Clinical trial NCT03986619: Evaluation of Methods for Measuring Food Reward and Food-related Behavior in Healthy Individuals
Official title: Evaluation of Methods for Measuring Food Reward and Food-related Behavior in Healthy Individuals - a Substudy of the PRESET Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Healthy Participants
Keywords: Food preferences
Food reward
Lead sponsor: Steno Diabetes Center Copenhagen
Sponsor class: OTHER
Collaborators: name: University of Copenhagen
name: University of Leeds
name: Aalborg University Hospital
name: IMotions A/S
First posted: 2019-06-14
Enrollment: 100
Sex: ALL
Minimum age (years): 30
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Normal-weight individuals

Brief summary: Most of the decisions and actions affecting energy balance are driven by implicit and explicit motivational processes. In modern obesogenic environment where highly palatable and energy-dense foods are easily available, it is of great interest to increase the understanding of both implicit and explicit processes of food-related behavior.

The aim of the present study is to examine whether biometric signatures in response to visual food stimuli during the already validated Leeds Food Preference Questionnaire (LFPQ) correlate with liking, wanting, food choice, or subsequent ad libitum food intake of those foods as assessed by the LFPQ and an ad libitum buffet meal.

Detailed description: Human behaviour including food reward plays a pivotal role in appetite regulation and the type and amount of food eaten. Food reward is a driver of eating behaviour and is defined as the momentary value of a food to the individual at the time of ingestion. Food reward contains the components of liking and wanting that can exist both with (explicit) and without (implicit) conscious awareness. Most of the daily decisions and actions affecting energy balance are driven by the non-conscious mind. Non-conscious processes are characterised by being unintentional and without awareness of the effects of triggering stimuli, e.g. food items. Contrary, conscious processes are typically assessed through participants' self-reports but limited by accuracy and desire to provide socially desirable answers. In a world of plenty where foods are always easily available with an overtly representation of highly palatable and energy-dense foods it is of great interest to understand the role of the non-conscious mind as a driver of food choice and intake. Intake of highly palatable and energy-dense foods is related to excess energy intake, high body mass index and weight gain. A possible explanation for this is that these foods are overall more rewarding and as a result of this, palatable.

A few previous studies have applied the use of innovative biometric measures, i.e. eye tracking, galvanic skin response, and facial expression to examine behavioural aspects of food intake that are without conscious awareness. However, there is a lack of studies examining how biometric responses to different food groups varying in energy density and palatability correlate with outcomes related to components of food reward and food intake. Therefore, in the present explorative study the above-mentioned biometric measurements will be combined with an existing validated method, the Leeds Food Preference Questionnaire (LFPQ), developed to examine both non-conscious and conscious components of food reward. The LFPQ assess four factors related to food reward within four combined food categories: food choice, implicit and explicit wanting, and explicit liking for high-fat sweet foods, low-fat sweet foods, high-fat savoury foods, and low-fat savoury foods. Biometric measurements of eye tracking can provide measures of attention, measurements of galvanic skin response can provide measures of arousal, and facial expressions analyses can provide information on emotional responses in response to selected stimuli, which in this study constitute pictures of food items. Furthermore, the study will include a taste task including an ad libitum intake of test foods in order to examine responses to actual food intake and to compare these responses to biometric responses to visual food stimuli.

Eligibility criteria: Inclusion Criteria:

* Age: from ≥30 to ≤70 years of age
* BMI: from 18.5 to 24.9 kg/m2

Exclusion Criteria:

* Unable to understand the informed consent and the study procedures;
* Allergic to the food items included in the taste test
* Self-reported history of an eating disorder in the past three years
* Self-reported weight change (\>5 kg) within three months prior to inclusion

Primary outcomes:
measure: Gaze duration bias (ratio)
timeFrame: 0 minutes (fasting)
Secondary outcomes:
measure: Attention
timeFrame: 0 minutes (fasting)
measure: Arousal
timeFrame: 0 minutes (fasting)
measure: Emotions
timeFrame: 0 minutes (fasting)
measure: Food choice
timeFrame: 0 minutes (fasting)
measure: Reaction time (ms)
timeFrame: 0 minutes (fasting)
measure: Implicit wanting
timeFrame: 0 minutes (fasting)
measure: Explicit liking
timeFrame: 0 minutes (fasting)
measure: Explicit wanting
timeFrame: 0 minutes (fasting)
measure: Subjective appetite
timeFrame: 0 minutes (fasting)
measure: Body weight (kg)
timeFrame: 0 minutes (fasting)
measure: Body mass index (kg/m^2)
timeFrame: 0 minutes (fasting)
measure: Fat mass (kg)
timeFrame: 0 minutes (fasting)
measure: Fat percentage (%)
timeFrame: 0 minutes (fasting)
measure: Fat free mass (kg)
timeFrame: 0 minutes (fasting)
measure: Waist circumference (cm)
timeFrame: 0 minutes (fasting)
measure: Hip circumference (cm)
timeFrame: 0 minutes (fasting)
measure: Systolic blood pressure (mmHg)
timeFrame: 0 minutes (fasting)
measure: Diastolic blood pressure (mmHg)
timeFrame: 0 minutes (fasting)
measure: Heart rate (bpm)
timeFrame: 0 minutes (fasting)
measure: HbA1c (mmol/mol and %)
timeFrame: 0 minutes (fasting)
measure: Sleep quality
timeFrame: 0 minutes (fasting)
measure: Sleepiness
timeFrame: 0 minutes (fasting)
measure: Self-reported health
timeFrame: 0 minutes (fasting)
measure: Physical activity
timeFrame: Minimum 6 hours after test day
measure: Chronotype
timeFrame: 0 minutes (fasting)
measure: Night eating
timeFrame: Minimum 6 hours after test day
measure: Eating behavior
timeFrame: Minimum 6 hours after test day
measure: Control over eating
timeFrame: Minimum 6 hours after test day
measure: Socioeconomic status
timeFrame: 0 minutes (fasting)
measure: Total energy intake (kcal or kJ)
timeFrame: 60 minutes after basal assessments
measure: Energy intake from specific food categories (kcal or kJ)
timeFrame: 60 minutes after basal assessments
measure: Explicit liking
timeFrame: 60 minutes after basal assessments
measure: Explicit wanting
timeFrame: 60 minutes after basal assessments
Other outcomes:
Locations:
city: Gentofte
country: Denmark
facility: Steno Diabetes Center Copenhagen
Study officials:
affiliation: Steno Diabetes Center Copenhagen
name: Kristine Færch, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Pedersen H, Diaz LJ, Clemmensen KKB, Jensen MM, Jorgensen ME, Finlayson G, Quist JS, Vistisen D, Faerch K. Predicting Food Intake from Food Reward and Biometric Responses to Food Cues in Adults with Normal Weight Using Machine Learning. J Nutr. 2022 Jun 9;152(6):1574-1581. doi: 10.1093/jn/nxac053.