Context 2


Technology Sector & The Path Forward


Inspired by the Nudge Unit, I continued my research into how and where cognitive biases are used to influence us. I found instances in advertising, the gambling industry, and most importantly, in everyday technology. Often, these tactics are used to take advantage of users to increase profit, without users being aware of them:



Facebook’s AI-driven news feed leverages confirmation bias to serve you content you like, and hide the content you may not like.






Uber leverages arbitrary goal setting to keep its drivers on the road for long hours.



Netflix’s autoplay feature leverages inertia to encourage binge-watching.






The list goes on. The most successful technology companies in the world have embedded themselves in people’s everyday lives. We return to these apps day after day because they are really engaging—even addicting. Behaviors like smoking, eating, drinking, and exercising are ALSO practices ingrained in daily routines. Because everyday technologies are context-aware, personal, and ubiquitous, they can effectively assist us to live healthier and happier lives.


Context-aware, personal, and ubiquitous technologies can effectively assist us to live healthier and happier lives.
The most successful technology companies in the world have  embedded themselves in
people’s everyday lives.

The pressing challenge for designers is to make wellness apps that people want to use for a long time. There are more than 256,000 health and wellness apps on major app stores worldwide, but they have high attrition. Recent research shows that 58% of mobile phone users have downloaded a health-related mobile app—indicating that health is on our minds—but continued active use of these

applications is low. Most users abandoned the health apps after the 10th use. The top reasons cited for discontinued use is that the apps took too much time and people lost interest. There are also simpler, pre-installed apps, such as Apple Fit and Google Health, that automatically and passively track your steps. However, their usage rates are low. A recent survey by Aetna showed that only 13% of the users surveyed used Google Health, and 21% used Apple Health. What’s more, 27% of users weren’t even aware the apps existed. Data without engagement is dead.

One strategy to overcome health app attrition is to incorporate health-related features into popular apps that people already use and love, so that the health apps don’t have to compete for attention with the “fun” apps. In other words, we don’t need to create more apps, instead we need a holistic wellness system that encourages synergies between apps.





The holistic wellness system provides timely and relevant feedback that fits into her daily routine to help Sally achieve her personal well-being goals. Counteracting present bias, the system provides ample opportunities for Sally to do the small things in the present that matter for her future. It’s important that the logic behind the suggestions given is transparent and the suggestions can be dismissed easily. Based on what gets dismissed and what gets adopted, the system learns and re-adjusts itself according to what Sally wants.

Decades of research tells us that providing feedback—such as seeing the number of steps that one has to take to meet one’s goal—is useful for changing behavior. We could provide feedback and nudges in the fun apps when they matter most. When designing for behavioral change, context matters. A user is more likely to walk when he wants to get somewhere than when he is watching TV at home. So with a holistic system, we could provide relevant and timely feedback that’s most likely to trigger action. For instance, when someone is about to order an Uber, we can show them the number of steps they still need to take to meet their daily goal. Another example is diet. We know that tracking food is laborious. However, we can embed this feature with Instagram, where people already take photos of their food.


For this wellness system to be effective long-term, it will also need to understand—and adapt to—the changing motivations of its users. Prior research suggests that we can frame wellness differently for people at different life stages. For example, a 20-year-old might not care much about long-term health, but she might be strapped for money and would want to save money by walking or biking. A rock climber might care more about certain strength training exercises than a runner. A new mom might be motivated by a completely different set of nudges than a 50-year-old man. The truth is these technology companies already know so much about us; we can democratize behavioral science and put these data to good use. Every user has a daily behavior pattern. A morning person and a night owl might be motivated to exercise at different times of the day. No one has a better grasp on each user’s pattern than technology companies like Google, Amazon, and Facebook. No one can better identify the pattern of patterns than those at the forefront of artificial intelligence and deep-learning. We have already developed hyper-targeted ads; now, we can appropriate the technologies to create hyper-targeted motivational strategies for well-being.