The role of machine learning in behavior change

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There is no one-size-fits-all solution to health behavior change. Some people respond to gentle nudges, others benefit more from structured programs, some take pride in being a Do-It-Yourselfer, others make more progress with a coach. Some can only change behavior if it is presented in the form of a game, others respond to social interactions.

At Fitbit R&D, we are interested in answering some fundamental questions: what motivates a person to lead a healthier life, what is a real or perceived barrier for change, and how Fitbit can help increase users' ability to make those changes. Answering these questions requires a multi-disciplinary approach. We take advantage of health science, behavior science, behavioral economics, and machine learning to deliver interventions. In this talk, we will review how machine learning is a key capability in making any solution smart and more personalized.

Key Speakers
  • Hulya Emir-Farinas
    Hulya Emir-Farinas - Director of Data Science at Fitbit R&D
    Hulya is passionate about turning ideas into data products. She enjoys developing high performing teams, mentoring data scientists, and working in cross functional teams. Her background is in Operations Research and she loves building analytical solutions that combine machine learning and optimization routines. In her career she has always had the luxury of working very close to technology and loves working on challenges around scaling up analytics methods in distributed computing environments.
  • Evelina.jpg
    Evelina Nedlund, associate editor at Employee Benefit News and Employee Benefit Adviser
    Evelina Nedlund is a reporter at Employee Benefit News and Employee Benefit Adviser. She can be contacted via email at