NPI Big Data Convening Summary
- 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.