Tag Archives: Frank Bentley
Health Mashups: Helping People Find Long-Term Trends Between Wellbeing and Activities in Their Lives
Frank Bentley is a Principal Staff Research Scientist at the Motorola Mobility Applied Research Center outside of Chicago, IL. He creates new mobile applications and services that help people connect with each other and with data about their lives. He then studies how these systems are integrated into daily life over weeks and months.
Do you sleep better on days when it’s warmer? Walk less on days packed with meetings? Gain weight on the weekends? A growing number of consumers are turning towards specialized devices that track particular aspects of their lives and wellbeing. Whether it’s the Zeo to track sleep, the FitBit to track daily step counts, the MOTOACTV to track workouts, or the WiThings scale to track their weight, there is currently a wealth of personal data that is being stored about daily activities. However, most of these services continue to be silos. Even where the ability to import data from one device into another’s service exists, data is only combined superficially, providing at most a graph of steps and weight over time, obscuring long-term and periodic interactions. The questions presented above cannot be answered without great effort – effort that many in the Quantified Self community devote to understanding themselves. But can it be easier?
We see the key value of tracking multiple aspects of one’s life to be understanding the interaction of data from wellbeing sensors with other sensors as well as with contextual data about a person’s life (where they spent time, how busy their day was, the weather, etc.). We want to enable people to discover these hidden trends in their lives without resorting to complex Excel files and a PhD in statistics.
The Health Mashups system
The Health Mashups system was built through a collaboration between KTH University and the Motorola Mobility Applied Research Center. It consists of a server that aggregates data from a variety of sensors and a mobile application to automatically capture a user’s context and display the resulting correlations calculated by the server. Users can connect their FitBit accounts for step count and sleep data as well as their WiThings account for weight data. An Android application uploads contextual information automatically each day including the number of hours busy on the user’s calendar as well as the current location at a city level and weather for that location. After the initial setup, no further actions are required from the user to keep this data flowing to our server (although we also support manual food and exercise logging through the mobile phone application). Each night, our server computes correlations between sensors and deviations on data from a given sensor and generates a feed of items that are statistically significant. This feed is then accessible on the phone or web for users to view and reflect upon. Users can see feed items such as: “You lose weight on weeks when it is warmer” or “Yesterday you walked much less than you normally do on Saturdays.” This eliminates the need for manual log books and messy Excel files, and opens Quantified Self-style investigations to those with no technical background.
We wanted to understand how a broad range of users would integrate this system into their lives. We conducted a two-month field trial and recruited ten diverse participants in Chicago and Stockholm to take part. They came from a wide range of ages and educational backgrounds and had a variety of reasons for participating: from particular issues with sleep or excessive weight that they wanted to address to a general curiosity to understand themselves better. Participants were given a FitBit and a WiThings scale and asked to use these in their lives for the first month. Whenever they had an insight about their wellbeing, they were asked to call us and leave a voicemail describing their insight. For the second month of the trial, they were given the Health Mashups interface on their phone and again were asked to call us with new insights.
For the first month of the trial, none of our participants called with insights across sensors or time scales. While many reported general trends (e.g. “I’ve been losing weight this week” or “Yesterday I didn’t walk as many steps as I thought I did”), their insights did not connect their sleep, weight loss, or step counts to each other in any way. Nor did they include insights about patterns on specific days of the week or comparisons/deviations from week to week.
In the second month, participants were able to understand their wellbeing in much deeper and complex ways. The system showed them insights across sensors and varying timescales. Our participants reported understanding and relating to these feed elements. The mashups data helped our participants to better understand how aspects of their lives were related and to make positive changes in their lives (e.g. eating a little less fried chicken on Sundays or walking more on specific days of the week).
The Future of Health Mashups
We see a promising future for personal data analytics related to one’s wellbeing. With massive amounts of wellbeing and contextual data now being collected, systems are needed that make sense of this data for people and allow them to focus on what is significant to their lives without a large amount of effort. With Health Mashups our participants could gain these insights, combining data that is automatically collected as they live their lives. We believe these types of insights have the power to raise awareness about situations that lead to poor life choices, resulting in positive changes in behavior and ultimately happier, healthier lives. This summer we will be conducting a larger quantitative study to investigate the impacts of this system across a wider group of participants. If you are interested in participating, you can register your interest here.