Hi there, my name is Kaylee Kruzan, and I'm a research
assistant professor at the Center for Behavioral
Intervention technologies in Northwestern University's
Feinberg School of Medicine. I'll be presenting today on some
work I did with my colleagues where we investigated the
potential of a self tracking technology to assist primary
care patients in managing their mental health symptoms.
First, it's important to note that mental health conditions
are incredibly prevalent worldwide. In the US, symptoms
of depression and anxiety are often first discovered in the
primary care context when people go in for routine visits. While
it's great that more people are being screened and their
symptoms are thus known. Many people who are diagnosed in
primary care settings do not receive treatment.
So what are digital mental health interventions? Well,
digital mental health interventions are typically
described as psychological or psychotherapeutic interventions
delivered in part by technology. And there's been an increased
interest in these devices, because they have many good
qualities. For example, these interventions are scalable and
can reach many people. They're cost effective, they're
efficacious, as shown by several meta-analyses, especially for
depression and anxiety. They're acceptable to many different
populations or groups. And they're flexible in terms of
format and platforms and functionalities.
Digital mental health interventions increasingly
incorporate self tracking functionalities, which can
support cognitive behavior therapy's focus on increasing an
individual's awareness of thoughts, attitudes, emotions
that impact mental health and well being. And CBT is the gold
standard treatment for anxiety and depression. This tracking
can occur manually, or it can occur passively, which relies on
sensors and the technology itself that can collect
continuous data on several behavioral and physiological
markers that are related to mental state.
So in this study, we had several aims, we aimed to better
understand the value of digital mental health interventions that
incorporate sensor data for primary care patients with
symptoms of depression or anxiety. Specifically, by
exploring individuals mental health self management
practices, they're interested in preferences for a self tracking
technology to assist them in mental health, self management,
and their envision comfort with sharing data from digital mental
health interventions with primary care providers.
So, experiences with mental health symptoms:
or, sorry, the mental health maintenance which responded or
correlated with good mental health. In periods of good
mental health participants were more regularly engaged in
activities that led to their mood like exercise connecting
with others socially. And the ease with which participants
engaged in these activities was a sign of good health.
poor mental health was characterized by symptoms that
made it difficult for participants to engage in these
activities, and often required participants to take action and
other ways to rebuild momentum. They described engaging in
focused activities that brought them a sense of accomplishment,
productivity and enjoyment. And these activities were were small
and task based typically. So most participants reported some
form of tracking physical health with wearables, but there was
limited tracking for mental health. And so participants that
did use self tracking for mental health largely discontinued use
because the tools were not perceived to be useful,
suggesting a need to reevaluate the utility of these
technologies in the mental health space. When asked if and
how they might envision a self tracking technology being useful
for mental health, participants describe to functions they
wanted to qualify, sorry, quantify the daily self care
activities that they engaged in. Rather than emphasizing
symptoms. Participants really wanted to see what they did to
help themselves on on the daily basis. And they wanted to begin
to discern patterns. So identify triggers and contributors to
mental health symptoms, and be able to do something about that
with that knowledge. We also found that participants saw
value in sharing, sharing data with providers for two main
reasons. They envision that sharing mood or passive sensor
data with their provider would help the provider to alert them
to declining mental health. They also imagined that sharing data
could help them to initiate conversations with their primary
care provider about their mental health.
So collectively, our participants spoke about
technology needs. And in considering these needs, we felt
it was necessary to comment on or consider potential service
needs to ensure that the technology could benefit
participants in moments of poor mental health, while also
fitting within a primary care setting. So first, sensor data
are often used to send users insights during periods of poor
mental health, and in an effort to empower them to make changes.
However, behavioral markers from sensor data were perceived to be
most valued by participants when they were maintaining mental
health. That is when they had good mental health, since their
data could be used to accommodate for shifting needs,
and provide differential feedback during these periods.
Given that our participants describe symptoms that affected
both motivation, and ability to engage in activities, and the
long standing concerns around digital mental health
engagement, we recognize the need to explore novel ways of
supporting user engagement with treatment. One way this is done
in clinical science literature is through the design of
complementary services or wraparound services, including
low intensity coaching, which we describe in more detail on the
paper. And that's it. Thank you so much for listening.