1. Big Data + Childhood Obesity Convening
January 20, 2016
Location: UC Berkeley- Women’s Faculty Club
Facilitator: Kristine Madsen, MD
School of Public Health, UC Berkeley
Note-taker and Recorder: Lydia Chen, Dietetics Student
Participants:
Jen Ahern, Epidemiologist
School of Public Health, UC Berkeley
Meredith A. Barrett, VP of Science and Research
Propeller Health, San Francisco
Stuart Kim-Brown, Director of Product Analytics
Under Armour Connected Fitness, San Francisco
Patricia Crawford, PhD, RD Director
UCANR
Daniel Fried, PhD Computer Scientist and Grad Student
Computer Science Division, UC Berkeley
Wendi Gosliner, PhD, RD Policy Researcher
UCANR
Tom Robinson, MD Obesity Researcher
Stanford University
Michael Widener, Geographer
University of Toronto
Eric Williams, Physicist and Director of Data Science
Omada Health, San Francisco
Summary Report of Ideas Discussed and Possible Mission Goals
In 2015, the Robert Wood Johnson Foundation invested a total of a billion dollars
to study childhood obesity and ways to prevent it. A few of its current long term goals
include making sure every child enters school at a healthy weight, incorporating more
physical activity into the school day, decreasing the consumption of sugar sweetened
beverages by children, and making sure children have access to healthy food. Within a
decade, RWJF wants to see the number of obese children in America significantly
decrease, and establish ways and means to keep that number down. RWFJ has contacted

2. and funded many different research groups, in hopes of achieving its goal. The UC
Nutrition Policy Institute was assigned to conduct research regarding big data and
childhood obesity. On January 20th, we (The UC NPI) invited professors from UC
Berkeley and several health and tech professionals (listed above) to a meeting to begin
discussion on where our research might lead. The goal of the meeting wasn’t to find
causes and associations to immediately fix obesity, but rather to discuss ways of using
big data outcomes to measure impact. Ideas that the participants came up with are
summarized, in first person, below.
Big data is useful in our society because we need information pertaining to all
sorts of children, not just a certain group. Helping individual children, while still
necessary, is now too long of a process if we want to fix this epidemic. We believe that
big data itself could be used as a method of intervention, with the supposed assistance of
real time feedback.
In terms of general data collection on food and environment, we can look at sales
receipts, study community flow via phone scans, and follow social media feeds. The food
that a family buys at a store, and the store itself, is a huge indicator of what goes into a
child’s diet, and may be linked to their overall health. The stories and thoughts that
people post on Facebook or Snapchat can help track what they’re consuming and their
location. We suggest making children’s health records a bit more transparent so clinics
can share information, in order to have easy access to a list of health-related problems a
child might have. Keeping school health records in online databases not only makes the
files easy to find, but all the data can be analyzed to see whether or not the students are
healthy. “Healthy habits” should be marketed, and should be done so in schools, with
incentives to motivate children. An emphasis on eating healthy foods at an early age, and
increasing physical activity time in school is the simplest way to promote well-being. A
huge indicator for childhood obesity is socioeconomic background, with trends often
showing that lower-class children tend to be heavier. We can collect data from SNAP to
see how these families are allocating their spending. While these ideas may seem a bit
lofty because big data on children may be difficult to obtain, we believe these are
necessary steps to take. Issues concerning privacy of children may make big data hard to
collect, but we can start by learning about their parents and schools with online tracking
technology and data from cell phones.
In order to see results and really improve our society, we need to make changes
easy. Forced education and spoon-feeding statistics to children can only partially get us to
our goal, leaving the rest up to direct, yet sustainable, intervention. With the help from
big data sets, we hope to find trends that relate to every child in the population, in order
to get rid of selection bias, and implement policy based on successful experimentation.