Endocrine Fellows Foundation Final Project Update Food Decision-making in Childhood

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1 Monica Serrano-Gonzalez, MD PGY-5, Year 2 Pediatric Endocrinology Fellow Children s Hospital Los Angeles Center for Endocrinology, Diabetes and Metabolism mserranogonzalez@chla.usc.edu; Office: Endocrine Fellows Foundation Final Project Update Food Decision-making in Childhood This is a cross-sectional study that aims to characterize the dietary-decision making process in 8-12 yearold lean and obese children, particularly as it pertains to the processing speed of health and taste attributes, by utilizing a novel, computer-based task. In Task 1 (Food Ratings), subjects will rate 60 different foods viewed on a computer screen (30 high- and 30 low-calorie images) in terms of tastiness, healthiness, and overall liking (ie, how much would you like to eat this food at the end of the experiment? ). Ratings will be made on a 5-point faces scale ( very bad to very good with a neutral option) and will be entered on a keyboard with corresponding faces on 5 keys. Block and stimulus order will be randomized across subjects. Per rating trial, a single color image will be presented at the center of a black screen in high resolution (1,680 x 1,050 pixels; LCD monitor). In Task 2 (Food Choices), subjects will make 100 binary choices among randomly presented pairs of foods. Food pairs will be coded a priori so that different combinations of tastiness and healthiness ratings are equally represented based on the subject s own food ratings from Task 1. The mouse cursor s x,y position will be tracked using Psychophysics Toolbox (temporal resolution 67 Hz). Recruitment strategies and logistics Overall, we have made significant strides towards conducting and recruiting for this study. At this time, we are focused on recruitment, with eleven children already having participated in the study. Our work to get the study to this point has included two main areas: recruitment strategies and logistics, and refinement of outcome measures. Preliminary analysis of data shows promising results that support our study hypotheses. We have polished the recruitment strategy holding multiple meetings with the Children s Hospital Los Angeles clinic teams for recruitment (i.e., General Pediatrics clinic, EMPOWER obesity clinic, and Pediatric Endocrinology clinic), placing flyers in the clinics, learning clinic flow and logistical details of recruiting within clinics and contacting interested and eligible individuals. We have also recently been working on adding two more recruitment sites: the BodyWorks (weight loss) program at the General Pediatrics clinic and the SIPA (Search to Involve Pilipino Americans) Summer Program. As well, we had

2 submitted several IRB amendments, including a Spanish-translated consent, Spanish-translated flyers, the parental CEBQ (Child Eating Behavior Questionnaire), and sharing output data with a collaborator outside our institution. Refinement of outcome measures We had initially focused on optimizing a novel, computer-based task for the study. This involved learning the software for the task (Matlab & Psychtoolbox) and computer coding, in order to adapt the task components for children. I completed an online course offered by the University of Vanderbilt, Introduction to Programming with Matlab, via Coursera, and earned the course certificate. I also established the set-up to run the task, including a customized keyboard, child-friendly mouse, and two Macbooks tailored for the study tasks. As a result, we are successfully running the child-friendly, modified version of both computer-based tasks (the Food Ratings task, and the Food Choices task). We also have refined a 24-hour dietary recall measure (ASA24 KIDS; National Cancer Institute) for the study, with both the parent and child now completing the dietary recall together, for improved accuracy 1,2. We also added two handouts on healthy-eating as educational incentives for the parents: USDA MyPlate, and U R What U Eat (DHHS/NIH). We are now following up with the parents one month after the study visit with a brief survey to assess their receptiveness to the two handouts, and to help inform future nutritional interventions. After attending the Obesity Society s ObesityWeek national conference (Los Angeles) in November, where I had the opportunity to discuss my study with other scientists and attend relevant sessions to this work, I wanted to add a parent questionnaire, Children s Eating Behavior Questionnaire (CEBQ) as an outcome measure that could be analyzed with the Food Choices data. We are also investigating the feasibility of adding a measure of impulsivity, such as a behavioral questionnaire, to our study protocol (e.g., UPPS-R-C) 3. We have also standardized the food image cues that are used in the computer tasks, eliminating any branding with labels on the images, and standardizing image characteristics between high- and lowcalorie groups. Branding has been shown to skew food perception 4 and so several images that had branding words removed. We also substituted several food images from a published food cues database (Blechert et al., food-pics image database) to help standardize image characteristics between the highand low-calorie groups, implementing Matlab-coded algorithms from the Blechert group to analyze food cues for characteristics such as: red/green/blue color, object size, contrast and brightness 5. However, when making even small changes to the computer task, as it is typically the case in computer programming, we often face the need to debug the computer code. Results I have performed a preliminary analysis of data collected thus far, in conjunction with a collaborating neuroscientist and statistician. In Task 1, Food Ratings, participants first rate food items for health, taste

3 and likeability. Subsequently, in Task 2, Food Choices, they choose between food pairs (Figure 1). We record the position of the mouse as the subject moves the mouse toward the food item of their choice. Paradigm validation Thus far, the data shows that the task paradigm works, as the participants are choosing food items in Task 2 (Food Choices) according to their food preferences from Task 1 (Food Ratings), as depicted in an average psychometric plot (Figure 2). Health and taste processing and dietary choice We next examined the influence of taste and health processing speed on the mouse trajectory. Because response times for choosing a food item in Task 2 differ within and between participants, the response time has to be normalized. Thus, response times were divided into 101 time bins (Figure 3, x-axis). We performed linear regressions for the relative health (health rating for the right food item health rating for the left food item) and the relative taste (taste rating for the right food item taste rating for the left food item) to predict the angle trajectory of the

4 mouse movement toward the food item that the child chooses. The resultant β coefficients across time (Figure 3, y-axis) show that the attribute of taste becomes significant as a predictor of the mouse trajectory at normalized time window t=76. On the other hand, the attribute of health thus far has not become significant. This is not very surprising, as in adults, health was not a significant predictor of the mouse trajectory in 32% of subjects 6. Health and taste processing and self-control We next examined the processing speed of taste and health in relation to self-control. The self-control success ratio (SCSR) is the percentage of time subjects chose the healthier food. When regressing the average time when taste becomes a significant predictor of the mouse trajectory, against the SCSR, the results are striking, with an R 2 = 0.58 and p= 0.029, showing that taste processing speed explains 58% of the variance in self-control. This result is strongly significant, even in our small sample. In contrast, the average time when health predicts the mouse trajectory is not a significant predictor of self-control (R 2 = 0.69, p= 0.169) (Figure 4). For each subject, we then calculated a measure of the computational advantage of taste. The computational advantage of taste is given by the earliest time at which taste significantly affected and continued to affect the trajectories, minus the earliest time at which health did. We then estimated a linear regression of the SCSR against the individual measures of the computational advantage for taste versus health. This regression tests the extent to which individual differences in self-control can be attributed to individual differences in the relative speed at which the health and taste attributes are processed. The result of this regression had an R 2 = 0.41 and a p= (Figure 4). Even though this is not yet significant, it s approaching significance and is likely not significant yet given the small sample size.

5 Goals/Timeline My first goal is to complete recruitment of 60 children by the end of December Next, is to analyze data and submit an abstract in March 2017 for the combined Pediatric Endocrine Society/European Society of Pediatric Endocrinology PES/ESPE meeting (September 2017), and to write the manuscript by the fall of In the future, I m interested in applying this computer task before and after a nutritional intervention to assess whether it can detect changes specifically in taste and health processing speed and its influence on choice. The idea is that this model can support the development of interventions that specifically increase the relative speed with which health information is processed. Thank you for your support,

6 References 1. Livingstone MBE, Robson PJ and Wallace JMW. Issues in Dietary Intake Assessment in Children and Adolescents. British Journal of Nutrition : S213-S Sobo EJ, Rock CL, Neuhouser ML, Maciel TL, Neumark Sztainer D. Caretaker-child interaction during children s 24 hour dietary recalls: Who contributes what to the recall record? Journal of the American Dietetic Association : Zapolski T, Stairs A, Fried Settles R, Combs JL and Smith G. The Measurement of Dispositions to Rash Action in Children. Assessment (1): Robinson T, Borzekowski DLG, Matheson DM and Kraemer HC. Effects of fast-food branding on young children s taste preferences. Arch Pediatr Adolesc Med (8): Blechert J, Meule A, Busch NK and Ohla K. Food-pics: an image database for experimental research on eating and appetite. Frontiers in Psychology : Sullivan N, Hutcherson C, Harris A and Rangel A. Dietary Self-Control Is Related to the Speed With Which Attributes of Healthfulness and Tastiness Are Processed. Psychological Science :13.