apps might, in combination, spark insights about whether one
shops to avoid certain feelings or whether one can actually buy
happiness (e.g., through purchasing experiences versus objects).
This cross functionality will add to the value provided by any
given application and could guide therapeutic recommendations.
Advances in data visualization will help individuals and
clinicians investigate and act upon trends. Aggregating data
across applications could help people identify disease markers
and profiles. Such a data bank would allow people to examine
the relationships between different aspects of their health and
behavior in light of population trends.
Tomorrow’s systems will also help patients and others
share their personal information in a controlled fashion. It
is clear that people will want to share data from different
aspects of their lives with clinicians, family, friends and others,
and that sharing preferences will vary considerably across
individuals. Today, many people use social media and self-
tracking technologies to share data and advice with peers.
The opportunity to share self-tracking data on sites such as
PatientsLikeMe has contributed to an impressive bank of
public health data that is changing the way patients evaluate
their medical options. Next-generation therapies may involve
a layering of crowd-sourced solutions on clinical suggestions.
Increasingly, patients expect that when they provide data about
their symptoms or treatment responses, they will have access to
it for personal investigation and anonymous sharing with peers.
In this way, people invest and divest health data depending on
by analyzing hundreds of speech qualities (Chang, Fisher, &
Canny, 2012), and to measure social dynamics related to well-
being (Lane et al., 2011). Ongoing research with machine
learning will combine many indicators from voice, expression,
movement and other behaviors detectable by smartphones.
The resulting emotional classifications may facilitate
meaningful assessment and highly tailored therapies.
Implications for clinicians and researchers
These advances in mobile technology will make mobile
technology increasingly relevant to mental health care. To
begin, they will provide clinicians with a more contextualized
understanding of patients’ struggles and an opportunity to
tailor treatment accordingly. Rich sets of population data will
eventually allow clinical researchers to redefine diagnostic
systems by examining clusters of symptoms and treatment
These advances will also allow clinicians to offer mobile
therapies as either adjuncts to or substitutes for psychotherapy,
addressing the need for affordable, nonstigmatizing and
effective treatment (Kazdin & Blase, 2011). Cognitive-behavioral therapy (CBT) is particularly amenable to mobile
interventions given its emphasis on self-monitoring and in situ
experimentation with alternative coping strategies. Preliminary
studies of mobile therapy based on CBT show promise.
Research has found that people use mobile therapies creatively
to increase self-awareness, cope with diverse stressors and
empathize with others (Morris et al., 2010).
Mobile tools and clinical interventions should complement
one another to create more psychologically intelligent
technologies and more sophisticated therapies. Early field