Detecting and Capitalizing on Physiological Dimensions of
Psychiatric Illness
Mark Matthews, Saeed Abdullah, Geri Gay
and Tanzeem Choudhury
Information Science, Cornell University, 219 Gates Hall, 14850, Ithaca, NY, U.S.A.
Keywords: Serious Mental Illness, Bipolar Disorder, Sensing, Smartphones, Mhealth.
Abstract: Serious mental illnesses, including bipolar disorders (BD), account for a large share of the worldwide
healthcare burden—estimated at $62.7B in the U.S. alone. Bipolar disorders represent a family of common,
lifelong illnesses associated with poor functional and clinical outcomes, high suicide rates, and huge societal
costs. Interpersonal and Social Rhythm Therapy (IPSRT), a validated treatment for BD, helps patients lead
lives characterized by greater stability of daily rhythms, using a 5 item paper-and-pencil self-monitoring
instrument called the Social Rhythm Metric (SRM). IPSRT has been shown to improve patient outcomes,
yet many patients struggle to monitor their daily routine or even access the treatment. In this paper we
describe how biological characteristics of bipolar disorder can be taken into consideration when developing
systems to detect and stabilize mood episodes. We describe the co-design of MoodRhythm, a smartphone
and web app, with patients and therapists. It is designed to support patients in tracking their health passively
and actively over a long period of time. MoodRhythm uses the phone’s onboard sensors to automatically
track sleep and social activity patterns. We report results of a small clinical pilot with experienced IPSRT
clinicians and patients with bipolar disorder and finish by describing the role physiological computing could
have not just in monitoring psychiatric illnesses according to existing broad categories of diagnosis but in
helping radically tailor diagnoses to each individual patient and develop interventions that take advantage of
idiosyncratic characteristics of each person’s illness in order to increase patient engagement in and
adherence to treatment.
1 INTRODUCTION
Bipolar disorder (BD) is recognized as one of the
most debilitating illnesses - responsible for more
disability-adjusted life years than all forms of cancer
– and affects approximately 2.6% of the population
worldwide (Merikangas et al., 2011). While the
median age of onset for bipolar disorders is 25, the
number of children and teens diagnosed in the past
decade has significantly increased (Moreno et al.,
2007). Individuals with bipolar disorder are
frequently severely disabled and their rate of
completed suicide is least 15 times that of the
general population (Harris and Barraclough, 1997).
Most patients with bipolar disorder (BD)
struggle to receive quality treatment and,
particularly, to access the psychosocial treatments
that have been shown to be critical to sustained
wellness and improved functioning. Thus, it is not
surprising that this common, lifelong, and life-
threatening illness (Murray and Lopez, 1996), is
associated with poor functional and clinical
outcomes (Judd et al., 2003), high suicide rates
(Baldessarini and Tondo, 2003), and huge societal
costs (Woods, 2000).
In the past decade, the biological components
and neurological dimensions of serious mental
illnesses like bipolar disorder have begun to be
identified (Craddock and Sklar, 2013). For many
years, scientists searched for the ‘bipolar’ gene.
Recent breakthroughs indicate that the picture is
much more complicated than this. Genetic variants
are increasingly tied to neurobiological deficits and
idiosyncratic characteristics that have been
witnessed in BD for some time. Several genes have
been identified that are associated with BD – some
are shared with Schizophrenia (Craddock and Sklar,
2013). People diagnosed with the illness can possess
some of these genes and not others. How each
person’s genetic beginnings interact with their
environment is crucial. In short, each person’s
bipolar disorder is unique to them; the particular
98
Matthews, M., Abdullah, S., Gay, G. and Choudhury, T.
Detecting and Capitalizing on Physiological Dimensions of Psychiatric Illness.
DOI: 10.5220/0005952600980104
In Proceedings of the 3rd International Conference on Physiological Computing Systems (PhyCS 2016), pages 98-104
ISBN: 978-989-758-197-7
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
manifestation of the illness depends on which genes
a person has. For example, empirical evidence
suggests that the MAOA gene is associated with
impulsive behaviour associated with manic episodes
in some forms of bipolar disorder (Preisig et al.,
2000).
Figure 1: Bipolar Symptoms and Smartphone Sensing.
We propose taking the neurobiological
components of bipolar disorder into consideration to
measure wellbeing and to engage patients in
treatment. The evolution and rapid dissemination of
smartphones has created unprecedented
opportunities for personalized data collection in an
extremely granular, unobtrusive, and even affordable
way. A recent community-based survey with over
1,500 people with serious mental illnesses found that
72% of patients with bipolar disorder owned and
used mobile phones regularly for calling, texting,
and the internet (Ben-Zeev et al., 2013).
Smartphone sensing capabilities are uniquely
suited to the detection of key parameters of bipolar
disorder: (1) sleep-wake/activity, (2) mental activity
and the nature and (3) the frequency of social
interaction (See Figure 1 below). Indeed, examining
the criteria for episodes of hypo/mania and
depression, one finds that key indicators of episode
onset (changes in activity, sleep, rate and intensity of
speech, frequency and intensity of social contact)
can all be passively collected from a smartphone.
Smartphone sensing might also support patients who
find self-tracking challenging and thereby increase
the quantity.
Neuropsychiatric disorders account for 28% of
the global burden of health (Mathers and Loncar,
2006). There is considerable potential for
physiological computing to identify novel methods
to monitor, diagnose and intervene in the treatment
of serious mental illness. In this paper, we describe
the grounding of our system development in
evidence-based therapy and basic research on the
physiological components of bipolar disorder. We
illustrate the potential of this approach by reporting
the positive results of a small clinical pilot with 3
patients and 3 clinicians who used MoodRhythm, a
smartphone app to monitor and stabilize biological
rhythms.
2 TREATMENT OF BIPOLAR
DISORDER
By far the most common treatment for bipolar
disorder is pharmacotherapy (i.e. treatment by
drugs). This is despite the fact that there is limited
evidence to support the efficacy of many drugs used
as part of treatment (Geddes and Miklowitz, 2013)
and that non-adherence to drug treatments can reach
as high as 60% (Strakowski et al., 1998). Evidence-
based treatment guidelines have suggested that
optimum management of bipolar disorder needs the
combination of pharmacotherapy and psychotherapy
(Goodwin and Consensus Group of the British
Association for, 2009) and while most
psychotherapies for bipolar disorder have been
repurposed from elsewhere, there is an increasing
recognition of a need for treatments that consider the
underlying neurobiological and psychosocial
mechanisms of bipolar disorder (Geddes and
Miklowitz, 2013).
One of the most prominent features of bipolar
disorder is its rhythmicity, including mood episodes
that cycle on an approximately regular basis and
symptoms that reflect disturbances in the body’s
natural rhythms (Levenson and Frank, 2011). A
number of theories and models have emerged over
the past several decades that relate sleep and
circadian rhythm disturbances to affective illnesses
such as bipolar disorder (Harvey, 2008). This
interest in biological rhythms has led to a
complementary interest in the social rhythms that
serve to regulate these biological rhythms and in
examination of the role of lifestyle regularity in
affective illnesses. A growing number of
investigations link social rhythms, mood changes,
and mood episodes in patients with affective
illnesses (e.g., (Haynes et al., 2006). Substantial
evidence now indicates that interventions targeting
social rhythms, sleep-wake rhythms, and light-dark
exposure may markedly improve affective illness
outcomes (e.g., Frank et al., 2005).
Interpersonal and Social Rhythm Therapy
(IPSRT) is a highly specific behavioral intervention
designed to regulate daily routines which, in turn, is
hypothesized to entrain underlying circadian
Detecting and Capitalizing on Physiological Dimensions of Psychiatric Illness
99
rhythms. The therapy was created specifically to
treat bipolar disorder, unlike most other available
treatments, and has been validated in a series of
single and multi-site studies (Frank et al., 2005,
Swartz et al., 2009). IPSRT targets activity patterns
as well as sleep timing and duration, factors that are
considered to mediate treatment outcomes. The
Social Rhythm Metric-5 (SRM) is a validated five-
item self-report assessment measure of the regularity
of daily routines (see Figure 2). The SRM helps to
record and quantify the regularity of social routines.
Figure 2: Paper-based Social Rhythm Metric.
Although the SRM has been proven effective for
tracking social routines, its paper-and-pencil format
has multiple disadvantages both as a clinical tool
and a research instrument. Even well intentioned
patients often forget to complete it or do so
inaccurately, particularly if concentration is
challenged by mania or depression. The paper
format is also not conducive to summarizing
collected data such as creating a graph of trends over
time that could be used in treatment to enhance
patients’ self-awareness of their social rhythms.
3 DESIGN PROCESS
In order to realize the potential of physiological
monitoring into practice requires that we develop the
algorithms and methodologies to transform raw data
into actionable knowledge. Yet, there are crucial
questions to consider when applying these
technologies including what is important to
individuals to track and how to ensure that they can
make sense of the collected data.
While there is a long history of using some forms
of sensing (e.g., activity and light exposure) in
efforts to understand and treat bipolar disorders,
patient adherence and acceptance of this technology
can limit the effectiveness of this approach (Prociow
et al., 2012). Individuals with bipolar disorder are
particularly sensitive to issues of stigma and often
refuse to use any device that might identify them as
‘different.’ Acceptance of novel treatments can be
an issue: even those who agree to wear devices such
as an actigraph, frequently do not do so consistently
(Camargos et al., 2013). Most important, the
promise of passive sensing has not yet had an impact
on clinical practice: the data collected via sensors
have been limited almost exclusively to research as
opposed to clinical settings.
Our goal in developing MoodRhythm was to use
patient smartphones to provide a combination of
active and passive methods to track daily rhythms, to
relay this information to clinicians, and to provide
feedback to patients to enable them to improve their
moods by establishing more regular daily rhythms.
In order to ensure the new system, MoodRhythm,
was well-accepted and met the needs of individuals
with bipolar disorder we sought to engage both
clinicians and individuals with bipolar disorder
integrally in the development of the application
through Participatory Design (Schuler and Namioka,
1993). This is a user-centered development
“approach towards computer systems design in
which the people destined to use the system play a
crucial role in designing it” (Schuler and Namioka,
1993). The end-user is involved at all phases of the
design process, having an equal hand in directing
development directions. Involving end-users early
on and working closely with them can help avoid
unpromising design paths, develop a more
comprehensive understanding of the target domain
and ultimately increase the likelihood that the
resultant technology meets the needs of the end
users.
Clinicians and patients worked in a Participatory
Design process with the research team to create
revise and finalize the initial implementation of the
MoodRhythm system. This entailed each member of
the design team using prototypes on their personal
devices over a minimum of 10 weeks each to
identify improvements and articulate new scenarios
of use.
The goal of IPSRT is to help patients establish
and maintain a stable social rhythm. MoodRhythm
allows patients to track the 5 basic activities used
in the prototypical paper version of the SRM: (1)
waking time, (2) first contact with another
individual, (3) starting their day, (4) dinner, and (5)
bedtime, but also to add custom activities that may
be more informative based on a particular patients
needs or habits (see Figure 3). The app allows
tracking of mood and energy as well on a 5 (very
low) to +5 (very high) scale.
PhyCS 2016 - 3rd International Conference on Physiological Computing Systems
100
Figure 3: Screens for the MoodRhythm prototype.
Patients can set daily targets and track how
closely they meet these target times. Notes can be
used to record additional information such as the
amount of medication taken or factors that may
have affected a patient’s routine or mood.
The diary is designed to provide an at-a-glance
summary of the patient’s successes in meeting their
rhythm goals for both the current and preceding
days. If the patient completes an activity within their
customizable time window (the default is 45
minutes), then the bar to the left turns green. When
the window is about to elapse and an event is not yet
recorded, the bar appears yellow (a “warning” that a
potential rhythm disruption is occurring). If a patient
misses the target, then the bar turns red.
Individuals with bipolar disorder have been shown
to possess “a hypersensitivity to reward-relevant
stimuli” (Nusslock et al., 2012). To explore ways to
increase patient adherence to self-report and to a
stable routine, we incorporated badges elements into
our system. MoodRhythm uses a behavioral reward
system to encourage rhythmicity - a series of badges
are given to users as they meet their therapeutic
goals. Badges are given based on user adherence to
self-report and their daily rhythmicity.
Table 1: Participant Feedback where 1 = Strongly
Disagree, 7 = Strongly Agree.
Mean SD
The way MoodRhythm works
overall is consistent
6.4 0.54
MoodRhythm has the functions
and capabilities I would expect it
to have
6.2 0.83
It is easy to learn to use
MoodRhythm
6.4 0.89
This product felt trustworthy 6.4 0.89
This app is attractive 5.2 2.04
I like using the interface of this app 6.2 1.30
Interacting with this product
require a lot of mental effort
1.6 0.54
The characters on the screen were
easy to read
6.4 0.89
I felt comfortable using the system 6.2 1
Overall, I am satisfied with how
easy it is to use this app
6 1
MoodRhythm also explores the possibility of
using a range of smartphone sensors to passively
detect social rhythms, levels of social interaction,
and other aspects of daily life that are critical to
wellness among individuals with bipolar disorder. It
takes advantage of a variety of sensor data sources
on the smartphone platform with the ultimate aim to
infer many of the activities included in the
prototypical, paper-based SRM instrument. Our
platform continuously collects data from the phone’s
light sensor, accelerometers, and microphone, as
well as information about phone usage events such
as screen unlocks and battery charging state.
Our work in this area also draws on several
years’ worth of peer-reviewed algorithm design and
empirical research in these areas, with sleep and
social inference accuracies approaching 85%–90%
with minimal intervention on the user’s part. We use
an empirically validated weighting of inputs from
audio, accelerometer, light level, screen unlock, and
charging state data sources in order to arrive at an
estimation of the time that the user spent sleeping in
a given 24-hour period (Rabbi et al., 2011). Another
algorithm computes the frequency and duration of
face-to-face conversations that a phone’s owner has
over the course of the day based on an analysis of
audio data continuously collected using the
smartphone’s built-in microphone. For a more
detailed description of the MoodRhythm system
please see (Voida et al., 2013).
4 STUDY
To assess the impact of MoodRhythm, we conducted
a small qualitative pilot study of the initial prototype
lasting two months and involving three patients and
three clinicians engaged in IPSRT. Each participant
completed a usability measure at the end of the
study.
Potential participants were identified through the
Depression and Manic-Depression Prevention
Program at Western Psychiatric Institute and Clinic,
Pittsburgh. Inclusion criteria required patients to be
already participating in a treatment program at the
clinic, to be able to provide informed consent and to
have a confirmed diagnosis of BD. Participants were
excluded if they were unwilling or unable to comply
with study procedures or had active suicidal ideation
requiring inpatient or intensive outpatient
management. No compensation was provided to
participants. The Institutional Review Board at the
University of Pittsburgh approved this research.
All three patients had used paper charts
Detecting and Capitalizing on Physiological Dimensions of Psychiatric Illness
101
previously to track their social rhythms and moods –
citing strengths of this medium as: “portable and
low upkeep” and “easy to use”. Weaknesses
mentioned included that this method makes it: “hard
to look at data points over long term” (2 patients),
and is inconvenient to use and results in invalid data:
“since I'm not very organized I sometimes lose them
or forget to take them out and make the notations I
should in a timely way. Counting on memory at the
end of the week rarely reflects what the reality was”.
Each participant completed a usability measure
based on agreement with several statements where a
1 (“Strongly Disagree”) to 7 (“Strongly Agree”). As
Table 2 above indicates, patient participants found
the app easy to learn and use, attractive and
trustworthy. This provides evidence that
MoodRhythm appears to overcome several
limitations of the paper-SRM and directly tackles the
challenges patients and clinicians face by providing
a convenient self-monitoring app, supporting clinical
interventions by graphing patterns and correlations
over time. A clinician commented accordingly: “I
think that this app could be a significant
contribution to the treatment of mental health
conditions and specifically bipolar disorder due to
the illness' proven sensitivity to life's rhythms.”
A principal factor related to the convenience of
using the smartphone to record activities:
First and foremost, it was convenient, which
meant that I remembered to note when
activities actually occurred and how I felt
(instead of trying to remember two days
later).” Patient 1
“It was right there for me with the rest of the
utilities I use every day on the phone. I never
had to look for it or a pen.” Patient 2
It fits my needs nicely. It's always with me”
Patient 3
Another key aspect of MoodRhythm was reducing
the time to receive feedback for patients, who
typically only go through the paper forms when they
see their psychiatrist: Getting the visual feedback
when my day worked within the targeted times gave
me more confidence that I could meet my doctor's
expectations.”(2 patients) This was echoed by a
clinician who commented: “The prompt feedback
that patient's receive via the device (i.e. items turn
green when a task is completed "on time") will much
more effective versus receiving the feedback only
when they are with their therapist.”
Making it easy to collect the data and providing
feedback opens up the possibility for patients and
therapists to take a longitudinal view of patient's
social rhythms and mood: “... it occurs to me how
very little of this very important information is
available to a therapist when interviewing or
treating an individual. We rely almost entirely on
patient memory to make treatment decisions and
when a person experiences depression, hypomania
and/or anxiety the memory is often blurry at best.”
Patients and clinicians had varied reactions to
sensing related to issues of privacy and awareness.
Patients indicated they were open to sensing in
areas where they felt their perception of the
behaviour was unreliable (My perception of social
interaction might be different than its perception. It
would be interesting to hear how this works”) or
where the behaviour was challenging to track
(“sleep is difficult to record and evaluate sleep.
Having something that makes this easier would help
me make sure I'm getting enough sleep and at the
right times.”).
However, patients tended to be interested in one
aspect of sensing, but not another. In response to a
question about using smartphone sensing to detect
levels of social interaction, one patient commented:
“That's just creepy.” One clinician echoed this
concern indicating that sensing might not be
universally accepted: “I think for a subset of
patient's this may be very helpful but overall
patient's may have concerns about privacy.
There may be ways to tackle these concerns by
being more transparent about how app sensing
works. Smartphone sensing, unlike devices like the
Fitbit, can naturally occlude is being tracked. One
patient reported never installing an app when it
requested access to her smartphone’s sensors such as
GPS or the microphone. She suggested that it might
be helpful explaining why granting access will help
the patient later on, show samples of what this data
will look like and explain how it is stored (i.e. raw
data or features of the data).
5 DISCUSSION
This study provides early yet encouraging evidence
that technologies that combine sensors and self-
report have promise in the treatment of serious
mental illness and may be well accepted by
individuals with bipolar disorder and their clinicians.
For the participants in this study, MoodRhythm
overcame the limitations of the paper-SRM and
directly tackled the challenges patients and
clinicians face by providing a convenient self-report
PhyCS 2016 - 3rd International Conference on Physiological Computing Systems
102
app, enabling long-term and symptomatic use via the
use of smartphone sensors and supporting clinical
interventions by graphing patterns and correlations
over time.
We believe that these and similar technologies
might be applied in a multi-phase manner. The
combination of self-report and passive sensing has
wide application for lifelong diseases like bipolar
disorder where it can be extremely challenging to
maintain regular self-tracking, but where self-report
information, particularly at the outset of treatment,
can be a highly valuable activity leading to critical
insights for the patient about the relationship
between social rhythm regularity and mood.
MoodRhythm aims to make recording daily
routine information easier for individuals with
bipolar disorder and provide more clinically relevant
information to the clinician. This work applied
several design approaches that could be applied in
the design of systems to support individuals with
psychiatric illness. They are to: 1) consider ways to
make therapeutically beneficial behaviours
appealing based on the pathology of the psychiatric
illness (e.g., reward susceptibility); (2) think of the
system as an agile tool that will need to be different
things to different patients at different times (e.g.,
allow different elements to be enabled/disabled) and
(3) communicate clearly how sensor inferences are
collected and what the benefits to them are.
Prior work applying technology to the treatment
of bipolar disorder have not accounted for the
significant biological aspects of the illness. While
this study’s results are clearly preliminary – a
rigorous evaluation of the resulting system is needed
– it does provide an example of how sensing and
patient-facing technologies can be designed and
deployed which take into consideration the
biological underpinnings of mental disorders. We
are currently completing a longer-term study
evaluating the system’s effectiveness against
existing interventions using the paperbased IPSRT
instrument (SRM5).
In addition to mood disorders, circadian
dysregulation has been implicated in cancer,
diabetes, obesity, bulimia nervosa, and Alzheimer’s
disease. Rhythm entraining interventions offer the
potential to help stabilize these disorders. Health
promotion/illness prevention strategies targeting
healthy lifestyles and cardiovascular disease
prevention may also take similar approaches.
Therefore, an app that takes into consideration
aspects of the neurocognitive characteristics of a
user group to promote sleep-wake regulation and
rhythm regularity has a wide range of potential
applications beyond our initial targets, although the
design of each app might have to be tailored to the
specific end-users.
6 CONCLUSION
Psychiatric illnesses represent a considerable burden
of diseases in the world. Increasingly the genetic
components that result in cognitive and behavioural
characteristics of these illnesses are being identified.
In the case of bipolar disorder, while current
research indicates that each person’s bipolar disorder
is unique, current clinical criteria place patients into
accurate but not highly specific categories. By
taking into account the pathology of the illness, there
is long-term potential to create more nuanced
diagnoses and treatment models on an individual-by-
individual basis. Smartphone sensors and
applications offer the possibility to both monitor and
engage patients, in order to create more nuanced
diagnoses and to take account of idiosyncratic neural
characteristics to increase engagement in treatment.
ACKNOWLEDGEMENTS
Mark Matthews’ work was supported by a Marie
Curie Fellowship (Project Number: 302530).
REFERENCES
Baldessarini, R. J. & Tondo, L. 2003. Suicide risk and
treatments for patients with bipolar disorder. JAMA,
290, 1517-1519.
Ben-Zeev, D., Davis, K. E., Kaiser, S., Krzsos, I. & Drake,
R. E. 2013. Mobile technologies among people with
serious mental illness: opportunities for future
services. Administration and Policy in Mental Health
and Mental Health Services Research, 40, 340-343.
Camargos, E. F., Louzada, F. M. & Nóbrega, O. T. 2013.
Wrist actigraphy for measuring sleep in intervention
studies with Alzheimer's disease patients: Application,
usefulness, and challenges. Sleep medicine reviews,
17, 475-488.
Craddock, N. & Sklar, P. 2013. Genetics of bipolar
disorder. The Lancet, 381, 1654-1662.
Ehn, P. 1993. Scandinavian design: On participation and
skill. Participatory design: Principles and practices,
41-77.
Frank, E., Kupfer, D. J., Thase, M. E., Mallinger, A. G.,
Swartz, H. A., Fagiolini, A. M., Grochocinski, V.,
Houck, P., Scott, J. & Thompson, W. 2005. Two-year
outcomes for interpersonal and social rhythm therapy
Detecting and Capitalizing on Physiological Dimensions of Psychiatric Illness
103
in individuals with bipolar I disorder. Archives of
general psychiatry, 62, 996-1004.
Geddes, J. R. & Miklowitz, D. J. 2013. Treatment of
bipolar disorder. The Lancet, 381, 1672-1682.
Goodwin, G. O. & Consensus Group of the British
Association For, P. 2009. Evidence-based guidelines
for treating bipolar disorder: revised second edition—
recommendations from the British Association for
Psychopharmacology. Journal of
Psychopharmacology, 23, 346-388.
Harris, E. C. & Barraclough, B. 1997. Suicide as an
outcome for mental disorders. A meta-analysis. The
British Journal of Psychiatry, 170, 205-228.
Harvey, A. 2008. Sleep and circadian rhythms in bipolar
disorder: seeking synchrony, harmony, and regulation.
American Journal of Psychiatry, 165, 820-829.
Haynes, P. L., Mcquaid, J., Ancoli-Israel, S. & Martin, J.
L. 2006. Disrupting life events and the sleep-wake
cycle in depression. Psychological Medicine, 36,
1363-1373.
Judd, L. L., Akiskal, H. S., Schettler, P. J., Coryell, W.,
Endicott, J., Maser, J. D., Solomon, D. A., Leon, A. C.
& Keller, M. B. 2003. A prospective investigation of
the natural history of the long-term weekly
symptomatic status of bipolar II disorder. Archives of
General Psychiatry, 60, 261-269.
Kyng, M. & Greenbaum, J. 1991. Cooperative design:
bringing together the practices of users and designers.
Information Systems Research.
Levenson, J. & Frank, E. 2011. Sleep and circadian
rhythm abnormalities in the pathophysiology of
bipolar disorder. Behavioral Neurobiology of Bipolar
Disorder and its Treatment. Springer.
Mathers, C. D. & Loncar, D. 2006. Projections of global
mortality and burden of disease from 2002 to 2030.
PLoS medicine, 3, e442.
Merikangas, K. R., Jin, R., He, J. & Et Al. 2011.
Prevalence and correlates of bipolar spectrum disorder
in the world mental health survey initiative. Archives
of General Psychiatry, 68, 241-251.
Moreno, C., Laje, G., Blanco, C., Jiang, H., Schmidt, A.
B. & Olfson, M. 2007. National trends in the
outpatient diagnosis and treatment of bipolar disorder
in youth. Archives of general psychiatry, 64, 1032-
1039.
Murray, C. J. & Lopez, A. D. 1996. The global burden of
disease, Vol. 1. Cambridge, MA: Harvard University
Press.
Nusslock, R., Almeida, J. R. C., Forbes, E. E., Versace,
A., Frank, E., Labarbara, E. J., Klein, C. R. & Phillips,
M. L. 2012. Waiting to win: elevated striatal and
orbitofrontal cortical activity during reward
anticipation in euthymic bipolar disorder adults.
Bipolar Disorders, 14, 249-260.
Preisig, M., Bellivier, F., Fenton, B. T., Baud, P., Berney,
A., Courtet, P., Hardy, P., Golaz, J., Leboyer, M. &
Mallet, J. 2000. Association between bipolar disorder
and monoamine oxidase A gene polymorphisms:
results of a multicenter study. American Journal of
Psychiatry, 157, 948-955.
Prociow, P., Wac, K. & Crowe, J. 2012. Mobile
psychiatry: towards improving the care for bipolar
disorder. International journal of mental health
systems, 6, 5.
Rabbi, M., Ali, S., Choudhury, T. & Berke, E. Passive and
In-Situ assessment of mental and physical well-being
using mobile sensors. 2011. ACM, 385-394.
Schuler, D. & Namioka, A. 1993. Participatory design:
Principles and practices, CRC Press.
Strakowski, S. M., Keck, P. E., Mcelroy, S. L., West, S.
A., Sax, K. W., Hawkins, J. M., Kmetz, G. F.,
Upadhyaya, V. H., Tugrul, K. C. & Bourne, M. L.
1998. Twelve-month outcome after a first
hospitalization for affective psychosis. Archives of
General Psychiatry, 55, 49-55.
Swartz, H. A., Frank, E., Frankel, D. R., Novick, D. &
Houck, P. 2009. Psychotherapy as monotherapy for
the treatment of bipolar II depression: a proof of
concept study. Bipolar disorders, 11, 89-94.
Voida, S., Matthews, M., Abdullah, S., Xi, M. C., Green,
M., Jang, W. J., Hu, D., Weinrich, J., Patil, P. &
Rabbi, M. MoodRhythm: tracking and supporting
daily rhythms. Proceedings of the 2013 ACM
conference on Pervasive and ubiquitous computing
adjunct publication, 2013. ACM, 67-70.
Woods, S. W. 2000. The economic burden of bipolar
disease. Journal of Clinical Psychiatry.
PhyCS 2016 - 3rd International Conference on Physiological Computing Systems
104