Identifying Cognitive Risks of Obesity Development in 12- to 24-Month-Old Infants

• In Event: Poster Session 12
  In Poster Session: PS 12 Topic - Health, Growth, Injury

Abstract

Objective: In the US, trends suggest the rate of childhood obesity will increase 5% this decade alone (Want et al., 2008). Identifying risk factors for obesity is an important arm of prevention science. Previous research reported parental obesity as increasing the risk of developing obesity in children (e.g., Shearrer et al, 2018). However, much is unknown about how parental obesity and early feeding experience contribute to developing cognitive patterns known to greatly increase obesity risk, i.e., heightened food cue sensitivity (Buvinger et al., 2017) and unhealthy food preferences (Ha et al., 2016).

Hypothesis: (1) Infants with higher weight-for-length percentiles will demonstrate heightened food cues sensitivity evidenced by greater preference for foods over objects. (2) Infants with higher weight-for-length percentiles will demonstrate unhealthy food preferences. (3) Maternal BMI will predict child’s high risk for obesity evidenced by hypothesis 1 and 2. (4) Mothers’ unhealthier food choice patterns and feeding practices will predict their child’s high risk for obesity evidenced by hypothesis 1 and 2.

Methods: This pilot study will recruit 20, full term, healthy 12- to 24-month-old infants and their mothers. Mothers self-report height and weight for themselves and infant. Mothers will complete online questionnaires via Qualtrics on demographic information, feeding practices (Infant Feeding Practices Survey-II; Fein et al., 2008) and infants’ eating styles (Baby Eating Behavior Questionnaire; Llewellyn et al., 2011). Computerized Food rating and choice tasks (Ha et al., 2016) will measure eating decisions on Qualtrics. Mothers will rate food healthiness, taste, preference and choice for themselves and infant, on 60 food items (30 healthy, 30 unhealthy food infants consume commonly) using 4- or 5-point Likert scales. Using a preferential looking paradigm, infant’s food cue sensitivity and unhealthy food preferences will be measured in matched 30 food-object pairs (10 sec for each pair) (Blechert et al., 2019) synchronously via Zoom. Recordings of the task will be coded manually by two independent experimenters.

Data Collection and Analysis Plan: This project has already been approved by our IRB, but COVID-19 prevented in-person recruitment. We will begin online data collection immediately and plan to complete before March 15. Recruitment will be aided by utilizing the ChildrenHelpingScience platform. Food cue sensitivity is computed as the ratio of looking time at food over total looking time at food and objects. Unhealthy food preference is computed as the ratio of unhealthy food items preferred over total number of preferred unhealthy and healthy foods. Regression analyses will be performed to estimate the predictive relationship of infant weight-for-length percentile on heightened food cue sensitivity and unhealthy food preferences. Regression equations will be replicated using maternal BMI to predict heightened food cue sensitivity and unhealthy food preferences in infants. A similar regression will use maternal decision weights of health and taste attributes and feeding practices to predict infant risk for obesity. Decision weights of health and taste attributes will be computed by fitting a linear regression model predicting food choices from taste and health ratings at the individual level. Decisions prioritizing taste define an unhealthy food choice pattern.

Authors

• Samuel Christopher Nelson, University of Missouri - Kansas City
  Presenting Author

• Seung-Lark Lim, University of Missouri-Kansas City
  Non-Presenting Author

• Oh-Ryeong Ha, University of Missouri - Kansas City
  Non-Presenting Author