US-based Fitbit is one of the leaders of the Wearable Device segment also known as “fitness trackers.” These products generate a lot of data as it relates to people’s sleep and activity cycles, data that the organization can use for product development purposes. Specifically, tracking and analyzing a user’s data can help Fitbit, through the use of machine learning, to recommend tailored and customized workout plans and sleep optimization strategies. 
The ability to provide customized fitness and sleep recommendations is incredibly powerful because it makes Fitbit’s fitness trackers that more valuable to consumers.   Originally, in the early days of the fitness tracker industry, there was value in simply being aware of one’s activity and sleep levels, ie. how many steps have I taken and how many hours have I slept? Currently, now these trackers should be able to go one level deeper, providing provide users with personalized products in the form of customized workouts. 
Via their Health Solutions Group, Fitbit has focused on taking their use of user-generated data to a whole new level, focusing on how healthcare treatment can be optimized for users based on their unique data.  
In the medium term, Fitbit is focused on providing additional machine learning-powered insights to their users via the integration of their recent 2/18/18 acquisition of Twine Health, a HIPAA-compliant health platform where patients can collaborate with doctors on the care plan for chronic diseases such as diabetes and hypertension.  Given Twine Health requires an element of tracking someone’s activity, weight, food intake, blood pressure, etc. this provides another area for machine learning to be able to help patients/providers. Through machine learning, not only will patients and providers know quickly if people are improving or worsening in terms of the symptoms of their conditions, but the technology can provide actionable and real-time recommendations on changes that will improve patient health. 
In the medium term, they are focused on building out their 4/30/18 partnership with Google to collaborate on the Wearables and Health space.  While some of the collaboration is geared towards combining activity data with people electronic medical records, the real value will be in their ability to use Google’s capabilities to analyze the fitbit user data . Specifically, Fibit will move their user data to the Google Cloud platform where they will be able to leverage “Google’s AI and machine learning capabilities and new predictive analytic algorithms to bring more meaningful data and insights to consumers.” 
In my humble opinion, while Supervised Machine Learning enables recommending personalized changes in activity, food intake, sleep, etc. that increase positive individual health outcomes, the true value for the Company will come from its ability to better understand the broader hidden “patterns and insights” within all of Fibit’s user data.
With this in mind, additional steps to take in the short run are to further look at the Fitbit user data via Unsupervised Machine Learning once they’re able to merge their data with patient electronic medical records. Are there certain predictors of chronic diseases? At this point, Fitbit should have a large enough user base that you can look at historical data and ask, what were some of the characteristics exhibited by people who ultimately exhibited hypertension, diabetes, and other chronic conditions? Additionally, what sort of behaviors (poor sleep, poor activity, high saturated fat diets, etc.) potentially led to those diseases? To accomplish this, you will need additional medical record data to further augment the data set so that you can include user data related to age, medical conditions, medicines taken, smoking status, etc. By reviewing all of this data in aggregate and understanding its structure, over time you can start identifying correlations between poor health and specific actions taken in the past. Although there are multiple attributes/actions that may ultimately lead to disease (gene predisposition, smoking, low activity, poor eating habits etc.) and it could be hard to isolate actions, you can see across a population, which of these attributes/actions are present in the average person with diabetes, for example. As a result, as time passes on, we are seeing a more holistic approach to medicine where many things contribute to someone’s well being, and armed with the insights from machine learning, you will be able to systematically employ preventive medicine.
However, a few more things are definitely worth considering as it relates to machine learning used to create actionable insights from wearable devices data. First, wearable devices for the most part are still not FDA endorsed, how can we use machine learning to better support the health insights derived from the data? Finally, more of a fun question, how does machine learning explain how there are people like Warren Buffett who eat McDonald’s and drink Coca Cola regularly but remain in great health?
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 Investor.fitbit.com. (2018). Fitbit, Inc. to Acquire Twine Health. [online] Available at: https://investor.fitbit.com/press/press-releases/press-release-details/2018/Fitbit-Inc-to-Acquire-Twine-Health/default.aspx
 Investor.fitbit.com. (2018). Fitbit and Google Announce Collaboration to Accelerate Innovation in Digital Health and Wearables. [online] Available at: https://investor.fitbit.com/press/press-releases/press-release-details/2018/Fitbit-and-Google-Announce-Collaboration-to-Accelerate-Innovation-in-Digital-Health-and-Wearables/default.aspx
 Penman, B. (2018). Fitbit Acquires Health-Coaching Company Twine, In A Move To Be A More Serious Tool. [online] News. Available at: https://www.wgbh.org/news/2018/03/05/science-and-technology/fitbit-acquires-health-coaching-company-twine-move-be-more-serious
 MobiHealthNews. (2018). Google, Fitbit collaboration aims to deliver comprehensive health data to care teams. [online] Available at: https://www.mobihealthnews.com/content/google-fitbit-collaboration-aims-deliver-comprehensive-health-data-care-teams