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

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

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

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 patient’s

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

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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

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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

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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 paper‐based IPSRT

instrument (SRM‐5).

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).

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