Development and validation of a new methodological platform to measure behavioral, cognitive, and physiological responses to food interventions in real time

Abstract

To fully understand the causes and mechanisms involved in overeating and obesity, measures of both cognitive and physiological determinants of eating behavior need to be integrated. Effectively synchronizing behavioral measures such as meal micro-structure (e.g., eating speed), cognitive processing of sensory stimuli, and metabolic parameters, can be complex. However, this step is central to understanding the impact of food interventions on body weight. In this paper, we provide an overview of the existing gaps in eating behavior research and describe the development and validation of a new methodological platform to address some of these issues. As part of a controlled trial, 76 men and women self-served and consumed food from a buffet, using a portion-control plate with visual stimuli for appropriate amounts of main food groups, or a conventional plate, on two different days, in a random order. In both sessions participants completed behavioral and cognitive tests using a novel methodological platform that measured gaze movement (as a proxy for visual attention), eating rate and bite size, memory for portion sizes, subjective appetite and portion-size perceptions. In a sub-sample of women, hormonal secretion in response to the meal was also measured. The novel platform showed a significant improvement in meal micro-structure measures from published data (13 vs. 33% failure rate) and high comparability between an automated gaze mapping protocol vs. manual coding for eye-tracking studies involving an eating test (ICC between methods 0.85; 90% CI 0.74, 0.92). This trial was registered at Clinical Trials.gov with Identifier NCT03610776.

Keywords: Automatic Gaze Mapping; Portion size memory; Portion-control plate; Satiety; Universal Eating Monitor.

Conflict of interest statement

The authors have no conflicts of interest to declare that are relevant to the content of this article. The funders and sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

© 2022. The Author(s).

Figures

Fig. 1

Potential mechanisms involved in portion control based on the effects of visual cues in tableware and food packaging. Two mediator pathways are considered, one at the time of serving (green arrows and circles); and one at the time of eating (orange arrows and circles). Stimuli are indicated in rectangles, mechanisms in circles and behaviors in triangles (see text for literature sources)

Fig. 2

Daily procedure for participants and components of the combined methodological platform. Plates used for this study are depicted on the bottom, right (for details, see text). Abbreviations: PCSE, portion-control self-efficacy scale; TFEQ, three-factor eating questionnaire; UEM, universal eating monitor; VAS, visual analogue scale questionnaire. Portion-control plate picture courtesy of Precise Portions LLC, Virginia, USA

Fig. 3

Universal Eating Monitor station at the University of Navarra. Left and upper right, room setting displaying the function for visual analogue scale (VAS) questionnaire on one of the screen monitors. Bottom right, lateral view outline of the anti-vibratory table hosting the high precision balance (Diagram courtesy of Borda Laboratorios, Madrid, Spain)

Fig. 4

Screenshot of the portion-size reconstruction software. The food photos were taken by a professional photographer using a digital camera with constant lightning and angle, and the same control dish. Portion sizes started at the equivalent of 1 tablespoon and followed by 20 kcal increments until the food filled about 80% of the plate (assumed to be the maximum volume physically fitting in the plate, based on the study protocol, which required selecting at least three meal components). The initial 1 tablespoon portion is based on 50% of the average small portion of cooked rice for Spanish consumers (Russolillo & Marques, 2008). The 20 kcal increments are based on previous research using full plates (Brunstrom, 2014). For carrots, due to their low energy density, 5 kcal increments were used instead

Fig. 5

Development of the video data coding protocol with Lightworks 14.0. Left, steps followed for the protocol development. Right, areas of interest (AOI) used for the initial gaze data analysis. Dwell time on mixed food areas (including more than one AOI) and time on food loaded onto the fork was also initially included

Fig. 6

Results from the memory reconstruction task for the first 20 subjects completing the study. Bars depict the comparison of eaten vs. recalled portion sizes after using each plate. Data are means ± SEM. a Calibrated plate. b Control plate

Fig. 7

Frequency distribution of the differences between target and actual extraction times for extraction times at 5, 10, 30, 60, and 90 min for 76 volunteers. Values on the X-axis are seconds

Fig. 8

Correlation for 14 pairs of data between proportional fixation times (% time on each AOI relative to total AOI time) as registered by two independent raters using the manual coding protocol in Lightworks 14.0 across two video-recordings

Fig. 9

Bland–Altman plot of AOI fixation times (s) collected with two methods (manual coding vs. automated gaze mapping) over the first 60 min upon starting the meal. Differences between methods are plotted against the mean of both methods for the vegetables, rice, and meatballs AOIs across a sample of ten recordings (five subjects using both plates each; n = 30 pairs of data). Dotted lines indicate the upper and lower limits of agreement, respectively. The two values with a difference > 10 in the Y-axis correspond to the rice AOI for a single subject (both plates). The value < – 6 in the Y-axis corresponds to the vegetables AOI for another subject (calibrated plate). Both subjects were female, with overweight