It’s my pleasure today to share with you our research on designing for emotional well-being: integrating
persuasion and customization into mental health technologies.
In recent years, the smartphone has become a portal for managing personal health. Many
people are now using mobile devices to track health metrics, access health services, and
get personalized suggestions from the systems. However, scholars have raised concerns about
persuasion and nudging from technologies. They argue that persuasive technologies may
undermine users’ ability to reflect on their situation and decide upon appropriate action.
In response to these challenges, HCI scholars have increasingly emphasized the need for
customizability and flexibility in mobile health technologies. Studies have found that
user customization can increase users' sense of control and support autonomy.
Yet, customization requires much user effort, and this can be burdensome for people
with motivational and cognitive challenges, such as those with depression and anxiety.
It’s unclear how this group of people interact with customizable technologies and their desired
level of customizability.
So we ask: What is the optimal level and type
of customizability in mental health technologies? In this research, we present the Intellicare
platform, a suite of mobile apps that help people manage their depression
and anxiety symptoms. This platform includes 12 individual apps each
targeting a specific behavioral or psychological treatment strategy. The apps are highly customizable
and interactive. Users can adapt the apps to their own conditions and needs.
You can find details about this platform in our paper.
We conducted interviews with 32 IntelliCare users to understand their experience with
the platform. We asked for their feedback on a variety of
things, and we were especially interested in understanding how app suggestions versus
user customization were most helpful. In the interest of time, I won’t go into
the details but only highlight a higher-level theme from the finding.
Which is the idea of human-AI collaboration, in particular the integration of system personalization
with user customization. Personalization is most helpful in providing
context-aware suggestions, tailored to users’ skill level, locations, and social context.
These can be easily achieved by passive sensing and machine learning techniques.
But the system may not know users’ physical and mental capacity. A few users said they
were partially disabled and receiving exercise suggestions from the app made them feel like
a failure. So the apps need to ask users to report their physical constraints and provide
tailored suggestions. Similarly, personalized suggestions should
also consider users' motivational and mental capacity, and not suggest activities they
can’t perform. This should be achieved by asking users to self-report their depression
symptoms. At the same time, users really appreciate the ability
to customize the apps. The most valued type of customization was creating to-do lists
and manually logging completed activities, which is basically this little app that asks you to create a checklist and check things off
These activities were considered intuitive, low-burden, and pleasurable. Our participants
said that checking things off is extremely satisfying. This could enhance users’ sense
of autonomy, their competence, and related to their own values. According
to the self-determination theory, autonomy, competence, and relatedness are three basic
human psychological needs, and can enhance well-being and satisfaction.
Another helpful customization feature is to annotate passively tracked data. For example,
if a user forgets to bring their phone with them someday, the systems should allow them
to annotate data and manually log their activities. This could complement algorithmic suggestions
when they fall short. In a nutshell, mental health technologies
will reach its full potential by combining machine intelligence with human intelligence.
Our research highlights that customization is an important way to augment user autonomy,
but it shouldn’t exceed users’ cognitive and mental capacity.