics for the <i>distal</i> and <i>proximal</i> condition.

<p>Group characteristics for the <i>distal</i> and <i>proximal</i> condition.</p