Using Technology to change behaviors and transform how people live
Credits: Advancing Women in Product Team, Kanika Kapoor, Aakrit Prasad, Jenny Tai, Aneesha Prakash
We have seen countless examples of how technology has the power to change human behavior.
Let’s take a more in-depth look at how companies like Fitbit, Headspace, and Samsung build products to impact how people live their lives through technology.
To share our perspectives with the broader community, AWIP hosted a panel of product experts from at Fitbit’s office in San Francisco.
The panel discussed how data can be used to enhance everyday health decisions: from when we eat, to when we exercise, to when we sleep. Our panelists discussed the technologies that enable and power the most successful products in the space, and in particular, how they affect people’s behaviors and lifestyles.
Our panel featured Gayathri Badrinath, a Digital Healthcare entrepreneur previously the Head of Global Marketing at Siemens, Divya Shah Samsung Digital strategy, Alyssa Simpson, VP Product at Figure 8 and Ashok Bania, Director PM at Headspace and was moderated by Jon Oakes, Fitbit SVP Product.
Here are some of the highlights from their discussion:
“What does behavior change mean? What products do you think bring that about effectively”
Behavior change is defined by the type of person you are, your goals, how much intrinsic motivation you have, and finally, the stage of change you are in. To illustrate, Ashok Bania of Headspace felt that Pokemon Go has been successful in bringing about a behavior change. Other panelists felt that Duolingo is another product that has made great strides in this space. . However, one of the challenges that all of our panelists acknowledged was that healthcare changes are harder to implement, as a result of higher barriers (e.g., staunch healthcare routines, than other verticals might experience such as online games like Farmville.
“How do you measure behavior change? How do you know your feature or product has been successful?”
Products like Fitbit use real-time data to measure the success of their features. The difficulty comes from mapping real-time healthcare data such as the number of steps or heartbeat to to long-term, historical healthcare data. Then it gets even more challenging when we need to do a correlation of the number of steps to strokes or cardiac arrests. Mental healthcare changes are even harder to measure. In order to properly measure the most nuanced of changes, Product Managers in this space would need to look at differentiation based on both intrinsic and extrinsic factors, such as natural disposition or external factors.
“What about Privacy? What kind of data is not “fair game” from users?”
The universal rule our panelists brought up was that data collected should not used to introduce unintended bias. Alyssa Simpson, Figure Eight mentioned a Visual Recognition feature she was working while t IBM. The software returned images of people in a wheelchair when the keyword tag “Loser” was used in a search in a pool of 80 million images. This was clearly a problem in that biases were unintentionally introduced by the collection of data from potentially biased audiences.
Merging lifestyle and consumer data with EHR data can also be tricky based on the healthcare data laws of the region a device is being operated from. Companies such as Fitbit and Samsung need to carefully consider the context in which they are using their data collected, especially the country from which it came as well as the laws that govern it. Security and privacy of consumer data should be paramount for any company that handles sensitive, PII (Personally Identifable Information).
“How should product managers think about privacy when designing these products? How do you deal with regulations?”
Companies should have standard, institutionalized methods of handling personal data collected. For starters, data should be saved in an unidentifiable way. An example of this could be a heathcare data company collaborating with the US Food and Drug Administration (the FDA); this way, data that is collected can be shared in a joint, secure manner and Product Managers would not have to make independent decisions related to the privacy of sensitive, consumer data.
Addiction can be caused by gamification in day-to-day life. How can gamification in day to day life be extended to make a lifestyle or behavioral change for the better?
At its core, gamification is a balance between intrinsic motivation and personal desires. Understanding the relationship amongst motivation, ability and the trigger to certain behaviors, the BJ Fogg model is important and likewise is identifying use cases and the audience. Long-term path behaviors like quitting smoking are fundamentally different from getting someone to get up from the couch and walking 10,000 steps. Companies such as Fitbit and also Apple Watch have clearly tapped into this realm, by getting millions to join different fitness challenges, which create intrinsic motivation when it’s with their peers.
What is the future of AI, ML, sensors? How can this tech be harnessed for good change?
Technology will start monitoring health passively because consumers have shown that they tend to jump from wearable to wearable. Machine Learning models could also reach households, and once that shift comes, consumers may no longer be scared of sharing their data. Machine Learning will also help machines to make better sense of context and meaning of data. Online recommendations are inaccurate and annoying, which can cripple the users’ experiences. However, in the future, the personalization algorithms are likely to be fine-tuned, which can lead to far more beneficial and successful experiences.
To watch the full panel discussion that was hosted by AWIP, check out our facebook page.
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